MOTORIST BEHAVIOUR AT RAILWAY LEVEL CROSSINGS: THE

PRESENT CONTEXT IN AUSTRALIA

Angela Wallace

Bachelor of Nursing (Australian Catholic University)

Graduate Diploma in Education (Griffith University)

Master of Public Health (Queensland University of Technology)

A thesis submitted for the Degree of Doctor of Philosophy,

Queensland University of Technology,

Centre for Accident Research and Road Safety – Queensland,

Brisbane, Australia.

2008

i

KEY WORDS

Level crossings, railway collisions, road users, heavy vehicles, older drivers,

younger drivers, human factors, risk management, road safety, educational interventions.

ii

ABSTRACT

Railway level crossing collisions in Australia are a major cause of concern for

both rail and road authorities. Despite the fact that the number of railway crash fatalities

in Australia has fallen in recent years, level crossing collisions constitute a significant

proportion of the national rail toll. Although rail transport is presently one of the safest

forms of land transport, collisions at level crossings are three times more likely to

involve fatalities as compared to all other types of road crashes (Afxentis, 1994). With

many level crossing fatalities and injuries resulting in coronial inquests, litigation and

negative media publicity, the actions of rail and road infrastructure providers and the

behaviour of motorists, pedestrians and rail users, come under close scrutiny.

Historically, research in this area has been plagued by the rail/road interface and the

separation of responsibilities between rail and road authorities reflecting the social and

political context in which they are contained. With the recent rail reform in Australia,

safety at level crossings has become a key priority area.

Accordingly, there is a need to better understand the scope and nature of motorist

behaviour at level crossings, in order to develop and implement more effective

countermeasures for unsafe driving behaviour. However, a number of obstacles have

hindered research into the area of level crossing safety. As with many road crashes, the

contributing causes and factors are often difficult to determine, however a recent

investigation of fatal collisions at level crossings supports the notion that human fault is

a major contributor (Australian Transport Safety Bureau, 2002a). Additionally, there is

a lack of reliable data available relating to the behavioural characteristics and

perceptions of drivers at level crossings. Studies that do exist have lacked a strong

theoretical base to guide the interpretation of results.

Due to the lack of financial viability of continuing to approach risk management

from an engineering perspective, the merits of human factor research need to be

examined for suitability. In Australia, there has been considerable recognition regarding

the importance of human factor approaches to level crossing safety (Australian

Transport Council, 2003). However, little attempt has been made by authorities to

scientifically develop and measure the effectiveness of road safety educational

iii

interventions. Therefore, there exists a significant need for developing targeted road

safety educational interventions to improve current risk management solutions at level

crossings.

This research program is the first of its kind in investigating motorist behaviour at

level crossings and the measuring the effectiveness of educational interventions for

improving driving safety. Although other ‘educational’ campaigns exist in this field, no

campaign or intervention has been guided by empirical research or theory. This thesis

adopted a multidisciplinary approach to theory, reviewing perspectives from

psychology, sociology and public health to explain driver behaviour at level crossings.

This array of perspectives is necessary due to the variety of behaviours involved in

collisions and near-misses at level crossings. The motivation underlying motorist

behaviour determines to a large extent how successful behaviour change strategies (e.g.

educational interventions) may be. Fishbein’s Integrated Model of Behaviour Change

(IM) based largely on the health belief model, theory of reasoned action and theory of

planned behaviour (Fishbein, 2000), assisted in the planning and development of a ‘one-

off’ targeted educational intervention specific for three different road user groups and in

questionnaire development to ascertain the present context of motorist behaviour at level

crossings. As no known research has been conducted that utilizes any psychosocial

model to explain or predict level crossing behavior within different road user groups,

this research program used this model as an exploratory tool rather than a tool to asses

the model’s capacity in explaining such behaviour. The difference between this model

and others is the inclusion of two important constructs in driving: skills (or abilities) and

environmental factors. Fishbein (2003) suggests that the model recognises the lack of

skills (or abilities) and/or environmental constraints may prevent a person from acting

on their intentions, in light of the fact that intention is viewed as the primary determinant

of behaviour. While the majority of behaviour change theories are limited by a range of

conceptual and contextual factors (Parker, 2004), the IM was used to assist this research

program as it appeared to be the most applicable model to examining level crossing

safety.

A variety of data collection methods were used in this research program as much

of what is currently known about level crossing collisions is derived from coroner’s

iv

findings and statistics. The first study (Study One) was designed to extend this

knowledge by undertaking a more thorough examination of contributing factors to level

crossing crashes and the road user groups at risk. This study used the method of

‘triangulation’ (i.e. combining research methods to give a range of perspectives)

whereby both qualitative (focus groups) and quantitative (modified Delphi technique)

research designs were utilised (Barbour, 1999, Bryman, 1992). With the discipline of

road safety research requiring methodological strategies that will enhance efforts to

conceptualise the multi-faceted nature of motorist behaviour at level crossings, this

application provided the robustness required. Results from the Delphi technique

indicated that older, younger and heavy vehicle drivers are considered to be three of the

highest risk road user groups by experts in the field. For the older driver group, experts

agreed that errors in judgment were the most important issue for this group when driving

at level crossings. Risk taking by younger drivers, such as trying to beat the train across

the crossing, was viewed as the central issue for the younger driver group. Like the

younger driver group, a concern by experts with the heavy vehicle group was intentional

risk taking at level crossings. However, experts also rated the length of heavy vehicles a

major concern due to the possibility of a truck over-hanging a crossing.

Results from focus groups with train drivers in Study One indicated that there are

unique problems associated with crossings in rural/regional areas compared to urban

areas. The metropolitan train drivers generally experienced motorist behaviour at active

crossings with flashing lights and boom gates while the regional train drivers

experienced behaviours at active crossings with boom gates, crossings with lights only

and passive crossings with stationary signs. In the metropolitan train driver group,

experiences of motorist behaviour at level crossings included: motorists driving around

boom gates, getting stuck under boom gates, queuing over congested crossings and

driving through the crossing after the red lights commence flashing. The behaviour of

motorists driving around boom gates was noted to occur quite regularly. The majority

of metropolitan train drivers reported that it was a common occurrence for motorists to

drive through a crossing when the lights are flashing both before and after the booms

were activated and some crossings were named as ‘black spots’ (locations where

motorists repeatedly violate the road rules). Vehicles protruding into the path of the

v

train and motorists entering congested crossings and then panicking and driving

backwards into the boom gates were also mentioned. Regional train drivers indicated

that motorists not stopping or giving way to trains is a continual problem at passively

controlled crossings (i.e. no boom gates or flashing lights). Regional train drivers

generally agreed that the majority of motorists obey protection systems; however some

motorists drive through flashing lights or drive around boom gates. Other high risk

behaviours included motorists attempting to beat the train across the crossing, speeding

up to go through flashing lights, and general risk taking by younger drivers in particular.

Motorists not allowing enough time to cross in front of the train or hesitating (stop-

starting) at crossings were also noted to be at high risk. There was a general perception

by regional train drivers that motorists are unable to judge the speed and distance of an

approaching train to determine a safe gap during which to cross. Local motorists were

also reported to be a problem at level crossings for regional train drivers. A theme

common to regional and metropolitan train drivers was the risk of catastrophic consequence

associated with level crossing collisions. The reasons given for this were the threat of

derailment, serious property damage, the high risk of a fatality, personal injury and, most

earnestly, the potential for enduring psychological consequences. Drivers uniformly spoke

about the continual fear they had of being involved in a collision with a heavy vehicle, and

many spoke of the effects that such collisions had on train drivers involved. For this reason,

train drivers were said to consider any near-miss incident involving trucks particularly

serious.

The second study undertaken as part of this research program (Study Two),

involved formative research as part of the planning, development and delivery of

behavioural interventions for each of the three road user groups identified in Study One.

This study also used both qualitative and quantitative data collection methods to provide

methodological triangulation and ensure reliability of the data. The overall objective of

the qualitative data collection was to obtain rich data using a qualitative mode of inquiry,

based on the key variables of attitudes, norms, self-efficacy (perceived behavioural

control), perceived risk, environmental constraints and the skills/abilities of drivers. The

overall objective of the quantitative data collection was to prioritise the issues identified

in order to direct and allocate project resources for intervention planning, development

and delivery. This combined recruitment strategy was adopted as it was an appropriate

vi

and practical data collection strategy within the qualitative and exploration

methodology. Information obtained from each of the groups was critical in assisting,

guiding, and identifying priority areas for message and material development. The use

of focus groups and one-on-one interviews provided insights into why drivers think or

do what they do at level crossings. The qualitative component of this study found that

for the older driver group, regional drivers hold a greater perception of risk at level

crossings than urban older drivers, with many recalling near-misses. Participants from

the urban older driver group indicated that level crossings are not as dangerous as other

aspects of driving, with many participants being doubtful that motorists are killed while

driving at level crossings. Both urban and regional younger drivers tended to hold a low

perception of risk for driving at level crossings, however many participants reported

having great difficulty in judging the distance a train is from a crossing. Impatience for

waiting at level crossings was reported to be the major reason for any risk taking at level

crossings in the younger driver group. Complacency and distraction were viewed by

heavy vehicle participants as two of the major driver factors that put them at risk at level

crossings, while short-stacking (when the trailer of the truck extends onto the crossing),

angle of approach (acute or obtuse angle) and lack of advance warning systems were

seen as the major engineering problems for driving a truck at level crossings. The

quantitative component of this study involving research with train drivers found that at

the aggregate train driver level, it is apparent that train drivers consider motorists’

deliberate violations of the road rules and negligently lax approach to hazard detection

as the predominant causes of dangerous driving at level crossings. Experts were

observed to rank risk taking behaviours slightly lower than train drivers, although they

agreed with train drivers that ‘trying to beat the train’ is the single most critical risk

taking behaviour observed by motorists.

The third study (Study Three) involved three parts. The aim of Part One of this

study was to develop targeted interventions specific to each of the three road user groups

by using Fishbein’s theoretical model (Integrated Model of Behaviour Change) as a

guide. The development of interventions was originally seen as being outside of the

scope of this project, however it became intertwined in questionnaire development and

thus deemed to be within the realms of the current mode of inquiry. The interventions

vii

were designed in the format of a pilot radio road safety advertisement, as this medium

was found to be one of the most acceptable to each of the road user groups as identified

in the formative research undertaken in Study Two. The interventions were used as a

‘one-off’ awareness raising intervention for each road user group. Part Two involved

the investigation of the present context of unsafe driving behaviour at level crossings.

This second part involved the examination of the present context of motorist behaviour

at level crossings using key constructs from Fishbein’s Integrated Model of Behaviour

Change (IM). Part Three involved trialing a pilot road safety radio advertisement using

an intervention and control methodology. This part investigated the changes in pre and

post-test constructs including intentions, self-reported behaviour, attitudes, norms, self-

efficacy/perceived behaviour control, perceived risks, environment constraints and

skills/ability. Results from this third study indicated that younger drivers recognise that

level crossings are potentially a highly dangerous intersection yet are still likely to

engage in risk taking behaviours. Additionally, their low levels of self-efficacy in

driving at level crossings pose challenges for developing interventions with this age

group. For the older driver sample, this research confirms the high prevalence of

functional impairments such as increasing trouble adjusting to glare and night-time

driving, restricted range of motion to their neck and substantial declines in their hearing.

While factors contributing to the over-representation of older drivers in collisions at

level crossings are likely to be complex and multi-faceted, such functional impairments

are expected to play a critical role. The majority of heavy vehicle drivers reported

driving safely and intending to drive safely in the future, however, there is a sub-set of

drivers that indicate they have in the past and will in the future take risks when

traversing crossings. Although this sub-set is relatively small, if generalised to the larger

trucking industry it could be problematic for the rail sector and greater public alike.

Familiarity was a common factor that was found to play a role in driving intention at

level crossings for all three road user groups. This finding supports previous research

conducted by Wigglesworth during the 1970’s in Australia (Wigglesworth, 1979).

Taken together, the results of the three studies in this research program have a

number of implications for level crossing safety in Australia. Although the ultimate goal

to improve level crossing safety for all motorists would be to have a combination of

viii

engineering, education and enforcement countermeasures, the small number of fatalities

in comparison to the national road toll limits this. It must be noted though that the

likelihood of creating behavioural change would be increased if risk taking at level

crossings by all motorists was detected and penalised, or alternatively, if perceptions of

such detection were increased. The instilling of fear in drivers with the threat of

punishment via some form of sanction can only be achieved through a combination of a

mass media campaign and increasing police presence. Ideally, the aim would be to

combine fear of punishment with the guilt associated with the social non-acceptability of

disobeying road rules at level crossings. Such findings have direct implications for

improving the present context of motorist behaviour at level crossings throughout

Australia.

ix

TABLE OF CONTENTS

KEY WORDS....................................................................................................................i

ABSTRACT..................................................................................................................... ii

TABLE OF CONTENTS................................................................................................ix

LIST OF FIGURES .......................................................................................................xv

LIST OF TABLES ........................................................................................................xvi

LIST OF APPENDICES ..............................................................................................xix

GLOSSARY OF ACRONYMS AND TERMS............................................................xx

STATEMENT OF ORIGINAL AUTHORSHIP ..................................................... xxii

ACKNOWLEDGMENTS ......................................................................................... xxiii

CHAPTER ONE: INTRODUCTION TO THE THESIS ...........................................1

1.1 INTRODUCTION ..........................................................................................................2

1.2 THE RESEARCH AREA .................................................................................................3

1.3 RATIONALE FOR THE RESEARCH.................................................................................4

1.4 THEORETICAL FRAMEWORK .......................................................................................5

1.5 RESEARCH QUESTIONS AND OBJECTIVES ....................................................................6

1.6 SCOPE OF RESEARCH ..................................................................................................8

1.7 THESIS OUTLINE .........................................................................................................8

1.8 SUMMARY..................................................................................................................9

CHAPTER TWO: LITERATURE REVIEW............................................................11

2.1 INTRODUCTION ........................................................................................................12

2.2 AUSTRALIA’S RAIL INDUSTRY ..................................................................................12

2.2.1 Economic importance...................................................................................12

2.2.2 Rail activities................................................................................................13

2.2.3 Reform agenda .............................................................................................13

2.2.4 Rail safety management ...............................................................................14

2.2.5 National strategy initiative ...........................................................................16

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2.2.6 Australasian railway level crossing behavioural strategy ............................17

2.2.7 Roles and responsibilities for level crossing safety .....................................18

2.3 LEVEL CROSSING PROTECTION SYSTEMS..................................................................19

2.3.1 Types of protection systems.........................................................................19

2.3.2 Definitions....................................................................................................21

2.3.3 Australian standards .....................................................................................22

2.3.4 International comparison of protection systems ..........................................24

2.4 LEVEL CROSSING STATISTICS IN AUSTRALIA ............................................................25

2.4.1 Data recording..............................................................................................25

2.4.2 Incidence of occurrences..............................................................................26

2.4.3 Incidence of fatalities ...................................................................................27

2.4.4 Cost of collisions..........................................................................................30

2.4.5 International statistical comparison..............................................................34

2.5 ENGINEERING MEASURES .........................................................................................36

2.5.1 Overview of engineering measures..............................................................36

2.5.2 Signage and markings ..................................................................................38

2.5.3 Rumble strips ...............................................................................................38

2.5.4 Sight distances..............................................................................................40

2.5.5 Information handling zones .........................................................................42

2.5.6 Crossing surface ...........................................................................................43

2.5.7 Approach speed limits..................................................................................43

2.5.8 Boom barriers...............................................................................................44

2.5.9 Conspicuity of trains ....................................................................................44

2.5.10 Train horns ...................................................................................................50

2.5.11 Low-cost warning systems...........................................................................51

2.5.12 Vehicle dimensions and performance ..........................................................53

2.5.13 Crossing closure ...........................................................................................54

2.5.14 Risk based scoring systems..........................................................................55

2.6 LAWS AND ENFORCEMENT .......................................................................................56

2.6.1 Overview of enforcement measures.............................................................56

2.6.2 Level crossing laws in Australia ..................................................................57

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2.6.3 Enforcing laws at level crossings.................................................................57

2.6.4 Fines and penalties .......................................................................................59

2.7 EDUCATION, PUBLICITY AND TRAINING....................................................................60

2.7.1 Overview......................................................................................................60

2.7.2 Driver training..............................................................................................61

2.7.3 Publicity .......................................................................................................61

2.7.4 Education programs and interventions.........................................................64

2.7.5 Level crossing safety programs and interventions .......................................66

2.7.5 Limitations of current level crossing safety programs.................................71

2.8 HUMAN FACTORS CONTRIBUTING TO COLLISIONS ....................................................72

2.8.1 Overview of accident causation ...................................................................72

2.8.2 Driver interaction with different protection systems ...................................77

2.8.3 Perception of risk .........................................................................................79

2.8.4 High risk behaviours and risk taking ...........................................................80

2.8.5 Familiarity ....................................................................................................86

2.8.6 Approach behaviour .....................................................................................87

2.8.7 Poor knowledge of road rules ......................................................................88

2.8.8 Distraction....................................................................................................89

2.8.9 Attentional blindness....................................................................................91

2.8.10 Hypovigilance and fatigue ...........................................................................91

2.8.11 Speeding.......................................................................................................92

2.8.12 Alcohol and drugs ........................................................................................95

2.8.13 Gender ..........................................................................................................96

2.9 RESEARCH QUESTIONS AND OBJECTIVES ..................................................................97

2.10 SUMMARY................................................................................................................98

CHAPTER THREE: THE IMPORTANCE OF THEORY FOR IMPROVING

MOTORIST BEHAVIOUR AT LEVEL CROSSINGS...........................................100

3.1 INTRODUCTION ......................................................................................................101

3.2 THE ROLE OF THEORY IN ROAD SAFETY STUDIES ....................................................101

3.3 THE INTENTION-BEHAVIOUR RELATION..................................................................102

3.4 INTEGRATED MODEL OF BEHAVIOUR CHANGE........................................................103

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3.4.1 Development of the model .........................................................................103

3.4.2 Criticisms of the model ..............................................................................105

3.4.3 Application to level crossing behaviour.....................................................106

3.5 SUMMARY..............................................................................................................106

CHAPTER FOUR: HIGH RISK AND VULNERABLE ROAD USERS AT

LEVEL CROSSINGS ..................................................................................................107

4.1 INTRODUCTION ......................................................................................................108

4.2 REVIEW OF EXISTING DATA ....................................................................................108

4.3 STUDY AIMS AND RESEARCH QUESTIONS................................................................109

4.4 METHOD.................................................................................................................110

4.4.1 Overview....................................................................................................110

4.4.2 Delphi technique ........................................................................................110

4.4.3 Focus groups ..............................................................................................115

4.4.4 Ethical clearance ........................................................................................119

4.5 RESULTS.................................................................................................................119

4.5.1 Delphi technique ........................................................................................119

4.5.2 Focus groups ..............................................................................................121

4.6 DISCUSSION............................................................................................................124

4.6.1 Study limitations ........................................................................................124

4.7 SUMMARY..............................................................................................................125

CHAPTER FIVE: PLANNING AND DEVELOPMENT OF INTERVENTIONS

FOR EACH ROAD USER GROUP...........................................................................127

5.1 INTRODUCTION ......................................................................................................128

5.2 HIGH RISK AND VULNERABLE ROAD USERS ............................................................129

5.2.1 Overview....................................................................................................129

5.2.2 Younger drivers..........................................................................................129

5.2.3 Older drivers.................................................................................................132

5.2.4 Heavy vehicle drivers...................................................................................140

5.3 QUALITATIVE RESEARCH WITH TARGET GROUPS ....................................................148

5.3.1 Objectives...................................................................................................148

xiii

5.3.2 Method .......................................................................................................148

5.3.3 Results ........................................................................................................152

5.3.4 Discussion ..................................................................................................170

5.4 QUANTITATIVE RESEARCH WITH TRAIN DRIVERS AND EXPERTS .............................173

5.4.1 Objectives...................................................................................................173

5.4.2 Method .......................................................................................................173

5.4.3 Results ........................................................................................................175

5.4.4 Discussion ..................................................................................................185

5.5 FRAMEWORK FOR INTERVENTION DEVELOPMENT ..................................................186

5.5.1 Overview....................................................................................................186

5.5.2 Social marketing ........................................................................................187

5.5.3 Intervention mapping .................................................................................188

5.6 SUMMARY..............................................................................................................190

CHAPTER SIX: THE PRESENT CONTEXT OF MOTORIST BEHAVIOUR AT

LEVEL CROSSINGS ..................................................................................................191

6.1 INTRODUCTION ......................................................................................................192

6.2 STUDY AIMS AND RESEARCH QUESTIONS...............................................................192

6.3 INTERVENTION DEVELOPMENT AND IMPLEMENTATION ..........................................194

6.3.1 Overview....................................................................................................194

6.3.2 Heavy vehicle drivers.................................................................................195

6.3.3 Older drivers ..............................................................................................196

6.3.4 Younger drivers..........................................................................................196

6.4 METHOD.................................................................................................................197

6.4.1 Research design..........................................................................................197

6.4.2 Questionnaire measures .............................................................................198

6.4.3 Pilot testing ................................................................................................206

6.4.4 Ethical considerations ................................................................................207

6.4.5 Sample size calculations ............................................................................207

6.4.6 Recruitment strategy ..................................................................................208

6.4.7 Sampling method .......................................................................................209

6.4.8 Procedure and response rate.......................................................................210

xiv

6.4.9 Data management.......................................................................................213

6.4.10 Dependent variables ...................................................................................214

6.4.11 Data analysis ..............................................................................................214

6.5 RESULTS.................................................................................................................215

6.5.1 Heavy vehicle drivers.................................................................................215

6.5.2 Older drivers ..............................................................................................231

6.5.3 Younger drivers..........................................................................................250

6.6 STUDY LIMITATIONS ..............................................................................................268

6.6.1 Widespread limitations ..............................................................................268

6.6.2 Heavy vehicle driver sample limitations....................................................269

6.6.3 Older driver sample limitations..................................................................271

6.6.4 Younger driver sample limitations.............................................................271

6.7 SUMMARY..............................................................................................................272

CHAPTER SEVEN: DISCUSSION ..........................................................................277

7.1 INTRODUCTION ......................................................................................................278

7.2 OVERVIEW OF THE STUDIES, METHODOLOGY AND KEY FINDINGS...........................280

7.2.1 High risk and vulnerable road users at level crossings ..............................281

7.2.2 Planning and development of interventions for each road user group.......285

7.2.3 The present context of motorist behaviour.................................................291

7.3 IMPLICATIONS FOR LEVEL CROSSING SAFETY.........................................................296

7.4 STRENGTHS AND WEAKNESSES OF THE RESEARCH .................................................300

7.5 SUGGESTIONS FOR FUTURE RESEARCH ...................................................................302

7.6 RECOMMENDATIONS TO INDUSTRY ........................................................................303

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LIST OF FIGURES

Figure 1: Components of railway level crossing safety ............................................ 16

Figure 2: Estimated cost of collisions at level crossings........................................... 33

Figure 3: Estimated cost of collisions with different types of control ...................... 33

Figure 4: Example of types of rumble strips............................................................. 39

Figure 5: Contributing factors in highway-railway grade crossing collisions. .......... 76

Figure 6: Perceived risk ratings by Canadian drivers ................................................ 80

Figure 7: Driving behaviour responses to potential hazards...................................... 83

Figure 8: Integrated Model of Behaviour Change ................................................... 104

Figure 9: Risk of driver being involved in a casualty road crash............................. 130

Figure 10: Projected older driver fatalities in Australia, 1995-2005. ...................... 134

Figure 11: Austroads class 3 vehicles degree of speeding....................................... 143

xvi

LIST OF TABLES

Table 1: Number of public level crossings by protection type .................................. 20

Table 2: Level crossing occurrences .......................................................................... 27

Table 3: Level crossing accident fatalities ................................................................ 28

Table 4: Car occupants killed due to being hit by a train at a level crossing............. 28

Table 5: Source of rail accident costs in Australia, 1999 ($ million) ........................ 32

Table 6: Estimated collisions with different types of protection .............................. 34

Table 7: Comparison of fatal collision rates ............................................................. 36

Table 8: Fatal road crashes and fatalities, Australia 1981 to 1998 .......................... 142

Table 9: Data collected during formative research phase ........................................ 152

Table 10: Data collected from truck drivers during formative research phase ........ 166

Table 11: Level of aggregate train driver assigned risk to motorist behaviours...... 177

Table 12: Level of regional train driver assigned risk to motorist behaviours ........ 179

Table 13: Level of urban train driver assigned risk to motorist behaviours ............ 180

Table 14: Comparison of means between urban and regional train drivers............. 181

Table 15: Level of expert assigned risk to motorist behaviours .............................. 183

Table 16: Comparison of means between train drivers and experts ........................ 184

Table 17: Items and scales included in the T1 questionnaire................................... 199

Table 18: Items and scales included in the T2 questionnaire................................... 200

Table 19: Structure of the Rural, Remote and Metropolitan Classifications .......... 201

Table 20: Response rates compared across road user groups .................................. 213

Table 21: Company attrition .................................................................................... 217

Table 22: Mean age between companies ................................................................. 217

Table 23: Mean overall scores between shift work and driving at level crossings.. 218

Table 24: Crash involvement between companies................................................... 219

Table 25: Road crashes during past 3 years ............................................................. 219

Table 26: Contributing factors to road crash............................................................ 220

Table 27: Knowledge of level crossing rules and facts............................................ 220

Table 28: Modified DBQ ......................................................................................... 221

Table 29: Self-reported driving behaviour at crossings ........................................... 221

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Table 30: Intended driving behaviour at crossings .................................................. 222

Table 31: Bivariate correlations between dependent and independent variables ... 223

Table 32: Attitudes towards driving at level crossings ............................................ 224

Table 33: Perceived behavioural control whilst driving at crossings ...................... 224

Table 34: Mean scores for subjective norms of others ............................................ 226

Table 35: Belief of the likelihood of being involved in a level crossing collision . 226

Table 36: Beliefs of design and environmental factors at level crossings .............. 227

Table 37: Exposure to level crossing driving........................................................... 228

Table 38: Mean scores for familiarity at level crossings (boom gates) .................. 229

Table 39: Mean scores for familiarity at level crossings (flashing lights).............. 229

Table 40: Mean scores for familiarity at level crossings (passive signs only)........ 230

Table 41: Participant group and area classification at both time points .................. 231

Table 42: Licence conditions as reported by participants........................................ 232

Table 43: Medical conditions suffered by participants ............................................ 233

Table 44: Driving ability.......................................................................................... 234

Table 45: Driving self-assessment ratings between groups ..................................... 234

Table 46: Comparison of crash involvement and rurality at Time 1 ....................... 235

Table 47: Road crashes during past 3 years ............................................................. 236

Table 48: Self-report contributing factors to road crash .......................................... 236

Table 49: Mean overall scores between road crashes and driving instruments ...... 237

Table 50: Bivariate correlations for older drivers ................................................... 239

Table 51: Hierarchical regression of constructs on intention at level crossings ...... 240

Table 52: Attitudes towards driving at level crossings ............................................ 241

Table 53: Perceived behavioural control whilst driving at level crossings.............. 241

Table 54: Subjective norms of others ...................................................................... 242

Table 55: Beliefs of environmental factors at level crossings ................................ 243

Table 56: Exposure to level crossing driving........................................................... 244

Table 57: Familiarity and area classification at Time 1........................................... 244

Table 58: Familiarity with driving at level crossings (boom gates) ....................... 245

Table 59: Familiarity with driving at level crossings (flashing lights) ................... 246

Table 60: Familiarity of driving at level crossings (passive signs only)................. 246

xviii

Table 61: Comparison of experimental group outcome variables ........................... 248

Table 62: Repeated measure analysis of variance for the outcome variables.......... 249

Table 63: Participant group and area classification at both time points .................. 250

Table 64: Education level of younger participants .................................................. 251

Table 65: Mean overall scores between licence type and outcome variables......... 251

Table 66: Mean overall scores between gender and outcome variables ................. 252

Table 67: Comparison of crash involvement and rurality at Time 1 ....................... 253

Table 68: Mean overall scores between road crashes and outcome variables ........ 254

Table 69: Bivariate correlations between dependent and independent variables ... 255

Table 70: Hierarchical regression of constructs on intention at level crossings ...... 257

Table 71: Attitudes towards driving at level crossings ............................................ 258

Table 72: Perceived behavioural control whilst driving at level crossings.............. 259

Table 73: Mean scores for subjective norms of others ............................................ 259

Table 74: Beliefs of environmental constraints whilst driving at level crossings... 260

Table 75: Exposure to level crossing driving........................................................... 261

Table 76: Familiarity and area classification at Time 1........................................... 261

Table 77: Familiarity with driving at level crossings (boom gates) ....................... 262

Table 78: Familiarity with driving at level crossings (flashing lights) ................... 263

Table 79: Familiarity with driving at level crossings (passive signs only)............. 264

Table 80: Comparison of experimental group instrument mean scores................... 265

Table 81: Repeated measure analysis of variance for the outcome variables.......... 267

xix

LIST OF APPENDICES

Appendix 1: Review of models/theories ………………………………….. 364

Appendix 2: Modified Delphi Technique (First Questionnaire) ………….. 386

Appendix 3: Modified Delphi Technique (Second Questionnaire) ………. 390

Appendix 4: Survey instrument used in Study Two ……………………… 397

Appendix 5: Support letter from RACQ inviting Younger Drivers to

participate ……………………………………………………

399

Appendix 6: Support letter from RACQ inviting Older Drivers to

participate ……………………………………………………

401

Appendix 7: Intervention and Control Radio Script for Each Road User

Group ………………………………………………………...

403

Appendix 8: Questionnaires used in Study 3 ……………………………... 412

xx

GLOSSARY OF ACRONYMS AND TERMS

ALCAM Australian Level Crossing Assessment Model

ARA Australasian Railways Association

ARRB Australian Road Research Board

ARSCIG Australian Railway Crossing Strategy Implementation Group

ARTC Australian Rail Track Corporation

ATC Australian Transport Council

ATSB Australian Transport Safety Bureau

BAC Blood Alcohol Concentration

DBQ Driver Behaviour Questionnaire

DIPNR Department of Infrastructure, Planning and Natural Resources

ESCAP Economic and Social Commission for Asia and the Pacific

GDP Gross Domestic Product

HBM Health Belief Model

HIV Human Immunodeficiency Virus

HSE Health and Safety Executive

IM Integrated Model of Behaviour Change

IGA Intergovernmental Agreement

ITS Intelligent Transport Systems

ITSRR Independent Transport Safety and Reliability Regulator

LGA Local Government Association

MoT Ministry of Transport

MUARC Monash University Accident Research Centre

NHTSA National Highway Traffic Safety Administration

NRTC National Road Transport Commission

NTSB National Transportation Safety Board

ODSAQ Older Driver Self Assessment Questionnaire

OECD Organisation for Economic Co-Operation and Development

QR Queensland Rail

QT Queensland Transport

xxi

RACQ Royal Automobile Club of Queensland

RIC Rail Infrastructure Corporation

RLX Railway Level Crossing

SA Shire Association

RTA N.S.W. Roads and Traffic Authority

STD Sexually Transmitted Disease

THC Tetrahydrocannabinol (cannabis)

U.S.A. United States of America

xxii

STATEMENT OF ORIGINAL AUTHORSHIP

The work contained in this thesis has not been previously submitted for a degree or

diploma at any other higher education institution. To the best of my knowledge and

belief, the thesis contains no material previously published or written by another person

except where due reference is made.

Signed: _____________________________________________________________

Date: _______________________________

xxiii

ACKNOWLEDGMENTS

There is of course many people to thank for assisting and supporting my journey

throughout this PhD. Firstly, I would like to thank my husband Matt for his endless

emotional support, particularly when it all seemed too hard. Without your support, I

honestly don’t think I could have sustained my motivation. Secondly, I would like to

thank my parents for their ongoing support of my continuous journey of study. The fact

that they are proud of me doing my PhD has been a significant factor in keeping me

going.

Of course I need to thank my primary supervisor Associate Professor Jeremy

Davey. Thank you for providing me with so many wonderful opportunities to travel

abroad to present at conferences, to conduct media interviews and establish important

contacts. You have helped me keep my sense of humour along the journey, which has

been a saving grace. Also to my associate supervisors Adjunct Professor Vic Siskind

and Associate Professor Barry Watson, thank you for guiding me in the process of

‘working through it all’. Additional to my supervisory team, I need to thank Dr. James

Freeman for helping me clarify things in the end and assisting me in putting all the

pieces in the puzzle together.

Finally, I would like to thank the Rail CRC for their financial assistance and the

rail industry for supporting my journey of inquiry. I received nothing but support from

rail authorities throughout Australia, and hope that this PhD provides ‘real-world’

recommendations for improving level crossing safety for the future.

1

CHAPTER ONE: INTRODUCTION TO THE THESIS

1.1 Introduction ……………………………………………………….. 2

1.2 The research area ……………………………………...................... 3

1.3 Rationale for the research …………………………………………. 4

1.4 Theoretical framework ………………………………..................... 5

1.5 Research objectives ……………………………………………….. 6

1.6 Scope of research ……………………………………..................... 8

1.7 Thesis outline ……………………………………………………... 8

1.8 Summary ………………………………………………………….. 9

2

1.1 INTRODUCTION

Railway level crossing collisions in Australia are a major cause of concern for

both rail and road authorities. Despite the fact that the number of railway collision

fatalities in Australia has fallen in recent years, level crossing collisions constitute a

significant proportion of the national rail toll. Although rail transport is presently one of

the safest forms of land transport, collisions at level crossings are three times more likely

to involve fatalities as compared to all other types of road crashes (Afxentis, 1994). Of

all the types of road crashes which occur, those that involve a collision between a motor

vehicle and train are amongst the most severe. Conservative estimates of level crossing

collisions involving motor vehicles are valued to be approximately $10 million per year

(Bureau of Transport and Regional Economics, 2002), however more recent estimates

from industry experts indicate that this figure is closer to $50 million per year. With

many level crossing fatalities and injuries resulting in coronial inquests, litigation and

negative media publicity, the actions of rail and road infrastructure providers and the

behaviour of motorists, pedestrians and rail users, come under close scrutiny.

Historically, research in this area has been plagued by the rail/road interface and the

separation of responsibilities between rail and road authorities reflecting the social and

political context in which they are contained. With the recent rail reform in Australia

and the public demand for safety at level crossings, motorist behaviour has become a

key priority area.

Accordingly, there is a need to better understand the scope and nature of motorist

behaviour at level crossings, in order to examine the key determinants of behaviour and

associated constructs (e.g. intentions, attitudes, norms, self-efficacy or perceived

behaviour control, perceived risks, skills and abilities of motorists, and perceived

environmental constraints). Additionally, there is great need to investigate whether

targeted educational interventions produce any statistically significant changes in such

constructs in order to develop future road safety countermeasures for both intentional

and unintentional unsafe driving behaviour.

This thesis documents a program of research undertaken for this purpose.

3

1.2 THE RESEARCH AREA

In Australia, at-grade road/railway crossings are commonly referred to as ‘level

crossings’ and there are a large number of these across the rail network. ‘Active’ and

‘passive’ crossings are the two major types of railway level crossings. There are

approximately 9400 public level crossings of which approximately 2650 (28%) have

active protection, 6060 (64%) have passive protection and the remainder have other

control or protection (Ford, 2002). Passive crossings have no dynamic devices to warn

drivers of an approaching train and feature a static array of signs that remain constant.

These crossings have no lights, bells, booms, gates or other active devices warning of an

approaching train (Wigglesworth, 2001). Alternatively the term ‘active’ crossing

encompasses crossings that display a range of dynamic devices to alert motorists of

upcoming danger and impose a requirement to stop in unsafe conditions. Active crossing

devices include features such as flashing lights, bells, boom barriers and wooden gates,

which are activated through track circuitry operated by an approaching train

(Wigglesworth, 2001).

State and Territory rail safety regulators have agreed to use the following

definition of a railway occurrence:

Any accident or incident involving a railway train or other railway vehicles

operated on rails, whether in motion or not, or other event on railway property

affecting the safety of persons and property.

Includes:

• Collision, derailment, fire, explosion, act of God, or other event; and

• Slips, trips and falls on trains or railway infrastructure

Excludes:

• Occurrences in repair shops, not involving a train in motion; and

• Assaults (for national reporting only. Individual State/Territory Rail Safety

Regulators may require assaults to be reported. The later definitions of assaults

are designed to facilitate consistency between those States/Territories)

4

Note: The classification of an occurrence by type (collision, derailment etc) is

determined by the ‘top event’ in the sequence (i.e. the event with the greatest

outcome). This may not necessarily be the final event in a chain of events.

(Australian Transport Safety Bureau, 2004, pxv)

1.3 RATIONALE FOR THE RESEARCH

This program of research was motivated by a strong desire from within the rail

industry to investigate motorist behaviour at level crossing safety to support current

engineering efforts. However, a number of obstacles have hindered research into the

area of level crossing safety. Firstly, level crossings are amongst the most multifaceted

of road safety issues due to the accumulation of rail infrastructure, trains and train

operations (Australian Transport Council, 2003).

Secondly, minimal research has been conducted into collision involvement

patterns of drivers at level crossings, both within Australia and internationally. While

some studies have been conducted in this area, they have tended to be general in

approach and have not specifically examined contributing factors to collision

involvement. As with many crashes, the contributing causes and factors are often

difficult to determine, however a recent investigation of fatal collision at level crossings

supports the notion that human fault is a major contributor (Australian Transport Safety

Bureau, 2002b).

Thirdly, there is a lack of reliable data available relating to the behavioural

characteristics and perceptions of motorists at level crossings. Studies that do exist have

lacked a strong theoretical base to guide the interpretation of results. With many level

crossing fatalities and injuries resulting in coronial inquests, litigation and negative

media publicity, the actions of rail and road infrastructure providers and the behaviour of

motorists, pedestrians and rail users, come under close scrutiny.

Fourthly, there is a need to better understand the scope and nature of motorist

behaviour at level crossings, in order to examine the key determinants of behaviour and

associated constructs (e.g. intentions, attitudes, norms, self-efficacy or perceived

behaviour control, perceived risks, skills and abilities of motorists, and perceived

5

environmental constraints). To date, there has been no research conducted either in

Australia or overseas that has measured changes in behavioural constructs from targeted

educational interventions for specific road user groups. Such investigations assist in

directing both human efforts and financial resources in developing suitable road safety

countermeasures in attempting to change both intentional and unintentional unsafe

driving behaviour at level crossings.

Although engineering approaches have traditionally dominated risk management

strategies in rail safety, research suggests that such technologies are reaching their point

of diminishing returns. Due to the lack of financial viability of continuing to approach

risk management purely from an engineering perspective, the merits of behavioural

research need to be examined for suitability. In Australia, there has been considerable

recognition regarding the importance of human factor approaches to level crossing

safety (Australian Transport Council, 2003). However, little attempt has been made by

authorities to scientifically examine the present context of motorist behaviour at level

crossings or measure changes in behavioural constructs after exposure to educational

interventions. With the spate of several heavy vehicle-train collisions observed in rural

Victoria during 2007, both the public and industry alike are seeking both explanations

and solutions to improve level crossing safety for both road and rail users.

1.4 THEORETICAL FRAMEWORK

This research program has adopted a multidisciplinary approach to theory,

reviewing perspectives from psychology, sociology and public health to explore motorist

behaviour at level crossings. The examination of a broad approach of perspectives is

necessary due to the variety of behaviours involved in collisions and near-misses at level

crossings. The motivation underlying motorist behaviour determines to a large extent

how successful behaviour change strategies (e.g. educational interventions) may be.

Although no one concept is more precise than the other, some are certainly more

useful (Stafford, 1993). Fishbein’s Integrated Model of Behavioural Change (IM) based

largely on the health belief model, theory of reasoned action and theory of planned

behaviour (Fishbein, 2000), appears to be the most useful in exploring motorist

behaviour at level crossings. The three major determinants of intentions measured both

6

directly and indirectly in this model are attitudes, norms and self-efficacy. What is

likely to make this model more suitable in exploring level crossing safety is its

incorporation of both environmental factors and the skills and/or abilities of drivers –

two very important aspects when considering the complex environment that drivers face

at many level crossings. Additionally, the IM includes other relevant predictors of

intention and behaviour such as demographics, personality characteristics, which are

assumed to affect intentions indirectly through influencing underlying beliefs (Fishbein

et al, 2003). This model appears to also be valuable in guiding the development of data

collection instruments. Other factors such as sensation seeking and habitual behaviours

are not systematic psychological theories per se, and are not detected using this IM.

Therefore, such factors will be appraised using conceptual frameworks for explaining

motivations of motorists.

While the IM proposed by Fishbein assists in the exploration of the entire program

of research, it is particularly relevant to Study Three. Study Three has two main

purposes. Firstly, it assists in the planning, development and measurement of change

associated with exposure to targeted educational interventions specific to each of the

three road user groups identified in Study One. Secondly, it examines the present

context of motorist behaviour at level crossings using key constructs from the IM. As

no known research has been conducted that utilises any psychosocial model to explain or

predict level crossing behavior within different road user groups, this research program

uses this model as an exploratory tool rather than a tool to asses the model’s capacity in

explaining such behaviour.

Chapter Three will specifically review Fishbein’s model, while an extensive

review of a variety of theoretical perspectives that have been used to explain motorist

behaviour such as the theory of planned behaviour, health belief model, deterrence

theory and social cognitive theory, are included as Appendix 1.

1.5 RESEARCH QUESTIONS AND OBJECTIVES

The first study in the research program will examine the following important

research questions:

• What types of motorists are at risk of a collision?;

7

• What are the behaviours that motorists exhibit that increase their chances of a

collision?;

• How frequently do incidents and collisions occur?; and

• How frequently are incidents recorded by train drivers?

The overall objective of the second study is to provide information and

understanding about the knowledge, attitudes, norms, beliefs and risk perceptions of

motorists in relation to driving at level crossings. Specific research questions for Study

Two include:

• Do motorists perceive that they are at risk of being involved in a level crossing

collision?;

• What level of knowledge of the road rules exists?;

• What source and medium is believed to be the most appropriate for road users?;

and

• What differences exist between train drivers and experts opinions in terms of

high risk behaviours at level crossings?

Study Three will involve three parts. The aim of Part One of this study is to

develop targeted interventions specific to each of the three road user groups in

accordance with Fishbein’s theoretical model (Integrated Model of Behaviour Change).

Part Two will involve the investigation of the present context of unsafe driving

behaviour at level crossings. This includes examining knowledge, attitudes, beliefs,

perceptions, self-reported and intended behaviour, and environmental constraints of each

road user group. This second part will also involve the examination of the present

context of motorist behaviour at level crossings using key constructs from Fishbein’s

Integrated Model of Behaviour Change. Part Three involves trialing a pilot road safety

radio advertisement using an intervention and control methodology. This part will

investigate the changes in pre and post-test constructs such as intentions, attitudes,

norms, self-efficacy or perceived behaviour control, perceived risks, and perceived

environment constraints whilst driving at level crossings.

8

1.6 SCOPE OF RESEARCH

This thesis examines the behaviours of motor vehicle drivers at railway level

crossings. Collisions within station precincts, those at non-crossing locations,

trespassing, suicides and those in freight terminals are not included in this thesis.

1.7 THESIS OUTLINE

This research program is the first of its kind in examining the present context of

motorist behaviour at level crossings using an integrated model of behaviour change.

Additionally, it is the first of its kind in measuring change in key behavioural constructs

after exposure to targeted educational interventions. Although other ‘educational’

campaigns exist in this field, no interventions have been guided by empirical research or

psychological theory. The structure of the thesis reflects the specific tasks undertaken as

part of the research.

Chapter Two: Reviews the available evidence relating to collisions and near-

misses at level crossings, drawing on a variety of data sources. The major issues

examined are: the prevalence of collisions at level crossings; the behaviour of road

users; the personal, social and environmental factors contributing to the behaviour; and

the effectiveness of current countermeasures to manage risk. At the conclusion of the

chapter, a number of key research questions are identified to guide the research.

Chapter Three: Reviews Fishbein’s integrated model to examine the present

context of motorist behaviour at level crossings and assist in the planning, development

and measurement of change associated with exposure to targeted educational

interventions.

Chapter Four: Documents the first study undertaken as part of the research. This

study involved two types of data collection methods: (1) a modified Delphi technique to

gather information from road and rail experts to gain an informed judgment, and (2)

focus groups with train drivers to provide insight into the risk taking behaviour of

motorists at level crossings.

Chapters Five: Documents the second study undertaken as part of the research. It

involved formative research with members of the three road user groups identified in the

first study, as part of the planning and development targeted educational interventions

9

for the third study. Two forms of formative research (qualitative and quantitative) were

used to provide methodological triangulation and ensure reliability of the data.

Chapter Six: Reports on the findings from the third study which involved an

exploration of the present context of motorist behaviour at level crossings (using key

constructs from Fishbein’s IM). Additionally, this third study involved the planning and

development of targeted educational interventions, and the measurement of change

associated with intervention exposure. Older (60 plus years), younger (17-24 years) and

heavy vehicle driver groups were recruited through various agencies. Pre and post-test

questionnaires were provided to participants. The majority of analyses conducted with

heavy vehicle drivers were of an exploratory nature due to small sample sizes.

However, investigations of older and younger drivers also involved parametric

inferential statistics.

Chapter Seven: Concludes the thesis by discussing the findings from all three

studies in the context of the literature reviewed, as well as identifying limitations of the

research undertaken. Additionally, it presents the implications of the research findings

for further research and provides recommendations to industry stakeholders.

1.8 SUMMARY

This chapter has provided a brief overview of some of the obstacles that have

hindered research into the area of level crossing safety, as well as discussing some of the

theoretical issues relevant to understanding motorist behaviour. To date, limited research

has been conducted into motorist behaviour at level crossings, both within Australia and

internationally. Furthermore, financial resources have not customarily been directed

towards researching motorist behaviour at level crossings as fatalities constitutes less

than 1% of the national road toll. However, with the recent spate of several heavy

vehicle-train collisions observed in rural Victoria and the media attention these

collisions have attracted, both the public and industry alike are seeking explanations and

solutions to improve level crossing safety for both road and rail users alike.

The foundations for the current research program will be laid in Chapter Two

which will review the research literature relevant to the study of motorist behaviour at

level crossings. The remaining chapters will present and discuss the findings of three

10

specific studies undertaken to improve the existing body of evidence relating to level

crossing motorist behaviour.

11

CHAPTER TWO: LITERATURE REVIEW

2.1 Introduction ………………………………………………………... 12

2.2 Australia’s rail industry ……………………………………………. 12

2.3 Level crossing protection systems …………………………………. 18

2.4 Level crossing collision statistics in Australia …………………….. 25

2.5 Engineering measures ……………………………………………… 36

2.6 Laws and enforcement ……………………………………………... 56

2.7 Education, publicity and training ………………………………….. 60

2.8 Human factors contributing to collisions ………………………….. 72

2.9 Research questions ………………………………………………… 97

2.10 Summary …………………………………………………………… 98

12

2.1 INTRODUCTION

The following chapter will review the available literature relating to the nature and

extent of level crossing collisions and the effectiveness of relevant engineering,

enforcement and educational countermeasures. Firstly, an overview of the railway

industry in Australia will be explored to gain a greater understanding of the context in

which level crossing safety exists. This review of engineering measures focuses on

presenting current and potential risk management solutions for protecting motorists at

level crossings. Given the limited scientific evidence that is available that evaluates

such engineering measures on influencing motorist behaviour, the review is limited in its

critical interrogation of the literature.

Among the key questions that are explored in this literature review are: how

prevalent are collisions at level crossings; what are the contributing human factors to

collisions at level crossings; what engineering, enforcement and educational

countermeasures are currently in place to reduce collisions at level crossings; and how

different road user groups are at risk of being involved in a collision. These questions

are informed by the examination of human factors contributing to collisions that makes

up a substantial portion of this chapter. In this examination a more critical interrogation

of the literature is presented for educational programs that aim to influence motorist

behaviour at level crossings.

The aim of the chapter will be to consolidate the available research evidence and

identifying gaps in current knowledge relating to motorist behaviour at level crossings.

By reviewing the literature, it will lay a foundation for the subsequent program of

research that is undertaken for this thesis.

2.2 AUSTRALIA’S RAIL INDUSTRY

2.2.1 Economic importance

Australia’s railway industry is critical to the nation’s economy (Hill, 2001, p101).

The 40,000 kilometre rail network “serves intercapital markets and many important

economic regions” (Hill, 2001, p1). The majority of companies in the industry are

private profitable ventures trading in highly competitive domestic and international

13

markets (Australian Rail Association Incorporated, 2002) and generate 1.6% of

Australia’s gross domestic product (GDP) with output of goods and services worth $8

billion each year, including exports worth $0.5 billion per year (excluding coal and iron

ore) (Australian Rail Association Incorporated, 2002).

2.2.2 Rail activities

The rail industry in Australia is very diverse, consisting of rail operators (freight,

passenger, tourist and heritage), manufacturers, suppliers, consultants, track access

corporations, maintenance and construction contractors, logistics providers and a wide

range of other companies covering all sectors of the industry (Australian Rail

Association Incorporated, 2002). Rail freight services are a fundamental part of the

distribution process for interstate freight and a range of regional produce and bulk export

commodities (Hill, 2001), while urban rail systems are an essential part of the transport

infrastructure of cities, providing enormous economic, social and environmental benefits

(Australian Rail Association Incorporated, 2004b). According to the Bureau of

Transport and Regional Economics (2002), rail has consistently carried around a third of

Australia’s domestic freight in terms of tonne-kilometres during the past 25 years,

although increased competition from road transport and lack of investment in rail

infrastructure has led to a steady decline in the proportion of non-bulk interstate freight.

Around 80% of rail transport activity in Australia, measured in terms of the distance

traveled by both freight and passenger trains, occurs in the eastern jurisdictions of New

South Wales, Victoria and Queensland (Australian Transport Safety Bureau, 2003).

2.2.3 Reform agenda

Rail transport in Australia has undergone significant reform since the early 1990’s,

triggered by competition from road transport and government activity (Bureau of

Transport and Regional Economics, 2002). In November 1996, the Federal government

announced its rail reform agenda to provide the impetus to make rail competitive and

thus ensure it continues to be an integral part of Australia’s national transport system

14

(Department of Transport and Regional Services, 2004). The most significant outcome

from this rail reform process has been the fragmentation of the industry into multiple

operational, management and regulatory jurisdictions (Department of Transport and

Regional Services, 2004). As the prominence of interstate rail operations increased and

the rail industry was opened up to private operators, pressure was mounting to eliminate

State-based obstacles (such as differing safety standards and accreditation, to efficient

interstate operations). Now, the rail industry is progressively more seen as a national

industry not as a series of State-based industries (Australian Rail Association

Incorporated, 2004a). The Federal government believed that this change was necessary,

with Federal, State and Territory transport ministers signing an ‘Intergovernmental

Agreement on Rail Safety’, which came into effect in May 1996. This

Intergovernmental Agreement (IGA) is both a recognition of and commitment to the

need for a nationally consistent approach to rail safety (Department of Transport and

Regional Services, 2004). Safety arrangements under the 1996 IGA have reached a stage

of maturity whereby the administrative process is now functioning and safety is

considered a high priority (Australian Transport Council, 2002).

2.2.4 Rail safety management

Australia’s rail industry has been changing rapidly as a result of initiatives by

Commonwealth, State and Territory governments to reform historic structures,

policies and practices, and by rail organisations to improve customer service,

safety, asset quality and commercial performance.

(Affleck Consulting Pty Ltd, 2003, pv)

However, according to the Australian Transport Council, the safety arrangements

in Australia “continues to expose shortcomings as the rail industry develops and in the

distinction between operators on the interstate and intrastate networks become

increasingly redundant” (Australian Transport Council, 2002, p3). These shortcomings

relate mainly to interface management, performance and risk information, role

differentiation and administration (Australian Transport Council, 2002). Interface

15

management between the rail and road sectors requires specific attention to safety

management processes. The emergence of level crossing safety as a key priority area is

the result of the changing face of Australia’s railway industry.

Although safety at level crossings is only one aspect of transport safety within the

whole transport system (Australian Transport Council, 2003), it constitutes an

identifiable proportion of the rail toll. This no doubt has been the impetus for the release

of the ‘National Railway Level Crossing Safety Strategy’ by the Australian Transport

Council (ATC) in August 2003. The objective of this strategy is to reduce the “number,

cost and trauma of crashes between trains and any road users by the most cost-effective

means” (Australian Transport Council, 2003, p3). Additionally, level crossing safety has

for the first time been included in the National Road Safety Action Plan 2003/2004 but

remains excluded from most State and Local government road safety strategies. With

level crossing safety currently high on the rail industry agenda and the growth in level

crossing safety activities occurring throughout Australia, research examining key

determinants of motorist behaviour and road safety countermeasures is timely. Figure 1

illustrates the multifaceted system in which level crossing safety is situated (Australian

Transport Council, 2003).

16

Figure 1: Components of railway level crossing safety

2.2.5 National strategy initiative

Prior to 2002, there had been considerable activity with respect to level crossing

safety in all parts of Australia, but without much consistency or coordination (Wallace et

al., 2006). Important initiatives were being undertaken by State level crossing

committees, road agencies and the rail industry but they were largely unsystematic,

independent and unrelated. Several issues of critical local importance arose and were

being addressed in individual jurisdictions, when government Transport Ministers at the

ATC requested a framework for national level crossing safety to be prepared. In 2003,

the ATC endorsed the National Railway Level Crossing Safety Strategy, together with a

draft action plan for specific initiatives. The Australian Railway Crossing Strategy

Implementation Group (ARCSIG) was formed to coordinate the wide range of activities

on behalf of the ATC via the governments' National Transport Coordinating Committee,

the Standing Committee on Transport and the Rail Group.

Regulation

Industry practiceInformation

FundingEnforcement

Governmentpolicy

Technology Communityresponse

RoadInfrastructure

Road Vehicles

RailInfrastructure

Train Drivers

Rail Vehicles

Road Users

Environment

Education

17

The national strategy identified that there were many government activities that

required some measure of improvement. Consequently, the national strategy broadly

covers education, engineering, enforcement and policy issues. Upgrades to protection

systems at specific sites are outside the scope of this strategy as the responsibility lies

with government and the rail sector. Each national strategy activity is owned by

individual agencies and ARCSIG distributes information and provides liaison. Several of

the actions have been completed and several more have been added to the program.

ARCSIG is active in further improving the coordination between policy, engineering,

road and rail, education and enforcement agencies including state and national level

crossing committees.

2.2.6 Australasian railway level crossing behavioural strategy

After commencement of this project in early 2004, the Australasian Railways

Association (ARA) initiated the development of a behavioural strategy in early 2005.

This strategy seeks to form partnerships between industry, government, and road and rail

stakeholders to develop education and enforcement programs. This strategy is aiming to

introduce ‘Operation Lifesaver’ in each jurisdiction. The merits of ‘Operation Lifesaver’

are discussed in Chapter Two (Literature Review). The objective of this strategy is to

reduce the number, cost and trauma associated with level crossing collision through

behavioural programs that are developed and delivered in all jurisdictions. This is

thought to be achieved by:

• Macro level programs – encompassing mass media programs focusing on radio,

television, billboards and press advertisements;

• Micro level programs – encompassing targeted education and awareness

initiatives (face-to-face education, schools, high risk groups, high risk locations);

and

• Enforcement – encompassing programs aimed at penalising non-compliance of

the road rules at level crossings.

(Australasian Railways Association, 2006)

18

2.2.7 Roles and responsibilities for level crossing safety

Numerous agencies share the responsibility for ensuring safety at level crossings,

with the strategic coordination and oversight occurring through each State and Territory

level crossing safety committee. For example, in New South Wales there are nine main

agencies that share responsibility for level crossings. Their role in this responsibility is

summarised below.

• Australian Rail Track Corporation (ARTC) - manages and maintains the NSW

country and interstate rail network under a 60-year lease from the State

Government. ARTC also maintains the remaining country rail network under

agreement to the Rail Infrastructure Corporation (RIC).

• Department of Infrastructure, Planning and Natural Resources (DIPNR) - drives,

co-ordinates and streamlines land use and transport planning, infrastructure

development and natural resource management in NSW.

• The Independent Transport Safety and Reliability Regulator (ITSRR) - ensure

public transport and commercial railway operations are safe for use by the

communities and businesses of NSW.

• Local Government Association and Shires Association of NSW (LGA & SA) –

are the peak bodies representing the interests of NSW metropolitan, regional and

rural councils to other spheres of government and the wider community.

• Ministry of Transport (MoT) - provide independent, considered policy advice

and financial and strategic coordination for the transport portfolio to improve

passenger and freight transport service outcomes for the people of NSW.

• NSW Police - aims to protect the community and property by, preventing,

detecting and investigating crime, monitoring and promoting road safety,

maintaining social order, performing and coordinating emergency and rescue

operations. Other major services include traffic control, communications,

intelligence analysis, anti-terrorist negotiation and security coordination.

• Rail Infrastructure Corporation (RIC) - owns the NSW country rail network on

behalf of the State Government.

• RailCorp - owns and maintains the rail infrastructure in the greater metropolitan

Sydney region and delivers CityRail and CountryLink passenger services. It was

19

created in 2004 to provide safer, cleaner, more secure and more reliable rail

transport.

• NSW Roads and Traffic Authority (RTA) - is responsible for promoting road

safety and traffic management, driver licensing and vehicle registration. It is also

responsible for the maintenance and development of the national highway and

State road network in NSW. It provides funding assistance to local councils for

regional roads and to a limited extent, for local roads.

(NSW Government, 2006)

2.3 LEVEL CROSSING PROTECTION SYSTEMS

2.3.1 Types of protection systems

In Australia, at-grade road/railway crossings are commonly referred to as ‘level

crossings’ and there are thousands of these across the rail network. Such road/rail

intersections are unique in the transport sector as they present the only case of two

different infrastructures placed under different responsibilities and traveled by vehicles

with dramatically different performances (United Nations, 2000). Level crossing

protection systems used 60 to 100 years ago, such as boom gates designed to prevent

frightened horses from charging forwards into approaching trains as well as red flashing

lights which emulate the red lanterns that railroad employees swung back and forth to

warn of trains, still feature as operating procedures for rail safety (Green, 2002). Despite

the fact the rail infrastructure in Australia has developed substantially over the past

century, the original infrastructure of level crossings has changed very little (McBride,

2002). Level crossings today occur across a variety of road access types (highways,

other public roads, private roads, and access for rail agencies), road user categories

(motor vehicles, pedestrians, agricultural machinery, and other work vehicles such as

plant machinery) and rail use types (main lines, secondary main lines, branch lines,

heavy haulage lines, and restricted lines subject to seasonal use or tourism activities)

(McBride, 2002). Although protection systems operating at level crossings are less

visually effective than those used at highway intersections (Green, 2002), rail authorities

20

do not have the financial resources to update all level crossing protection systems in

Australia (Afxentis, 1994).

‘Active’ and ‘passive’ crossings are the two major types of railway level crossings

in Australia. Active crossings incorporate devices that warn motorists when it is safe to

use the crossing, either by visual or auditory cues (boom gates and/or twin alternating

flashing red lights). Passive crossings do not warn the motorist of the proximity of an

approaching train (stop or give-way signs only). Passive crossings (n=6060, 64%) are

the most common protection system for level crossings in Australia, followed by active

crossings (n=2649, 28%), with the remainder having other control or protection (Ford,

2002). Table 1 below illustrates the types of level crossings in each jurisdiction.

Queensland has the largest number of level crossings across the rail network with 37%

of Australia’s total number of level crossings (n=3500), followed by Victoria with 24%

(n=2209), 16% in Western Australia (n=1518) and 12% in New South Wales (n=1144).

Table 1: Number of public level crossings by protection type

Source: Ford & Matthews (2002)

21

2.3.2 Definitions

2.3.2.1 Passive crossings

Passive crossings have no dynamic devices to warn motorists of an approaching

train and feature a static array of signs that remain constant. These crossings have no

lights, bells, booms, gates or other ‘active’ device warning of an approaching train

(Wigglesworth, 2001). According to the Australian Standard (AS1742.7 – 1993)

‘Manual of uniform traffic devices – Part 7: Railway crossings’ (Standards Australia,

1993), a passive crossing is defined as:

Control of the movement of vehicular or pedestrian traffic across a railway level

crossing by signs and devices, none of which are activated during the approach or

passage of a train, and which rely on the road user detecting the approach or

presence of a train by direct observation.

2.3.2.2 Active crossings

Active crossing devices include features such as flashing lights, bells, boom

barriers and wooden gates, which are activated through track circuitry operated by an

approaching train (Wigglesworth, 2001). Flashing light crossings consist of (at least)

two sets of twin alternating flashing red lights installed to face each road approach

direction and are activated through track circuitry (Wigglesworth, 2001). Boom barrier

crossings are equipped with two booms hinged about a horizontal axis, one on each side

of the crossing, and are supplemented by flashing lights and warning bells which are

also activated through track circuitry operated by an approaching train (Wigglesworth,

2001).

According to the Australian Standard (AS1742.7 – 1993) ‘Manual of uniform

traffic devices – Part 7: Railway crossings’ (Standards Australia, 1993), an active

crossing is defined as:

Control of the movement of vehicular or pedestrian traffic across a railway level

crossing by devices such as flashing light signals, gates or barriers, or a

combination of these, where the device is actuated prior to and during the passage

of a train through the crossing.

22

2.3.3 Australian standards

A project commissioned by Austroads to examine safety at passive railway level

crossings that was timed to coincide with the ‘7th International Symposium on Railroad-

Highway Grade Crossing Research and Safety’, involving a workshop with

representatives from key stakeholder organisations, found that there needed to be some

changes made to the previous Australian Standard (1742.7-1993) for road signs and

marking at both active and passive level crossings.

The project identified four issues that required investigation. Firstly, the issues of

how best to implement a system of crossing identification numbers at both types of

crossings to enable control centres and emergency services to receive and transmit

accurate locations in the event of collisions or technical faults with protection systems

were raised (Cairney et al., 2002b). The second issue was whether it is possible to

develop an advance warning sign that coveys the message that a passive crossing is

approaching. Anecdotal evidence suggests that it is not clear that the distinction

between advance warning of passive and active crossings is intuitively obvious to

Australian motorists. A study by Mitsopoulos et al (2002) found that many road users

appear to be unaware that many level crossings do not have active protection. In this

driving simulator study, the majority of participants believed that all or almost all of the

railway level crossings in Victoria have active protection, although the actual proportion

is less than half (Mitsopoulos, 2002). The third issue was whether it is feasible to

provide a warning that high-speed trains use the railway line. The fourth issue suggested

by the project was whether it is feasible to provide an indication of general level of risk

associated with level crossings (e.g. use advisory speed as a way of indicating the risk to

road users) (Cairney et al., 2002b).

The current Australian Standard (1742.7) ‘Manual of uniform traffic control

devices’ (Part7: Railway crossings) was approved on behalf of the Council of Standards

Australia on the 19th January 2007 (Standards Australia, 2007). The principal changes

and additions to the previous Australian Standard (1742.7-1993) are summarised as

follows:

• The Standard now promotes use of the red background position sign, R6-25, for

new or replacement signs in preference to the open ‘crossbuck’ sign, R6-24;

23

• Provision is made for active advance warning of the activation of railway

crossing signals under certain conditions;

• More detail is given for sight distance requirements at passive control crossings

for stop and give-way sign control;

• The need to avoid unsafe queuing of traffic on railway crossings upstream of

traffic signals is recognised and the use of corrective measures including signs

and box markings are specified; and

• Standards for pedestrian crossing treatments at railway crossings have been

substantially upgraded and now include provision for people with disabilities.

(Standards Australia, 2007, p2)

However, there remain a few safety issues that are not able to be addressed by this

current Standard. Firstly, the length and mass of heavy vehicles is the most prominent

issue with this Standard.

Long and heavy vehicles with slow acceleration from the stopped position can,

when sight distance along the railway is poor, have difficulty starting up and

clearing a crossing before the arrival of a previously unseen train unless there is

some form of active control to warn that a train is coming. Likewise, long vehicles

can present problems if there is an intersection or other vehicular check point

close to the crossing on the departure side and the rear of such a vehicle

inadvertently fails to clear the conflict area.

(Standards Australia, 2007, p5)

Secondly, the Standard indicates that crossings that are infrequently used (such as

those used seasonally or in isolated areas) “present operational and management

challenges that can only be partially met (if at all in some cases) by application of this

Standard” (Standards Australia, 2007, p5). Additionally, the Standard does not provide

any guidance about upgrading crossings to the next treatment (i.e. passive control to

active control or active control to crossing closure), but rather refers readers to the

ALCAM (Australian Level Crossing Assessment Model) model.

24

2.3.4 International comparison of protection systems

2.3.4.1 Developed countries

In the United States in 2001, there were 154,084 public crossings and 98,430

private crossings (Federal Railroad Administration., 2004) intersecting 200,000 miles of

railroad track (Hall, 2004). In the United Kingdom, there are approximately 8000 level

crossings of various types on the UK national rail network, of which approximately 30%

allow only pedestrians to cross the railway (Rail Safety and Standards Board, 2004). Of

the crossings that are used by motor vehicles (n=5600), there are 1675 (34%) active

crossing and 3967 (66%) passive crossings (Rail Safety and Standards Board, 2004).

During the past ten years in the United Kingdom there has been an overall decreasing

trend in the total number of crossings across the rail network. The Rail Safety and

Standards Board (2004) propose that this is a result of the recognition that level

crossings represent a high risk element of the network that needs to be controlled and

lessened. In Finland, at the beginning of 2001 on the rail network of 5854 km there were

5162 level crossings, with 82% (n=4219) having passive protection (Pajunen, 2004).

These three examples illustrate that level crossings occur in large numbers in rail

networks in developed countries.

2.3.4.2 Developing countries

In developing countries, unprotected crossings are very frequent whereby a large

number of crossings are unofficial with construction of the crossing being done by local

individuals without approval from either the railway or road authorities (United Nations,

2000). The Indian Railway network has a route length of 62,495 kilometres with a total

of 40,445 level crossings being an average of one level crossing every 1.5 kilometres

(United Nations, 2000). Of this total number of level crossings in India, nearly 40% have

active protection (manual barriers) while 7% are completely unprotected with neither

passive or active protection systems (United Nations, 2000). In recent years, there has

seen a shift in the Indian Railways policy regarding protection systems of level

crossings. Level crossings with high traffic volumes (either by road or rail) are now

‘manned’ to open gates to road users (United Nations, 2000). Additionally, subject to the

availability of government funds, crossings which have a ‘traffic moment’ (i.e. train

25

movements x motor vehicle movements) of 100, 000 per day or more are being replaced

by either under or over-passes (United Nations, 2000). Vietnam has one of the densest

railway system with level crossings in Asia, with more than three times the level

crossing density of India (United Nations, 2000). Vietnam’s railway system has

approximately 4842 level crossings over a total network length of only 2712 kilometres

(one level crossing every 0.5 kilometres) (United Nations, 2000). Just under 75% (n=3,

600) of level crossings are unofficial crossings (not provided by Vietnam Railways) and

nearly 93% of all level crossings in Vietnam have no protection system (United Nations,

2000). In contrast to other countries throughout the world, Iran has a relatively small

number of level crossings. In Iran, there is a total of 5995 kilometres of railway network

with a total of 418 level crossings (344 official and 74 unofficial) (United Nations,

2000), equating to one crossing every 14.3 kilometres (United Nations, 2000). Of these

total number of crossings, 217 (69.52%) are equipped with active protection (warning

lights and barrier) and the remainder have no form of protection (United Nations, 2000).

2.4 LEVEL CROSSING STATISTICS IN AUSTRALIA

2.4.1 Data recording

Currently, exact statistics of level crossing fatalities in Australia are difficult to

determine. There are a couple of significant reasons for this. Firstly, vehicle-train

collisions are rare events, and therefore it is difficult to obtain valid information about

preceding behaviour (i.e. likelihood of a suicide attempt) and the frequency of near-

misses. Secondly, the ATSB, the chief organisation for transport safety, includes some

level crossing fatalities in the national road toll and some in the national rail toll. When

both a motor vehicle and a train are involved in a fatal collision, the ATSB counts the

fatalities as motor vehicle fatalities rather than level crossing fatalities (following ICD-9)

(Australian Transport Safety Bureau, 2002b). The ATSB term a 'level crossing accident'

as those involving a collision between a railway train or other railway vehicle and a road

vehicle or person on a public street (trams on public streets excluded) (Australian

Transport Safety Bureau, 2002b). A person is counted as a level crossing fatality if the

26

death occurs up to 12 months after the collision. For the purposes of this thesis, only

vehicle-train collisions are being examined.

Thirdly, most jurisdictions in Australia use different methods of categorising and

recording collisions. Until there exists a systematic method for ensuring that all

jurisdictions categorise and record collisions identically, it is difficult to make accurate

comparisons in collision data. Cairney (2002) recommends that the usefulness of

information from collisions at level crossings would be greatly improved by including

the variables that are currently collected for road crashes, such as vehicle type and driver

characteristics. As a consequence of the differing methods of recording of level crossing

collisions in Australia, there is a lack of definitive evidence available relating to the

extent and nature of level crossing collisions. Therefore, for the purposes of the review

of collision and fatality data for this thesis, data from the ATSB will be predominantly

used.

2.4.2 Incidence of occurrences

Due to the large number of near-misses at level crossings that do not involve a

fatality but have the potential to result in a catastrophic event such as a train derailment,

it is important to examine the occurrences of near-misses as well as collisions involving

fatalities. To date, the ATSB is unaware of the exact number of level crossing

occurrence rates in each jurisdiction (Australian Transport Safety Bureau, 2004),

however anecdotal evidence from train drivers suggests that such occurrences are far

more frequent than reported.

A level crossing ‘occurrence’ as provided by the ATSB (2004):

Any collision of a train or rolling stock with either a road vehicle, person, level

crossing safety equipment or gate, or any other occurrence that compromises

safety at a level crossing. Includes: cases of road vehicles causing damage to

gates, barriers or other equipment at level crossings; near miss incidents; any

case of a train running onto a level crossing when not authorised to do so; any

failure of equipment at a level crossing which could endanger users of the road or

path crossing the railway.

27

The latest available data from the ATSB on level crossing occurrences is provided

in Table 2. This data excludes level crossing occurrences involving tramways crossing

roadways and is provided to the ATSB from each jurisdiction in Australia. Data from

2001 and 2002 indicates that Queensland (n=673) has the highest number of occurrences

followed by Victoria (n=543) and New South Wales (n=526).

Table 2: Level crossing occurrences

Source: Australian Transport Safety Bureau (2004) ‘rail occurrences’

2.4.3 Incidence of fatalities

A study by the ATSB focusing on collision details recorded in the ‘fatality crash

database’, a national database holding records of collisions on public roads resulting in

at least one fatality, indicated that in any given year covered by this database, level

crossing collisions constituted no more than one per cent (1%) of fatal road crashes

(Australian Transport Safety Bureau, 2002b). The actual annual figures range from

0.5% to 1.0%, with an average of 0.7% (Australian Transport Safety Bureau, 2002b).

This database covered the years 1988, 1990, 1992, 1994, 1996, 1997 and 1998 (only part

of the year to date, as some of the relevant coroner’s reports have not yet been received)

(Australian Transport Safety Bureau, 2002b). The reason for recordings of these years is

that the database was only updated every second year up to 1996 and annually after this

year. The 87 cases in the study sample of the fatality database are the fatal collisions at

level crossings that occurred in these years. Compared to other road crashes, the number

of fatalities per 100 fatal level crossing collisions was slightly higher than the number of

fatal road crashes (Australian Transport Safety Bureau, 2002b). The ATSB database

indicates that there were 120 fatalities per 100 fatal level crossing collisions compared

with 113 fatalities per 100 other fatal road crashes (Australian Transport Safety Bureau,

2002b).

28

Table 3 shows the total number of deaths in Australia due to level crossing

collisions each year in the period 1997-2002, the period for which data is currently

available from the ATSB. It should be noted that these are fatalities due to collisions

between trains and road vehicles or pedestrians on public streets. Deaths due to

collisions between trains and road vehicles on private roadways are excluded as well as

suicides. A suicide is defined here as a death that a coroner has found to be a suicide.

Table 3: Level crossing accident fatalities

Source: Australian Transport Safety Bureau. (2003b). N.B. This data includes both motor vehicle occupants and pedestrians.

From 1997-2002, there were 74 deaths due to collisions between trains and motor

vehicles at level crossings (see Table 4). These 74 deaths represented less than 1% of the

national road toll in the same period. Nearly 70% of the car occupants who died as a

result of level crossing collisions were males, but no particular age group stands out

(Australian Transport Safety Bureau, 2003). Occupants of pick-up trucks or vans are

included with only three such fatalities seen in this time series (Australian Transport

Safety Bureau, 2003).

Table 4: Car occupants killed due to being hit by a train at a level crossing

Source: Australian Transport Safety Bureau. (2003b).

29

2.4.3.1 Nature of fatalities

According to the ATSB study of 87 fatal collisions at level crossings (Australian

Transport Safety Bureau, 2002b), the point of impact was more often the front of the

train rather than the side of the train. In 66% of this study sample, the point of impact

was at the front of the train, with 16% being at the side of the train and the remaining

being unknown (Australian Transport Safety Bureau, 2002b). The majority of collisions

occurred in a rural area or urban centre away from a capital city (67%), while 18%

occurred in a capital city and in the remainder of cases the location was unknown

(Australian Transport Safety Bureau, 2002b). With the bulk of collisions not occurring

in capital cities, it stands that only ten percent of collisions occurred at crossings with

boom gates. Forty-four (44%) percent of collisions occurred where the protection

system was passive, while 41% occurred where the crossing was some other type of

active warning system (Australian Transport Safety Bureau, 2002b).

Weather conditions were of little significance in contributing to collisions at level

crossings, as indicated in the ATSB fatality database. Eighty-five percent (n=73) of

collisions occurred in fine weather, with 84% of collisions occurring on a dry road

(Australian Transport Safety Bureau, 2002b). Compared to other fatal road crashes,

adverse weather or road conditions accounted for a slightly higher number of fatalities at

level crossings, with 13% (n=11) being due to such conditions compared with 9%

(n=1043) of other fatal road crashes (Australian Transport Safety Bureau, 2002b). In

terms of time of day, 83% of collisions at level crossings occurred in daylight (excluding

dawn or dusk) (Australian Transport Safety Bureau, 2002b). The majority of collisions

occurred on a weekday during the day (63%), while 14% occurred at night, two percent

at dawn and one percent at dusk, with the remainder unknown (Australian Transport

Safety Bureau, 2002b).

Unintended road user error was found to be more common in level crossing

collisions than other fatal road crashes, as indicated by the ATSB database (Australian

Transport Safety Bureau, 2002b). Forty-six percent (n=40) of level crossing collisions,

while only 22% (n=2708) of other fatal road crashes were recorded to be due to

unintended road user error (Australian Transport Safety Bureau, 2002b). The influence

of alcohol and/or drugs was significantly less common with only 9% (n=8) of collisions

30

involving alcohol and/or drugs, compared to 31% (n=3746) of other fatal road crashes

(Australian Transport Safety Bureau, 2002b).

Fatigue was not considered to be a major factor in level crossings collisions, with

3% (n=3) involving fatigue compared with 8% (n=925) of other fatal road crashes

(Australian Transport Safety Bureau, 2002b). Excessive speed was also less likely to be

a major factor, with only 7% (n=6) of level crossing collisions involving speed

compared to 23% (n=2794) of other fatal road crashes (Australian Transport Safety

Bureau, 2002b). ‘Other risk taking’, as recorded by the ATSB fatality database,

accounted for a similar percentage of fatalities, with 3% (n=3) involving risk taking

compared with 5% (n=560) of other fatal road crashes (Australian Transport Safety

Bureau, 2002b). The ATSB does not specify what ‘other risk taking’ is defined as.

2.4.4 Cost of collisions

Level crossing collisions have been shown to result in enormous human and

financial cost to society (Lobb, 2001). The Bureau of Transport and Regional

Economics (2002) provide the following information pertaining to collisions:

Accident costing is an inexact science. Cost estimates depend on the particular

costing approaches used, the number of accident cost components that can be

estimated, the quality and quantity of available data and the value of key

parameters used (such as the discount rate).

(px)

Although the quality of data used in the Bureau of Transport and Regional

Economics was reported as being low compared to the quality of data available for the

costing of aviation accidents and road crashes (Bureau of Transport and Regional

Economics, 2002), the total cost of rail collisions was estimated in 1999 to be $196

million (Bureau of Transport and Regional Economics, 2002). The total for level

crossing collisions was estimated to be approximately $10 million (Bureau of Transport

and Regional Economics, 2002). However, both railway experts and insurance

companies in Australia have revoked this estimation stating that it is grossly

31

conservative. Table 5 below illustrates the source of rail accident costs in Australia in

1999.

In 2003, Peter Cairney, an Australian expert in road safety, estimated the cost of

collisions at level crossings using the casualty and collision data presented by Ford and

Matthews (2002) and estimates of collision costs provided in 2000 by the Bureau of

Transport Economics (BTE) (Bureau of Transport Economics, 2000). The estimates

calculated by Cairney (2003) were based on these two documents, although Cairney

recognises some incompatibilities in the approaches adopted by the authors. This data

on property collisions was extracted from spreadsheets provided by Ford and Matthews

(2002) and the value of a property damage collision developed by the BTE allocated to

these collisions. Cairney (2003) points out that it is unknown how the distribution of

costs arising from collisions with trains compares to the distribution for all traffic

crashes, and hence regards his procedure as an approximation. Cairney (2003) also

extracted the total number of fatalities, serious injuries and minor injuries from Ford and

Matthews (2002) spreadsheets and applied relevant BTE costs for each. Figure 2

(Cairney, 2003) shows these totals for each jurisdiction and each class of protection

system is illustrated in Figure 3 (Cairney, 2003).

32

Table 5: Source of rail accident costs in Australia, 1999 ($ million)

Source: Bureau of Transport and Regional Economics. (2002). N.B. All figures in 1999 dollars, are based on a discount rate of 4 percent and are rounded to the nearest million dollars.

33

Figure 2: Estimated cost of collisions at level crossings

Figure 3: Estimated cost of collisions with different types of control

34

The breakdown of cost by protection system is illustrated in the table below.

Cairney (2003) suggests that 1996 was an exceptional year, with much higher costs than

subsequent years. The average cost of collisions, including 1996, was estimated by

Cairney (2003) to be $21 million at all crossings, comprising $13.8 million at active

crossings and $7.1 million at passive crossings.

Table 6: Estimated collisions with different types of protection Type of protection 1996 1997 1998 1999 2000

Passive: Give-way 14.4 4.4 5.9 3.3 1.1 Passive: Stop - - - 1.0 5.2 Sub-total at Passive Crossings 14.4 4.4 5.9 4.3 6.3 Active 19.0 11.5 17.5 8.6 12.5 Other control 0.3 - - - - Total 33.9 16.0 23.5 12.9 18.7

N.B. Totals slightly exceed column entries due to rounding errors and small sums in some individual cells. This table excludes NSW.

2.4.5 International statistical comparison

Level crossings are a characteristic of railways in most countries of the world.

Developing countries are known to have very poor level crossing safety performance,

with Vietnam, Thailand and Bangladesh having the highest collision and casualty rates

in the Economic and Social Commission for Asia and the Pacific (ESCAP) region. The

countries of this region include but are not limited to India, Vietnam, Thailand, the

Philippines, Bangladesh, the Islamic Republic of Iran and the Russian Federation. A

report (United Nations, 2000) evaluating the cost-effective systems for level crossing

protection in this region found that:

Railways are ill-equipped to be in a position to monitor level crossing safety

effectively and to take both corrective and pro-active measures to improve the

safety of their level crossing installations.

(p1)

Few of these railway systems appear to have an adequate safety information

system, which would support any rigorous assessment of safety hazards and risks.

(p122)

35

In terms of developed countries, international comparisons indicate that Australia

performs rather well. According to an Austroads project requested by the Australian

Transport Council (2002b) that was timed to coincide with the ‘7th International

Symposium on Railroad-Highway Grade Crossing Research and Safety’, held at Monash

University in Melbourne in 2002:

Australia appears to have fewer fatalities per 100,000 population at level

crossings than New Zealand, the United States and Finland, and to have

considerably fewer fatal crashes at passively controlled crossings than New

Zealand and Finland.

(Cairney et al., 2002b)

This report also indicates that in the United States in 2000, there were 425

fatalities at level crossings (Hall, 2002), a rate of approximately 0.15 fatalities per

100,000 people. Finland experienced an average of 7.9 fatal collisions per year and 10.6

fatalities per year at passive crossings, which is equivalent to 0.15 fatal collisions per

100,000 population and 0.21 fatalities per 100,000 population. The majority of crashes

in Finland occur at passively protected crossings (74%) and therefore appear to have a

higher fatality rate than the United States (Pajunen, 2002, Cairney et al., 2002b). In New

Zealand, there are approximately 7 fatal collisions and 9 fatalities per year, with 0.18

fatal collisions per 100,000 population and 0.23 fatalities per 100,000 population

(Cairney et al., 2002b).

Schmid and Watson (2004) attempted to compare level crossing data from selected

countries around the world and examined different formulae that are commonly used to

evaluate the risk at level crossings. However, this analysis, presented at the ‘8th

International Level Crossing Safety and Trespass Prevention Symposium’ in Sheffield,

England, was fraught with errors. Unfortunately the corrected data remains unavailable.

36

Table 7: Comparison of fatal collision rates

* estimated on basis of proportion of collisions at passive crossings. Source: Cairney, P., Gunatillake, T. and Wigglesworth, E. (2002).

2.5 ENGINEERING MEASURES

2.5.1 Overview of engineering measures

With human factors being a relatively new area of research in transport safety,

particularly rail safety, many of the protection and warning devices used at level

crossings are based on tradition, rather than sound human factors research and design.

Despite protection and warning devices at level crossings being visually less effective

than protection and warning devices used at highway intersections (Green, 2002), rail

authorities do not have the required resources to upgrade such systems to meet

worldwide standards such as the American Railroads Standards (Afxentis, 1994).

However, level crossings, particularly passive crossings, present inherent dangers to

road users and as long as they exist, rail authorities will remain at risk of being found

liable, in either whole or in part, for collisions at level crossings (Stephen, 2002).

Engineering design and warning systems are the most common element for which rail

authorities are found to be liable (Stephen, 2002).

Cairney (2003) states that “the form of traffic control implemented at a railway

level crossing greatly affects the decision that has to be made by the driver of the road

vehicle and the safety of the crossing” (p1). Where devices have been upgraded,

engineering solutions have demonstrated some ability to optimise the intrinsic safety of

roads at level crossings. There are a variety of engineering factors that need to be

considered in order to reduce collision risk at level crossings. These include signage and

markings; rumble strips; sight distances; information handling zones; crossing surface;

37

approach speed limits; boom barriers; conspicuity of trains; train horns; low-cost

warning systems; vehicle dimensions and performance; and crossing closure. Each of

these factors is considered by risk based scoring systems that assess risk at individual

crossings.

Despite such warning devices alerting motorists to be drive safely at level

crossings, the behaviour of the motorist approaching a crossing can have a significant

effect on the probability that a collision with a train will occur (Carroll et al., 1995). A

variety of motorist errors may contribute to a collision between a motor vehicle and

train, despite the use of warning and protection systems at level crossings. These include

(but are not limited to):

• Failure to detect the train before it reaches the crossing;

• Failure to recognise the potential hazard of a train; and

• Failure to correctly estimate when the train will arrive at the crossing.

(Carroll et al., 1995)

In part, these errors can be traced to the quality of the train-related information

(visual and audible) needed by motorists to take appropriate action when approaching

and crossing a level crossing. Carroll et al. (1995) suggest that the visibility of the train,

configuration and geometry of the crossing, and the peripheral vision of the motorist, are

all factors that impact on a motorist’s ability to acquire the information necessary in time

to avoid a collision. Train visibility at passive crossings with no active warning devices

or protection systems, is a particular concern. As such, it can be said that no matter how

skilled or experienced a motorist is, the physical environment and engineering systems

of a level crossing may contribute to a collision between a motor vehicle and a train.

Therefore, it is essential that motorists are able to detect trains approaching or at a

crossing and recognise the associated dangers as early as possible in their approach to a

crossing, in order to avoid a collision.

As part of a cooperative approach in reforming the rail industry in Australia, the

Australian Rail Operations Units was established to work with industry and the States

and Territories to develop a national voluntary ‘Code of Practice for the Defined

Interstate Rail Network’, that aims to assist in harmonising operational and engineering

38

procedures across the industry. This code is currently being implemented in the rail

industry throughout Australia.

2.5.2 Signage and markings

The Australian Standard (1742.7-2007) for signing and marking treatments at both

passive and active crossings is very comprehensive. This Standard:

…specifies traffic control devices to be used to control and warn traffic at and in

advance of railway crossings at grade. It specifies the way in which these devices

are used to achieve the level of traffic control required for the safety of rail traffic

and road users, including pedestrians. Requirements and guidance are also given

in appendices on the illumination and reflectorisation of signs, on their

installation and location, and on selection of the appropriate sign size.

This current Standard draws attention to the need for meaningful cooperation on

both safety and maintenance issues at level crossings between relevant road and rail

authorities (Standards Australia, 2007). It clearly asserts that the safety of road users is

dependent on their ability to detect the approach of a train and that any changes in

infrastructure or operation contemplated by one authority that may increase the risk

associated with operation of the crossings, needs to be agreed by the other authority

(Standards Australia, 2007).

2.5.3 Rumble strips

Rumble strips are either grooves or rows of raised pavement markers that are

placed perpendicular to the direction of travel to alert drivers to an approaching change

of roadway condition or hazard requiring substantial speed reduction (Washington State

Department of Transportation, 2004). The intention of rumble strips is to provide both

an auditory and physical (vibration) stimuli that can be felt by both the driver and

vehicle occupants. The corrugation height for passenger vehicles can be quite shallow

(around 8mm), yet still produce significant stimulus in a vehicle (Rechnitzer, 2002). The

39

use of tactile surface texture on roadways is a common design measure, and varies from

'rumble strips' on the edge of roads to 'corrugated surfaces' on the road way. Figure 4

illustrates some examples of rumble strips (Harwood, 1993).

Figure 4: Example of types of rumble strips

The usage of rumble strips at level crossings in Australia have been used from

time to time, but this use has not been systematic nor do the installations appear to have

been evaluated (Cairney, 2003). There has been a proposal to install a low cost rumble

strip system at passive crossings in Australia, which is only active when a train is

approaching (Rechnitzer, 2002). This rumble strip system is intended to be used in

conjunction with existing visual warning signs on approach and at the level crossing.

This system has been designed due to the infrequency of trains and the absence of

protection systems at passive level crossings, to warn motorists when a train is

40

approaching or not (Rechnitzer, 2002). Activation of this system would occur by an

electronic signal transmitted from the approaching train.

A review of studies of rumble strips in reducing road crashes at road intersections

performed by Harwood (1993), showed that although studies generally show a reduction

in collisions, it is not possible to quantify their effectiveness due to small sample sizes or

inadequate methods. Additionally, Harwood (1993) reports that rumble strips have not

been as effective as speed reduction measures. In the Netherlands, the effectiveness of

rumble strips in reducing speed, were examined as part of a large-scale project to

improve safety at level crossings commissioned by Railinfrabeheer B.V. Observations

were conducted on video recordings at 16 level crossings in the before-and-after study.

These video recordings were analysed quantitatively by determining the speed of free-

driving passenger cars and by conducting a time analysis of non-stopping/stopping

behaviour of pedestrians, bicyclists and motorists (van der Horst, 2002). This study

concluded that transversal rumble strips do not appear to be effective in reducing the

speed of free-driving passenger cars, neither on 50 nor on 80km/h roads (van der Horst,

2002). Cumulative speed-distributions did not differ significantly before and after

installation of these transversal rumble strips (van der Horst, 2002).

2.5.4 Sight distances

The sight of an approaching train may be obscured from a motorist’s view for a

range of reasons. According to Ward and Wilde (1995) “limited sight distance along the

tracks will compound the motorist’s difficulty in detecting an approaching train and the

estimation of its rate of approach” (p33). Objects in the driving scene, signs other than

the crossing signs, vegetation, buildings/structures, are all possible visual distractions

that may take a driver’s attention away from an approaching train (Caird, 2002). Failure

to detect a train by a motorist is largely the result of sight distance, particularly at

passive crossings (Berg, 1982). Some motorists may be aware that they have a restricted

sight distance, however may continue to appraise the prevailing hazard but still engage

in compensatory modification of their approach behaviour. Approach behaviour is

therefore modified to sustain a degree of perceived situation risk (Ward and Wilde,

41

1996). On the contrary, many motorists do not recognise that sight limitations present a

problem when driving at level crossings. Although sight distance is regarded by most

experts as being an important engineering factor in level crossing collisions, Ward and

Wilde (1996) argue that “evidence that restricted lateral visibility at railway crossings is

hazardous has not been forthcoming in spite of its apparent plausibility”. There has been

numerous research that has found that sight distance and level crossing collisions are not

correlated (Russell, 1974b, Zalinger et al., 1977, van Belle et al., 1975). Ward and

Wilde (1996) suggest that “the lack of evidence to link restricted lateral visibility with

higher accident rates seems paradoxical in light of the intuitive relationship between

these two variables” (p63). Schoppert and Hoyt (1968) state, whilst commenting on this

paradox:

This does not seem logical: sight distance should be one of the most important

variables. If the driver cannot see the crossing and down the track an adequate

distance, then he and his vehicle are being expected to perform beyond their

physical limitations.

(p29)

According to Dewer (2002) adequate sight distance depends on the speeds of both

train and motor vehicle. Mortimer (1988) argues that train visibility is sharply reduced

at level crossings that are at an angle to the road, with a significant number of level

crossings in the United States having angles between 60 and 90 degrees (approximately

80%) and 4% having very sharp angles of less than 30 degrees. It has been demonstrated

that the risk of a collision is seven times higher when the road is parallel to the tracks

and the train is approaching from behind the driver’s field of vision (Caird, 2002).

Alexander (1989) proposes that the limits of head movement for scanning at level

crossings are considered to be 110 degrees to the left and 140 degrees to the right.

Therefore, truck drivers may be at a disadvantage, with the design of truck cabins

limiting views for drivers. Dewer (2002) suggests that trucks typically have more

difficulty stopping and accelerating, hence the need for greater sight distance to stop and

to cross the tracks ahead of the train after stopping at the crossing. Dewer (2002)

recommends that a minimum of eleven (11) seconds of travel time by the train should be

42

available to the truck driver in order to accommodate acceleration capability and length

of large trucks.

2.5.5 Information handling zones

According to Tustin (1986) the situation faced by a motorist of any vehicle at a

crossing occurs in three areas or zones. Information handling zones are particular areas

of the road that motorists make decisions about the level crossing ahead (Tustin, 1986).

The three zones include:

• Approach zone – This zone is the area of the road in which motorists begin to

formulate actions needed to avoid colliding with trains. Scanning for trains or

signals, recognising any hazards, and deciding the proper course of action, are

behaviours that motorists use in this zone. The motorist must be aware of the

crossing ahead, with information usually provided to the driver by an advance

warning sign or pavement markings. The driver must take notice of the crossing

through visual observations, control devices or sounds from the train horn

(klaxon). Advance warning systems should be place in an area that provides

sufficient warning to motorists to alter their speed and take appropriate driving

action as required.

• Non-recovery zone – This zone begins at the point along the road where

motorists must make a decision to stop (after stopping at a ‘Stop’ sign or giving

way at a ‘Give Way’ sign) if a train is approaching. If the stop/go decision is

delayed beyond the beginning of the non-recovery zone, the amount of road

remaining will be insufficient to avoid a collision with an oncoming train. The

non-recovery zone ends at the beginning of the hazard zone, and starts at the

stopping sight distance required by the vehicle speed. Proper design and

installation of warning systems and control devices will provide the majority of

drivers with the information needed to make the decision (in time) to stop if

required. Provided with such information, the motorist must operate their

vehicle as required by the prevalent conditions (e.g. visibility of an approaching

train).

43

• Hazard zone – This zone is the rectangle formed by the width of the road and

distance measured along the road on either side of the tracks. This zone is the

area where stopped or approached vehicles are capable of colliding with

approaching or stationary trains. The objective of this zone is for the motorist to

cross the tracks safely. Obeying warning signals and protection systems is

crucial for crossing safely in this zone.

(p31-33)

2.5.6 Crossing surface

Improving the quality of the surface on approach to level crossings is one such

treatment that initiates great debate. Vehicles that are aware of a crossing ahead, but are

unable to stop in time due to poor quality road surfacing, may be at risk of a vehicle-

train collision. According to Glennon and Engr (2005), rough crossing surfaces are

sometimes alleged to be a contributing factor in distracting motorists from their primary

task of searching for a train. It is unknown however the extent to which crossing surface

is responsible for collisions at level crossings. More research is needed into the

correlation between crossing surface and distraction at level crossings.

2.5.7 Approach speed limits

A field study conducted by Ward and Wilde (1995) was undertaken in Ontario,

Canada to compare motorist approach behaviour between day and night-time conditions.

All observations of approach behaviours were conducted without the crossing signals

being activated. Day and night observation periods were made within the same level

crossing and in close temporal proximity to minimise confounding and any migration of

motorist characteristics (Ward and Wilde, 1995). This study found that approach

behaviour to the crossing examined was far more conservative at night than during the

day. Motorists in this study had consistently lower approach speeds throughout the

approach way and navigated the crossing at a slower overall speed at night (Ward and

Wilde, 1995). Additionally, there was some evidence of less braking overall at night,

with Ward and Wilde suggesting that the slower speeds were indicative of a slower

initial speed prior to the approach way, rather than by subsequent modification of speed

44

through braking during the final approach. Ward and Wilde (1995) argue that these

differences could not be attributed to systematic demographic changes in day and night-

time motorist populations.

2.5.8 Boom barriers

A program was conducted in Victoria during a 19-year period (1971-1989) in

which 91 metropolitan and major urban level crossings were upgraded from flashing

lights only status to boom barrier status (Wigglesworth and Uber, 1991). There was

significant reduction in fatal vehicle-train collisions during this period, with the

mortality rate being reduced from 5.71 to 0.33 (deaths per 100 crossing years). This

study compared these reductions with control sites (consisting wherever possible of the

next crossing on the same line as the upgraded crossing), which saw mortality rates

increase from 1.22 to 1.63 deaths per 100 crossing years (Wigglesworth and Uber,

1991). A second comparison was also made in this study – between the same 91

crossings and the 82 crossings on the same lines where the flashing light installations

had remained unchanged for the complete 19-year period. The mortality rate for this

second comparison group rose from 1.31 to 2.77 deaths per 100 crossing years.

Although this study was conducted a few decades ago, it is clear evidence that upgrading

level crossings with flashing lights to boom barrier status reduces fatal collisions at level

crossings (Wigglesworth and Uber, 1991).

2.5.9 Conspicuity of trains

Alerting devices that enhance train conspicuity, allowing attraction of motorist

attention, increase the likelihood that a motorist will see the approach of a train in

sufficient time to safely cross at a level crossing. There is no doubt that under certain

conditions, the failure of a motorist to detect an approaching train is a major contributing

factor in vehicle-train collisions. Passive crossings without flashing lights or boom

barriers make it difficult for motorists to detect a moving train and correctly estimate its

time of arrival to the crossing. With the majority of level crossings in Australia being

passively protected, it can be difficult for motorists to detect a train at night. One

45

important factor in the failure of motorists to detect an approaching train is the lack of

visual properties on the train, other than its standard headlight (Carroll et al., 1995).

Although the majority of vehicle-train collisions in Australia occur during daylight

hours, when normalised for differences in traffic volume between day and night periods,

collision rates are most likely to be substantially higher at night than during the day

(Darzentas and McDowell, 1981, Leibowitz, 1985, Russell, 1974b, Schoppett and Hoyt,

1968, Swedish Road and Traffic Research Institute, 1981). According to Ward and

Wilde (1995), it is apparent from differences between daytime and night-time collision

rates that there are qualitative differences between (1) the conditions encountered by

motorists during these different periods at level crossings and (2) the style of approach

behaviour enacted by motorists (p32).

Conspicuity depends primarily on the contrast between an object and its

background to make an object obvious. At night, lighting on trains assists with

achieving such a contrast. Historically, standard train headlights have provided a visual

signal to motorists of an approaching train at level crossings, however headlights were

not specifically designed for that purpose (Carroll et al., 1995). Strobe lights, ditch

lights, crossing lights, oscillating devices, paint schemes and reflective materials, have

all been equipped on locomotives to increase train conspicuity (Carroll et al., 1995).

Train conspicuity research has been conducted since the 1970’s, and since this time

there has been many technological changes, both in the vehicle’s internal environment as

well as in alerting device material technology and techniques (Carroll et al., 1995).

During the mid 1990’s, the Volpe National Transportation Systems Center (Volpe

Center) evaluated the effectiveness of available auxiliary external alerting devices that

may improve train conspicuity at level crossings (Carroll et al., 1995). This study

evaluated a variety of external visual alerting devices including several light systems, as

well as paint schemes and reflective materials. The Volpe Center used a multifaceted

methodology to evaluate how external alerting devices contribute to the ability of a

motorist to:

• Detect the approach of a locomotive before the train reaches the level crossing;

• Recognise the associated potential hazard; and

• Accurately estimate when the train will arrive at the level crossing.

46

Results from this evaluation indicated that alerting light design and operation can

improve train conspicuity. Other results were found:

• Passive alerting devices are considered to be of only limited effectiveness in

enhancing train conspicuity. Passive alerting devices should be used only as a

secondary technique to reduce collisions at level crossings.

• The use of auxiliary external alerting lights can be effective in improving train

conspicuity.

• Multiple lights, light intensity, spatial dimensions and angle, and pattern all

contribute to increasing the effectiveness of the visual alerting signal and

providing more attention by motorists.

• Train approach speed, sight distances and ambient light conditions should be

considered when specifying minimum and maximum levels for alerting light

luminous intensity and effective intensity.

• The provision of a low-beam intensity control which supplies a lower luminous

intensity level for the entire alerting light system, similar to a ‘dimmer’ switch

currently used for standard headlights, would reduce the potential for glare.

A ‘cross-eyed’ alerting light beam pattern with lights angled inward

and focused an extended distance down the track, appears to have the

positive features of a wider system beam width and range in front of

the train as well as less potential for blinding motorists.

(Carroll et al., 1995)

In Australia, controlled field trials of auxiliary alerting lights have been conducted

to evaluate the most effective warning to motorists (Hughes and Coles, 1986). These

trials concluded that a 100 watt combination driving/fog lights whereby two lights are

angled 7.5 degrees inward (cross-eyed) provided ample warning to motorists and

improved track illumination directly ahead and to the side for the train driver (Carroll et

al., 1995, Hughes and Coles, 1986). A Western Australian field study that reviewed

three lighting systems [(i) locomotive headlights alone, (ii) locomotive headlights and

ditch lights and (iii) locomotive headlights, ditch lights and a single roof-mounted strobe

light], concluded that neither of the two auxiliary lighting treatments improved the

47

conspicuity of the locomotive over that achieved by the standard headlights (Cairney et

al., 2002a). The results from this study indicated that a single strobe light did not

improve detection when added to trains already fitted with both headlights and crossings

lights (Cairney et al., 2002a). More recently, an Austroads report examined the prospects

for improving the conspicuity of trains at passive crossings (Cairney, 2003). This report

highlighted the importance of addressing the critical aspects of vehicle-train collisions at

passive crossings for train conspicuity, which included:

• Daytime collisions are approximately 70% of the problem, night-time collisions

approximately 30%; and

• A vehicle is struck by a train in 65% of cases, and a vehicle collides with the side

of the train in approximately 35% of cases. In fatal collisions, a higher

percentage (approximately 80%) involved the vehicle being struck by the train.

(Cairney, 2003, p29)

Cairney (2003) suggests two possibilities for addressing the issue of daytime

collisions. Additional lighting is one such possibility, however there is limited research

that has tested the effects of coloured strobe lights on the daytime conspicuity of trains,

and Cairney (2003) questions whether there are any suitable colours that have not been

preempted for other uses. The second issue is that of the effects of colour schemes

which contrast with the backgrounds against which a train is seen. Again Cairney (2003)

suggests that the effect of paint schemes in enhancing the brightness contrast they can

offer with the environment is limited and no data relating to train conspicuity is

available. With regards to collision reductions, this report suggests that “evaluating the

effectiveness of conspicuity treatments to trains in terms of collision reductions will not

be practical, due to the small number of collisions available for comparison, unless the

proportion of collisions prevented by the treatment is exceptionally high” (Cairney,

2003, pix). However, due to there being a smaller number of trains (approximately

2300) than passive crossings (approximately 6000), alerting devices on trains are likely

to cost less than installing low-cost active warning systems at passive crossings

(Cairney, 2003).

48

The 2002-2003 Annual Report of the Department of Transport and Regional

Services refers to the implementation of a number of key strategies to support safety

transport services; lead the development nationally of more consistency in rail regulatory

arrangements; and develop the Commonwealth’s role in rail safety and investigation.

Arising from this report, the House of Representatives (Standing Committee on

Transport and Regional Services) inquired into some of the measures that have been

proposed to improve train conspicuity and reduce level crossing collisions. From this

inquiry in June 2004, the House of Representatives recommended that:

The Australian Government takes steps, through the Transport Ministers Council,

to require that all locomotives and rolling stock in the Australian rail industry are fitted

with standard reflective strips or reflective paint and that all locomotives are fitted with

rotating beacon lights.

(2004, p12)

The Australian ‘Code of Practice for the Defined Interstate Rail Network’

specifies for lighting that:

• Headlights

Headlights may be fitted in a single central assembly, or as two

individual headlights positioned each side of the centre line of the

locomotive.

Each headlight should be protected by a separate circuit breaker, and

controlled by switches for off/on, main and dim positions.

Each lamp should be supplied from a separate circuit. The two units

should not use common components except for the headlight circuit

breaker and control switches.

Each headlight should incorporate at least two lamps, and should have

provision for adjustment in both the vertical and horizontal planes.

• Coupler lights

Protected coupler lights are optional equipment but should be

provided to illuminate the front of the locomotive in the vicinity of the

49

coupler and the ground below. They should not project light

forwards.

Switches for these lights should be located on both sides of the

locomotive at both ends and should be accessible from track

formation level.

• Road visibility lights

Low level lights should be provided on the ends of each locomotive to

provide road users and pedestrians with enhanced warning of the

presence or approach of a locomotive or train.

These lights should be positioned below the anti-climb beams and

near the outside of the underframe, and should be capable of

adjustment in both the horizontal and vertical planes. Each light

should normally be set to face in towards the track centre line; the

included angle between the lights shall be 10 – 15 degrees.

They should be protected from incidental damage under normal

operating conditions.

The lights should not be less than 100 Watts each and may be

combined with or incorporated into the fog or low visibility lights.

(Commonwealth of Australia, 2002, p50-51)

Illumination at crossings is another treatment option for improving conspicuity of

trains at level crossings. Detection of a slow moving train is far more difficult than a

fast moving train. This is one reason that illumination at crossings is paramount. Some

research in the U.S.A. has shown that crossing lights were the most effective treatment

of all lighting treatments for detection of trains at a level crossing (Federal Railroad

Administration, 2001). Other research has found that benefits of night-time illumination

at level crossings is approximately 30%, with benefits being somewhat greater at passive

crossings (Russell and Konz, 1980). It has also been suggested that greater illumination

is required at crossings that have greater frequency of trains which are slow or stopped at

crossings at night (Dewer, 2002). As train speed increases, the chance of a vehicle-train

collision decreases. Although the majority of vehicle-train collisions in Australia occur

50

during daylight hours, when normalised for differences in traffic volume between day

and night periods, collision rates are most likely to be substantially higher at night than

during the day (Darzentas and McDowell, 1981, Leibowitz, 1985, Russell, 1974b,

Schoppett and Hoyt, 1968, Swedish Road and Traffic Research Institute, 1981).

2.5.10 Train horns

Train horns (klaxons) are an important part of a train’s safety procedures, as it

serves as both an early warning for motorists as well as emergency alarm to convey

urgency to motivate a motorist to correct their action and clear the crossing (Russo,

2003). However, there has been little research into the effectiveness of such warning

devices for motorists, particularly with regards to the ambient noise inside the vehicle

being dominated by noise resulting from the vehicle’s operation (Rapoza, 2002). Much

of the literature has focused on noise levels to address complaints by residents who live

near tracks. A study by the Rail Safety Directorate at Transport Canada investigated

horn placement on locomotives and emitted sound to address excessive loudness

complaints from crews and residents near tracks (Transportation Development Centre,

2003). This study found that the location of a train horn was very important to its

effectiveness and a horn’s harmonic content is more important than its fundamental

frequency (Transportation Development Centre, 2003). An investigation for the Rail

Safety and Standards Board in the U.K. found that the environmental impact of noise

from the warning horns could be reduced with the horns still clearly audible at 400

metres by using devices that are 6 dB quieter than those currently specified for speeds of

160 km/h or less (Hardy, 2004). Another study investigating how to increase the

effectiveness of train horns without increasing intensity found that “train horns could be

made more effective by ensuring substantial mid-frequency energy, shifting the spectral

centroid higher, and increasing musical dissonance” (Russo, 2003, p51). In terms of the

effect that bans on train horns have on level crossing collisions, Zador’s (2003) study

found statistically significant and substantial increases in collisions (ranging from an

increase of 43.3% for active crossings with flashing lights to 52.6% for passive

crossings). However, other studies do not support these findings.

51

According to a study conducted by Aurelius and Korobow (1971) a train horn

would be an unacceptable nuisance if it had enough output to be totally effective in

warning motorists. Their study found that:

• The sound pressure level to alert a motorist (reliably) traveling at up to 50 miles

per hour (80 kilometres per hour) was 105 decibels;

• The warning level immediately outside the vehicle rose to 109 decibels, at speeds

between 50 and 65 miles per hour (80 to 105 kilometres per hour);

• High figures make no provision for age-related, occupation-related, or leisure-

related activities resulting in hearing loss, nor do they allow for the effect of

gusting wind; and

• Present horns cannot reliably warn motorists early enough of an oncoming train

(in high speed encounters).

Wigglesworth (2002) supports this view, proposing that enhancing the train horn is

an unacceptable nuisance and there is little point in further research “unless new

technology intervenes in some way” (p5).

2.5.11 Low-cost warning systems

Many motorists are “accustomed to driving in a well-engineered and carefully

controlled traffic environment with prior notification of any threat” (Wigglesworth,

2002, p9). When motorists (who live in urban areas) travel to more rural areas, they

expect to be told if a train in approaching a level crossing. There are studies that support

this argument. A study by Sanders (1976) found that 35% of Californian drivers thought

all level crossings had active warning systems. Installing flashing lights or boom

barriers (active protection) at all passive crossings in Australia, is not cost-effective for

the number of fatalities per year. Wigglesworth estimates that to install active protection

(approximately $300 000 per crossing) at all public passive crossings in Australia

(approximately 6000), this would cost approximately $1.8 billion. Government

authorities in Australia are unable to provide such funding to upgrade all level crossings

52

to active protection. Therefore, one alternative is to develop and evaluate installation of

low-cost warning devices at passive crossings. According to Jordon (2006)

With the high costs associated with traditional active level crossing protection,

many traffic engineers in the 1970’s and 1980’s expressed frustration at the

inability to be able to fund active protection at many of the passive crossings that

were the scenes of fatal crashes.

(p1)

The rationale for low-cost warning devices is basically to stretch the dollar further.

With many of these passive crossings being in isolated rural areas (with small road

traffic and train volumes), rail industry budgets typically are not able to provide more

active protection systems. In the early 1990’s in Victoria, a group of engineers united

to originate the Victorian Rail Level Crossing Committee. This committee’s interest in

alternative level crossing protection systems, alongside several Coronial Inquests into

fatalities of motorists at passive crossings in Victoria’s north, added weight to the

concerns for safety at passive crossings (Jordon, 2006). According to Jordon (2006) the

intention of such devices is “ not to replace the existing active device with a new low

cost option, but rather to have an additional device which could be used to improve the

conspicuity of selected passive crossings at the time that a train was in the vicinity” (p1).

The major reason for the high cost of warning systems is that track circuits are

electrically isolated sections of rail which are placed in electrical contact with other

sections by the passage of a train (Cairney et al., 2002b). “Modern sensing and

communications technology offers the possibility of lower-cost options for active

signaling which do not depend on track circuits for train detection” (Cairney et al.,

2002b, p16).

In 1999, a trial of five low-cost warning devices took place in Melbourne by

VicRoads and VicTrack, with the aim to improve road safety at remote rural rail

crossings by improving the conspicuity of the crossing at the time that a train is

approaching. The five devices included: Doppler radar unit, two magnetometers, an in-

train transmitter, and an induction loop. As a result of this trial, a Low Cost Level

Crossing Warning Device (LCLCWD) has been developed in conjunction with a

53

commercial partner that allows train detection to be achieved by loop detectors placed

between the tracks (Jordon, 2006). This device has robust detectors and radio linked

information transmission, is ideally suited to rural locations remote from mains power,

and is environmentally sustainable technology (Jordon, 2006). Additionally, it is

powered by solar cells with battery storage, which allows substantial savings (Cairney et

al., 2002b). In late 2004, the first real-life installation of the device took place. This

device has been undergoing ‘blind’ testing under full train conditions near Creswick for

nearly a year (Jordon, 2006). VicRoads and VicTrack are planning for this device to

soon become an accepted treatment for passive crossings in remote areas throughout

Victoria.

2.5.12 Vehicle dimensions and performance

The variety of vehicles that cross level crossings must be taken into account in the

design and operation of crossings (Tustin, 1986). Motorcycles, tractors, bicycles, trucks,

buses, vans, are types of vehicles other than cars that may travel over a level crossing.

These vehicles have widely different characteristics that may directly affect the design

elements of a crossing (Tustin, 1986). Of equal importance is the cargo/freight that

these vehicles carry (such as children in buses or hazardous materials in trucks) (Tustin,

1986). According to Tustin (1986):

Long vehicles, and vehicles carrying heavy loads, have longer braking distances

and slower acceleration capabilities; hence, long vehicles may be exposed to a

crossing for an even greater period of time than that in proportion to their length.

(p35)

Tustin et al. (1986) also suggests that the width of the vehicle may be a factor in

considering the width of the road surface and the length of the crossing surface

measured along the track. Trucks with greater widths are now becoming more

commonplace on Australian roads. The combination of under-clearance and wheelbase,

is another important component that is required to be considered in the design of level

crossings. Long trucks with low clearance may become lodged on a crossing if the

54

grades of the crossing and its approaches are not adequate (Tustin, 1986). Braking and

acceleration performance of a vehicle is another issue pertinent to level crossing safety.

The function of a vehicle’s braking performance is subject to stopping sight distance.

If a crossing experiences a significant percentage of heavy trucks, any given sight

distance will dictate a slower speed of operation to allow for the braking

performance of these vehicles.

(Tustin, 1986, p35)

The acceleration of vehicles is also of key importance. Clearing the crossing

before a train reaches the crossing, is of particular importance for trucks. Coupled with

their long lengths, trucks have relatively poor acceleration capabilities, which make

them hazardous at passively protected level crossings. The design and condition of the

crossing surface, is also considered an important safety aspect (Tustin, 1986). Crossings

and approaches built on a steep rise are often difficult and time consuming for vehicles,

let alone trucks, to cross (Tustin, 1986). All of these factors must be taken into account

by rail authorities when B-double approved routes are applied for by the heavy vehicle

industry.

2.5.13 Crossing closure

From the literature reviewed, it is evident that level crossings present a major

hazard to motorists and are typically the greatest cause of fatalities and injuries in rail

operations. The first treatment option at hazardous level crossings should always be

closure of the crossing. Crossing closure can be achieved either by: grade separation

(bridge or tunnel), closing the crossing to road traffic, or closing the crossing to rail

traffic through relocation or abandonment of the rail line (Glennon and Engr, 2005). In

considering the need and prioritising crossing closure, screening tools are required. In

Australia, closure of a level crossing is assessed by use of the Risk Based Scoring

System. Rail authorities actively seek to close level crossings, both public and private,

wherever possible. Level crossings can be nominated for closure by the score, local

governments, Main Roads department, the rail industry or the general public.

55

2.5.14 Risk based scoring systems

Models to assess risk at individual level crossings have received great attention

during the past few decades, with models existing for more than 60 years (Mok and

Savage, 2005). A comprehensive system for assessing risks at level crossings in

Australia and for assessing the likely impact of different treatments on these risks was

developed by Queensland Rail in 1999. The Queensland Level Crossing Safety Project

Team researched available risk scoring systems for level crossings, and found that there

was no single risk scoring system that was satisfactory for the purpose of assessing

safety risk at level crossings within Queensland. Therefore, they developed a new

system that used the best features of the systems that the team reviewed. This resulted in

the Australian Level Crossing Assessment Model (ALCAM) (Hughes, 2002). The

ALCAM is a process that objectively assesses, evaluates and prioritises the safety risks

of level crossings. It is also a method to determine the best treatment for individual sites.

This model allows for considerable improvements in safety at level crossings,

particularly at passive crossings where the use of common active treatments is not

possible (Hughes, 2002). Risk assessment for any given crossing involves examining the

matrix formed by the risk factors (29 possibilities) and the possible collision mechanism

(19 possibilities) (Cairney, 2003). The risk assessment consists of a total score (the sum

of all the cell entries) plus a score for each of the risk factors and a score for each of the

possible collision mechanisms. After determining that a level crossing is high risk and

what mechanisms are contributing to the risk, the system then becomes a valuable tool in

assisting to determine the treatments that are appropriate to reduce the risk (Hughes,

2002). The model allows for risk reduction strategies to occur in one of the following

ways:

• By installing new treatments at the level crossing (e.g. improved signage);

• By changing the characteristics of the level crossing (e.g. changing the road

speed or improving sight distances); or

• By changing road and the rail vehicle volumes at the level crossing (e.g. by

diverting road traffic to a nearby bridge over the railway line).

(Hughes, 2002)

56

This model also has the capacity to assess benefit/cost for proposed improvement

works, ensuring that each dollar is spent where it can generate the greatest safety

improvement. The factors include:

• Road and rail volumes and speeds at existing level crossings;

• Safety treatments;

• Number of tracks;

• Road quality;

• Vision of road vehicles to train;

• Vision of rail vehicles to road; and

• Adjacent road geometry.

(Williams and Creber, 2005)

It is important to note that this scoring system does not use information regarding

previous collisions that have occurred at level crossings to calculate a risk score,

although it has been found that the system produces results that have a very strong

correlation with collision history (Hughes, 2002). In May 2003, all Transport Ministers

in Australia agreed to adopt this innovative method of risk assessment. Additionally, this

model has been adopted by other countries such as the United Kingdom.

2.6 LAWS AND ENFORCEMENT

2.6.1 Overview of enforcement measures

The basic objective of enforcement is to ensure that the perceived costs of

infringing a law outweigh the perceived benefits of doing so…Research on traffic

law enforcement tends to indicate that the absolute size of the sanction is less

important than the subjective probability of apprehension. In other words, the

threat of detection tends to be a more effective deterrent than the severity of the

punishment.

(Pickett, 1996, p40)

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2.6.2 Level crossing laws in Australia

2.6.2.1 Australian road rules

The document ‘Australian Road Rules’ approved by the Australian Transport

Council and published by the National Road Transport Commission in 1999, provides

rules to be followed by all road users. They are part of a national scheme to provide

uniform road laws throughout Australia (National Road Transport Commission, 1999).

2.6.3 Enforcing laws at level crossings

2.6.3.1 Traditional methods of enforcement

Traditional methods of police enforcement for red light running involve police

patrols. However, police resources are typically limited in the amount of traffic

enforcement that can be conducted. According to Porter and Berry (2001) motorists

learn to slow down and obey the road rules when they see police, but they are aware that

enforcement is irregular and inconsistent. The traditional method for a driver violating

level crossings to be ticketed is if a nearby police officer observed them or a train driver

recorded their details and forwarded the information to police. The number of crossings

in Australia that have histories of violations and/or collisions makes the deployment of a

police officer at crossings both economically infeasible and practically impossible due to

limited police resources. As such, enforcement programs must be both cost-effective

and undisruptive to natural traffic flow.

Police presence may cause distractions at complex level crossing intersections

(Fitzpatrick et al., 1997), with automated enforcement technology being the most

practical solution at active level crossings. In smaller towns where more passive

crossings exist, police patrols at level crossings may have some deterrent effect. No

studies have been carried out in Australia that investigate any traditional enforcement

programs at level crossings. Although studies for other driving behaviours have found

positive results with enforcement programs, such results can not be generalised to other

driving behaviours as they do not produce similar results (Tay, 2005).

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2.6.3.2 Automated enforcement technology

“Automated enforcement typically consists of detection equipment and cameras to

capture images of drivers violating the law” (Lammert, 1999, p284). This information

technology countermeasure works in a similar way to red light cameras as it takes a

photo of any vehicle that illegally crosses the level crossings. Some research has

indicated that such devices improve safety at level crossings. In a trial in California,

level crossing enforcement systems cut the violation rate by 78-92% (Rathbone, 1995).

Pickett and Grayson (1996) suggest that an integrated program of red light cameras

coupled with high profile police campaigns offer the greatest change of success in

reducing red light violations at level crossings.

As part of a study by the Department of Infrastructure in Victoria (Public

Transport Safety Victoria, 2005), a Redflex Traffic Camera System was installed at

Springvale Road, Nunawading to observe motorist behaviour at this busy crossings.

This crossing was chosen for numerous reasons:

• Train drivers regularly report near miss incidents at the crossing;

• High volumes of rail and road traffic;

• Adjacent to a major road intersection; and

• Frequent road congestion at the railway line.

(Public Transport Safety Victoria, 2005)

This study found that on average more than 200 incidents of motorist non-

compliance with the road rules occurred each weekday, 116 incidents occurred on

Saturdays and 101 incidents occurred on Sundays. The three main high risk behaviours

taken by motorists included:

• Entering the crossing while the warning lights were flashing, warning bells were

ringing or boom barriers were closing;

• Entering the crossing while warning lights were flashing, warning bells were still

ringing and boom barriers were opening; and

• Entering the crossing even though the crossing or the road ahead was blocked

and it was not possible to drive through and to clear the rail tracks without

stopping.

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Of most concern with these findings is the frequency with which motorists stopped

on the crossing when it was blocked due to congestion ahead of the crossing. This

behaviour occurred at all times including when the flashing lights and audible signals

were indicating the approach of a train (Public Transport Safety Victoria, 2005). The

overall goal of this study was to provide footage to identify times and days when risk

taking behaviours by motorists are most likely to occur so that police are deployed to

deter traffic offences.

In New South Wales, RailCorp currently utilises ‘closed circuit television’

(CCTV) cameras across its network. These cameras record images of vehicles and

pedestrians using the crossing and signs at the level crossing notify users if a camera is

in operation. According to RailCorp, it collects, uses, stores and discloses CCTV images

for the purpose of:

• Monitoring and recording any offence or infringement, which may be reported to

the Roads & Traffic Authority or appropriate law enforcement agencies;

• Investigating safety/security incidents on or about level crossings;

• Monitoring the ongoing safety on or about level crossings and assess the need for

additional safety or maintenance measures; and

• Statistical or research purposes related to level crossing safety.

Images taken by RailCorp’s CCTV may be disclosed to the New South Wales’

Roads and Traffic Authority (RTA) or other law enforcement agencies such as the New

South Wales’ Police Service or Australian Federal Police (CityRail, 2005). However,

there have been no evaluation studies as to how cost-effective the installation of such

CCTV’s have been in reducing collisions at crossings in either New South Wales or any

other jurisdiction.

2.6.4 Fines and penalties

2.6.4.1 Overview

The fines for violating road rules at level crossings have historically been very low

in some jurisdictions in Australia in view of such a high risk interface between that of

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rail and road. Previous to March 2007, the fine for disobeying road rules at level

crossings in Queensland was $45. Since 1st March 2007, Queensland Transport

introduced tougher penalties for risky driving at level crossings, with fines now being

$225 and 3 demerit points. Fines for disobeying road rules at level crossings are slightly

higher in New South Wales ($300 and 3 demerit points). Up until June 2007, Victoria’s

fines were lower than that of Queensland’s. However, as a consequence of the Kerang

train disaster in early June 2007 in which a semi-trailer collided with a V/line train

(killing 11 passengers and injuring at least 22), the Victorian government announced a

toughening on penalties for infringements at level crossings. These penalties have risen

from $177 and 3 demerit points to $430 and four demerit points. Additionally, a new

offence has been introduced for speeding to ‘beat a train’, crossing tracks when lights

and bells are operating, or weaving in between boom gates that have descended. Such

behaviours now carry a fine of 30 penalty units ($3304), 4 demerit points and automatic

3 month licence suspension.

2.7 EDUCATION, PUBLICITY AND TRAINING

2.7.1 Overview

It can be said that no matter how skilled or experienced a motorist is, the physical

environment and engineering systems of a level crossing may contribute to a collision

between a motor vehicle and a train. In light of the ongoing research and experience of

the systems approach to road safety, the role that road user education, driver training and

publicity campaigns play in influencing safe driving behaviour is being re-examined

(World Health Organization, 2004). Motorists have a tendency to choose a certain level

of performance that depends on how they wish to cope with the task demanded of them,

which makes the distinction between performance and behaviour important in the

examination of such road safety interventions (Henderson, 1991, Haid, 2002). The

World Health Organization suggests that when used in support of legislation and law

enforcement, education, publicity and training can create shared social norms for safety

(World Health Organization, 2004). As there is currently no effective model for the

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relationships between how people drive and how they learn to do so safely, road safety

educational countermeasures must be developed through carefully designed human

factors and educational research (Henderson, 1991, Sentinella, 2004).

2.7.2 Driver training

Driver training is one area that has received great criticism. Driver training is

generally perceived by the public as being able to improve young driver’s behaviour.

According to Simpson (Simpson, 1995) there is a public perception that to attain its loss

reduction potential, the focus of driver training should be on those aspects of the driving

task that are linked to the risk of collision. However, research suggests that driver

training of a traditional and conventional nature contributes little to reductions in risk

among drivers of all age and experience groups (Mayhew, 1996, Woolley, 2000,

Christie, 2001). Christie (2001) argues that “the degree to which driver training can

address the negative influence of driver age and experience on crash risk and

involvement is probably limited…..and driver training for learner and novice drivers

may contribute to increased crash risk by encouraging early driver licensing” (p6).

Additionally, Haworth et al’s (2000) Australian research supports this view stating that

“despite the strong belief in the effectiveness of driver training courses by those

involved, there is no clear evidence that they are effective in lowering crash rates” (p18).

International studies reveal similar results. A well-known review of 30 scientific

evaluations of formal driver training, motorcycle rider training programs and advanced

training courses for novice drivers, found little support for the claim that driver training

is an effective countermeasure (Mayhew, 1996). Although it is important to examine

driver training as a road safety countermeasure, this form of countermeasure is not

within the scope of this thesis.

2.7.3 Publicity

The use of intensive road safety publicity campaigns are adopted by most

jurisdictions in Australia. Publicity, also known as public education, is usually

employed through television, radio, print media, press conferences and displays. Such

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publicity campaigns usually have mass media advertising as the most visible component

of the campaign. Speeding and drink-driving are generally the subjects of such

campaigns, due to these behaviours being leading contributing factors in fatal road

crashes. The Global Road Safety Partnership, a hosted program of the International

Federation of Red Cross and Red Crescent Societies (IFRC) based in Geneva, suggests

that there are three main types of campaigns:

• To raise awareness of an issue or to inform (e.g. about new laws);

• To change attitudes (e.g. to improve public acceptance of road safety

countermeasures); and

• To change behaviour, as part of a package of measures (e.g. engineering and/or

enforcement related to speeding).

(Global Road Safety Partnership, 2006)

The effectiveness of such campaigns has been the topic of great debate for many

years. Typically, measures of whether such campaigns are effective have been confined

to recall of the message or assessment of how the public received the message

(Henderson, 1991). However, Avery (1973) proposed that neither reception nor recall

were related to whether or not safety-related behaviours had been modified by the

message. Christie (2002) argues that positive change in behaviours has been reported

when the information consists of only a simple, single message (e.g. use a child restraint

and/or install a smoke alarm).

The continuing debate in Australia has been fuelled by the Victorian Transport

Accident Commission’s (TAC) use of highly emotive and dramatic road safety

advertising. The TAC campaigns have been extensively evaluated with mixed results

(Cameron, 1993, Newstead et al., 1995, White, 2000, Cameron, 2000, Tay, 2004).

Some studies have observed no significant impact on driver safety as an indirect result

of publicity campaigns, whilst other researchers have found campaigns to be extremely

effective in reducing road trauma. Donovan (1999) evaluated 12 television commercials

(four drink driving, four speeding, two fatigue and two inattention) selected from a pool

of approximately 100 commercials from Australia and New Zealand (including TAC

ads). The study design used for this evaluation was a monadic, independent sample, with

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potential target audience respondents being intercepted at suburban and city shopping

centres. The results indicated that there was no direct evidence of the inherent

superiority of dramatic, highly emotional ads (such as the TAC ads), over other less

dramatic, low emotion ads (Donovan, 1999). Additionally, the study found that several

of the 12 ads had substantially higher impact as a passenger than as a driver. Although

this evaluation used ‘likelihood’ dependent variables for analysis (i.e. (1) likelihood of

complying with the recommended behaviour as a driver, (2) and as a passenger, the

likelihood of influencing the driver to comply with the recommended behaviour),

Christie (2002) suggests that there are significant limitations to this evaluation by not

using injury and/or crash (or collision) reduction as outcome measures.

Common methodological limitations associated with evaluating the effectiveness

of campaigns have been highlighted by Christie (2002). Christie (2002) claims that the

effectiveness of publicity campaigns has generally received a poor record in crash (or

collision) and injury reduction terms. Evaluation of campaigns have rarely used injury

and/or crash (or collision) reduction as outcome measures, using acceptance, popularity,

and/or improvements in knowledge and skill as criterion measures (Duperrex, 2002,

Christie, 2002, O’Neill, 2002). Other common methodological limitations include the

simultaneous investigation of several variables using a correlational design, and the

reliance on limited outcome measures. Another explanation that may account for the

limited effectiveness of educational programs found in some research, relates to the

underlying effectiveness of the campaign development. Often campaigns are

commissioned by advertising agencies and are developed based on intuitive aspirations

rather than being guided by theoretical models and evaluative research.

According to a report to the TravelSafe Committee in Queensland, on achieving

high levels of compliance with road safety laws, Elliott noted that “missionary zeal

and/or a desire to ‘alleviate the carnage’ often means pressure applied to road safety

authorities and to politicians to do something” (Elliott, 1992). Elliott’s advice to this

TravelSafe Committee also included resisting temptation to just “do something” and

continue to apply proven methods of enforcement supported by publicity (Elliott, 1992).

The combination of supporting publicity with enforcement is not disputed by

experts. Homel et al (1988) concluded in their study of specific and general deterrence,

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that both mass media and visible enforcement are important for campaigns to have any

significant impact. However, there are areas in road safety that make the justification of

the use of police resources difficult. Level crossing safety is one such area. Therefore,

educational interventions designed using theoretical models and evaluative research,

allowing the targeting of existing perceptions, attitudes, beliefs and behaviours, is seen

to be a more cost-effective method of attempting to change intention of motorists at

level crossings.

2.7.4 Education programs and interventions

Education of the general public (publicity) and education of specific road users are

two distinct forms of road safety education, although they are both often referred to as

‘road safety education’ in the literature. Targeted education interventions/programs, on

the other hand, are principally designed with specific road user groups in mind. These

interventions/ programs are usually designed using best-practice principles of road

safety education (Elkington, 2003). With motorist error being considered an important

factor in the causation of road crashes, great emphasis has historically been placed on

reducing error through education programs. However, with the recent emphasis on a

systems approach to improving road safety, the reliance solely on education has been

questioned. This debate can be resolved by examining the effectiveness of such

programs by considering the empirical evidence. However, an exhaustive review of the

literature established that there is very little research that examines the effectiveness of

educational interventions for specific road user groups.

Di Pietro and Ivett (2003) propose that “the contribution of road safety education

and training as a road safety countermeasure should not only be evaluated against

immediate outcomes relevant to the public health agenda, but also against the future

requirements of maintaining positive health outcomes” (p1). They suggest that such

countermeasures play an imperative role in preparing road users by developing skills

and understandings, which contribute to the development of a ‘road safety culture’ in the

community. Their argument includes the notion that “whilst it is sensible to fund

countermeasures which will rapidly reduce the road toll, it is equally important that

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adequate funding also be allocated to education and training as part of a longer term

strategy” (p1).

Pedestrian education is one road safety area that has received positive results in

terms of education. A recent and extensive review of pedestrian safety in developed and

developing countries showed that it can change observed road crossing behaviour,

although its ability to reduce the risk of pedestrian injury in road crashes is largely

unknown (Duperrex, 2002). Another study (Leadbeatter, 1997) that found positive

results is the evaluation of the ‘RoadSmart’ program. RoadSmart is a primary school

road safety education program developed by VicRoads. Leadbeatter (1997) evaluated

the ‘Walking Safely’ component of the program and found that if teachers implement

the program in the intended manner, children cross roads more safely. A study

examining a program of educational and environmental (access prevention)

interventions designed to reduce the incidence of illegal and unsafe crossings by

pedestrians of the rail corridor at a suburban station in New Zealand (Lobb, 2001), found

that awareness-inducing interventions appear to increase knowledge of safe behaviours,

yet it is unclear as to whether they reduce injury rates (Lobb, 2001).

The argument against developing and implementing education

interventions/programs is largely led in Australia by Dr. Ron Christie a well-known

psychologist and road safety consultant. Christie (2002) argues that there is little

scientific evidence that road safety education contributes to reduced risk, injury or

fatality among those targeted. He suggests that such measures divert funds, resources

and attention away from better-based and more effective countermeasures (Christie,

2002). He also argues that road safety professionals and governments need to be more

questioning of the worth of educational and training approaches and have the courage to

say ‘NO’ to advocates, lobbyists and politicians who want to expend funds and

resources on unproven programs (Christie, 2002). Christie’s argument however is based

on the major functions of public health evaluation. Such initiatives, programs and

interventions rely on a reduction in death and injury, being cost-effective in terms of

expenditure of funds and resources, and do no harm (MacIntyre, 2000, Turnock, 1997).

The argument against education programs also encompasses the short-term

effectiveness. Additionally, Grossman and Garcia (1999) found in their study that

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examined systematic reviews of health promotion programs to increase seatbelt wearing

among young children, that the benefits were short-lived. This is one argument that must

be recognised in the development of any type of road safety intervention that is

educational in nature.

Review of the literature has found that most evaluation studies or systematic

reviews have been conducted on educational campaigns or long-running programs.

Very little research has focused on theoretically developed interventions that are brief in

nature. Although anecdotal evidence would indicate that such interventions would likely

to be ineffective, no research in level crossing safety has ever been conducted in the

delivery of educational interventions per se. Therefore, efforts to intervene on specific

road user groups through a single intervention educational approach deserve

consideration. An appraisal of level crossing safety programs both in Australia and

overseas is provided below.

2.7.5 Level crossing safety programs and interventions

2.7.5.1 Overview

Despite research that suggests an informed approach offers a more efficient and

less costly process with increased probability of success (Witte, 2000), it appears that the

development and analysis of effective messages to promote safe driving behaviour is

limited in the rail safety literature. In reviewing the literature on educational programs

for motorist safety, it must be noted that the context of a level crossing educational

program or intervention would be vastly different from generic road safety education

programs or interventions. The reason for this is that a level crossing program and/or

intervention would target specific issues pertaining to crossing safety rather than

providing general educational information.

2.7.5.2 Australian interventions and programs

To date, there has been no scientifically evaluated level crossing education

program or interventions for motorists in Australia. However, there have been

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numerous attempts by many jurisdictions to try to ‘educate’ the public about both

pedestrian and motorist safety at level crossings.

In South Australia, the ‘Don’t Play with Trains’ safety campaign (2004/05 –

2006/07) aims to inform all road users that their behaviour is the key factor in collisions

at level crossings. The television and radio commercials were first aired in June 2005

and again in July 2006 during National Rail Safety Week (23-29 July 2006). The

campaign was also aired in February 2007. Two commercials were developed (one

commercial for motorists and the other for pedestrians) with the aim to demonstrate that

risk taking at level crossings for both motorists and pedestrians is hazardous

(Department for Transport Energy and Infrastructure, 2006). The commercial for

motorists focuses on two intertwining scenarios. The first scenario seen is that of two

young men driving a work utility vehicle in a rural/remote area whilst talking about

football. The utility comes across an unprotected level crossing and the driver does not

stop at the crossing as the commercial leads the viewer to believe that the driver assumes

there is no train coming. The viewer observes the utility being hit by the train with the

viewer believing it is a fatal collision. The other scenario is that of a middle-aged mother

with two primary school-aged children in the back of the car. She is talking to the

children about what they did at school when she enters a metropolitan level crossing

behind a truck and gets caught when the boom gates are descending. The view also

observes this car being hit by the train with the viewer believing that it is a fatal

collision. An audio script is read by a male while both scenarios are played out. At the

end of the commercial the voiceover says “don’t play with trains”.

The impetus for this campaign was brought about by the collision between a

passenger train and a bus and car on the level crossing at Park Terrace, Salisbury in

South Australia in October 2002. Tragically, four people died (including the bus driver)

and 26 people sustained serious injuries. The investigation into this collision resulted in

a number of recommendations including the development of a public education

campaign highlighting the dangers of level crossings.

The NSW Roads and Traffic Authority (RTA), with assistance from the Railway

Infrastructure Corporation, conducted research into motorist behaviour and collisions at

level crossings. This market research found that rural/remote community attitudes to

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level crossings included complacency and high risk behaviour. The objectives of this

campaign were to:

• Increase awareness of the importance of obeying road rules at level crossings;

• Create awareness of the dangers caused by the stopping limitations of trains; and

• Increase safe behaviours by motorists using railway level crossings.

(NSW Roads and Traffic Authority (RTA), 2006)

In Victoria, a $1 million advertising campaign was launched in November 2005,

including television, radio and outdoor advertising. This campaign was prompted by the

number of traffic offences at the Springvale Road level crossing that was part of a video

camera trial. More than 5000 offences were recorded during 28 days of recording. A

combination approach is being used in Victoria, with trialing of new road safety

initiatives as well as greater enforcement at six of the highest risk level crossings in

Melbourne. The first phase of the education campaign targeted metropolitan motorists

with the campaign ‘Don’t risk it. Always keep the crossing clear’, while in regional

Victoria, the campaign is ‘Don’t risk it. Slow down and be prepared to stop’. Other

initiatives that are being trialed as part of this campaign include:

• A trial of the effectiveness of yellow cross-hatching (yellow grid lines painted on

the road) at particular level crossings to prevent queuing;

• A review of the operation of traffic signals located near crossings to ensure they

do not trap vehicles on crossings;

• Trialing new illuminated signs to warn pedestrians of the approach of second

trains; and

• A review of the safety features of Victoria’s 3,000 crossings to determine the

most appropriate treatment for each.

(Metlink Victoria, 2005)

The next phase of this education campaign, developed with VicRoads, Transport

Accident Commission (TAC), Connex, V/Line and Victoria Police, aims to also target

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high risk pedestrians including children, teenagers, the elderly, cyclists and the mobility

impaired.

In Queensland, the ‘Rail Smart’ campaign was launched in July 2006 as an

initiative that brings together a number of safety related activities that QR conducts on

an ongoing basis, including safety awareness and education activities which addresses

level crossing safety, corridor trespassing and general rail user safety issues. This

campaign is broadcast through a 30 second television commercial, which has two main

themes: (1) QR Rail Smart (generic rail safety message) and (2) specific safety messages

such as ‘be alert near level crossings’. This campaign is also delivered through:

• Television Community Service Announcements;

• Supporting collateral;

• Rail property media placements;

• Development of a Rail Smart message in conjunction with the Queensland Police

Service and the Queensland Police Citizens Youth Club;

• Incorporating Rail Smart activities in the QR Reds Talent ID Clinics throughout

regional and metropolitan Queensland during the 2007 Super14 Season; and

• Delivering Rail Smart messages through QR's community education unit (CEU)

as part of its state-wide primary, secondary and tertiary student programs. (In

2005 160,659 students were addressed in 500 schools in Qld).

(Queensland Rail, 2007)

2.7.5.3 International programs

‘Operation Lifesaver’ is probably the most well-known education and awareness

program for level crossing safety in the world. This program is currently run in the

United States and Canada as a non-profit program, with its mission to educate both

drivers and pedestrians to make safe decisions at level crossings. The program began in

1972 as a one-time, six-week only public awareness campaign in the state of Idaho

(Hall, 2002). This campaign focused on several key audiences, with volunteer presenters

delivering messages to driver education students, professional truck drivers, school bus

drivers and law enforcement agencies. During this six-week program in Idaho, a

reduction in fatalities was observed. The following year, the program was rolled-out

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into other states, with lowering of collision rates observed. By 1986, there were 49

independent Operation Lifesaver programs operating in the United States. These

programs focus on the three E’s of road safety: engineering, enforcement and education.

A study by Savage (2006) which used a negative binomial regression to estimate

whether variations in Operation Lifesaver activity are related to the number of collisions

and fatalities at level crossings. Annual data on the experiences of 46 states from 1996-

2002 were used, as only data for this period was able to be analysed due to incomplete

and questionable data from previous years. Additionally, the effect on the number of

fatalities cannot be concluded with statistical certainty, due to a small number of

fatalities compared with collisions. Savage (2006) suggests that there is considerable

year-to-year random variability in the number of fatalities, hence making it more

difficult to find statistical robust relationships. Savage (2006) also suggests that

quantifying and evaluating enforcement activities is more difficult than quantifying the

effects of engineering solutions. Although Savage’s (2006) study only used annual data

from 1996-2002, due to being the only complete and accurate data since 1972, an earlier

study by Mok and Savage (2005) analysed pooled data from 1975-2001. This study

disaggregates the improvement in level crossing collisions and fatalities into its

constituent causes. Negative binomial regressions were also used in this data analysis.

The findings from this study suggest that approximately:

• Two-fifths (40%) of the decrease in collisions and fatalities are due to factors

such as reduced drunk driving and improved emergency medical response;

• One-fifth (20%) of the decrease in collisions and fatalities is due to the

installation of gates and/or flashing lights;

• One-tenth (10%) of the decrease in collisions and fatalities is due is due to

closure of crossings resulting from line abandonments or consolidation of

infrequently used crossings;

• One-seventh (14%) of the decrease in collisions and fatalities is due is due to

‘Operation Lifesaver’; and

• One-seventh (14%) of the decrease in collisions and fatalities is due is due to the

installation of additional lights on locomotives (during the mid 1990’s).

(Mok and Savage, 2005)

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2.7.5 Limitations of current level crossing safety programs

Reviewing the evidence of the effectiveness of road safety campaigns reveals that

many have typically been developed in a haphazard manner, and may have limited

effectiveness in improving road safety. Mass media advertising, such as those used by

all jurisdictions in Australia to tackle level crossing safety and the ‘Operation Lifesaver’

program in the United States and Canada, have neither been evaluated for effectiveness

in terms of self-reported or intended behaviour. Additionally, in Australia campaigns

have typically been isolated (i.e. not involving increased police presence) and therefore

have not attempted to apply principles for maximising the effectiveness of road safety

campaigns. It is noted that observational studies or recordings is an expensive road

safety initiative, and is most likely economically not viable for all level crossings in any

country. However, to date there has been no effort or attempt to develop any type of

campaigns on theory.

It is well known that theoretically grounded campaigns developed in accordance

with research and targeting specific road safety issues can provide a more effective

means of risk management. Indeed research investigating the effectiveness of

educational programs targeting specific road safety issues has found programs to be

highly effective in reducing road crashes (Cameron, 1993, Durkin, 1999, Powles, 1993).

Finally, evaluations of cost/benefit ratios by Guria (1999) of road safety initiatives

programs, suggests that education and advertising campaigns produce high incremental

returns as compared to alternative methods of risk management such as engineering

approaches. These positive research outcomes coupled with this favorable economic

evaluation, suggests that the current investment in road safety programs is below optimal

and warrants further research into theoretical models and evaluative research appropriate

for effective program development.

With engineering approaches at level crossings no longer being a viable option in

terms of cost, the role of targeted educational interventions in informing future programs

needs to be examined. Without the evaluation of targeted interventions for specific road

user groups, campaigns and programs generated by government authorities will no doubt

be developed without the application of theory or the findings from scientific evidence.

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2.8 HUMAN FACTORS CONTRIBUTING TO COLLISIONS

2.8.1 Overview of accident causation

There are two perspectives on human factors in accident causation: the ‘old view’

and the ‘new view’ (Dekker, 2002). The ‘old view’ (Reason, 2000, American Medical

Association., 1998) sees human error as a cause of failure by which it is seen in the

following way:

• Human error is the cause of most accidents;

• The engineered systems in which people work are made to be basically safe;

their success is intrinsic. The chief threat to safety comes from the inherent

unreliability of people; and

• Progress in safety can be made by protection these systems from unreliable

humans through selection, procedurelisation, automation, training and discipline.

(Dekker, 2002, p372)

The ‘new view’ sees human error not as a cause but as a symptom of failure

(American Medical Association., 1998, Hoffman, 2000, Reason, 2000, Woods, 1994)

and includes the views:

• Human error is a symptom of trouble deeper inside the system;

• Safety is not inherent in systems. The systems themselves are contradictions

between multiple goals that people must pursue simultaneously. People have to

create safety;

• Human error is systematically connected to features of people tools, tasks and

operating environment. Progress on safety comes from understanding and

influencing these connections; and

• Human error represents a substantial movement across the fields of human

factors and organisational safety and encourages the investigation of factors that

easily disappear behind the label ‘human error’.

(Dekker, 2002, p372)

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Recent studies have found that unsafe driving acts can be classified into three

distinct categories: errors, lapses and violations (Reason, 1990b). According to Reason

et al. (1990b), in considering the human contribution to accidents, it is necessary to

make a distinction between these categories. Previous research has indicated that these

three main types of aberrant driver behaviour have different psychological origins and

demand different modes of remediation (Parker, 2000). According to Parker, Reason,

Manstead and Stradling (1995):

• Lapses are typically absent-minded behaviours with consequences mainly for the

perpetrator, posing no threat to other road users;

• Errors are typically misjudgments and failures of observation that may be

hazardous to others; and

• Violations involved deliberate contraventions of safe driving practice.

Such measures of driving behaviour are typically examined within the Driver

Behaviour Questionnaire (DBQ) (Reason, 1990b). There have been several versions of

the DBQ over the years, with Lawton et al’s (1997a) study subsequently extending the

violations scale by added new items to the original DBQ. Factor analysis conducted by

Lawton et al (1997a) showed that violations can be split into two distinctive scales

according to the reason why drivers violate. The aggressive violation scale contains an

interpersonally aggressive component, while the ordinary violation scale contains

deliberate deviations from safe driving without a explicitly aggressive aim (Lajunen,

2004b). Violations rather than errors or lapses have been found in the literature to be

related to road crash involvement (Lajunen, 2004b, Parker, 2001, Reason, 1990b),

however research with elderly drivers has shown relatively high error and lapses scores

in predicting road crash involvement (Parker, 2000). It has been shown that errors,

lapses and violations are three empirically distinct classes of behaviour, however it is

difficult to identify such behaviours without the reconstruction of the sequence of events

in any particular crash (or collision).

“Reconstructing the human contribution to a sequence of events that led up to an

accident is not easy” (Dekker, 2002, p.373). Accident investigators and coroners are

rarely there when events unfold around the people under investigation, and as a result

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their actions and assessments may appear not only controversial, but in fact perplexing

when seen from a different point of view (Dekker, 2002). Investigators and safety

authorities, thanks to hindsight, consider that they know more about the incident or

accident than the people involved (Dekker, 2002). Dekker (2002) argues that hindsight:

• Means being able to look back, from the outside, on a sequence of events that led

to an outcome that has already happened;

• Allows almost unlimited access to the true nature of the situation that surrounded

people at the time (where they actually were versus where they thought they

were; what state their system was in versus what they thought it was in); and

• Allows investigators to pinpoint what people missed and should not have missed;

what they did not do but should have done.

(p373)

From this hindsight perspective, the entire sequence of events can be exposed.

However, according to Dekker (2002) this contrasts fundamentally with the point of

view of the people who were inside the situation as it unfolded around them. To these

insiders, the outcome was unknown, nor the entirety of surrounding circumstances,

although they contributed to the course of the sequence of events on the basis of what

they understood to be an evolving situation (Dekker, 2002). For the majority of

accidents in complex situations, Dekker (2002) argues that people were doing exactly

the sorts of things they would usually be doing – the things that usually lead to success

and safety. Wigglesworth’s (2001) study of detailed police reports prepared for the

coroner in level crossings fatalities, supports this notion in one sense. This study

concluded that in most cases level crossing collisions occurred to a law-abiding citizen

going about his or her daily work and was attributed to human overload rather than any

breach of regulation (Wigglesworth, 2001). Reason (1997), Sanne (1999) and Woods et

al (1994) argue that mishaps are more usually the result of everyday influences on

decision making than isolated cases of erratic individuals behaving unrepresentatively.

Dekker (2002) argues that people are doing what makes sense to them given the

situational indications, operational pressures, and organisational norms presented to

them at the time. To put it simply, people do not want to be involved in a road crash and

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they are generally doing what makes sense to them at the time (Dekker, 2002). Such

behaviour is the result of errors and lapses only, and relies on the different concepts of

performance and behaviour.

Performance and behaviour are two fundamental components of driving that are

inextricably intertwined and need to be recognised in both the development of road

safety countermeasures and factors contributing to road crashes. Performance relates to

“our abilities to perceive and react to circumstances in an appropriate and- timely

manner. It is the manifestation of what we commonly refer to as ‘skill’ (Henderson,

1991). Behaviour refers to what a driver actually does on the road, not what they are

capable of doing. According to Henderson (1991), behaviour “embraces not only

performance as in fact performed, but also attitudes to the task, cultural differences and

pressures, and the way we perceive and respond to risk” (p4). There is great difficulty

however for road safety researchers in investigating behaviour, as information is harder

to come by and is usually only able to be examined in advanced laboratory conditions

(Henderson, 1991).

It must be noted that the act of driving is taken for granted by most motorists,

however it is a complex task that is dependent on a variety of cognitive and psychomotor

performance abilities to be intact, such as alertness, attention, multitasking, memory,

coordination, and visual spatial perception (Moller, 2004). According to Ogden (1996),

driving can be described by three essential tasks: navigation, guidance and control.

These three tasks require the driver to receive inputs from a driving environment,

process them, make predictions about alternative actions, decide which are the most

appropriate, execute the actions, observe their effects through feedback and process new

information (Lay, 1990). Wang (2002) suggests that “essential in performing these tasks

is a driver’s ability to make relatively accurate estimates of the safety of the driving

environment” (p253).

With regards to the view taken to explain accident causation at level crossings,

there appears to be two schools of thought. According to research conducted by the

University of Calgary, the study of human factors of level crossing collisions attempts to

place human error in the context of perceptual, memory, cognitive and motor

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capabilities (Caird, 2002). Figure 5 illustrates factors contributing to vehicle-train

collisions at level crossings (Caird, 2002).

Figure 5: Contributing factors in highway-railway grade crossing collisions.

However, according to one Australian expert in level crossing safety, the human

factor approach reviews the system as a whole and does not apportion blame, but rather

seeks to observe patterns of human behaviour that differ from designer expectations and

consequently to gain an understanding of the human limitations that have resulted in

such differences (Wigglesworth, 1978, Wigglesworth, 2001). This specifically relates to

passive level crossings in that the designer expectation is that motorists will look in both

directions to see if a train is approaching, at a speed that enables them to stop if

necessary (Wigglesworth, 2001, Leibowitz, 1985, Richards, 1990, Russell, 1993).

Australian coroners have tended to agree with this view of Wigglesworth by reporting

that from a systematic point of view, level crossing collisions usually have more than

one contributing factor (i.e. being the non-observance of the Australian Road Rules).

Some coroners have indicated that there are numerous causal factors relating to:

• Road design (the number of entry/exit points);

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• Road traffic lights and the inter-link with the level crossing warning system;

• The width of the crossing;

• Probable lack of awareness by road vehicle drivers of the road traffic rules as

they relate to level crossings;

• The lack of ‘near hit safety’ reporting at level crossings; and

• The lack of a focused body to oversight and undertake risk based assessments of

level crossing safety.

(Australian Transport Safety Bureau, 2002a, pvi)

2.8.2 Driver interaction with different protection systems

2.8.2.1 Active level crossings

Drivers are notified when approaching an active (automated) level crossing of a

train’s presence or an approaching train through the use of flashing lights and/or (visual)

and bells (aural). These warning systems are activated by the train circuitry for a period

of approximately thirty seconds before the arrival of the train. Such systems provide a

much better assessment of the likelihood of an oncoming train than passive level

crossings (stop or give-way signs only). Warning and protection systems at actively

protected level crossings (flashing lights and/or boom gates) have been found to have a

very high level of reliability. Stott (1987) in his research found that collisions occurred

as a result of the failure of the motorist to pay attention to the warning systems at

crossings rather than a mechanical failure of the warning system.

Actively protected crossings usually bring about a reduction in delay to the road

user, but also places responsibility on the motorist to observe and obey the road traffic

signals. Generally, driver observance at active level crossings is of a high standard

(Pickett, 1996) but violations do occur, which may or may not lead to a collision.

According to Pickett and Grayson (1996) there are three categories of drivers that have

been identified as likely to be involved in an accident at actively protected level

crossings:

• Those who are unwilling to stop because they believe they have plenty of time to

cross before the train arrives;

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• Those who are unable to stop because they are too close to the tracks at the onset

of flashing lights or because someone is driving too close behind; and

• Those who are unaware of the signals because they are inattentive or distracted.

This list of categories suggested by Pickett and Grayson (1996) is somewhat

incomplete as it does not include those drivers that lack knowledge and awareness about

warning and/or protection systems or those drivers that find these systems complex and

confusing. Additionally, it doesn’t include drivers who are familiar with a particular

level crossing. Wigglesworth’s study (1979) of 85 consecutive fatal collisions involving

motor vehicles and trains at all types of level crossings in Victoria, found from detailed

police reports prepared for the coroner that most fatalities were not linked with breach of

regulations, but instead pointed to the presence of stressors such as family bereavement,

financial or employment problems, or distraction of the driver by events inside or

outside the car. This study concluded that in most cases, the accident occurred to a law-

abiding citizen who was not in breach of any regulations, but rather going about their

daily work (Wigglesworth, 1979). Familiarity was found to be linked with at least 86%

of these fatalities as drivers lived locally and were familiar with the existence of the

crossing (Wigglesworth, 1979).

2.8.2.2 Passive level crossings

Passive crossings usually have extremely low volumes of both rail and road traffic

and are traveled over by the same small cohort of local drivers who may or may not be

representative of the driving population generally (Wigglesworth, 2001). Although the

assumption by the public and some researchers is to upgrade passive crossings to active

protection systems to make them safer, this has been rebutted as active protection

systems continue to be linked with collisions. In August 2004, 3 people were fatally

injured when their car was hit by a train while queuing over the St. Albans level crossing

in Melbourne which was actively protected with both flashing lights and boom barriers.

Therefore, it can be seen that even actively protected crossings may also be an

environment for error-producing decisions.

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2.8.3 Perception of risk

Risk perception is the subjective assessment of the probability of a specified type

of accident happening and how concerned we are with the consequences. To

perceive risk includes evaluations of the probability as well as the consequences of

a negative outcome.

(Sjöberg et al., 2004, p8)

There is an extensive body of literature devoted to the psychological work on the

subject of perceived risk. However, it is generally unknown how and why individuals

differ in their judgment of risk (Kraus and Slovic, 1988). The perception of driving risk

is reported to be subject to systematic distortions (Rafaely et al., 2006). This is

particularly evident in the younger driving population (i.e. under 25 years of age).

Perceived risk (a driver’s feelings about the level of risk for a particular driving

situation) has been demonstrated to be lower for younger drivers than for older drivers.

Younger drivers typically underestimate the risk of being involved in a road crash

(Deery, 1999, Dejoy, 1992), linking them to risk taking behaviour (Glendon et al., 1996)

as well as risky driving behaviour (Deery, 1999, Harre, 2000). Gardner and Steinberg’s

(2005) simulated driving study found that the presence of peers as passengers influenced

risk decision-making and perceived risk. Compared to when the driver was alone,

participants reportedly took more risks, focused more on the benefits than the possible

costs of risky driving behaviour, and make riskier decisions (Gardner and Steinberg,

2005). Additionally, some research has indicated that young drivers have been shown to

perceive their own risk as lower than that of their peers (Finn and Bragg, 1986).

Compared with the younger driver population, older drivers have been found to perceive

their driving ability better than or equal to that of their peers (Holland, 1993, Freund et

al., 2005), and better than their younger counterparts (i.e. younger drivers aged under 25

years) (Groeger and Brown, 1989).

Recent studies in Canada reveal that motorists view level crossings as a low risk

safety problem (Beirness et al., 2003). This research found that when “examined in the

context of other road safety issues, highway/railway crossing safety is viewed as being

amongst the least serious” (p9). The survey was administered by telephone and 1209

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drivers agreed to participate. Figure 6 shows the average ratings provided by the

random sample of Canadian drivers (Beirness et al., 2003). A scale of 1 representing

‘not a problem at all’ to 6 representing ‘an extremely serious problem’ was used. As

can be seen, many survey participants believe that the most serious road safety issues are

drink driving and red light running (average rating of 5 or greater), while

highway/railway crossing safety received an average rating of 3.0. This rating was also

below safety issues of vehicle defects and distracted drivers (Beirness et al., 2003). This

has been the only known research that has included level crossing driving with other

driving behaviours.

Figure 6: Perceived risk ratings by Canadian drivers

2.8.4 High risk behaviours and risk taking

To understand risk taking at a deeper level, it is important to understand the

cognitive and perceptual processes underlying driving behaviour in an environment that

has potential traffic hazards. Deery (1999) conducted an extensive review of hazard and

risk perception among novice drivers. He recommends that “before defining risk and

hazard perception, it is important to note that they are concepts that reflect, at least in

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part, drivers’ subjective experiences and thus should be distinguished from objective

risk” (p226). The subjective experience of risk in potential traffic hazards, is referred to

as risk perception (Deery, 1999). Brown and Groeger (1988) propose that there are two

inputs to determine risk perception:

• Information regarding the potential hazards in the traffic environment; and

• Information on the ability of the driver (and the capabilities of the vehicle) to

prevent those potential hazards from being transformed into actual accidents.

Figure 7 illustrates the domain of hazard perception such as information about

potential traffic hazards and the process of identifying hazardous objects and events, and

judging and quantifying their potential to be dangerous (Armsby et al., 1989, Brown,

1988). This model also illustrates driver’s beliefs about their ability in handling

hazardous situations, which is the result of their self-assessed driving ability (Deery,

1999). Hazard perception, using this model, involves elements of both driving skill and

subjective experience. In comparison to risk perception, risk acceptance is the degree to

which a driver is willing to accept risk, with the driver having a level of perceived risk

or risk threshold (Deery, 1999). Deery (1999) suggests that because “driving is

essentially a self-paced activity, a driver determines the difficulty of his/her task by

setting and accepting different risk thresholds” (p226). He proposes that a driver’s

motivation determines their risk threshold for behaviour alternatives, with there being

several reasons why a driver may take a risk including:

• Driver may exhibit poor risk perception – e.g. the driver may perceive low levels

of risk in maintaining a relatively high driving speed. Poor risk perception may

result from a driver misjudging the distance to a level crossing or the braking

distance of their vehicle. It may also occur from the driver over-estimating their

ability to deal with a problem should it arise;

• Driver may possess relatively poor levels of driving skill – e.g. the time taken to

detect a level crossing and make a decision to slow down may be excessive,

thereby delaying the response time to release the accelerator or brake; and

• Driver may have a high level of risk acceptance – e.g. they may correctly

perceive the risk of driving over a passive level crossing without scanning for

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trains, and may also possess a relatively high level of driving skill, but they may

have chosen to accept the risk to minimise time delays.

(Deery, 1999, p228)

Additional to the performance of high risk behaviours by drivers, research has

indicated that risk taking has been linked to the involvement in road crashes. However,

only a handful of studies have actually measured the association between injury

resulting from a road crash and risk taking behaviour (Bell, 2000, Crutcher et al., 1994,

Perneger, 1991, Rajalin, 1994, Turner, 2004). Risky driving, in the broad sense,

encompasses behaviours such as drink driving, drug driving, speeding, red light running,

tailgating, weaving in and out of traffic, unsafe overtaking and distracted driving

(Beirness and Simpson, 1988, Jessor, 1977, Jonah and Dawson, 1987). When drivers

ignore the warning signals and signs at level crossings, often in an attempt to ‘beat the

train’, this act can be interpreted as risk taking (Reason, 1997). However, research

indicates that the cause of fatal collisions at level crossings is largely not the result of

deliberate violations of the road rules, but rather inattention (Wigglesworth, 1979).

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Figure 7: Driving behaviour responses to potential hazards

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Risk acceptance has also been examined by Wilde (Wilde, 1982, Wilde, 2001).

Wilde developed of theory of ‘risk homeostasis’ in which individuals are prepared to

accept a given target level of risk, resulting from a gap between the perceived level of

risk and an (exogenously given) target level of risk (Gossner and Picard, 2005). Other

researchers argue that the ‘risk homeostasis’ theory is possible only when under very

restrictive conditions (Janssen and Tenkink, 1988). Janssen and Tenkink (1988)

argue that risk compensation can occur under more general conditions: that is, the

safety effects anticipated from engineering measures will be discounted to a large

degree by shifts in behaviour. This is supported by conclusions from an OECD report

(1990) that indicates that behavioural adaptation or compensation does not eliminate

the safety gains obtained, but it may reduce the effectiveness of road safety programs

in a number of cases (Gossner and Picard, 2005). Given such empirical evidence that

motorists adapt or compensate their behaviour to changes in their driving environment

(such as road safety engineering and traffic countermeasures), it would be reasonable

to assume that motorists would drive more safely at high risk intersections such as

level crossings. However, the evidence suggests otherwise.

At level crossings, it is important to recognise that within the sub-set of

motorists whom engage in high risk behaviours, there are two main types of

individuals. As discussed previously, some individuals purposively and knowingly

engage in risky behaviours such as trying to ‘beat the train’ for the thrill of it or

possibly because of impatience at having to wait for the train to pass. When motorists

ignore the warning signals and signs at level crossings, often in an attempt to ‘beat the

train’, this act can be interpreted as risk taking (Reason, 1997). Willful violations

include driving around the gates and driving around cars stopped at the crossing

(Abraham et al., 1998, Berg, 1981, Meeker, 1997). Results from Berg et al (1981) and

Abraham et al (1998) suggest that the timing of crossing signals as a reason motorists

crossed when flashing lights were still activated. In Berg et al’s (1981) study, drivers

were more likely to cross after the signal had been activated for more than 30 seconds.

Unnecessary waiting times to the presence of slower moving trains (e.g. freight) on

track circuits designed to accommodate faster trains (e.g. passenger), are identified as

a reason motorists violate warnings signals and signs (Berg, 1981). Respondents to

Abraham et al’s (1998) study indicated that they violated warning signals or

protection systems because the train was not in sight or because it had been stopped

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for an unreasonable amount of time. Such assumptions by drivers are inherently

dangerous. Caird et al (2002) report that:

• Restricted visibility may prevent motorists approaching flashing light

crossings from seeing the train and they therefore erroneously assume that a

train is not present;

• If a train is stopped near a crossing with multiple tracks, the signals observed

by the motorist may actually be for another train approaching from either the

opposite direction or from behind the stopped train on a parallel track. These

types of collisions are called second train collisions; and

• Motorists’ judgments of how far away a train is from a crossing may be

affected by perceptual factors, such as looming.

In order to further understand the types of individuals who are most at risk for

vehicle-train collisions, Witte and Donohue (2000) surveyed 891 randomly selected

residents from the U.S.A. These researchers found that whilst the majority of

respondents perform safe driving behaviours, a sub-group of 10-20% labeled ‘risk

seekers’ engaged in extremely risky behaviours (Witte, 2000). More specifically,

approximately 10% of the respondents perceived trying to ‘beat the train’ across the

tracks to be exciting, and almost 14% reported that they would drive around the gates

with the flashing lights even if a train was in sight. Analyses revealed that members

of this ‘risk seeker’ sub-group were disproportionately male, with a high propensity to

engage in other risky behaviours such as smoking, drinking alcohol and fighting

(Witte, 2000). These sub-group members were more likely to have experienced

personal frustration at level crossings and to exhibit biased judgment processes

regarding their ability to ‘beat the train’. Additionally, they also reported high

‘sensation seeking’ tendencies to engage in novel experiences and to minimise

boredom. The researchers suggest that these strong sensation seeking tendencies

predispose this sub-group to experience greater frustration and demonstrate higher

judgment distortions, which consequently leads to riskier behaviour.

Other individuals inadvertently put themselves and others at risk through failing

to successfully recognise a dangerous situation (Wigglesworth, 1979, Abraham et al.,

1998, ARRB Transport Research., 2002, Berg, 1982, Caird, 2002, Leibowitz, 1985,

Meeker, 1997, Pickett, 1996, Shinar, 1982). These motorists are engaging in risky

behaviours due to a lack of awareness of approaching trains or an inability to judge a

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safe distance to cross in front of an oncoming train. Unintentional violators, who are

unable to stop at a level crossing for a variety of reasons (such as speeding or being

too close to the intersection at the onset of the flashing lights without causing danger

to other road users), have made a conscious decision to cross the intersection and

generally feel that circumstances leave them with no choice but to violate the road

rules.

2.8.5 Familiarity

Familiarity with a level crossing may influence a motorist’s behaviour in

numerous ways (Caird, 2002). “Crossing familiarity and an expectation that a train

will not be present have the potential to lull drivers into complacency or poor looking

habits” (Caird, 2002, p101). Motorists that expect long delays may violate traffic

signals and possibly reduce their scanning behaviour to detect a train’s approach

(Abraham et al., 1998). Abraham et al’s (1998) study revealed that 87% of motorists

who committed a violation at an active crossing used the crossing regularly. This

finding suggests that familiarity may encourage motorists to take greater risks

(Abraham et al., 1998). However, determining an accurate picture of a motorist’s

familiarity with a level crossing is difficult as fatally injured motorists are unable to

testify (Wigglesworth, 1979). To date, no data is recorded by the ATSB on familiarity

of motorists.

In case study investigations (Wigglesworth, 1979, National Transportation

Safety Board, 1986), findings have indicated that motorists involved in crossing

collisions were familiar with the crossing. Wigglesworth’s (1979) study of 85

consecutive fatalities in Victoria, Australia between 1973 and 1977 found that 73 of

the 85 motorists (86%) were considered to be familiar with the crossings at which

their collision occurred. These motorists lived within one mile of the level crossing,

classifying them as familiar with the crossing. However, motorists who did not live

close by to the crossing but were familiar with it due to employment or other reasons,

could actually be familiar with the crossing (Wigglesworth, 1979). Additionally,

motorists who lived close by to the crossing may rarely drive over it as there is no

need to. The National Transportation Safety Bureau’s (NTSB) (1986) study found

similar results to Wigglesworth’s (1979) study. The NTSB study estimated that

nearly 85% of motorists involved in a fatal collision were familiar with the crossing.

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However, this study did not indicate the classification of familiarity unlike

Wigglesworth’ study.

Overall, it is highly likely that motorists who are familiar with particular

crossings may become complacent and take greater risks than whilst driving at

unfamiliar crossings. According to Pickett (1996) motorists may “also base their

decision to cross on their previous experience of either the same crossing (familiarity)

or of other crossings (association). The concept of mental set says that when people

are exposed to the same phenomenon repeatedly, they come to expect it” (p5). Pickett

(1996) also suggests another situation, with motorists familiar with one crossing,

transferring their experience to a new crossing and not being as vigilant in the new

situation.

2.8.6 Approach behaviour

Once a motorist has recognised the presence of a crossing, it is the

responsibility of the motorist to also determine whether an approaching train is

imminent (Ward and Wilde, 1995). However, according to Ward and Wilde (1996)

motorists approaching crossings may be uncertain about the probability of

encountering a train, particularly at passive crossings. Ward and Wilde (1996)

suggest that “uncertainty will be high without reliable knowledge of the scheduling of

train arrivals based on past experience, either because (i) the trains at the site do not

adhere to a regular schedule, or (ii) there is no prior experience with the site” (p63).

‘Decisional uncertainty’ may lead to inconsistent behaviour within and between

motorists, particularly when a decision to stop at a level crossing is not feasible (e.g.

traveling to fast to stop safely) or not warranted (e.g. when a train has stopped a long

distance from crossing) (Ward and Wilde, 1995, Ehrlich, 1989). A motorist must

determine how to respond at level crossings, based upon joint consideration of their

own approach parameters and those of the train (i.e. direction of approach, estimation

of speed and distance from crossing) (Meeker, 1989).

The approach parameters of a motorist include the restriction of lateral sight

distance. When there is limited sight distance to dense vegetation or approach angles,

it would be logical to assume that such restrictions are associated with greater

incidence of collisions because of the greater hazard associated with the obstruction of

lateral visibility (Ward and Wilde, 1996). Nevertheless, there has been no consistent

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demonstration of any relationship between lateral sight distance and collision history

(Russell, 1974a, Zalinger et al., 1977, van Belle et al., 1975). Ward and Wilde (1996)

offer an explanation for this phenomenon based on the concept of risk homeostasis.

They suggest that motorists compensate their behaviour (such as speed reduction) in

response to the perceived risk associated with restricted visibility, particularly at

passive crossings (Wilde et al., 1987, Ward and Wilde, 1996). Such behaviour,

proposed by Ward and Wilde (1996) is expected to maintain a ‘more-or-less’ constant

safety margin. This notion has been supported by their study in examining the effects

of enhancing lateral sight distances at a passive crossing in Ontario, Canada. The

study’s methodology also included parallel observations with an untreated site to

control for secular confounding (Ward and Wilde, 1996). The findings of their

research met their expectations – “improvement at lateral sight distances resulted in

an upstream shift toward longer search durations and a tendency towards faster

approach speeds, but failed to produce a calculated net safety benefit” (p63).

Additionally, it was found that enhancement treatment at the treatment site reduced

perceived risk by local motorists (Ward and Wilde, 1996).

As can be seen, there has been little extensive research published on the safety

issue of approach behaviour at level crossings. Other than a few well-known

published studies in the area, little recent research is available. This is an area greatly

lacking in level crossing safety and requires further investigation, particularly in

Australia.

2.8.7 Poor knowledge of road rules

According to Donohue, Wendelken, Crone and Bunge (2005), “a detailed

account of how we use rules to make decisions would constitute an important advance

in our understanding of human behavior” (p1140). However, no research to date has

focused on knowledge of road rules and level crossing driving behaviour, although

anecdotal evidence suggests that many Australian drivers have a poor knowledge of

road rules at level crossings.

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2.8.8 Distraction

Driver distraction is a serious problem in road safety and has long been

suggested to be a contributing factor to collisions (Treat, 1977). Although there is a

considerable body of evidence to support the notion that motorists can become

distracted while they drive, until recently little attention has been paid to distraction as

an important risk factor for road crash involvement. Although the number of road

crashes in Australia for which distraction is a contributing factor is unknown,

international evidence indicates that distraction is a significant contributing factor to

road trauma (Regan, 2003). National data in Australia has in the past not recorded

distraction in a detailed manner, but as Regan and Young (2003) suggest “although

the full extent to which distraction is a causal factor in road crashes in Australia is not

yet known, there is converging evidence that it is likely to be a significant problem

here” (p1).

While Lam (2002) proposes that distraction comprises a variety of activities,

situations and circumstances that a motorist may be exposed to while performing a

driving task, Ranney et al (2000) argue that it may be characterised as any activity

that takes a driver’s attention away from the task of driving. The vast majority of

driving is characterised by activities involving straightforward control of the vehicle,

such as sustaining appropriate speed, headway, and lane position within the road

environment. These familiar activities require very little cognitive processing and

motorists are often able to engage in other cognitive activities simultaneously without

noticeable detriment to the driving task (Ranney, 2000). Brown (2003) suggests

however that safe driving of a motor vehicle requires a wide range of skills and

abilities, with driving performance being sub-optimal when the driver is distracted,

either from within or external to the vehicle (Brown, 2003). Cognitive overload and

cognitive underload are the two elements that are identified as being fundamental to

motorist distraction (Wallace, 2003). Researchers generally agree that distraction can

take place even when a driver is concentrating on the task at hand.

The National Highway Traffic Safety Administration (NHTSA) proposes that

there are four distinct, although not mutually exclusive, forms of motorist distraction.

These include: visual, auditory, biomechanical (physical) and cognitive (Royal,

2002). While Regan and Young (2003) have classified motorist distraction in their

review of the literature as being either technology based distraction or non-technology

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based distraction, with some overlap occurring. While other researchers suggests that

internal-to-vehicle and external-to-vehicle are the two major classifications of

distraction (Wallace, 2003, Brown, 2003).

Recent evidence suggests that there are numerous factors and individual

differences influencing the probability of being distracted (Wallace, 2003), with in-

vehicle distraction and external-to-vehicle distraction being the distinction in the

literature. With the emergence of more complex vehicle systems and electronic

devices available to drivers, overload of drivers is estimated to increase as new

technologies proliferate the market. Recent concerns about safety implications of

technology based distractions, centre on the magnitude and nature of demands these

devices place on drivers (Ranney, 2000, Min, 1998).

Distraction is frequently cited as another category of unsafe actions at level

crossing, with one U.S.A. study (National Transportation Safety Board., 1998) citing

motorist distraction as a probable cause in approximately 20% of collisions reviewed.

The types of distractions that have been documented in level crossing collisions

include the use of mobile phones; internal cognitive processes (e.g. day dreaming or

worrying); conversation with passengers; external distraction by objects or other

vehicles; and using in-vehicle devices (e.g. radio/CD player) (Caird, 2002). With the

advent of Intelligent Transport Systems in cars, level crossing collisions involving

distraction of motorists may become more prevalent. Currently, the Australian

Transport Safety Bureau records distractions as unintended motorist behaviour in the

data, and such behaviours are more common in level crossing collisions than in other

fatal road crashes. However, this data does not specify the incidence of distraction as

a major contributing factor to level crossing collisions in Australia nor the types of

distractions. According to Britain’s Health and Safety Executive (HSE) distraction at

level crossings may result in motorists failing to abide by warning signals and

crossing rules (Health and Safety Executive, 2005). The HSE (2005) suggests that

distraction issues may include:

• Noise: noisy surroundings may mean that users cannot hear oncoming trains;

• Groups: level crossing users in groups may behave differently to individuals,

for example when trying to keep together; and

• Visual distractions: visual distractions, such as shops and advertisements, may

draw the user’s attention away from the information and warnings at the

crossing.

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2.8.9 Attentional blindness

According to Simons (2000) “although we might intuitively believe that

unusual, unexpected and salient objects will capture attention, leading to awareness,

they often do not” (p150). Attentional blindness is a major side-effect of top-down

processing (consciously directed attention in the pursuit of predetermined goals or

tasks), and in interactive tasks (such as driving), portions of the scene may go

unnoticed (Cater et al., 2002, Mack and Rock, 1998). This phenomenon denotes the

failure to see highly visible objects that a person may be looking at directly, when

attention is elsewhere (Mack, 2003, Memmert, 2006). Although typically a visual

phenomenon, there can also be an auditory and tactile component. The phenomenon

of ‘attentional blindness’ has been the topic of debate in level crossing safety in recent

years, as it has serious life-and-death consequences. It points itself to the serious

dangers of inattention whilst driving at level crossings, although it is difficult to

accurately determine the extent to which it may play a part in fatalities at level

crossings.

2.8.10 Hypovigilance and fatigue

Hypovigilance and fatigue have long been regarded as probable contributing

factors in many road crashes. Although vigilance and/or fatigue have not been

identified as major contributing factors in level crossing collision data per se, these

factors may have contributed to some collisions.

Hypovigilance has been defined as “a naturally occurring state in which some

individuals need more sensory stimulation to stay awake” (Dahl, 1996, p44).

Vigilance has been defined as having two broad conceptions. The first is related to

the physiological processes underlying alertness or wakefulness; while the second is

related to information processing and sustained attention (Thiffault, 2003). Fatigue

on the other hand is a general term used in the literature, which relates to both

physiological and psychological processes. According to Lyznicki et al (1998) fatigue

reflects a decreased capacity to perform, along with the subjective states which are

associated with decreased performance. Long hours spent driving (time-on-task) has

been found to produce fatigue and a deterioration of driving performance. A study by

Summala and Mikkola (1994) found that 60% of fatal sleep-related road crashes in

Finland occurred within the first hour of driving. The circadian rhythm (time-of-day)

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effect has also been found to play a major role in fatigue (Pack et al., 1995).

According to Thiffault and Bergeron (2003):

The characteristics of road geometry and roadside environment, including other

factors that define the driving task, can have an impact on driving performance

by affecting arousal, alertness and information processing. An under-

demanding monotonous road environment with low traffic density can produce

fluctuations of arousal that decrease alertness and vigilance.

(p382)

As such, through the habituation process, low variation may tend to lead to

decreases in arousal, which may induce driving fatigue (Thiffault, 2003).

Countermeasures to rupture monotony have long been a topic of debate, with

the equilibrium between distraction and monotony of the foremost importance.

However, interventions discussed in the literature have been described as positive

distracters as they attempt to direct the motorist’s attention away from internal

thoughts and back to the external environment by providing a rupture in monotony.

There have been numerous suggestions as to how to rupture monotony. Brown (1991)

proposes that it could be beneficial to integrate novelty and variety into the motorist’s

task and environment, while Nelson (1997) suggests that partial perceptual

restrictions render the landscape more interesting and lessens boredom and monotony.

However, such suggestions have yet to be systematically evaluated in terms of their

effect on motorist fatigue and vigilance.

With the majority of studies indicating that fatigue is more frequently related to

monotonous road environments (such as driving on highways or on rural roads), it is

important not to rule out fatigue and hypovigilance as probable contributing factors in

passive level crossing collisions. Although rumble strips have been recommended by

Australian experts to alert drivers before they approach a level crossing, such trials are

only in the early stages of development. Visual stimuli prior to the placement of

rumble strips may be another option for countermeasure future development.

2.8.11 Speeding

Speeding has not been identified as a major contributing factor in level crossing

collisions, although may be one of several factors or influences in the chain of events

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commonly leading to a collision. Speeding has been found to increase the incidence

and severity of road crashes (Elvik, 2004b, Australian Transport Council, 2000,

Buzeman, 1998, Johnston, 2004, Richter, 2004), with the severity of injuries resulting

from a road crash also being directly related to the pre-crash speed of the vehicle

(Frith, 2003).

Frith, Strachan and Patterson (2005) have summarised the relationship between

speed and road crash risk from a review of the literature.

• Between 1987 and 1988, 40 states in the United States of America raised the

speed limit on interstate highways from 55mph (88 km/h) to 65 miles/hour

(104 km/h). This resulted in an increase in average car speeds of about 3

miles/hour (5 km/h). Over the same period there was an increase in deaths on

these roads of between 20% and 25% (Transportation Research Board, 1998);

• During the 1973 fuel crisis, the New Zealand Government reduced rural speed

limits from 55 miles/hour (88 km/h) to 50 miles/hour (80 km/h), leading to an

8-10 km/h reduction in average rural speeds. The drop in speed led to a

significant drop in injuries, as compared with urban roads which were

unaffected by the speed-limit change. On main inter-city roads the number of

deaths dropped by 37%, serious injuries decreased by 24% and minor injuries

decreased by 22%. The corresponding reductions for urban areas were 15%,

9% and 4% (Frith and Toomath, 1982);

• In Australia the speed limit on Melbourne’s rural and outer freeway network

was increased from 100 km/h to 110 km/h in 1987 and then changed back to

100 km/h in 1989. Compared to a control area where the speed limit remained

the same, the injury crash rate per kilometre traveled increased by 25% when

the speed limit increased, and decreased by 19% when the speed limit

decreased (Sliogeris, 1992);

• A review of the studies on speed-limit changes from several countries (South

Africa, Belgium, Finland, France, Great Britain, Germany, U.S.A., and New

Zealand) where a speed limit was reduced or a new limit was introduced found

a reduction in road crashes ranging from 8% to 40% (Fieldwick, 1993) ; and

• Patterson et al. examined the effect of changes in speed limits on deaths on

rural interstates in the U.S.A. Road crash deaths in the groups of states that

raised their speed limits to 75 miles/hour and 70 miles/hour rose by 38% and

35%, respectively, relative to fatality levels in the states that did not change

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their speed limits(Patterson et al., 2002).

(Frith et al., 2005, p24)

Fergusson et al. (2003) suggest that travelling above the speed limit is part of a

spectrum of risk taking behaviours that is associated with an elevation in crash risk.

Additionally, the concept of speeding forms part of violations of road rules, with

speeding being a deliberate deviation from the road rules (Kontogiannis, 2002,

Mesken, 2002, Parker, 1995b). However, the culture of speeding is virtually

perceived by most as ‘normal’, with many motorists believing that they can ‘safely

speed’ (Blincoe et al., 2006). Rothengatter (1991) proposes that many motorists

regard speeding as one of the least serious traffic offences, while Holland and Conner

(1996) suggest that speeding is seen as being socially acceptable and that motorists

believe that there is little chance of either being apprehended by police or causing a

road crash.

Police enforcement is one of the most frequently used tools in reducing speed

limit violations. The aim of enforcement is to deter motorists from speeding by

increasing one of the disadvantages of speeding – the perceived likelihood of being

caught (Frith et al., 2005). According to Goldenbeld and van Schagen (2005):

Given the fact that in many countries traffic law enforcement forms a central

part of the road safety program, there is still a considerable limitation in the

extent of the scientific knowledge about the most (cost-)efficient ways of

enforcing traffic violations in general and speed violations in particular.

(p1136)

While there is a large body of evidence that suggests that speed cameras can

reduce motorists’ speed as well as reducing frequency of collisions and severity of

trauma, not all motorists respond to speed cameras in the same way (Blincoe et al.,

2006). Corbett and Simon (1999) found in their study, that motorists react in a

complex way to speed related issues and speed camera enforcement. Their study

confirms findings from Corbett (1995) and Lex Service’s (1997) findings that

motorists can be categorised into specific groups (depending on their speed and

camera-related driving behaviour). The categories proposed include:

• Conformers – those motorists who always or nearly always comply with speed

limits. This group is most in favour of cameras;

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• Deterred – those motorists who reduce their speeds since roadside cameras

have been in place. Cameras have made this group change their behaviour;

• Manipulators – those motorists who slow down for the camera then speed

outside the camera zone. Difficult group to target with anti-speed messages;

and

• Defiers – those motorists who exceed speed limits and have not reduced their

speed since speed camera introduction. Difficult group to target with anti-

speed messages.

(Corbett and Simon, 1999)

At level crossings, speed has not been identified as a major contributing factor

in level crossing collisions (albeit limited detailed data exists for level crossing

collisions in Australia). Nonetheless, speed may be one of several factors or

influences in the chain of events commonly leading to a level crossing collision.

2.8.12 Alcohol and drugs

Alcohol and drugs have been identified as contributing to 9% of fatal level

crossing collisions, compared with 30% in other fatal road crashes (Australian

Transport Safety Bureau, 2003). Although there has been a general reduction in

alcohol-related road crash fatalities and serious injuries during the past two decades,

drink driving remains a major public health problem for motorised countries

throughout the world (Sweedler, 1997). Drink driving is major factor in fatal road

crashes, with a significant percentage of fatal road crashes involving a driver with an

illegal blood alcohol content (Federal Office of Road Safety, 1997). Approximately

one-third of all fatal road crashes where the driver has been tested for alcohol

consumption are associated with blood alcohol concentrations (BAC) above the legal

limit (Ferguson et al., 1999), with approximately 70% of those drivers having a BAC

more than three times the legal limit (Single, 1997). Major contributing factors to the

decrease in road deaths has come about by improvements to roads and vehicles,

enactment of road safety legislation, intensive public education, and enhanced police

enforcement aided by improved enforcement technology (Federal Office of Road

Safety, 1997).

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Over the past decade, there has been a substantial increase in driving fatalities

involving drug use. Drugs other than alcohol have been found to be associated with

approximately 23% of heavy vehicle driver fatalities (Degenhardt, 2006). According

to Drummer et al. (2004) in 2001, 16.5% of driver fatalities had used cannabis (THC)

or stimulant/amphetamine type drugs, whilst in 2002 this percentage had risen to

20.4%. International studies indicate that 5-30% of motorists involved in road crashes

were affected by drugs (Del Rio, 2002, Athanaselis, 1999, Longo, 2000, Sjogren,

1997). Evaluating the influence of drug use on driving is fraught with difficulty, with

research being restricted in several ways:

• Most subjects are young healthy males, who are not regular drug users;

• Drug concentrations used are low to moderate;

• Sample size is usually small;

• The validity of the tests used with regard to road safety is not clear; and

• The test situation in the laboratory results in an increased effort by the subjects

to compensate for drug effects.

(Krueger and Vollrath, 2000)

However, a recent case-control study by Drummer et al (2004) found that

cannabis, amphetamines and combinations of psychoactive drugs significantly

increase a motorist’s risk of a serious road crash. Although there is significantly more

research being conducted in this area, it is likely that little gains are to be made

specifically in the prevention of level crossing collisions with the development of

alcohol and drug countermeasures.

2.8.13 Gender

The relevance of gender to road safety has long been recognised and it has been

the contribution of male drivers to fatal and serious crashes which has, to date,

attracted the most attention.

(Dobson, 1998, p11)

Male drivers have historically been over-represented in road crash fatalities and

are more likely to be killed than female drivers for every kilometre traveled (Dobson,

1998). Statistics indicate that while male drivers may be more at risk of being

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involved in a fatal road crash, female drivers are more at risk of sustaining serious

injury in a road crash (Federal Office of Road Safety, 1996). A project exploring

factors associated with risk of road crashes among young women (19-24 years) and

middle-aged women (46-51 years), found that “crash involvement is related to several

factors including feeling stressed and rushed, low life satisfaction, usual alcohol

consumption…and being born in a non English speaking country” (Dobson, 1998,

p9). Dobson et al (1998) suggests that errors, lapses and violations are related to both

the incidence of crashes and responsibility for those crashes in both young and

middle-aged women drivers.

2.9 RESEARCH QUESTIONS AND OBJECTIVES

This literature review highlights the fact that there is little known about the

exact causes of collisions at level crossings or about the effectiveness of reducing

collisions at crossings. The first study in the research program will examine the

following important research questions:

• What types of motorists are at risk of a collision?;

• What are the behaviours that motorists exhibit that increase their chances of a

collision?;

• How frequently do incidents and collisions occur?; and

• How frequently are incidents recorded by train drivers?

The overall objective of the second study is to provide information and

understanding about the knowledge, attitudes, norms, beliefs and risk perceptions of

motorists in relation to driving at level crossings. Specific research questions for

Study Two included:

• Do motorists perceive that they are at risk of being involved in a level crossing

collision?;

• What level of knowledge of the road rules exists?;

• What source and medium is believed to be the most appropriate for road

users?; and

• What differences exist between train drivers and experts opinions in terms of

high risk behaviours at level crossings?

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Study Three involved three parts. The aim of Part One of this study was to

develop targeted interventions specific to each of the three road user groups in

accordance with Fishbein’s theoretical model (Integrated Model of Behaviour

Change). Part Two involved the investigation of the present context of unsafe driving

behaviour at level crossings. This included examining knowledge, attitudes, beliefs,

perceptions, self-reported and intended behaviour, and environmental constraints of

each road user group. This second part also involved the examination of the present

context of motorist behaviour at level crossings using key constructs from Fishbein’s

Integrated Model of Behaviour Change. Part Three involved trialing a pilot road

safety radio advertisement using an intervention and control methodology. This part

investigated the changes in pre and post-test constructs such as intentions, attitudes,

norms, self-efficacy or perceived behaviour control, perceived risks, and perceived

environment constraints whilst driving at level crossings.

2.10 SUMMARY

As can been seen, there is an extensive body of published literature related to

engineering measures for improving safety at level crossings. However, there is

limited published literature in the areas of human factors, education or enforcement

for this complex driving environment. This literature review has attempted to provide

a greater understanding of the possible critical factors involved in vehicle-train

collisions. Although vehicle-train collisions are rare events, and therefore it is

difficult to obtain valid information about preceding behaviour and the frequency of

near-misses, unintended road user error has been blamed by transport authorities as

playing a significant role in such collisions. However, anecdotal evidence also

suggests that risk taking at level crossings makes up a large proportion of near-miss

incidents.

It must be noted though that as long as level crossings present inherent dangers

to motorists (and as long as they exist), rail authorities will remain at risk of being

found liable (either in whole or in part) for fatal collisions (Stephen, 2002). With

more than 9400 level crossings throughout Australia, it is a difficult task (some would

say impossible) for rail authorities to provide engineering systems that will

completely protect all motorists from involvement in a collision. With this in mind,

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educational interventions for improving motorist behaviour at level crossings may be

the answer that rail authorities are searching for. However to date, little rigorous

research has been conducted that has either explored the determinants of behaviour

and associated key constructs or measured changes in behavioural constructs or

statistically significant interactions between such constructs. Additionally, no

research has been guided by theory in exploring motorist behaviour at level crossings.

As a consequence, little is known about the role that educational interventions play in

changing intention of motorists. Without such research, it is difficult for transport

authorities to justify expenditure on campaigns targeting behaviour.

Although there are a number of models/theories that may be useful in guiding

this research on level crossing motorist behaviour (see Appendix 1), the ‘Integrated

Model of Behavioural Change’ (Fishbein, 2000) is the model chosen to assist in the

exploration of the entire program of research. Justification for the application of this

model to this program of research is provided. Although this model assists in all

aspects of this research program, it is particularly relevant to Study Three. It must be

noted that this model does not directly assess the predictive utility of Fishbein’s

model, but rather utilises it to assist in exploration of level crossing motorist

behaviour.

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CHAPTER THREE: THE IMPORTANCE OF THEORY FOR IMPROVING MOTORIST BEHAVIOUR AT LEVEL CROSSINGS

3.1 Introduction …………………………………………………………. 101

3.2 The role of theory in road safety evaluation studies ………………... 101

3.3 The intention-behaviour relation ……………………………………. 102

3.4 Integrated model of behaviour change ……………………………… 103

3.5 Summary ……………………………………………………………. 106

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3.1 INTRODUCTION

Explaining human behaviour in all its complexity is a difficult task which can

be approached at many levels (Ajzen, 1991). The motivation underlying road user

behaviour determines to a large extent how successful behaviour change strategies

may be. Although there are a number of models/theories that may be useful in

guiding this research program on level crossing motorist behaviour (as reviewed in

this chapter), the ‘Integrated Model of Behavioural Change’ (Fishbein, 2000) includes

two important constructs in driving: skills (or abilities) and environmental factors.

Fishbein (2003) suggests that the model recognises the lack of skills (or abilities)

and/or environmental constraints may prevent a person from acting on their

intentions, in light of the fact that intention is viewed as the primary determinant of

behaviour. While the majority of behaviour change theories are limited by a range of

conceptual and contextual factors (Parker, 2004), the IM was used to assist this

research program as it appeared to be the most applicable model to examine level

crossing safety.

While the IM proposed by Fishbein assists in the exploration of the entire

program of research, it is particularly relevant to Study Three. As no known research

has been conducted that utilises any psychosocial model to explain or predict level

crossing behavior within different road user groups, this research program used this

model as an exploratory tool rather than a tool to assess the model’s capacity in

explaining such behaviour. An extensive review of other models/theories and their

application to level crossing safety is provided in Appendix 1.

3.2 THE ROLE OF THEORY IN ROAD SAFETY STUDIES

In road safety research, some researchers argue that the role of theory has

historically been overlooked with many research programs having little theoretical

basis to guide the design of studies and the interpretation of study findings (Elvik,

2004a). Elvik (2004a) proposes that this deficiency in a strong theoretical basis means

that few results of road safety evaluation studies can be ruled out on theoretical

grounds. According to Risser and Nickel (2004), one of the major problems that has

contributed to this lack of a strong theoretical basis is the deficiency of meta-theory

that keeps the unstable and context-dependent theories under better control.

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Additionally, Risser and Nickel (2004) suggest that “there is no, and there never will

be any, valid unified theory of human behaviour. Therefore, there will never be a

convincing unified theory of driver behaviour” (p52). More recently though, many

attempts have been made to develop theories designed to predict or explain the

findings of road safety evaluation research (Elvik, 2004a). These many theories have

been developed and used extensively to predict health behaviours which interpret a

person’s intention to act as the most immediate and important predictor of subsequent

action (Sheeran, 2004).

3.3 THE INTENTION-BEHAVIOUR RELATION

Understanding the intention-behaviour relation is fundamental as it offers a

method of organising and understanding the large number of influences within the

area of road safety (Gordon and Hunt, 1998). Intentions have been defined by

Triandis (1980) as the instructions that people give themselves to perform a particular

behaviour or to achieve certain goals. Such intentions are the culmination of the

decision-making process and according to Ajzen (1991), Gollwitzer (1990) and Webb

and Sheeran (2004) they signal the end of the reflection about a certain behaviour and

encapsulate the standard of performance that one has set for themselves, one’s

commitment to that performance, and the amount of time and effort that will be

expended during action. The construct of intention as being central to theories of

health behaviour also requires the understanding as to how well intentions actually

predict behaviour.

How well intentions predict behaviour and behaviour change has been the

subject of great debate by researchers and psychologists alike. Although many health

behaviour studies have found that intentions predict corresponding behaviour quite

well, there has long been a concern by researchers that there are observed

discrepancies between behaviour and intention (Sheppard et al., 1988, Campbell,

1963, Blumer, 1955). According to Gillholm et al. (2000) studies of the intention-

behaviour relation have primarily been concerned with the intention to perform single

behaviours, however in the real-world people commonly face a choice between a

multitude of behaviours.

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3.4 INTEGRATED MODEL OF BEHAVIOUR CHANGE

The more one knows about the factors that underlie the performance or

nonperformance of any given behaviour, the more likely it is that one can

design successful interventions to change or reinforce that behaviour.

(Fishbein, 2003)

3.4.1 Development of the model

The ‘Integrated Model of Behaviour Change’ (IM) developed by Fishbein

(Fishbein, 2000), is a framework for “identifying the factors underlying the

performance or nonperformance of any behaviour clearly defined in terms of action,

target, circumstance and time” (2003, p3). Fishbein, a leading behavioural scientist,

suggests that although there may be an infinite number of variables that may directly

or indirectly influence the performance or nonperformance of a given behaviour, only

a limited number of variables are needed to be considered when predicting,

understanding, changing or reinforcing a given behaviour (Fishbein, 2003). Four

major theories of behaviour and behaviour change represent this limited set of

variables: (1) the health belief model, (2) social cognitive theory, (3) theory of

reasoned action, and (4) theory of planned behaviour (Fishbein, 2003). Based on

these four theories, Fishbein suggests that there are five key variables: (1) intentions,

(2) attitudes, (3) norms, (4) self-efficacy or perceived behavioural control, and (5)

perceived risk (Fishbein, 2003).

Also included in this model are skills (or abilities) and/or environmental

constraints. Fishbein (2003) suggests that the model recognises the lack of skills (or

abilities) and/or environmental constraints may prevent a person from acting on their

intentions, in light of the fact that intention is viewed as the primary determinant of

behaviour. He proposes that intentions alone are not the singular determinant of

behaviour, and different factors may satisfy or increase the intention/behaviour

relationship (Fishbein et al., 2003). In short, Fishbein proposes that intentions are

viewed as a function of the attitude towards performing the behaviour, perceived

norms (governing the performance of that behaviour) and self-efficacy (the person’s

perception of their ability to perform that behaviour when barriers to performance are

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present). Each of these three main determinants is measured both directly and

indirectly.

According to Middlestadt (1996), a standard elicitation procedure is required to

develop indirect belief-based measures. This procedure identifies the significant social

referent groups that form the basis of normative orientation toward the behaviour, the

relevant barriers towards performance of the behaviour, and the underlying (indirect)

sets of anticipated outcomes that form the basis of attitudes toward the behaviour

(Middlestadt, 1996, Fishbein et al., 2003). Therefore, this model proposed by Fishbein

(2003) assumes that these fundamental beliefs determine the overall (direct) measures

of attitude, norm and self-efficacy. Also included in this model are distal variables

(such as demographics, personality characteristics) as they propose that they are

relevant predictors of intentions and behaviour, but are assumed only to affect

intentions (indirectly) through their influence on underlying beliefs (Fishbein et al.,

2003). The figure below illustrates the model proposed by Fishbein (2000).

Figure 8: Integrated Model of Behaviour Change

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3.4.2 Criticisms of the model

Although the IM has not been used extensively in the literature, probably due to

its more recent development, there has been some validation of its worthiness in

predicting behaviour (Sheppard, 1988, Van den Putte, 1991). However, Fishbein

himself suggests that:

…a striking feature of the model is the complexity of the causal model of

intention formation compared with the apparent simplicity of the intention-

behaviour relationship. In fact, the IM does not actually address the ‘causes’ of

performing the behaviour except insofar as it defines intention as the primary

cause while recognizing the lack of ability and/or environmental constraints

may act negatively to prevent one from acting on one’s intentions.

(2003, p4)

Fishbein (2001) in his article examining utilising intervention theory to model

factors influencing behavior change, proposes that this does not mean that the

intention-behaviour correlation is low. He suggests that there are many studies that

show a substantial correlation whilst measuring both intentions and behaviour

(Fishbein et al., 2001), although he notes that such considerable correlations only

address the issue of statistical conclusion validity (Cook and Campbell, 1979), whilst

he suggest that the IM is a causal model of intention formation and ensuing

behaviour. From this however, he proposes that the model is a “complex causal model

of intentions but essentially a correlational model for behaviour” (Fishbein et al.,

2003, p5). Fishbein goes on to emphasise that this conception suggests an exploration

of the causal role of attitudes, norms and self-efficacy in performance of the target

behaviour, in contrast to forming positive intentions toward the behaviour (2003).

Other authors have suggested that the causal link between intentions and behaviour

could be intensified by other variables in the model. Some authors argue that

attitudes may have a direct influence on behaviour (Bentler, 1981, Kleinke, 1984,

Kraus, 1995), while Ajzen (1991) and Bandura (1986) argue that self-efficacy can

also affect behaviour directly (Fishbein et al., 2003). While the majority of behaviour

change theories are limited by a range of conceptual and contextual factors (Parker,

2004), this integrated model of behaviour change appears to be the most applicable

model to examine variables with regards to level crossing safety.

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3.4.3 Application to level crossing behaviour

This model developed by Fishbein, has historically been utilised for research

examining HIV and STD prevention interventions. No known research has applied

this model to road safety. Therefore, this research is a ‘world-first’ in applying this

model to motorist behaviour at level crossings.

3.5 SUMMARY

The aim of this chapter was to present the merits and limitations of Fishbein’s

integrated model of behaviour change (IM) for exploring motorist behaviour at level

crossings and factors contributing to such behaviours. While the IM is relatively new

to the area of road safety, this model appears to provide a comprehensive framework

for understanding the variety of factors than can either encourage or discourage

unsafe driving behaviour at level crossings, than do other theories or models (as

presented in Appendix 1). Since behaviour change models have not been used in

exploring level crossing motorist behaviour, there is no doubt that this model

embodies an important perspective from which to examine level crossing driving

behaviour.

As stated previously, while the IM proposed by Fishbein assists in the

exploration of the entire program of research, it is particularly relevant to Study Three

for intervention and questionnaire development. As no known research has been

conducted that utilises any psychosocial model to explain or predict level crossing

behavior within different road user groups, this model was used as an exploratory tool

rather than a tool to asses the model’s capacity in explaining such behaviour. The

capacity to explain motorist behaviour at level crossings is enhanced through the use

of this model as it includes two important constructs in driving: skills (or abilities) and

environmental factors (Fishbein, 2000). With level crossings being high risk

intersections (often in complex driving environments), the inclusion of these two

elements for explaining behaviour is paramount.

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CHAPTER FOUR: HIGH RISK AND VULNERABLE ROAD USERS AT LEVEL CROSSINGS

4.1 Introduction …………………………………………………………… 108

4.2 Review of existing data ……………………………………………….. 108

4.3 Study aims and research questions ……………………………………. 109

4.4 Method ………………………………………………………………... 110

4.5 Results ………………………………………………………………… 119

4.6 Discussion …………………………………………………………….. 124

4.7 Summary ……………………………………………………………… 125

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4.1 INTRODUCTION

This chapter documents the first study undertaken as part of this research

program. This first study used the method of ‘triangulation’ (i.e. combining research

methods to give a range of perspectives) whereby both qualitative and quantitative

research designs were utilised. Two types of data collection methods were utilised in

this study. The first method (modified Delphi technique) involved a method of

structured collection and processing of information gathered from road and rail safety

experts in Australia to gain an informed judgment. This was designed to establish the

behaviours and types of road users that may increase their risk of being involved in a

level crossing collision. A modified Delphi technique is a particularly useful tool

when accurate information about a specific problem is limited or unavailable. The

second method (focus groups) involved the investigation of train drivers’ experiences

of incidents and collisions at level crossings. This provided an insight into the

incidents at level crossings to ascertain which road user groups are most at risk and

how they can be effectively targeted by educational countermeasures. Focus groups

with train drivers was seen as an important tool as it offers the opportunity to gain

valuable insights into participant’s perceptions and experiences, as well as providing

‘high density’ qualitative information (Nicolson and Anderson, 2003).

Much of what is currently known about level crossing collisions is derived from

coroner’s findings and statistics. This study is designed to extend this knowledge by

undertaking a more thorough examination of unsafe behaviours and the road user

groups at risk.

While the principal aim of this study is to address the specific research

questions identified below, it will provide a foundation for Study Two and Study

Three. These later studies will investigate the effectiveness of an educational

countermeasure for three road safety groups identified to be at risk by the current

study and examine the present context of motorist behaviour at level crossings.

4.2 REVIEW OF EXISTING DATA

Currently, exact statistics of level crossing fatalities in Australia are difficult to

determine. The most significant reason is that most jurisdictions in Australia use

different methods of categorising and recording such collisions. As a consequence of

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these differing methods, there is a lack of definitive evidence available relating to the

extent and nature of level crossing collisions. Due to the large number of incidents at

level crossings that do not involve a fatality but have the potential to result in a

catastrophic event such as a train derailment, it is important to examine the

occurrences of incidents as well as collisions involving fatalities. To date, the

Australian Transport Safety Bureau is unaware of the exact number of level crossing

near miss rates in each jurisdiction (Australian Transport Safety Bureau, 2003),

however anecdotal evidence from train drivers suggests that near misses are far more

frequent than reported.

Due to this relatively small number of fatalities each year involving collisions

between trains and motor vehicles and limited data availability from the ATSB, it is

difficult to determine the major causes of collisions. However, the ATSB data does

indicate that older drivers (aged 60 years and over) are over-represented in level

crossings collisions compared to other fatal road crashes (Australian Transport Safety

Bureau, 2003). However, there are likely to be other road user groups that are also at

risk of being involved in a level crossing collision. With the number of heavy

vehicles on the road expected to increase dramatically over the next decade, the risk

of a heavy vehicle-train collision (that results in a train derailment) is also expected to

increase. Therefore, since there is very little useful data available to determine key

road user groups at risk of a vehicle-train collision, this study’s purpose was to

identify such road user groups through two data collection methods.

4.3 STUDY AIMS AND RESEARCH QUESTIONS

The purpose of this study was to identify road user groups at risk and key unsafe

motorist behaviours. As described above, there are a small number of fatalities each

year involving collisions between trains and motor vehicles and limited data

availability from the Australian Transport Safety Bureau (ATSB). Hence, it is

difficult to determine the major causes of level crossing collisions. Evidence from rail

authorities in all jurisdictions indicate that there are a small number of collisions but a

large number of ‘near-misses’.

Specific research questions for this study included:

• What types of motorists are at risk of a collision?;

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• What are the behaviours that motorists exhibit that increase their chances of a

collision?;

• How frequently do incidents and collisions occur?; and

• How frequently are incidents recorded by train drivers?

4.4 METHOD

4.4.1 Overview

Two forms of data collection (quantitative and qualitative) were used to provide

methodological triangulation and ensure reliability of the data. The triangulation

method uses multiple methods and perspectives to collect and interpret data to seek

convergences of findings to enhance validity. This study consisted of two phases: (1)

consensus of an expert panel (modified Delphi technique) and (2) focus group

discussions with train drivers.

4.4.2 Delphi technique

4.4.2.1 Background information

When accurate information is unavailable, as in the field of level crossing safety

in Australia, the Delphi technique is a particularly useful tool. The Delphi technique

is one method that has been used to determine priorities, set goals and establish future

directions in a specific area (Linstone, 1975). Additionally, it is useful for non-

interacting groups (whose members are geographically distant) and for ‘hot topics’

facing the community (level crossing safety is currently one ‘hot topic’ in both the rail

industry and community).

Consensus methods are one technique in dealing with conflicting scientific

evidence (Jones, 1995). According to Jones and Hunter (1995), such methods allow a

wider range of study types to be considered than is customary to statistical reviews. In

addition, these methods allow a superior function for the qualitative assessment of

evidence. One of the best known consensus methods is the Delphi technique. The

Delphi technique takes its name from the Delphic oracle's skills of interpretation and

foresight (Jones, 1995). The Delphi technique is particularly useful when accurate

information is unavailable. It consists of a series of sequential questionnaires or

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‘rounds’, interspersed by controlled feedback that seeks to gain the most reliable

consensus of opinion of a group of experts (Linstone, 1975). The first round

questionnaire is qualitative and its role is to identify issues to be addressed in later

rounds. The second and subsequent rounds are more specific, with the questionnaire

seeking quantification of earlier findings, usually through rating or ranking

techniques. Each participant is provided with a summary of opinions before

answering the next questionnaire. The survey is the most common technique of

Delphi application. The Delphi survey has three special features: 1) anonymity of

participants, 2) iteration and controlled feedback between rounds, and 3) statistical

summary of group response (Linstone, 1975). Adler and Ziglio (1996) give three

considerations that are important for Delphi applications:

• The problem does not lend itself to precise analytical techniques but can

benefit from subjective judgments on a collective basis;

• The problem at hand has no monitored history nor adequate information on its

present and future development; or

• Addressing the problem requires the exploration and assessment of numerous

issues connected with various policy options.

Typically, the Delphi technique commences with an open-ended questionnaire

that is given to a panel of selected experts to solicit specific information about a

particular subject or content area (Custer et al., 1999). In subsequent rounds of this

procedure, participants rate the relative importance of individual items as well as

making changes to the phrasing or substance of the items. Through a series of rounds

(typically three) this process is designed to yield consensus of the subject (Custer et

al., 1999). A modified Delphi technique is similar to the usual Delphi technique in

terms of both procedure and intent. However, the major modification consists of

commencing the process with a set of meticulously selected items. Review of the

literature and interviews with experts in the area, are the two main sources from

which pre-selected items are drawn (Custer et al., 1999). Custer et al. (1999) suggests

that the primary advantages of this modified Delphi technique is that it provides solid

grounding in previously developed work, and it improves the initial round response

rate.

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The purpose of these survey questionnaires was to identify what experts believe

are the key road user groups and target behaviours that are risk factors for a vehicle-

train collision at a level crossing, as no national dataset is available in Australia that

accurately provides detailed demographic or behavioural characteristics of drivers

involved in collisions. Studies by the ATSB provide only a description of some of the

circumstances that appear to be prevalent in fatal collisions at level crossings but in no

way claims to be definitive of the nature and causes of level crossing collisions

(Australian Transport Safety Bureau, 2002b). The ATSB (2002b) states that the

sample sizes are too small to enable in-depth analysis.

In road safety, the Delphi technique has been used to assess expert opinion on

countermeasures against unlicensed driving (Knox, 2002) as well as speeding (Silock,

1999). Based on the above criteria, characteristics of the Delphi technique were

considered particularly suitable for the consideration of identifying road user groups

at risk in level crossing collisions. As no data is currently available in Australia that

accurately provides demographic or behavioural characteristics of motorists involved

in vehicle-train collisions, the Delphi technique was one tool that may assist in

informing the direction of Study Two and Study Three.

4.4.2.2 Sample and procedure

A list of experts in level crossing safety in Australia was compiled in

consultation with the Australasian Railway Association and state rail authorities. This

list included experts from the following agencies:

• Australian Rail Association (ARA)

• Australian Rail Track Corporation (ARTC)

• National and State level crossing safety steering groups (e.g. National Railway

Level Crossing Safety Strategy Implementation Group, QLD Level Crossing

Safety Steering Group)

• Departments of Planning and Infrastructure (DPI)

• Queensland Rail (QR)

• Queensland Transport (QT)

• Main Roads, WA

• Main Roads, Queensland

• VicRoads

• National Road Transport Commission (NRTC)

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• Queensland University of Technology (QUT)

• Pacific National

• Monash University Accident Research Centre (MUARC)

• Australian Road Research Board (ARRB)

• State Coroner’s Offices

• N.S.W. Road and Traffic Authority (RTA)

• Police Departments

• Parliamentary TravelSafe Committees

• RACQ/RACV/NRMA

Survey questionnaires (with Information Sheets) were distributed to 48 experts

(both rail and road industry experts) throughout Australia. It was determined that rail

and road safety experts in level crossing safety from different regions of the country,

as well as industry experience and perspective would provide some breadth of insight

into level crossing safety issues. The rationale for using these two different industry

panelists was that although both groups came from different parts of the industry, they

represent unique perspectives of expert opinion. Both perspectives are important as

they will assist in informing development of educational interventions. An

explanation of the objectives and procedure of each Delphi round is detailed below:

• Round 1

Unstructured and open-ended questionnaire;

Experts can express their opinion on any issue they feel relevant;

and

Questionnaire contains a synopsis of the issues and is educative,

informative as well as being explorative.

• Round 2

Questionnaire developed from feedback in Round 1;

Quantitative statements that participants rated;

Answers from Round 1 fed back to participants through a summary

at the beginning of Round 2 questionnaire;

Self-rating of participants on their level of expertise; and

Final analysis and conclusions drawn.

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Round One of this survey (48 surveys sent) was sent out by mail on the 28th

July 2004. A reminder letter was sent out to all experts on the 10th August 2004 to

encourage them to participate. Consent was implied if the questionnaire was

completed and returned. A total of 24 experts returned a completed questionnaire

(response rate of 50%). The Round Two survey was then developed with feedback

from the Round One survey and was sent on the 30th September 2004 to 45 experts (3

experts stated that they were unable to participate in the survey for unknown reasons).

A reminder letter was sent out to all experts on the 13th October to encourage them to

participate. Consent was implied if the questionnaire was completed and returned. A

total of 27 surveys were returned (response rate of 60%).

4.4.2.3 Assessment tools

As stated earlier, a modified Delphi technique was used for both rounds. The

first round included open-ended questions to solicit specific information about a

particular subject or content area. This first round questionnaire included carefully

selected items drawn from both the literature and interviews (informal) with experts in

the field. Round Two questionnaire participants rate the relative importance of

individual items as well as making changes to the phrasing or substance of the items.

Round One of the modified Delphi technique was developed to elicit broad

responses to questions about warning and protection systems; types of motorists at

risk of collision; unsafe motorist’s behaviours; and demographics. Opinions and

recommendations from Round One were provided to the panel and then displayed in

rating items in Round Two. The second questionnaire (Round Two) was quantitative,

and was developed to obtain ratings (using Likert scales) of the types of motorists at

risk and unsafe motorist behaviours. These Likert scales ranged from 1 = Very

Important to 5 = Not at All Important. Pilot testing of the two questionnaires was

conducted to help to identify ambiguities and improve feasibility of administration.

Both of these questionnaires were piloted on road and rail safety practitioners and

researchers.

4.4.2.4 Analysis

Opinions and recommendations from Round One were provided to the panel

and then displayed in ranking items in Round Two. This second round was used to

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elicit opinions on more specific items than in the first round. Descriptives were

conducted using the open-ended answers in the Round One survey. Data from the

Likert scales from Round Two were entered into the Statistical Package for Social

Sciences (SPSS) and descriptive analyses were conducted (i.e. mean and standard

deviation etc.).

4.4.3 Focus groups

4.4.3.1 Background information

Focus groups with train drivers were seen as a critical element in examining the

prevalence of ‘near miss’ incidents. The purpose of this second phase is to explore

train drivers’ experiences of motorist behaviour at level crossings. As train drivers

potentially have a different perspective of the road user groups and key target

behaviours at risk of a vehicle-train collision from experts in the area, exploring train

driver’s experiences and opinions is seen as a valuable method of data collection. No

known research has been conducted in Australia that has explored the perceptions of

train drivers specifically in relation to level crossings. This phase focused on train

drivers’ experiences of:

• Motorist behaviour at level crossings;

• Vehicle types involved in level crossing incidents;

• “Near-misses” at level crossings;

• Perceptions on motorist behaviour at level crossings;

• Perceptions of current safety and safety actions at level crossings; and

• Impact of level crossing incidents.

Focus groups are one of the most favoured and accepted methods of collecting

qualitative data, as they offer the opportunity to gain valuable insights into

participant’s perceptions and experiences (Nicolson, 2003). Focus groups allow data

to be collected from a number of people in a relatively short period of time (Beyea,

2000) as well as providing ‘high density’ qualitative information (Nicolson, 2003).

Focus groups were selected as the most appropriate data collection too with train

drivers, as they offered the opportunity to explore and gather rich data on level

crossing safety as well as gaining insights into train drivers’ experiences. The group

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experience with trains drivers is also advantageous as it facilitates discussion within a

supportive environment (Nicolson, 2003). Kitzinger (1995) states:

The idea behind the focus group method is that group processes can help people

to explore and clarify their view in ways that would be less easily accessible in

a one to one interview…When group dynamics work well the participants work

alongside the researcher, taking the research in new and often unexpected

directions.

(p299)

According to The Health Communication Unit at the University of Toronto

(2002), there are three main types of focus groups:

• Exploratory focus groups – used to increase understanding of an issue, to

generate hypotheses, in concept development and pilot testing;

• Phenomenological focus groups - seek to understand the experiences and

outlook of respondents (as consumers, potential consumers and/or opinion

leaders); and

• Clinical focus groups – used to examine unconscious mechanisms operating

within people that impact on their behavioural or predispositions to behaviour.

However, each of these types of focus groups has both advantages and

disadvantages. Advantages of focus groups include:

• The main advantage of focus group methodology for collecting information is

that it allows for in-depth discussion and probing on an issue of interest. You

can collect opinions of more than one person in one session and the interaction

between group participants can result in increased elaboration on a topic and

broader insight into understanding an issue;

• Focus groups provide a tremendous amount of information at a reasonable

cost. In most cases they are less costly than conducting 8–12 in-depth

interviews and cheaper than most quantitative data collection methods;

• Opinions of more people are obtained within a shorter time frame, compared

to in-depth interviews; and

• Clients can benefit by observing the group if a room with a one way mirror is

used.

(Kitzinger, 1995)

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Whilst disadvantages include:

• Potential for participants to influence one another’s opinions;

• Focus groups do not provide quantifiable information about a population;

• The number of questions which can be asked is limited as the response time

for each question is increased by the number of participants;

• While focus groups provide the researcher with in-depth responses to their

questions, this type of data is more difficult to analyse than quantitative data;

• The quality of the information collected is dependent on the skills of the

moderator;

• For some populations and topics focus groups are not effective because the

social context influences the responses more than what the researcher would

want; and

• Focus groups can be difficult to conduct with populations which have hearing,

cognitive or communicative impairments.

(Kitzinger, 1995)

4.4.3.2 Sample and procedure

The subject pool for focus group discussions with train drivers was identified by

Queensland Rail (QR) through the QR Research Manager. Train drivers were

informed of the opportunity to participate in the focus groups by QR managers and

Union representatives. Information sheets and consent forms were provided to the

participants prior to commencement of the focus group. The focus groups took place

during October 2004, with one focus group being held in Rockhampton

(Regional/Freight services) and another focus group being held in Brisbane

(Citytrain). Seventeen train drivers from QR participated in the study: eight (8) from

the Brisbane metropolitan area and nine (9) from regional areas of Rockhampton and

Gladstone.

Each focus group discussion ran for approximately 90 minutes and moderated

by the same two researchers. Participants were informed that participation was purely

voluntary and that their responses would remain anonymous through de-identification

of any collected data. Information sheets were provided to the participants by the

Traincrew Managers prior to the focus group and posters advertising the sessions

were placed on noticeboards in the Traincrew staff areas. Consent forms were signed

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by participants at prior to commencement of the discussion. Contact details of the

counseling service at Queensland Rail were provided to each of the participants on the

information sheet.

The first session was conducted in a provincial city with train drivers from

regional/freight service areas and the second with train drivers from the metropolitan

area. Both focus groups were conducted on Queensland Rail premises in a private

meeting room.

Each group had a full discussion of each of the items on the agenda and all

respondents were given sufficient opportunity to air their views. The researchers’

main objective was to facilitate discussion between participants. To allow external

validation, both group discussions were tape recorded and subsequently transcribed.

Written notes were also made during the discussion. Transcribed data from these

tapes will be de-identified to protect each participant’s identity. No theoretical testing

or development was necessary for these focus groups, as this study is exploratory and

the aim of the focus groups was to gather information on the frequency and

characteristics of level crossing incidents as experienced by the train drivers. Codes

were generated from the data collected from these two focus groups, with themes

being identified independently for each focus group and then merged together for

sorting of similar themes. During each session, an inductive approach was used,

which allowed the researchers to be flexible in exploring issues and themes as they

arose.

In consideration of the sensitive nature of involvement in accidents and

fatalities, participants were not asked directly if they had been involved in a vehicle-

train collision but were encouraged to discuss their experiences.

4.4.3.4 Assessment tools

An agenda for the focus group discussions was developed in accordance with

guidelines for conducting focus groups. This agenda included: welcome, review of

agenda, review of goal of the meeting, review of ground rules, introductions,

questions and answers, closing comments. Questions were developed with input from

experts in the area and were based on the themes identified from the survey

questionnaire (consensus of expert panel) results. The types of questions included:

nature, frequency and reporting of incidents at level crossings, types of vehicles

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involved in incidents, types of behaviours observed at level crossings, the impact of

incidents, and current protection systems.

4.4.3.5 Analysis

Raw data collected from the focus group discussions was collected in a

relatively unstructured form (by use of tape recordings) and then transcribed. The data

was then analysed through a qualitative thematic analysis process.

4.4.4 Ethical clearance

Ethical clearance for data collection for Study One was gained from the

Queensland University of Technology Human Research Ethics Committee (QUT Ref.

No. 3550H).

4.5 RESULTS

4.5.1 Delphi technique

The response rate for Rounds One and Two were 50% (n=48) and 60% (n=45)

respectively. In Round Two, participants were asked how many years experience

they had in the field, with a mean of 22.8 years (S.D. 12.57) being observed. The

division between those participants who were working in rail, road, road and rail, and

enforcement was 51.9%, 25.9%, 7.4% and 14.8% respectively.

Themes from Round One included experts viewing the most common motorist

issues as: risk taking, inattention, disobeying road rules, complacency, motorist error,

low expectations of trains, and lack of education. The engineering and design issues

that experts believed contribute to collisions at level crossings included: sighting

distances, short stacking problems (particularly with trucks) and angles of approach.

One environmental issues was raised by many experts with regards to contributing to

level crossing collisions: the position of a crossing may restrict some motorists from

being able to see activated warning systems (such as flashing lights) when the sun is

shining brightly.

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Many experts made comment on safety management issues, with some raising

the need for consistency of approach with understanding of the complexity of level

crossing safety. One expert commented:

“The interaction between a train and a vehicle or other road user is a complex

event. The vehicle, road user, or the infrastructures are the primary elements of

railway level crossings. These operate within a wider environment of

economics, community culture, scientific understanding, etc. All of these

contribute to potential conflicts.” - Participant Round 1.

The road user groups identified by the expert panel as being ‘at risk’ included:

general motorists, younger and older drivers, heavy vehicles, rural road users, buses,

fleet and local drivers. Apart from the general motorists group, there were four

groups that were identified by the majority of respondents as being ‘high risk’. These

included heavy vehicles, rural road user, older drivers and younger drivers. There

were four key areas that participants raised that they believed were major motorist

factors that contribute to collisions at level crossings. These included:

• Behaviour – some drivers try to beat trains or drive around boom gates;

• Training – drivers are not trained to deal with level crossings;

• Understanding – consequence and severity of accidents with trains; and

• Inattention – train never comes at this time so on auto pilot.

A range of behaviours and issues associated with these groups were identified

and utilised in the ranking processes of the Round 2 questionnaire.

The Round Two questionnaire was developed from the first round

questionnaire, with responses being re-introduced to the panel as factors for rating

(Likert scales). Results from this second questionnaire highlighted the importance of

specific items within the constructs of ‘major contributing factors’ and specific

behaviours and issues of each ‘road user group’. Within the construct of major

contributing factors, motorist behaviours that were found to be ‘very

important/important’ included:

• Low expectation of coming across a train while driving (89.3%);

• Not slowing down to scan for a train at passive crossings (85.7%);

• Not stopping at Stop signs at passive crossings (85.7%);

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• Inattention by the motorist when driving (82.1%); and

• Lack of detection devices for breaches (no “red light” cameras at level

crossings) (75.0%).

For each of the road user groups a number of items that may contribute to

increased risk were presented for ranking. For the older driver group, there was a

high level of agreement that errors in judgment (e.g. misjudging time needed to cross

safely) were ‘very important/important’ risk factors (78.6%). For the younger driver

group, 92.8% ranked trying to beat the train across the crossing as ‘very

important/important’. Factors for the heavy vehicle group that were ranked as ‘very

important/important’ included: trying to beat the train across the crossing (75.0%)

and length of vehicle causing overhang on the crossing (67.9%). Factors particular to

rural road users that were ranked as ‘very important/important’ included: low

expectation of a train (89.3%); complacency due to familiarity (89.3%) and not

scanning for a train at give way signed crossings (85.8%).

4.5.2 Focus groups

All participants in the focus groups were male, reflecting gender profile of this

workforce. The mean years of industry experience for the metropolitan group was 24

years (range 1 to 34 years) and for the regional group was 23 years (range 5 months to

42 years). The majority of participants were train drivers (n=15), while two

participants were Train Guards. The majority of the drivers revealed they had

experienced an incident or fatality (including suicides) during their career with two

drivers reporting they had experienced more than five fatalities each. All participants

had experienced near misses with road users at level crossings.

Analysis revealed that there are strong differences between the experiences of

Regional/Freight participants and the Citytrain participants.

4.5.2.1 Citytrain

The metropolitan train drivers generally experienced motorist behaviour at

active crossings with flashing lights and boom gates while the regional train drivers

experienced behaviours at active crossings with boom gates, crossings with lights

only and passive crossings with stationary signs.

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In the metropolitan train driver group, experiences of motorist behaviour at

level crossings included: motorists driving around boom gates, getting stuck under

boom gates, queuing over congested crossings and driving through the crossing after

the red lights commence flashing. The behaviour of motorists driving around boom

gates was noted to occur quite regularly with one participant commenting it happens

“everyday…they reverse back and then drive around them”. The majority of train

drivers believed that it was very common that motorists would drive through the

crossing when the lights are flashing both before and after the booms were activated

and some crossings were named as black spots where motorists repeatedly offend.

Vehicles protruding into the path of the train and motorists entering congested

crossings and then panicking and driving backwards into the boom gates were also

mentioned.

For the metropolitan group, trucks were also mentioned, with the issue of

getting stuck under the boom gates due to the overhang of the vehicle on the crossing.

Comments were made that in some cases the boom gates have closed on the rear

trailers of trucks without the truck driver even realising. Motorcyclists trying to beat

the train were mentioned as frequent high risk road user behaviour.

4.5.2.2 Regional/Freight

With regards to motorist behaviour, the regional group participants noted that

motorists not stopping or giving way at passively controlled crossings is continuing to

be a major concern and that behaviours differ with location. Interaction with

engineering devices was discussed and although it was agreed that generally people

stop for the barriers, high risk motorist behaviours at active crossings included

running the flashing lights and disobeying the boom barriers by driving around or

straight through them. Other high risk behaviours included motorists attempting to

beat the train across the crossing, speeding up at flashing lights, and general risk

taking by younger drivers in particular. Participants spoke of motorist behaviour that

they perceived were due to inattention such as, getting stuck under lowering boom

gates and not seeing the train and subsequently driving into its path. Motorists not

allowing enough time to cross in front of the train and hesitating or stop-starting at the

crossing were also noted to be at high risk. There was a general perception from these

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behaviours that motorists are unable to judge the speed and distance of an

approaching train to determine a safe gap during which to cross.

Mention was made of risk taking in motorcyclists and younger drivers, however

the main cause of concern was heavy vehicles, as breaches at level crossings are

common, and the potential of a heavy vehicle-train collision is highly likely to

injure/kill the train driver and possibly derail the train. Heavy vehicles were noted to

be slower to get across the crossing due to vehicle length and acceleration. High risk

behaviour in this road user group included not stopping at passive crossings, trying to

beat the train across the crossing and going around or through boom gates.

There was agreement by the regional group that about 60% of ‘near misses’ are

due to poor visibility (such as angle of approach) with only 30-40% actually due to

the motorist. Perceptions on why motorists engage in risk taking behaviours at

crossings were a common theme throughout the discussion. These perceptions were

generally based on the train driver’s interpretation of the vehicle approach and

reactions of the motorist. In the cabin of a train, train drivers are often able to see the

vehicle on approach to the crossing and make predictive judgments about their

behaviour. Common thoughts included: “You see a car or truck… coming up the

road…and you think ’Is this bloke gonna stop or what?' ”. On seeing the motorists in

the vehicle, interpretations of frustration and impatience were made, such as:

You can actually see the look on the motorist’s face…when you’re on a train,

the boom gates come down and they are... ‘Aw the train’ they’re not real happy,

especially when they are going to work.

A thorough range of perceptions on why motorists enact in risk taking

behaviours at crossings were given by the participants including those factors that are

attributable to the environment rather than human factors. There was general

agreement in the regional group that motorists’ behaviour was influenced by the

motorists’ complacency, lack of knowledge and low perception of risk. Impatience

by the motorist was a commonly recognised theme with comments that, being in

hurry, time pressures, and anger and frustration with waiting for the train to pass,

being noted by both groups of participants.

For the regional train drivers, possible reasons for local motorist behaviours

included low expectations of coming across a train due to the infrequency of trains at

crossings (and subsequent complacency) and knowledge of the waiting times when

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stopping for a train leading to beat the train behaviours. This perception of motorists

trying to beat the train, lights or boom gates to save time was a recurring theme with

both groups.

The lack of enforcement of the road rules at crossings emerged as a strong

theme in both the Citytrain and regional groups, with participants noting that

motorists know they won’t get caught. This was also suggested for the small amount

of motorists that engage in risk taking or thrill seeking behaviours. In the regional

group, motorist inattention and distraction were common issues identified, with the

concepts of motorists’ not seeing the train or not registering that they are at a crossing

mentioned. Motorist knowledge was also thought to be a contributing factor to

behaviour with comments that there is low knowledge of a train’s stopping distance,

public misconceptions on the ability of the train to stop and the poor understanding of

the meaning of warning lights. One train driver commented, “…people say that to

me, 'Why don’t we stop for them?' ”. Due to the high number of motorists that do not

leave enough time to cross safely, it was also thought that motorists did not have the

ability to judge the distance and speed of an approaching train to determine a safe gap

during which to cross. Perceptions on the behaviours of truck drivers included

possible influences of fatigue, time pressures, delays in waiting for long trains and

frustration, drivers not knowing the length of their vehicle, and misjudging the time

taken to cross safely.

For both the regional and metropolitan train driver groups, there appeared to

exist a culture that train drivers are ‘whingers’ if they report all near-miss incidents at

level crossings. Many train drivers in both groups indicated that the reporting

mechanism for such events (i.e. transmission over the radio to control) does not allow

any anonymity and thus they are reluctant to report the numerous incidents that they

encounter.

4.6 DISCUSSION

4.6.1 Study limitations

This first study used ‘methodological triangulation’ (i.e. combining research

approaches to give a range of perspectives) whereby both qualitative (focus groups)

and quantitative (modified Delphi technique) research designs were utilised (Barbour,

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1999, Bryman, 1992). With the discipline of road safety research requiring

methodological strategies that will enhance efforts to conceptualise the multi-faceted

nature of motorist behaviour at level crossings, this application provided the

robustness required. However, like all research, this first study is not without its

limitations.

One of the foremost limitations of focus groups include the responses of each

participant potentially not being independent as a few dominant focus group members

can skew the session. However, with the experience and skills of the moderator, it is

anticipated that this limitation was reduced substantially. Additionally, analysing

results from these focus groups requires proficiency and a good understanding of

qualitative research. With the author having many years of experience in focus group

analysis in both health research and road safety research, it is anticipated that this

limitation was also reduced considerably.

With regards to limitations of the Delphi technique, it has been suggested that

success depends of the quality of the participants (Linstone, 1975, Masser and Foley,

1987). Identifying participants who are knowledgeable about level crossing safety

was a challenge. Although it was determined that rail and road safety experts in level

crossing safety from different regions of the country would provide different

perspectives to gain ensure a breadth of insight into level crossing safety issues, it is

difficult to guarantee that the choice of these experts was unsurpassed. However,

with the assistance of the Australasian Railway Association (ARA) in the compilation

of this list of experts, it is anticipated that the quality of these experts was of a high

standard.

4.7 SUMMARY

This study was designed to explore the first four research questions identified as

part of this program of research. In doing so, it identified road user groups at risk and

their key target behaviours in level crossing collisions. Results from methodological

triangulation research indicate that there are three main road user groups at risk: older

drivers (60+ years), younger drivers (17-24 years) and heavy vehicles. These results

extend the available evidence relating to contributing factors involved in vehicle-train

collisions. Additionally, they provide justification for targeting specific road user

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groups in attempting to change behavioural constructs necessary for safe driving at

level crossings.

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CHAPTER FIVE: PLANNING AND DEVELOPMENT OF INTERVENTIONS FOR EACH ROAD USER GROUP

5.1 Introduction …………………………………………………………… 128

5.2 Vulnerable road users at level crossings ……………………………… 129

5.3 Qualitative research with target groups ……………………………….. 148

5.4 Quantitative research with train drivers and experts ………………….. 173

5.5 Framework for intervention development …………………………….. 186

5.6 Summary ……………………………………………………………… 190

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5.1 INTRODUCTION

This chapter documented Study Two, the formative research undertaken as part

of the planning, development and delivery of behavioural interventions for each of the

three road user groups. Additionally, it provided an overview of the road safety issues

specific to each of the three road user groups identified in Study One: younger

drivers, older drivers and heavy vehicle drivers.

Formative research uses social science methods to assess the beliefs,

perceptions, and behaviours of a specific group (Vastine et al., 2005). The resulting

data allow for the design and development of an intervention that is tailored to the

group’s requirements and preferences (Vastine et al., 2005). Additionally, formative

research demonstrates an interest in understanding target groups and can in theory

build trust, collaboration and cooperation, and ultimately acceptance of the project

(Vastine et al., 2005). This type of research forms the basis for developing effective

strategies, including communication channels, to try to influence behaviour change.

Two forms of formative research data collection (qualitative and quantitative)

were used to provide methodological triangulation and ensure reliability of the data.

As discussed in the previous chapter, this method uses multiple methods and

perspectives to collect and interpret data to seek convergences of findings to enhance

validity. Qualitative data collection involved formative research with members from

each of the three road user groups using semi-structured interviews and focus group

discussions from both urban and regional samples. Quantitative data collection

involved both train drivers in regional and urban settings, and experts in the field.

The same recruitment methods used in Study One for train drivers and experts in the

field were used in this second study. As both train drivers and experts assisted with

identifying the three road user groups at risk in Study One, they were also deemed to

be valuable in assisting with formative research in this second study. The overall

objective of this study was to obtain rich data on the key variables of attitudes, norms,

self-efficacy (perceived behavioural control), perceived risk, environmental

constraints and the skills/abilities of drivers and prioritise project resources for

intervention planning, development and delivery. Information obtained from both

forms of data collection (i.e. qualitative and quantitative) was critical in assisting,

guiding, and identifying priority areas for message and material development and

development of data collection measures (i.e. questionnaires).

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Typically there are two key approaches to planning behavioural interventions.

One is ‘social marketing’, which is often used by transport agencies to plan mass

media campaigns, the other ‘intervention mapping’ is based on the importance of

planning behavioural interventions that are founded on theory and evidence. Both

approaches will be discussed in this chapter. Additionally, findings from the

formative research are discussed in detail.

5.2 HIGH RISK AND VULNERABLE ROAD USERS

5.2.1 Overview

Findings from Study One indicated that there are four main road user groups at

risk of being involved in a level crossing collision. These groups include younger

drivers, older drivers, heavy vehicle drivers and rural road users. As rural road users

comprise a number of distinct road user groups, including older, younger and heavy

vehicle drivers, rural road users are not examined per se. While not exhaustive, this

examination will provide an overview of the characteristics of the contributing factors

to general road crashes are described below, with application to level crossing safety.

Reference is also made to the literature (see Chapter Two – Literature Review) that

examines human factors contributing to road crashes (or level crossing collisions).

5.2.2 Younger drivers

The over-involvement of young drivers in road crashes is widely understood in

Australia to be a most serious and to date largely intractable road safety

problem. Young people aged 15-24 make up 15% of the population but account

for 31% of fatalities.

(Triggs, 1998)

5.2.2.1 Crash statistics and patterns of younger drivers

A study by Cavallo and Triggs (1998), found that Victorian first year drivers are

approximately three to five times more likely to be involved in a casualty road crash

than more experience drivers. Figure 9 illustrates the relationship between age and

casualty road crash risk in Victoria (when compared to the lowest crash risk group –

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40-49 age group) (VicRoads., 2000). This pattern is also typical for drivers in other

Australian jurisdictions as well as overseas.

Figure 9: Risk of driver being involved in a casualty road crash

Maycock et al. (1991) conducted a study in Britain of drivers initially licensed

to drive at different ages (i.e. 17, 20, 25, 36 and 50 years respectively) who traveled

approximately 12 000 kilometres per year, and found that road crash risk whilst

driving solo in the first few years decreased by approximately 31% due to age and

approximately 59% due to experience. According to Gregersen and Bjurulf (1996) “it

seems that experience as well as age related factors are of vital importance. It also

seems clear that experience is of greater importance than age, at least from 17 years of

age” (p231).

5.2.2.2 The role of experience

According to an extensive review conducted by Christie (2001), both driver age

and experience (or inexperience) contributes to road crash risk and crash involvement.

Research conducted in Australia, the United States, Canada, United Kingdom and

Sweden supports the notion that as drivers accumulate greater age (along with

maturity) and experience, road crash risk decreases (Levy, 1990, Drummond and Yeo,

1992, Maycock, 1991, Mayhew, 1995, Gregersen, 1996). Compared to older drivers,

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young drivers are at higher risk of being involved in a road crash (Williamson,

2003a). Until the age of 60, when declines in functional ability actually increase, both

increasing age and experience contribute to reduced road crash risk.

Road safety experts agree that there are two main reasons for the over-

representation of young drivers in road crashes: age and inexperience. Gusfield

(1991) proposes that road safety researchers centre of young drivers being willing to

take risks, and that it is necessary to consider what the motor vehicle represents to

young drivers and how their leisure time is spent. According to Gusfield (1991), the

motor vehicle signifies adulthood, autonomy from parents and school, as well as

being a status symbol (to opposite sex). Christie (2001) argues that “youthfulness and

inexperience tend to run in parallel for most new drivers” (p5). Experience-related

factors for young drivers, according to Mayhew and Simpson’s (1995) review of

driving experience, are far inferior in the novice driver compared to experienced

drivers. Deery (1999) proposes that a reason for the complexity of the young novice

driver road crash problem is that the task of driving is itself extremely complex.

Although young drivers learn quickly the road rules and how to handle a vehicle

(Hall, 1996), young drivers have limited experience to develop the complex, higher-

order perceptual and cognitive skills required to safely interact with the driving

environment (Deery, 1999). Critical to their road crash involvement, the skills that

young drivers often fall short on include:

• Hazard perception (i.e. detecting, recognising, and dealing with traffic

hazards);

• Attentional control (i.e. attending to the right things, in the right amounts, at

the right time);

• Timesharing (i.e. dealing with changing workloads); and

• Calibration (i.e. matching one’s performance with task demands).

(Deery, 1999, p228)

The distinction between age and experience has been referred to as ‘driving

style’ (or behaviour) and ‘driving skill’ (or performance) (Deery, 1996, Elander,

1993). Driving skill is concerned with limitations of performance on aspects of the

driving task, and is expected to improve with practice or training (although some

researchers propose that training is not beneficial to young drivers). Whereas driving

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style is concerned with decision-making aspects of driving, such as the way drivers

choose to drive or driving habits developed over time (Deery, 1999). Driving speed

and following distance, are two such examples of driving style (Deery, 1999).

5.2.2.3 Driving at level crossings

Although younger drivers are not over-represented in level crossing collisions

data in Australia, there have been some highly publicised collisions involving younger

drivers at level crossings. Preliminary research with young drivers through use of

focus group discussions and one-on-one interviews revealed that metropolitan drivers

have very poor knowledge of protection systems other than active crossings that have

boom gates and/or flashing lights, with approximately one quarter of participants

stating they didn’t know about passive crossings (that only have a Stop or Give-way

sign). Also of concern with this younger group was the low level of knowledge of the

meaning of road markings (such as yellow painted hatching across the tracks) at level

crossings. Additionally, some younger driver hold the belief that flashing lights are

‘cautionary’ lights (like amber light at traffic lights) and that drivers don’t need to

stop when lights are activated at a crossing. Overall, younger drivers stated that they

believe they are at low risk of being involved in a collision. As such, their

performance and behaviour with regards to driving at level crossings warrants

investigation.

5.2.3 Older drivers

Increasing concern is centered on the high crash risk of older drivers. Although

older drivers currently represent only a small number of accidents, the older

driver problem is highlighted when accident statistics are presented per

distance traveled.

(Andrea et al., 2001, p1)

In comparison to younger drivers, Charlton, Oxley, Fildes and Les (2001b)

suggest that when distance is taken into account, older drivers behave similarly to

novice drivers in terms of risk. The documented road crash risk of older drivers,

particularly in the 80 plus years age group, is significantly higher than the overall

average (Di Stefano and Macdonald, 2003). However, the elevation in crash risk does

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not automatically represent collision risk, because older drivers tend to have greater

physical frailty therefore when they do crash are likely to have injuries (Di Stefano

and Macdonald, 2003). It is indisputable that older drivers’ performance tends to

deteriorate significantly with increasing age, and there is accumulating research

(Daigneault, 2002, Stutts, 1998, Lundberg, 1998) supporting the view that a major

causal factor in this deterioration is cognitive decrements related to the ageing process

(Di Stefano and Macdonald, 2003). However, Eby and Kostyniuk (1998a) and Janke

(1994) propose that the functional impairments of older drivers do not necessarily

reduce their ability to drive safely.

5.2.3.1 Overview of the ageing driving population

Older people constitute the fastest growing sector of the driving population and

are believed to represent a high risk to road safety, given their high crash rate

per distance traveled.

(Wood, 2002, p214)

Predictions by Young (1990) indicate that the proportion of older Australians is

likely to double between 1990 and 2030 from 12% to 25%. This prediction of

Australian older people is mirrored in Japan, the United States and Western Europe

(Fildes, 1997). This increase in older people in the population extends to the driving

population. It has been suggested that “tomorrows older generation is much more

mobile than the current generation as cars and travel have become more achievable

and acceptable” (Fildes, 1997, p9). Driving for people aged 60 plus years is often

equated with mobility, and perceived as necessary to maintaining independence,

autonomy and self-esteem (Carp, 1988). According to Fildes (1997) there is evidence

to suggest that loosing the ability to drive is coupled with increased depression and

dependence on others. However, given that the elderly are continuing to drive well

into their eighth and ninth decades of life (Jette and Branch, 1992), the increase in this

driving population has important implications for road safety. Shaheen and Niemeier

(2001) suggest that there are “important consequences associated with an increased

reliance by the elderly on the automobile” (p156). As such, road crash rates create a

potentially significant problem.

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5.2.3.2 Crash statistics and patterns of older drivers

Older drivers are generally perceived to be cautious drivers, however road crash

statistics indicate that older drivers (aged 70 + years) have a higher serious injury road

crash risk than all other age groups (including younger drivers). While it is known

that older drivers have relatively few road crashes, they are much more likely to be

severely or fatally injured given crash involvement (Fildes, 2004, Charlton, 2001a).

When distance is taken into account, the risk of a road crash for an older driver is

similar to that of novice drivers (Charlton, 2001a, Daigneault, 2002). Research

conducted by Fildes et al (2001) established projections of the future road crash risk

for the older driver population, taking into account driving behaviour, population

migration, personal wealth and health, infrastructure and technological impacts

(Fildes, 2001). This projection is illustrated in Figure 10. This research predicted

that without active road safety intervention, there would be an overall three-fold

increase in fatal road crashes involving older drivers (Fildes, 2001). By 2005, this

research indicated that there would be an increase of 261% of males and 336%

increase for females in older driver fatalities.

Figure 10: Projected older driver fatalities in Australia, 1995-2005.

Older drivers are typically involved in multiple vehicle road crashes and road

crashes in complex driving situations (Wood, 2002, Andrea et al., 2001). They are

involved in a disproportionate number of road crashes involving intersections, with

failure to stop, inattention and turning across traffic (McGwin and Brown, 1999a,

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Ryan, 1998) and are commonly found ‘at-fault’ in road crashes (Preusser et al., 1998).

According to Drakopoulos and Lyles (2001), the “intersection environment presents

one of the greatest challenges for driver mental capacity due to the presence of

conflicting vehicular and pedestrian traffic movements, and the need for quick

decision-making in response to signs, signals, other drivers’ actions….” (p87). A

recent Australasian study of ‘blackspot’ road crash sites of older drivers found that the

principal problem for older drivers was selecting safe gaps in conflicting traffic,

manifesting itself at intersections controlled by stop or give-way signs, or traffic

signals (Fildes et al., 2000). World-wide road crash data supports this research

(McGwin and Brown, 1999b, Benekohal et al., 1994). Charlton et al (2001a) suggests

that a higher proportion of older drivers’ travel time is spent in high risk environments

(urban areas rather than on freeways or rural areas), which places them at higher

average risk per unit time or distance driven, compared to their younger counterparts.

However, the types of road crashes are different between those of younger drivers.

Older drivers are more likely to be involved in crashes at lower speeds, during

daylight hours, on dry roads, and be well under the legal BAC limit (Fildes et al.,

1994, Jette and Branch, 1992). Additionally, those aged 75 years and over

substantially exceed any other driver age group in terms of casualty road crash risk

(Diamantopoulou, 1996). International statistics show similar trends to research

conducted in Australia.

5.2.3.3 Physical ability and cognitive performance

The deterioration in driving performance in older drivers is multi-factorial,

however physical ability and cognitive performance play pivotal roles. The effects of

physical and cognitive deterioration with age has been “universally acknowledged and

widely studied” (King, 2004, p8). Physiological changes associated with ageing have

been found to affect perceptual, motor and cognitive abilities during the task of

driving (Eby, 1998b, Daigneault, 2002, Lundberg, 1998, Stutts, 1998). Shaheen and

Niemeier (2001) conducted an extensive literature review to accumulate quantitative

details and statistics concerning changes in physical factors in five key areas of

impairment: (1) vision, (2) hearing, (3) cognitive response, (4) cognitive attention and

memory, and (5) physical strength. Oxley et al (1995) added to this review and

provided more specific areas of degeneration:

• Visual acuity;

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• Attention capacity;

• Contrast sensitivity;

• Cognitive processing ability;

• Visual field loss;

• Decision time deterioration;

• Dark adaptation and glare recovery;

• Loss of memory capacity;

• Auditory capacity;

• Neuromuscular and strength loss;

• Perceptual performance;

• Postural control and gait changes;

• Motion perception; and

• Reaction time.

(p24)

However, vision is the most recognised age-related physical decline associated

with driving performance. Vision is an important factor in driving a vehicle, with

visual impairment becoming significantly more prevalent with increasing age (Attebo

et al., 1996) through both the normal ageing process as well as the increased

prevalence of eye disease (Wood, 2002). Age-related visual impairment (such as

declines in visual field, visual processing speed, visual search, low light sensitivity,

dynamic and static visual acuity, perception of angular movement and movement in

depth, resistance to glare and contrast sensitivity, have all been found to be associated

with varying degrees of increase in road crash risk (Shinar and Scheiber, 1991, Kline

et al., 1992, Ball et al., 1993). The ageing process results in yellowing and cloudiness

of the crystalline lens, a decrease in pupil size and alterations in the integrity of the

macular pigment and neural pathways (Wood, 2002, Weale, 1992). Haegerstrom-

Portnoy, Schneck and Brabyn (1999) suggest that these changes lead to the reductions

in light sensitivity, increased glare sensitivity and reduced visual acuity. According to

Simms (1985), nearly 90% of the information translated to a driver is visual, with the

efficiency of the person’s visual perception skills likely to influence the driver’s

competence on the road.

A recent study in Queensland indicated that there is little correlation between

self-rated driving ability and actual performance (Carberry et al., 2004). This study

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specifically examined the relationship between eye disease and driving performance.

Results suggest that drivers with visual impairment perform worse at driving tasks

such as obstacle avoidance and sign detection. This has serious implications for

complex driving environments such as level crossings. With many older drivers

being unaware of their visual limitations and the extent to which the ageing process

impairs the visual field and visual acuity, informing older drivers of their visual

limitations may assist them to modestly adapt their driving behaviour (Ball and

Owsley, 1991). However, the role that self-regulatory behaviour plays in reducing

road crash risk is a topic of great debate.

5.2.3.4 Self-regulatory driving behaviour

The processes involved in self-regulation and the factors that influence the

adoption or avoidance of self-regulatory behaviours are complicated and not

well understood.

(Charlton, 2001a, p2)

There is much speculation that older drivers self-regulate their driving

behaviour in response to a functional impairment or difficulties they experience when

driving. This speculation includes the skill in regulating driving according to their

own ability to reduce the incidence and severity of a collision. Some research

evidence advocates that caution and conservativeness prevail when it comes to

driving practices of older road users (Eberhard, 1996, Winter, 1988). According to

Charlton et al (2003), such examples of self-regulatory behaviour in the literature has

included:

• Driving more slowly;

• Traveling shorter distances;

• Making fewer trips;

• Avoiding difficult driving conditions (night-time, peak travel times and other

stress-inducing situations);

• Preferences for longer time gaps when turning or merging; and

• Avoidance of simultaneous activities while driving.

Charlton et al’s (2003) study of 656 drivers aged 55 years and older (including

29 former drivers) in Victoria, examined the extent and nature of self-regulatory

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driving behaviour and the characteristics of those who self-regulate and those who do

not. The results indicated that self-regulators tended to be female, aged 75 plus years,

with vision problems, with arthritis, lower ratings of speed of decision-making for

safe driving, not the principal driver and not married (Charlton et al., 2003). Charlton

(2003) notes:

While it is likely that many older drivers adjust their driving adequately, it is

also possible that some fail to self-regulate appropriately and, as a

consequence, may be at higher risk of crash involvement. There is some

evidence to suggest that older drivers, or at least some older drivers, do not

self-regulate adequately. Indeed, if self-regulation was entirely successful, crash

statistics would not show an over-representation of older drivers in serious

casualty crashes.

(p9)

One notable finding from this research is that road crash risk was not

necessarily related to lower crash involvement (Charlton et al., 2003). Another study

supporting self-regulatory behaviour of older drivers found that older drivers with

small reductions in spatial vision (particularly acuity in the presence of glare and

binocular deficits) recognised their limitations and restricted their driving to the day

(West, 2003). Contrary to such findings of self-regulatory behaviour, reports on older

drivers’ self-assessment of their driving ability indicate that older drivers both under-

estimate the risk of being involved in a road crash and over-estimate their ability to

handle the driving task and feel they have total control to avoid road crashes (Brainin,

1980, Matthews, 1986). However, self-regulation (or the lack of it) not only involves

older drivers, but people of all ages. A study by Rothman, Klein and Weinstein

(1996) found that people of all ages are poor at recognising the relationship between

their actions and potential driving risks. This study also found that many people

perceive themselves as less likely than their peers to suffer harm (Rothman, 1996).

5.2.3.5 Medical conditions

Medical conditions and their association with road crash risk has also been the

source of debate. Although there has been little rigorous research examining the

impact of medical conditions on driving ability or road crash involvement, some

research has indicated that medical conditions such as diabetes, cardiac conditions

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(Koepsell, 1994), a history of falls, kidney problems or stroke (Lyman, 2001)

increases the likelihood of an older driver being involved in a road crash. However,

as Fildes et al (1997) point out, evidence of crash associations with medical

conditions “is not always conclusive and the mechanisms not well understood” (px)

and that the “extent to which drivers with these conditions self-regulate their driving

will also influence their crash involvement” (px).

5.2.3.6 Driving at level crossings

As can be seen, there are many sensory, cognitive and physical functions that

deteriorate as people age which may ultimately impair their driving ability at place

them at high risk of being involved in a crash. Although this review did not attempt be

an exhaustive review of the effects of ageing on driving performance, it has

highlighted some important factors: (i) there is little correlation between self-rated

driving ability and actual performance; (ii) drivers with visual impairment perform

worse at driving tasks such as obstacle avoidance and sign detection; and (iii) the self-

regulatory behaviour of older drivers is a domain that is not entirely understood.

Preliminary research with drivers aged 60 years and over through the use of

focus group discussions and one-on-one interviews revealed that older drivers vary in

their perception of risk, with half of the participants stating that they are not at risk of

being involved in a level crossing collision, while the other half stated they are at

some degree of risk. Participants that stated they are not at any risk whilst driving at a

level crossing referred to using self-regulatory behaviour such as avoiding certain

‘risky’ crossings or becoming familiar with crossings they use regularly. Those

participants that stated they may be at risk whilst driving at a level crossing cited that

inattention or poorly designed crossings could be significant factors. Pressure from

other drivers was also a common theme that emerged from this preliminary research.

Although most older drivers indicated that they don’t take risks at level crossings,

many declared that they have had a close-call at a level crossing and many stated

visibility at night-time was a particular concern for them. Of particular concern with

this older driver group, was that the majority of urban participants indicated that

passive crossings (i.e. Stop or Give-way signed crossings) were ‘only in the bush’.

With the results of this preliminary research as well as the fact that older drivers are

over-represented in fatal collisions at level crossings, understanding why ageing

drivers are at greater risk of such collisions is of key important for road safety.

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5.2.4 Heavy vehicle drivers

5.2.4.1 Overview of the safety in the heavy vehicle industry

Long distance trucking makes an often under-estimated but very substantial

contribution to the Australia economy and society…. These achievements of the

long distance trucking industry come with a cost. These costs include safety

problems facing drivers and the general motoring public.

(Quinlan, 2001, p33)

Although the past decade has seen a decline in fatal road crashes involving

articulated trucks (long haul), incidents still account for a significant and

disproportionate number of all road fatalities (Quinlan, 2001). It must be noted

though, that in only about 25% of such fatal road crashes, the truck driver is held

responsible. Nevertheless, driving a truck for a living remains one of the most

dangerous occupations.

There are a variety of ways in evaluating safety in the long haul trucking

industry, including indications of the hazardous nature of this industry. From a

financial point of view, there are two distinct factors:

• Claims on Compulsory Third Party (CTP insurance) have escalated for this

industry during the past decade; and

• Comprehensive insurance have seen commercial vehicle insurance rates rise

dramatically (in some cases by more than 30%).

(Quinlan, 2001)

The alternative view - that of comparing road crash statistics to other transport

industries - indicates that despite funds being expended on upgrading roads than

maintaining an ‘ageing rail network’, the safety performance of the trucking industry

is be far worse than the main alternative of land transport (that of rail) (Quinlan,

2001).

5.2.4.2 Crash statistics and patterns of heavy vehicle crashes

In 1995, the Federal Office of Road Safety (now Australian Transport Safety

Bureau) commissioned analyses on the nature of urban truck road crashes in Australia

(Sweatman et al., 1995). A large body of work was undertaken for these analyses

including a literature review, mass data analysis, detailed post-crash analysis of fatal

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crashes, analysis of accident black spots and in-depth investigation. The findings

from these analyses include:

• There were around 1000 serious heavy vehicle crashes per year in urban areas

costing the community approximately $100 million per year;

• Serious crashes formed 50-75% of serious rigid truck crashes and 25-50% of

articulated truck crashes;

• Majority of causalities were drivers of cars involved in crashes with heavy

vehicles;

• Majority of fatal truck crashes occur in non-urban areas;

• Non-urban truck crashes are significantly worse than urban crashes;

• Most fatal truck crashes occur in lower speed zones; and

• Precipitating factors in fatal crashes included inappropriate behaviour,

inattention, disregards of traffic controls and excess speed (often on the part of

the other vehicle or pedestrian).

Table 8 provides a summary of the annual number of fatal road crashes as

recorded by FORS. During this period (1981-1998), it is evident that there was a

downward trend in the total number of road crashes and fatalities involving all

vehicles, however this trend is not observed in fatal road crashes involving vehicle

vehicles. Since 1989, there is an observed significant improvement in both fatal road

crashes involving trucks and other vehicles. Overall, the safety performance of

articulated trucks has shown a considerable improvement since the late 1980’s.

However, trucks remain over-represented in fatal road crashes (whereby the rate of

involvement in fatal road crashes is compared to the proportions of vehicles

constituted by heavy trucks) and account for a significant component of the national

road toll (Quinlan, 2001). As noted earlier, most persons killed in fatal road crashes

involving heavy vehicles are not truck drivers, but rather members of the public

(Quinlan, 2001). According to an inquiry of safety in the long haul trucking industry

(Quinlan, 2001), this safety issue adds a fundamental public safety dimension in

reviewing the safety performance of this industry outside that of truck driver safety.

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Table 8: Fatal road crashes and fatalities, Australia 1981 to 1998

Source: Quinlan, M. (2001).

In comparison to other countries, the Australian fatal truck road crash rate per

kilometre traveled is 47% greater than that of the U.S.A., 39% higher than the U.K.,

comparable to both Germany and Canada, 20% lower than Sweden, 45% lower than

France and 55% lower than New Zealand (Haworth et al., 2002). However, these

researchers (Haworth et al., 2002) propose that the higher fatality rates observed on

Australian roads compared to the U.S.A. and U.K. may largely be explained by the

lower proportion of truck travel on divided and limited access roads in Australia, as

well as possibility speed limits.

5.2.4.3 Speeding

There are grounds to believe that the link between speed and safety outcomes

may be even more critical for heavy vehicles than for light vehicles.

(National Road Transport Commission, 2004, p16)

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According to a report released in 2004 by the National Road Transport

Commission (2004), concern and action on heavy vehicles has largely focused on the

small percentage of drivers that exceed the speed limits by a substantial margin.

However, this report claims that although those drivers represent a significant safety

hazard, low level speeding (by just a few km/hour) also involves substantial risk

(National Road Transport Commission, 2004). ‘Low level’ speeding is asserted to be

important to overall safety outcomes because it is far more prevalent than extreme

speeds (National Road Transport Commission, 2004). To measure compliance of

heavy vehicles over 12 tonne with speed limits, all such vehicles are required to be

fitted with a speed limiter (designed to limit peak highway speeds to 100km/hr)

(National Road Transport Commission, 2004). However, speed distribution data

indicates that there are many non-compliant truck drivers on the road. ARRB

Transport Research conducted a study for Austroads on heavy vehicle compliance

with speed (George, 2002b) with six years of data (years 1995-2000) and 45 million

observations. This investigation found that for aggregated Australian data (excluding

Western Australia and Northern Territory) averaged over the years 1995-2000, 17%

of the observed class 3 vehicles and 26% of the class 9 vehicles were detected

exceeding the posted speed limit (i.e. 100km/hr) (George, 2002b). Figure 11 shows

the cumulative frequency plot for class 3 vehicles (two axle rigid vehicles) observed

exceeding the posted speed limit (George, 2002b).

Figure 11: Austroads class 3 vehicles degree of speeding

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5.2.4.4 Fatigue and sleep deprivation

Fatigue has been shown to affect mental alertness, thereby decreasing an

individual’s ability to operate a vehicle safely and increasing the risk of human error

that could lead to fatalities and injuries. Drowsiness slows reaction time, decreases

awareness, and impairs judgment. Driver impairment due to drowsiness is known to

be a major contributing factor in many crashes involving commercial-vehicle drivers.

(Hanowski et al., 2007, p1)

Fatigue and sleep deprivation have long been recognised as being critical safety

issues in the trucking industry, with driver fatigue being found to be a major cause of

long-haul truck road crashes (Haworth et al., 1988, National Transportation Safety

Board, 1995). The two most important factors that are likely to increase fatigue

include: long periods of exposure to a monotonous task such as driving (Hamelin,

1987, Krueger, 1989) and driving in the early hours of the morning (Feyer et al.,

1995). According to the ‘Fatigue Expert Group’ (2001) there are considerable

incentives in the economic and social profile of the transport industry for scheduling,

trip planning and consequent driver practices that increase the risk of fatigue for the

driver (Economic Associates Pty Ltd, 2002). Additionally, competitive pressures,

payment systems, contracting arrangements and even the unintended consequences of

the driving hours regime combine to create an environment in which fatigue has

become an accepted part of industry practice (Economic Associates Pty Ltd, 2002,

p5).

However, in recent years, there have been a large number of changes in the

long-haul trucking industry in Australia, with many of the changes having an impact

on fatigue management practices. In 1998 Queensland’s Department of Transport

(Queensland Transport) implemented the National Driving Hours Policy to better

manage heavy vehicle driver fatigue. The policy was introduced through the

Transport Operations (Road Use Management - Fatigue Management) Regulation

1998 and makes use of log books and prescriptive driving, and work and rest limits

(Queensland Transport, 2007). An evaluation of this program was conducted over a

six year period (1996-2002), with findings indicating that drivers are now less likely

to report speeding to meet a deadline, feeling tired, and experiencing difficulty

concentrating (Burgess-Limerick and Bowen-Rotsaert, 2002). Other jurisdictions in

Australia have implemented the National Driving Hours Policy (New South Wales,

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Victoria, South Australia and Tasmania) which prescribes maximum working hours

for heavy vehicle drivers on a daily and weekly basis, including provision for short

and long work breaks (Economic Associates Pty Ltd, 2002). This regulation also

prescribes requirements for the maintenance of logbooks for recording of driving

working hours (Economic Associates Pty Ltd, 2002). Western Australia and the

Northern Territory are the only two jurisdictions in which this regulation does not

apply. These two jurisdictions have introduced Fatigue Management Codes of

Practice under Occupational Health and Safety legislation. In the Australian Capital

Territory, driving hours are not regulated. However, such regulations have been

criticised for focusing on a symptom of poor fatigue management (i.e. hours actually

worked). In their report for the National Road Transport Commission, the Australian

Transport Safety Bureau and the New Zealand Land Transport Authority, the ‘Fatigue

Expert Group’ (2001) concluded however that the prescribed driving hours do not

account for circadian patterns/time of day factors (especially night work). It is

therefore evident that fatigue management practices in multifaceted economic and

social environments such as the long-haul trucking industry are fraught with continual

barriers in their implementation and will no doubt receive repeated criticism.

5.2.4.5 Drug use

Drug use within the heavy vehicle industry is not within the scope of this

research program, however it is important to acknowledge that there is a clear

association between stimulant use by truck drivers and road crash risk (Swann, 2002).

Drug use by truck drivers is a great public concern for two reasons. Firstly, it has

been said that heavy vehicle traffic is expected to increase 75% in the period to 2010

and secondly truck drivers that test positive to stimulants have an crash risk similar to

car drivers who test positive to a BAC of 0.10 – 0.15 (such levels of alcohol represent

gross impairment) (Swann, 2002). Therefore, acknowledging the problem that drug

use poses to truck safety and other road users is important.

According to the Victorian Parliamentary Road Safety Committee (1996), it is

well documented that use of various types of illegal drugs is highly prevalent in the

general population. At the core of drug use is the intent to alter physical and/or

mental function, which is also capable of impairing driving skills (Marowitz, 1995).

Research conducted by Drummer (2002) supports this assumption. Controlled studies

reveal that drug use contributes to the physiological and mental impairment of the

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driving task. Of most concern to road safety experts are the debilitating effects of

drug use on driving skills and behaviour and the prevalence of drug use in the driving

population (Ramsay and Prem, 2000). The drugs that are of particular interest to road

safety researchers include cannabis and the central nervous system stimulants. These

drugs are extensively reported as the ‘drugs of choice’ by heavy vehicle drivers that

have been found to drug drive (Victoria Police State Highway Task Force, 1995,

Hartley and Arnold, 1996, Swann, 2002). Quinlan (2001) states:

The use of drug stimulants by truck drivers to combat fatigue has long been a

feature of the long distance trucking industry in Australia (since at least the

1970s).

(p77)

However, Quinlan (2001) suggests that “given the illegality of many stimulant

drugs, obtaining accurate information on the extent and nature of the practices has

always been difficult. It is therefore impossible to discuss the use of drugs with

complete precision” (p78). However, surveys of truck drivers suggest that there is a

consistent pattern whereby drug use is widespread within the long-haul trucking

industry (Quinlan, 2001). Enforcement practices largely rely on on-road detection

which represents a time-consuming task by police and is only like to detect some of

the most extreme cases (Quinlan, 2001). Quinlan (2001) also proposes that there has

also been evidence of the supply of stimulants by employers and the removal of

incriminating evidence by tow truck operators in the event of a collision.

5.2.4.6 Sharing the road

An inquiry coordinated by the Motor Accidents Authority of New South Wales

to investigate safety in the long-haul road transport industry, found that specific

mention was made of the fact that some car drivers expect trucks to have similar

performance characteristics to cars and motorists in general appear to have received

no indication of the significant differences as part of their driver education (Quinlan,

2001). This inquiry found a strong case for including information about heavy vehicle

characteristics (e.g. signage, braking, turning) in the formal training of general

motorists as part of the licensing process (Quinlan, 2001). The inquiry formed the

view that:

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… inadequate understanding of heavy vehicles amongst other road users is an

issue that needs to be addressed both because it exacerbates safety risks to

other road users and places additional unnecessary pressure of truck drivers.

(Quinlan, 2001, p83)

This issue of ‘sharing the road’ has also received great attention in the U.S.A.

Allocation of funding to educate the motoring public on how to share the road safely

with commercial motor vehicles is part of the ‘Share the Road/No-Zone Campaign’

which works in conjunction with regulatory agencies to increase the public’s

recognition of truck and bus limitations. Although this program can assist in

educating motorists about certain areas around a truck where the truck driver cannot

see motorists, it is not designed to influence the perception or beliefs about trucks by

motorists.

5.2.4.7 Driving at level crossings

Investigations by the ATSB of fatal collisions at level crossings involving heavy

vehicles indicate that for the majority of collisions, the collision was a consequence of

motorist behaviour (either through error, lapses or violations). Although more than

one contributing factor have generally been found in each of the investigations at

level crossings, ‘failing to stop’ by the truck driver has been cited in numerous

investigation reports.

Behaviour that is common among many drivers is a rolling stop. By not coming

to a complete stop, drivers have less time to notice the approach of a train. In

addition, checking for a train while the vehicle is still moving can make the

remaining distance too short to be able to stop.

(Coghlan, 1997)

Additionally, familiarity, complacency, expectations of trains and visibility

problems (such as sighting distances) have also been cited as contributing to truck-

train collisions.

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5.3 QUALITATIVE RESEARCH WITH TARGET GROUPS

5.3.1 Objectives

Specifically, the research explored the following issues:

• Knowledge of road rules;

• Exposure to different protection systems;

• Awareness of collisions and near-misses;

• Design of crossings;

• Familiarity of crossings;

• Importance of the problem in relation to other road safety issues;

• Attitudes, beliefs and perception of risk;

• Effectiveness of enforcement and penalties;

• Cognitive ability to judge train speed and stopping distances; and

• Constructs of message acceptance and delivery.

5.3.2 Method

5.3.2.1 Ethical clearance

Ethical clearance for data collection for this formative research phase was

gained from the Queensland University of Technology Human Research Ethics

Committee (QUT Ref. No. 3550H).

5.3.2.2 Sampling

A non-random sampling technique was used in this formative research. The two

types of non-random sampling techniques used were purposive and convenience

sampling. The purposive sampling technique was used primarily for focus groups

discussions. A purposive sample is one that is chosen by the researcher subjectively

and attempts to obtain a sample that is representative of that population (Tilley, 1990).

Although this type of sampling allows information to be obtained from the target

population, it is likely to overweigh sub-groups in the population that are more readily

accessible. Convenience sampling technique was used for semi-structured interviews.

A convenience sample also has limitations. Although convenience samples comprise

of participants who are available in a convenient manner to researchers, there is no

randomness (Tilley, 1990). Thus the likelihood of bias is considered somewhat high.

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However, these two types of sampling techniques were the most appropriate and

practical within the budget and resources of the project.

This combined approach (purposive and convenience sampling) was seen as an

appropriate and practical data collection strategy within the qualitative and

exploration methodology. Focus groups offer the opportunity to explore and gather

rich data on a specific issue and gain insights into the experiences and perceptions of

its participants. One major advantage of this data collection tool is that data can be

collected from numerous people in a relatively short period of time (Beyea, 2000).

Semi-structured interviews is one of the most frequently used qualitative methods,

and allows for focused, conversational, two-way communication to obtain a range of

insights on level crossing safety. These interviews used a script, consisting of a set of

questions as a starting point to guide interaction with the participant (Sampson, 1972).

Nevertheless, as the aim is to capture as much information as possible on the

participant’s views about level crossing safety, the interview follows the participants

thinking and poses new questions from answers given by the participant (Sampson,

1972). Consequently, each interview can be quite different from other interviews

(Drever, 1995, Wengraf, 2001).

Both focus groups and semi-structured interviews were conducted with

participants from each of the three road user groups in both urban and regional areas

in Queensland by trained researchers in road safety. Locations to conduct the focus

groups and interviews were selected to utilise settings that were familiar, practical and

non-threatening. Older drivers were mainly recruited through existing social and

charity organisations. In the metropolitan area, bowls clubs and social organisations

were approached and given flyers and posters advertising an invitation to take part in

a discussion on road safety at the organisation’s premises. In the regional areas, the

charity ‘Meals on Wheels’ and the ‘60 and Better’ program were approached and

were also sent posters and flyers for the sessions. The criteria for participation for the

older group included:

• Aged 60 years or over; and

• Have a valid driver’s licence.

Younger drivers were recruited through the education facilities of Universities

and TAFE colleges in both regional and urban areas within Queensland. Approval

was sought from both of these educational facilities to conduct either focus groups or

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interviews with students on their campuses. Focus groups in the urban area were

advertised on first year student’s noticeboards. Through arrangements with the

Course Coordinator of one of the University’s departments, students were given

academic credit for attending the sessions. Interviews in the urban area were

conducted through convenience sampling on the campuses of a large TAFE college

and as well as a University where focus groups were also conducted. Students were

approached in public areas by the interviewers and invited to participate in an

interview. These participants were given a gift of $10 for their time. In the regional

areas, interviews were conducted through convenience sampling on the campuses of a

TAFE college and a University in central Queensland. Students were approached in

public areas by the interviewer and invited to participate in an interview. These

participants were also given a gift of $10 for their time. The criteria for participation

for the younger group included:

• Aged between 17 to 24 years; and

• Have a valid learners’ permit or driver’s licence.

Truck drivers were recruited through convenience sampling at a large

‘Truckstop’ in Brisbane. Assistance was sought from the Queensland Trucking

Association (QTA) to gain permission from the Truckstop to conduct interviews on

site. On approval from the Truckstop, two trained interviewers approached drivers on

their meal breaks and invited them to participate in an interview. The interviews were

conducted over a period of two days. All participants received a travel mug as a gift

for their time. The criteria for participation for the heavy vehicle group included:

• Have a valid heavy vehicle licence; and

• Drive a heavy vehicle.

5.3.2.3 Procedure

Information sheets and consent forms were provided to each participant prior to

commencement of focus groups and semi-structured interviews. Participants were

informed that participation was purely voluntary and that their responses would

remain anonymous through de-identification of any collected data. Consent forms

were signed by participants prior to commencement of the discussion/interview.

During data gathering, verification and validation of responses was strengthened

through the use of interviewer probes, follow-up questions and reiteration. For the

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focus group discussion, the researcher’s aim was to facilitate discussion between

participants to ensure dominant participants didn’t control the discussion. During

each focus group, an inductive approach was used, which allowed the researcher to be

flexible in exploring issues and themes as they arose. To allow external validation,

focus group discussions were tape recorded with permission from participants and

subsequently transcribed. Transcribed data from these tapes will be de-identified to

protect each participant’s identify. The focus group sessions took approximately 60

minutes to administer, whilst interviews took approximately 20 minutes.

5.3.2.4 Materials

An agenda for the focus group session was developed in accordance with

guidelines for conducting focus groups. This agenda included: welcome, review of

agenda, review of goal of the meeting, review of ground rules, introductions,

questions and answers, and closing comments. The moderator’s guide was

synonymous with the interview schedules (although within the group setting greater

flexibility was required to allow the group process to occur) and was developed

specifically for each target group. Questions were developed with input from experts

in the area, and were based on the themes identified from Study One (train drivers and

expert panel participation). The focus group moderator’s guide was developed with

the interview schedules, and were piloted and re-drafted, with the final guides

designed to gather data on:

• Demographic information;

• Exposure to level crossings;

• Knowledge of level crossing types, road rules and safety information;

• Attitudes and beliefs to driving at level crossings;

• Self-reported behaviours;

• Behaviours of other motorists; and

• Road safety message acceptance and delivery.

5.3.2.5 Data analysis and reportage

Raw data collected from the focus group discussions was collected in a

relatively unstructured form (by use of tape recordings) and then transcribed. The data

was then analysed through a qualitative thematic analysis process (Wengraf, 2001).

Although the moderator’s guide was designed to gather information on a range of

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topics, reportage is broken down into key themes that emerged from the data. These

themes provide the framework for the categorisation of the responses, with

participant’s own words being identified in the text by the use of italics. There were

five major themes that consistently emerged throughout the data from the younger and

older driver groups, while six themes emerged from the truck driver group. Results of

this thematic analysis are presented according to these themes, with regional and

urban responses being integrated (older and younger drivers only). Following

presentation of these key themes, a summary of the key differences between regional

and urban responses is summarised.

5.3.3 Results

5.3.3.1 Sample characteristics

A total of 122 participants were involved in this formative research phase, with

70 participants (57.4%) taking part in the semi-structured interviews and 52 (42.6%)

in the focus group sessions. In total, there were 51 females (41.8%) and 71 male

(58.2%) involved in this formative research phase. Table 9 illustrates data collection

from each of the road user groups in both regional and urban areas.

Table 9: Data collected during formative research phase Target Group Component of Data

Collection Location n

Heavy vehicle drivers (all areas)

Semi-structured interviews

Major truck stop 26

Young drivers (urban)

Semi-structured interviews and focus groups

Queensland Transport Licensing Centre and University

25

Young drivers (regional)

Semi-structured interviews

TAFE and University 28

Older drivers (urban)

Semi-structured interviews and focus groups

Senior Citizens Bowls Clubs

23

Older drivers (regional)

Focus groups Meals on Wheels 60 and Better program

20

5.3.3.2 Older drivers

There were a total of twenty-three participants in the urban group of older

drivers. Nineteen people took part in two focus groups and four participants were

interviewed, in the metropolitan Brisbane area. Ten participants were male (43.5%)

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and 13 were female (56.5%). The mean age of the group was 76.3 years (range 61-89

years) and the mean years licensed as a driver was 50.22 years (range 17-75 years).

The majority of participants were married (n=11, 47.8%) or widowed (n=6, 26.1%)

and were born in Australia (n=22, 95.7%). About half the participants (n=11, 49.9%)

stated they traveled 50km or more from home ‘once a month’ or more.

Twenty participants took part in three focus groups in a regional town in

Queensland. Eleven were males (55%) and 9 were females (45%). The mean age of

the group was 67.99 years (range 61-85 years) and the mean years licensed as a driver

was 46.95 years (range 30-69 years). The majority of participants were married (n=9,

45%) or widowed (n=6, 30%) and were born in Australia (n=19, 95%). The majority

of participants drove a vehicle four or more days per week (n=18, 90%) and 75%

(n=15) encountered both active and passive crossings regularly (everyday or a few

times per week). Traveling 50km or more from home was quite common with 70%

(n=14) of participants reporting that this occurred ‘once a week’ to ‘once a month’.

Perception of risk

There was a mixed perception of risk among regional older participants, with

half perceiving that they are not at risk of being involved in a level crossing collision,

whilst the other half believed that there is some degree of risk. Those participants that

believed they are not at risk whilst driving at level crossings believe they follow the

road rules, they use protective behaviours such as avoiding certain crossings, or

becoming more familiar with crossing. Those regional participants that believed they

are at some risk believe inattention and poor designed crossings are likely factors that

would increase their level of risk. The majority of regional participants reported rarely

taking risks at level crossings, although many stated that other motorists are the ones

that take risks frequently at crossings.

There appeared to be a high level of awareness that collisions between trains

and motor vehicles occurs, with one regional participant stating some accurate

statistics. Near-misses were frequent with many participants in this regional group.

Many participants stated that they had close-calls with particular crossings and

therefore tried to avoid these crossings. It is a wake-up call for sure. Most of the

participants that recalled near-misses indicated that they were quite shaken by the

experience and try to be more cautious at crossings. Various reasons were suggested

as to why they were involved in a near-miss with some suggesting that complacency

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played a big role. Some participants suggested that you look but you don’t always see

as being the reason why they experienced a close call with a train.

The majority of urban participants felt that level crossings were not high on the

safety agenda (in comparison to drink driving, speeding etc). A couple of urban

participants asked if anyone was ever killed while driving at level crossings and were

surprised to hear how many motorists are fatally injured each year in Australia. The

response given by one participant is it that many? appeared to sum up the low level of

perceived risk that this group held. However, the majority of urban participants felt

that level crossings were often complex driving environments that could place them at

risk, but they maintained that protective behaviours (such as only driving during the

day or at familiar crossings) would reduce their risk of being involved in a collision.

The belief that I drive on roads that I know well appeared to be widespread in this

urban group. Most of the urban participants stated being familiar with a number of

local crossings and thought that due to this familiarity, it made driving at such

crossings easier and safer. Unfamiliar crossings (such as those in areas where they

rarely or never driver) were believed to cause more risk to participants and many

stated that they try to avoid driving over such crossings. Some participants indicated

that they could get confused in unfamiliar environments, particularly crossings that

are complex. The notion that there are too many lights and signs going on at some

level crossings was felt by many participants in the urban group. Overall, urban

participants felt that due to low driving exposure at crossings and ensuring they obey

the road rules at crossings, that there would be an extremely low risk of being

involved in a collision with a train. Near-misses were not as common with urban

participants, with only one participant stating that they got stuck on a crossing due to

congestion ahead of the crossing. This participant suggested that it was a scary

experience.

Self-reported and intended driving behaviour

Both the regional and urban participants indicated that they obey the rules at

level crossings and that it is other drivers that put them at risk. Many participants

noted that it is the young drivers particularly that cause them greatest concern when

driving at level crossings. No participant admitted to intentionally violating or would

be willing to violate the road rules at level crossings such as driving through activated

warning systems or failing to stop/give-way to trains at passive crossings. One urban

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participant stated that you’d have to be stupid to take risks at railway crossings, while

one regional participant stated that the hoons will always try to beat the train.

Risk factors for being involved in a collision

Although both the urban and regional older driver groups indicated that they

don’t take risks at level crossings, many participants in the urban group mentioned the

difficulties that they sometimes face when driving in such complex traffic

environments (such as at busy urban level crossings). Behaviour of other motorists

was considered to be an issue with driving safely at level crossings for the older urban

group. Most participants in this group felt that other motorists put pressure on them

by beeping the horn, yelling at them, or driving around their vehicle when they are

waiting for flashing lights to cease flashing. The majority of participants felt that

such behaviours were unnecessary and that younger drivers were largely responsible

for such behaviours. Other perceived risk factors for the urban group were similar to

those of the regional group. Confusion and distraction were discussed, with

comments that unfamiliar crossings can be confusing, roads with many turning lanes

were confusing and signs, billboards and other objects on the road were distracting.

Many participants in the urban group mentioned protective or compensatory

behaviours such as being diligent, driving in off-peak times and getting a relative to

drive in an unfamiliar environment. The majority of participants in the urban group

mentioned the difficulty they face with the ageing process and how it influences their

driving ability. Decreased vision, poorer hearing, slower reaction times and reflexes

were the predominant factors that were declared to pose problems when driving at

level crossings. When you can’t see or hear as well as you used to it makes it difficult.

Such comments were widespread among urban participants.

Among the older regional group, there was a very high level of familiarity with

local crossings and knowledge of different types of crossings. Many regional

participants indicated that they avoid driving at unfamiliar crossings as this could be

more stressful than driving at crossings that they regularly use. Poor visibility at

nighttime (due to degeneration of their vision) was also stated by many participants as

being a problem for driving at level crossings. This was a reason that many

participants stated that they avoid driving through crossings at night or when there are

low levels of light (such as dusk or dawn). Some regional participants mentioned that

their reflexes and flexibility were not the same as they used to be. Reflexes and

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flexibility were felt to be an issue at crossings that have a sharp angle of approach

where participants have to turn to look back up the track. Many participants in this

group indicated they as they got older, driving generally became harder physically for

them. Quite a few participants also suggested that due to degenerating vision and

hearing that driving at level crossings was stressful for them and that they would

avoid situations that they were not confident driving. Like their urban counterparts,

pressure from other drivers was a common issue that emerged in this older group.

Many regional participants stated that other drivers pressure them by beeping the

horn, yelling at them, and overtaking them at the crossing. All participants felt that

such behaviours were unnecessary and the majority held the view that younger drivers

were responsible for such behaviours.

Knowledge and perceptions of laws and enforcement

There was a very high level of knowledge about the different types of protection

systems at level crossings (in both regional and urban areas), although very few

regional participants knew of the fines and penalties for breaches at level crossings.

One regional participant stated that whatever they are, they should be higher. The

majority of regional participants indicated that they rarely (if ever) see police

enforcing the road rules at level crossings and that there subsequently would be a low

risk of any motorist being caught by police. One of the participants remarked that

police have better things to keep them occupied and implied that it would be a waste

of police resources to do patrols at crossings in regional areas. Although yellow

hatching road markings (denoting ‘keep clear’) are more likely to be in urban areas,

no regional participant could correctly explain what such road markings mean, with

many participants stating that they had never seen any crossing in regional areas that

had such road markings.

The urban group did not have the same level of knowledge about the different

types of level crossings as their regional counterparts, as passive crossings (with Stop

or Give-way signs only) were thought to be only in the bush. All participants

indicated that they were familiar with active crossings, especially with their local

level crossings. Like their regional counterparts, there was generally a poor level of

knowledge about penalties and fines for breaches at crossings, with many participants

stating that they never see police enforcing the road rules at crossings. Although

many active crossings in urban areas have yellow hatching road makes that denote

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‘keep clear’, only a couple of urban participants could state the meaning of such road

markings. Many participants claimed that they have never seen any yellow road

markings even though they regularly drove over the local crossing that had these

markings. Generally, the urban group had a high level of knowledge of the road rules

for driving at level crossings although some participants did not know that it was

illegal to queue over a crossing.

Usefulness of education and publicity

Most regional participants felt that some education about level crossing safety

was a good idea, however others could not really see the point of it as there is a low

risk of being involved in a collision compared to other areas of driving (i.e. speeding,

drink driving etc). There was a general agreement that road safety advertisements

served to reinforce rather than change their behaviour as all participants felt they were

already safe drivers. ‘Informational’ television campaigns were considered to be

effective for many participants in this group, especially when combined with visual

effects. A number of regional participants stated that it was the visual images that they

recalled when watching road safety advertisements. Many of the regional group

suggested the use of positive rather than negative messages, such as supplementing

messages with tips on how to drive safely (e.g. towing a caravan across a crossing).

Credible sources of information delivery included police, ambulance officers or train

drivers. Additional to television campaigns, the majority of participants indicated that

talk-back radio would be an effective delivery method for this group as they consider

that experts talking about the issue to be highly credible sources of information.

Public talks from police, ambulance officers or train drivers were also seen as being

valuable to this age group.

Many urban participants felt that education or publicity for level crossing safety

was not necessary as it would be a waste of time for older drivers as they already did

the right thing at level crossings. Most of the urban group believed that if there was

to be an education campaign for older drivers, then a reminder campaign about

driving safely at level crossings and changing risk perception of older drivers would

be the most effective. Like their regional counterparts, urban participants felt that

‘informational’ television campaigns would be effective for many participants in this

group, especially when combined with visual effects. Additional to television

campaigns, the majority of urban participants indicated that talk-back radio would be

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an effective delivery method as they frequently listen to the radio and value the

sources of information that talk-back radio provides. This group of urban participants

also suggested public talks from police, ambulance officers or train drivers as being

valuable in the delivery of messages.

Differences between regional and urban group responses

The most notable differences between regional and urban older group responses

were related to risk perception and knowledge about the different types of protection

systems. Regional drivers appear to believe that they are at greater risk of being

involved in a level crossing collision with many recalling near-misses. The main

reasons given for such near-misses largely included complacency due to familiarity of

local level crossings. On the other hand, urban drivers generally held the perception

that level crossings were not as dangerous as other aspects of driving, with many

participants doubtful that motorists die at such intersections. Many participants in the

urban group also were surprised that transport authorities were investing financial

resources into level crossing education programs for motorists as the majority

believed that it is not a major road safety priority. Urban drivers also had a poor level

of knowledge about the different types of protection systems, other than active

systems with boom gates (such as in metropolitan areas), with some participants

unaware that some level crossings have no active protection system to warn motorists

of an approaching train.

5.3.3.3 Younger drivers

There were a total of twenty-five urban participants, with approximately half

taking part in focus groups (n=13, 52%) and half in interviews (n=12, 48%) in the

Brisbane area. Fourteen participants were male (56%) and 11 were female (44%). The

mean age of the group was 20.4 years (range 17-28 years) and the mean years

licensed as a driver was 2.74 years (range 0.17-10 years). The majority of participants

(n=23, 92%) held an open driver’s licence, were single (n=19, 76%) and were born in

Australia (n=23, 92%).

Of the regional drivers that took part in this research phase (n=28), ten

participants were male (35.7%) and 18 were female (64.3%). All of these participants

took part in the semi-structured interview in regional Queensland. The mean age of

the group was 19.78 years (range 17-24 years) and the mean years licensed as a driver

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was 2.56 years (range 0.08-8 years). Three quarters of the participants held an open

driver’s licence (n=21, 75%). Nineteen participants stated they were single (67.9%)

and most were born in Australia (n=26, 92.9%).

Perception of risk

Overall, there was a low perceived level of risk among regional participants as

they felt that they are familiar with level crossings in their area. Additionally, the

majority of this group believed that at crossings with low train traffic volumes they

are less likely to be involved in a collision than at crossings with more train traffic

volumes. It there are more trains then there is more risk. The majority of regional

participants felt that they are a safe driver and would be able to avoid a collision at a

level crossing. There was a small number of participants that were concerned that

inattention, poor visibility of trains, stalling their car on a crossing, or being a

passenger (i.e. not being in control of the vehicle), could pose some degree of risk of

being involved in a collision with a train. Being familiar with a level crossing was

deemed by half of the participants as making them drive more carefully, whilst the

other half indicated that it would make them more complacent when driving at these

crossings. Male participants were more likely to perceive that being familiar with

certain crossings would make them less likely to be involved in a collision with a

train, while female participants were more likely to hold the perception that driving at

crossings on a regular basis would make them more likely to be involved in a

collision with a train due to being on ‘auto-pilot’, with one regional participant stating

that sometimes you get to your destination and can’t remember how you got there.

Urban participants also appeared to believe that there was little risk whilst

driving at level crossings. The majority of urban participants felt that they were not at

risk of being involved in a level crossing collision. Those participants that perceived

there was some degree of risk of being involved in a collision, thought it would be

due to either equipment malfunction (such as faulty boom gates/flashing lights) or

stalling their car at the crossing. Many participants in this group raised the concern

that if they stalled their vehicle on the crossing then this would be extremely

dangerous. That’s how people get killed. It became apparent from the responses from

many participants in the urban group that there is an urban myth that the majority of

collisions between trains and motor vehicles are a result of motorists stalling their car

on a crossing. This issue was raised on numerous occasions during the discussions,

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with many participants fearing that it could happen to anyone. Urban participants that

spoke of performing high-risk behaviours (such driving through flashing lights whilst

the boom gate is descending) indicated that they felt that their chances of being hit by

a train were small. One participant commented that you have more chance of being hit

by a bus. Like their regional counterparts, younger urban participants were divided in

their beliefs about familiarity and complacency. The group appeared to be equally

divided as to whether they drive more or less carefully at crossings that they drive

through frequently. No particular gender in the urban group stood out as holding one

belief over the other.

Self-reported and intended driving behaviour

Most regional participants stated that many motorists (including themselves)

disobey road rules at level crossings and that their ability to judge the distance of a

train as justification for why they took risks. There is usually plenty of time. Many

drivers admitted to risk taking behaviours such as not stopping at Stop signs, going

through flashing lights, racing the train to avoid waiting at boom gates, following the

car in front across the passive crossing without scanning for a train, and driving

through a crossing even when poor visibility. Those regional participants that were

concerned about there being some degree of risk at level crossings were less likely to

admit to disobeying the road rules at level crossings. Waiting for a train to go through

the crossing was seen by many regional participants as really annoying, especially

with a freight train. Many regional participants stated that long freight trains (with

more than 30 wagons) would take ages to go through the crossing and therefore they

would try to beat the train to the crossing to avoid delays. This included disobeying

road rules such as not stopping at a crossing or going through the activated flashing

lights. Participants that took risks to beat a freight train didn’t view this as risk-taking

as they believed that they had sufficient time to cross in front of the freight train.

Many urban participants also admitted to risk taking behaviours such as driving

around boom gates, queuing over crossings, racing the train to avoid waiting at boom

gates, going through flashing lights, and not stopping at Stop signs. The majority of

these participants that admitted to such risk taking behaviours indicated that this risk

taking was less dangerous than risk taking such as drink driving, speeding or driving

through red lights at an intersection. More people die drink driving than being hit by

trains…surely. Justifying their risk taking behaviours was common in the urban

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group, with many participants stating that ‘no one gets hurt’ when they disobey road

rules. Some participants stated that they frequently disobey the rules at level crossings

and have never had a near-miss with a train. The reasons for such justifications

included: infrequent trains, being able to judge the distance that the train is from the

level crossing, and that trains will slow down if they observe a car at a crossing.

Although most urban participants didn’t encounter passive crossings in rural or

remote areas, there were many participants that stated they would be willing to violate

road rules at passive crossings as there would rarely be a train. Some participants

stated that they would drive through passive crossings in rural or remote areas even if

a train was visible as they believed that it would be easy to judge the speed and

distance of the train.

Risk factors for being involved in a collision

A number of near-miss stories were told by younger regional participants which

were said to be at bad crossings where there were numerous occurrences. It was

unknown if these occurrences were just by local residents or by tourists as well. The

general perception among regional participants was that these crossings were due

purely to design issues and were not due to risk taking by either themselves or other

motorists. Many regional participants discussed visibility, angle of approach and

actual awareness of being at a level crossing as two key safety issues in regional

areas. Issues of the poor visibility of the train and driving through crossings at night

were also mentioned by many participants. At some crossings, it is almost impossible

to see if a train is coming. Many younger regional participants raised their concern

that there is often little lighting on the sides of trains which make them difficult to see

at night. Influencing factors to drive safely at level crossings for younger regional

participants also included past experiences of near-misses.

Near-misses were considered to be a wake-up call for some regional

participants as they believed they may have become complacent with certain

crossings as there is a low volume of train traffic through the crossing on a daily

basis. Having friends or family members as passengers was another issue raised by

the majority of participants. It was suggested that when family members are present

in their vehicle (such as parents) then they would always obey the rules at crossings

and not take risks. When friends were passengers in their vehicle, some participants

(particularly female participants) indicated that they would want their friends to think

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I am a good driver. Male regional participants appeared less likely to be worried what

their friends thought, but equally concerned with female participants what their family

thought of their driving at level crossings.

Pressure from other drivers whilst waiting at a level crossing was raised by

many of the urban participants. The majority of these participants indicated that they

felt that other drivers forced them to drive through level crossings whilst the boom

was lifting and the flashing lights still activated. Some participants even suggested

that vehicles behind theirs would beep their horn to hurry them across the level

crossing. One participant suggested that he would just give them the finger to let the

other motorist know that he wasn’t moving. Peers as passengers were also seen as a

key element in driving safely at level crossings, with many urban participants stating

they would take more risks when driving by themselves. The risk of damaging their

car was a big influence on whether participants drove safely at crossings or not. Some

participants stated their car was very important to them and if they were involved in a

collision with a train (and not injured) then they would be distressed if their vehicle

was destroyed as they had ‘saved up’ their money to purchase it. Some younger

urban participants indicated that if they were involved in a collision with a train that it

would be a slow speed (because the train would slow down) and that only their

vehicle would be damaged. Not everyone dies if their car gets hit, do they?

Driving on ‘auto-pilot’ was stated by a small number of urban participants as

being common at crossings they are familiar with. Complacency was suggested to be

common whilst driving generally, but some participants stated that they were very

complacent at local crossings that they drive over regularly. Being familiar with a

level crossing was deemed by most participants as making no difference to the level

of risk, although some participants stated that there is less risk at familiar crossings as

they know the risks.

Knowledge and perceptions of laws and enforcement

All younger regional participants were aware of the different types of crossings

due to the sample area having a range of both passive and active crossings. Road

rules at level crossings were known by the majority of regional participants and in

contrast to their urban counterparts, this group discussed a greater awareness of safety

issues at level crossings. There was a general perception that there was little

enforcement by police a crossings and a very low likelihood of being fined.

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Participants indicated that police have other matters to attend to and that booking

motorists disobeying the road rules at level crossings was low on the policing agenda

in regional areas. You never see them out there.

The majority of younger drivers in the urban group stated that they were not

familiar with the type of level crossings outside of the metropolitan area as they rarely

drove in rural or remote areas. Some metropolitan participants even indicated that

they did not know that there were passive crossings with only a Stop or Give-way

sign. Among the urban group, there was generally a very low level of knowledge

about yellow hatching road markings that denote ‘keep clear’. Only a few

participants could correctly explain what such road markings mean. Many of the

urban participants indicated that they had never seen a crossing with such markings in

the metropolitan area. I’ve never even seen them, was one comment from a younger

urban participant. Knowledge about penalties and fines for breaches at crossings was

also quite poor in the urban group, with the general perception among these

participants that they have a very small chance of being caught for disobeying road

rules at crossing due to lack of police enforcement. Many of the urban participants

commented that they weren’t even aware that they could be fined for driving through

activated flashing lights or for queuing across a level crossing.

Usefulness of education and publicity

Nearly all of the regional participants thought that mass media campaigns were

the most effective method in changing their and others behaviour, with consequences

of a collision being an important message to many in this group. Most of the younger

regional participants reported that they listen to the radio when driving, and that using

radio as a delivery method would reach many younger drivers. Some participants

stated that they thought shock television campaigns were the best for young drivers

while other participants indicated that they would just tune out to shock style

campaigns. A suggestion for an education video for schools was also raised by many

of the regional participants. Some participants also mentioned including increasing

awareness about level crossing safety in the licensing process and when young drivers

learn to drive (such as through driving schools). One participant indicated that she had

never seen any questions about level crossings on her licence exam. Additionally, the

majority of participants in the regional group believed that it is also important to

continue to be made aware of risks at crossings, not just at the initial licensing.

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Nearly all of the younger urban participants thought that mass media campaigns

were effective in changing their and others behaviour. A couple of female

participants stated that they would change the channel if such shock-style campaigns

came on the television and that such campaigns would only be effective for some

young drivers. Television commercials that clearly showed consequences and that

they could relate to on a personal level were seen as the most effective tool for

communicating to most members of the urban group. However, many participants

also stated that radio was also an effective tool as they frequently listen to the radio

whilst driving. Additionally, many members in the urban group indicated that they

need to be ‘given the information’ about the dangers of driving rather than having to

seek the information. Creating an awareness of the risks at level crossings was

thought by many as important in level crossing education. This included targeting

risk at passive crossings for urban drivers that travel to rural and remote areas (such as

going on a holiday). Some younger urban participants suggested providing

information such as how difficult it is to judge the speed of a train and how difficult it

is for a train driver to avoid a collision at a crossing. One participant commented that

approaching trains are like an illusion…all of a sudden they are there.

Differences between regional and urban group responses

Interestingly for the younger driver group, there appeared to be more

similarities in their responses than differences. The most notable difference between

these two groups was knowledge about different types of protection systems. Like the

older urban group, the younger urban group also had a poor level of knowledge about

passively protected crossings (i.e. Stop or Give-way signs only) outside of the

metropolitan area. Overall, there was very little difference between regional and

urban group responses in relation to perception of risk, reported driving behaviour,

risk factors for being involved in a collision with a train, perceptions of laws and

enforcement and the usefulness of education and publicity campaigns.

Both groups believe that they are at low risk of being involved in a collision

with a train as they believe that they have the skills and abilities to avoid a collision.

Interestingly though, some participants in both groups indicated that they find it

difficult to judge the distance a train is from a crossing which appears to oppose such

views of being able to avoid a collision. Additionally, there was very little difference

in reported driving behaviour, with many participants from regional and urban areas

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stating that they frequently took risks at level crossings. Impatience for waiting at

crossings was evident in both of these groups, with regional drivers being frustrated

with the time it takes for freight trains to pass while urban drivers were frustrated with

waiting for protection systems (i.e. boom gates or flashing lights) to turn off. Like the

inconsistency in both group’s perceived level of risk at level crossings, there appeared

to be a paradox in self-reported driving behaviour. Most participants in both groups

stated that they frequently take risks at crossings (such as driving through flashing

lights or when a boom gate is descending), however the majority of participants

mentioned that other motorists caused them to take risks at crossings.

5.3.3.4 Heavy vehicle drivers

All participants in the truck driver group were male (n=26) reflecting the gender

profile of this workforce. Each driver took part in a semi-structured interview at a

major Brisbane Truckstop. Postcodes of drivers’ home addresses indicated that

participants were from a range of areas within the states of South Australia, Victoria,

New South Wales and Queensland. The mean age of the group was 46.3 years (range

26-68 years) and the mean years licensed as a driver was 29.2 years (range 8-54

years). The mean number of years driving trucks was 23.3 years (range 4-54 years).

Fifteen stated they were married / in a de facto relationship (57.7%) and the majority

were born in Australia (n=25, 96.2%). There were high levels of hours driven per

week reported, with 21 participants reporting driving more than 60 hours per week

(mean = 80.8 hrs per week).

When asked to estimate what percentage of the level crossings they encountered

were actively controlled, almost half the participants (n=12, 54.5%) stated ‘none’

were actively controlled, 22.7% (n=5) estimated that up to 40% were active and

22.6% (n=5) estimated 41-100% were active crossings. When asked specifically

about crossings that have flashing lights only and no boom barrier, 42.3% of

participants (n=11) estimated that 61-80% of the crossings were of this type. Twenty

three percent (n=6) of drivers reported being involved in a road crash during the past

five years, with all of those drivers reporting they were at fault in the collision. Traffic

offences history is illustrated in Table 10.

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Table 10: Data collected from truck drivers during formative research phase Offence type n % Driving offences 24 92.3 Speeding offences 16 61.5 Log book offences 12 46.2 Other offences 5 19.1

Perception of risk

Level crossing safety was low on the scale of important road safety issues for

most of the truck driver participants, with some stating fatigue, drugs and speeding

were the key road safety problem areas. Other motorists were largely seen by

participants as causing many of the safety risks at level crossings. Although other

motorists were mentioned by most participants as increasing their risk of being

involved in a collision, very few participants elaborated further about how other

motorists place trucks at risk. Those participants that indicated there could be some

chance that they would be involved in a collision with a train specified design issues

and limitations of their truck as being paramount in increasing their risk.

On the other hand, participants that held the perception that there would be very

little if at all no chance of being involved in a collision with a train made statements

such as you don’t mess with trains, I always obey the rules at crossings and it would

be too costly. Interestingly, older truck drivers (i.e. over 55 years) appeared to hold

the perception that they could be involved in a collision compared to the younger

participants interviewed. The reasons for this may possibly be related to older truck

drivers being aware of more collisions (through both media and fellow colleagues)

than their younger counterparts. As such, they may no longer believe that they are

invincible or they may not be as confident driving a truck as they once were.

Many participants were aware of a colleague being involved in a heavy vehicle-

train collision, with some participants stating they would not divulge such

information. Some participants mentioned that the media played a role in ensuring

that any heavy vehicle-train collisions are viewed as both dramatic and potentially

catastrophic, even when the truck driver was not entirely to blame for the collision.

Self-reported and intended driving behaviour

Most participants stated that they always try to drive safely at level crossings,

with a handful of participants stating that they sometimes take risks. One participant

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suggested that if there is no train near the crossing, then I might take a risk, while

another participant stated that out in the middle of nowhere, you can safely get across

without stopping. Those participants that admitted to taking risks at level crossings

said they would often try to ‘beat the train’ if they thought they would be waiting any

length of time at the crossing (e.g. for a freight train). Waiting at level crossings for

long trains to pass (such as a freight train with 40-50 wagons) was seen as frustrating

for some participants as it would ‘break their journey’. Sometimes there are 40

wagons on the backs of the trains. Many of these risk taking drivers held the

perception that it was more difficult to brake to stop at level crossings than it was to

just put your foot down.

Risk factors for being involved in a collision

There were a range of potential risk factors for driving a truck at level crossings.

Fatigue was listed by many participants as being one possible factor increasing the

likelihood of a collision. There appeared to be a general belief that within the

trucking industry there are a sub-set of truck drivers that ignore the warning signs of

fatigue which has an over-arching effect on the safety of driving (for both the truck

driver and other motorists). Some participants stated that it is part-and-parcel of

driving trucks, but that it can be managed either through having quality sleep, taking

breaks whilst driving or working for companies that abide by fatigue management

laws. There appeared to be differing views about the effectiveness of such fatigue

management laws, with one driver suggesting that it is far too limiting while another

saying that it was about time that something was done about drivers work ridiculous

hours. A few of the older participants (over 55 years) stated that their younger

counterparts consume either illicit or licit substances in order to alleviate work-related

fatigue. Many of the younger participants would not comment on such issues,

although one young participant suggested that some drivers do what it takes to stay

awake at the wheel.

Complacency was also listed as a possible factor increasing the likelihood of a

collision. Complacency was mentioned to be linked with driving outside the

metropolitan area such as in rural or remote areas. Several participants mentioned

that some truck drivers may hold the belief that I’ve never seen a train here yet at

certain level crossings with small train volumes, while other participants mentioned

that some truck drivers may hold the belief that a train will slow down to avoid a

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collision with a truck. There was a general consensus among participants that

complacency is not just within the trucking industry but extends to the general

motorist. Like complacency, distraction was seen by many participants as being a

risk factor for driving trucks generally and at level crossings. Distraction was seen by

many of the participants as being related to in-vehicle distraction such as wireless

communication devices (such as map and other complex visual displays), talking on a

mobile phone or eating/drinking. These tasks were thought to impair a truck driver’s

visual search patterns, reaction times and decision-making processes, causing them to

drive unsafely on the road generally or at level crossings.

Judgment errors, such as a truck not clearing a crossing (when the trailer of the

truck extends onto or over the rail tracks and is known as ‘short-stacking), were

believed by many to be a problem that will just get bigger with the increase in vehicle

lengths in Australia. Some drivers indicated that they sometimes have difficulty in

knowing if their trailer has completely cleared a crossing, with a few stating that this

issue can make level crossings hazardous for trucks. Judgment errors appeared to be

linked to how familiar a truck driver is with a particular level crossing. Being

unfamiliar with a particular level crossing was another factor raised by many

participants as being a possible factor in causing a collision. Some participants

suggested that if they hadn’t crossed a level crossing before then they would be

uncertain if their trailer would over-hang the crossing and this would pose a safety

risk. No participant hinted at the stress that a train driver may feel if they are

approaching a level crossing when a truck is over-hanging the tracks.

Blinding sun or faulty protection systems were mentioned as being key reasons

why truck drivers couldn’t be blamed for causing the collision. These two factors

were mentioned by many of the interview participants as being problematic for

driving at level crossings. Some participants recalled hearing of collisions that

involved a truck driver not being able to see activated flashing lights and suggested

that this is a common problem due to the height of the cabin in relation to the height

of the warning systems. Other pressing design and engineering issues for participants

included angle of approach, visibility restrictions, road surfaces, and inadequate

stopping distances. Of these issues, the angle of approach and visibility were seen as

the most problematic for the majority of drivers. Participants indicated that crossings

that have an approach angle that is oblique (i.e. an angle, such as an acute or obtuse

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angle, that is not a right angle or a multiple of a right angle) would be sub-optimal for

safely negotiating their truck. Dog-leg approaches are the worst.

Knowledge and perceptions of laws and enforcement

This truck driver group displayed a very high level of knowledge of the road

rules and types of level crossings that exist. Most participants could recall the location

of crossings that they regularly drove through on their designated routes. All

participants knew the road rules at level crossings and with statements such as trains

always have right of way and we respect trains. Many of the participants talked about

trains being a larger mass than their heavy vehicle and the fact that it would be messy

if they collided with a train. The majority of participants indicated that there is a very

low level of enforcement by police at crossings, however all believe police

enforcement would be ineffective as drivers would inform other drivers on the two-

way radio (UHF). One participant suggested that word would spread quickly. Many

participants indicated seeing some enforcement at level crossings during their careers,

however they all believed that unless there are cameras installed to observe for

violation of road rules then enforcement is a waste of time and money.

Improving safety for trucks

In terms of improving safety for trucks at level crossings, advance warning

systems were seen as a key by all participants to reducing the likelihood of a collision.

Many participants also mentioned that trains need to be better lit up at night as they

are difficult to see, particularly when a truck is driving parallel to a train as it is

difficult to see the headlights, with one participant suggesting that it is impossible to

see trains at night. Most participants indicated that it is also impossible to hear a train

approaching a level crossing as the engine underneath a truck cabin is so noisy that

you can’t hear yourself think sometimes. As such, it was suggested that warning bells

and alarms should be made louder at crossings with large volumes of heavy vehicle

traffic.

Usefulness of education and publicity

The majority of participants had never experienced any form of level crossing

safety education and comments indicated that both engineering and education needs

should be addressed for truck driver safety:

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Fix physical road issues first and follow with education – education is pointless

if you can’t perform it.

Make people safe. You can make the crossing as safe as you like but its people

that do the unsafe things, make the people safe.

While a number of participants made positive or insightful comments about

educating truck drivers about the dangers of level crossings, a small number felt that

education was not the answer. Most participants indicated that financial efforts

shouldn’t be targeted towards their driving behaviour at crossings, but rather that of

other motorists. Additionally, some participants suggested that educating train

drivers was also an important tool for improving their own safety at crossings. Some

participants held the belief that train drivers should be made more aware of the

difficulties that trucks face at level crossings (such as braking and accelerating times)

as they can slow down if they want to.

Those participants that held the perception that education was needed to inform

and educate truck drivers about the safety of issues of driving trucks at level

crossings, raised a variety of delivery methods for reaching truck drivers. One

method that was considered acceptable to truck participants was an article in trucking

magazines (e.g. ‘Big Rig’, ‘Truck and Life’ or ‘Owner Driver’). Most participants

stated that they read trucking magazines during breaks and thought they were a

credible source of information. Articles that focused on ‘real-life experiences’ were

considered the most interesting to read. Existing training sessions with trucking

companies was also suggested by many participants as being an appropriate method

for delivery of education. Other ideas included radio safety advertisements, billboards

near level crossings or posters at truckstops.

5.3.4 Discussion

As stated previously, information obtained from each of the three road user

groups was critical in assisting, guiding, and identifying priority areas for message

and material development, as well as the development of data collection measures

(i.e. questionnaires). The reportage of the results of both interview and focus groups

was broken down into key themes that were generated by the data. These themes

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provided the framework for the categorisation of the responses, with participants’ own

words being identified in the text by the use of italics.

The results of the qualitative formative research from each of the three road user

groups revealed some interesting differences between the three groups. For the

younger drivers however, there were more similarities than differences between

responses from regional and urban participants. Younger drivers demonstrated low

risk perception of the consequences to unsafe driving behaviour at level crossings,

with risk taking being reported at high levels for this group. The regional group

reported a high acceptance of risk taking behaviours such as trying to ‘beat the train’,

while participants in the urban group reported driving through activated protection

systems (either before or after a train had passed through the level crossing). Both

groups however held the belief that they could such risks were acceptable as they

consider that they have the skills and abilities to safely do so. As such risk perception

and risk acceptance are the two key issues that were highlighted in researching this

group. The most acceptable method of delivering educational interventions for this

group appears to be through radio or television. A scenario involving risking their

friends’ lives when driving unsafely was a common theme for developing messages.

For the older participants, most displayed high levels of knowledge and reported

low risk taking behaviour, with age-related factors being acknowledged by the group

as being risk factors for their involvement in a collision. However, this group

indicated that they employed compensatory and protective measures to reduce or

control their level of risk (e.g. driving during the day, going slower, being more alert,

and avoiding crossings they felt were dangerous). According to the participants in the

older group, educational strategies would best be focused on reminder or re-affirming

messages as they believe they always obey crossing rules and try to drive as safely as

possible. Radio messages or public talks at social groups appeared to be the most

acceptable method of delivery for educational campaigns for the majority of

participants in the older group.

Heavy vehicle drivers indicated a high level of knowledge of safety issues, with

engineering and design being a major factor contributing to near-misses or the risk of

being involved in a collision at a level crossing. Risk taking was acknowledged as

minority behaviour in the industry, with time pressures, complacency, monotony,

inattention and fatigue noted to be risk factors. There were mixed opinions about

educational interventions targeted towards heavy vehicle drivers, with most drivers

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indicating that the most acceptable methods would be in sharing of personal

experiences (through trucking magazines) and awareness raising (through company

training sessions).

A comparison of the three road user groups indicates that only younger drivers

admit to violating the road rules at level crossings. Both regional and urban younger

drivers admit to past risk taking behaviours at crossings as well as holding intentions

to take risks in the future. Older drivers typically reported that their skills and

abilities to drive safely at level crossings were sometimes reduced due to degeneration

of their vision, hearing and reflexes. Many older drivers suggested that they

‘compensate’ for these difficulties by driving at familiar crossings during times that

cause them least stress (i.e. during the day). Older drivers also generally believe that it

is other motorists that take risks at crossings and pressure them to drive unsafely.

Heavy vehicle drivers appear to be the most familiar group with the different type of

protection systems, which is not surprising given their exposure to driving in both

urban and regional areas. However, this group held the strongest beliefs that

engineering and design of crossings caused them to potentially drive unsafely due to

limitations of their vehicle (e.g. visibility restrictions, ability to quickly clear a

crossing). Although many truck drivers held strong beliefs regarding their plight

with warning and protection systems at level crossings, they also acknowledged that

they may become complacent when driving due to time pressures, fatigue and

monotony.

These findings highlight the continual need to acknowledge the distinction

between performance (the abilities of drivers to perceive and react to circumstances in

an appropriate and timely manner) and behaviour (what drivers are able to do,

including the perception and acceptance of risk, as well as peer pressure). With

research suggesting that behaviour change not being among the outcomes that can be

accomplished by educational interventions alone (Henderson, 1991), level crossing

interventions may achieve an increase in the awareness of the safety problem or

behaviour, assist in the formation of beliefs (especially when not held formally), and

contribute to the ‘road safety culture’ in the community (Henderson, 1991, Di Pietro,

2003).

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5.4 QUANTITATIVE RESEARCH WITH TRAIN DRIVERS AND EXPERTS

5.4.1 Objectives

Although the qualitative formative research assisted in understanding the

meanings that the target groups assign to level crossing safety as well as exposing the

psychological processes underlying unsafe behaviours, quantitative research was

required to help interpret these qualitative findings as well as cross-validate and

quantify at risk behaviours.

5.4.2 Method

5.4.2.1 Ethical clearance

Ethical clearance for data collection for this feedback phase was gained from

the Queensland University of Technology Human Research Ethics Committee (QUT

Ref. No. 3550H).

5.4.2.2 Sampling

A non-random sampling technique (convenience sampling) was also used for

this quantitative formative research. Although non-random samples are not able to be

analysed for significance, this type of non-probability sampling was the most

appropriate and practical within the budget and resources of the project.

A large rail organisation in Queensland was the subject pool for train drivers.

These drivers were identified through the organisation’s Train Crew Managers in both

metropolitan and regional areas within Queensland. Train Crew Managers were each

sent 30 questionnaires to distribute to their train drivers (i.e. 60 questionnaires sent

out in total). A list of experts in level crossing safety in Australia was compiled in

consultation with the Australasian Railway Association and state rail authorities. This

list was used for the modified Delphi technique questionnaires in Study 1. Both of

these groups completed questionnaires to ascertain any differences between beliefs of

train drivers and experts in the field.

5.4.2.3 Procedure

Information sheets were provided with each survey questionnaire sent to both

train drivers and experts. Participants were informed that participation was purely

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voluntary and that their responses would remain anonymous. Consent was implied

with return of the completed questionnaire. The questionnaire took approximately 10

minutes to complete. Participants were provided with a reply-paid envelope in which

to return their completed questionnaire.

For the train drivers, supervisors in both the regional and urban area were

mailed questionnaires each during February/March 2006 to distribute to train drivers.

All questionnaires were returned within a few weeks from being mailed. For the

experts in the field, each expert was mailed a survey questionnaire during January

2006. A reminder letter was sent a few weeks later to obtain a maximum response

rate.

5.4.2.4 Materials

A 19-item questionnaire using a Likert scale (1=low risk; 5=very high risk) was

developed for this quantitative formative research. The survey questionnaire sent to

both train drivers and experts are provided in Appendix 4. This questionnaire was

developed from results obtained from the qualitative formative research conducted

with each of the three road user groups. Themes that emerged from this qualitative

research (i.e. high risk behaviours of drivers) were selected by a panel of 3 road safety

experts (separate to those experts participating in this survey). Reliability analysis

was undertaken to provide information about the relationships between individual

items in the 19-item instrument. Reliability is considered to be the correlation of an

item, scale or instrument with a hypothetical one which truly measures what it is

supposed to (McGraw and Wong, 1996). The set of 19 items shows good internal

consistency with this sample, recording a Cronbach alpha value of .860.

5.4.2.5 Data analysis

The data collected was analysed using the Statistical Package for Social

Sciences (SPSS) Version 15.0. Given the nature of the survey questionnaire, there

was minimal missing data, with cases not being excluded from analyses if they had

missing data due to the minimal likelihood of bias.

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5.4.3 Results

A total of 47 train drivers from both regional (n=22, 73.3%) and urban (n=25,

83.3%) areas agreed to participate in this feedback survey, while thirty six (36) survey

questionnaires were sent to experts in the field, with 32 completing and returning the

questionnaire (88.8%). The response rates for both groups were above average for

both groups. No demographic data was collected from the survey questionnaire sent

to either train drivers or the expert panel. Both of these groups had been surveyed

previously on two occasions using the modified Delphi Technique, and it was not

deemed relevant in this formative quantitative research phase.

A number of results are presented in this section. Firstly, an examination of the

differences between urban and regional train driver risk ratings. An independent t-test

was used to assess the differences in overall mean scores as well as individual items.

Additionally, these risk ratings were ranked (by mean order) from highest (mean

score greater than 4.50) to moderate (mean score of 3.50 or less) risk. Secondly, an

examination of the differences between train drivers (aggregate) and expert’s risk

ratings. An independent t-test was also used to assess the differences in overall mean

scores as well as individual items. Additionally, these risk ratings were ranked (by

mean order) from highest (mean score greater than 4.50) to moderate (mean score of

3.50 or less) risk. Each of these comparisons is displayed in tables.

Table 11 presents the findings from the aggregate data from both urban and

regional train drivers. As can be seen in Table 11 below, aggregate ratings of risk

proscribed by the train drivers leaned toward the more dangerous end of the spectrum.

To this end, 10 of the 19 acts were regarded as belonging to the highest echelon of

dangerous behaviours at level crossings, polling an average risk rating of between

4.51 and 5.00. Not surprisingly, at the head of this list was the act of trying to beat

the train across the crossing, where the driver knowingly gambles on their ability to

safely clear a crossing before an approaching train crosses. Also in the highest risk

category were behaviours that implied crossing in poor visibility situations, crossing

with undue haste (overtaking, bypassing boom gates, crossing in-front of a visible

train), crossing without due attention (not looking for a second train, queuing, not

noticing passive crossings) and ignoring warning devices (not stopping for passive

crossings, driving through an activated active crossing). The next seven behaviours

were regarded by the drivers as high risk, and polled an average risk rating of between

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4.50 and 3.51. These behaviours again demonstrated the common ground of acts of

undue haste or negligence, but seemingly also implied a level of safety consideration.

For example, included behaviours such as scanning on approach without stopping and

crossing when the train is distant are obviously dangerous, but demonstrate at least

some consideration of risk by the driver. The two remaining behaviours, crossing

before boom gates began to descend or had fully ascended, were regarded as

presenting only moderate levels of risk. This is likely because, though these

behaviours are against the law and hold the potential for harm, they do show some

deference to the active protections.

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Table 11: Level of aggregate train driver assigned risk to motorist behaviours Item

Aggregate Class Rating

Aggregate Mean

Aggregate S.D.

Try to beat the train across the crossing Highest 4.91 0.28 Driving around the boom gates Highest 4.81 0.68 Drive in front of train when it is 'close' to the crossing Highest 4.77 0.52 Going thru the crossing as soon as one train has passed Highest 4.74 0.57 Queuing up over a congested crossing Highest 4.72 0.71 Not looking at passive crossings Highest 4.68 0.51 Go thru flashing lights (at crossings with flashing lights only) when train visible Highest 4.64 0.60 Going across the crossing when unable to see if there is a train coming (poor visibility) Highest 4.62 0.61 Not stopping at all at passive crossings Highest 4.57 0.54 Overtaking cars that are stopped at the crossing Highest 4.55 0.97 Following the car in front across the crossing without looking High 4.43 0.71 Trying to get through the crossing before the boom gates come down (moving) High 4.30 0.93 Speeding on approach to crossings High 4.21 0.83 Going through flashing lights High 4.11 0.73 Going at passive crossings when the train is visible but 'far away' High 3.79 0.88 Looking or scanning on approach and then not stopping or slowing if no train seen High 3.68 0.86 Slowing and rolling thru STOP signed crossings High 3.62 1.09 Going thru flashing lights before boom gates start to go down Moderate 3.43 1.35 Going thru flashing lights before boom gates start to come up Moderate 2.85 1.30

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The findings from the comparison of risk ratings between urban and regional

train drivers found some interesting trends. When Tables 12 and 13 below are

compared, the similarities and differences of the drivers’ ratings are drawn into focus.

At first glance the ratings appear fairly consistent between the two groups. The act of

consciously trying to beat the train across the tracks again draws most severe ratings,

and again the behaviours of crossing before boom gates begin to descend or have fully

ascended again rate as the least dangerous. Similar also is the risk ratings skew

toward rating the behaviours as generally dangerous, and the distribution of numbers

of behaviours between the highest, high and moderate risk groups. An independent t-

test was also conducted to compare the risk ratings of train drivers. An overall mean

score was computed for the 19 items. Table 14 presents the statistics for each item.

The mean score for regional train drivers was 4.28 (n=22) while the mean score for

the urban group was 4.29 (n=25). There was no significant difference between these

mean scores [t(45)= -.121, p=.904,ns]. Only one item ‘go through flashing lights (at

crossings with flashing lights only) when a train is visible’ was found to have a p-

value of 0.05.

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Table 12: Level of regional train driver assigned risk to motorist behaviours Item

Regional Class Rating

Regional Mean

Regional S.D.

Try to beat the train across the crossing Highest 4.86 0.35 Going thru the crossing as soon as one train has passed Highest 4.77 0.43 Driving around the boom gates Highest 4.68 0.94 Drive in front of train when it is 'close' to the crossing Highest 4.68 0.65 Going across the crossing when unable to see if there is a train coming (poor visibility) Highest 4.64 0.66 Queuing up over a congested crossing Highest 4.64 0.95 Following the car in front across the crossing without looking Highest 4.59 0.67 Not looking at passive crossings Highest 4.55 0.60 Not stopping at all at passive crossings High 4.50 0.60 Trying to get through the crossing before the boom gates come down (moving) High 4.50 1.01 Go thru flashing lights (at crossings with flashing lights only) when train visible High 4.45 0.67 Overtaking cars that are stopped at the crossing High 4.45 1.18 Speeding on approach to crossings High 4.32 0.78 Going through flashing lights High 4.05 0.78 Looking or scanning on approach and then not stopping or slowing if no train seen High 3.73 0.88 Going at passive crossings when the train is visible but 'far away' High 3.73 0.88 Slowing and rolling thru STOP signed crossings High 3.59 1.18 Going thru flashing lights before boom gates start to go down Moderate 3.50 1.14 Going thru flashing lights before boom gates start to come up Moderate 3.05 1.25

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Table 13: Level of urban train driver assigned risk to motorist behaviours Item

Urban Class Rating

Urban Mean

Urban S.D.

Try to beat the train across the crossing Highest 4.96 0.20 Driving around the boom gates Highest 4.92 0.28 Drive in front of train when it is 'close' to the crossing Highest 4.84 0.37 Go thru flashing lights (at crossings with flashing lights only) when train visible Highest 4.80 0.50 Queuing up over a congested crossing Highest 4.80 0.41 Not looking at passive crossings Highest 4.80 0.41 Going thru the crossing as soon as one train has passed Highest 4.72 0.68 Not stopping at all at passive crossings Highest 4.64 0.49 Overtaking cars that are stopped at the crossing Highest 4.64 0.76 Going across the crossing when unable to see if there is a train coming (poor visibility) Highest 4.60 0.58 Following the car in front across the crossing without looking High 4.28 0.73 Going through flashing lights High 4.16 0.69 Speeding on approach to crossings High 4.12 0.88 Trying to get through the crossing before the boom gates come down (moving) High 4.12 0.83 Going at passive crossings when the train is visible but 'far away' High 3.84 0.90 Slowing and rolling thru STOP signed crossings High 3.64 1.04 Looking or scanning on approach and then not stopping or slowing if no train seen High 3.64 0.86 Going thru flashing lights before boom gates start to go down Moderate 3.36 1.52 Going thru flashing lights before boom gates start to come up Moderate 2.68 1.34

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Table 14: Comparison of means between urban and regional train drivers

Mean Item Urban Regional t df Sig Driving through the crossing when lights flashing before boom gates go down 3.36 3.50 .352 45 .726 Trying to get through the crossing before the boom gates come down (moving) 4.12 4.50 1.412 45 .165 Going through flashing lights before boom gates start to come up 2.68 3.05 .959 45 .342 Slowing and rolling thru STOP signed crossings 3.64 3.59 -.152 45 .880 Going thru the crossing as soon as one train has passed 4.72 4.77 .313 45 .755 Not stopping at all at passive crossings 4.64 4.50 -.882 45 .382 Not looking at passive crossings 4.80 4.55 -.1726 45 .091 Going at passive crossings when the train is visible but 'far away' 3.84 3.73 -.433 45 .667 Drive in front of a train when it is 'close' to the crossing 4.84 4.68 -1.042 45 .303 Queuing up over a congested crossing 4.80 4.64 -.781 45 .439 Driving around the boom gates 4.92 4.68 -1.204 45 .235 Trying to beat the train across the crossing 4.96 4.86 -1.173 45 .247 Speeding on approach to crossings 4.12 4.32 .811 45 .421 Overtaking cars that are stopped at the crossing 4.64 4.45 -.647 45 .521 Looking or scanning on approach and then not stopping or slowing if no train seen 3.64 3.73 .343 45 .733 Going through flashing lights 4.16 4.05 -.533 45 .597 Go through flashing lights (at crossings with flashing lights only) when a train is visible 4.80 4.45 -2.017 45 .050 Following the car in front across the crossing without looking 4.28 4.59 1.509 45 .138 Going across the crossing when unable to see if there is a train coming (poor visibility) 4.60 4.64 .202 45 .841 Global Mean Score 4.29 4.28 -.121 45 .904

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Table 15 illustrates the assigned risk levels by the panel of experts. Experts

ranked ‘driving around boom gates’ as the highest risk behaviour by motorists, with a

mean of 4.91 (n=32). The lowest ranked item was ‘going through flashing lights

before the boom gates start to go down’, with a mean of 2.63 (n=31). Additionally, an

independent t-test was conducted to compare overall mean scores between train

drivers and experts. This mean score was computed for each case to provide an

overall mean of the 19-items. Statistics for each item is displayed in Table 16. The

mean score for the expert panel was 4.06 (n=32) while the mean score for the train

drivers was 4.29 (n=47). There was no significant difference between these mean

scores [t(77)=-2.334, p=.998, ns]. Only one item ‘overtaking cars that are stopped at

the crossing’ was found to have a p-value of less than 0.05. The p-value for this item

was .001.

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Table 15: Level of expert assigned risk to motorist behaviours Item Class Rating Mean S.D. Driving around the boom gates Highest 4.91 .296 Try to beat the train across the crossing Highest 4.90 .396 Overtaking cars that are stopped at the crossing Highest 4.88 .421 Drive in front of train when it is 'close' to the crossing Highest 4.84 .374 Queuing up over a congested crossing Highest 4.78 .420 Going thru the crossing as soon as one train has passed Highest 4.72 .634 Not looking at passive crossings Highest 4.66 .602 Go thru flashing lights (at crossings with flashing lights only) when train visible Highest 4.63 .660 Going across the crossing when unable to see if there is a train coming (poor visibility) High 4.34 .701 Following the car in front across the crossing without looking High 4.22 .608 Not stopping at all at passive crossings High 4.13 .833 Going through flashing lights High 4.00 .816 Trying to get through the crossing before the boom gates come down (moving) High 3.94 1.014 Speeding on approach to crossings High 3.90 .870 Going at passive crossings when the train is visible but 'far away' Moderate 3.40 .932 Looking or scanning on approach and then not stopping or slowing if no train seen Moderate 2.88 1.070 Slowing and rolling thru STOP signed crossings Moderate 2.75 1.218 Going thru flashing lights before boom gates start to come up Moderate 2.65 1.082 Going thru flashing lights before boom gates start to go down Moderate 2.63 1.185

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Table 16: Comparison of means between train drivers and experts

Mean Item Experts Train

Drivers t df Sig Driving through the crossing when lights flashing before boom gates go down 2.63 3.43 -2.720 77 .180 Trying to get through the crossing before the boom gates come down (moving) 3.94 4.30 -1.629 77 .821 Going through flashing lights before boom gates start to come up 2.65 2.85 -.730 76 .374 Slowing and rolling thru STOP signed crossings 2.75 3.62 -3.301 77 .312 Going thru the crossing as soon as one train has passed 4.72 4.74 -.190 77 .780 Not stopping at all at passive crossings 4.13 4.57 -2.909 77 .062 Not looking at passive crossings 4.66 4.68 -.195 77 .481 Going at passive crossings when the train is visible but 'far away' 3.40 3.79 -1.836 75 .532 Drive in front of a train when it is 'close' to the crossing 4.84 4.77 .672 76 .145 Queuing up over a congested crossing 4.78 4.72 .412 77 .312 Driving around the boom gates 4.91 4.81 .764 77 .120 Trying to beat the train across the crossing 4.90 4.91 -.152 76 .703 Speeding on approach to crossings 3.90 4.21 -1.579 76 .890 Overtaking cars that are stopped at the crossing 4.88 4.55 1.758 77 .001 Looking or scanning on approach and then not stopping or slowing if no train seen 2.88 3.68 -3.695 77 .518 Going through flashing lights 4.00 4.11 -.601 76 .929 Go through flashing lights (at crossings with flashing lights only) when a train is visible 4.63 4.64 -.092 77 .925 Following the car in front across the crossing without looking 4.22 4.43 -1.339 77 .055 Going across the crossing when unable to see if there is a train coming (poor visibility) 4.34 4.62 -1.840 77 .192 Global Mean Score 4.06 4.29 -2.334 77 .998

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5.4.4 Discussion

Data from both urban and regional train drivers provided an insightful comparison

of assigned risk levels for a list of commonly observed level crossing behaviours by

motorists. Rated as most dangerous was the act of trying to beat the train across the

crossing, a behavioural description that implies conscious risk taking on the part of the

motorist. Similar themes were repeated among the other upper echelon of dangerous

behaviours, specifically crossing with undue haste, crossing in poor visibility situations

and ignoring warning devices. These behaviours can be categorised as violations on the

part of the motorists, acts that imply a willful disregard of safe crossing protocols by the

motorists, as well as indifference toward the risks involved in their behaviours. The

remaining behaviours in this upper echelon of risk were indicative of negligent lapses,

where motorists are crossing without due attention to the conditions in front of them.

While these behaviours do not involve deliberate violation on the part of the motorist,

they remain deserving of examination as dangerous behaviours for the reason that they

are indicative of a passive approach to risk assessment while driving. Hence, at an

aggregate level, it is apparent that train drivers consider motorists’ deliberate protocol

violations and negligently lax approach to hazard detection as the predominant causes of

danger at level crossings (Lawton, 1997b, Caird, 2002). Mirroring the aggregate level

analysis, the assigned risk levels show noteworthy consistency between the urban and

regional groups. With only one exception, each of the level crossing behaviours retain

the same risk categorisation. Where discrepancies are observed, they appear on the

surface to be mostly minor differences in the rank order which the items appear. This is

notable in that it indicates that the both sets of train drivers, presumably drawing on their

own experiences of either urban or regional locales, still share a similar view of where the

danger lies at levels crossings.

The comparison between the aggregate assigned risk levels by train drivers and

those assigned by experts also revealed some interesting results. Both the panel of

experts and train drivers ranked the items ‘driving around the boom gates’ and ‘trying to

beat the train across the crossing’ as being the highest risk taking behaviours. Experts

also ranked ‘overtaking cars stopped at the crossing’ as being very high risk (4.88), while

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train drivers ranked ‘drive in front of a train when it is close to the crossing’ (4.77) as

being a very high risk taking behaviour. Overall, experts were observed to rank the

behaviours lower (4.06) than train drivers (4.29). This may be due to the nature of train

drivers observing a large number of near-misses throughout their careers and the

possibility that they experience great frustration with motorists that take risks at level

crossings (findings from the focus groups discussions with train drivers in both urban and

rural areas). Additionally, experts in the field may have inadvertently made a comparison

of risk levels at level crossings and risk levels whilst driving generally. This would no

doubt have produced lower mean scores than those reported by train drivers.

5.5 FRAMEWORK FOR INTERVENTION DEVELOPMENT

5.5.1 Overview

The planning, development and delivery of behavioural interventions to road user

groups can be applied using two different approaches. ‘Social Marketing’ techniques and

‘Intervention Mapping’ are two techniques that have been applied in the delivery of

behavioural interventions. However, although social marketing principles have been

widely used in public health and social psychology, its basis is not driven by theory. This

is a major limitation of this approach, and its use is therefore restricted in developing

behavioural interventions. However, for the purposes of comparison, the merits and

limitations of both techniques will be reviewed.

It must be noted though that as this thesis did not have a sole purpose of evaluating

the effectiveness of the behavioural interventions developed in Study Three, intervention

mapping was not used in a prescriptive manner. As such, the systematic investigation of

the merit, worth, or significance of the interventions was not examined. What

intervention mapping did provide though was the emphasis on two significant factors that

are required: (1) combining theory with evidence (including industry experts such as

those used in Study One of this thesis) and (2) recognising that individual behaviour is

influenced by the environments in which individuals live, their social networks,

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organisations, communities and societies. A detailed comparison of intervention mapping

with the popular social marketing techniques is provided.

5.5.2 Social marketing

Social marketing is the use of commercial marketing principles to promote the

adoption of a behaviour that will improve the health or well-being of the target audience

or of society as a whole (Weinreich, 1999, Nutbeam, 2004). To be effective, road safety

programs must precisely specify its target audience, use customised methods to reach the

target audience and involve the target audience in developing the program (Nutbeam,

2004). Social marketing does not rely purely on educating the target audience about an

issue, but rather uses persuasive messages developed through formative research with

members of the target audience (Weinreich, 1999). This framework involves research and

re-evaluation at each stage to assess if the program is developing appropriately (Nutbeam,

2004, Weinreich, 1999). According to Weinreich (1999) this process consists of five

general stages:

• Planning;

• Message and materials development;

• Pre-testing;

• Implementation; and

• Evaluation and feedback.

This process is non-linear but rather of feedback and adjustment that may require

re-visiting past stages to make changes based on new information (Weinreich, 1999).

The first step, the planning phase, forms the foundation on which the process is built.

This phase must be based on understanding the problem being addressed, the targeted

audiences, and the environment in which the program will operate (Weinreich, 1999).

Formative research is used to analyse such factors and develop a workable strategy for

influence behaviour change. Step Two uses information learned in the formative planning

phase to design messages to be conveyed to the target audiences, as well as the materials

to carry such messages (Weinreich, 1999). The pre-testing phase (Step Three) involves

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using a range of methods to test messages, materials and proposed strategies with the

target audience members to determine what works best to achieve objectives of the

program (Weinreich, 1999). Step Four involves introducing the program to the target

audiences (Weinreich, 1999). Preparation and monitoring the implementation is

paramount to ensuring success of the program. Step Five, the evaluation and feedback

phase, assesses the effects of the program as a whole as well as the individual elements of

the strategy (Weinreich, 1999). Evaluation occurs throughout the process of program

development, and feedback is used at each stage to improve the program (Weinreich,

1999).

Fishbein (2001) proposes that while there has been a growing recognition of the

value of theory-based behavioural interventions, it is seldom clear how theory is actually

used in the development (and evaluation) of such interventions. Intervention mapping is a

protocol for the development of theory-based behavioural intervention, by providing

guidelines and tools for the application of theory as well as the translation of theory in

actual program materials and activities (Bartholomew et al., 1998, Hoelscher et al.,

2002).

5.5.3 Intervention mapping

Intervention mapping also provides researchers and program planners with a

framework for effective decision-making at each step in intervention planning,

implementation and evaluation (Bartholomew et al., 2006). This concept is “based on the

importance of planning programs that are based on theory and evidence” (Bartholomew

et al., 2006, p8). The term ‘evidence’ refers to data from research studies as represented

in scientific literature as well as opinion and experience of community members and

industry experts (Bartholomew et al., 2006). As such, theoretical and empirical evidence

is brought together to meet a road safety need. The concept of intervention mapping

provides a detailed framework for this process to occur in a systematic and logistical

approach.

The primary concept of intervention mapping is that individual behaviour is

influenced by different causes at various environmental levels (such as environments in

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which individuals live, including family, social networks, organisations, communities and

societies) (Kok et al., 2004, Bartholomew et al., 1998, Bartholomew et al., 2001).

Intervention mapping consists of five key steps: (1) creating a matrix of proximal

program objectives, (2) selecting theory-based intervention methods and practical

strategies, (3) designing and organising a program, (4) specifying adoption and

implementation plans, and (5) generating program evaluation plans (Bartholomew et al.,

1998). Intervention mapping has been used to develop intervention programs for a variety

of health behaviour programs including asthma management, sun protection, nutrition,

adolescent risk taking, cancer screening, HIV prevention and acute stroke therapy.

Although intervention mapping has not been found in the literature to have been applied

to road safety interventions, the merits of intervention mapping must not be overlooked.

The capacity of intervention mapping to provide road safety program planners with the

ability to integrate the “wealth of information, theories, ideas, and models to develop

interventions that are logical and appropriate in their foundations and are practical and

acceptable in their administration” is underestimated.

Intervention mapping provided valuable information to assist with the guiding and

identifying priority areas for message and material development in Study Three.

However, as this program of research did not have a sole purpose of evaluating the

effectiveness of the behavioural interventions, it was not used in a prescriptive manner.

Hence, the systematic investigation of the merit, worth, or significance of the

interventions was not examined. What intervention mapping did provide though was the

emphasis on two significant factors that are required: (1) combining theory with evidence

(including industry experts such as those used in Study One of this thesis) and (2)

recognising that individual behaviour is influenced by the environments in which

individuals live, their social networks, organisations, communities and societies. This

environmental change concept is important in its application to road safety, and

particularly important in attempting to improve level crossing safety in the heavy vehicle

industry.

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5.6 SUMMARY

This chapter documented Study Two, the formative research undertaken as part of

the planning and development of interventions for each of the three road user groups, as

well as the development of data collection measures. As stated previously, intervention

mapping was used to assist in the planning and development of the interventions, but was

not used in a prescriptive manner. The next chapter will provide findings from Study

Three. Study Three involved the examination of the present context of motorist

behaviour at level crossings as well as the planning, development and measurement of

change associated with exposure to the interventions for each of the three road user

groups.

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CHAPTER SIX: THE PRESENT CONTEXT OF MOTORIST BEHAVIOUR AT LEVEL CROSSINGS

6.1 Introduction …………………………………………………………… 192

6.2 Study aims and research questions ……………………………………. 192

6.3 Intervention development and implementation ……………………….. 194

6.4 Method ………………………………………………………………... 197

6.5 Results ………………………………………………………………… 215

6.6 Study limitations ……………………………………………………… 268

6.7 Summary ……………………………………………………………… 272

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6.1 INTRODUCTION

This chapter documents the third study undertaken as part of the program of

research. There were three parts to this final study. The aim of Part One of this study was

to develop targeted interventions specific to each of the three road user groups in

accordance with Fishbein’s theoretical model (Integrated Model of Behaviour Change).

The development of interventions was originally seen as being outside of the scope of

this project, however, it became intertwined in questionnaire development and thus

deemed to be within the realms of the current mode of inquiry. The aim of Part Two was

to investigate the present context of unsafe driving behaviour at level crossings. This

included examining the personal, social and environmental factors contributing to unsafe

driving intention and driving behaviour at level crossings. The aim of Part Three was to

trial a pilot road safety radio advertisement using an intervention and control

methodology.

The Integrated Model of Behaviour Change (IM) developed by Martin Fishbein,

was useful in this exploratory investigation. Firstly, it provided a foundation on which to

build and develop each of the three intervention messages. Secondly, it assisted in

designing questionnaires based on the key variables of Fishbein’s model (i.e. intentions;

attitudes; norms; self-efficacy or perceived behavioural control; perceived risks; skills or

abilities; and environmental constraints). The design of these questionnaires was based on

the premise that intentions alone are not the singular determinant of behaviour, as

motorist behaviour at level crossings appears to be influenced by other factors. Thirdly,

it assisted in examining the factors that contribute to the intention to drive unsafely at

level crossings as well as self-reported driving behaviour. As such, it assisted in

explaining why personal, social and environmental factors are important in the future of

both engineering and human factor solutions to improving level crossing safety.

6.2 STUDY AIMS AND RESEARCH QUESTIONS

With the assistance of Fishbein’s Integrated Model of Behaviour Change (IM), an

exploratory investigation of three different road user groups was achieved. Two aims and

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eight research questions were addressed in the final study of the research program. This

summary outlines the findings relevant to each of the aims and research questions.

The first aim was to examine what changes in driving intention and reported

driving behaviour are influenced by exposure to the intervention message.

Research Question 1: Does exposure to the intervention message produce safer

driving intention and self-reported driving behaviour at level crossings?

The second aim was to examine the personal, social and environmental factors that

influence driving intention to perform behaviours related to unsafe driving at level

crossings.

Research Question 2: Is there a relationship between gender and unsafe driving

intention among older and younger drivers?

Research Question 3: Is there a relationship between distance traveled per week

and unsafe driving intention among heavy vehicle drivers?

Research Question 4: Is there a relationship between familiarity and unsafe driving

intention?

Research Question 5: Is there a relationship between attitudes and unsafe driving

intention?

Research Question 6: Is there a relationship between self-efficacy and unsafe

driving intention?

Research Question 7: Is there a relationship between subjective norms and unsafe

driving intention?

Research Question 8: Is there a relationship between beliefs about environmental

constraints and driving intention?

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6.3 INTERVENTION DEVELOPMENT AND IMPLEMENTATION

6.3.1 Overview

It is well-known that during the past few decades, road safety advertising has

typically had an emphasis on highly graphic (shock style) campaigns such as those used

for drink driving. However, with research now indicating that this style of advertising is

largely ineffective (Christie, 2002), advertising that conveys information to viewers in

order to change beliefs is most likely to be more effective. As discussed earlier in Chapter

Two (Literature Review), it can be understood that some education campaigns that have

typically been developed in a haphazard manner, may have limited effectiveness in

improving road safety. However, it has also be identified that theoretically grounded

campaigns developed in accordance with research and targeting specific road safety

issues can provide a more effective means of risk management. Although the current

project was not a ‘campaign’ per se but rather single interventions, the development and

assessment of educational interventions is just as important in building such campaigns.

The IM (Fishbein, 2003) was utilised in the development of these targeted radio road

safety intervention messages that aimed to change beliefs, attitudes (and subjective norm

and perceived control) and intentions, whilst acknowledging the influence of the

environment (at both the level crossing site level as well as the social

network/community/organisational level) and skills and abilities of motorists.

The interventions were designed in the format of a pilot radio road safety

advertisement, as this medium was found to be one of the most acceptable to each of the

road user groups as identified in the formative research undertaken in Study Two. This

medium was also used as it was within the budget of the project and was likely to be

applied in the ‘real-world’. Radio has been found to be an effective medium for instilling

road safety awareness among drivers (Wong et al., 2004). The radio messages were used

as a one-off targeted awareness raising intervention for each road user group. Each radio

message had a 90 second duration. It was intended that these interventions may be used

in the future by government agencies in support of other mass media campaigns (such as

television, billboards etc) and police enforcement, with the view that campaigns be

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targeted towards specific road user groups (such as heavy vehicles, older and younger

drivers).

The content of the radio message was different with all three road user groups (see

Appendix 7). However, what was consistent between each of the advertisements was the

purposive non-focus on detection or legal sanctions. As the likelihood of detection at any

level crossing is extremely low in most parts of Australia, focusing on this enforcement

countermeasure would most certainly not be a deterrent to any road user group.

Crossings rarely have police presence or are monitored by cameras (such as red-light

cameras at major intersections) due largely to level crossing fatalities being much lower

than other road safety issues such as drink driving, seat-belt wearing or speeding. There

are only a few level crossings scattered throughout capital cities in Australia that have

video and digital still cameras installed, and even fewer that use such devices for

applying fines and penalties to drivers. Without such enforcement activities to support

educational interventions, it is argued that campaigns used by government agencies will

for the most part be fruitless (Donovan, 1999). This is one of the pitfalls in level crossing

safety research.

6.3.2 Heavy vehicle drivers

The content used for the heavy vehicle radio advertisement was based on the

Victorian level crossing campaign ‘Don’t Risk It’ developed by the Department of

Infrastructure. This campaign provided information for heavy vehicle drivers as well as a

variety of other road user groups. However, information provided for heavy vehicle

driving was supported by the formative research conducted in Study Two. This

formative research indicated that heavy vehicle drivers face three key safety issues at

level crossings: (1) stopping distances, (2) short-stacking and (3) visibility. Therefore,

the information provided to heavy vehicle drivers in this ‘Don’t Risk It’ campaign was

used for ‘suggested actions’ in the intervention advertisement for the heavy vehicle

group.

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6.3.3 Older drivers

Older drivers were provided with a radio advertisement based on the premise that

they have a wealth of driving experience, but that all level crossings are in some way

different from the next and unfamiliar crossings may be confusing for some older drivers.

Additionally, it acknowledged that older motorists may be vulnerable to being pressured

by other road users, and that it is essential to follow the road rules at level crossings even

if being pressured to take risks. This road user group was also informed that although

they are often very conscious of day-to-day risks, sometimes it can be the unexpected

danger, the once in a lifetime event, that they are not prepared for. Informative research

with this group in Study Two indicated that older drivers believe that they always ‘do the

right thing’ and ‘we don’t take risks’. Many of the drivers that participated in the focus

groups in this formative research phase stated that it is ‘other drivers’ that may pressure

them into having to take risks. Hence, to build a rapport with drivers that listened to this

intervention was the initial step before providing them with actions for maintaining their

safe driving behaviour.

Participants randomly allocated to the older driver control group were also provided

with a road safety message (vision and driving). It was decided that the control group

should receive information about a separate road safety issue that was relevant to this age

group. The reason for this was two-fold. Firstly, it became apparent from conversations

whilst recruiting participants for this group that participants were expecting to be

involved in important road safety research, with part of this being listening to pilot radio

advertisements. Secondly, discussions with experts in the field of road safety suggested

that if older drivers feel that they are contributing to the research (such as pilot radio

advertisements) then they will continue to be participants (i.e. limit the risk of attrition).

Therefore, all participants listened to either information about driving at level crossings

(intervention group) or vision and driving (control group).

6.3.4 Younger drivers

Informative research with younger drivers found that this road user group was

particularly concerned about harming either their friends or their motor vehicle if

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involved in a road crash or level crossing collision. Many participants in the focus

groups conducted during the formative research, stated that they were however willing to

take risks at crossings to save time. The intervention for this group was centered on the

themes of: (1) knowing the different types of crossings, (2) knowing that trains are not

able to stop in time due their speed and mass and (3) waiting at level crossings whilst a

train is going through may take an extra minute but might save their life. Such themes

were found to be prominent with participants in the formative research phase. The

method for delivery of the intervention was an online survey in which participants

listening to the radio ad either about level crossing safety (intervention) or wearing of

seat belts (control). Each participant had to click on the file to open and listen to the radio

advertisement. This was provided immediately to participants after they had completed

the first questionnaire online.

6.4 METHOD

6.4.1 Research design

There were three parts to this final study. Part One involved the development of

road safety radio advertisements specific for each of the three road user groups. Part Two

involved the investigation of the present context of unsafe driving behaviour at level

crossings. A questionnaire (Time 1) was completed by members of each of the three road

safety groups identified as being ‘at risk’ of a vehicle-train collision. This questionnaire

was then subsequently followed by Part Three of the study. Part Three involved trialing a

pilot road safety radio advertisement using an intervention and control methodology. For

the older and younger driver groups, the design was experimental as participants were

randomly allocated to either the intervention or control group. A post-test questionnaire

(Time 2) was then completed for Part Three by members of each of the three road user

groups. Although for the heavy vehicle driver group the intention was to also use a

quasi-experimental intervention and control design in which there is no randomisation to

intervention or control groups, difficulties arose over time with the recruitment of this

road user group and therefore no control group was able to be recruited. The greatest

challenge was the poor involvement of companies in the study. Although the project

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sought the assistance of the Queensland Trucking Association and actively approached a

multitude of trucking companies, only five agreed to participate. This was an extremely

disappointing response rate, particularly with the months of effort in approaching

companies and offering incentives to participants. The exact reasons for refusal by

companies to participate in this research remain unknown. However, from discussions

with some trucking company managers that refused to participate, there appeared to be a

sense of either level crossing safety not being a priority safety area for their employees or

that disclosure of employee driving practices could leave their company open to scrutiny.

Other limitations of recruiting from the heavy vehicle industry are discussed in greater

detail later in the chapter.

6.4.2 Questionnaire measures

6.4.2.1 Overview

Two questionnaires were developed for each of the three road user groups: Time 1

(pre-intervention) and Time 2 (post-intervention). Both of the questionnaires was slightly

different for the three road user groups and is provided in Appendix 8. Tables 17 and 18

below illustrate the differences between the Time 1 questionnaire give to the different

road user groups. Questionnaires were piloted on members from each of the road user

groups (i.e. older and younger drivers, and truck drivers).

Each section and instrument of the questionnaires is discussed following the two

tables.

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Table 17: Items and scales included in the T1 questionnaire Older Drivers

Younger Drivers Heavy Vehicle Drivers

Socio-demographic

Gender, age, employment status, postcode, driving experience, licence restrictions

Gender, age, employment status, postcode, driving experience

Gender, age, employment status, shift work status, postcode, driving experience, licence type

Driving Patterns Frequency and distance travelled per week

Frequency and distance travelled per week

Frequency and distance travelled per week

Health and Driving Self-Assessment

Conditions that may affect driving ability

n/a n/a

Driving Assessment n/a High risk driving behaviours

n/a

History of Road Crashes Involvement in road crashes and contributing factors

Involvement in road crashes and contributing factors

Involvement in work-related road crashes and contributing factors

General Driving Behaviour Modified Driver Behaviour Questionnaire (DBQ)

Modified Driver Behaviour Questionnaire (DBQ)

Modified Driver Behaviour Questionnaire (DBQ)

Level Crossing Driving Behaviour (Self) – past 6 months and next 6 months

Frequency of driving at different types of level crossings and frequency of risk taking behaviours and future intention to risk take

Frequency of driving at different types of level crossings and frequency of risk taking behaviours and future intention to risk take

Frequency of driving at different types of level crossings and frequency of risk taking behaviours and future intention to risk take

Level Crossing Driving Behaviour (Important Others)

Driving behaviour of friends and family

Driving behaviour of friends and family

Driving behaviour of friends and colleagues

Influencing Factors of Driving at Level Crossings

Beliefs of factors that influence self-driving at level crossings

Beliefs of factors that influence self-driving at level crossings

Beliefs of factors that influence self-driving at level crossings

Environmental Constraints

Environmental conditions that affect driving at level crossings

Environmental conditions that affect driving at level crossings

Environmental conditions that affect driving at level crossings

Road Rules Knowledge of level crossing rules

Knowledge of level crossing rules

Knowledge of level crossing rules

Level Crossing Collision Perception of likelihood of being involved in a RLX collision

Perception of likelihood of being involved in a RLX collision

Perception of likelihood of being involved in a RLX collision

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Table 18: Items and scales included in the T2 questionnaire Older Drivers

Younger Drivers Heavy Vehicle Drivers

General Driving Behaviour

Modified Driver Behaviour Questionnaire (DBQ)

Modified Driver Behaviour Questionnaire (DBQ)

Modified Driver Behaviour Questionnaire (DBQ)

Level Crossing Driving Behaviour (Self) – past 6 months and next 6 months

Frequency of driving at different types of level crossings and frequency of risk taking behaviours and future intention to risk take

Frequency of driving at different types of level crossings and frequency of risk taking behaviours and future intention to risk take

Frequency of driving at different types of level crossings and frequency of risk taking behaviours and future intention to risk take

Level Crossing Driving Behaviour (Important Others)

Driving behaviour of friends and family

Driving behaviour of friends and family

Driving behaviour of friends and colleagues

Influencing Factors of Driving at Level Crossings

Beliefs of factors that influence self-driving at level crossings

Beliefs of factors that influence self-driving at level crossings

Beliefs of factors that influence self-driving at level crossings

Environmental Constraints

Environmental conditions that affect driving at level crossings

Environmental conditions that affect driving at level crossings

Environmental conditions that affect driving at level crossings

Road Rules Knowledge of level crossing rules

Knowledge of level crossing rules

Knowledge of level crossing rules

Level Crossing Collision Perception of likelihood of being involved in a RLX collision

Perception of likelihood of being involved in a RLX collision

Perception of likelihood of being involved in a RLX collision

Radio Message (Intervention or Control)

Recall of message, slogans and information, likelihood that message influenced driving behaviour

Recall of message, slogans and information, likelihood that message influenced driving behaviour

Recall of message, slogans and information, likelihood that message influenced driving behaviour

6.4.2.2 Demographics

A variety of questions were included in the Time 1 questionnaire to determine each

participant’s age, gender, employment status, licence type and/or conditions (e.g.

corrective lenses and/or medical condition requiring a medical certificate), and postcode.

The Rural, Remote and Metropolitan Areas (RRMA) classification was used, which is a

geographical classification based on statistical local areas (SLAs) and allocates each SLA

in Australia to a category based on population numbers and an index of remoteness

(Department of Primary Industries and Energy, 1994). The structure of the RRMA

classification is displayed in Table 19 below.

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Table 19: Structure of the Rural, Remote and Metropolitan Classifications Zone Classification Category Metropolitan 1 Capital cities 2 Other metropolitan centres (urban centre population > 100,000 Rural 3 Large rural centres with populations 25,000 – 99,000 4 Small rural centres with population 10,000 – 24,999 5 Other rural areas with population < 10,000 Remote 6 Remote centres with population > 5,000 7 Other remote areas with population < 5,000

Younger drivers were also asked about their highest educational level attained.

Additional to employment status, heavy vehicle drivers were also asked about shift work,

the length of time having held their licence to drive a heavy vehicle, and the hours and

distance driver on an average week. Therefore, the questionnaires for each road user

group were slightly different with regards to socio-demographic information.

6.4.2.3 Crash history

Each of the three road user groups was asked questions about their crash

involvement during the past three years. Heavy vehicle drivers were specifically asked

about their crash involvement whilst driving a truck. For those participants that reported

having been involved in a road crash, they were asked questions about the number of

crashes, who was at fault, any factors that may have caused the crash and if any person

was injured or died as a result of the crash.

6.4.2.4 Medical conditions

Older drivers were asked a range of questions related to what medical conditions

they have been diagnosed with. This instrument was developed by reviewing the

literature on older drivers and making some assumptions about influences that medical

conditions may have on driving ability. As this instrument is made up of medical

conditions, reporting the Cronbach’s alpha coefficient for the 17 items is unnecessary.

6.4.2.5 Knowledge of level crossing road rules

Four questions were asked about knowledge of level crossing road rules at each

time point. It was necessary to gain an understanding about the road rules that drivers

were unaware of. The questions included: what twin flashing lights mean, whether boom

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gates stay down a lot longer than normal intersections, what yellow hatching road

markings mean, and if there are fines for failure to stop at a level crossing.

6.4.2.6 General driving behaviour

The measure of driving behaviour used for each road user group was a modified

version of the Driver Behaviour Questionnaire (DBQ). This questionnaire has become

widely used in examining self-reported driving behaviour (Lajunen, 2003). This

instrument has been extensively used in a variety of road safety research in many

countries throughout the world (Davey et al., 2007). Reason et al’s (1990b) original DBQ

focused on two distinct behaviours: errors and violations. Errors are defined as driving

actions that are not planned, whilst violations are defined as deliberate deviations from

safe driving practices (Reason, 1990b). However, both behaviours are potentially

dangerous in the context of driving (Lajunen, 2004b). ‘Slips and lapses’ was later

identified as focusing on attention and memory failures, however these were not

considered to affect driving safely significantly. Lajunen and Summala (2003) suggest

that errors are more serious mistakes such as failure of observation or misjudgments,

while lapses are more likely to be associated with memory and attention difficulties.

Lawnton et al (1997a) revised the original DBQ to differentiate violations as either

aggressive or ordinary. Aggressive violations are considered to be associated with an

interpersonally aggressive element, whilst ordinary violations do not aspire to being

aggressive, but are still considered deliberate violations (Lawton, 1997a). Research has

confirmed different loading of factors. Aberg and Rimmo’s (1998) research has

confirmed the three factors (errors, lapses and violations), while Sullman et al’s (2002)

research on New Zealand truck drivers confirmed four factors (errors, lapses, ordinary

violations and aggressive violations).

A modified version of the DBQ was used in both questionnaires (i.e. Time 1 and

Time 2) for each of the three road user groups. As there is no known research that has

utilised the DBQ for level crossing driving, it was considered necessary to include

additional items. For older drivers and younger drivers, the instrument consisted of 24

items. This instrument was developed by researchers at the Centre for Accident Research

and Road Safety – Qld, based at the Queensland University of Technology. The internal

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consistency of these modified DBQ’s for younger and older drivers was examined

through calculating Cronbach’s alpha reliability coefficient. Similar to previous research

with younger drivers (Ulleberg & Rundmo, 2003) and older drivers (Parker, 2000) the

instrument appears to exhibit relative internal consistency (0.802 and 0.801 respectively).

As professional truck drivers are often under time pressure, it was necessary to

include a modified version of the DBQ to assess its usefulness in determining

relationships such as crash involvement (Davey et al., 2007). Research by Sullman et al

(Sullman, 2002) was reviewed and although the 28-item DBQ used was considered

adequate for other research purposes, it was deemed to not be extensive enough for the

current study. A total of 41 items was included in the modified DBQ for heavy vehicle

drivers (24 items included in older and younger driver questionnaires as well as 17 items

regarding professional driving). This instrument was also developed by researchers at the

Centre for Accident Research and Road Safety – Queensland, based at the Queensland

University of Technology. The internal consistency of this modified DBQ for heavy

vehicle drivers was examined through calculating Cronbach’s alpha reliability

coefficient. Similar to previous research with heavy vehicle drivers (Sullman, 2002), the

instrument appears to exhibit relative internal consistency (0.834).

Factor analysis was also administered on both instruments (41-item and 24-item).

Principal components analysis with varimax rotation was implemented to determine the

factor structure both of these modified DBQ’s. One item ‘have one or two alcoholic

drinks before driving’ had to be removed from the factor analysis for the heavy vehicle

driver instrument (41-item) as there was no variance found when computed by SPSS.

With this item removed, a twelve-factor solution accounted for 23.38% of the total

variance. Different methods of factor extraction and rotation were undertaken, however

none yielded any better factor structure results. According to Ozkan, Lajunen and

Summala (2006):

It is likely that the number of items, various versions of DBQ, different sampling

strategies (i.e., postal survey), different target populations (i.e., elderly drivers),

driving context (i.e., work and leisure), different traffic cultures and environment

all influence the stability of the DBQ structure.

(p387)

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6.4.2.7 Self-reported driving behaviour at level crossings

Participants were asked a range of questions related to driving behaviour at level

crossings that were considered by the research team to be high risk driving. These

statements were developed from a combination of formative research, review of the

literature as well as observations of driving behaviour at level crossings. A 20-item

instrument was developed for both younger and older drivers for both questionnaires (i.e.

Time 1 and 2). Unfortunately, the heavy vehicle driver instrument had only 19 items as

one item (seen police enforcing the road rules at rail crossing) was unintentionally deleted

during questionnaire compilation. Participants were asked to score how they had driven

during the past six months at level crossings (for Time 1) and past month (Time 2). The

scale ranged from 0=Not at all likely to 5=Very likely. The alpha coefficient for both the

younger driver (0.978) and heavy vehicle driver (0.758) instruments were considered

acceptable, while the older driver (0.655) instrument was slightly less than the acceptable

value of 0.7.

6.4.2.8 Intended driving behaviour at level crossings

As well as reported driving behaviour, behaviour intention at level crossings was

also measured. The same scale for reported behaviour was used for intention. However,

participants were asked how likely they would behaviour in the next six months of

driving (0=Not at all likely to 5=Very likely). The alpha coefficient for older drivers

(0.626) was a little low, while the younger drivers (0.925) and heavy vehicle driver

(0.858) instruments were considered acceptable. Like the scale for self-reported driving

behaviour at level crossings, unfortunately the heavy vehicle driver instrument had only

19 items instead of 20. One item ‘seen police enforcing the road rules at rail crossing’

was unintentionally deleted during questionnaire compilation.

6.4.2.9 Attitudes towards driving at level crossings

A direct measure of attitude was derived from 8 items from the 15-item instrument

bipolar scales (-3 to +3). The first 8 items in this instrument directly measured attitude

towards driving at level crossings, while the last 7 items directly measured control. Each

of the bipolar scales was recoded in SPSS using unipolar (1-7) scales. Participants were

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asked to complete statements by rating pairs of adjectives whilst driving at level crossings

(e.g. bad-good, more confusing-less confusing). Cronbach’s alpha statistics for the 8

items in this 15-item instrument were above the acceptable level of 0.7 for both heavy

vehicle drivers (0.865) and older drivers (0.892), however was slightly lower than the

acceptable level of 0.7 for younger drivers (0.622).

6.4.2.10 Self-efficacy/perceived behavioural control of driving at level crossings

A direct measure of self-efficacy/perceived behaviour control was derived from the

last 7 items in the 15-item instrument that used bipolar scales (-3 to +3). The first 8 items

in this instrument directly measured attitude towards driving at level crossings, while the

last 7 items measure control. Each of the bipolar scales was recoded in SPSS using

unipolar (1-7) scales. Participants were asked to complete statements by rating pairs of

control adjectives whilst driving at level crossings (e.g. up to me-not up to me, dependent

on other motorists-dependent on me). Cronbach’s alpha statistics for the 7 items in this

15-item instrument were above the acceptable level of 0.7 for heavy vehicle drivers

(0.763), older drivers (0.705) and younger drivers (0.733).

6.4.2.11 Subjective norms other important others

A 14-item instrument for directly measuring the construct of subjective norm was

developed for each of the road user groups. The Cronbach’s alpha statistic for internal

consistency for younger drivers (0.704) was above the acceptable 0.7 level, however both

the heavy vehicle (0.610) and older driver (0.579) instruments showed lower internal

consistency.

6.4.2.12 Perceived risk of a level crossing collision

A scale was developed for participant’s perception of the likelihood that they would

be involved in a collision with a train at any point in the future. The scale was from 0

(not at all likely) to 5 (very likely).

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6.4.2.13 Environmental constraints whilst driving at level crossings

Participants were asked a range of questions about their beliefs of specific

environmental and engineering factors at level crossings. The frequency each of the

driving situations occurred was recorded from 1=Never to 5=Always. Heavy vehicle

drivers were asked a total of 13 questions specifically for driving a truck, while younger

and older drivers were asked only nine (9) questions. Each of the instruments had an

acceptable alpha coefficient with the younger driver instrument being the highest (0.818),

followed by they heavy vehicle instrument (0.806) and older driver instrument (0.741).

6.4.2.14 Familiarity with different protection systems

Participants were also asked how frequently they drove through different level

crossing protection systems (i.e. active crossings with boom gates, passive crossings with

flashing lights and passive crossings with stop or give-way signs only). Response items

included: never, once a year, twice a year, monthly, weekly or daily. Familiarity with

each of the types of protection systems was then recoded to participants being either

familiar or unfamiliar drivers.

6.4.3 Pilot testing

Time 1 (Part One) questionnaires for the three road user groups were piloted after

compiling the findings from the formative research and available literature in the area.

Road and rail safety experts in the field as well as a selection of participants from each of

the road user groups assisted with the pilot testing. Managers from the participating truck

depots piloted the questionnaires, while drivers aged 60 plus years assisted in piloting the

older driver questionnaire. Young drivers (aged 17–24 years) that worked with the

researcher served as participants for piloting the young driver questionnaire. Items were

refined based on initial responses and interpretations. As the Time 2 questionnaire

contained almost identical items to that of Time 1, it was deemed unnecessary to pilot

this questionnaire as well.

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6.4.4 Ethical considerations

Ethical clearance for data collection for this final study was gained from the

Queensland University of Technology Human Research Ethics Committee (QUT Ref.

No. 3550H). All participants were informed that:

• Their participation was strictly voluntary and their confidentiality was assured;

• Any data collected will be unidentifiable and purely for research purposes;

• Personal data would only be accessibly by members of the research team;

• There would be no direct benefits to them or any risks by participating in the

study;

• If they would like further information they could contact the chief investigator;

and

• If they had any concerns or complaints about the ethical conduct of the project

they should contact the research ethics officer (telephone number provided).

6.4.5 Sample size calculations

Typically a number of factors must be known or estimated to calculate sample size.

These include (1) the effect size (usually the difference between two groups); (2) the

population standard deviation (for continuous data); (3) the desired power of the

experiment to detect the postulated effect; and (4) the significance level (Sudman, 1976).

However, in this study, there is no prior research that can inform the estimate of the

population standard deviation of any of the variables used. Therefore, a statistician was

involved in the calculation of the sample size for this study. Sudman (1976) suggests that

a minimum of 100 participants is needed for each major group in a sample and for each

minor sub-group, a sample of 50 participants is necessary. To allow detection of a

difference in driving behaviour at level crossings between intervention and control

groups, as well as cost considerations, a sample size of 200 for each of the road user

groups was identified as being an appropriate sample. It was hoped for both the older and

younger drivers that this sample would be split in halves for regional and metropolitan

areas, and then split again in halves for intervention and control participants (e.g. 50

intervention regional older drivers, 50 control regional older drivers etc). For the heavy

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vehicle drivers, it was known that this group would be the most difficult road user group

to recruit from (advice from other researchers) and the aim was to recruit 200 drivers in

total, while ensuring that all companies recruited traveled in both regional and

metropolitan areas. Limitations of recruiting from the heavy vehicle industry are

discussed in detail in ‘study limitations’ (6.7).

6.4.6 Recruitment strategy

6.4.6.1 Older drivers

Drivers aged 60 years and over were recruited through the Royal Automobile Club

of Queensland (RACQ). RACQ was approached to assist with recruitment of 200 drivers

in both regional and urban areas. A letter from the General Manager for External

Relations was sent to a randomly selected list of 1000 older drivers (500 urban and 500

regional) during June 2006. This letter supported the research being conducted but did

not state it was specifically on the subject of railway level crossings. Although bias

exists in all research, it was important that the research limited the amount of possible

bias and therefore the omission of the exact nature of the research was deemed by

statisticians as being vital. The letter stated that if RACQ members would like to

participate, they should telephone or email the project officer to leave their contact

details. The project officer was employed by the larger project as the contact person for

all external relations as well as assisting with data collection.

6.4.6.2 Young drivers

Young drivers aged 17-24 years were also recruited through the Royal Automobile

Club of Queensland (RACQ). As the RACQ assisted with the recruitment of older

drivers, it was deemed an efficient and inclusive means to also recruiting young drivers.

Like the older driver recruitment strategy, a letter from the General Manager for External

Relations was also sent to a randomly selected list of 1000 young drivers (500 urban and

500 regional) during December 2006. This letter supported the research being conducted

but did not state it was specifically about driving at level crossings. To ensure there was

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limited bias, the omission of the exact nature of the research was deemed by statisticians

as a vital part of the recruitment strategy.

6.4.6.3 Heavy vehicle drivers

The Queensland Trucking Association (QTA) assisted with recruitment for this

research. A list of ten major truck companies that were likely to participate was provided

by the QTA. Each of the trucking companies were contacted and informed about the

project and invited to participate. As an incentive for participation, safety managers were

told that their company would be provided with a ‘risk profile’ of the aggregate data from

their drivers. This incentive was considered to be a motivating factor for the participating

companies. Additionally, each of the heavy vehicle drivers was informed that if they

completed both questionnaires then they would be mailed two movie tickets.

6.4.7 Sampling method

6.4.7.1 Older and younger drivers

A non-probability method (voluntary response sampling) was used for both the

older and younger driver samples. As members of both these road user groups were

invited to participate through a mail out letter from the RACQ, they voluntarily decided

to participate in the research. The advantage of this type of sampling is that it is efficient

for collecting large amounts of information and flexible in collecting information

regarding attitudes, beliefs, and reported and intended behaviours. The disadvantage of

this method is that the sample may include individuals with strong opinions about the

issue, and can therefore be biased (voluntary response bias). However, given that

participants were informed that they would receive two movie tickets for completion of

both questionnaires, this may have been very appealing to invited participants and

therefore the sample may not have just included individuals with strong opinions about

level crossings. With a limited budget for recruitment, this method of sampling was found

to be the most efficient way of recruiting participants quickly.

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6.4.7.2 Heavy vehicle drivers

A non-probability sampling method (purposive/judgmental) was used for the

collection of data from this industry. Although probability sampling is the preferred

method by statisticians and social scientists alike, in the ‘real-world’ of research it is not

always possible to use a probability sample. The advantage of this type of sampling is

that it is less expensive, efficient and easy to implement (Foreman, 1991). Purposive

sampling can also be very useful when a target sample needs to be reached quickly and

when sampling for proportionality is not the primary concern (Colman, 1995). For the

purposes of the current mode of inquiry, this type of sampling was within the budget of

the project as well as the most appropriate and suitable for sampling within the trucking

industry.

However, the limitations of this sampling method must be addressed. One major

limitation of this type of sampling is the introduction of bias due to the sample pattern

(Foreman, 1991). Therefore, non-probability samples cannot depend upon the rationale

of the theory of probability; however they can serve a purpose when there is reliable

information about the sample (or site). Assistance from the Queensland Trucking

Association (QTA) offered extra validity of the data as there was professional judgment

of the companies involved. The other limitation of this type of sampling is that it limits

the usefulness of the data for statistical interpretation (Tilley, 1990, Colman, 1995).

However, it does not necessarily mean that non-probability samples are not representative

of the population involved. It was the not the intention of this research to work out what

proportion of the trucking population gives a particular response, but rather to obtain an

idea of the range of responses on the attitudes, beliefs and intentions of driving at level

crossings. With the involvement of small, medium and large trucking companies in the

research, this assisted with ensuring the sample was representative.

6.4.8 Procedure and response rate

6.4.8.1 Older drivers

Of the 1000 letters that were sent by RACQ, 186 members telephoned the project

officer to provide contact details for sending the first questionnaire (Time 1). This

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response rate equated to 18.6%. Participants were then randomly selected, using a table

of random numbers, to either the intervention or control group. The first questionnaire

was sent in mid-July 2006 to the 186 older drivers that agreed to participate. This

questionnaire also included an Information Sheet. Of the 186 questionnaires sent at Time

1, 152 completed questionnaires were returned. The response rate for Time 1 was

therefore 15.2% (response rate of 81.7% for those that originally agreed to participate).

Six (6) weeks after receiving the completed Time 1 questionnaire from participants, a

research assistant with a degree in Psychology telephoned participants and played the

road safety radio advertisement intervention or control message. Telephoning took place

both during the day and in the evening to capture as many participants as possible.

During this telephoning period (one week), 137 participants were able to be contacted.

The response rate was therefore 13.7% (response rate of 73.7% for those that originally

agreed to participate). The intervention and control messages are provided in Appendix 7.

The Time 2 questionnaire (post-intervention) was sent to the 137 participants that

listened to either the control or intervention radio advertisement. This was sent 4 weeks

after being exposed to the advertisement. There were 109 participants that completed and

returned this second questionnaire, which was a response rate of 10.9% (response rate of

58.6% for those that originally agreed to participate).

6.4.8.2 Younger drivers

In December 2006, a letter was sent from the RACQ supporting this research was

sent by post to 1000 of its younger members aged 17-24 years. Members were informed

of an online survey (two in total) that would take approximately 20 minutes to complete.

This survey was developed by an external company that had been employed by the

Centre for Accident Research and Road Safety – Qld (CARRS-Q) previously. Incentives

for completing both online surveys were two movie tickets. The sample of participants

was divided into intervention and control groups by month of birth. With online surveys,

randomisation of participants is often difficult and therefore this method was deemed

most suitable. Therefore, participants born between January and June were allocated into

the intervention group. Participants born between July and December were allocated into

the control group.

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In December 2006, 149 younger drivers accessed the online survey and completed

the first questionnaire. There were 100 participants born between January and June

(intervention group) and 49 born between July and December (control group). This

equated to a response rate of 14.9%. At the completion of this first online questionnaire,

each participant listened to an online audio file either about safe driving at level crossings

or the wearing of seatbelts. Participants of the Time 1 questionnaire were sent an email 4

weeks after completion of the first questionnaire informing them it was time to complete

the second questionnaire (Time 2). The total response rate for this second questionnaire

was 88 (30 control group and 58 intervention group). A reminder email was sent two

weeks after this email to remind Time 1 participants to complete the second questionnaire

in order to receive the movie tickets. The response rate for this questionnaire from the

Time 1 questionnaire was 59.1%.

6.4.8.3 Heavy vehicle drivers

Five (5) companies that were invited to participate through the Queensland

Trucking Association agreed to participate in the research. The other five (5) companies

stated industrial relation issues and poor staffing as limiting them from participating.

During July through to September 2006, data collection sessions were arranged with each

of the five (5) participating truck companies. As well as the author, a research assistant

with a degree in Psychology collected the data. An information sheet was provided to

each participant with the Time 1 (pre-intervention) questionnaire. Consent was implied

with completion of the questionnaire. Time 1 questionnaires were completed by

participants in a designated quiet area (such as the lunch room) at the truck depot. Each

participant provided mailing details to send the post-intervention (Time 2) questionnaire

to. This method was deemed the most suitable for the trucking companies as it ensured

confidentiality of questionnaires. Forty-five (45) heavy vehicle drivers turned up to the

lunch room and said they would be happy to participate. Each driver was given an

Information Sheet to read and consent was implied by completing the questionnaire. All

participants were informed that no information could be traced by their company and that

the data would become aggregate data to be analysed.

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Once participants had completed the Time 1 questionnaire, they listened to the

intervention message. The intervention was a pilot radio road safety message specific for

driving at railway level crossings. As participants completed the questionnaire at different

times, often due to varying literacy levels, it was important to ensure that other

participants could not overhear the intervention being played. Some truck drivers

completed the questionnaire in 15 minutes while others took up to 45 minutes.

Therefore, an iPoD with earphones was used for each participant to listen to the radio

message. Each participant was then informed they would be receiving another

questionnaire in the mail. Depending on arrangements made with each of the truck

company’s operations manager, some participants were informed that they would receive

two movie tickets in the mail once both questionnaires were completed.

The Time 2 questionnaire (post-intervention) was sent to the 45 truck drivers that

completed the Time 1 questionnaire. This questionnaire was sent 4 weeks after

participants were exposed to the radio advertisement. A follow-up reminder was sent 2

weeks later to those drivers that did not return the Time 2 questionnaire. A total of 11

drivers completed and returned the Time 2 questionnaire. This response rate was

therefore 24.4%.

Table 20: Response rates compared across road user groups Road User Group

Letter sent

T1 sent

T1 received

T2 Sent

T2 received

Older drivers 1000 186 152 137 109 Younger drivers 1000 - 149 - 88 Heavy vehicle drivers - - 45 45 11

6.4.9 Data management

Data was entered using the Statistical Package for the Social Sciences (SPSS)

version 15.0. Before commencement of data analysis, all databases were cleaned to check

for accuracy of data entry, missing values and outliers. For both the heavy vehicle

drivers and older drivers, there was minimal missing data. However, for the younger

driver group, one instrument, ‘subjective norms’ had a significant number of missing data

for the first three items and therefore these three items were excluded from both Time 1

and Time 2 to ensure equal comparisons. As this road user group completed their

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questionnaires online, it is possible there was electronic saving problems for the page in

which this instrument was displayed.

The level of significance for measurement was set at p<.05. A more rigorous

significance level (α = .01) was used for any post-hoc comparisons, to guard against

inflation of the Type 1 error rate (i.e. rejecting the null hypothesis when it is true).

6.4.10 Dependent variables

One variable was selected to act as the dependent variable in order to operationalise

Fishbein’s integrated model: driving intention at level crossings (Time 1). Although

intentions are not a precise predictor of future behaviour, they are the primary

determinant of behaviour and signify a preparedness to engage in particular behaviours

(Fishbein et al., 2001). Since both the older and younger driver samples utilised an

experimental design (pre and post-testing), the variable ‘intention’ (Time 1) was used as

it was not contaminated by the effects of exposure to the intervention.

6.4.11 Data analysis

A combination of descriptive statistics, parametric testing and repeated measures

was undertaken for this final study. For the heavy vehicle group, both Analysis of

Variance (ANOVA) and paired sample t-tests were performed. Since the entire sample

of heavy vehicle drivers received the intervention (no control group), then paired sample

t-tests were the most appropriate statistic. Given that both the younger and older driver

groups were randomly allocated to either the intervention or control group, split-plot in

time analysis were used instead of paired sample t-tests to test the effect of exposure to

the intervention. Additionally, hierarchical regression was performed on these two

groups to predict which variables influence intention to drive safely at level crossings.

These regressions used Time 1 variables only, as the influence of exposure to the

intervention would have contaminated the prediction of intention.

One major assumption of the ANOVA is that each group is an independent random

sample from a normal population. For the heavy vehicle sample, it was impossible to

randomly select groups (i.e. companies) from the normal population as companies needed

to give their permission for their drivers to participate in the research. Many trucking

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companies that were approached declined to participate in the research and therefore non-

probability purposive sampling was used. Due to the variety of trucking companies that

participated in the research, the sample was deemed representative (i.e. characteristics

correspond to, or reflect, those of the original population or reference population). An

alpha level of .05 was used for all ANOVA’s and r was calculated as the effect size

(Rosenthal, 1991).

A paired sample t-test is used to verify the significance of the difference between

two sets of paired data (with no control group). This t-test is used to compare two

population means where there are two samples in which observations in one sample can

be paired with observations in the other sample (Shier, 2004). A typical example of this is

the pre and post-test observations on the same subjects (Colman, 1995). Although

attrition was high for the heavy vehicle sample between Time 1 (n=45) and Time 2

(n=11), statistical advice indicated that paired sample t-tests could be conducted on these

two time points. Additionally, as regression can only be applied to sample sizes larger

than 50, it was not able to be applied to the heavy vehicle dataset. However, hierarchical

regression was able to be applied to both the younger and older driver samples. In

hierarchical regression, independent variables are not inserted into the model all at once,

but rather they are entered into the model in an order specified by the researcher (or

informed by theory). By doing this, each predictor variable (independent variable) can be

assessed in terms of what it adds to the model at its own point of entry (Tilley, 1990).

This type of regression assists in determining which factors/constructs may satisfy or

increase the intention/behaviour relationship.

6.5 RESULTS

6.5.1 Heavy vehicle drivers

6.5.1.1 Company characteristics

Four trucking companies agreed to participate in the current study. Due to

confidentiality agreements with each of the trucking companies, pseudonyms are

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provided for each company. All of the companies had large depots located in Brisbane.

A profile of each trucking company is provided below.

• ‘Abbotts’ is one of the pioneer companies of integrated logistics in Australia as

well as being an industry leader in the provision of warehousing, transportation

and supply chain management. It has a fleet of over 4200 vehicles traveling to and

from more than 250 sites throughout Australia. This fleet is considered to be a

large size fleet.

• ‘Carters’ provides refrigerated transport services throughout Australia including

local distribution in Sydney and Brisbane. Its fleet consists of over 140 company

owned prime movers, 180 refrigerated vans and 35 rigid trucks. This fleet is

considered to be a medium size fleet.

• ‘Rogers’ is a leading provider of transport and third party warehousing and

distribution services throughout Australia. Roger’s line haul fleet of interstate

prime movers covers all capital cities as well as regional areas in Queensland.

This company fleet consists of around 50 prime movers, 60 flat-top trailers, 15

skel trailers, 18 drop-deck trailers, 15 curtain-sided trailers , 6 prairie wagon

trailers and 100 containers. This fleet is considered to be a medium size fleet.

• ‘Thompsons’ is a smaller private enterprise that provides freight transport

throughout Brisbane, Gympie, Hervey Bay, Maryborough and Bundaberg, as well

as to Sydney. More specific information about this company was not released by

company managers as it was deemed confidential. This fleet is considered to be a

small size fleet.

6.5.1.2 Sample attrition

The attrition rates for participants from each of the companies are illustrated in the

table below. Abbott’s had the best retention rate of participants, while Thompson’s had

the poorest retention rates.

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Table 21: Company attrition Time 1 Time 2

Company n % n % Abbotts 13 28.9 5 45.5 Thompsons 6 13.3 1 9.1 Rogers 13 28.9 4 36.3 Carters 13 28.9 1 9.1 Total 45 100.0 11 100.0

6.5.1.3 Demographics

At Time 1, the mean age for drivers was 47.4 years (S.D. 9.203), with the youngest

participant being 30 years and the oldest participant being 64 years. By Time 2, the mean

age of participants increased to 51.4 years (S.D. 7.061). The table below compares the

means of the four companies at both time points.

Table 22: Mean age between companies Time 1 Time 2

Company Mean Age Mean Age ‘Abbott’s 47 50 ‘Thompsons’ 50 49 ‘Rogers’ 49 53 ‘Carters’ 45 55

The majority of participants were full-time workers at Time 1 (93.3%), with 42.2%

stating that they work shift work. Most of the participants had a MC (B-double or road

train) licence (66.7%), drove between 4-6 days per week (71.1%), and drove between 51-

72 hours per week (62.2%). Nearly 16% (n=7) of drivers reported driving more than 5000

kilometres per week, while 26.7% reported driving less than 1000 kilometres per week.

A mean of 25.2 years (S.D. 10.724) was observed for number of years licensed, with a

range of 2 to 40 years.

ANOVA’s were also calculated to examine differences between shift workers and

each of the driving instruments. Results are presented in the table below.

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Table 23: Mean overall scores between shift work and driving at level crossings Instrument Shift Work n df Mean F Sig DBQ Shift work 18 1, 41 0.70 3.398 0.72 No shift work 25 0.55 Self-reported driving behaviour Shift work 19 1, 43 1.31 4.330 0.043 No shift work 26 1.16 Intended driving behaviour Shift work 18 1, 42 1.40 11.984 0.001 No shift work 26 1.08 Subjective norms Shift work 18 1, 42 5.82 0.027 0.871 No shift work 26 5.87 Attitude Shift work 18 1, 42 5.43 0.027 0.871 No shift work 26 5.48 Perceived behavioural control Shift work 19 1, 43 5.81 .495 .486 No shift work 26 5.62 Beliefs about environmental constraints Shift work 19 1, 42 2.36 6.641 0.014 No shift work 25 1.97

A significant effect was found for three of the driving instruments. An effect was

revealed for reported driving behaviour F (1, 43) = 4.330, p <0.05, intended driving

behaviour F (1, 42) = 11.984, p <0.01, and beliefs about environmental constraints F (1,

42) = 6.641, p <0.05. It appears that truck drivers that reported working shifts are

significantly more likely to take risks when driving generally and at level crossings, and

hold beliefs that environmental constraints affect their driving at level crossings.

6.5.1.4 Crash history

Nine participants (20.0%) reported at Time 1 of having been involved in a road

crash whilst driving a truck during the past 3 years. Table 24 illustrates the number of

drivers from each company involved in a road crash during the past 3 years. As can be

seen, there was very little difference between the numbers of crashes from each company,

however, three out of the six Thompson’s participants (50%) reported being involved in a

crash.

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Table 24: Crash involvement between companies Company Involved in Crash *

(n) Total Participants

(n) Abbott’s 2# 13 Thompson’s 3 6 Roger’s 2 13 Carter’s 2# 13 Total 9 45

N.B. * denotes being involved in a crash during past 3 years. # denotes one participant being involved in 2 crashes during past 3 years.

Of those participants who reported being involved in a crash during the past 3

years, seven (77.7%) reported being involved in one road crash, whilst two participants

(22.3%) reported that they were involved in two road crashes. Interestingly, no

participant who had been involved in a road crash stated that it was their fault. Six

(66.6%) participants stated that the crash was ‘not at all’ their fault, while the other three

participants did not answer the question. Additionally, no participant reported that they

had been involved in a level crossing collision. Table 25 illustrates the seriousness and

type of road crashes that participants reported being involved in.

Table 25: Road crashes during past 3 years Crash seriousness and type n % Seriousness Damage only 7 77.7 Slight injury (to any person) 0 0 Serious injury (to any person) 1 11.1 Fatality (to any person) 1 11.1 Level crossing collision 0 0 Type Head on collision with another vehicle 0 0 Rear-end collision with another vehicle 1 11.1 Angular (i.e. side-on) collision with another vehicle 3 33.3 Collision with a pedestrian 0 0 Collision with another object (e.g. parked car, animal, tree) 1 11.1

Overturned vehicle 1 11.1

Participants were also asked at Time 1 what they believed were contributing factors

to their road crash during the past 3 years, with the scale of 0=Not at all true,

1=Somewhat true and 2=Very true. Four (44.4%) participants stated that they ‘couldn’t

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see the other vehicle or object’, while two (22.2%) participants stated that they were

‘listening to music or the CB radio’ and two (22.2%) stated that they were ‘distracted by

something outside the vehicle’. Participants could give more than one answer to being a

contributing factor.

Table 26: Contributing factors to road crash

Type n % Been drinking alcohol before driving 0 0 Felt tired 0 0 Driving too fast for the conditions 0 0 Talking to a passenger in the vehicle 0 0 Listening to music or the CB radio 2 22.2 Talking on a mobile phone 0 0 Trying to pick up something from the seat or floor 0 0 Adjusting the radio/cassette/CD or fan/air conditioning 0 0 Checking the instruments (e.g. fuel gauge or speedometer) 1 11.1 Daydreaming 0 0 Talking on the CB radio 1 11.1 Distracted by something inside the vehicle 0 0 Distracted by something outside the vehicle 2 22.2 Couldn’t see the other vehicle / object 4 44.4

N.B. n=participants that reported contributing factor being ‘somewhat true’ or ‘very true’.

6.5.1.5 Knowledge of level crossing road rules

Less than a third of participants (n=14) answered all four questions correctly at the

first time point. Eleven (24.4%) participants answered only two questions correctly at

Time 1, which decreased to 9.1% (n=1) at Time 2. Table 27 below shows the differences

between time points.

Table 27: Knowledge of level crossing rules and facts Time 1 Time 2 Type

n % n % All questions correct 14 31.1 3 27.3 Three questions correct 20 44.4 7 63.6 Two questions correct 11 24.4 1 9.1 No correct questions 0 0 0 0

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6.5.1.6 General driving behaviour

Mean scores for the modified Driver Behaviour Questionnaire (DBQ) are presented

in the table below. All self-reported mean scores were relatively low, indicating that

participants believe that they are driving safely most of the time.

Table 28: Modified DBQ Time 1 Time 2

Company n Mean n Mean Abbotts 13 .57 5 .77 Thompsons 6 .65 1 .83 Rogers 11 .57 3 .83 Carters 13 .68 1 .32

1=Not at all to 5=Very often

Results of paired sample t-tests indicate that there was no significance difference in

the pre and post-intervention paired data sets for general driving behaviour (i.e. DBQ)

before the intervention (M=0.70, S.D.=0.370) to after the intervention (M=0.73,

S.D.=0.420), t(9) = -.563, p = 0.589.

6.5.1.7 Self-reported driving behaviour at level crossings

Mean scores of the reported driving behaviour at level crossings at Time 1 and

Time 2 is presented in Table 29 below. Carters had the highest mean scores at both Time

1 and Time 2. Results of paired sample t-tests indicate that there was no significance

difference in the pre and post-intervention paired data sets for self-reported driving

behaviour at level crossings of truck drivers before the intervention (M=1.22,

S.D.=0.370) to after the intervention (M=1.20, S.D.=0.242), t(11) = 2.12, p = 0.836.

Table 29: Self-reported driving behaviour at crossings Time 1 Time 2

Company n Mean n Mean Abbotts 13 1.15 5 1.13 Thompsons 6 1.18 1 1.00 Rogers 13 1.15 4 1.24 Carters 13 1.38 1 1.63

1=Not at all to 5=Very often

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6.5.1.8 Intended driving behaviour at level crossings

Mean scores the intended driving behaviour at level crossings at both Time 1 and

Time 2 is presented in the Table 30 below. Like self-reported driving behaviour, means

were relatively low, with Carters being the highest at both time points. Results of paired

sample t-tests indicate that there was no significance difference in the pre and post-

intervention paired data sets for self-reported intended driving behaviour at level

crossings of truck drivers before the intervention (M=1.26, S.D.=0.497) to after the

intervention (M=1.29, S.D.=0.437), t(11) = -0.112, p = 0.913. Table 30: Intended driving behaviour at crossings

Time 1 Time 2 Company n Mean n Mean Abbotts 13 1.06 5 1.11 Thompsons 5 1.05 1 1.00 Rogers 13 1.24 4 1.50 Carters 13 1.39 1 1.63

1=Not at all likely to 5=Very likely

Bivariate correlations were also undertaken to examine the relationships between

driving intention at level crossings (Time 1) and each of the independent variables

(outcome variables). As shown in Table 31, there was a weak positive correlation

between the driver behaviour questionnaire and driving intention at level crossings (Time

1) [r = .315, p < .05]. This indicated that participants that drive unsafely on the roads

generally are more likely to drive unsafely at level crossings. A significant negative

correlation was found between shift-work and driving intention at level crossings (Time

1) [r = -.471, p < .01]. In other words, participants that worked shift-work were more

likely to hold strong intentions to drive unsafely at level crossings. Additionally, a strong

positive correlation was found for kilometres per week drive and driving intention at level

crossings (Time 1) [r = .405, p < .01] which indicates that truck drivers that drive greater

distances per week have stronger intentions to drive unsafely at level crossings.

Moreover, a significant correlation was found for perceived risk of a level crossing

collision and driving intention at level crossings (Time 1) [r = .630, p < .01]. In other

words, participants that had stronger intentions to drive unsafely at level crossings were

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more likely to have higher perceptions of risk with being involved in a collision at a level

crossing. As predicted, self-reported driving behaviour (Time 1) was significantly

correlated with intended driving behaviour (Time 1) [r =.647, p < .01]. Since the

sample size at Time 1 (n=45) dropped considerably at Time 2 (n=11), correlating

independent variables with self-reported driving behaviour at level crossings (Time 2)

would have most likely produced unreliable correlation data and therefore excluded from

bivariate correlations.

Table 31: Bivariate correlations between dependent and independent variables

Dependent Variable

Independent variables

Driving Intention at Level Crossings (T1)

Age -.134 Years held licence -.112 Shift-work -.471 ** Kilometres per week drive .405 ** Crashes during past 3 years .017 Driver behaviour questionnaire .315 * Attitudes towards driving at level crossings -.201 Self-efficacy of driving at level crossings -.228 Normative beliefs of others driving at level crossings -.033 Beliefs of environmental constraints whilst driving at level crossings .153 Perceived risk of crash at a level crossing .630 ** Driving intention at level crossings (T1) - Self-reported driving behaviour at level crossings (T1) .647 **

* p < .05 ** p < .01 *** p < .001

6.5.1.9 Attitudes towards driving at level crossings

The first eight items from the 15-item instrument with bipolar scales (i.e. -3 to +3)

directly measured attitudes towards driving at level crossings. As stated previously, each

of the bipolar scales was recoded using unipolar (1-7) scales. Participants were asked to

complete statements by rating pairs of adjectives whilst driving at level crossings (e.g.

bad-good, more confusing-less confusing). Mean scores indicated that at Time 1 their

was generally a positive attitude towards driving at level crossings, however at Time 2,

two companies (Rogers and Carters) recorded a less positive attitude towards driving at

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level crossings. Table 32 below illustrates the mean scores of each company at both time

points.

Results of paired sample t-tests indicate that there was no significance difference in

the pre and post-intervention paired data sets for self-reported attitudes towards driving at

level crossings of truck drivers before the intervention (M=4.67, S.D.=1.571) to after the

intervention (M=4.81, S.D.=1.457), t(9) = -0.353, p = 0.733.

Table 32: Attitudes towards driving at level crossings Time 1 Time 2

Company n Mean n Mean Abbotts 12 5.33 5 5.25 Thompsons 6 5.73 1 5.38 Rogers 13 5.12 2 3.44 Carters 13 5.11 1 4.75

1=Negative attitude to 7=Positive attitude

6.5.1.10 Perceived behaviour control whilst driving at level crossings

The second seven items from the 15-item instrument with bipolar scales (i.e. -3 to

+3) directly measured perceived behavioural control (self-efficacy) whilst driving at level

crossings. As stated previously, each of the bipolar scales was recoded using unipolar (1-

7) scales. Participants were asked to complete statements by rating pairs of adjectives

whilst driving at level crossings (e.g. not up to me-up to me, out of my control-under my

control). Mean scores at Time 1 indicated that generally participants believe that they

have control over their driving behaviour at level crossings, however mean scores for

three of the companies dropped at Time 2.

Table 33: Perceived behavioural control whilst driving at crossings Time 1 Time 2

Company n Mean n Mean Abbotts 13 5.58 5 5.60 Thompsons 6 6.05 1 5.57 Rogers 13 5.32 2 4.71 Carters 13 6.03 1 7.00

1=Negative attitude to 7=Positive attitude

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Results of paired sample t-tests indicate that there was no significance difference in

the pre and post-intervention paired data sets for self-reported self-efficacy whilst driving

at level crossings of truck drivers before the intervention (M=5.14, S.D.=1.467) to after

the intervention (M=5.56, S.D.=0.777), t(9) = -1.010, p = 0.342.

6.5.1.11 Subjective norms of others

Participants were asked a range of questions of their beliefs about how their

colleagues, friends and other motorists drive at level crossings. The means scores

relating to the safe driving behaviour of other motorists was substantially worse than

participant’s colleagues or their friends. This is illustrated with the mean score of ‘other

motorists generally obey the rules at level crossings’ (T1=2.68, T2=3.09) being quite a lot

higher than ‘your colleagues generally obey the rules at level crossings’ (T1=1.55,

T2=1.64) and ‘your friends generally obey the rules at level crossings’ (T1=1.55,

T2=1.82). Mean scores for ‘it is generally possible to judge a train’s speed’ (T1=4.44,

T2=4.00) indicated that participants believe that it is generally not possible to judge a

trains speed. However, the mean score for ‘it is generally safe to disobey the rules at

level crossings’ (T1=3.57, T2=3.91) indicated that some participants believe that they can

disobey the rules at level crossings and not be at risk of a collision with a train. Table 35

below displays the means scores for each of the items in this instrument at Time 1 and

Time 2.

Results of paired sample t-tests indicate that there was no significance difference in

the pre and post-intervention paired data sets for the subjective norms of others (i.e.

friends, colleagues and other motorists) before (M=2.70, S.D.=0.500) or after the

intervention (M=2.80, S.D.=0.469), t(7) = -0.498, p = 0.636.

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Table 34: Mean scores for subjective norms of others Time 1 Time 2

Item n Mean n Mean Your colleagues generally obey the rules at level crossings 42 1.55 11 1.64

Your friends generally obey the rules at level crossings 42 1.55 11 1.82

Other motorists generally obey the rules at level crossings 40 2.68 11 3.09

Your colleagues generally think it important to obey the rules at level crossings 41 1.41 11 1.64

Your friends generally think it important to obey the rules at level crossings 41 1.46 11 1.64

Other motorists generally think it important to obey the rules at level crossings 42 2.45 11 2.45

It is generally safe to disobey the rules at level crossings 42 3.57 11 3.91

It is generally possible to judge a train’s speed 41 4.44 11 4.00It is generally safe to cross if you can’t see a train, even if the lights are flashing 42 4.10 11 4.27

It is generally safe to roll slowly through a crossing instead of stopping 42 4.02 10 3.80

Trains generally run to a regular timetable 42 3.45 10 4.20Penalties need to be tougher for violating road rules at level crossings 42 2.17 10 1.70

The main deterrent for breaking the rules at level crossings is fear of getting caught 41 3.41 11 2.64

Generally it is more important to use common sense at level crossings than strictly follow the road rules 42 3.31 10 2.90

1=Strongly agree to 5=Strongly disagree

6.5.1.12 Perceived risk of a level crossing collision

Participants were asked about their perception of how likely it is that they would be

involved in a collision at a level crossing whilst driving a truck. The table below

illustrates that at all time points, the mean scores were extremely low, indicating that

most participants believe that they will never be involved in a level crossing collision.

Table 35: Belief of the likelihood of being involved in a level crossing collision Time 1 Time 2

Belief N Mean n Mean Likelihood of collision with train 45 0.33 11 0.09

0=Not at all likely to 5=Very likely

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Results of paired sample t-tests indicate that there was no significance difference in

the pre and post-intervention paired data sets of perception of being involved in a level

crossing collision before the intervention (M=0.45, S.D.=1.214) to after the intervention

(M=0.09, S.D.=0.302), t(11) = 0.938, p = 0.371.

6.5.1.13 Environmental constraints whilst driving at level crossings

The items ‘blinding sun makes it difficult to see if the red flashing lights are

activated’ (T1=3.16, T2=2.73), ‘other drivers (such as cars) do stupid things that put you

in a dangerous situation’ (T1=2.98, T2=2.55) and ‘the design of the road is an S bend and

it is difficult to see if a train is approaching or at the railway crossing’ (T1=2.71,

T2=2.82), scored the highest means for both time points. Formative research with truck

drivers had indicated that these were significant issues whilst driving a truck at level

crossings, which supports results from this current sample. Table 36: Beliefs of design and environmental factors at level crossings

Time 1 Time 2

Belief n Mean n Mean

Blinding sun makes it difficult to see if the red flashing lights are activated 45 3.16 11 2.73

The design of the road is an ‘S’ bend and it is difficult to see if a train is approaching or at the railway crossing 45 2.71 11 2.82

The height of the truck’s cabin makes it difficult to see a train or the warning systems 45 1.53 11 1.55

Warning systems on the road approaching the crossing are not adequate to inform trucks there is a railway crossing ahead 45 2.49 11 2.00

The mass of the truck makes it difficult to brake in time to stop at a railway crossing 45 1.96 11 2.18

Road surface is often poor and it is difficult to stop 44 2.30 11 2.27Boom gates and/or flashing lights are often faulty 45 2.09 11 1.91Other drivers (such as cars) do stupid things that put you in a dangerous situation 45 2.98 11 2.55

Intersections ahead of a railway crossing often cause your truck to overhang the tracks 45 1.84 11 1.36

Noise from the truck’s engine is too loud to hear an approaching train 45 1.80 11 1.73

When you have to stop at a railway crossing, it takes a long time before your truck is able to get over the crossing 45 2.16 10 2.40

All ‘Stop’ signs should be changed to ‘Give Way’ signs at railway crossings as these are easier for trucks to get through the crossing

45 1.80 11 2.00

My truck has stalled on the tracks at a railway crossing 45 1.09 11 1.00(1=never, 2=almost never, 3=sometimes, 4=almost always, 5=always)

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Not surprisingly, results of paired sample t-tests indicate that there was no

significance difference in the pre and post-intervention paired data sets of beliefs of

participants about the design and environmental factors influencing safety at level

crossings before (M=2.29, S.D.=0.501) or after the intervention (M=1.98, S.D.=0.380),

t(10) = 1.754, p = 0.113. Although participants were provided with information

acknowledging possible environmental/design difficulties they may face when driving a

truck at level crossings, there was very little difference in beliefs regarding their

perceived environmental influences from being exposed to the intervention message.

6.5.1.14 Familiarity with different protection systems

Approximately 20% (n=10) of participants reported never driving through level

crossings with boom gates whilst driving for work, while 40% of drivers reported driving

through them on a daily basis. Driving through passive crossings with flashing lights or

just a stop/give-way sign was less common than driving through boom gates. Over a

third of participants reported that they never drive over any type of passive crossing

whilst driving a truck. Table 37 illustrates participant reported exposure to driving at

level crossings.

Table 37: Exposure to level crossing driving

Boom gates

Flashing Lights only

No lights or boom gate Type n % n % n % Daily 18 40.0 9 20.0 12 26.7Weekly 12 26.7 8 17.8 5 11.1Monthly 5 11.1 9 20.0 8 17.8Yearly 0 0 3 6.7 5 11.1Never 10 22.2 16 35.6 15 33.3

The three items in the questionnaires were then recoded to compare those

participants that either drive over active crossings or passive crossings regularly. Those

participants that stated they never or yearly drove over either active or passive crossings

were combined to form a group of being unfamiliar with that type of crossing whilst

driving a truck. The recoding of these variables is used for the inferential statistics.

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A series of one-way ANOVA’s was then calculated to examine differences between

familiarity with level crossings (boom gates, flashing lights or passive) in relation to each

of the driving instruments.

Table 38: Mean scores for familiarity at level crossings (boom gates) Instrument Familiarity N df Mean F Sig DBQ Familiar 34 1, 41 0.65 3.284 .770 Unfamiliar 9 0.47 Self-reported driving behaviour Familiar 35 1, 43 1.27 4.918 .320

Unfamiliar 10 1.07 Intended driving behaviour Familiar 34 1, 42 1.21 .005 .946 Unfamiliar 10 1.22 Subjective norms Familiar 34 1, 42 5.89 0.250 .620 Unfamiliar 10 5.71 Attitude Familiar 34 1, 42 5.25 .003 .953 Unfamiliar 10 5.28 Perceived behavioural control Familiar 35 1, 43 5.79 1.499 .227 Unfamiliar 10 5.39 Beliefs about environmental constraints Familiar 34 1, 42 2.17 0.683 .413

Unfamiliar 10 2.02

There were no significant results in relation to drivers that are familiar with boom

gated crossings and any of the driving instruments.

Table 39: Mean scores for familiarity at level crossings (flashing lights)

Instrument Familiarity N df Mean F Sig DBQ Familiar 25 1, 41 0.69 5.534 .024 Unfamiliar 18 0.50 Self-reported driving behaviour Familiar 26 1, 43 1.31 8.591 .005

Unfamiliar 19 1.10 Intended driving behaviour Familiar 25 1, 42 1.26 1.267 .267 Unfamiliar 19 1.14 Subjective norms Familiar 25 1, 42 6.09 3.754 .059 Unfamiliar 19 5.52 Attitude Familiar 19 1, 42 5.40 .829 .368 Unfamiliar 25 5.07 Perceived behavioural control Familiar 26 1, 43 6.03 9.355 .004 Unfamiliar 19 5.25 Beliefs about environmental constraints Familiar 26 1, 42 2.25 3.215 .080

Unfamiliar 18 1.97

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A significant effect for the driver behaviour questionnaire F (1, 41) = 5.534, p

<0.05, self-reported driving behaviour at level crossings F (1, 43) = 8.591, p <0.05 and

self-efficacy whilst driving at level crossings F (1, 43) = 9.355, p <0.05 were revealed.

This indicated that truck drivers that were familiar with driving at crossings with only

flashing lights recorded higher levels of unsafe driving at level crossings generally,

slightly higher risk taking driving behaviour and higher levels of self-efficacy/perceived

behavioural control than unfamiliar drivers.

Table 40: Mean scores for familiarity at level crossings (passive signs only)

Instrument Familiarity n df Mean F Sig DBQ Familiar 24 1, 41 0.66 1.978 .167 Unfamiliar 19 0.55 Self-reported driving behaviour Familiar 25 1, 43 1.27 1.567 .217

Unfamiliar 20 1.17 Intended driving behaviour Familiar 24 1, 42 1.20 0.013 .909 Unfamiliar 20 1.22 Subjective norms Familiar 24 1, 42 5.89 0.093 .762 Unfamiliar 20 5.80 Attitude Familiar 24 1, 42 5.26 0.000 .998 Unfamiliar 20 5.26 Perceived behavioural control Familiar 25 1, 43 5.79 0.533 .469 Unfamiliar 20 5.59 Beliefs about environmental constraints Familiar 25 1, 42 2.27 3.867 .056

Unfamiliar 19 1.96

There were no significant results in relation to drivers that are familiar with passive

crossings (stop or give-way sign only) and any of the driving instruments.

6.5.1.15 Intervention message recall

All participants received an intervention message immediately after completion of

the first questionnaire (Time 1). The second questionnaire (Time 2) asked participants if

they recalled the radio message they received about level crossings, what slogans or

information they recalled (if any) and how likely it was that their driving behaviour was

influenced from receiving the message. At Time 2 (post-test), five participants recalled

having received the intervention message about level crossings, while four participants

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stated that they received a message about fatigue. As there was no intervention message

about fatigue, these four participants obviously didn’t recall the intervention at all.

However, no participant could recall any slogans or information provided to them in the

intervention about level crossings.

6.5.2 Older drivers

6.5.2.1 Sample attrition

At Time 1, 152 participants completed questionnaires, with this figure dropping to

109 at Time 2. The attrition rates for intervention and control groups are displayed in the

table below. Additionally, this table illustrates the attrition for metropolitan and

rural/remote participants. Interestingly, the number of participants in intervention and

control groups remained equal at both time points. With regards to area classification, at

Time 1 there was similar percentages of metropolitan (55.3%) and rural/remote (44.1%)

participants.

Table 41: Participant group and area classification at both time points Time 1 Time 2

n % n % Group Intervention 76 50.0 54 49.5 Control 76 50.0 55 50.5 Total 152 - 109 - Area Classification Metropolitan 84 55.3 70 64.2 Rural/remote 67 44.1 39 35.8 Missing 2 0.7 0 0.0

6.5.2.2 Demographics

At Time 1, the mean age for participants was 69.2 years (S.D. 6.842), with the

youngest participant being 60 years and the oldest participant being 89 years. At Time 2

the mean age of participants was approximately the same (69.4 years). At Time 1, more

than two-thirds of participants were male (69.1%) and 21.7% of participants reported still

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being in paid work. Approximately half of all participants reported having a condition on

their drivers licence as described in Table 42 below.

Table 42: Licence conditions as reported by participants Time 1 Condition n % No condition 77 50.7 S condition only (corrective lenses required) 46 30.3 M condition only (medical certificate require) 12 7.9 Both S & M conditions 16 10.5 Missing 1 0.7

Older drivers were also asked about their any medical conditions that they suffer

from. Nearly half of the sample (42.2%) reported high blood pressure and a fifth (22.4%)

reported either some hearing loss or deafness. Twenty-seven participants (17.8%)

reported either neck, back or limb disorders. With safe driving at level crossings

involving both listening for trains and movement of the neck to scan for trains, it is of

concern that so many participants in this sample reported medical conditions that restrict

both of these behaviours. This may be one of the factors that is important in the over-

representation of older drivers in level crossing collisions.

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Table 43: Medical conditions suffered by participants Time 1 Medical Condition n % High blood pressure 64 42.1 Heart disease 16 10.5 Chest pain/Angina 19 12.5 Any condition requiring heart surgery 11 7.2 Palpitations/Irregular heart beat 14 9.2 Head/Spinal injury 8 5.3 Seizures, fits, convulsions or epilepsy 1 0.7 Abnormal shortness of breath 4 2.6 Blackouts or fainting 2 1.3 Stroke 2 1.3 Dizziness or vertigo 10 6.6 Double vision 3 2.0 Colour blindness 1 0.7 Kidney disease 3 2.0 Diabetes 10 6.6 Neck, back or limb disorders 27 17.8 Hearing loss or deafness 34 22.4

6.5.2.3 Self-assessment of driving ability

Participants were asked a range of questions relating to their driving ability at Time

1. All mean scores for driving ability were relatively good (i.e. close to 0) indicating that

participants in general believe that have no problems with driving. The highest mean

observed was ‘having more trouble adjusting to glare and/or night driving than you did

previously’ (1.34), while the mean for ‘have regular health and vision checks’ was 3.82.

It appears that vision is of the greatest driving concern from these items, yet not all

drivers are having regular check-ups. Table 44 below illustrates the means for each of

the items in this instrument.

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Table 44: Driving ability Item n Mean Reactions to unexpected situations slower than they used to be 151 1.06Have trouble judging the distance of other vehicles, or changing focus from your instrument panel to the road 152 0.27

Having more trouble adjusting to glare and/or night driving than you did previously 152 1.34Ever get surprised by pedestrians or other vehicles coming from your left or right while you are focusing straight ahead 152 0.55

Some traffic situations or other drivers upset you 151 1.21Have trouble driving through, or turning at busy intersections or roundabouts 152 0.36Feel uncomfortable driving in unfamiliar territory 152 1.06Find that you are easily distracted or that your thoughts wander while you are driving 152 0.53Have regular health and vision checks 152 3.82

0=Not at all to 5=Very often

Health and vision checks was then recoded to be aligned with the other items (i.e.

‘Very often’ was recoded to 0 and ‘Not at all’ was recoded to 5). This ensured that those

participants that reported having regular checks would receive a low score for this

question when the total instrument score was calculated. The total instrument score for

each participant was then recoded into three categories: green rating (0-5), yellow rating

(6-15) and red rating (16+). These categories represented the self-reported driving ability

of participants (green=no problems driving, yellow=some problems driving, red=quite a

lot of problems driving). The purpose of recoding into three categories was to compare

ratings between gender and rurality. Table 45 below demonstrates the differences

between gender and rurality. Percentages of ratings are shown as being within the group

(i.e. gender or rurality).

Table 45: Driving self-assessment ratings between groups Green Rating Yellow Rating Red Rating Total

Participants in Group

(n) n % n % n %

Gender Male 105 39 37.5 59 56.7 6 5.8 Female 47 12 26.1 31 67.4 3 6.5 Rurality Metropolitan 84 28 33.7 51 61.4 4 4.8 Rural/remote 67 23 34.8 39 59.1 4 6.1

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From Table 45 it is clear that a greater percentage of male participants report

having very few problems driving (green rating =37.5%) compared to female participants

(green rating=26.1%). There was little difference in ratings between metropolitan and

rural/remote participants. Analysis of variances was also undertaken to examine

differences between gender and rurality and the mean scores for this instrument. No

significant effect was found for gender and self-reported driving ability F (1, 149) =

0.974, p =.325 or rurality and self-reported driving ability F (2, 149) = 2.241, p =.110.

6.5.2.4 Crash history

At Time 1, 29 participants in total reported having been involved in a road crash

during the past 3 years. Table 46 below illustrates the differences between rural/remote

and metropolitan driver crash involvement.

Table 46: Comparison of crash involvement and rurality at Time 1 Rurality

Involved in a crash

(n)

Involved in a crash (%)

Total participants

(n) Metropolitan 20 23.8 84 Rural/remote 9 13.4 67 Missing - - 1 Total 29 19.1 152

Metropolitan drivers (23.8%) were more likely to have been involved in a collision

during the past 3 years than rural/remote drivers (13.4%), which is not surprising given

the larger driver population in metropolitan areas. Five participants reported having been

involved in 2 collisions, while one participant reported having been involved in three

collisions during the past 3 years. One metropolitan participant reported having been

involved in a level crossing collision with a fatality occurring in that collision. The

majority of drivers that reported having been involved in a collision during past 3 years

indicated that it was damage only (89.7%) and an angular/side on collision (44.8%).

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Table 47: Road crashes during past 3 years Time 1 n % * Seriousness Damage only 26 89.7 Slight injury (to any person) 1 3.4 Serious injury (to any person) 0 0 Fatality (to any person) 1 3.4 Level crossing collision 1 3.4 Type Head on collision with another vehicle 0 0 Rear-end collision with another vehicle 10 34.5 Angular (i.e. side-on) collision with another vehicle 13 44.8 Collision with a pedestrian 0 0 Collision with another object (e.g. parked car, animal, tree) 5 17.2 Overturned vehicle 0 0

* Percentage of drivers that reported having been involved in a crash during past 3 years (i.e. 29 drivers in total)

Self-reported contributing factors to these collisions revealed that five drivers

(17.2%) were distracted by something outside the vehicle, two were daydreaming (6.8%)

and two were listening to the radio (6.8%). Table 48 below illustrates the self-reported

contributing factors to road crashes during the past 3 years.

Table 48: Self-report contributing factors to road crash Time 1 Type n %* Been drinking alcohol before driving 0 0 Felt tired 1 3.4 Driving too fast for the conditions 0 0 Talking to a passenger in the vehicle 1 3.4 Listening to the radio 2 6.8 Talking on a mobile phone 0 0 Trying to pick up something from the seat or floor 0 0 Adjusting the radio/cassette/CD or fan/air conditioning 0 0 Checking the instruments (e.g. fuel gauge or speedometer) 0 0 Daydreaming 2 6.8 Distracted by something inside the vehicle 1 3.4 Distracted by something outside the vehicle 5 17.2

* Percentage of the 29 drivers that reported being involved in a crash during past 3 years.

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Analysis of variances was also undertaken to examine differences between

participants that reported being involved in a road crash during the past 3 years and each

of the driving instruments at Time 1.

Table 49: Mean overall scores between road crashes and driving instruments

Instrument Crash History n df Mean F Sig DBQ Crash 28 1, 142 0.42 .008 .931 No crash 115 0.43 Self-reported driving behaviour Crash 29 1, 140 0.10 .512 .476

No crash 112 0.13 Intended driving behaviour Crash 26 1, 137 0.11 .707 .402 No crash 112 0.14 Subjective norms Crash 26 1, 136 3.80 2.459 .119 No crash 111 3.62 Attitude Crash 26 1, 140 5.57 .334 .564 No crash 115 5.43 Perceived behavioural control Crash 26 1, 142 6.04 3.047 .083 No crash 117 5.76 Beliefs about environmental constraints Crash 28 1, 140 1.52 4.424 .037*

No crash 113 1.77

A significant effect was found for one of the driving instruments. An effect was

revealed for beliefs about environmental constraints when driving at level crossings F (1,

140) = 4.424, p <0.05. Therefore, this indicates that older drivers that reported having

had a crash during the past 3 years were less likely to believe that environmental

constraints influences their ability to drive safely at level crossings. Therefore, drivers

who hadn’t had a crash during the past 3 years were more likely to believe that

environmental factors play a role in safe driving behaviour at level crossings.

6.5.2.5 Knowledge of level crossing road rules

Participants were asked four questions about road rules and facts about level

crossings. Only a fifth of drivers (19.1%) answered all questions correctly, while four

participants (2.6%) did not answer any questions correctly. This is an interesting finding

as anecdotal evidence suggested that drivers have a poor knowledge of the road rules at

level crossings but it had never been confirmed by research in Australia.

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6.5.2.6 General driver behaviour

All mean scores for items on the modified driver behaviour questionnaire (DBQ)

were very low (i.e. less than 1) at both time points. The mean sample score at Time 1

was 0.43 (S.D.=0.266) which remained relatively similar at Time 2 (M=0.45,

S.D.=0.290).

6.5.2.7 Self-reported driving behaviour at level crossings

At Time 1, the mean score for this instrument was 0.12 (S.D.=0.183) indicating that

participants believe they drive safely at level crossings.

6.5.2.8 Intended driving behaviour at level crossings

Sample mean scores for intended driving behaviour at level crossings at Time 1

(M=0.14, S.D.=0.202) were very low, indicating that participants intend to drive safely at

level crossings in the future. A correlation analysis was undertaken to explore the

relationships between the dependent variable driving intention at level crossings (Time 1)

and the independent variables. Table 50 illustrates the significant relationships that were

revealed. As shown in this table, there was a weak correlation between participants’ self-

reported driving ability and driving intention at level crossings (Time 1) [r = .196, p <

.05]. In other words, a self-reported poorer driving ability was associated with a stronger

intention to perform high risk behaviours at level crossings. As expected, driving

intention at level crossings (Time 1) and self-reported driving behaviour at level

crossings (Time 1) were highly correlated [r = .792, p < .01].

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Table 50: Bivariate correlations for older drivers Dependent Variable

Independent variables Driving Intention at Level Crossings (T1)

Age .038 Gender -.050 Conditions on licence .077 Rurality .048 Hours per week drive -.132 Driving ability (self-reported) .196 * Crashes during past 3 years .072 DBQ .119 Attitude -.015 Perceived behavioural control .004 Subjective norms -.132 Environmental constraints .167 Perceived risk of collision -.042 Driving intention - Self-reported driving behaviour .762 **

* p < .05 ** p < .01 *** p < .001

To further analyse the prediction of driving intention at level crossings, a

hierarchical multiple regression was used. This 3-step hierarchical regression analysis

was undertaken to assess the contribution of the components of the integrated model (IM)

to the prediction of behavioural intention, along with measures of environment,

skills/ability (self-reported driving ability) and perceived risk. Three blocks of variables

were used to predict intention at Time 1: (i) environmental constraints and driving ability,

(ii) attitudes, subjective norms and perceived behavioural control, and (iii) perceived risk.

In this way, it was possible to assess the additional predictive utility of the attitudes,

subjective norms, and perceived behaviour control and the ability of environmental

constraints and compliance with road laws to mediate the influence of perceived risk.

The overall 3-step regression model was not significant, and no outcome variables

were found to be predictors of intention to drive safely at level crossings. At Step 1,

environmental constraints and driving ability variables accounted for only 4.4% of the

variance in behavioural intention. The addition of attitudes, subjective norms and

perceived behavioural control (F change = .771, NS) led to small (6.3%) increases in the

amount of variance explained, however the final addition of perceived risk (F change =

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1.178, NS) led to even smaller (7.3%) increases in the overall amount of variance

explained by the model.

Table 51: Hierarchical regression of constructs on intention at level crossings Variables Mean S.D. B Std

Error β R2 Adj R2 ∆ R2

Step 1 Environmental constraints 1.73 .567 .055 .035 .153

Driving ability .89 .468 .071 .041 .163 .044 .028 Step 2 Attitude 5.46 1.152 -.001 .018 -.004 Subjective norms 3.07 .449 -.055 .043 -.121 Perceived behavioural control 5.81 .728 -.028 .028 -.101

.063 .022 Step 3 Perceived risk .21 .721 -.029 .026 -.101 .073 .024 .010

* p<.05 ** p<.01 ***p<.001

6.5.2.9 Attitudes towards driving at level crossings

The first eight items from the 15-item instrument with bipolar scales (i.e. -3 to +3)

directly measured attitudes towards driving at level crossings. As stated previously, each

of the bipolar scales was recoded using unipolar (1-7) scales. Participants were asked to

complete statements by rating pairs of adjectives whilst driving at level crossings (e.g.

bad-good, more confusing-less confusing). Mean scores indicated that at Time 1 their

was generally a positive attitude towards driving at level crossings, however one item

relating to road rules suggested that participants were unsure if they were not strict

enough or too strict (M=3.85).

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Table 52: Attitudes towards driving at level crossings Time 1

Item n Mean Design of crossings (bad) 146 5.18Design of crossings (unsafe) 148 5.02Design of crossings (confusing) 150 5.85Design of crossings (difficult to obey rules) 147 5.91Road rules (bad) 147 6.12Road rules (not strict enough) 147 3.85Road rules (confusing) 150 5.79Road rules (not practical) 146 5.94

1=Negative attitude to 7=Positive attitude

6.5.2.10 Perceived behavioural control whilst driving at level crossings

The second seven items from the 15-item instrument with bipolar scales (i.e. -3 to

+3) directly measured self-efficacy and perceived behaviour control whilst driving at

level crossings. As stated previously, each of the bipolar scales was recoded using

unipolar (1-7) scales. Participants were asked to complete statements by rating pairs of

adjectives whilst driving at level crossings (e.g. not up to me-up to me, out of my control-

under my control). Mean scores at Time 1 indicated that participants generally believe

that they have control over their driving at level crossings, however one item ‘other

motorists influence my driving’ (harder to obey rules to easier to obey rules) scored a

lower mean (M=4.95) which indicated that some participants feel that they can’t always

obey the road rules at level crossings because of other motorists behaviour. Such

behaviour may include peer pressure for older drivers. Table 53 below illustrates the

mean scores observed at Time 1 for perceived behavioural control (self-efficacy).

Table 53: Perceived behavioural control whilst driving at level crossings

Time 1 Item n Mean Obeying the rules (not up to me) 149 6.75Obeying the rules (out of my control) 147 6.49Obeying the rules (dependent on other motorists) 148 6.55Obeying the rules (dependent on time constraints) 148 6.65Other motorists influence my driving (harder to obey rules) 146 4.95Other motorists influence my driving (more confusing) 144 5.45Other motorists influence my driving (more stressful) 144 5.20

1=Negative attitude to 7=Positive attitude

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6.5.2.11 Subjective norms of important others

Older drivers were asked a range of questions about how their family, friends and

other motorists drive at level crossings. Mean scores for family and friends obeying the

rules at level crossings were substantially higher than other motorists obeying the rules at

level crossings at Time 1. This indicated that older participants believe that motorists

other than their family and friends do not obey the rules at level crossings as often as their

important others (i.e. family and friends). The item ‘penalties need to be tougher for

violating road rules at crossings’ scored a relatively high mean indicating that older

participants believe that the current penalties are not tough enough for violators. Table

54 below illustrates the mean scores of each item at Time 1.

Table 54: Subjective norms of others Time 1

Item n Mean Your family generally obeys the rules at rail crossings 149 1.21Your friends generally obey the rules at rail crossings 143 1.66Other motorists generally obey the rules at rail crossings 147 2.71Your family generally think it important to obey the rules at rail crossings 150 1.17Your friends generally think it important to obey the rules at rail crossings 142 1.56Other motorists generally think it important to obey the rules at rail crossings 146 2.50

It is generally safe to disobey the rules at rail crossings 151 4.69It is generally possible to judge a train’s speed 150 4.35It is generally safe to cross if you can’t see a train, even if the lights are flashing 151 4.74

It is generally safe to roll slowly through a crossing instead of stopping 151 4.60Trains generally run to a regular timetable 149 3.81Penalties need to be tougher for violating road rules at rail crossings 149 2.05The main deterrent for breaking the rules at rail crossings is fear of getting caught 147 3.30

Generally it is more important to use common sense at rail crossings than strictly follow the road rules 151 3.87

1=Strongly agree to 6=Strongly disagree

6.5.2.12 Perceived risk of a level crossing collision

Participants were asked about their perception of how likely it is that they would be

involved in a collision at a level crossing whilst driving. At Time 1, the perceived risk

was extremely low (M=0.19).

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6.5.2.13 Environmental constraints whilst driving at level crossings

The items ‘my car stalled on the tracks at a rail crossing’ and ‘intersections ahead

of a rail crossing often cause your car to overhang the tracks’ scored the lowest mean at

Time 1. At Time 1 (M=2.64) the item ‘difficult to hear an approaching train when the

windows are up’ scored the highest mean. With such relatively low means across all

items, it indicates that older participants don’t believe that the design and environment

play a crucial role in their driving at level crossings. Table 55 illustrates the mean scores

for all items at Time 1.

Table 55: Beliefs of environmental factors at level crossings

Time 1 Item n Mean Blinding sun makes it difficult to see if the red flashing lights are activated 146 2.00The design of the road makes it difficult to see if a train is approaching or at the rail crossing 147 1.90

Warning systems on the road approaching the crossing are not adequate to inform drivers there is a rail crossing ahead 145 2.04

Road surfaces are often poor and it is difficult to stop 146 1.50Boom gates and/or flashing lights are often faulty 145 1.40Other drivers do stupid things that put you in a dangerous situation 145 1.81Intersections ahead of a rail crossing often cause your car to overhang the tracks 145 1.20

Difficult to hear an approaching train when the windows are up 145 2.64My car stalled on the tracks at a rail crossing 147 1.00

1=Never, 2=Almost never, 3=Sometimes, 4=Almost always, 5=Always

6.5.2.14 Familiarity with difference protection systems

Table 56 illustrates participant reported exposure to driving at level crossings. The

majority of participants reported that they never drive through boom gates (57.2%), never

drive through crossings with flashing lights (61.2%) and never drive through passive

crossings without lights or boom gates (59.9%).

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Table 56: Exposure to level crossing driving Boom gates Flashing Lights only No lights or boom gate

Type n % n % n % Never 87 57.2 93 61.2 91 59.9Once a year 9 5.9 14 9.2 20 13.2Twice a year 14 9.2 15 9.9 8 5.3Monthly 14 9.2 11 7.2 6 3.9Weekly 17 11.2 11 7.2 13 8.6Daily 8 5.3 4 2.6 9 5.9Missing 3 2.0 4 2.6 5 3.3

The exposure instrument was then recoded to compare those participants that either

drive through each type of crossing regularly or not. Those participants that stated they

never, yearly or twice a year drove over either active or passive crossings were combined

to form a group of being unfamiliar with that type of crossing whilst driving. Not

surprisingly participants that lived in rural or remote areas were more familiar with

crossings that had only a stop or give-way sign and red flashing lights, however, the same

percentage of metropolitan and rural/remote participants reported being unfamiliar with

boom gates (i.e. 74%).

Table 57: Familiarity and area classification at Time 1 Familiar Unfamiliar

Protection System n % n % Boom gates Metropolitan 21 13.8 61 40.1 Rural/remote 17 11.2 49 32.2 Flashing red lights Metropolitan 6 3.9 75 49.3 Rural/remote 20 13.2 46 30.3 Give-way or Stop sign only Metropolitan 9 5.9 72 47.3 Rural/remote 19 12.5 46 3.9

A series of one-way analysis of variances were calculated to examine differences

between familiarity of older drivers with level crossings (boom gates, flashing lights or

passive) in relation to each of the driving instruments. Drivers that were considered

245

‘familiar’ with level crossings were those that reported driving over a type of crossing

daily, weekly or monthly. Results are presented in the tables below.

Table 58: Familiarity with driving at level crossings (boom gates) Instrument Familiarity n df Mean F Sig DBQ Familiar 37 2, 138 0.52 3.986 .021* Unfamiliar 103 0.39 Self-reported driving behaviour Familiar 39 2, 136 0.17 2.180 .117 Unfamiliar 99 0.10 Intended driving behaviour Familiar 36 1, 135 0.17 1.686 .196 Unfamiliar 100 0.12 Subjective norms Familiar 36 1, 135 3.58 0.969 .327 Unfamiliar 100 3.68 Attitude Familiar 36 1, 138 5.26 1.515 .221 Unfamiliar 103 5.52 Perceived behavioural control Familiar 38 1, 140 5.86 0.231 .631 Unfamiliar 103 5.79 Beliefs about environmental constraints Familiar 38 1, 138 1.69 0.118 .732

Unfamiliar 101 1.73 * p<.05

The instrument ‘Driver Behaviour Questionnaire’ was the only significant factor for

familiarity with boom gated crossings [F (2, 138) = 3.986, p <0.05]. This indicated that

older drivers that were familiar with driving at crossings with boom gates recorded higher

levels of unsafe driving generally than unfamiliar drivers.

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Table 59: Familiarity with driving at level crossings (flashing lights) Instrument Familiarity n df Mean F Sig DBQ Familiar 26 1, 139 0.39 .584 .446 Unfamiliar 114 0.44 Self-reported driving behaviour Familiar 26 1, 135 0.21 7.989 .005*

Unfamiliar 111 0.10 Intended driving behaviour Familiar 25 1, 135 0.22 5.707 .018* Unfamiliar 111 0.12 Subjective norms Familiar 23 1, 133 3.62 .049 .825 Unfamiliar 111 3.65 Attitude Familiar 26 1, 138 5.23 1.336 .250 Unfamiliar 113 5.51 Perceived behavioural control Familiar 26 1, 140 5.77 .119 .730 Unfamiliar 115 5.82 Beliefs about environmental constraints Familiar 26 1, 138 1.73 .006 .939

Unfamiliar 113 1.72 * p<.05

A significant effect for two instruments: self-reported driving behaviour F (1, 135)

= 7.989, p <0.05 and intended driving behaviour F (1, 135) = 5.707, p <0.05 were

revealed. This indicated that older drivers that were familiar with driving at crossings

with only flashing lights recorded higher levels of unsafe driving and intended driving

behaviour at level crossings than unfamiliar drivers.

Table 60: Familiarity of driving at level crossings (passive signs only) Instrument Familiarity n df Mean F Sig DBQ Familiar 26 1, 139 0.38 .714 .399 Unfamiliar 114 0.43 Self-reported driving behaviour Familiar 28 1, 136 0.19 3.763 .054

Unfamiliar 109 0.11 Intended driving behaviour Familiar 28 1, 135 0.16 .390 .533 Unfamiliar 108 0.13 Subjective norms Familiar 26 1, 133 3.62 .101 .751 Unfamiliar 108 3.66 Attitude Familiar 27 1, 138 5.07 4.343 .039* Unfamiliar 112 5.56 Perceived behavioural control Familiar 27 1, 140 5.77 .174 .677 Unfamiliar 114 5.83 Beliefs about environmental constraints Familiar 27 1, 1.77 .185 .668

Unfamiliar 111 137 1.72 * p<.05

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A significant result was found for the scale attitudes towards driving at level

crossings F (1, 138) = 4.343, p <0.05 and familiarity with passive crossings (stop or give-

way sign only). This indicated that participants that are familiar with driving at passive

crossings are more likely to have a negative attitude towards driving at level crossings

generally.

6.5.2.15 Intervention message recall

All participants received an intervention message via telephone six weeks after

returning their Time 1 questionnaire. Of the 76 control participants, only 33 indicated

that they remembered what radio message they received (43.4%). Forty-four (44)

participants in the intervention group (n=76) recalled that they had received a radio

message about level crossing safety (57.9%). These low recall figures were staggering

given that all participants were prompted of either receiving a message about vision or

level crossings. Of the participants that recalled receiving the radio messages, only nine

reported remembering any slogans while 23 reported remembering information from the

radio messages (14 control participants and 9 intervention participants). Participants were

also asked how likely it was that their driving behaviour was influence from receiving

either the control or intervention message. Only two intervention and three control

participants indicated that it was ‘very likely’ that their driving behaviour had been

affected by listening to the message. Therefore, it can be concluded that there generally

was very poor recall of both the intervention and control messages.

6.5.2.16 Effect of intervention exposure

A comparison of means between the intervention and control groups was

undertaken at both Time 1 and Time 2. Table 61 illustrates the mean scores for all

instruments at Time 1 and Time 2 for both the intervention and control groups for the

older driver sample.

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Table 61: Comparison of experimental group outcome variables Intervention Group Control Group

Instrument

T1 Mean

T2 Mean

T1 Mean

T2 Mean

DBQ .41 .45 .44 .45Self-reported driving behaviour .13 .08 .12 .09Intended driving behaviour .12 .11 .15 .14Attitudes 5.61 5.47 5.32 5.45Perceived behavioural control 5.86 5.94 5.76 5.77Subjective norms 3.06 3.18 3.00 3.05Beliefs of environmental constraints 1.62 1.70 1.77 1.61Perceived risk of a crash .19 .19 .20 .53

To ascertain the effect of the intervention, a split-plot in time analysis (repeated

measures) was undertaken for each of the outcome variables. Separate analyses were

undertaken rather than multivariate ANOVAs since the dependent variables were

predicted to have different outcomes and the outcomes of interest were the individual

variables rather than the set of variables. Results of the series of split-plot analyses reveal

that beliefs about environmental constraints (F = 4.769, df = 1, 95, P< 0.05) was the

only outcome variable to have significantly changed over time between groups. The

associated Partial Eta squared was .048, indicating a small effect size. Perceived risk of a

collision at a level crossing (F = 4.651, df = 1, 104, P< 0.05) was observed to have a

significant time effect (within-subject test), however between groups tests indicates that

the variable group is not significant. Results from the repeated measure analyses are

presented in Table 62.

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Table 62: Repeated measure analysis of variance for the outcome variables Instrument df Wilks’ A

Effect sizes(Partial Eta

squared)F Sig

DBQ

Time 1, 96 .972 .028 2.737 .101

Group x Time 1, 96 .977 .023 2.241 .138

Self-reported driving behaviour

Time 1, 94 .984 .016 1.486 .226

Group x Time 1, 94 .999 .001 .117 .733

Intended driving behaviour

Time 1, 100 .994 .006 .611 .436

Group x Time 1, 100 .984 .016 1.598 .209

Attitude

Time 1, 100 .994 .006 .611 .436

Group x Time 1, 100 .984 .016 1.598 .209

Perceived behavioural control

Time 1, 100 .985 .015 1.547 .217

Group x Time 1, 100 .998 .002 .221 .639

Subjective norms

Time 1, 87 .977 .023 2.050 .156

Group x Time 1, 87 1.000 .000 .004 .950

Environmental constraints

Time 1, 95 .994 .006 .581 .448

Group x Time 1, 95 .952 .048 4.769 .031*

Perceived risk of a collision Time 1, 104 .957 .043 4.651 .033*

Group x Time 1, 104 .979 .021 2.279 .134

* p<.05 ** p<.01 ***p<.001 Note: to interpret effect sizes: .02=small magnitude; .15=medium magnitude; .35=large magnitude (Cohen, 1988).

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6.5.3 Younger drivers

6.5.3.1 Sample attrition

At Time 1, 149 participants completed the online questionnaire, with this figure

dropping to 88 at Time 2. The attrition rates for intervention and control groups are

displayed in the table below. Interestingly, like the older driver group, the ratio for

intervention to control remained relatively similar between time points. Additionally, this

table illustrates the attrition for metropolitan and rural/remote participants. As can be

seen, the majority of participants at both time points were from metropolitan areas.

Table 63: Participant group and area classification at both time points Time 1 Time 2

n % n % Group Intervention 100 67.1 58 65.9 Control 49 32.9 30 34.1 Total 149 100.0 88 100.0 Area Classification Metropolitan 92 61.7 53 60.2 Rural/remote 57 38.3 35 39.8 Total 149 100.0 88 100.0

6.5.3.2 Demographics

At Time 1, the majority of participants were female (n=83, 55.7%), had an open

licence (n=118, 79.2%) and were employed (n=135, 90.6%). The mean age of

participants was 21.64 years (range 18 to 24 years). The education level attained by

participants is illustrated in Table 64. The majority of participants reported having either

received a university degree or having done some university (49.0%).

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Table 64: Education level of younger participants Time 1 Level n % Some high school 7 4.7 Year 12 40 26.8 Some Uni 28 18.8 Uni Degree 45 30.2 Some TAFE 3 2.0 TAFE Degree 19 12.8 Other 7 4.7

To examine the differences between licence type and each of the driving

instruments, a series of analysis of variances was undertaken. The influence of gender on

driving was also examined using a series of ANOVA’s. Tables 65 & 66 illustrate the

ANOVA’s undertaken.

Table 65: Mean overall scores between licence type and outcome variables

Instrument Licence n df Mean F Sig DBQ Provisional 31 1, 148 0.60 .012 .913 Open 118 0.61 Self-reported driving behaviour Provisional 31 1, 148 0.31 .226 .635

Open 118 0.36 Intended driving behaviour Provisional 31 1, 148 0.26 .702 .404 Open 118 0.35 Subjective norms* Provisional 31 1, 148 3.55 .741 .846 Open 118 3.32 Attitude Provisional 31 1, 148 4.06 .269 .605 Open 118 3.97 Perceived behavioural control Provisional 31 1, 148 4.98 .583 .446 Open 118 5.12 Beliefs about environmental constraints Provisional 31 1, 148 2.25 2.184 .142

Open 118 2.50 * Instrument excluded first 3 items due to large amounts of missing data

There were no significant results in relation to licence type and any of the driving

instruments.

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Table 66: Mean overall scores between gender and outcome variables Instrument Licence n df Mean F Sig DBQ Male 66 1, 148 0.65 1.867 .174 Female 83 0.57 Self-reported driving behaviour Male 66 1, 148 0.46 4.878 .029*

Female 83 0.27 Intended driving behaviour Male 66 1, 148 0.44 4.549 .035* Female 83 0.25 Subjective norms* Male 66 1, 137 3.40 1.148 .291 Female 83 3.31 Attitude Male 66 1, 148 4.03 .246 .620 Female 83 3.96 Perceived behavioural control Male 66 1, 148 4.97 2.272 .134 Female 83 5.19 Beliefs about environmental constraints Male 66 1, 148 2.48 .199 .656

Female 83 2.42 * Instrument excluded first 3 items due to large amounts of missing data

A significant effect was found for two of the driving instruments. An effect was

revealed for self-reported driving behaviour at level crossings [F (1, 148) = 4.878, p

<0.05] as well as intended driving behaviour at level crossings [F (1, 148) = 4.549, p

<0.05]. Therefore, this indicates that young male drivers were significantly more likely

to report current and predict their future driving at level crossings to be less than their

female counterparts. This is not surprising given the amount of literature supporting

research findings that young males are more likely to take risks whilst driving (Laapotti

and Keskinen, 1998, Arnett, 2002).

6.5.3.3 Crash history

Fifty-nine (39.6%) participants reported having been involved in a collision during

the past 3 years. Table 67 below illustrates the differences in crash involvement between

rural/remote and metropolitan participants.

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Table 67: Comparison of crash involvement and rurality at Time 1 Rurality

Involved in a crash

(n)

Involved in a crash (%)

Total participants

(n) Metropolitan 44 47.8 92 Rural/remote 15 26.3 57 Total 59 39.6 149

Metropolitan drivers (47.8%) were more likely to have been involved in a collision

during the past 3 years than rural/remote drivers (26.3%), which is not surprising given

the larger driver population in metropolitan areas. Five participants reported having been

involved in 2 collisions, while one participant reported having been involved in three

collisions during the past 3 years. One metropolitan participant reported having been

involved in a level crossing collision with a fatality occurring in that collision.

The majority of participants involved in a collision, reported that it was damage

only (79.6%), and 11 participants reported that it was at an intersection (18.6%). Three

participants (5.1%) reported that they were involved in a roll-over, while 28.8% (n=17)

stated that the collision was their fault completely. Participants reported a variety of

contributing factors to their collisions. The most prevalent contributing factor was

reported as ‘listening to music’ (50.8%), followed by ‘daydreaming’ (16.9%), ‘talking to

a passenger’ (15.2%) and ‘speeding’ (13.6%). Ten percent (n=6) of participants reported

fatigue as contributing to their crash, while only one participant reported talking on a

mobile phone as contributing to their crash.

Analysis of variances was also undertaken to examine differences between

participants that reported being involved in a road crash during the past 3 years and each

of the outcome variables at Time 1. No significant effects were found for crash

involvement and any of the driving instruments (see Table 68).

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Table 68: Mean overall scores between road crashes and outcome variables Instrument Crash History n df Mean F Sig DBQ Crash 59 1, 148 0.63 .605 .438 No crash 90 0.59 Self-reported driving behaviour Crash 59 1, 148 0.40 .812 .369

No crash 90 0.32 Intended driving behaviour Crash 59 1, 148 0.38 .816 .368 No crash 90 0.30 Subjective norms* Crash 56 1, 137 3.28 1.175 .263 No crash 82 3.39 Attitude Crash 59 1, 148 4.00 .030 .862 No crash 90 3.98 Perceived behavioural control Crash 59 1, 148 5.17 .747 .389 No crash 90 5.04 Beliefs about environmental constraints Crash 59 1, 148 2.45 .000 .986

No crash 90 2.45 * Instrument excluded first 3 items due to large amounts of missing data

6.5.3.4 Knowledge of level crossing road rules

Participants were asked four questions about road rules and facts about level

crossings. Less than a fifth of drivers (14.8%) answered all questions correctly, while

three participants (2.0%) did not answer any questions correctly. Compared to older and

heavy vehicle participants, younger participants scored substantially lower in knowledge.

This is an interesting finding as younger drivers would most likely have been exposed to

some information about level crossing road rules in the licensing process.

6.5.3.5 General driving behaviour

Mean scores for items on the modified driver behaviour questionnaire were very

low (i.e. less than 1) at both time points. The mean score at Time 1 was 0.61

(S.D.=0.338) which remained relatively similar at Time 2 (M=0.77, S.D.=0.329).

6.5.3.6 Self-reported driving behaviour at level crossings

At Time 1, the mean score for this instrument was 0.35 (S.D.=0.530) and increased

to 1.23 (S.D.=1.894) at Time 2. Both these scores are relatively low, indicating that

younger participants believe they drive safely at level crossings.

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6.5.3.7 Intended driving behaviour at level crossings

Mean scores for driver intention at Time 1 (M=0.33, S.D.=0.565) was very low,

indicating that younger participants report that they intend to drive safely at level

crossings in the future. A correlation analysis was undertaken to explore the relationships

between the dependent variable driving intention at level crossings (Time 1) and the

outcome variables. Table 69 illustrates the significant relationships that were revealed.

As shown in Table 69, there was a weak negative correlation between participants’

gender and driving intention at level crossings (Time 1) [r = -.173, p < .05]. In other

words, being a male participant was associated with a stronger intention to drive unsafely

at level crossings. In addition, there was a significant negative correlation between the

participants’ self-efficacy whilst driving at level crossings and driving intention at level

crossings (Time 1) [r = -.216, p < .01]. This negative relationship indicated that lower

self-efficacy levels were associated with a stronger intention to drive unsafely at level

crossings.

Table 69: Bivariate correlations between dependent and independent variables Dependent Variable

Independent variables Driving Intention at Level Crossings (T1)

Age -.068 Gender -.173 * Educational level -.099 Licence type .069 Rurality .114 Hours per week drive .036 Crashes during past 3 years .074 DBQ .253 ** Attitude .033 Perceived behavioural control -.216 ** Subjective norm .152 Environmental constraints .103 Perceived risk of collision .025 Driving intention - Self-reported driving behaviour .809 **

* p < .05 ** p < .01 *** p < .001

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To further analyse the prediction of driving intention at level crossings, a

hierarchical multiple regression was used. This 3-step hierarchical regression analysis

was undertaken to assess the contribution of the components of the integrated model (IM)

to the prediction of behavioural intention, along with measures of environment,

skills/ability (compliance with road laws) and perceived risk. Three blocks of variables

were used to predict intention at Time 1: (i) environmental constraints and compliance

with road laws, (ii) attitudes, subjective norms and perceived behavioural control, and

(iii) perceived risk. In this way, it was possible to assess the additional predictive utility

of the attitudes, subjective norms, and perceived behaviour control and the ability of

environmental constraints and compliance with road laws to mediate the influence of

perceived risk.

The overall 3-step regression model was not significant, however perceived

behavioural control (self-efficacy) was found to be a significant predictor of intention to

drive safely at level crossings (p<.01). At Step 1, environmental constraints and

compliance with road laws variables accounted for only 1.6% of the variance in

behavioural intention. The addition of attitudes, subjective norms and perceived

behavioural control (F change = 3.365, p<.05) led to small (8.6%) increases in the

amount of variance explained, however the final addition of perceived risk (F change =

.048, NS) led to even smaller (8.7%) increases in the overall amount of variance

explained by the model.

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Table 70: Hierarchical regression of constructs on intention at level crossings Variables Mean S.D. B Std

Error β R2 Adj R2 ∆ R2

Step 1 Environmental constraints 2.52 .815 -.011 .061 -.016

Compliance with road laws# 2.99 .471 -.136 .103 -.113

.016 .002 Step 2 Attitude 4.06 .827 .071 .070 .103 Subjective norm 3.35 .788 -.002 .069 -.003 Perceived behavioural control 5.13 .870 -.185 .060 -.284**

.086 .052 Step 3 Perceived risk 2.49 .766 -.014 .064 -.019 .087 .045 .000

* p<.05 ** p<.01 ***p<.001 # Compliance with road laws was used instead of driving ability as this measure is likely to be more accurate in a younger driver sample

6.5.3.8 Attitudes towards driving at level crossings

The first eight items from the 15-item instrument with bipolar scales (i.e. -3 to +3)

directly measured attitudes towards driving at level crossings. As stated previously, each

of the bipolar scales was recoded using unipolar (1-7) scales. Participants were asked to

complete statements by rating pairs of adjectives whilst driving at level crossings (e.g.

bad-good, more confusing-less confusing). Mean scores indicated that at Time 1 their

was generally a positive attitude towards driving at level crossings, however one item

scored less than a mean of 3.0. The item ‘design of crossings (difficult to obey rules to

easy to obey rules)’ had a mean of 2.90 at Time 1. This indicated that younger drivers

believe that it is sometimes difficult to obey the road rules at level crossings due to the

design of crossings. Table 71 illustrates the mean scores for each item.

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Table 71: Attitudes towards driving at level crossings Time 1

Item n

Mean

Design of crossings (bad) 149 4.21Design of crossings (unsafe) 149 4.13Design of crossings (confusing) 149 3.41Design of crossings (difficult to obey rules) 149 2.90Road rules (bad) 149 4.47Road rules (not strict enough) 149 4.54Road rules (confusing) 149 4.34Road rules (not practical) 149 3.91

1=Negative attitude to 7=Positive attitude

6.5.3.9 Perceived behavioural control whilst driving at level crossings

The second seven items from the 15-item instrument with bipolar scales (i.e. -3 to

+3) directly measured self-efficacy and perceived behaviour control whilst driving at

level crossings. As stated previously, each of the bipolar scales was recoded using

unipolar (1-7) scales. Participants were asked to complete statements by rating pairs of

adjectives whilst driving at level crossings (e.g. not up to me-up to me, out of my control-

under my control). Mean scores at Time 1 indicated that younger participants generally

believe that they have control over their driving at level crossings, however one item

‘other motorists influence my driving’ (more stressful to more relaxing) scored a lower

mean (M=3.96) which indicated that some participants feel that it is sometimes stressful

driving at level crossings because of other motorist’s behaviour. Table 72 illustrates the

mean scores observed at Time 1 for self-efficacy/perceived behavioural control.

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Table 72: Perceived behavioural control whilst driving at level crossings Time 1

Item n Mean

Obeying the rules (not up to me) 149 4.96Obeying the rules (out of my control) 149 5.30Obeying the rules (dependent on other motorists) 149 5.91Obeying the rules (dependent on time constraints) 149 6.05Other motorists influence my driving (harder to obey rules) 149 5.35Other motorists influence my driving (more confusing) 149 4.11Other motorists influence my driving (more stressful) 149 3.96

1=Negative attitude to 7=Positive attitude

6.5.3.10 Subjective norms of important others

As stated previously, the first three items in this instrument comprised large

amounts of missing data, possibly due to online electronic data saving problems.

Therefore, the first three items of this instrument were not included in mean scores.

Table 73 below illustrates the mean scores for Time 1. The lowest mean score ‘generally

safe to roll slowly through a crossing instead of stopping’ (M=1.90) suggests that

younger drivers are prepared to take a risk and not stop at crossings.

Table 73: Mean scores for subjective norms of others Time 1

Item n Mean

Your family generally obeys the rules at rail crossings - -Your friends generally obey the rules at rail crossings - -Other motorists generally obey the rules at rail crossings - -Your family generally think it important to obey the rules at rail crossings 143 4.30Your friends generally think it important to obey the rules at rail crossings 142 5.30Other motorists generally think it important to obey the rules at rail crossings 142 4.68

It is generally safe to disobey the rules at rail crossings 142 4.54It is generally possible to judge a train’s speed 142 2.86It is generally safe to cross if you can’t see a train, even if the lights are flashing 141 2.82

It is generally safe to roll slowly through a crossing instead of stopping 142 1.90Trains generally run to a regular timetable 142 2.01Penalties need to be tougher for violating road rules at rail crossings 141 2.51The main deterrent for breaking the rules at rail crossings is fear of getting caught 141 3.41

Generally it is more important to use common sense at rail crossings than strictly follow the road rules 141 2.65

1=Strongly agree to 6=Strongly disagree

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6.5.3.11 Perceived risk of a level crossing collision

Younger participants were asked about their perception of how likely it is that they

would be involved in a collision at a level crossing whilst driving. The younger sample

mean score (M=2.42) at Time 1 were relatively higher than both older drivers (M=0.19)

and truck drivers (M=0.33). There appeared to be a perception among younger drivers in

this sample that they are at risk of being involved in a level crossing collision.

6.5.3.12 Environmental constraints whilst driving at level crossings

At Time 1, the items ‘design of the road makes it difficult to see if a train is

approaching’ (M=3.12) and ‘difficult to hear an approaching train when the windows are

up’ (M=3.02) scored the highest means for younger drivers. The lowest mean observed

at Time 1 was ‘my car stalled on the tracks at a rail crossing’. Table 74 presents Time 1

mean scores for environmental constraints.

Table 74: Beliefs of environmental constraints whilst driving at level crossings Time 1

Item n Mean

Blinding sun makes it difficult to see if the red flashing lights are activated 149 2.35The design of the road makes it difficult to see if a train is approaching or at the rail crossing 149 3.12

Warning systems on the road approaching the crossing are not adequate to inform drivers there is a rail crossing ahead 149 2.71

Road surfaces are often poor and it is difficult to stop 149 2.35Boom gates and/or flashing lights are often faulty 149 1.99Other drivers do stupid things that put you in a dangerous situation 149 2.99Intersections ahead of a rail crossing often cause your car to overhang the tracks 149 2.29

Difficult to hear an approaching train when the windows are up 149 3.02My car stalled on the tracks at a rail crossing 149 1.19

1=Never, 2=Almost never, 3=Sometimes, 4=Almost always, 5=Always

6.5.3.13 Familiarity with different protection systems

Table 75 illustrates participant reported exposure to driving at level crossings. The

majority of participants reported that they never drive through boom gates (53.0%), never

drive through crossings with flashing lights (58.4%) and never drive through passive

261

crossings without lights or boom gates (52.3%). As such, it is evident that more than half

of the participants were more likely to be unfamiliar with driving at level crossings.

Table 75: Exposure to level crossing driving Boom gates Flashing Lights only No lights or boom gate

Type n % n % n % Never 79 53.0 87 58.4 78 52.3Once a year 11 7.4 21 14.1 22 14.8Twice a year 17 11.4 15 10.1 9 6.0Monthly 15 10.1 14 9.4 8 5.4Weekly 17 11.4 12 8.1 10 6.7Daily 10 6.7 0 0 22 14.8

Like the other two road user groups, the exposure instrument was then recoded to

compare those participants that either drive through each type of crossing regularly or

not. Those participants that stated they never, yearly or twice a year drove over either

active or passive crossings were combined to form a group of being unfamiliar with that

type of crossing whilst driving. Surprisingly, only 4% of participants that lived in

metropolitan areas were familiar with crossings that had only flashing red lights, while

similar percentages for familiarity with passive crossings (stop or give-way signs) were

reported for metropolitan and rural/remote drivers.

Table 76: Familiarity and area classification at Time 1 Familiar Unfamiliar

Protection System n % n % Boom gates Metropolitan 27 18.1 65 43.6 Rural and Remote 15 10.1 42 28.2 Flashing red lights Metropolitan 6 4.0 86 57.7 Rural and Remote 20 13.4 37 24.8 Give-way or Stop sign only Metropolitan 19 12.8 73 49.0 Rural and Remote 21 14.1 36 24.2

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A series of one-way analysis of variances were calculated to examine differences

between familiarity of younger drivers with level crossings (boom gates, flashing lights

or passive) in relation to each of the driving instruments. Drivers that were considered

‘familiar’ with level crossings were those that reported driving over a type of crossing

daily, weekly or monthly. Results are presented in the tables below.

Table 77: Familiarity with driving at level crossings (boom gates) Instrument Familiarity n df Mean F Sig DBQ Familiar 42 1, 148 0.67 1.869 .174 Unfamiliar 107 0.58 Self-reported driving behaviour Familiar 42 1, 148 0.55 8.346 .004* Unfamiliar 107 0.27 Intended driving behaviour Familiar 42 1, 148 0.46 3.241 .074 Unfamiliar 107 0.28 Beliefs about others’ driving* Familiar 39 1, 137 3.25 1.052 .410 Unfamiliar 99 3.39 Attitudes towards driving at level crossings Familiar 42 1, 148 4.09 .841 .361

Unfamiliar 107 3.95 Self-efficacy whilst driving at level crossings Familiar 42 1, 148 4.99 .746 .389

Unfamiliar 107 5.13 Beliefs about environmental constraints Familiar 42 1, 148 2.54 .786 .377

Unfamiliar 107 2.41 * Instrument excluded first 3 items due to large amounts of missing data

The instrument on self-reported driving behaviour at level crossings was the only

significant factor for familiarity with boom gated crossings [F (1, 148) = 8.346, p <0.05].

This indicated that younger drivers that were familiar with driving at crossings with boom

gates recorded higher levels of unsafe driving at level crossings generally than unfamiliar

drivers.

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Table 78: Familiarity with driving at level crossings (flashing lights) Instrument Familiarity n df Mean F Sig DBQ Familiar 26 1, 148 0.73 4.432 .037* Unfamiliar 123 0.58 Self-reported driving behaviour Familiar 26 1, 148 0.53 3.498 .063

Unfamiliar 123 0.31 Intended driving behaviour Familiar 26 1, 148 0.53 3.886 .051 Unfamiliar 123 0.29 Beliefs about others’ driving* Familiar 25 1, 137 3.37 .977 .516 Unfamiliar 113 3.34 Attitudes towards driving at level crossings Familiar 26 1, 148 4.33 5.142 .025*

Unfamiliar 123 3.92 Self-efficacy whilst driving at level crossings Familiar 26 1, 148 5.23 .705 .402

Unfamiliar 123 5.06 Beliefs about environmental constraints Familiar 26 1, 148 2.58 .743 .390

Unfamiliar 123 2.42 * Instrument excluded first 3 items due to large amounts of missing data

A significant effect for two instruments: driver behaviour questionnaire F (1, 148)

= 4.432, p <0.05 and attitudes towards driving at level crossings [F (1, 148) = 5.142, p

<0.05] were revealed. This indicated that younger drivers that were familiar with driving

at crossings with only flashing lights recorded a higher level of unsafe driving generally

but had a more positive attitude towards driving at level crossings.

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Table 79: Familiarity with driving at level crossings (passive signs only) Instrument Familiarity n df Mean F Sig DBQ Familiar 40 1, 148 0.71 5.660 .019* Unfamiliar 109 0.57 Self-reported driving behaviour Familiar 40 1, 148 0.49 4.042 .046 Unfamiliar 109 0.30 Intended driving behaviour Familiar 40 1, 148 0.46 2.829 .095 Unfamiliar 109 0.29 Beliefs about others’ driving* Familiar 38 1, 137 3.31 1.037 .431 Unfamiliar 100 3.37 Attitudes towards level crossings Familiar 40 1, 148 4.14 1.779 .184

Unfamiliar 109 3.93 Self-efficacy whilst driving at level crossings Familiar 40 1, 148 5.14 .182 .671

Unfamiliar 109 5.07 Beliefs about environmental constraints Familiar 40 1, 148 2.50 .215 .643

Unfamiliar 109 2.43 * Instrument excluded first 3 items due to large amounts of missing data

A significant result was found for the instrument ‘driver behaviour questionnaire’

[F (1, 148) = 5.660, p <0.05] and familiarity with passive crossings (stop or give-way

sign only). This indicated that younger participants that are familiar with driving at

passive crossings are more likely to drive unsafely on the road generally than participants

that reported being unfamiliar with passive crossings.

6.5.3.14 Intervention message recall

Recall of what message younger drivers received was better than the two other road

user groups. Fifty-seven (64.7%) recalled either receiving the message about wearing

seatbelts at all times when driving or safe driving at level crossings. Only three (3.4%)

participants recalled any of the slogans from either of the messages, while eighteen

participants (20.5%) recalled information about either of the messages. Only four (4.5%)

participants reported that by listening to the road safety messages their driving behaviour

has been affected.

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6.5.3.14 Effect of intervention exposure

A comparison of means between the intervention and control groups was

undertaken at both Time 1 and Time 2. Table 80 below illustrates the mean scores for all

outcome variables at Time 1 and Time 2 for both the intervention and control groups for

the younger driver sample. Mean scores for the driver behaviour questionnaire increased

for both the intervention and control groups from Time 1 to Time 2, indicating that those

participants that remained in the study at Time 2 were more prepared to take risks on the

road than the Time 1 younger driver sample. Additionally, mean scores for attitudes

towards driving at level crossings also increased for both intervention and control groups,

however this indicated that the Time 2 sample had more positive attitudes towards

driving at level crossings. Also of importance is the considerable decrease in the mean

scores for perceived risk of a collision at a level crossing, which indicates that the sample

of participants that remained at Time 2 were less likely to believe that they are at risk of a

level crossing collision.

Table 80: Comparison of experimental group instrument mean scores Intervention Group Control Group

Outcome Variables

T1 Mean

T2 Mean

T1 Mean

T2 Mean

Driver behaviour questionnaire .64 .75 .65 .80Self-reported driving behaviour .35 1.26 .45 1.17Intended driving behaviour .32 .30 .40 .36Attitudes towards driving at level crossings 4.09 4.41 4.11 4.36

Self-efficacy of driving at level crossings 5.12 4.55 4.96 4.73

Normative beliefs of others driving at level crossings* 3.25 3.21 3.51 3.00

Beliefs of environmental constraints whilst driving at level crossings 2.44 2.62 2.74 2.77

Perceived risk of a collision at a level crossing 2.53 1.29 2.40 1.30

* First 3 items removed from both Time 1 and Time 2 mean scores due to missing data in Time 1

To ascertain the effect of the intervention for the younger driver sample, a split-plot

in time analysis (repeated measures) was undertaken for each of the outcome variables.

Separate analyses were undertaken rather than multivariate ANOVAs since the dependent

266

variables were predicted to have different outcomes and the outcomes of interest were the

individual variables rather than the set of variables.

Results of the series of split-plot analyses reveal that normative beliefs (F = 6.196=

1, 81P< 0.05) was the only outcome variable to have significantly changed over time

between groups. The associated Partial Eta squared was .071 indicating a small to

medium effect size. A number of outcome variables (see Table 81) were observed to have

a significant time effect (within-subject test), however between groups tests indicates that

the variable groups were not significant. Results from the repeated measure analyses are

presented in the table below.

267

Table 81: Repeated measure analysis of variance for the outcome variables Instrument df Wilks’ A

Effect sizes(Partial Eta

squared)F Sig

DBQ

Time 1, 86 .793 .207 22.434 .000***

Group x Time 1, 86 .993 .007 .606 .438

Self-reported driving behaviour

Time 1, 86 .871 .129 12.774 .001**

Group x Time 1, 86 .998 .002 .169 .682

Intended driving behaviour

Time 1, 86 .996 .004 .358 .551

Group x Time 1, 86 .999 .001 .058 .811

Attitudes

Time 1, 86 .940 .060 5.520 .021*

Group x Time 1, 86 .999 .001 .088 .768

Self-efficacy

Time 1, 86 .831 .169 17.436 .000***

Group x Time 1, 86 .964 .036 3.252 .075

Normative beliefs

Time 1, 81 .908 .092 8.180 .005**

Group x Time 1, 81 .929 .071 6.196 .015*

Environmental constraints

Time 1, 86 .985 .015 1.290 .259

Group x Time 1, 86 .993 .007 .565 .454

Perceived risk of a collision Time 1, 86 .688 .312 38.952 .000***

Group x Time 1, 86 .998 .002 .142 .707

* p<.05 ** p<.01 ***p<.001 Note: to interpret effect sizes: .02=small magnitude; .15=medium magnitude; .35=large magnitude (Cohen, 1988).

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6.6 STUDY LIMITATIONS

6.6.1 Widespread limitations

Like all research, this final study is not without its limitations. There are

widespread limitations in the data collected from each of the three road user groups,

as well as specific limitations of the data collection methodologies within these

groups.

Firstly, one of the widespread limitations of this final study is that data for all

three road user groups was drawn exclusively from only one jurisdiction in Australia.

Every effort was made to ensure that there was a diverse sample of participants in

both the older and younger driver groups, with an equal number of recruitment letters

being sent to rural/remote and metropolitan RACQ members. Additionally,

randomisation to either the intervention or control group was conducted for

rural/remote and metropolitan participants. With financial constraints limiting the

scope of data collection extending outside of Queensland, some caution needs to be

exercised when generalising these results to other jurisdictions in Australia or abroad.

However, anecdotal evidence from train drivers throughout Australia suggests that

there is very little difference in the behaviours and road user groups most at risk of a

collision with a train.

Secondly, this investigation suffers from the typical perceived limitation

associated with self-report data. The main criticism of self-report data is that it is

affected by social desirability bias. It goes without saying in road safety research that

social desirability will have some impact upon the findings obtained. It has been

suggested that ‘deviant’ individuals would minimise the number of crashes they were

involved in or other illegal driving activities (e.g. drink driving) (Hatakka, 1997).

However, it is important to note that any resulting associations found would be under-

estimates of real associations, rather than over-estimates (Hatakka, 1997, Lawton,

1997a, West, 1995). For the current investigation, the use of self-report would have

potentially been a problem if actual (observed) driving behaviour was monitored.

However, since actual driving behaviour was not monitored, the degree of

correspondence between self-report and observed driving behaviour is insignificant.

Thirdly, the use of incentives was considered to be suitable for each of the three

road user groups, however may not have been great enough for completion of both

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questionnaires. Two movie tickets were provided to each of the participants if they

completed both questionnaires. This may have been a great enough incentive to

originally agree to participate, but over time more incentives may have been needed to

ensure low attrition rates. However, limited financial resources did not allow four

movie tickets to be purchases for all participants (at a total cost of approximately $44

per participant).

Finally, as this intervention was provided as a ‘one-off’ intervention, it appears

that the recall was extremely poor. The intervention would have been enhanced if

participants were exposed to the intervention on several occasions over a period of a

few weeks, rather than just once. However, again due to the limited budget in which

the intervention could be rolled out, a ‘one-off’ brief intervention was the most

achievable method for delivery. A larger budget, such as that spent by governments

in developing road safety awareness campaigns, would have possibly achieved greater

recall of this intervention as participants could have been exposed to a range of

delivery modes (i.e. radio, television, billboards) over a longer period of time.

6.6.2 Heavy vehicle driver sample limitations

Data collection from the trucking industry was fraught with difficulty. The

greatest challenge was the poor involvement of companies in the study. Although the

project sought the assistance of the Queensland Trucking Association and actively

approached a multitude of trucking companies, only five agreed to participate. This

was an extremely disappointing response rate, particularly with the months of effort in

approaching companies and offering incentives to participants. To the author, it

appears that the reluctance by many companies to participate in this road safety

research may have stemmed from either the belief that level crossing safety is not

high on their safety agenda or a distrust of disclosing driving behaviours of employees

in fear of scrutiny of the company’s safety compliance. The lack of involvement

from the trucking industry therefore resulted in a relatively small sample size that may

limit the validity and generalisability of the findings, as well as the range of statistical

analyses that could be undertaken. It could also be argued that those companies that

were willing to participate, had better safety records that those companies that refused

to participate. As such, this study may have only captured the companies with

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comprehensive OHS management systems and a focus on ensuring a culture of safety

within their workplace.

Additionally, this small sample size prevented randmonisation of companies to

either the intervention or control group from occurring. This was a major limitation

for this road user group and further techniques and incentives for company participant

may need to be developed in order to increase the industry’s participation rates over

longer periods of time.

The administration of the questionnaire was another area that limited the

response rate from truck drivers. Although the first questionnaire (Time 1) was

completed face-to-face by drivers during morning tea breaks at their depot, the Time 2

questionnaire was posted to the drivers. This was deemed the most cost-effective data

collection tool for this subsequent questionnaire, however it appears that truck drivers

are less willing to complete and return questionnaires outside of their workplace. As

there was a significant attrition between Time 1 and Time 2, it can therefore be

assumed that it would have been more effective for participants to have face-to-face

involvement with completion of both questionnaires. Mean safety scores on many of

the items in the questionnaire are higher at Time 2 than they are at Time 1 which

would suggest that drivers that completed both questionnaires were likely to drive

more safely than those that only completed the first questionnaire. Hence, it could be

said that participants that were willing to complete both questionnaires had an interest

in road safety as well as level crossing safety. Although the author sent reminder

letters to those participants that hadn’t returned the questionnaire within 3 weeks of

being posted, focus group research at the truck depots may have assisted with further

data collection for this group. However, the lack of financial resources did not allow

this to occur.

Fourthly, although a modified version of the DBQ was developed for this

sample of truck drivers, the purpose of the original DBQ was to measure driving

behaviours specifically amongst car drivers, not truck drivers. According to Sullman

et al (2002), the vehicle dynamics of a truck differ greatly to that of cars and therefore

this may influence the types of driving behaviour that truck drivers engage in. As

such, this must be kept in mind when interpreting the findings from the DBQ with the

heavy vehicle sample. Finally, although self-report data generally has limitations

(such as social desirability bias), a study of New Zealand truck drivers comparing

self-report speeds with mean speeds observed by the Land Transport Safety Authority

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(LTSA), found that truck drivers accurately reported the speeds at which they traveled

on the road (Walton, 1999). Such studies therefore provide support for the validity of

self-report measures of driving behaviour for heavy vehicle drivers.

6.6.3 Older driver sample limitations

One of the most obvious limitations with the older driver sample is that there

were several couples (i.e. husband and wives) that participated in the research. It

remains unknown if these couples completed the questionnaires entirely on their own,

or whether they talked about the questionnaires with each other during completion.

Although all participants were informed that they should complete the questionnaire

by themselves, no guarantees can be made about this as they were postal

questionnaires rather than one-on-one interviews. Additionally, each participant was

randomly allocated to receive either the intervention or control road safety message

(via telephone). Once again, although all participants were asked not to discuss what

message they received with their spouse, it is unknown if any couple talked about

what road safety message they heard when contacted by a research assistant.

Consequently, there may a small amount of contamination with questionnaires that

were completed by couples.

6.6.4 Younger driver sample limitations

There were two leading limitations with the younger driver sample. Firstly,

there was some undiscovered online survey saving problems (with one particular

instrument) that were not detected until both databases were received from the

external online survey company. As such, it was too late to contact participants to re-

enter their data for this instrument. It appeared that one particular page did not save

correctly for some participants, and consequently this instrument was not able to be

included in pre and post-test comparisons for this younger driver sample. The

discovery of this problem was very disappointing, as the author was particularly

interested in this instrument for the younger driver sample. The second limitation

for this road user group was that of participants completing more than one survey (at

both time points) to try to gain more movie tickets. As the data was cleaned and

checked rigoursly for any duplicate information (such as personal information like

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email addresses), approximately half a dozen duplicate entries were discovered. This

resulted in the sample being slightly smaller than originally thought to be when the

databases were received. This was certainly an interesting find by the author,

however was not surprising given that movie tickets were deemed by many younger

drivers (during focus group discussions) as being a great incentive for participation.

6.7 SUMMARY

With the assistance of Fishbein’s Integrated Model of Behaviour Change (IM),

an exploratory investigation of three different road user groups was achieved. Two

aims and eight research questions were addressed in the final study of the research

program. This summary outlines the findings relevant to each of the aims and

research questions.

The first aim was to examine what changes in driving intention and self-

reported driving behaviour are influenced by exposure to the intervention message.

Research Question 1: Does exposure to the intervention message produce safer

driving intention and self-reported driving behaviour at level crossings?

There were no significant changes to either driving intention or self-reported

driving behaviour in any of the three road user groups. However, given that the

intervention was a ‘one-off’ intervention due to limited financial resources for

implementing a large scale education program such as those developed by transport

authorities, this result was expected.

The second aim was to examine the personal, social and environmental factors

that influence driving intention to perform behaviours related to unsafe driving at

level crossings.

Research Question 2: Is there a relationship between gender and unsafe driving

intention among older and younger drivers?

A relationship between gender and unsafe driving intention was only found for

the younger driver sample. Young male participants were significantly more likely to

report both current and future driving at level crossings to be less safe than their

female counterparts. This is not surprising given the amount of literature supporting

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research that young males are more likely to take risks whilst driving generally

(Laapotti and Keskinen, 1998, Arnett, 2002) or be less ‘safety-orientated’ than their

female counterparts (Meadows and Stradling, 1999). However, this finding has

implications for targeting younger drivers as it appears that educational programs may

need to direct efforts largely towards young male drivers as opposed to their female

counterparts. The challenge remains for program designers to implement

interventions that have the prospect to produce lasting behaviour change in young

males.

Research Question 3: Is there a relationship between distance traveled per week

and unsafe driving intention among heavy vehicle drivers?

A relationship was found between distance traveled per week and unsafe driving

intention in the heavy vehicle driver sample. A strong association was found between

the number of kilometres driven per week in a heavy vehicle and driving intention at

level crossings. Although it is unknown if the participants that reported longer

distances traveled were abiding by fatigue management policies and practices by

exceeding prescribed driving hours, a number of safety issues are known to be

associated with increased driving exposure. “A direct consequence of the long hours

worked by drivers is to resort to stimulant drugs. Drivers use drugs not for pleasure

but to combat fatigue and stay at the wheel longer” (Quinlan, 2001, p4). Research

conducted by Williamson et al (2003b) found that there is a strong association

between tight schedules, delivery time bonus/penalties and performance-based

payment systems (e.g. kilometre-based rates) and both chronic injury and the

propensity of drivers to engage in dangerous driving practices (such as speeding and

excessive hours). Given that speeding heavy vehicles are significantly over-

represented in crashes, the finding from the current research requires further

investigation.

Research Question 4: Is there a relationship between familiarity and unsafe

driving intention?

Familiarity with driving at level crossings was found to be associated with

unsafe future driving intention within all three samples. Some literature has suggested

that familiarity may encourage motorists to take greater risks (Abraham, 1998).

According to Pickett (1996) drivers may “also base their decision to cross on their

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previous experience of either the same crossing (familiarity) or of other crossings

(association). The concept of mental set says that when people are exposed to the

same phenomenon repeatedly, they come to expect it” (p5). Pickett (1996) also

suggests another situation, with drivers familiar with one crossing, transferring their

experience to a new crossing and not being as vigilant in the new situation. However,

Wigglesworth (1979) proposes that determining an accurate picture of familiarity with

a particular level crossing is difficult. In Wigglesworth’s study of 85 consecutive

fatalities in Victoria (between 1973 and 1977), he found that 73 of the 85 motorists

(86%) that were fatally injured were considered to be familiar with the crossing at

which their collision occurred (Wigglesworth, 1979). These motorists lived within

one mile of the level crossing. The finding that familiarity is associated with higher

levels of risk taking has significant implications for all three road user groups, but

particularly the heavy vehicle industry given the potential for a major catastrophic

event to occur if a truck collides with a train.

Research Question 5: Is there a relationship between attitudes and unsafe

driving intention?

No relationship between attitudes and unsafe driving intention was found for

any of the driver samples. Attitudes are defined as a person’s overall evaluation of a

particular behaviour leading to either positive or negative outcomes (Lajunen, 2004a).

Given that some research has suggested that attitudes have a greater influence on

driving intentions in a ‘safe’ driving environment while normative influences were

more influential in a ‘risky’ driving environment (Trafimow and Fishbein, 1994), this

finding is not surprising. The current research findings support Trafimow and

Fishbein’s (1994) investigation suggesting that normative factors appear to be more

influential in intentions to drive unsafely at level crossings by all three road user

groups than attitudinal factors.

Research Question 6: Is there a relationship between self-efficacy and unsafe

driving intention?

A relationship between self-efficacy and unsafe driving intention was found for

both the older and younger driver samples. Self-efficacy has been defined as an

individual’s capacity to organise, control and execute certain behaviours to attain

specific performances (Bandura, 1977a). However, since self-efficacy is regarded as

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a coderterminant of behaviour (Ajzen, 1991), it relies on the accuracy of an

individual’s perception of their control over a driving situation (Sheeran, 2003). Self-

efficacy was shown to be central for both the younger and older driver samples. Older

drivers reported lower levels of self-efficacy of driving at level crossings as the

majority believe that other motorists influence their driving and their ability to obey

the road rules. Such findings however contradict previous research into speeding.

Elliot et al (Elliott, 2003b) in their research found that older drivers had greater

perceived behavioural control (perceived self-efficacy) then younger drivers when it

came to complying with speed limits. Self-efficacy was also found to be associated

with the driving intention of younger drivers. The current research produced similar

results to previous research with younger drivers. Elliot et al (Elliott, 2003b) in their

research about driver’s compliance with speed limits found that younger drivers (and

particularly males) had lower levels of self-efficacy than older drivers. Some research

has indicated that individuals with higher levels of self-efficacy report significantly

greater rates of success in changing behaviour (Wells-Parker et al., 2000). However,

one of the major assumptions of Fishbein’s Integrated Model (IM) is that self-efficacy

has primarily an indirect effect on behaviour (via their effects on intention) (Fishbein

et al., 2003). Thus, if young drivers have already formed strong intentions to perform

unsafe driving at level crossings (but are not doing so), then little will be

accomplished by developing interventions designed to increase self-efficacy (Fishbein

et al., 2003). This has important implications for designing educational programs for

young drivers.

Research Question 7: Is there a relationship between subjective norms and

unsafe driving intention?

A relationship was found between subjective norms and unsafe driving intention

for both the older and younger driver samples. Subjective norm consists of a person’s

beliefs about whether significant others (such as friends, family or colleagues) think

that they should engage in a particular behaviour (Conner, 1996). Older driver

participants hold the belief that other motorists do not obey the rules at level crossings

as often as their important others (i.e. family and friends). This finding also supports

the formative research undertaken with older drivers in that they hold the belief that

their age group drives more safely at level crossings than other road user groups.

Although older drivers are generally not characterised by risk-taking behaviours, their

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driving errors reflect functional, cognitive and perceptual impairment (Eby, 1998b).

Although the current research is not able to confirm causal relationships per se, it has

highlighted that prevalence of functional impairments in older drivers aged 60 plus

years. Further research should be directed at examining any associations between

cognitive and perceptual impairment for driving at level crossings for this age group.

For the younger driver sample, there was a small to medium effect size found

for normative beliefs. The observed shifts in normative beliefs for this age group (17-

24 years) may possibly be due to the perceived risk of the driving environment at

level crossings. Some research has suggested that attitudes have a greater influence

on driving intention in a ‘safe’ driving environment, while normative influences are

more influential in a ‘risky’ driving environment (Trafimow and Fishbein, 1994). Of

the three road user groups, younger drivers hold a greater perception of risk whilst

driving at level crossings. Therefore, it could be argued that the level crossing

environment is perceived a ‘risky’ driving environment by younger drivers, which

would explain why normative beliefs are significantly changed over time compared to

other outcome variables.

Research Question 8: Is there a relationship between beliefs about

environmental constraints and driving intention?

Although the heavy vehicle group reported greater frustration with

environmental factors such as the design of the approaching road and flashing lights

being difficult to see with the sun, there was no association with driving intention in

any of the three samples.

The subsequent chapter will combine the findings of all three studies and

endeavor to amalgamate the implications of the research in pursuit of attaining an

improved understanding of motorist behaviour at level crossings. Such pursuits will

hopefully create a more comprehensive approach to the future development of

effective educational countermeasures. This chapter also considers the contribution of

the research findings to the management of risk at level crossings in Australia.

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CHAPTER SEVEN: DISCUSSION

7.1 Introduction …………………………………………………………… 278

7.2 Overview of the studies, methodology and key findings ……………... 280

7.3 Implications for level crossing safety …………………………………. 296

7.4 Strengths and weaknesses of the research …………………………….. 300

7.5 Suggestions for future research ……………………………………….. 302

7.6 Recommendations to industry ………………………………………… 302

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7.1 INTRODUCTION

Level crossing collisions in Australia are a major cause of concern for both rail

and road authorities. When a catastrophic collision occurs (such as a heavy vehicle-

train collision resulting in a derailment with numerous fatalities), it receives great

media attention, and the actions of rail/road infrastructure providers and the behaviour

of motorists comes under close scrutiny. It must be noted though that as long as level

crossings present inherent dangers to motorists (and as long as they exist), rail

authorities will remain at risk of being found liable (either in whole or in part) for

fatal collisions (Stephen, 2002). With more than 9400 level crossings throughout

Australia and limited funds for upgrading crossings, it is an arduous task for rail

authorities to provide engineering systems that will completely protect all motorists

from involvement in a collision. Historically, research in the level crossing arena has

focused on engineering solutions to protect both rail and road users, however more

recently, the rail industry has sought new approaches to improving risk management

initiatives. One such approach is the examination of motorist behaviour at level

crossings. To date, little rigorous research has been conducted that has either

explored the determinants of behaviour and associated key constructs or measured

changes in behavioural constructs or statistically significant interactions between such

constructs. Additionally, no research has been guided by psychological theory in

exploring motorist behaviour at level crossings. As a consequence, little is known

about the driving intentions of motorists and their beliefs of factors influencing their

driving in these complex environments. Without such research, it is difficult for

transport authorities to justify expenditure on campaigns targeting behaviour.

The ultimate goal to improve level crossing safety would be to have a

combination of engineering, education and enforcement countermeasures for all level

crossings. However, the small number of fatalities in comparison to the national road

toll limits this. To put level crossings into perspective, such collisions are a

significant problem for the rail industry, yet they represent less than 1% of the

national road toll and are rare events. However, they remain tragic events that cause

tremendous personal, social, financial and infrastructural anguish. Since vehicle-train

collisions are rare events, it is difficult to obtain valid information about preceding

behaviour and the frequency of near-misses. However, unintended road user error has

been blamed by transport authorities as playing a significant role in such collisions.

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Anecdotal evidence also suggests that risk taking at level crossings makes up a large

proportion of near-miss incidents. It must be noted that the act of driving is taken for

granted by most motorists, however it is a complex task that is dependent on a variety

of cognitive and psychomotor performance abilities to be intact, such as alertness,

attention, multitasking, memory, coordination, and visual spatial perception (Moller,

2004). Hence, from a systematic point of view, level crossing collisions are far more

complex than the immediate causal factor being the non-observance of the road rules.

This background provided the foundation for developing and guiding the three studies

conducted in this program of research.

The primary aim of the research program was to investigate the present context

of motorist behaviour at level crossings in relation to key variables of attitudes,

norms, self-efficacy (perceived behavioural control), perceived risk, environmental

constraints and the skills/abilities of drivers. Three studies were conducted to

investigate high risk and vulnerable road user groups, the relationship between

behavioural constructs, and changes in driving intention after exposure to an

intervention. Both qualitative and quantitative data collection methods were utilised to

provide methodological triangulation and reliability of the data. Prior to this research

program, there have been no documented attempts to investigate the present context

of motorist behaviour at level crossings or the usefulness of targeted educational

interventions for specific road user groups. Given the legal obligations of rail and road

authorities, as well as the public’s scrutiny of protection systems at level crossings, a

greater understanding of motorist behaviour and the context of safe driving at level

crossings remains an important priority for rail/road authorities, legislators,

investigators and researchers alike.

In this chapter, the foremost empirical findings from this research program are

examined and interpreted in relation to the exploration of Fishbein’s integrated model

(IM), as well as practical implications for the future development of government

‘campaigns’ to improve motorist vigilance and promote abidance of road rules at level

crossings. Furthermore, a summary outlining the findings relevant to each of the aims

and research questions is presented.

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7.2 OVERVIEW OF THE STUDIES, METHODOLOGY AND KEY FINDINGS

A variety of data collection methods were used in this research program as

much of what is currently known about level crossing crashes is derived from

coroner’s findings and crash statistics. Since there are relatively few fatalities per

year (in relation to other fatal road crashes) it is difficult to determine patterns of

contributing factors or high risk road user groups. Therefore, a variety of data

collection measures were utilised to ensure ‘methodological triangulation’ (i.e. the use

of more than one method to investigate a phenomenon).

Study One was designed to extend the current knowledge of fatal collisions at

level crossings by undertaking a more thorough examination of contributing factors to

level crossing crashes and the road user groups at risk. This study applied the

principles of methodological triangulation by using both quantitative and qualitative

research methods. Quantitative data was collected using a modified Delphi technique,

whilst qualitative data was collected using focus group discussions. With the

discipline of road safety research requiring methodological strategies that will

enhance efforts to conceptualise the multi-faceted nature of motorist behaviour at

level crossings, this application provided the robustness required.

Study Two involved formative research as part of the planning, development

and delivery of behavioural interventions for each of the three road user groups

identified in Study One. This study also used both qualitative and quantitative data

collection methods to provide methodological triangulation and ensure reliability of

the data. The overall objective of the qualitative data collection was to obtain rich data

using a qualitative mode of inquiry, based on the key variables of attitudes, norms,

self-efficacy (perceived behavioural control), perceived risk, environmental

constraints and the skills/abilities of drivers. The overall objective of the quantitative

data collection was to prioritise the issues identified in order to direct and allocate

project resources for intervention planning, development and delivery. This combined

recruitment strategy was adopted as it was an appropriate and practical data collection

strategy within the qualitative and exploration methodology. Information obtained

from each of the groups was critical in assisting, guiding, and identifying priority

areas for message and material development. The use of focus groups and one-on-one

interviews provided insights into why drivers think or do what they do at level

crossings.

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Study Three involved three parts. The aim of Part One of this study was to

develop targeted interventions specific to each of the three road user groups using

Fishbein’s integrated model. The development of interventions was originally seen as

being outside of the scope of this project, however it became intertwined in

questionnaire development and thus deemed to be within the realms of the current

mode of inquiry. The interventions were designed in the format of a pilot radio road

safety advertisement, as this medium was found to be one of the most acceptable to

each of the road user groups as identified in the formative research undertaken in

Study Two. The interventions were used as a ‘one-off’ awareness raising intervention

for each road user group. Part Two involved the investigation of the present context

of unsafe driving behaviour at level crossings. This second part involved the

examination of the present context of motorist behaviour at level crossings using key

constructs from Fishbein’s Integrated Model of Behaviour Change (IM). This study

did not assess the capacity of Fishbein’s model, but rather used it to assist in

exploration of the issues surrounding unsafe driving at level crossings. This issue is

also an important one to examine as no research has ever been undertaken in either

Australia or overseas that has explored the determinants of behaviour and associated

key constructs. Part Three involved trialing a pilot road safety radio advertisement

using an intervention and control methodology. This part investigated the changes in

pre and post-test constructs including intentions, self-reported behaviour, attitudes,

norms, self-efficacy/perceived behaviour control, perceived risks, environment

constraints and skills/ability. This issue is an important one to address as there is no

documented literature that has examined educational interventions for level crossing

safety by explaining intention using a psychosocial theoretical model.

7.2.1 High risk and vulnerable road users at level crossings

Study One provided evidence that there are three main road user groups at risk

of being involved in a level crossing collision. Data collected via a modified Delphi

technique (panel of experts) established that there are four road user groups at ‘high

risk’ of a level crossing collision: heavy vehicles, older drivers, younger drivers, and

rural drivers. Additionally, there were four key areas that experts believed were major

motorist factors that contribute to collisions at level crossings. These included:

• Intentional behaviour – disobeying road rules and trying to beat the train;

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• Unintentional behaviour – inattention, distraction, complacency;

• Training – drivers are not trained to negotiate level crossings; and

• Understanding – consequence and severity of collisions with trains.

For each of the road user groups identified by the modified Delphi technique

(panel of experts), a number of items that may contribute to increased risk were

presented for ranking. For the older driver group, there was a high level of agreement

that errors in judgment (e.g. misjudging time needed to cross safely) were ‘very

important/important’ risk factors. For the younger driver group, nearly all of the

experts ranked trying to beat the train across the crossing as ‘very

important/important’. Factors for the heavy vehicle group that were ranked as ‘very

important/important’ included: trying to beat the train across the crossing and length

of vehicle causing overhang on the crossing. Factors particular to rural road users that

were ranked as ‘very important/important’ included: low expectation of a train;

complacency due to familiarity and not scanning for a train at give way signed

crossings.

Data collected from focus group discussions with train drivers in both

metropolitan and regional areas revealed that there are strong differences in

experiences of motorist behaviour at level crossings. The metropolitan train drivers

generally experienced motorist behaviour at active crossings with flashing lights and

boom gates while the regional train drivers experienced behaviours at active crossings

with boom gates, crossings with lights only and passive crossings with stationary

signs. In the metropolitan train driver group, experiences of motorist behaviour at

level crossings included: motorists driving around boom gates, getting stuck under

boom gates, queuing over congested crossings and driving through the crossing after

the red lights commence flashing. The behaviour of motorists driving around boom

gates was noted to occur quite regularly. The majority of metropolitan train drivers

reported that it was a common occurrence for motorists to drive through a crossing

when the lights are flashing both before and after the booms were activated and some

crossings were named as ‘black spots’ (locations where motorists repeatedly violate

the road rules). Vehicles protruding into the path of the train and motorists entering

congested crossings and then panicking and driving backwards into the boom gates

were also mentioned.

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Regional train drivers indicated that motorists not stopping or giving way to

trains is a continual problem at passively controlled crossings (i.e. no boom gates or

flashing lights). Regional train drivers generally agreed that the majority of motorists

obey protection systems; however some motorists drive through flashing lights or

drive around boom gates. Other high risk behaviours included motorists attempting to

beat the train across the crossing, speeding up to go through flashing lights, and

general risk taking by younger drivers in particular. Motorists not allowing enough

time to cross in front of the train or hesitating (stop-starting) at crossings were also

noted to be at high risk. There was a general perception by regional train drivers that

motorists are unable to judge the speed and distance of an approaching train to

determine a safe gap during which to cross. Local motorists were also reported to be

a problem at level crossings for regional train drivers. There was a general consensus

that motorists that are familiar with certain crossings hold low expectations of trains

being at a crossing and therefore are more likely to be complacent with scanning of

trains and the road rules. Interestingly, there was general agreement by the regional

group that about 60% of ‘near misses’ are due to poor visibility (such as angle of

approach) with only 30-40% actually due to the motorist. In the regional group,

motorist inattention and distraction were common issues identified, with the concepts

of inattentional blindness (not seeing the train coming) being a key concern

mentioned. However, the main cause of concern for both regional and metropolitan

train drivers was noted to be heavy vehicles.

A theme common to regional and metropolitan train drivers was the risk of

catastrophic consequence associated with level crossing collisions. The reasons given

for this were the threat of derailment, serious property damage, the high risk of a

fatality, personal injury and, most earnestly, the potential for enduring psychological

consequences. Drivers uniformly spoke about the continual fear they had of being

involved in a collision with a heavy vehicle, and many spoke of the effects that such

collisions had on train drivers involved. For this reason, train drivers were said to

consider any near-miss incident involving trucks particularly serious. This is in

marked contrast to the general view of near-crashes involving cars, which most train

drivers seem to rationalise and dismiss as a minor danger and acceptable part of their

job. Another emergent theme relating to heavy vehicle size and mass was the fact that

heavy vehicles have significantly more difficulty negotiating level crossings

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successfully. This concern was reflected by the regional and metropolitan drivers in

different ways.

While regional train drivers were more inclined to discuss the difficulties heavy

vehicle drivers face in gauging the time required to cross the crossing safely, their

metropolitan counterparts cited concerns related to unintentional overhang of long

vehicles. This overhang was said to be especially prevalent in high traffic areas, where

a heavy vehicle driver underestimated the space required to clear a crossing, and were

thus unable to obtain safe clearance. Both the regional and metropolitan drivers

reached consensus that these risks often existed not because of dangerous behaviour

on the part of the heavy vehicle drivers, but inadvertently from a general ignorance as

to the actual size of their vehicle. Urban train drivers were further inclined to implicate

traffic, road design and crossing location as factors increasing this risk.

The consensus of train drivers regarding risk caused by unsafe heavy vehicle

motorist behaviour, was that it occurred frequently and willfully. A common

perception, especially among the regional train driver group, was that heavy vehicle

drivers often deliberately increased their speed in order to ‘beat the train’ across the

crossing. Attributions for this behaviour centered on a perceived desire to avoid delay.

While some drivers took the view that this behaviour was generated by impatience or

recklessness, others cited the intense time pressures that truck drivers are often placed

under. To this end, the drivers in the regional sample were able to cite that drivers

employed by two specific trucking companies were chief offenders. The wilful unsafe

behaviour by the heavy vehicle drivers emerged as a more serious problem for the

regional sample, with many examples of repeated violations at crossings suggesting it

to be of primary concern. However, train drivers within the metropolitan sample were

also able to identify variations of this behaviour, such as the deliberate avoidance of

boom gates.

For both the regional and metropolitan train driver focus groups, lack of

enforcement at level crossings also emerged as strong themes. Additionally, motorist

knowledge was also thought to be a contributing factor to behaviour with a general

consensus by both regional and metropolitan train drivers that there is low level of

knowledge of a train’s stopping distance, public misconceptions on the ability of the

train to stop and the poor understanding of the meaning of warning lights.

Furthermore, both regional and metropolitan train drivers reported that motorists in

general do not have the ability to judge the distance of a train or the speed of an

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approaching train. Evidence of this perception was noted to be the frequency of near-

misses with vehicles at crossings.

7.2.2 Planning and development of interventions for each road user group

Study Two was undertaken as part of this research program involved formative

research as part of the planning, development and delivery of behavioural

interventions for each of the three road user groups identified in Study One. This

study also used both qualitative and quantitative data collection methods to provide

methodological triangulation and ensure reliability of the data. The overall objective

of the qualitative data collection was to obtain rich data using a qualitative mode of

inquiry, based on the key variables of attitudes, norms, self-efficacy (perceived

behavioural control), perceived risk, environmental constraints and the skills/abilities

of drivers. The overall objective of the quantitative data collection was to prioritise

the issues identified in order to direct and allocate project resources for intervention

planning, development and delivery. This combined recruitment strategy was adopted

as it was an appropriate and practical data collection strategy within the qualitative

and exploration methodology. Information obtained from each of the groups was

critical in assisting, guiding, and identifying priority areas for message and material

development.

7.2.2.1 Qualitative research

Qualitative formative research from each of the three road user groups revealed

that risk taking behaviours and attitudes differed greatly between the three road user

groups as well as between urban and regional settings.

For the older participants, most displayed high levels of knowledge and reported

low risk taking behaviour, with age-related factors acknowledged by the group as

being risk factors for their involvement in a collision. No older participant admitted

to intentionally violating or would be willing to violate the road rules at level

crossings such as driving through activated warning systems or failing to stop/give-

way to trains at passive crossings. Many participants mentioned the difficulty they

face with the ageing process and how it influences their driving ability. Decreased

vision, poorer hearing, slower reaction times and reflexes were the predominant

factors that were declared to pose problems when driving at level crossings. Poor

visibility at nighttime (due to degeneration of their vision) was also stated by many

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participants as being a problem for driving at level crossings. This was a reason that

many participants stated that they avoid driving through crossings at night or when

there are low levels of light (such as dusk or dawn). Some participants mentioned that

their reflexes and flexibility were not the same as they used to be. Reflexes and

flexibility were felt to be an issue at crossings that have a sharp angle of approach

where participants have to turn to look back up the track. Many participants in this

group indicated they as they got older, driving generally became harder physically for

them. Quite a few participants also suggested that due to degenerating vision and

hearing that driving at level crossings was stressful for them and that they would

avoid situations that they were not confident driving.

Although both the urban and regional older groups indicated that they don’t take

risks at level crossings, many participants in the urban group mentioned the

difficulties that they sometimes face when driving in such complex traffic

environments (such as at busy urban level crossings). Behaviour of other motorists

was considered to be an issue with driving safely at level crossings for the older urban

group. Most participants felt that other motorists put pressure on them by beeping the

horn, yelling at them, or driving around their vehicle when they are waiting for

flashing lights to cease flashing. The majority of participants felt that such behaviours

were unnecessary and that younger drivers were largely responsible for such

behaviours.

The most notable differences between regional and urban older group responses

were related to risk perception and knowledge about the different types of protection

systems. Regional motorists appear to believe that they are at greater risk of being

involved in a level crossing collision with many recalling near-misses. The main

reasons given for such near-misses largely included complacency due to familiarity of

local level crossings. On the other hand, urban motorists generally held the

perception that level crossings were not as dangerous as other aspects of driving, with

many participants doubtful that motorists die at such intersections. Many participants

in the urban group also were surprised that transport authorities were investing

financial resources into level crossing education programs for motorists as the

majority believed that it is not a major road safety priority. Urban motorists also had

a poor level of knowledge about the different types of protection systems, other than

active systems with boom gates (such as in metropolitan areas), with some

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participants unaware that some level crossings have no active protection system to

warn motorists of an approaching train.

According to the majority of participants in the older driver group, educational

strategies would best be focused on reminder or re-affirming messages as they believe

they always obey crossing rules and try to drive as safely as possible. Both regional

and urban participants felt that ‘informational’ television campaigns would be

effective for many participants in this group, especially when combined with visual

effects. Additional to television campaigns, the majority of participants indicated that

talk-back radio would be an effective delivery method as they frequently listen to the

radio and value the sources of information that talk-back radio provides. Public talks

from police, ambulance officers or train drivers were also seen as being valuable to

this age group. Hence, radio messages or public talks at social groups appeared to be

the most acceptable method of delivery for educational campaigns for the majority of

participants in the older group.

Younger drivers demonstrated low risk perception of the consequences to

unsafe driving behaviour at level crossings, with risk taking being reported at high

levels for this group. Participants that spoke of performing high-risk behaviours (such

driving through flashing lights whilst the boom gate is descending) indicated that they

felt that their chances of being hit by a train were small. The majority of these

participants that admitted to such risk taking behaviours indicated that this type of risk

taking was less dangerous than other risk taking behaviours such as drink driving,

speeding or driving through red lights at an intersection. Some participants stated that

they frequently disobey the rules at level crossings and have never had a near-miss

with a train. The reasons for such justifications included: infrequent trains, being able

to judge the distance that the train is from the level crossing, and that trains will slow

down if they observe a car at a crossing. Those participants that perceived there was

some degree of risk of being involved in a collision, thought it would be due to either

equipment malfunction (such as faulty boom gates/flashing lights) or stalling their car

at the crossing. Many participants in this group raised the concern that if they stalled

their vehicle on the crossing then this would be extremely dangerous. Additionally,

the majority of this group believed that at crossings with low train traffic volumes

they are less likely to be involved in a collision than at crossings with more train

traffic volumes. Male participants were more likely to perceive that being familiar

with certain crossings would make them less likely to be involved in a collision with a

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train, while female participants were more likely to hold the perception that driving at

crossings on a regular basis would make them more likely to be involved in a collision

with a train due to being on ‘auto-pilot’. Peers as passengers were also seen as a key

element in driving safely at level crossings, with many participants stating they would

take more risks when driving by themselves. The risk of damaging their car was a big

influence on whether younger driver participants drove safely at crossings or not.

Some participants stated their car was very important to them and if they were

involved in a collision with a train (and not injured) then they would be distressed if

their vehicle was destroyed as they had ‘saved up’ their money to purchase it.

Overall, there was very little difference between regional and urban group

responses. Both groups believe that they are at low risk of being involved in a

collision with a train as they believe that they have the skills and abilities to avoid a

collision. Interestingly though, some participants in both groups indicated that they

find it difficult to judge the distance a train is from a crossing which appears to

oppose such views of being able to avoid a collision. Impatience for waiting at

crossings was evident in both the urban and regional groups, with regional drivers

being frustrated with the time it takes for freight trains to pass while urban drivers

were frustrated with waiting for protection systems (i.e. boom gates or flashing lights)

to turn off. The regional group reported a high acceptance of a range of risk taking

behaviours (‘beat the train’), while participants in the urban group tended to report

lower risk taking behaviours (poor knowledge of road rules). Of particular concern

with both the regional and urban younger drivers was their willingness to take risks.

Like the inconsistency in both group’s perceived level of risk at level crossings, there

appeared to be a paradox in self-reported driving behaviour. Most participants in both

groups stated that they frequently take risks at crossings (such as driving through

flashing lights or when a boom gate is descending), however the majority of

participants mentioned that other motorists caused them to take such risks.

Nearly all of the younger participants thought that mass media campaigns were

effective in changing their and others behaviour. The most acceptable method of

delivering educational interventions for this group appears to be through radio or

television. Some participants stated that they thought shock television campaigns were

the best for young drivers while other participants indicated that they would just tune

out to shock style campaigns. A scenario involving risking their friends’ lives when

driving unsafely was a common theme for developing messages.

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Heavy vehicle drivers indicated a high level of knowledge of safety issues, with

engineering and design being a major factor contributing to near-misses or the risk of

being involved in a collision at a level crossing. A unanimous complaint from heavy

vehicle drivers was that level crossings are not designed in a manner which is user-

friendly to their vehicles. The metropolitan environment was cited as a primary

example, where choice of crossing location, traffic and other roadway factors can lead

to unintentional short-stacking and overhang (e.g., required to stop while part of

vehicle remains on crossing). On the other hand, regional crossings were also said to

cause difficulties, with design faults and location choice having detrimental effects on

sight distances and train visibility. A further comment was inadequate warning of

approaching crossings, which drivers perceived greatly reduced their ability to take

appropriate action. Heavy vehicle drivers suggested that such shortcomings in design

and protection systems were responsible for the majority of unsafe driver behaviour

observed at level crossings.

However, a high level of familiarity with their routes and with the crossings

they encountered while traveling these routes, was also nominated by heavy vehicle

drivers as a major factor associated with their crossing behaviour. Here, it was discussed

that drivers sometimes suffered lapses of concentration, resulting in them failing to follow

appropriate safe crossing behaviour at level crossings. Drivers tended to defend their own

perceived complacency as a function of high levels of familiarity with the crossings and,

furthermore, expressed a degree of confidence in their ability to identify which crossings

were most dangerous and required most attention. While a practical approach on the

surface, this familiarity was sometimes observed to breed overconfidence in their abilities

to control their vehicle, which resulted in the drivers being generally less inclined to

engage in safe crossing behaviours.

Though the heavy vehicle drivers were generally inclined to identify the above

two factors as the predominant causative agents of their unsafe crossing behaviour, a

minority raised the spectre of wilful risk taking among drivers. These reports included

both confessions of individual involvement, as well as second-hand reports from and

observations of other drivers’ behaviour. The most prominent motive for unsafe

behaviour was a desire to avoid delay in getting to their destination. These drivers

frequently cited the delay caused by not only waiting for trains to pass, but also the

significant time lost involved in deceleration and re-acceleration. Others were more

direct, citing the personal frustration and impatience associated with stopping at

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crossings for professional drivers. These participants stated that trains should be

slowed down at crossings with large volumes of heavy vehicles.

At the core of each of these behaviours were perceptions of time pressures due

to the rigid timetabling imposed by the trucking companies. Many drivers attempted

to rationalise their behaviour by stating that, although they knew not obeying level

crossing warning systems to be dangerous, they managed their risk level by limiting

this behaviour to crossings where they perceived such behaviours to be ‘safe’. It is

worth noting that drivers were, in general, uncomfortable when discussing wilful risk

taking, often attributing it to ‘other drivers’. This points to a general awareness that this

behaviour is not perceived as responsible, safe or socially acceptable.

There were mixed opinions about educational interventions targeted towards

heavy vehicle drivers, with most drivers indicating that the most acceptable methods

would be in sharing of personal experiences (through trucking magazines), awareness

raising (through company training sessions) or radio messages.

7.2.2.2 Quantitative research

For the quantitative component of Study Two’s formative research, both train

drivers and a panel of experts completed questionnaires to ascertain differences in

beliefs of high risk behaviour by motorists at level crossings. This 19-item

questionnaire (using Likert scales) was developed from results obtained from the

qualitative formative research conducted with each of the three road user groups.

Themes that emerged from this qualitative research (i.e. high risk behaviours of

drivers) were selected by a panel of three road safety experts (separate to those

experts participating in this survey). An independent t-test was conducted to compare

overall mean scores between the two groups: train drivers and experts. There was no

significant difference between overall mean scores. However, both experts and train

drivers ranked the items driving around the boom gates and trying to beat the train

across the crossing as being the highest risk taking behaviours. Experts also ranked

overtaking cars stopped at the crossing as being very high risk, while train drivers

ranked drive in front of a train when it is close to the crossing as being a very high

risk taking behaviour. Overall, experts were observed to rank the behaviours lower

than train drivers. This may be due to the nature of train drivers observing a large

number of near-misses throughout their careers and the possibility that they

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experience great frustration with motorists that take risks at level crossings (findings

from the focus groups discussions with train drivers in both urban and rural areas).

7.2.3 The present context of motorist behaviour

There were three parts to the final study (Study Three). The aim of Part One of

this study was to develop targeted interventions specific to each of the three road user

groups using Fishbein’s model (Integrated Model of Behaviour Change). Part Two

involved the investigation of the present context of unsafe driving behaviour at level

crossings. This included examining the personal, social and environmental factors

contributing to unsafe driving intention and driving behaviour at level crossings. Part

Three involved trialing a pilot road safety radio advertisement using an intervention

and control methodology. A summary of the findings from Part Two and Part Three

are presented below.

7.2.3.1 Heavy vehicles at level crossings

Research undertaken with heavy vehicle drivers in Study Three suggested that

truck drivers hold a number of beliefs in relation to other motorists and environmental

constraints at level crossings. Firstly, heavy vehicle participants indicated that other

motorists drive unsafely more frequently than their colleagues or friends. This finding

is not surprising, given the comradeship that is known to exist amongst truck drivers.

Secondly, the majority of participants believe that they will never be involved in a

level crossing collision, although some indicated that it is acceptable to sometimes

disobey road rules at level crossings. Given that level crossing collisions are rare

events, it is not surprising that this small sample would have a low perception of risk

at level crossings. What is surprising is that participants believe that it is generally

not possible to judge a train’s speed, but that it is still safe to cross when warning

systems indicate that there is a train approaching the crossings. Two environmental

(or engineering) factors were considered to be the major cause of unsafe crossing

behaviour by truck drivers: blinding sun making it difficult to see if flashing lights are

activated and the design of the approaching road (such as an ‘S’ bend) makes it

difficult to see if a train is approaching a crossing. These two issues were a recurring

theme that was raised by truck drivers at different stages of the research program.

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The most common solution raised by participants is advanced warning systems (on

the road approaching the level crossing).

Truck drivers that work shifts were found to be significantly more likely to take

risks when driving generally on the road and also at level crossings. These shift

workers were also considerably more likely to hold the belief that environmental

constraints affect their ability to drive safely at level crossings. Given that flexible

approaches to prescriptive driving hours regulations may lead to improved road safety

outcomes (Mabbott, 2001), one would expect that reducing shift workers exposure to

level crossings through route changes, may have a long-term effect on level crossings

collision rates for the heavy vehicle industry. Familiarity was also a key issue with

driving a truck at level crossings. Participants that were familiar with driving at

crossings with only flashing lights recorded higher levels of unsafe driving at level

crossings generally, slightly higher risk taking driving behaviour and higher levels of

self-efficacy than unfamiliar drivers. The current study gained information relating to

self-reported driving behaviour at level crossings as well as self-reported exposure

(i.e. how frequently a participant crosses a particular protection system). The purpose

of obtaining such data was to gain information on exposure to the three types of

protection systems at level crossings. The finding that familiarity is associated with

higher levels of risk taking has significant implications for the heavy vehicle industry

given the potential for a major catastrophic event to occur if a truck collides with a

train.

As can be seen, there are a number of findings from this research that have

implications for improving level crossing safety for both heavy vehicle drivers and the

greater public. However, the nature of professional truck driving (i.e. commercial

practices and arrangements) poses some significant obstacles to achieving improved

safety outcomes. Firstly, with the increasing adoption of ‘just-in-time’ ware housing

polices comes an increase in the importance of the express freight sector, as well as

both overnight distribution and long distance transport (George, 2002a). Secondly, in

recent years that has been significant growth in specialist vehicle types (such as B-

doubles) which has changed the mix of heavy vehicles on the road as well as a higher

proportion of heavy vehicles in the traffic stream (George, 2002a). The combination

of these two factors weighs heavily on whether or not safety can be maintained, let

alone improved, at level crossings.

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7.2.3.2 Older drivers at level crossings

There were a number of interesting findings from the older driver sample in

Study Three. Older drivers were asked a number of questions about their health and

driving ability. Of greatest concern is that many participants reported substantial

decline in their hearing, restricted range of motion to their neck and having increasing

trouble adjusting to glare and night-time driving. These factors have significant

implications for older drivers (60 plus years) traversing level crossings. Firstly,

hearing warning systems and trains approaching a crossings (passive crossings in

particular), is fundamental in ensuring safe driving behaviour. Secondly, with

restricted movement to the neck whilst driving, adequate scanning for trains at

crossings that have approach angle that is oblique (i.e. an angle, such as an acute or

obtuse angle, that is not a right angle or a multiple of a right angle) would be sub-

optimal for safely negotiating such level crossings. Thirdly, have difficulty with glare

when driving may have an affect on the ability of older drivers to accurately observe

if flashing lights are activated at crossings without boom gates. These three factors

may very well play a critical role in the over-representation of older drivers in level

crossing collisions.

Self-efficacy was shown to be central for older drivers at level crossings. Older

drivers reported lower levels of self-efficacy of driving at level crossings as the

majority believe that other motorists influence their driving and their ability to obey

the road rules. Such findings however contradict previous research into speeding.

Elliot et al (Elliott, 2003b) in their research found that older drivers had greater

perceived behavioural control (perceived self-efficacy) then younger drivers when it

came to complying with speed limits. Within the current study, anecdotal evidence

from experts in the field (Study One) revealed that older drivers may feel pressured by

other motorists to take risks at level crossings. The beliefs held by experts in Study

One were observed to have support by the findings of Study Three. Subjective norms

of other motorists driving were also found to score higher for older drivers. Older

driver participants hold the belief that other motorists do not obey the rules at level

crossings as often as their important others (i.e. family and friends). This finding

supports the formative research undertaken with older drivers in that they hold the

belief that their age group drives more safely at level crossings than other road user

groups. Also of interest is the poor level of knowledge of the road rules at level

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crossings. Anecdotal evidence had suggested that many motorists are not familiar

with the road rules at level crossings; this older driver sample certainly supports this.

Like the heavy vehicle participants, familiarity with driving at level crossings

was found to be a factor related to unsafe driving of older drivers both generally and

at level crossings. Older drivers that were familiar with driving at crossings with

boom gates recorded higher levels of unsafe driving generally than unfamiliar drivers.

Additionally, it was found that older drivers that were familiar with driving at

crossings with only flashing lights recorded higher levels of unsafe driving and

intended driving behaviour at level crossings than unfamiliar drivers. Furthermore,

the study found that older participants that are familiar with driving at passive

crossings are more likely to have a negative attitude towards driving at level crossings

generally. It appears that many older drivers use self-protective behaviours such as

avoiding driving at level crossings during times they perceive may be ‘riskier’ to them

(i.e. dusk, peak traffic periods). This process of self-regulation has been suggested to

be related to deterioration in vision, general health and crash involvement (Ragland et

al., 2004). It could be argued that this self-regulation also involves using roads that

older drivers are familiar with; including level crossings. Interventions such as

awareness raising of how familiarity may play a role in crashes at level crossings for

older drivers, may assist in discontinuing the current over-representation of this road

user group in level crossing collisions.

7.2.3.3 Younger drivers at level crossings

There are a number of interesting findings from the younger driver sample.

Firstly, compared to older and heavy vehicle participants, younger participants scored

substantially lower in their knowledge of road rules at level crossings. This is an

interesting finding as younger drivers would most likely have been exposed to some

information about level crossing road rules in the licensing process. Secondly,

younger drivers reported higher levels of risk perception (i.e. involvement in a

collision with a train at a level crossing) compared to older and heavy vehicle drivers.

Historically risk analysts propose that the most accurate measurement of driving risk

is collision rates, however using such rates have limitations for level crossings. Even

though collision rates at level crossings are substantially lower than that of the

national road toll, it does not necessarily mean that the risk is not high (Wang, 2002).

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Wang (2002) proposes that a high risk could have been perceived but avoided. This

finding was also surprising given that research has found that drivers that believe they

are less vulnerable to crashes than other drivers, have less incentive to engage in self-

protective behaviours (McKenna, 1993, Horswill et al., 2004). It would appear that

the findings from the younger driver sample contradict findings from this previous

research. One explanation for this is that although younger drivers hold the

perception that level crossings are high risk intersections, they are less likely to be

fatally injured at a level crossing than from speeding or other high risk driving

behaviours. Another explanation could be their low exposure to level crossing driving

compared to their participation in other unsafe driving behaviours.

The third remarkable finding is that related to self-efficacy. Self-efficacy has

been defined as an individual’s capacity to organise, control and execute certain

behaviours to attain specific performances (Bandura, 1977a). However, since self-

efficacy is regarded as a coderterminant of behaviour (Ajzen, 1991), it relies on the

accuracy of an individual’s perception of their control over a driving situation

(Sheeran, 2003). For the current younger driver sample, self-efficacy was found to be

a predictor of intention to drive safely at level crossings. As such, lower levels of

self-efficacy were associated with a stronger intention to drive unsafely at level

crossings. The current research produced similar results to previous research with

younger drivers. Elliot et al (2003b) in their research about driver’s compliance with

speed limits found that younger drivers (and particularly males) had lower levels of

self-efficacy than older drivers. Some research has indicated that individuals with

higher levels of self-efficacy report significantly greater rates of success in changing

behaviour (Wells-Parker et al., 2000). However, one of the major assumptions of

Fishbein’s Integrated Model (IM) is that attitudes, norms and self-efficacy (as well as

their underlying beliefs) have primarily an indirect effect on behaviour (via their

effects on intention) (Fishbein et al., 2003). Thus, if young drivers have already

formed strong intentions to perform unsafe driving at level crossings (but are not

doing so), then little will be accomplished by developing interventions designed to

increase self-efficacy (Fishbein et al., 2003). This has important implications for

designing educational programs for young drivers. Since the majority of younger

drivers appear to hold intentions to drive safely at level crossings, future interventions

should be directed at increasing relevant skills and abilities and/or assisting with

environmental constraints.

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Another noteworthy finding of this study with younger drivers is that young

male participants were significantly more likely to report both current and future

driving at level crossings to be less safe than their female counterparts. This is not

surprising given the amount of literature supporting research that young males are

more likely to take risks whilst driving generally (Laapotti and Keskinen, 1998,

Arnett, 2002) or be less ‘safety-orientated’ than their female counterparts (Meadows

and Stradling, 1999). However, this finding has implications for targeting younger

drivers as it appears that educational programs may need to direct efforts largely

towards young male drivers as opposed to their female counterparts. The challenge

remains for program designers to implement interventions that have the prospect to

produce lasting behaviour change in young males.

Finally, the finding that familiarity with driving at level crossings was

associated with unsafe driving was not surprising given that this result was seen in

both the older and heavy vehicle samples. What is surprising is that younger drivers

are most likely to be less familiar with different designs of level crossings than the

older and heavy vehicle participants, yet they report more unsafe driving behaviours

compared to these other two samples.

7.3 IMPLICATIONS FOR LEVEL CROSSING SAFETY

Previous research into unsafe driving at level crossings supports the notion that

from a systematic point of view, level crossing collisions are far more complex than

the immediate causal factor being the non-observance of the road rules (Caird, 2002).

Although the act of driving is taken for granted by most motorists, it is however a

complex task that is dependent on a variety of cognitive and psychomotor

performance abilities to be intact, such as alertness, attention, multitasking, memory,

coordination, and visual spatial perception (Moller, 2004). As level crossings are the

interface between the rail and road, and are considered to require similar cognitive

processes to road intersections, they pose a unique problem for drivers. Driving

through intersections is possibly one of the most complex conditions drivers

encounter, as multifarious perceptions, decisions and maneuvers are required to

successful negotiate and cross intersections (Trbovich and Harbluk, 2003). This

background provided the foundation for the three studies conducted in this research.

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In the course of conducting this research, a number of significant implications for

managing risk at level crossings have been identified.

Firstly, it has been shown that older drivers hold low levels of self-efficacy for

driving at level crossings, with other motorists influencing their ability to safely

negotiate crossings. If this perception of driving control at level crossings is

influenced by older driver’s functional, perceptual or cognitive impairments, remains

unknown.

What this research has highlighted is the prevalence of older driver impairments

in some important functional abilities that may be directly related to level crossing

driving: increasing trouble adjusting to glare and night-time driving, restricted range

of motion to the neck and substantial declines in hearing. Although factors

contributing to the over-representation of older drivers in collisions at level crossings

are likely to be complex and multi-faceted, such functional impairments are expected

to play a critical role. So too is the ability of older drivers to adopt safe driving

practices by self-regulating their driving behaviour. The connection between

familiarity and unsafe driving practices for older drivers, also poses another problem

for ensuring safety for this age group. Some research has indicated that many older

drivers are aware of what generally constitutes a risky driving situation, yet are

unaware that some driving conditions (such as complex intersections) have become

specifically risky for them (Holland and Rabbitt, 1992). This notion may hold true for

older drivers at level crossings. As such, they may over-estimate their own driving

ability while under-estimating their risk of being involved in a vehicle-train collision.

Secondly, younger drivers recognise that level crossings are potentially a highly

dangerous intersection, albeit that collisions at crossings are rare events, yet are still

likely to engage in risk taking behaviours. Additionally, their low levels of self-

efficacy in driving at level crossings pose challenges for developing interventions

with this age group. If the success of changing behaviour is related to increasing

driver’s levels of self-efficacy (Wells-Parker et al., 2000), then future interventions

are more likely to be effective if they are directed at increasing relevant skills and/or

abilities as well as assisting with environmental constraints. Given that experience

constitutes the core of skills and abilities of drivers, and the lack of financial resources

to actively protect younger drivers at any level crossing they encounter, how such

interventions are applied in the ‘real-world’ is likely to be an arduous task. It would

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appear that the licensing process is one critical element in preparing younger drivers

for the multitude of hazardous driving scenarios that exist in the driving environment.

Thirdly, most heavy vehicle drivers reported driving safely and intending to

drive safely in the future. However, there is a sub-set of drivers that indicate they

have in the past and will in the future take risks when traversing crossings. Although

this sub-set is relatively small, if generalised to the larger trucking industry it could be

problematic for the rail sector and greater public alike. The fact remains that heavy

vehicle-train collisions, although rare events, have the potential to be catastrophic in

terms of fatalities and injuries, environmental disaster, delays in the rail network, and

extensive damage to property. In June 2007, such a catastrophic disaster was

observed in northern Victoria when a train was derailed killing 11 train passengers.

With this tragic outcome came major media attention and great negativity towards the

trucking industry. Nevertheless, such events are likely to continue with the

commercial traffic on the eastern corridor of Australia forecast to double in the next

5-10 years, and the complex set of social practices associated with professional truck

driving placing pressure on truck drivers. The two greatest pressures are allied with

the increasing adoption of ‘just-in-time’ ware housing (increasing the importance of

the express freight sector, as well as both overnight distribution and long distance

transport) and the significant growth in specialist vehicle types (i.e. B-doubles) which

drastically changes the mix of heavy vehicles on the road as well as a higher

proportion of heavy vehicles in the traffic stream (George, 2002a).

The ultimate goal to improve level crossing safety for all motorists would be to

have a combination of engineering, education and enforcement countermeasures for

all level crossings. However, the small number of fatalities in comparison to the

national road toll limits this. To date, there’s been very little enforcement at

Australia’s 9400 level crossings and police have indicated their resources will

continue to be largely concentrated on speeding, drink/drug driving, fatigue and

seatbelt usage. Unless police ministers around the country change policies to increase

police presence at ‘dangerous’ crossings, the role that enforcement practices takes will

remain the same. Nevertheless, the likelihood of creating behavioural change would

be increased if violations at level crossings by all motorists was detected and

penalised, or alternatively, if perceptions of such detection were increased. Since

deterrence is achieved through instilling fear in drivers with the threat of punishment

via some form of sanction (Elliott, 2003a), it is aimed mostly at the ‘marginal group’

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who lie between the law abiding citizens (for who deterrence is not necessary) and the

undeterrable citizens (for who deterrence is ineffective) (Zimring and Hawkins,

1976). Further research that examines the appropriateness of deterrence theory may

provide another perspective for which to tackle level crossing safety.

As described previously, there exists in Australia a comprehensive system for

assessing risks at level crossings and the likely impact of different treatments on these

risks. After determining a level crossing is high risk and what makes up the risk, the

system is used to determine treatments appropriate to reduce the risk. This

assessment system has the capacity to assess benefit/cost for proposed improvement

works, ensuring that each dollar is spent where it can generate the greatest safety

improvement. Unless significant financial resources are bestowed by both

Commonwealth and State governments to upgrade all level crossings to actively

protect road users, the current assessment system will continue to allocate the limited

financial resources to the nominated crossings. Although making wide-spread

changes to current operational level crossings may be considered unfeasible, future

level crossing design (including upgrade programs) should accommodate for the

changing mix of trucks in the traffic stream. More specifically, the results of this

research indicate that the level crossing design process should include consideration

of the requirements and limitations of driving large vehicles, including: (a) length of

vehicle, (b) maneuverability, and (c) visibility. Research across the Canadian rail

network, comparable to Australia’s in terms of its size and layout, supports such

findings, having demonstrated that the design of level crossings often do not

accommodate for the specific needs of heavy vehicles (Gou and Bellvigna-Ladoux,

2003). Furthermore, the implementation of advanced warning systems such as

increasing the existence of early road signage may prove fruitful in improving safety.

In addition to enforcement and design factors, an educational awareness

campaign at the national level that is viewed by both metropolitan and rural drivers

would highlight the risks of driving for different age groups. However, such

campaigns must be developed and directed specifically to road user groups (i.e.

younger drivers, older drivers etc) if they are to have any substantial effect on

improving safety for all motorists. For the trucking industry, there may appear some

merit in implementing educational awareness campaigns that highlight to large

vehicle drivers the increased risks associated with operating trucks on and around

level crossing. Such an initiative could be conducted at a company level through the

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supervision or induction process and possibly reinforced through corresponding

regulatory governing bodies. The output of this exploratory research can be

conceptualised as the ‘groundwork’ required in building specific, targeted road safety

educational countermeasures for different road user groups.

7.4 STRENGTHS AND WEAKNESSES OF THE RESEARCH

The strength of this research program is found in the contribution of knowledge

about the influencing factors on motorist safety at level crossings. This research

program is the first of its kind in the world. Although interventions have been

developed in other countries, none have been guided by empirical research or theory.

The motivation underlying motorist behaviour determines to a large extent how

successful behaviour change strategies may be. This research program has been

guided by Fishbein’s Integrated Model of Behaviour Change (IM) to assist in the

planning and development of interventions as well as guiding instrument development

to ascertain the present context of motorist behaviour at level crossings.

What makes this research unique is its inclusion of train driver’s perspective

with those of three distinct road user groups (i.e. older and younger drivers, and heavy

vehicle drivers). In doing so, this research has added to the existing literature as well

as providing greater insight into why motorists take risks at level crossings.

Furthermore, a multi-method approach to investigating factors that influence unsafe

driving behaviour at level crossings allowed for a more thorough examination of the

issues. The method of ‘triangulation’ (i.e. combining research methods to give a range

of perspectives) whereby both qualitative and quantitative research designs were

utilised, provided the robustness required within the discipline of road safety research.

In addition to the strengths of this research program, there are weaknesses. One

limitation of this research is the low response rate among heavy vehicle drivers. This

response rate resulted in a small sample size that potentially limits the validity and

generalisability of the findings, as well as the range of statistical analyses that could

be undertaken. The reluctance by many trucking companies to participate in this

research may have stemmed from either the belief that level crossing safety is not

high on their safety agenda or a distrust of disclosing driving behaviours of employees

in fear of scrutiny of the company’s safety compliance. As such, this study may have

only captured trucking companies with comprehensive OHS management systems and

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a focus on ensuring a culture of safety within their workplace. Additionally, the high

attrition rate observed for the heavy vehicle sample may have been partly attributable

to the length and number of questionnaires. Moreover, this small sample size also

prevented randmonisation of companies to either the intervention or control group in

Study Three from occurring. This was a major limitation for this road user group and

further techniques and incentives for company participant may need to be developed

in order to increase the industry’s participation rates in road safety research over

longer periods of time.

This investigation also suffers from the typical perceived limitation associated

with self-report data. The main criticism of self-report data is that it is affected by

social desirability bias. It goes without saying in road safety research, that social

desirability will have some impact upon the findings obtained. It has been suggested

that ‘deviant’ individuals would minimise the number of crashes they were involved

in or other illegal driving activities (e.g. drink driving) (Hatakka, 1997). However, it

is important to note that any resulting associations found would be under-estimates of

real associations, rather than over-estimates (Hatakka, 1997, Lawton, 1997a, West,

1995). For the current investigation, the use of the self-report data would have

potentially been a problem if actual driving behaviour was monitored. However,

since actual driving behaviour was not monitored, the degree of correspondence

between self-report and observed driving behaviour is most likely to be insignificant.

Since there exists no standardised measures of attitudes, norms, self-efficacy,

driving intention, perceived risk, and environmental constraints for level crossing

behaviour, the researcher was required to develop scales to directly and indirectly

measure such constructs. This may have affected the validity and reliability of the

instruments. Another widespread limitation of this investigation is that data was

drawn exclusively from only one jurisdiction in Australia. Every effort was made to

ensure that there was a diverse sample of participants with an equal number of

recruitment letters being sent to rural/remote and metropolitan drivers. However, with

financial constraints limiting the scope of data collection extending outside of

Queensland, caution needs to be exercised when generalising these results to other

jurisdictions in Australia or abroad.

Finally, although this research has found some important factors that may

influence unsafe driving by motorists at level crossings, explaining the causal

mechanism is complicated. From a systematic point of view, level crossing collisions

302

are far more complex than the immediate causal factor being the non-observance of

the road rules (Caird, 2002). The exploratory nature of this research program limits

conclusions and generalisations being made. As such, similar investigations are

needed in examining these road user groups before these findings can be exclusively

applied at the macro level. Suggestions for further research are provided in the

subsequent section.

7.5 SUGGESTIONS FOR FUTURE RESEARCH

Although the multi-method approach to investigating factors that influence

unsafe driving behaviour at level crossings allowed for a more thorough examination

of the issues, further qualitative research would be useful to clarify and expand on

some findings of this research program. These include how truck drivers can reduce

their risk of being involved in a catastrophic event if their truck collides with a train.

Even though anecdotal evidence indicated that road safety interventions which

are brief in nature (i.e. one-off interventions) are not likely to be highly effective, no

research in level crossing safety has ever been conducted that has examined such

interventions. Due to the limited financial resources that both federal and state

governments currently allocate to improving level crossing safety, efforts to intervene

on specific road user groups through a single intervention educational approach

deserved consideration. Although this research concluded that ‘one-off’ brief

interventions are ineffective for changing driver intention for level crossings, the

output of this exploratory research can be conceptualised as the ‘groundwork’.

Considering that current mass media campaigns for level crossing safety do not

include any empirically guided interventions, this research is an important step for the

rail industry in ensuring that mass media campaigns are both informed by road safety

theory as well as the motivations underlying driving behaviour of the road user groups

(target audience) for which they are designed to target.

However, it must be kept in mind that with motorist error being considered an

important factor in the causation of level crossing collisions, reliance solely on

education is not the solution. How rail and road authorities combine the mix of

education, enforcement and engineering solutions to improve level crossing safety is

and will continue to be an arduous task.

303

7.6 RECOMMENDATIONS TO INDUSTRY

Based on the findings from this research program, there are a number of key

recommendations for improving level crossing safety.

The first of the key recommendations relates to the reporting of near-miss

incidents at level crossings by train drivers. Currently, it appears that it is up to a train

driver’s discretion whether or not they report near-miss incidents. The formative

research undertaken indicated that there exists a culture among train drivers that they

are ‘whingers’ if they report all near-miss incidents at level crossings. Many train

drivers indicated that the reporting mechanism for such events (i.e. transmission over

the radio to control) does not allow any anonymity and thus they are reluctant to

report the numerous incidents that they encounter. Additionally, train drivers hold the

belief that even if they do report such incidents, the likelihood of any action taken by

police is extremely low. Due to the large number of near-misses at level crossings

that do not involve a fatality but have the potential to result in a catastrophic event

such as a train derailment, it is important to identify the occurrences of near-misses as

well as collisions involving fatalities. As such, a key recommendation is for the

review of the current system for reporting of near-miss incidents to maximise the

accuracy of such events.

The second key recommendation relates to the recording of national collision

data. Findings from a review conducted by Ford and Matthews (2002) found that

most jurisdictions in Australia have “widely differing methods of categorising and

recording the level crossing characteristics and accident data” (p10). As a

consequence of the differing methods of recording of level crossing collisions in

Australia, there is a lack of definitive evidence available relating to the extent and

nature of level crossing collisions. Coupled with the inaccuracy of near-miss

incidents from low levels of reporting by train drivers, it is difficult to determine how

best to direct countermeasure resources. Cairney (2002) recommends that the

usefulness of information from collisions at level crossings would be greatly

improved by including the variables that are currently collected for road crashes, such

as vehicle type and driver characteristics.

Level crossing collisions have been shown to result in enormous human and

financial cost to society (Lobb, 2001). Although there have been attempts to

determine level crossing collision costs, the accuracy of such reports have been

304

revoked by both railway experts and insurance companies alike. It has been

suggested that estimations are grossly conservative. The most recent report assessing

the socioeconomic costs of vehicle-train collisions was conducted by the Bureau of

Transport and Regional Economics (2002). This analysis covered collisions that

occurred in Australia only during 1999. This report concluded that the total cost of

level crossing collisions was $32 million, with approximately $10 million related to

vehicle-train collisions (Bureau of Transport and Regional Economics., 2002). It is

recognised that costs of level crossing collisions cannot be quantified exactly,

however in order to receive more appropriate levels of funding from government

agencies to invest in improving safety and developing effective countermeasures, a

report that more accurately determines the direct and indirect costs needs to be

undertaken.

The design of crossings and absence of advanced warning systems appear to be

major issues for motorists, particularly heavy vehicles. As described previously, there

exists in Australia a comprehensive system for assessing risks at level crossings and

the likely impact of different treatments on these risks. After determining a level

crossing is high risk and what makes up the risk, the system is used to determine

treatments appropriate to reduce the risk. This assessment system has the capacity to

assess benefit/cost for proposed improvement works, ensuring that each dollar is spent

where it can generate the greatest safety improvement. Although making wide-spread

changes to current operational level crossings may be considered financially

unfeasible, future level crossing design (including upgrade programs) should

accommodate the changing mix of trucks in the traffic stream. It is evident from this

research that heavy vehicle drivers hold the belief that they experience significant

difficulty in negotiating crossings with an ‘S’ bend approaching road. Advanced

warning systems may assist drivers in preparing for a train at these crossing when

visibility on the approach road is reduced. As such, further investigation of this

matter needs to be considered.

Another strong recommendation that has emerged from this research is the need

to develop educational and awareness materials specific to different road user groups.

Although the interventions developed are one example of interventions for these road

user groups, the output of this exploratory research can be conceptualised as the

‘groundwork’ required in building specific, targeted road safety educational

countermeasures. The research found that there are unique issues that motivate

305

different road user groups. However, there is one common element for all road user

groups: familiarity. Familiarity with driving at level crossings was found to be

significantly associated with unsafe driving intentions for all road user groups. This

factor therefore should be included in future educational and awareness raising

campaigns as it is a critical element that has been found to be related to fatal level

crossing collisions (Wigglesworth, 1979). Additionally, it is advised that future

campaigns be developed and directed specifically to distinct road user groups if they

are to have any substantial effect on improving safety for all motorists.

For the trucking industry, there may be some merit in implementing educational

awareness campaigns that highlight to heavy vehicle drivers the increased risks

associated with operating trucks on and around level crossing. Such an initiative could

be conducted at a company level through the supervision or induction process and

possibly reinforced through corresponding regulatory governing bodies or

associations. Messages could also be delivered through trucking magazines as this

medium may indeed provide the most appropriate source for message acceptance in

this group.

The development of education and awareness materials for the older driver

group would need to be substantially different. What this research has confirmed is

the high prevalence of functional impairments for drivers that are 60 plus, such as

increasing trouble adjusting to glare and night-time driving, restricted range of motion

to their neck and substantial declines in their hearing. Although factors contributing

to the over-representation of older drivers in collisions at level crossings are likely to

be complex and multi-faceted, such functional impairments are expected to play a

critical role. Self-regulation for older drivers appears to play role in their driving at

level crossings and other busy intersections. Although many older drivers may be

aware of what generally constitutes a risky driving situation, they may be unaware

that some driving conditions (such as complex intersections) have become specifically

risky for them (Holland and Rabbitt, 1992). This notion may hold true for older

drivers at level crossings. As such, they may over-estimate their own driving ability

while under-estimating their risk of being involved in a vehicle-train collision.

Including such information in education and awareness raising materials may be

beneficial in reaching the older driver group.

Younger driver’s low levels of self-efficacy while driving at level crossings

pose challenges for developing educational and awareness raising materials for the

306

17-24 year age group. If the success of changing behaviour is related to increasing

driver’s levels of self-efficacy (Wells-Parker et al., 2000), then future interventions

are more likely to be effective if they are directed at increasing relevant skills and/or

abilities as well as assisting with environmental constraints. Given that experience

constitutes the core of skills and abilities of drivers, and the lack of financial resources

to actively protect younger drivers at any level crossing they encounter, how such

interventions are applied in the ‘real-world’ is likely to be an arduous task. It would

appear that the licensing process is one critical element in preparing younger drivers

for the multitude of hazardous driving scenarios that exist in the driving environment.

Another is most likely to be through driving schools. Driving instructors have a

“critical role in providing high-quality learning opportunities for novice drivers”

(Bailey, 2003,p1). Inclusion of mandatory level crossing driving for learner drivers

may provide an opportunity to ensure young drivers are exposed to high risk

intersections such as level crossings.

Coupling enforcement programs with educational and awareness raising

campaigns will no doubt provide some improvement in driver behaviour at level

crossings. How this can be achieved is a continuous challenge. Nevertheless, the

likelihood of creating behavioural change would be increased if violations at level

crossings by all motorists was detected and penalised, or alternatively, if perceptions

of such detection were increased. Increasing current fines/penalties and developing a

uniform national approach to offences at level crossings, is the preliminary step

towards deterring motorists in violating the road rules. Only recently in Queensland

and Victoria has there been a substantial increase in fines and penalties, with the

catalyst in Victoria being the Kerang tragedy in June 2007. In Victoria, fines

increased for motorists that speed to ‘beat a train’, cross when lights and bells are

activated, or weave between boom gates, to $3304 (30 penalty units), 4 demerit points

and an automatic 3 month licence suspension. Such legislative initiatives together

with increased police enforcement will hopefully provide some level of deterrence to

motorists. However, before moving ahead with either educational or enforcement

initiatives, an analysis of the benefits and the costs of implementing any educational

or awareness raising campaign should be undertaken. Assessing the costs and

benefits may also serve as a basis for prioritising separate measures or measure

packages (such as the combination of education and enforcement) (SWOV Institute

for Road Safety Research, 2005).

308

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APPENDICES

364

Appendix 1: Review of models/theories

365

THEORY OF PLANNED BEHAVIOUR

Extension to the theory of reasoned action

The theory of planned behaviour (Ajzen, 1985), an extension of the theory of

reasoned action (Fishbein, 1967), is one model that has been successful in predicting

behaviour in road safety. This theory has previously been used to understand

pedestrians’ road crossing decisions and predicting their intentions (Evans, 2003,

Evans, 1998), to examine intention to commit driving violations (Parker, 1992b), as

well as modifying beliefs and attitudes to exceeding the speed limit (Parker, 1996)

and driver’s compliance with speed limits (Elliott, 2003b). Although this theory has

been contentious in its ability to predict behaviour, there is no doubt that this theory

embodies an important perspective from which to examine level crossing behaviour.

The theory of reasoned action (Fishbein, 1967, Fishbein, 1975) is one of the

most widely studied social psychology models of attitude and behaviour. This theory

developed in 1967, includes attitude as one of the important factors influencing

behavioural intention (Severin, 2001) and assumes that people are rational in making

decisions based on the information available to them. According to Ajzen and

Fishbein (1980), a person will normally act in accordance with their intentions

although a perfect correlation between intention and behaviour does not exist. The

figure below illustrates the theory of reasoned action (Ajzen and Fishbein, 1980).

Theory of Reasoned Action

Subjective

Norm

Attitude

Toward the Behaviour

Behavioural

Intention

Behaviour

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The theory of reasoned action suggests that a person’s behaviour is determined

by their intention to perform the behaviour and that this intention is a function of their

attitude towards the behaviour as well as their subjective norm (Ajzen and Fishbein,

1980). Attitude is defined as being the “affective and instrumental evaluations of

performing the behavior by the individual”(Rhodes and Courneya, 2004, p2) while

subjective norm is defined as the “social pressure on the individual to perform or not

to perform a particular behaviour” (Rhodes and Courneya, 2004, p2). The

determinants of the attitudinal and normative constructs (i.e. information people have

about themselves and the world in which they live) are important in influencing

behaviour (Ajzen and Fishbein, 1980). Beliefs are underlying to a person’s attitudes

and subjective norms, and ultimately determine behavioural intention as well as

behaviour (Ajzen and Fishbein, 1980). According to Fishbein (1967) this theory

seeks not only to predict human behaviour but also understand it, thus making it

important to identify and analyze the determinants of intentions.

Fishbein and Ajzen’s (1980) theory of reasoned action has received strong

empirical support over the years (Sheppard, 1988), however many attempts have been

made to improve its predictability, either by adding new variables or by making

changes to its internal structure (Bagozzi, 1992). In 1985, Ajzen extended the TRA

and proposed an additional variable, ‘perceived behavioural control’ to the model to

account for the limitation in dealing with behaviours over which people have

incomplete volitional control (Taylor, 1995). According to Taylor and Todd (1995),

perceived behavioural control is the person’s control beliefs weighted by the

perceived facilitation of the control factor that either inhibits or facilitates the

behaviour. Perceived behavioural control is also regarded as a co-determinant of

behaviour, although it is largely dependent on the accuracy of people’s perceptions of

control (Ajzen, 1991, Sheeran, 2003). Studies have indicated that measurements of

perceived behavioural control improve predictions of intention from attitude and

subjective norm as well as predictions of behaviour from intention (Ajzen, 1991,

Netemeyer et al., 1991, Schifter and Ajzen, 1985, Gärling, 1992). This additional

variable accounts for situations where a person has less than complete control over

their behaviour and may be relevant for level crossing behaviour (Taylor, 1995).

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Principles and overview

The theory of planned behaviour (Ajzen, 1985) was developed out of the need

to deal with the limitations of the theory of reasoned action in terms of behaviours in

which people have incomplete volitional control (Doll, 1992). According to the

theory of planned behaviour (see Figure 17), behaviour can be predicted from a

person’s intentions to perform the behaviour and from their perceptions of control

over the behaviour. This theory proposes that behaviour intentions are the main

determinant of behaviour (Elliott, 2003b) and intention is assumed to be influenced by

perceived behavioural control in addition to attitude and subjective norm (Ajzen and

Madden, 1986, Gärling, 1992, Netemeyer, 1990). By considering these influences,

this theory is “in this way extended to include the prediction of a broader class of

behaviours than those which are under volitional control” (Gillholm, 1996, p3).

Fundamentally, this theory hypothesizes three conceptually independent determinants

of intention:

• Attitude toward the behaviour which refers to the degree to which a person has

a favorable or unfavorable evaluation or appraisal of the behaviour in

question;

• Subjective norm (social factor) which refers to the perceived social pressure

to perform or not to perform the behaviour; and

• Perceived behavioural control which refers to the perceived ease or difficulty

of performing the behaviour and is assumed to reflect past experience as well

as anticipated impediments and obstacles.

(Doll, 1992)

Attitudes are a person’s overall evaluation of the behaviour (e.g. “it is good to

stop at a level crossing when the flashing lights are activated”). Recent evidence

suggests that it is important to differentiate affective from instrumental attitudes in

relation to behaviours, which might realistically be considered as having an affective

element (Lawton, 1997, Parker, 1998, Parker, 1996). Instrumental attitudes are

related to benefits (e.g. “it is beneficial to obey warning systems at level crossings”)

and disadvantages (e.g. “it takes up time waiting for a train to pass”), while emotional

attitudes refer to emotions related to safety at level crossings (e.g. “not proceeding

368

through a level crossing when the train has passed but the flashing lights are still

activated, makes me feel silly”).

The role of beliefs in the theory of planned behaviour is that at the most basic

level of explanation, behaviour is a function of salient information or beliefs, relevant

to the behaviour (Doll, 1992). Subjective norms consist of a person’s beliefs about

whether significant others (e.g. spouse, colleagues, friends) think that he or she should

engage the behaviour (Conner, 1996). According to Miller (1965), people can hold a

great many beliefs about any given behaviour, but they can concentrate on only a

relatively small number at any given moment. As such, it is these salient beliefs that

are thought to be the main determinant of a person’s intentions and actions (Doll,

1992). Doll and Ajzen (1992) propose that there are three types of salient beliefs:

behavioural beliefs (assumed to influence attitudes towards the behaviour); normative

beliefs (constitute the underlying determinants of subjective norms); and control

beliefs (provide the basis for perceptions of behavioural control).

Perceived behavioural control forms the third variable or predictor of intentions.

This refers to a person’s perception of the extent to which performance of the

behaviour is easy or difficult (Conner, 1996). As such, in the theory of planned

behaviour, perceived behavioural control has both direct and mediated (by

behavioural intention) effects on behaviour (Lajunen, 2004). According to Ajzen

(1991) and Sheeran, Trafimow and Armitage (2003), perceived control is (with

intention) also considered a co-determinant of behaviour, although perceived control-

behaviour relationship is dependent on the precision of people’s perceptions of

control. The concept of perceived behavioural control is not original to the theory of

planned behaviour as it also appears in the health belief model (Rosenstock, 1966)

where it is termed barriers, and also in the model of interpersonal behaviour (Triandis,

1977) where it takes the form of facilitating conditions (Ajzen, 2002a). However,

Bandura’s work on self-efficacy is perhaps the originator of perceived behavioural

control (Bandura, 1977a, Bandura, 1989, Bandura, 1997). Like attitude and subjective

norm, perceived behavioural control can be measured by asking direct questions to a

person about their capability to perform a behaviour or indirectly on the basis of

beliefs about ability to deal with specific inhibiting or facilitating factors (Ajzen,

2002a).

369

Theory of Planned Behaviour

Application to level crossing behaviour

The theory of planned behaviour has been applied to driver behaviour and

efforts to predict driver violations on this basis, are delivering initial results (Parker,

1995, Parker, 1992b, Parker, 1992a). Given that there is strong support from studies

on drivers’ compliance with speed limits (Elliott, 2003b), it seems realistic that

desirable changes in drivers’ attitudes, subjective norms, and perceptions of control

may lead to consequent changes in their intentions and behaviour. However, previous

studies examining driver behaviour have emphasized the importance of distinguishing

between aberrant behaviour constituting (deliberate) violations and behaviour

resulting from errors and lapses (Reason, 1990).

With regards to driver behaviour at level crossings, Pickett and Grayson (1996)

suggest that there are three main reasons for non-observance of signals at active

crossings:

• Drivers who are unwilling to stop because they believe they have plenty of

time to cross before the train arrives;

• Drivers who are unable to stop because they are too close to the stop line at the

Subjective

Norm

Attitude Toward the Behaviour

Behavioural

Intention

Behaviour

Perceived Behavioural

Control Actual Behavioural

Control

Behavioural

Beliefs

Normative

Beliefs

Control Beliefs

370

onset of flashing lights, or because someone is driving too close behind; and

• Drivers who are unaware of the signals because they are inattentive or are

distracted.

According to Pickett and Grayson (1996) drivers may base their decision to

cross at a level crossing on their previous experience of either the same crossing

(familiarity) or of other crossings (association). When drivers are exposed to the

same phenomenon repeatedly, as in the concept of ‘mental set’, they come to expect it

(Pickett, 1996). Additionally, drivers familiar with one level crossing may transfer

their experience to a new crossing, and be complacent in the new situation (Pickett,

1996). Both of these situations may lead to drivers’ non-observance of warning and

protection systems. However, findings from Pickett and Grayson’s (1996) British

study involving analysis of a sample of witness statements obtained by the British

Transport Police during a campaign on red signal violations at level crossings,

indicates that the majority of signal violations at level crossings are intentional and

deliberate.

Evidence from fatal accidents in Victoria, Australia contradicts this British

study on red signal violations. In 1979, Wigglesworth examined 85 consecutive fatal

crashes involving motor vehicles and trains at both active and passive level crossings,

with data being taken from detailed police reports prepared for the coroner. This

study indicated that at least 86% of drivers killed in the level crossing crash lived

locally and were therefore familiar with the existence of this crossing (Wigglesworth,

1979). This Victorian evidence suggests that the drivers killed at level crossings were

not violating warning or protection systems, but rather law-abiding citizens going

about their daily work and attributable to human overload unrelated to any breach of

regulation (Wigglesworth, 2001). Therefore, it would seem that from these Victorian

coroner’s reports, that the category that fatalities generally lie in is “those who are

unaware of the signals because they are inattentive or are distracted”. In accordance

with previous studies emphasizing the importance of distinguishing between aberrant

behaviour constituting (deliberate) violations and behaviour resulting from errors and

lapses (Reason, 1990), it appears that these fatal accidents are a result of the latter.

While evidence suggests that drivers that violate road rules are more likely to be

crash-involved (Parker), it is unknown if this can be translated to level crossing

crashes.

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Criticisms of the theory of planned behaviour

Although many health behaviour studies have found that intentions predict

corresponding behaviour quite well, there has long been a concern by researchers that

there are observed discrepancies between these two constructs (Campbell, 1963,

Blumer, 1955). Sheeran et al. (2004) propose that there are three processes that

underlie this discrepancy between intention and behaviour. Intention viability is the

first process that is defined as being “impossible for most decisions to find expression

in the absence of particular abilities, resources, or opportunities” (Sheeran, 2004, p3).

The second process is intention activation refers to the “extent to which contextual

demands alter the salience, direction, or intensity of a focal intention relative to other

intentions” (Sheeran, 2004, p4). The third process suggested by Sheeran et al. (2004)

concerns intention elaboration in which people may “fail to engage in, or elaborate in

sufficient detail, an analysis of the particular actions and contextual opportunities that

would permit realization of their intention”(Sheeran, 2004, p5).

The original derivation of the theory of planned behaviour (Ajzen, 1985) does

not define intention in relation to actual performance but rather defines intention in

terms of trying to perform a given behaviour. Early work on this theory however,

showed strong correlations between measures of the model’s variables that raised

questions about trying to perform a given behaviour and measures that dealt with the

actual performance of the behaviour (Ajzen and Madden, 1986). However, according

to Doll and Ajzen (1992) the relative importance of intention and perceived

behavioural control in the prediction of behaviour is expected to vary across situations

and across different behaviours.

HEALTH BELIEF MODEL

Origins and overview

The health belief model (HBM) developed by Rosenstock (1974), is one of the

most widely used conceptual frameworks for understanding health behavior in terms

of individual decision-making. This model which was developed during the 1950s

and influenced by works of social psychologist Kurt Lewin, was originally designed

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to systematically explain and predict preventative health behaviour and focused on the

relationship between health behaviours, practices and utilization of health services

(Rosenstock, 1974). This model has been applied to numerous public health issues

including breast-self examination, immunization, sexual risk behaviours and the

transmission of infectious diseases.

One of the major assumptions of the HBM is that the beliefs of the individual

determine behaviour to a greater extent than the objective environment (Rosenstock,

1974). Salazar (1991) suggests that the model concentrates on phenomenological

aspects of the individual, to a lesser degree, the history or past experiences of the

person. The HBM proposes that the likelihood of a person adopting a given health-

related behaviour is a function of that individual's perception of a threat to their

personal health, and their belief that the recommended behaviour will reduce this

threat (Rosenstock, 1974). The key variables of the HBM include:

• Perceived threat:

Perceived susceptibility (an individual’s subjective perception of

the risk of contracting an illness/condition); and

Perceived severity (concerning the seriousness of contracting the

illness/condition);

• Perceived benefits (the believed effectiveness of strategies designed to reduce

the threat of the illness/condition);

• Perceived barriers (the potential negative consequences that may results from

taking particular health actions, including physical, psychological, and

financial demands);

• Cues to action (events such as physical or environmental, that motivate an

individual to take action);

• Self-efficacy (the belief in being able to successfully execute the behaviour

required to produce the desired outcomes) – introduced by Bandura in 1977;

and

• Other variables – diverse demographic, sociopsychological, and structural

variables that affect an individual’s perceptions and thus indirectly influence

health-related behaviour.

(Rosenstock, 1994)

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Individual Perceptions Modifying Factors Likelihood of Action

Health Belief Model

Application to level crossing behaviour

In road safety, the HBM has been used in predicting and understanding safety

helmet use among schoolboy cyclists by analyzing attitudes and beliefs (Rutter, 1998)

as well as seat belt use among passengers (Simsekoglu, 2004). Driver behaviour at

level crossings could be explained by the health belief model by using the following

approach:

Likelihood of

behavioural change

Perceived threat of

illness/condition

Cues to action

Knowledge

Demographics

Perceived benefits versus barriers to

behavioural change

Perceived susceptibility

and seriousness of illness/condition

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Concept Definition Application Perceived susceptibility

One’s opinion of chances of a collision

Define road user groups at risk; Personalize risk based on behaviours and features of groups; Heighten perceived susceptibility if too low

Perceived severity

One’s opinion of how serious a collision would be

Specify consequences of the risk and severity of a collision

Perceived benefits

One’s opinion of the efficacy of the advised action to reduce risk or seriousness of impact

Define action to take: how, where, when; clarify the positive effects to be expected

Perceived barriers

One’s opinion of the tangible and psychological costs of the advised action

Identify and reduce barriers through reassurance, incentives and assistance

Cues to action Strategies to activate “readiness”

Provide ‘how-to information’, promote awareness

Self-efficacy Confidence in one’s ability to take action

Provide training and guidance in performing action

Threat perception includes two components: susceptibility to a crash and

anticipated severity of the consequences of the crash (e.g. likelihood of being killed).

Behavioural evaluation consists of two distinct sets of beliefs: those related to barriers

(e.g. inconvenience for waiting for the train to pass, or peer pressure such as ‘beat the

train’) to safe level crossing behaviour and those concerning benefits (e.g. increased

safety). In addition to threat perception and behavioural evaluation, ‘cues to action’

and ‘health motivation’ components are included in the HBM. Cues to action refers

to triggers to safe level crossing behaviour (e.g. Signs alerting driver to danger ahead)

whereas health motivation refers to ones readiness to be concerned about health and

safety matters generally.

Criticisms of the health belief model

The health belief model has been criticized widely in the literature for a number

of reasons. Firstly, it has been criticized for not taking into account factors that are

involved in the decision-making process when performing a particular behaviour.

As the model is a psychosocial one, it can only account for variance in

behaviours which can be explained by attitudes and beliefs…Therefore, several

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factors involved in the decision-making process (such as environmental,

intrapersonal and cultural factors) are neither measured nor identified.

(Toepell, 2003, p38)

Secondly, the prediction of behavioural intention (as opposed to the prediction

of actual behaviour) has been reported by Toepell (2003) to be a function lacking in

the model. Although not a criticism of the model as such, Toepell (2003) suggests that

very few studies implement the model in its entirety when predicting health

behaviour, and typically only use the variables of perceived susceptibility, severity,

barriers and benefits.

DETERRENCE THEORY

Overview

Deterrence theory has primarily been found in the domain of criminology and

sociology, although it has strong links to psychology. This theory has been used

extensively in Australia and other countries to guide the development and evaluation

of many road safety countermeasures (Homel, 1986, Elliott, 2003b, Watson, 2004a).

David South, an Australian psychologist who specialized in alcohol and traffic safety,

argues that “the reduction in the road toll…has arguably been the most successful

example of public action to minimize a social problem in Australia, and there is solid

evidence that general deterrence programs have played a major role” (1998, p76).

However, some road safety researchers would disagree. Harrison (1998) questions

South’s contention that there is ‘solid’ evidence supporting the role of general

deterrence, but recognizes that deterrence principles have played a pre-eminent role in

policy making for road safety in Australia.

Erickson, Gibbs and Jensen (1977) propose that deterrence theory should not

only be concerned with the objective properties of legal sanctions, but perceptions of

sanctions. Therefore, deterrence theory is primarily concerned with the manner in

which legal sanctions deter illegal behaviours through the mechanisms of fear and

perceived risk (Homel, 1986, Homel, 1988). Classical deterrence theory asserts that

the effectiveness of a legal threat is a function of the perceived certainty, severity and

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swiftness of punishment (Homel, 1986, Vingilis, 1990). An important distinction is

made in deterrence theory between specific and general deterrence (Homel, 1986,

Akers, 1994). Specific deterrence relates to the individual level in which an offender

is deterred from re-offending through direct exposure to sanctions, while general

deterrence is at the population level in which the community is deterred through the

threat of legal sanctions (Homel, 1986). Stafford and Warr (1993) however, have

criticized classical deterrence theory for its failure to effectively account for the effect

of punishment avoidance on behaviour. They suggest that it is imperative to not only

consider the effect of a person’s direct experience with punishment and punishment

avoidance, but also their indirect or vicarious experiences obtained through interaction

with their peers (Stafford, 1993). As a consequence, Stafford and Warr (Stafford,

1993) proposed a reconceptualisation of deterrence theory to incorporate these key

principles. As a result of this reconceptualisation, specific and general deterrence are

no longer mutually exclusive processes operating on different populations, but can

operate conjointly on individuals (Watson, 2004a). This reconceptualisation of

deterrence theory has been supported by Piquero and Paternoster’s (1998) study in

examining drink driving behaviour. This study found that intentions to drink and

drive were affected by both personal and vicarious experiences, as well as experience

of punishment and punishment avoidance.

Application to level crossing behaviour

Enforcing traffic laws and deterring drivers from engaging in driving behaviour

that increases crash risk, has been found to be an effective way of reducing crash risk

in respect to drink driving and speeding behaviour (Cavallo and Cameron, 1992,

Cameron et al., 1992). According to Christie (2001), targeted deterrence and

enforcement measures have a high probability of changing driver behaviour, although

these measures are seldom popular with drivers and some sections of the media. This

efficacy of enforcement and deterrence is thought to be due to the higher risk of

detection for such behaviour relative to the risk of being involved in a collision

(Christie, 2001). According to Elliott (1992), this provides enforcement with a greater

potential to influence the motivation and behaviour of drivers.

In the arena of level crossing safety, enforcement is a mechanism that has

typically been overlooked. Red light cameras as a means of collecting data about road

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user violations with possible use as a deterrent or enforcement mechanism have not

been used widely in Australia. However, the Australian Transport Council’s national

strategic direction to improve level crossing safety has highlighted the need for

ensuring legislation and enforcement are appropriate for the potential consequences of

a vehicle-train collision (Australian Transport Council, 2003). According to

deterrence theory, the decision to intentionally drive unsafely at level crossings (e.g.

drive around boom gates, drive through red flashing lights), should be mainly

influenced by a driver’s perceptions of the risk of apprehension, as well as the

certainty, swiftness and severity of punishment. Given the pre-eminent role of this

theory in road safety policy in Australia, it is very surprising that deterrence theory

has not been used to explain level crossing driver behaviour.

Criticisms of deterrence theory

Deterrence theory has been criticized by many experts for a number of reasons.

Vingilis (1990) has argued that deterrence theory fails to account for a wide range of

non-legal factors that may influence compliance with the law. These factors include:

social sanctions and rewards, moral commitment to the law, and the opportunity for

the commission of crime (Vingilis, 1990). Likewise, Akers (1990) argues that

deterrence theory is not a general or complete model of criminal behaviour as “the

primary concepts and valid postulates of deterrence and rational choice are

subsumable under general social learning or behavioural principles” (p655). Zimring

& Hawkins (1976) suggest that deterrence is aimed mostly at the marginal group who

lie between the law abiding citizens for who deterrence is not necessary and the

undeterrable citizens for who deterrence is ineffective. Ezzat Fattah, a leading

Canadian criminology academic, questions the real role of deterrence by suggesting

that its primary or direct effect may hide its real effects (Fattah, 1976). He suggests

that deterrence is less effective in controlling or eliminating habitual, unthinking

behaviour.

One would wonder whether the main function of legal sanctions with regard to

drivers’ behaviour is intimidation (curbing or modifying the behaviour through

the fear of punishment) or education (by instilling habitual modes of

performance in compliance with the traffic standards and regulations).

(Fattah, 1976, p48)

378

The conditions for which deterrence is more effective have been proposed by

Barry Elliott, a leading Australian road safety researcher. These conditions include:

• In controlling rational behaviour than in inhibiting impulsive behaviour;

• When motivation to engage in the prohibited behaviour is low and less

effective when the motivation is high (as in compulsive behaviour);

• When the behaviour is not habitual and requires thought processes and is

intentional – to the extent behaviour is already habitual, deterrence is least

effective;

• In assisting in the development of habitual modes of behaviour; and

• In controlling intentional behaviour (violations) rather than in eliminating

negligent behaviour (such as errors).

(Elliott, 2003a, p5-6)

SOCIAL COGNITIVE THEORY

Origins and overview

Bandura (1977b) first proposed the social learning theory (recently re-labeled

social cognitive theory) and asserted that the processes surrounding conforming

behaviour and deviant behaviour were similar. A basic tenet of this theory is that

social interaction informs and reinforces behaviour in which learning occurs through

direct experience and continues in ways that are dependent upon the negative and

positive consequences of that experience (Akers, 1979). Added to this tenet, is the

element taken from Bandura’s (1977b) social cognitive concepts, by which behaviour

can be learned through observation or modeling (Akers, 1990). Whilst deterrence

theory is concerned with the influence of legal sanctions on criminal behaviour, social

cognitive theory focuses on the overall social setting in which behaviours are

performed and the way in which they are differentially rewarded or punished (Akers,

1990).

Of the social cognitive theories that have been developed over time, Akers’ is

the most widely used to investigate a range of deviant or non-conforming behaviour.

Ronald Akers, in collaboration with Robert Burgess, developed a form of social

learning theory during the mid-1960’s, to explain criminal or deviant behaviour

379

(Akers, 1990, Akers, 1994). This theory drew on aspects of both sociological theory

(Sutherland’s Differential Association theory) and psychological theory (Skinner’s

operant conditioning and Bandura’s social learning concepts). Although this theory

has been developed over time, the fundamental concepts remain consistent with its

origins. This theory consists of four main concepts: differential association, imitation,

definitions and rewards. Differential association relates to the interaction and

subsequent identification with a group or an individual, with a person’s behaviour

being inclined to be compatible with that of the group. The most influential groups are

the family and peer group (Akers, 1979). The dimension of differential association

with this theory begins from interactions with the group with which one associates,

while the normative dimensions are those relating to the group’s evaluation of

different behaviours (Akers, 1996).

Imitation refers to behaviour that occurs after direct observation of that

behaviour or one similar by another person, with family and peer groups providing the

basis for most imitation or modeling (Akers, 1979). The media may be a powerful

source of imitation or modeling. Definitions are the norms, attitudes and orientations

that are learned through interaction with significant groups, with behaviour being

deemed as good or bad through these interactions and is learned by imitating the

behaviour of others (Akers, 1979). Verbal or cognitive definitions can be reinforced,

which occurs when the approval for the behaviour exceeds condemnation (Akers,

1979, Akers and Lee, 1996). Rewards (differential reinforcement) is the mechanism

through which behaviour is secured and sustained, and will either be strengthened or

weakened by reward or punishment (Akers et al., 1979). Akers’ (1979) proposes that

similar acts that are rewarded will provide rewards when one act is rewarded to a

greater extent than an alternative. Although Akers’ theory has demonstrated extensive

support in studies on both adolescent and adult populations, this theory has not been

utilized widely in the field of road safety.

Application to level crossing behaviour

In road safety, Aker’s social cognitive theory has been used to examine

unlicensed driving (Watson, 2004b) as well as analyzing road accidents involving

child pedestrians (Howarth and Repetto-Wright, 1978, Howarth, 1974). Watson’s

(2004b) study found that Akers’ theory was better able to predict unlicensed driving

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than deterrence theory, which was thought to be due to the more comprehensive

framework that social cognitive theory provides over deterrence theory. Additionally,

social cognitive theory was able to include the contributions that the attitudes of

family, friends and others have in developing personal attitudes towards behaviour

(Watson, 2004b).

Although social cognitive theory has not been applied to level crossing safety to

date, it may offer some heuristic advantages over other theories. Firstly, it provides a

comprehensive method of addressing a variety of important factors such as formal and

informal sanctions, direct and indirect experiences, and punishment and punishment

avoidance. Secondly, in applying this theory to level crossing behaviour, the primary

process that influences unsafe behaviour would be through an individual’s interaction

and involvement with their social groups (differential associations). Involvement in

such social groups allows opportunities for the imitation of models and learning of

attitudes (either positive or negative) towards behaviours such as risk taking at level

crossings. The likelihood of unsafe driving at level crossings would increase with

either neutral or favorable definitions towards the behaviour. The individual’s

anticipated balance of reinforcement for unsafe driving behaviour at level crossings

(differential reinforcement), further contributes to a decision to take risks or not. This

is dependent upon an individual’s prior and current rewards and punishments for risk

taking at level crossings, as well as anticipated rewards and punishments for different

behaviour.

Criticisms of social cognitive theory

Although this theory has been used widely in a variety of settings, Lapinski and

Witte (1998) suggest that one element missing from the model is motivation.

According to Delaney, Lough, Whelan and Cameron (2004), the theory appears to

assume that people with high levels of self-efficacy and outcome expectations

consistent with the recommended response, would be motivated to act. Other

researchers have reported that normative pressures have little impact on health

behaviour (deVries, 1990, Kok, 1991, Lechner, 1995). According to Bandura (1998)

this raises the question of whether normative influences are ineffectual or whether

they need to be measured more comprehensively as different forms of social outcome

expectations.

381

SENSATION SEEKING

The possibility that sensation seeking underlies risk taking in driving has been

the subject of a considerable amount of research during the past few decades. The

interest in driver’s personality as a potential underlying causal factor in driver

behaviour was brought about by Tillman and Hobbs (1949) proclamation that ‘a man

drives as he lives’ (Jonah, 1997). Accordingly, sensation seeking has been found to

be higher in males than females (Jonah, 1997). Age is another factor that is relevant

for the motivation to seek sensation. Studies indicate that there is a decline in

sensation seeking with age (Giambra et al., 1992), with one study showing that it rises

between ages 9 and 14, peaks around age 20 and then declines steadily thereafter

(Zuckerman, 1994b).

Studies have demonstrated that there is a significant positive relationship

between risky driving and the theoretical construct of sensation seeking (Jonah, 1997).

Jonah’s (1997) literature review of this relationship found that the majority of the 40

studies reviewed showed a positive relationship between sensation seeking and risky

driving, with correlations in the 0.3-0.4 range. These studies focused mainly on drink

driving, speeding and following too closely. Additionally, these studies have found a

stronger relationship between self-reported driving behaviour and sensation seeking,

rather than with crash involvement or traffic offences, which may be a result of

weaknesses in crash measures used. This review postulates that high sensation seekers

adapt their behaviour to take advantage of safety improvements (e.g. protection

systems) to a greater extent than low sensation seekers, and that consequently they

exhibit greater risky driving (Jonah, 1997). Some researchers believe that sensation

seeking tendencies can not be controlled but rather a trait.

According to Zuckerman (1994a) sensation seeking “is a trait defined by the

seeking of varied, novel, complex, and intense sensations and experiences and the

willingness to take physical, social, legal and financial risks for the sake of such

experiences” (p27). Central to this trait is the optimistic tendency to approach novel

stimuli and explore the environment (Jonah, 2001). The broader trait referred to by

Zuckerman (1994a) as ‘impulsive sensation seeking’ is closely associated with

Eysenck’s (1983) ‘psychoticism dimension’. Evidence suggests that individual

differences are embedded in biological differences in the brain and may in fact to

some extent be hereditary (Zuckerman, 1994b, Eysenck, 1983). Zuckerman (1994b)

382

proposes that monamine neurotransmitters like dopamine, norepinephrine and

serotonin underlie the trait of sensation seeking. Zuckerman developed a

psychometric test to diagnose individual differences based on the bio-developmental

sources of an individual’s motivation to sensation seek (Eysenck, 1983, Zuckerman,

1994b, Horvath and Zuckerman, 1993). This ‘sensation seeking score’ is one measure

in terms of how sensation seeking is operationally defined. Although Zuckerman’s

test has been subjected to empirical validation (Burns and Wilde, 1995), it has been

found that there is a relatively moderate empirical connection between the test and

real-life reflections of the related disposition (Rosenbloom and Wolf, 2002).

Application to level crossing behaviour

According to Witte and Donohue (2000), the lack of perceived susceptibility

toward vehicle-train collision may be due to either errors in judgment, sensation

seeking tendencies or a lack of personal experience with someone who has been

involved in a collision at a level crossing. Jonah (1997) proposes that:

Some high SSs [sensation seekers] will not be influenced by educational

measures since these drivers actually enjoy the thrill of risky driving and

avoiding negative consequences. Furthermore, enforcement programs may fail

to deter some high SSs’ risky driving since they enjoy the thrill of breaking the

law and avoiding detection. These drivers may only be protected by engineering

solutions, whereby they do not have to alter their behaviour to improve their

safety (e.g. airbags, anti-lock brakes). On the other hand, it is conceivable that

high SSs would readily adapt to any perceived improvements in their level of

risk by accepting greater risks.

(p663)

Results from Witte and Donohue’s (2000) formative evaluation suggest that the

majority of respondents engage in safe driving at level crossings, with 10-20%

reporting extremely risky behaviours such as trying to ‘beat the train’. The authors

suggest that high sensation seekers are motivated to avoid frustrating and dull

experiences, admitting they have increased susceptibility to being in a vehicle-train

collision (Witte, 2000). These findings also suggests that risky drivers are habituated

383

to high levels of fear because of prior close calls with trains, and engage in biased

judgments about their abilities to ‘beat the train’ because they have ‘made it’ before.

It is difficult to discern as to what role sensation seeking plays in fatal collision

at level crossings. Previous research conducted by Wigglesworth (1979) suggests that

sensation seeking has not played a role in fatal level crossings collisions. This study of

85 consecutive fatal crashes involving motor vehicles at all types of level crossings in

Victoria, examined the detailed police reports prepared for the coroner. Wigglesworth

(2001) suggests that these reports indicate that “in most cases, the accident occurred

to a law-abiding citizen going about his or her daily work and was attributable to

human overload unrelated to any breach of the regulation” (p311). Since this time,

some may argue that sensation seeking of drivers has increased. However, due to

limited data supporting the role of sensation seeking in fatal crashes, this construct

was not included in any data collection measures.

HABITUAL BEHAVIOUR

According to Ouellette and Wood (1998), habits can be defined as “behavioural

tendencies to repeat responses given a stable supporting context” (p55). Such

repetition and practice of a response in a given setting, allows cognitive processes that

initiate and control the response to become automatic and be performed quickly in

parallel with other activities and little focal attention (Goldenbeld, 2000). Sutton

(1993) argues that almost all behaviours are able to be repeated and that many health

behaviours must be repeated if they are to be effective. For an individual, habits are

essential in handling every day activities as they facilitate information processing

contributing to the efficiency of behavioural routines (Goldenbeld, 2000). Older

people develop individual lifestyles in which habits play a crucial role.

Repeated behaviours however, create an important problem for the theories of

reasoned action and planned behaviour (Sutton, 1993, Ronis, 1989). Although the

theory of planned behaviour incorporates automatic processes, it generally assumes

reasoned processes underlying attitudes and actions (Ajzen, 2002b). This theory

postulates that human social behaviour is reasonable (Bamberg, 2003), however many

theorists challenge this standpoint and argue than human behaviour can be automatic

or habitual (Aarts et al., 1998, Aarts and Dijksterhuis, 2000, Ouellette, 1998, Triandis,

384

1977, Ronis, 1989, Fazio, 1990, Bagozzi, 1981). It is proposed that when behavior is

performed repeatedly and becomes habitual, it is guided by automated cognitive

processes, rather than being preceded by complex decision processes (i.e. a decision

based on attitudes and intentions) (Aarts et al., 1998). Hence, it is based on habitual

or automatic processes, rather than reasoned decision-making. To test this assumption,

past behaviour measures play an important role. It is argued that if social behaviour

is reasoned, frequency of prior behaviour should have only an indirect link to later

behaviour with its effect being mediated by intention and perceived behaviour control

(Bamberg, 2003). Bamberg and colleagues (2003) point out that if added to the

regression equation, past behaviour is normally found to significantly improve the

prediction of later behaviour over and above the effects of intentions and perceptions

of behaviour control. It can be said that, such findings indicate that rather than being

completely reasoned, the behaviour is partly under the direct control of the stimulus

situation (i.e. that it habituates with repeated performance) (Bamberg, 2003). This

perspective considers that frequency of past behaviour is an indicator of habit

strength, which can be used as an independent predictor of later action (Bamberg,

2003).

According to Ajzen (2002b) strong and unmediated links between prior and

later behaviour imply habituation in a process that bypasses intentions. This is often

taken as evidence for habituation of behaviour and as complementing the reasoned

mode of operation assumed by behaviour-intention models such as the theory of

planned behaviour (Ajzen, 2002b). When a habit starts to develop, behaviour is

thought to come under the control of stimulus cues and on future occasions, presence

in a similar situation is sufficient to activate the automatic response chain (Ajzen,

2002b, Aarts et al., 1998, Ouellette, 1998). Therefore, for habitual behaviour to

occur, a stable context is crucial. Thus, habit is defined as the tendency to repeat past

behaviour in a stable context (Ouellette, 1998).

‘Routinization of behaviour’ is also consistent with a reasoned action

perspective according to Ajzen (2002b). The habituation viewpoint affirms that

routinized behaviour is under the control of stimulus cues, however, the reasoned

action perspective proposes that such behaviour is guided by automatically activated

or spontaneous attitudes and intentions (Ajzen, 2002b). According to the principles of

the theory of planned behaviour, attitudes and intentions – once formed and well-

established – are activated automatically and guide behaviour without the requirement

385

of conscious supervision (Ajzen and Fishbein, 2000). Thus, it does not propose that

individuals review their behavioural, normative and control briefs prior to every

enactment of a frequently performed behaviour (Ajzen, 2002b).

Road safety researchers have argued that to a large extent, driver behaviour is

influenced by habit. Seat belt usage (Bentler, 1979) and travel mode choice are two

behaviours that are claimed to be habit-like behaviours. Sutton et al. (1990) in their

study found that there is a substantial habitual component to wearing seat belts and

that this behaviour is not under volitional control. Wittenbraker et al. (1983) found

similar results in their study and argue that prior behaviour, or habit should be added

to the model of reasoned behaviour. More recently, Hatakka, Keskinen, Katila and

Laapotti (1997) found in their study that self-reported risk driving habits and skills for

careful driving, were good predictors for traffic violations and crashes in both

correlational and longitudinal methodologies.

Application to level crossing behaviour

With regards to level crossing safety, driving over the same level crossing/s on a

regular basis eventually becomes a habit in the sense that little cognitive effort is

required for continued execution of the behaviour. So long as the context remains

relatively unchanged, such routinized behaviour is performed in a largely automatic

manner with little conscious control. Research conducted by Wigglesworth (1979)

indicates that of 85 consecutive fatal crashes in Victoria during the 1970’s, 86% of

those drivers killed in a level crossings collision were local residents who were

familiar with the crossing. However, this research does not detail how frequently the

driver was exposed to driving at this level crossing.

The perspective argued by Bamberg and colleagues (2003) that past behaviour

is an indicator of habit strength, may provide useful to examining habitual behaviour

at level crossings in the current study. Although it is not possible to test for

significant associations between past behaviour and collisions at level crossings in the

current study, an instrument pertaining to self-report past behaviour will be included

to test for any association between past behaviour and involvement in any crashes

during the past year.

386

Appendix 2: Modified Delphi Technique (First Questionnaire)

387

Dear Panelists,

Thank-you for agreeing to take part in this 1st questionnaire on Motorist

Behaviour at Railway Level Crossings. You have been identified as a key stakeholder

and we are seeking your participation in this project. There will be 2-3 short survey

questionnaires to complete during this project.

This 1st questionnaire asks you to write your answers in the space provided after

each question. We will then combine all the answers to give us a broad overview of

all the issues. The 2nd questionnaire which will be sent to you in late August, will

give you a summary of the findings from this 1st questionnaire and then you will be

asked to rank issues/items for importance. Depending on whether we require more

information, a 3rd questionnaire will be sent to you. This 3rd questionnaire will

summarize the 2nd questionnaire and also ask you to rank issues/items of importance.

Your individual contribution to each questionnaire is very important to

improving level crossing safety in Australia. If you would like to clarify any question

in the 1st questionnaire, please contact the Project Officer (Ms Nadja Ibrahim) on 07

3864 4926 or email: n2.ibrahim@qut.edu.au

Dr. Jeremy Davey

Chief Investigator

Centre for Accident Research & Road Safety - Qld

388

Questions:

• What do you believe are the major factors contributing to vehicle-train

collisions at railway level crossings?

• Please comment on the current engineering devices/design at level crossings

and their impact on the safety of motorists.

• What types of motorists or vehicles do you consider are most at risk of being

involved in a crash?

• In your opinion, what specific things do motorists do to contribute to vehicle-

train collisions at level crossings? i.e. What are the behaviours that put them

at risk?

• Do you think motorists behave differently between active (lights and/or boom

gates) and passive crossings (no protection systems)? If yes, how do they

behave differently?

• In your opinion, why do motorists behave in this way at level crossings?

• In your opinion, do interactions between different motorists and/or vehicles

contribute to vehicle-train collisions?

389

Demographics

What State/Territory do you currently work in? ______________

Which field do you work in?

Road Rail Other (Please Specify) ________________________

How long have you worked in this current field?

_______________ yrs

Which type of organisation do you mainly work for? Please choose one only.

National Government Organisation State Government Organisation National Non-Government Organisation State Non-Government Organisation Police Service Academic Institution Private Business/Company Other (Please Specify) ________________________

Which of these areas within your organisation are you mainly involved in? Please choose one only.

Policy Project Management Research and/or Development Enforcement Engineering Education Communications/Marketing Other (Please Specify) ________________________

What is your gender?

Male Female

390

Appendix 3: Modified Delphi Technique (Second Questionnaire)

391

Dear Panelists,

We are now seeking your participation in the next round of these questionnaires

on Motorist Behaviour at Railway Level Crossings.

The Centre for Accident Research and Road Safety – Queensland (CARRS-Q)

based at the Queensland University of Technology, is investigating motorist

behaviour at level crossings. The first phase of this 3-year project is to identify (by

key stakeholders) target behaviours and target groups that may place motorists at risk

of a vehicle-train collision.

Your opinion is very valuable to us and we thank-you for your time and

participation. If you would like any further information about this project, please do

not hesitate to contact me or the project team

Dr. Jeremy Davey

Chief Investigator

Centre for Accident Research & Road Safety - Qld

392

Major Contributing Factors

A number of factors contributing to vehicle-train collisions at railway level crossings were identified in the previous questionnaire. Please indicate how important you believe each of these is to a collision or a near miss. Circle one number on the scale for each factor, according to how important you believe the factor is in regards to vehicle-train collisions or near misses.

Ver

y im

port

ant

Not

at a

ll im

port

ant

Motorist Behaviour

1. Trying to beat the train across the crossing 1 2 3 4 5

2. Inattention by the motorist when driving 1 2 3 4 5

3. Disobeying the warning signs or signals at crossings 1 2 3 4 5

4. Speeding on approach to crossing 1 2 3 4 5

5. Queuing over crossings 1 2 3 4 5

6. Not stopping at ‘stop’ signs at passive crossings 1 2 3 4 5

7. Not slowing down to scan for a train at passive crossings 1 2 3 4 5

8. Driving around closed boom gates at active crossings 1 2 3 4 5

9. Alcohol / drugs use 1 2 3 4 5

10. Becoming confused at active crossings 1 2 3 4 5

Motorist Error

11. Misjudging train speed 1 2 3 4 5

12. Misjudging distance of train from the crossing 1 2 3 4 5

13. Misjudging the time needed to cross safely 1 2 3 4 5

14. Not seeing level crossing on approach 1 2 3 4 5

Motorist Expectations

15. Low expectation of coming across a train while driving 1 2 3 4 5

16. Familiarity with train timetables 1 2 3 4 5

17. Low perceived chance of a crash occurring 1 2 3 4 5

18. Perception of long delays when stopping for a train to pass 1 2 3 4 5

19. Not expecting a second train

1 2 3 4 5

Motorist Lack of Knowledge, Training or Experience

20. Urban motorists’ inexperience with passive level crossings

1 2 3 4 5

21. Lack of education / training on level crossing safety during 1 2 3 4 5

393

initial driver licensing stage

22. Lack of awareness about the consequences of driving unsafely at

level crossings (i.e. severity of crashes)

1 2 3 4 5

23. Lack of knowledge about a trains stopping distance 1 2 3 4 5

24. Poor knowledge of the road rules in relation to level crossings 1 2 3 4 5

Engineering Issues

25. Road markings are ineffective as they are usually ignored by

motorists

1 2 3 4 5

26. Inadequate sighting distances for motorists at crossings 1 2 3 4 5

27. Traffic congestion leading to queuing over crossing 1 2 3 4 5

28. Inadequate warning signage at crossings 1 2 3 4 5

29. Short stacking situations (i.e. where there is insufficient stacking

distance from the nearest road intersection for the crossing to be

cleared safely by long vehicles)

1 2 3 4 5

30. Inadequate warning devices at crossings 1 2 3 4 5

31. Environmental/design issues affecting ability to see warning

devices (e.g. sun glare)

1 2 3 4 5

32. Inconsistent signage between crossings 1 2 3 4 5

33. Inconsistent approaches by authorities to risk treatment /

management at crossings

1 2 3 4 5

34. Human visual limitations making speed judgment of

approaching trains difficult

1 2 3 4 5

Lack of Enforcement

35. Lack of enforcement of road rules at level crossings by police 1 2 3 4 5

36. Low fines and penalties for potential severity of collision 1 2 3 4 5

37. Lack of detection devices for breaches (no “red light” cameras at

level crossings)

1 2 3 4 5

394

Road User Groups

A number of road user groups at risk of vehicle-train collisions were identified in the previous questionnaire. These include: general motorists, younger motorists, older motorists, long haul/heavy vehicle drivers, rural motorists, bus drivers, local drivers and fleet drivers. For each of the road user groups, there are a number of factors that may contribute to their risk of a vehicle-train collision. Please indicate how important you believe each of these factors is to each group.

Ver

y im

port

ant

Not

at a

ll im

port

ant

General Motorists

1. Errors of judgment (e.g. misjudging time needed to cross safely) 1 2 3 4 5

2. Speeding on approach to crossing 1 2 3 4 5

3. Motorist fatigue 1 2 3 4 5

4. Trying to beat the train across the crossing 1 2 3 4 5

5. Driving impatiently 1 2 3 4 5

6. Lack of knowledge about safe driving behaviour at crossings 1 2 3 4 5

7. Misjudging level of risk 1 2 3 4 5

8. Driving while distracted (e.g. talking on a mobile phone) 1 2 3 4 5

9. Queuing over crossing 1 2 3 4 5

10. Community norms relating to behaviour at level crossings (e.g.

“no-one stops at this crossing”)

1 2 3 4 5

Younger Motorists (17-24 years)

11. Trying to beat the train across the crossing 1 2 3 4 5

12. Inexperience with crossings 1 2 3 4 5

13. Driving impatiently 1 2 3 4 5

14. Misjudging level of risk 1 2 3 4 5

15. Lack of training on crossings during licensing process 1 2 3 4 5

Older Motorists (60 years and over)

16. Vision impairment 1 2 3 4 5

17. Confusion/anxiety at crossings 1 2 3 4 5

18. Slow reaction times 1 2 3 4 5

19. Decision making difficulties 1 2 3 4 5

20. Errors of judgment (e.g. misjudging time needed to cross safely) 1 2 3 4 5

395

Long Haul/Heavy Vehicle Drivers

21. Difficulties with vehicle braking and acceleration 1 2 3 4 5

22. Trying to beat the train across the crossing 1 2 3 4 5

23. Length of vehicle causing overhang on the crossing 1 2 3 4 5

24. Risk taking due to commercial time pressures 1 2 3 4 5

25. Extended time taken to safely clear crossing 1 2 3 4 5

26. Truck driver fatigue 1 2 3 4 5

27. Low expectation of a train being on crossing 1 2 3 4 5

28. Not scanning for a train at ‘give way’ signed crossings 1 2 3 4 5

29. Trying to beat the train across the crossing 1 2 3 4 5

30. Complacency due to familiarity with crossings 1 2 3 4 5

31. Community norms relating to behaviour at level crossings (e.g.

“no-one stops at this crossing”)

1 2 3 4 5

Bus Drivers

32. Bus driver distraction (e.g. school bus driver) 1 2 3 4 5

33. Extended time taken to safely clear crossing 1 2 3 4 5

34. Trying to beat the train across the crossing 1 2 3 4 5

35. Difficulties with vehicle braking and acceleration 1 2 3 4 5

36. Queuing over crossing 1 2 3 4 5

37. Risk taking due to commercial time pressures 1 2 3 4 5

Local Drivers (motorists that live within 15km of a level crossing)

38. Low expectation of a train being on crossing 1 2 3 4 5

39. Queuing over crossing 1 2 3 4 5

40. Complacency due to familiarity with the crossings 1 2 3 4 5

41. Inattention while driving 1 2 3 4 5

42. Trying to beat the train across the crossing 1 2 3 4 5

43. Driving impatiently 1 2 3 4 5

44. Community norms relating to behaviour at level crossings (e.g.

“no-one stops at this crossing”)

1 2 3 4 5

Fleet Drivers

45. High level of exposure to crossings (i.e. drive over numerous

crossings per day)

1 2 3 4 5

46. Speeding on approach to crossing 1 2 3 4 5

47. Motorist fatigue 1 2 3 4 5

396

48. Lack of knowledge about safe driving behaviour at crossings 1 2 3 4 5

49. Misjudging level of risk 1 2 3 4 5

50. Risk taking due to commercial time pressures 1 2 3 4 5

51. Driving while distracted (e.g. talking on a mobile) 1 2 3 4 5

397

Appendix 4: Survey instrument used in Study Two

398

MOTORIST BEHAVIOURS AT RAILWAY LEVEL CROSSINGS

In our recent research with the general public, the following behaviours were reportedly performed at railway level crossings. Please indicate what level of risk you would attribute to each behaviour, in terms of risk of a

vehicle-train collision.

Please put one “X” in the appropriate box for each of the behaviours.

Low

ris

k

Som

e ri

sk

Mod

erat

e ri

sk

Hig

h ri

sk

Ver

y hi

gh r

isk

1. Driving through the crossing when the lights are flashing but before the boom gates start to go down

2. Trying to drive through the crossing when the boom gates are actually coming down

3. Driving through the crossing when the lights are flashing and the boom gates have started start to go up

4. Slowing and rolling through STOP signed crossings if no train visible (rather than coming to a complete stop)

5. Driving through the crossing as soon as one train has passed, without looking for a second train, at a passive or crossing with lights only

6. Not stopping at all at STOP signed passive crossings 7. Not looking for trains at passive crossings 8. Drive through passive crossings when the train is visible but still

some distance away

9. Drive through passive crossings when the train close to the crossing 10. Queuing over a congested crossing 11. Driving around the boom gates 12. Try to beat the train over the crossing 13. Speeding on approach to crossings 14. Overtaking cars that are stopped at either an active or passive

crossing

15. Looking or scanning on approach to a passive crossing and then not topping or slowing if no train seen

16. Driving through a crossing when the lights are flashing and the train is not visible (at crossings with lights only)

17. Driving through a crossing when the lights are flashing and the train is visible (at crossings with lights only)

18. At passive crossings, following the car in front across the crossing without looking

19. Driving across the crossing when unable to see if there is a train coming (poor visibility due to shrubs, cane, parked cars etc)

Thank you for your time and participation

399

Appendix 5: Support letter from RACQ inviting Younger Drivers to participate

400

29th November 2006 Dear Member

Younger Driver Safety Study The RACQ is calling for volunteers to be a part of a ground-breaking study to understand the driving behaviour of young drivers aged 17 - 24 years. As part of its ongoing commitment to road safety, the RACQ actively supports the valuable research work carried out by the Centre for Accident Research and Road Safety – Queensland (CARRS-Q) at Queensland University of Technology. CARRS-Q is a joint initiative of the Motor Accident Insurance Commission and QUT, which conducts applied research into areas of road safety and accident prevention as well as administering a road safety research grant scheme on behalf of the MAIC. As the RACQ’s representative on the CARRS-Q Board of Management, I am acutely aware of the significance of the work carried out by the Centre and its value to Queensland motorists. That is why I am pleased to draw your attention to this innovative research study into younger driver behaviour to be undertaken by the RACQ and CARRS-Q in order to develop more effective road safety campaigns. The RACQ is seeking your participation. Very little time is required of participants. Online surveys will take approximately 20 minutes to complete. In total, 2 surveys will need to be completed. As an incentive for young drivers to participate, 2 movie tickets will be posted to you on completion of the questionnaires. Participants will be asked a range of questions about road safety campaigns and their driving behaviour. Please be assured that any personal information provided by you will remain confidential and used solely for statistical research purposes. To participate in this research, you will need to access one of the websites below and use login details provided. Please note that to assist the online server in coping with internet traffic from the survey, two separate websites are being used. We ask you to read the following directions to ensure that you access the correct site. If you were born in the months January to June, use the following details to participate:

Website: http://rail.carrsq.net.au/S212/ Username: drive2006 Password: road06

If you were born in the months July to December, use the following details to participate:

Website: http://rail.carrsq.net.au/S313/ Username: motor2006 Password: road06

Thank you for your cooperation. Yours sincerely Gary Fites General Manager External Relations

401

Appendix 6: Support letter from RACQ inviting Older Drivers to participate

402

15 May 2006

Dear Member

Older Driver Safety Study The RACQ is calling for volunteers to be a part of a ground-breaking study to understand why drivers aged 60 years and over are involved in more road crashes. As part of its ongoing commitment to road safety, the RACQ actively supports the valuable research work carried out by the Centre for Accident Research and Road Safety – Queensland (CARRS-Q) at Queensland University of Technology. CARRS-Q is a joint initiative of the Motor Accident Insurance Commission and QUT, which conducts applied research into areas of road safety and accident prevention as well as administering a road safety research grant scheme on behalf of the MAIC. As the RACQ’s representative on the CARRS-Q Board of Management, I am acutely aware of the significance of the work carried out by the Centre and its value to Queensland motorists. That is why I am pleased to draw your attention to this innovative research study into older driver behaviour to be undertaken by the RACQ and CARRS-Q in order to develop more effective road safety campaigns. While age is not the sole indicator of driving ability, drivers aged 60 years and over are involved in more fatal crashes per kilometres travelled than other drivers. Gradual declines in visual, cognitive and motor functions can make drivers vulnerable to crashes in complex situations that require high levels of visual perception, attention, and rapid response. As a result, older drivers are more likely to be involved in crashes at intersections and in other such traffic situations. The RACQ is seeking your participation. Very little time is required of participants. Surveys will take approximately 20 minutes to complete and will be sent to you in the mail (with reply-paid envelopes included). In total, 2 surveys will need to be completed. If you would like to participate in this research, please phone Mr Nicholas Stenson on 07 3864 4926 or email n.stenson@qut.edu.au Survey questionnaires will be sent to you in the mail and you will then be phoned by a member of the research team to ask your opinion about road safety campaigns that you may have seen or heard. Please be assured that any personal information provided by you will remain confidential and used solely for statistical research purposes. For your participation, you will be sent 2 movie tickets once you have completed the surveys. Thank you for your cooperation. Yours sincerely Gary Fites General Manager External Relations

403

Appendix 7: Intervention and Control Radio Script for Each Road User Group

404

Heavy vehicle intervention (radio script)

Pre-amble

Accidents at railway crossings are like lighting strikes. They don’t happen all

the time and you never think it will happen to you, but, if it does, you probably won’t

survive it. Every year, though, trucks hit trains, lives are lost and millions of dollars

of damage is done.

Source introduction

If you’ve been driving trucks up and down Australia, you will know what

happens when a train hits a B-Double, and you’ll know it’s not pretty.

Suggested action

We’re not going to tell you how to do your job. If you’re a truck driver, you

know the roads and you know where the dangerous spots are. Use your experience to

protect yourself at railway crossings:

• Think about Stopping Distances – some crossings have lights and boom gates,

others just have a stop or give-way sign, but all require you to stop. Prepare

early, make sure you give your truck enough time to pull up.

• Look out for Short-stacking – this is when your truck hangs over the crossing

because there is not enough space ahead to clear it. Make sure that your path

is clear before you start to cross. You don’t want to realise your stuck half-

way.

• Check for Problems with Visibility - some conditions can make it harder to

check for trains at crossings. Don’t gamble at s-bend roads, in glare or when

roadside objects obscure your view. Slow down and make sure you are safe to

cross.

Re-cap

Work is currently underway fixing up the roads at level crossings, but with

thousands of railway crossings in Australia, it is going to take some time to fix them

405

all up. Until then, think about the crossings you pass, and take care to protect yourself

and others. So remember…Keep your brain in gear: Stay alert at railway crossings.

Official end

This message is brought to you by the Motor Accident Insurance Commission

in the interests of road safety.

406

Older Driver Intervention (radio script)

Pre-amble

Each year, accidents at railway level crossings claim the lives of Australian

drivers. It’s tempting to think that these deaths are hoons or inexperienced drivers.

But often those killed at level crossings are just normal people. Twenty-six percent

(26%) of them are aged over 60.

Source introduction

As older drivers, we have a wealth of experience behind the wheel, but this

doesn’t make us immune to the dangers on the road. While we are very conscious of

day to day risks, sometimes it can be the unexpected danger, the once in a lifetime

event, that we are not prepared for.

Suggested action

This is what you need to know to protect yourself at level crossings:

• Know your crossings – some have lights and boom gates, others just have a

stop or give-way sign, but all require you to give way to trains.

• Plan your trip – know what crossings you are likely to pass and how to

negotiate them. If you find certain crossings confusing, ask someone you trust

for advice.

• Take your time – no one has the right to rush you on the road. Following the

rules at level crossings makes the road safer not only for yourself, but for other

drivers as well.

Re-cap

The stakes at railway crossings are high. Being prepared may not just save your

life, but those of the people you care most about. So remember, every time you use a

level crossing…Be Cautious. Be Smart. Be Safe.

407

Official end

This message is brought to you by the Motor Accident Insurance Commission

in the interests of road safety.

408

Older Driver Control (radio script)

Pre-amble

Your eyes are one of the most important senses for driving. As drivers get

older, their eyesight is less clear and they cannot see as far.

Source introduction

For drivers aged 60 years and over, it is often hard to see detail such as traffic

signs, and it may be difficult to see objects approaching from the side. Handling glare

and recovering from glare is another problem for mature drivers. As such, mature

drivers can have problems with oncoming headlights or the afternoon sun. Eyesight is

also not as good at twilight or at night time.

Suggested action

If you are aged 60 years and over, it is important to maintain your eyesight to

drive safely. Two things you can do to make sure your eyesight is at its best are:

• Have regular check-ups at the Optometrist at least every two years and

• Keep the prescription of your glasses up-to-date

Other things you can do to ensure you drive safely are always to make sure your

windscreen is clean to reduce glare and limit your driving at night or at twilight.

Re-cap

So next time you walk past an Optometrist, book in for a check-up. It may just

save your life.

Official end

This message is brought to you by the Motor Accident Insurance Commission

in the interests of road safety.

409

Younger Driver Intervention (radio script)

Pre-amble

Each year, accidents at rail level-crossings claim the lives of young Australian

drivers. Some of them are taking stupid risks. Some weren’t paying attention. But

most of them get hit just trying to save a little bit of time.

Source introduction

When you see what happens when a train hits a car, you don’t ever forget the

carnage, the twisted metal and the lives lost. What you don’t often see is the long-

term damage they do: to families, to mates and to lives of those left behind.

Suggested action

When it comes to accidents at level-crossings, there aren’t any second chances.

This is what you need to know to protect yourself at these crossings:

• Know your crossings – some have lights and boom gates, others just have a

stop or give-way sign. It’s important to remember, however, that all of these

crossings require you to stop.

• Know your trains – trains travel at speeds up to 100 km/hr and way over 120

tonnes. At that speed and with size, they can take up to a kilometre to stop.

Don’t assume you can beat them across the tracks, and don’t think they will be

able to stop in time to avoid a collision if you try to.

• Know your facts – the stop time at most level-crossings is shorter than you

think, around the same time as a change of traffic lights. In the long-run,

adding an extra minute to your journey makes no difference to your arrival

time, but it might save your life.

Re-cap

So remember, every time you reach a level-crossing, think about what you’re

doing. A minute of your time can save a lifetime of regrets.

410

Official end

This message is brought to you by the Motor Accident Insurance Commission

in the interests of road safety.

411

Younger Driver Control (radio script)

Pre-amble

Road safety education has been talking about seatbelts for a long time.

However, statistics show us that small group of car occupants, both drivers and

passengers, still routinely travel without seatbelts. If that’s not enough, the hospital

wards are full of proof that some people still aren’t getting the message.

Source introduction

So this time, we’ll let the numbers speak for themselves. 50 – the number of

Queenslanders killed each year in car accidents who weren’t wearing seatbelts. 244 –

the number of Queenslanders hospitalised each year because of car accidents, who

weren’t wearing seatbelts. 10 – the number of times more likely you are to die in a

car accident, if you aren’t wearing a seatbelt.

Suggested action

The numbers don’t lie. A seatbelt can be the difference between walking away

from an accident, and never walking again.

Re-cap

Next time you get into a car, take a second and buckle up. It’s that simple.

Official end

This message is brought to you by the Motor Accident Insurance Commission

in the interests of road safety.

412

Appendix 8: Questionnaires used in Study 3

413

ID Number: __ __ __ - __ Staff Use Only

Centre for Accident Research and Road Safety – Queensland

Young Drivers Pre-Test Questionnaire

2006

Associate Professor Jeremy Davey Deputy Director Centre for Accident Research & Road Safety – Queensland Queensland University of Technology Telephone: 07 3864 4574 Email: j.davey@qut.edu.au

Angela Wallace PhD Scholar Centre for Accident Research & Road Safety – Queensland Queensland University of Technology Telephone: 0402 240 234 Email: angela.wallace@student.qut.edu.au

414

1. Personal Information We would like to start by asking you some questions about yourself. Please circle the number that best describes you.

What is your gender? Male………………………………………………….……

Female…………………………………………………… 1 2

What is your age? _________ Years

Are you currently involved in any paid work?

Yes, I am currently working …………………………… No, I am not currently working ………………………..

1 2

If you are in paid work, what is your occupation?

_______________________________________

What is the postcode where you live?

What type of drivers licence do you hold? Learners ……....………………………………………… Provisional ……………………………………………… Open licence …………………………………………….

1 2 3

2. Education What is the highest level of education that you have completed? Please circle one number that best describes your answer. Some secondary or high school …………………………………………………………………………….. Year 12 …………………………………………………………………………………………………………. Some university …………………………………………………………………………………………………. University degree ………………………………………………………………………………………………... Some TAFE ……………………………………………………………………………………………………… TAFE Diploma or Certificate …………………………………………………………………………………… Other (Please specify) ______________________________________________________________

1 2 3 4 5 6 7

3. Driving Patterns We would like to ask you some questions about how often you drive.

On average, how many days of the week do you drive? ______________________ days per week

On average, how many hours per day do you drive? ______________________ hours per day

415

4. Driving Self-Assessment

The next few questions relate to your driving. We would like for you to answer these questions as truthfully as possible. (Circle one number for each statement) How often do you…..

Nev

er

All

the

time

Wear your seatbelt while driving 0 1 2 3 4 5

Drive more than 10km/hr above the speed limit in built-up areas 0 1 2 3 4 5

Stop at ‘Stop’ signs 0 1 2 3 4 5

Feel tired when driving at night-time 0 1 2 3 4 5

Drive more than 10km/hr above the speed limit on open roads 0 1 2 3 4 5

Get angry at the actions of other drivers 0 1 2 3 4 5

Drive more than 10km/hr above the speed limit during day-time 0 1 2 3 4 5

Drive after having a few drinks 0 1 2 3 4 5

Enjoy driving 0 1 2 3 4 5

Prefer not to wear a seatbelt 0 1 2 3 4 5

Drive more than 10km/hr above the speed limit at night-time 0 1 2 3 4 5

Feel tired when driving during the day 0 1 2 3 4 5

Enjoy driving faster than other traffic 0 1 2 3 4 5 5. Road Crashes The next few questions are about any road crashes you have been involved in as a Driver in the past 3 years. By crash, we mean any collision that involved either injury to another person or yourself, damage to property, damage to another vehicle, or damage to the vehicle you were driving. Please mention only those crashes that you were involved in as a Driver. How many crashes have you been involved in as a Driver during the past 3 years? (Write number on line)

__________

If you have not been in a crash as a Driver during the past 3 years, please skip to Question 6. Of the crashes that you have been involved in as a Driver during the past 3 years, how many resulted in: Damage only ______________ Slight injury (to any person)

______________

Serious injury (to any person)

______________

Fatality (of any person)

______________

416

Did any of the road crashes occur at either an intersection or a railway level crossing? No ………………………….………… 0 Yes ………………………….………… 1

Accident at an Intersection …………………… Accident at a Railway Level Crossing ……….

1 2

Thinking about your most recent crash (during the past 3 years), what type of collision was it?

(Circle one number for each statement)

Yes No

Head on collision with another vehicle

1 2

Rear-end collision with another vehicle

1 2

Angular (i.e. side-on) collision with another vehicle

1 2

Collision with a pedestrian

1 2

Collision with another object (e.g. parked car, animal, tree)

1 2

Overturned vehicle

1 2

Other

1 2

Was the crash your fault?

Not at all Partially Fully (Circle one number only) 0 1 2

Were the following statements true or false at the time of the crash?

(Circle one number for each statement)

True False

I had been drinking alcohol before driving

1 2

I felt tired

1 2

I was driving too fast

1 2

I was talking to a passenger in the vehicle

1 2

I was listening to music or the radio

1 2

I was talking on a mobile phone

1 2

I was trying to pick up something from the seat or floor

1 2

I was adjusting the radio/cassette/CD or fan/air conditioning

1 2

I was checking the instruments (e.g. fuel gauge or speedometer)

1 2

417

(Circle one number for each statement)

True False

I was daydreaming

1 2

I was distracted by something inside the vehicle

1 2

I was distracted by something outside the vehicle

1 2

6. General Driving Behaviour No one is perfect. Even the best drivers make mistakes, do foolish things, or bend the rules at some time or another. Some of these behaviours are trivial, but some are potentially dangerous. For each item below you are asked to indicate HOW OFTEN, you drive in the following way.. On the scale of 0 ‘Never’ to 5 ‘Nearly all the time’, please circle the number which best sums up your answer.

(Circle one number for each statement) How often do you…..

Nev

er

Har

dly

ever

Occ

asio

nally

Qui

te o

ften

Freq

uent

ly

Nea

rly a

ll th

e tim

e

Attempt to overtake someone that you hadn’t noticed to be signalling a right turn

0 1 2 3 4 5

Get into the wrong lane when approaching a roundabout or a junction

0 1 2 3 4 5

Miss ‘Stop’ or ‘Give Way’ signs and narrowly avoid colliding with traffic having right of way

0 1 2 3 4 5

Misread the signs and exit from the roundabout on the wrong road

0 1 2 3 4 5

Fail to notice that pedestrians are crossing when turning into a side street from a main road

0 1 2 3 4 5

Drive especially close to the car in front as a signal to its driver to go faster or get out of the way

0 1 2 3 4 5

Forget where you left your car in the car park

0 1 2 3 4 5

Queuing to turn left onto a main road, you pay such close attention to the mainstream of traffic that you nearly hit the car in front

0 1 2 3 4 5

Hit something when reversing that you had not previously seen 0 1 2 3 4 5

Cross a junction knowing that the traffic lights have already turned against you

0 1 2 3 4 5

On turning left nearly hit a cyclist who has come up on your inside 0 1 2 3 4 5

Disregard the speed limits late at night or very early in the morning 0 1 2 3 4 5

Attempt to drive away from the traffic lights in third gear 0 1 2 3 4 5

Fail to check your rear-view mirror before pulling out, changing lanes, etc.

0 1 2 3 4 5

418

(Circle one number for each statement) How often do you…..

Nev

er

Har

dly

ever

Occ

asio

nally

Qui

te o

ften

Freq

uent

ly

Nea

rly a

ll th

e tim

e

Have an aversion to a particular class of road user, and indicate your hostility by whatever means you can

0 1 2 3 4 5

Become impatient with a slow driver in the outer lane and overtake on the inside

0 1 2 3 4 5

Underestimate the speed of an oncoming vehicle when overtaking 0 1 2 3 4 5

Switch on one thing, such as the headlights, when you meant to switch on something else, such as the wipers

0 1 2 3 4 5

Brake too quickly on a slippery road, or steer the wrong way in a skid 0 1 2 3 4 5

Intending to drive to destination A, you ‘wake up’ to find yourself on the road to destination B, perhaps because the latter is your more usual destination

0 1 2 3 4 5

Drive even though you realise you may be over the legal blood-alcohol limit

0 1 2 3 4 5

Get involved in unofficial ‘races’ with other drivers 0 1 2 3 4 5

Realise that you have no clear recollection of the road along which you have just been travelling

0 1 2 3 4 5

Angered by another driver’s behaviour, you give chase with the intention of giving him/her a piece of your mind

0 1 2 3 4 5

7. Driving at Railway Level Crossings Please circle how frequently you drive through the following types of railway level crossings when you drive.

(Circle one number for each statement) N

ever

Onc

e a

year

Twic

e a

year

Mon

thly

Wee

kly

Dai

ly

Boom gates with flashing lights

0 1 2 3 4 5

Flashing lights only (no Boom Gate)

0 1 2 3 4 5

Only STOP or GIVE WAY sign

0 1 2 3 4 5

419

How often have you ever engaged in the following behaviours at a Railway Level Crossing? On the scale of 0 ‘Not at all’ to 5 ‘Very often’, please circle the number which best sums up your answer. If you have never driven over any type of railway level crossing (i.e. with or without boom gates), please circle ‘Not Applicable’.

(Circle one number for each statement) I have: N

ot a

t all

Very

O

ften

Not

App

licab

le

Driven through a rail crossing when lights are flashing, but boom gates are yet to descend

0 1 2 3 4 5 n/a

Driven through a rail crossing when boom gates are descending

0 1 2 3 4 5 n/a

Driven through a rail crossing when booms gates are beginning to rise, but lights are still flashing

0 1 2 3 4 5 n/a

Rolling through a rail crossing without stopping, if no train is visible

0 1 2 3 4 5 n/a

Driven through a rail crossing after the first train has passed, without looking for a second

0 1 2 3 4 5 n/a

Failed to look for a train before crossing a rail crossing

0 1 2 3 4 5 n/a

Driven through a rail crossing when the train is visible, but still some distance away

0 1 2 3 4 5 n/a

Driven through a rail crossing when the train is close

0 1 2 3 4 5 n/a

Queued over a rail crossing

0 1 2 3 4 5 n/a

Driven around boom gates to cross a rail crossing

0 1 2 3 4 5 n/a

Tried to beat the train across the rail crossing

0 1 2 3 4 5 n/a

Sped on approach to a rail crossing

0 1 2 3 4 5 n/a

Driven through a rail crossing when the lights are flashing, but no train is visible

0 1 2 3 4 5 n/a

Driven through a rail crossing when the lights are flashing, and the train is visible

0 1 2 3 4 5 n/a

Scanned on approach to a rail crossing, but failed to stop at it

0 1 2 3 4 5 n/a

Stopped on the yellow hatching road markings

0 1 2 3 4 5 n/a

Followed another vehicle across the rail crossing without looking for myself

0 1 2 3 4 5 n/a

Driven through a rail crossing when visibility is impaired

0 1 2 3 4 5 n/a

Driven through a rail crossing without realising it until after the vehicle is over the tracks

0 1 2 3 4 5 n/a

Seen police enforcing the road rules at a rail crossing

0 1 2 3 4 5 n/a

420

We would like to know the likelihood that you will engage in the following behaviours in the next 6 months of driving at Railway Level Crossings. Even if you rarely drive through railway level crossings, please indicate how you would drive if you were to drive through a crossing. On the scale of 0 ‘Not at all likely’ to 5 ‘Very likely’, please circle the number which best describes your answer. (Circle one number for each statement) In the next 6 months of driving, it is likely I will: N

ot a

t all

likel

y

Very

lik

ely

Drive through a rail crossing when lights are flashing, but boom gates are yet to descend

0 1 2 3 4 5

Drive through a rail crossing when boom gates are descending

0 1 2 3 4 5

Drive through a rail crossing when booms gates are beginning to rise, but lights are still flashing

0 1 2 3 4 5

Roll through a rail crossing without stopping, if no train is visible

0 1 2 3 4 5

Drive through a rail crossing after the first train has passed, without looking for a second

0 1 2 3 4 5

Fail to look for a train before crossing a rail crossing

0 1 2 3 4 5

Drive through a rail crossing when the train is visible, but still some distance away

0 1 2 3 4 5

Drive through a rail crossing when the train is close

0 1 2 3 4 5

Queue over a rail crossing

0 1 2 3 4 5

Drive around boom gates to cross

0 1 2 3 4 5

Try to beat the train across the rail crossing

0 1 2 3 4 5

Speed on approach to a rail crossing

0 1 2 3 4 5

Drive through a rail crossing when the lights are flashing, but no train is visible

0 1 2 3 4 5

Drive through a rail crossing when the lights are flashing, and the train is visible

0 1 2 3 4 5

Scan on approach to a rail crossing, but fail to stop at it

0 1 2 3 4 5

Stop on the yellow hatching road markings

0 1 2 3 4 5

Follow another vehicle across the rail crossing without looking for myself

0 1 2 3 4 5

Drive through a rail crossing when visibility is impaired

0 1 2 3 4 5

Drive through a rail crossing without realising until after the vehicle is over the tracks

0 1 2 3 4 5

See police enforcing the road rules at a rail crossing

0 1 2 3 4 5

421

Rate each of the following statements according to the degree they reflect your personal views. On the scale of ‘1’ ‘Strongly Disagree’ to ‘6’ ‘Strongly Agree’, please circle the number which best describes your answer. (Circle one number for each statement)

Stro

ngly

D

isag

ree

Stro

ngly

A

gree

Your family generally obeys the rules at rail crossings 1 2 3 4 5 6

Your friends generally obey the rules at rail crossings 1 2 3 4 5 6

Other motorists generally obey the rules at rail crossings 1 2 3 4 5 6

Your family generally think it important to obey the rules at rail crossings 1 2 3 4 5 6

Your friends generally think it important to obey the rules at rail crossings 1 2 3 4 5 6

Other motorists generally think it important to obey the rules at rail crossings

1 2 3 4 5 6

It is generally safe to disobey the rules at rail crossings 1 2 3 4 5 6

It is generally possible to judge a train’s speed 1 2 3 4 5 6

It is generally safe to cross if you can’t see a train, even if the lights are flashing

1 2 3 4 5 6

It is generally safe to roll slowly through a crossing instead of stopping 1 2 3 4 5 6

Trains generally run to a regular timetable 1 2 3 4 5 6

Penalties need to be tougher for violating road rules at rail crossings 1 2 3 4 5 6

The main deterrent for breaking the rules at rail crossings is fear of getting caught

1 2 3 4 5 6

Generally it is more important to use common sense at rail crossings than strictly follow the road rules

1 2 3 4 5 6

We would like to ask you some questions about your attitude towards railway level crossings. Please circle one number in each table. As a motorist, I believe that the design of rail crossings are:

Bad Good -3 -2 -1 0 1 2 3

Unsafe Safe

-3 -2 -1 0 1 2 3

As a motorist, I believe that the design of rail crossings are:

Confusing Easily Understood

-3 -2 -1 0 1 2 3

422

As a motorist, I believe that the design of rail crossings are:

Difficult for obeying

road rules

Easy for obeying

road rules -3 -2 -1 0 1 2 3

As a motorist, I believe that road rules at rail crossings are:

Bad

Good

-3 -2 -1 0 1 2 3

Not Strict Enough

Too Strict

-3 -2 -1 0 1 2 3

As a motorist, I believe that road rules at rail crossings are:

Confusing

Easily Understood

-3 -2 -1 0 1 2 3

Not

Practical

Practical -3 -2 -1 0 1 2 3

Obeying the road rules at rail crossings is:

Not up to me

Up to me

-3 -2 -1 0 1 2 3

Out of my

control Under my

control -3 -2 -1 0 1 2 3

Obeying the road rules at rail crossings is:

Dependent on other

motorists

Dependent on me only

-3 -2 -1 0 1 2 3

423

Obeying the road rules at rail crossings is:

Dependent on time

constraints

Not dependent

on time constraints

-3 -2 -1 0 1 2 3

Other motorists influence my driving behaviour at rail crossings, making it:

Harder to obey road

rules

Easier to obey road

rules -3 -2 -1 0 1 2 3

More

confusing Less

confusing -3 -2 -1 0 1 2 3

More

stressful More

relaxing -3 -2 -1 0 1 2 3

When you are driving at a Railway Level Crossing, how often do each of the following occur? On the scale of 0 ‘Never’ to ‘5’ ‘Always’, please circle the number which best describes your answer.

(Circle one number for each statement) N

ever

Alw

ays

Blinding sun makes it difficult to see if the red flashing lights are activated

0 1 2 3 4 5

The design of the road makes it difficult to see if a train is approaching or at the rail crossing

0 1 2 3 4 5

Warning systems on the road approaching the crossing are not adequate to inform drivers there is a rail crossing ahead

0 1 2 3 4 5

Road surfaces are often poor and it is difficult to stop

0 1 2 3 4 5

Boom gates and/or flashing lights are often faulty

0 1 2 3 4 5

Other drivers do stupid things that put you in a dangerous situation

0 1 2 3 4 5

Intersections ahead of a rail crossing often cause your car to overhang the tracks

0 1 2 3 4 5

Difficult to hear an approaching train when the windows are up

0 1 2 3 4 5

My car stalled on the tracks at a rail crossing

0 1 2 3 4 5

424

The following section tests your knowledge about railway level crossings. Circle the number that best describes your answer. When the twin red lights start flashing this means?

A train is coming but it is still safe to cross………….. You must stop and not enter the crossing ………….. Don’t know ………………………………………………

1 2 3

Boom gates usually stay down a lot longer than traffic lights take to change at an intersection

True ………………………………………………………False ……………........…………………………………. Don’t know ………………………………………………

1 2 3

Yellow hatching road markings means keep clear

True ………………………………………………………False …………………………………………………….. Don’t know ………………………………………………

1 2 3

There are fines for not stopping at railway crossings

True ………………………………………………………False …………………………………………………….. Don’t know ………………………………………………

1 2 3

How likely is it, that as a Driver you will be involved in a crash with a train at a railway level crossing?

Not at all Likely

Very Likely

0 1 2 3 4 5

We would like to be able to match this questionnaire with others at a later date. However, we wish to preserve your anonymity. One way to do this is to collect some information from you that will not identify you, but will allow us to match the questionnaires. Please assist us by providing:

First 3 letters of your mother’s maiden name (i.e. Surname before marriage): What is your Date of Birth? _______________________ (e.g. 3rd July 1986)

Thank you for completing this questionnaire. Please return this questionnaire in

the reply-paid envelope supplied.

425

ID Number: __ __ __ - __ Staff Use Only

Centre for Accident Research and Road Safety – Queensland

Young Drivers Post-Test Questionnaire

November 2006

Associate Professor Jeremy Davey Deputy Director Centre for Accident Research & Road Safety – Queensland Queensland University of Technology Telephone: 07 3864 4574 Email: j.davey@qut.edu.au

Angela Wallace PhD Scholar Centre for Accident Research & Road Safety – Queensland Queensland University of Technology Telephone: 0402 240 234 Email: angela.wallace@student.qut.edu.au

426

General Driving Behaviour No one is perfect. Even the best drivers make mistakes, do foolish things, or bend the rules at some time or another. Some of these behaviours are trivial, but some are potentially dangerous. For each item below you are asked to indicate HOW OFTEN, if at all, this kind of thing has happened to you. On the scale of 0 ‘Never’ to 5 ‘Nearly all the time’, please circle the number which best sums up your answer.

(Circle one number for each statement) How often do you…..

Nev

er

Har

dly

ever

Occ

asio

nally

Qui

te o

ften

Freq

uent

ly

Nea

rly a

ll th

e tim

e

Attempt to overtake someone that you hadn’t noticed to be signalling a right turn

0 1 2 3 4 5

Get into the wrong lane when approaching a roundabout or a junction

0 1 2 3 4 5

Miss ‘Stop’ or ‘Give Way’ signs and narrowly avoid colliding with traffic having right of way

0 1 2 3 4 5

Misread the signs and exit from the roundabout on the wrong road

0 1 2 3 4 5

Fail to notice that pedestrians are crossing when turning into a side street from a main road

0 1 2 3 4 5

Drive especially close to the car in front as a signal to its driver to go faster or get out of the way

0 1 2 3 4 5

Forget where you left your car in the car park

0 1 2 3 4 5

Queuing to turn left onto a main road, you pay such close attention to the mainstream of traffic that you nearly hit the car in front

0 1 2 3 4 5

Hit something when reversing that you had not previously seen 0 1 2 3 4 5

Cross a junction knowing that the traffic lights have already turned against you

0 1 2 3 4 5

On turning left nearly hit a cyclist who has come up on your inside 0 1 2 3 4 5

Disregard the speed limits late at night or very early in the morning 0 1 2 3 4 5

Attempt to drive away from the traffic lights in third gear 0 1 2 3 4 5

Fail to check your rear-view mirror before pulling out, changing lanes, etc.

0 1 2 3 4 5

Have an aversion to a particular class of road user, and indicate your hostility by whatever means you can

0 1 2 3 4 5

Become impatient with a slow driver in the outer lane and overtake on the inside

0 1 2 3 4 5

Underestimate the speed of an oncoming vehicle when overtaking 0 1 2 3 4 5

Switch on one thing, such as the headlights, when you meant to switch on something else, such as the wipers

0 1 2 3 4 5

427

(Circle one number for each statement) How often do you…..

Nev

er

Har

dly

ever

Occ

asio

nally

Qui

te o

ften

Freq

uent

ly

Nea

rly a

ll th

e tim

e

Brake too quickly on a slippery road, or steer the wrong way in a skid 0 1 2 3 4 5

Intending to drive to destination A, you ‘wake up’ to find yourself on the road to destination B, perhaps because the latter is your more usual destination

0 1 2 3 4 5

Drive even though you realise you may be over the legal blood-alcohol limit

0 1 2 3 4 5

Get involved in unofficial ‘races’ with other drivers 0 1 2 3 4 5

Realise that you have no clear recollection of the road along which you have just been travelling

0 1 2 3 4 5

Angered by another driver’s behaviour, you give chase with the intention of giving him/her a piece of your mind

0 1 2 3 4 5

Driving at Railway Level Crossings How often have you engaged in the following behaviours whilst driving at a railway level crossing in the past 2-3 weeks? On the scale of 0 ‘Not at all’ to 5 ‘Very often’, please circle the number which best sums up your answer. If you have not driven over any type of railway crossing during the past 2-3 weeks, please circle ‘Not Applicable’.

(Circle one number for each statement) I have: N

ot a

t all

Very

O

ften

Not

App

licab

le

Driven through a rail crossing when lights are flashing, but boom gates are yet to descend

0 1 2 3 4 5 n/a

Driven through a rail crossing when boom gates are descending

0 1 2 3 4 5 n/a

Driven through a rail crossing when booms gates are beginning to rise, but lights are still flashing

0 1 2 3 4 5 n/a

Rolling through a rail crossing without stopping, if no train is visible

0 1 2 3 4 5 n/a

Driven through a rail crossing after the first train has passed, without looking for a second

0 1 2 3 4 5 n/a

Failed to look for a train before crossing a rail crossing

0 1 2 3 4 5 n/a

Driven through a rail crossing when the train is visible, but still some distance away

0 1 2 3 4 5 n/a

Driven through a rail crossing when the train is close

0 1 2 3 4 5 n/a

428

(Circle one number for each statement) I have: N

ot a

t all

Very

O

ften

Not

App

licab

le

Queued over a rail crossing

0 1 2 3 4 5 n/a

Driven around boom gates to cross a rail crossing

0 1 2 3 4 5 n/a

Tried to beat the train across the rail crossing

0 1 2 3 4 5 n/a

Sped on approach to a rail crossing

0 1 2 3 4 5 n/a

Driven through a rail crossing when the lights are flashing, but no train is visible

0 1 2 3 4 5 n/a

Driven through a rail crossing when the lights are flashing, and the train is visible

0 1 2 3 4 5 n/a

Scanned on approach to a rail crossing, but failed to stop at it

0 1 2 3 4 5 n/a

Stopped on the yellow hatching road markings

0 1 2 3 4 5 n/a

Followed another vehicle across the rail crossing without looking for myself

0 1 2 3 4 5 n/a

Driven through a rail crossing when visibility is impaired

0 1 2 3 4 5 n/a

Driven through a rail crossing without realising it until after the vehicle is over the tracks

0 1 2 3 4 5 n/a

Seen police enforcing the road rules at a rail crossing

0 1 2 3 4 5 n/a

We would like to know the likelihood that you will engage in the following behaviours in the next 6 months of driving at railway level crossings. Even if you rarely drive through railway level crossings, please indicate how you would drive if you were to drive through a railway crossing. On the scale of 0 ‘Not at all likely’ to 5 ‘Very likely’, please circle the number which best describes your answer. (Circle one number for each statement) In the next 6 months of driving, it is likely I will: N

ot a

t all

likel

y

Ve

ry

likel

y

Drive through a rail crossing when lights are flashing, but boom gates are yet to descend

0 1 2 3 4 5

Drive through a rail crossing when boom gates are descending

0 1 2 3 4 5

Drive through a rail crossing when booms gates are beginning to rise, but lights are still flashing

0 1 2 3 4 5

Roll through a rail crossing without stopping, if no train is visible

0 1 2 3 4 5

Drive through a rail crossing after the first train has passed, without looking for a second

0 1 2 3 4 5

Fail to look for a train before crossing a rail crossing

0 1 2 3 4 5

429

(Circle one number for each statement) In the next 6 months of driving, it is likely I will: N

ot a

t all

Like

ly

Very

Li

kely

Drive through a rail crossing when the train is visible, but still some distance away

0 1 2 3 4 5

Drive through a rail crossing when the train is close

0 1 2 3 4 5

Queue over a rail crossing

0 1 2 3 4 5

Drive around boom gates to cross

0 1 2 3 4 5

Try to beat the train across the rail crossing

0 1 2 3 4 5

Speed on approach to a rail crossing

0 1 2 3 4 5

Drive through a rail crossing when the lights are flashing, but no train is visible

0 1 2 3 4 5

Drive through a rail crossing when the lights are flashing, and the train is visible

0 1 2 3 4 5

Scan on approach to a rail crossing, but fail to stop at it

0 1 2 3 4 5

Stop on the yellow hatching road markings

0 1 2 3 4 5

Follow another vehicle across the rail crossing without looking for myself

0 1 2 3 4 5

Drive through a rail crossing when visibility is impaired

0 1 2 3 4 5

Drive through a rail crossing without realising until after the vehicle is over the tracks

0 1 2 3 4 5

See police enforcing the road rules at a rail crossing

0 1 2 3 4 5

Rate each of the following statements according to the degree they reflect your personal views. On the scale of ‘1’ ‘Strongly Disagree’ to ‘6’ ‘Strongly Agree’, please circle the number which best describes your answer. (Circle one number for each statement)

Stro

ngly

D

isag

ree

St

rong

ly

Agr

ee

Your family generally obeys the rules at rail crossings 1 2 3 4 5 6

Your friends generally obey the rules at rail crossings 1 2 3 4 5 6

Other motorists generally obey the rules at rail crossings 1 2 3 4 5 6

Your family generally think it important to obey the rules at rail crossings 1 2 3 4 5 6

Your friends generally think it important to obey the rules at rail crossings 1 2 3 4 5 6

Other motorists generally think it important to obey the rules at rail crossings

1 2 3 4 5 6

It is generally safe to disobey the rules at rail crossings 1 2 3 4 5 6

It is generally possible to judge a train’s speed 1 2 3 4 5 6

430

(Circle one number for each statement)

Stro

ngly

D

isag

ree

Stro

ngly

A

gree

It is generally safe to cross if you can’t see a train, even if the lights are flashing

1 2 3 4 5 6

It is generally safe to roll slowly through a crossing instead of stopping 1 2 3 4 5 6

Trains generally run to a regular timetable 1 2 3 4 5 6

Penalties need to be tougher for violating road rules at rail crossings 1 2 3 4 5 6

The main deterrent for breaking the rules at rail crossings is fear of getting caught

1 2 3 4 5 6

Generally it is more important to use common sense at rail crossings than strictly follow the road rules

1 2 3 4 5 6

We would like to ask you some questions about your attitude towards railway level crossings. Please circle one number in each table. As a motorist, I believe that the design of rail crossings are:

Bad Good -3 -2 -1 0 1 2 3

Unsafe Safe -3 -2 -1 0 1 2 3

Confusing Easily Understood

-3 -2 -1 0 1 2 3

Difficult for obeying

road rules

Easy for obeying

road rules -3 -2 -1 0 1 2 3

As a motorist, I believe that road rules at rail crossings are:

Bad

Good

-3 -2 -1 0 1 2 3

Not Strict Enough

Too Strict

-3 -2 -1 0 1 2 3

431

As a motorist, I believe that road rules at rail crossings are:

Confusing

Easily Understood

-3 -2 -1 0 1 2 3

Not

Practical

Practical -3 -2 -1 0 1 2 3

Obeying the road rules at rail crossings is:

Not up to me

Up to me

-3 -2 -1 0 1 2 3

Out of my

control Under my

control -3 -2 -1 0 1 2 3

Obeying the road rules at rail crossings is:

Dependent on other

motorists

Dependent on me only

-3 -2 -1 0 1 2 3

Obeying the road rules at rail crossings is:

Dependent

on time constraints

Not dependent

on time constraints

-3 -2 -1 0 1 2 3

Other motorists influence my driving behaviour at rail crossings, making it:

Harder to obey road

rules

Easier to obey road

rules -3 -2 -1 0 1 2 3

More

confusing Less

confusing -3 -2 -1 0 1 2 3

More

stressful More

relaxing -3 -2 -1 0 1 2 3

432

When you are driving at railway level crossings, how often do each of the following occur? On the scale of 0 ‘Never’ to ‘5’ ‘Always’, please circle the number which best describes your answer.

(Circle one number for each statement) N

ever

Alw

ays

Blinding sun makes it difficult to see if the red flashing lights are activated

0 1 2 3 4 5

The design of the road makes it difficult to see if a train is approaching or at the rail crossing

0 1 2 3 4 5

Warning systems on the road approaching the crossing are not adequate to inform drivers there is a rail crossing ahead

0 1 2 3 4 5

Road surfaces are often poor and it is difficult to stop

0 1 2 3 4 5

Boom gates and/or flashing lights are often faulty

0 1 2 3 4 5

Other drivers do stupid things that put you in a dangerous situation

0 1 2 3 4 5

Intersections ahead of a rail crossing often cause your car to overhang the tracks

0 1 2 3 4 5

Difficult to hear an approaching train when the windows are up 0 1 2 3 4 5 My car stalled on the tracks at a rail crossing

0 1 2 3 4 5

The following section tests your knowledge about railway level crossings. Circle the number that best describes your answer. When the twin red lights start flashing this means?

A train is coming but it is still safe to cross………….. You must stop and not enter the crossing ………….. Don’t know ………………………………………………

1 2 3

Boom gates usually stay down a lot longer than traffic lights take to change at an intersection

True ………………………………………………………False ……………........…………………………………. Don’t know ………………………………………………

1 2 3

Yellow hatching road markings means keep clear

True ………………………………………………………False …………………………………………………….. Don’t know ………………………………………………

1 2 3

There are fines for not stopping at railway crossings

True ………………………………………………………False …………………………………………………….. Don’t know ………………………………………………

1 2 3

433

How likely is it, that as a Driver you will be involved in a crash with a train at a railway level crossing?

Not at all Likely

Very Likely

0 1 2 3 4 5

Your Date of Birth: __________________________________ (e.g. 3rd July 1986)

Thank you for completing this questionnaire. Please return this questionnaire in the reply-paid envelope supplied.

434

ID Number: __ __ __ - __ Staff Use Only

Centre for Accident Research and Road Safety – Queensland

Drivers 60 + years Pre-Test Questionnaire

August 2006

Associate Professor Jeremy Davey Deputy Director Centre for Accident Research & Road Safety – Queensland Queensland University of Technology Telephone: 07 3864 4574 Email: j.davey@qut.edu.au

Angela Wallace PhD Scholar Centre for Accident Research & Road Safety – Queensland Queensland University of Technology Telephone: 0402 240 234 Email: angela.wallace@student.qut.edu.au

435

1. Personal Information We would like to start by asking you some questions about yourself. Please circle the number that best describes you.

What is your gender? Male………………………………………………….……

Female…………………………………………………… 1 2

What is your age? _________ Years

Are you currently involved in any paid work?

Yes, I am currently working …………………………… No, I am not currently working ………………………..

1 2

If you are in paid work, what is your occupation?

_______________________________________

What is the postcode where you live?

Do you have any conditions on your Drivers Licence?

No conditions….………………………………………… ‘S’ condition only (corrective lenses required)…….… ‘M’ condition only (medical certificate required) …….. Both ‘S’ & ‘M’ conditions ……………………………….

1 2 3 4

2. Driving Patterns We would like to ask you some questions about how often you drive.

On average, how many days of the week do you drive? ______________________ days per week

On average, how many hours per day do you drive? ______________________ hours per day 3. Health We would now like to ask you some questions about your health. Have you ever had, or been told by a Doctor that you have any of the following medical conditions?

(Circle one number for each statement)

Yes No

High Blood Pressure 1 2

Heart Disease 1 2

Chest Pain / Angina 1 2

Any condition requiring Heart Surgery 1 2

Palpitations / Irregular Heart Beat 1 2

Head Injury / Spinal Injury 1 2

Seizures, Fits, Convulsions or Epilepsy 1 2

Abnormal Shortness of Breath 1 2

Blackouts or Fainting 1 2

Stroke 1 2

436

(Circle one number for each statement)

Yes No

Dizziness, Vertigo or Problems with Balance 1 2

Double Vision or Difficulty Seeing 1 2

Colour Blindness 1 2

Kidney Disease 1 2

Diabetes 1 2

Neck, Back or Limb Disorders 1 2

Hearing Loss or Deafness, or had an Ear Operation or a Hearing Aid 1 2

4. Driving Self-Assessment

The next few questions relate to your driving. We would like for you to answer these questions as truthfully as possible. (Circle one number for each statement) N

ot a

t al

l

Very

O

ften

Are your reactions to unexpected situations slower than they used to be?

0 1 2 3 4 5

Do you have trouble judging the distance of other vehicles, or changing focus from your instrument panel to the road?

0 1 2 3 4 5

Are you having more trouble adjusting to glare and/or night driving than you did previously?

0 1 2 3 4 5

Do you ever get surprised by pedestrians or other vehicles coming from your left or right while you are focusing straight ahead?

0 1 2 3 4 5

Do some traffic situations or other drivers upset you?

0 1 2 3 4 5

Do you have trouble driving through, or turning at busy intersections or roundabouts?

0 1 2 3 4 5

Do you feel uncomfortable driving in unfamiliar territory?

0 1 2 3 4 5

Do you find that you are easily distracted or that your thoughts wander while you are driving?

0 1 2 3 4 5

Do you have regular health and vision checks?

0 1 2 3 4 5

5. Road Crashes The next few questions are about any road crashes you have been involved in as a Driver in the past 3 years. By crash, we mean any collision that involved either injury to another person or yourself, damage to property, damage to another vehicle, or damage to the vehicle you were driving. Please mention only those crashes that you were involved in as a Driver. How many crashes have you been involved in as a Driver during the past 3 years? (Write number on line)

__________

437

If you have not been in a crash as a Driver during the past 3 years, please skip to Question 6. Of the crashes that you have been involved in as a Driver during the past 3 years, how many resulted in: Damage only ______________ Slight injury (to any person)

______________

Serious injury (to any person)

______________

Fatality (of any person)

______________

Did any of the road crashes occur at either an intersection or a railway level crossing? No ………………………….………… 0 Yes ………………………….………… 1

Accident at an Intersection …………………… Accident at a Railway Level Crossing ……….

1 2

Thinking about your most recent crash (during the past 3 years), what type of collision was it?

(Circle one number for each statement)

Yes No

Head on collision with another vehicle

1 2

Rear-end collision with another vehicle

1 2

Angular (i.e. side-on) collision with another vehicle

1 2

Collision with a pedestrian

1 2

Collision with another object (e.g. parked car, animal, tree)

1 2

Overturned vehicle

1 2

Other

1 2

Was the crash your fault?

Not at all Partially Fully (Circle one number only) 0 1 2

Were the following statements true or false at the time of the crash?

(Circle one number for each statement)

True False

I had been drinking alcohol before driving

1 2

I felt tired

1 2

I was driving too fast

1 2

I was talking to a passenger in the vehicle 1 2

438

(Circle one number for each statement)

True False

I was listening to music or the radio

1 2

I was talking on a mobile phone

1 2

I was trying to pick up something from the seat or floor

1 2

I was adjusting the radio/cassette/CD or fan/air conditioning

1 2

I was checking the instruments (e.g. fuel gauge or speedometer)

1 2

I was daydreaming

1 2

I was distracted by something inside the vehicle

1 2

I was distracted by something outside the vehicle

1 2

6. General Driving Behaviour No one is perfect. Even the best drivers make mistakes, do foolish things, or bend the rules at some time or another. Some of these behaviours are trivial, but some are potentially dangerous. For each item below you are asked to indicate HOW OFTEN, if at all, this kind of thing has happened to you. On the scale of 0 ‘Never’ to 5 ‘Nearly all the time’, please circle the number which best sums up your answer.

(Circle one number for each statement) How often do you…..

Nev

er

Har

dly

ever

Occ

asio

nally

Qui

te o

ften

Freq

uent

ly

Nea

rly a

ll th

e tim

e

Attempt to overtake someone that you hadn’t noticed to be signalling a right turn

0 1 2 3 4 5

Get into the wrong lane when approaching a roundabout or a junction

0 1 2 3 4 5

Miss ‘Stop’ or ‘Give Way’ signs and narrowly avoid colliding with traffic having right of way

0 1 2 3 4 5

Misread the signs and exit from the roundabout on the wrong road

0 1 2 3 4 5

Fail to notice that pedestrians are crossing when turning into a side street from a main road

0 1 2 3 4 5

Drive especially close to the car in front as a signal to its driver to go faster or get out of the way

0 1 2 3 4 5

Forget where you left your car in the car park

0 1 2 3 4 5

Queuing to turn left onto a main road, you pay such close attention to the mainstream of traffic that you nearly hit the car in front

0 1 2 3 4 5

Hit something when reversing that you had not previously seen 0 1 2 3 4 5

439

(Circle one number for each statement) How often do you…..

Nev

er

Har

dly

ever

Occ

asio

nally

Qui

te o

ften

Freq

uent

ly

Nea

rly a

ll th

e tim

e

Cross a junction knowing that the traffic lights have already turned against you

0 1 2 3 4 5

On turning left nearly hit a cyclist who has come up on your inside 0 1 2 3 4 5

Disregard the speed limits late at night or very early in the morning 0 1 2 3 4 5

Attempt to drive away from the traffic lights in third gear 0 1 2 3 4 5

Fail to check your rear-view mirror before pulling out, changing lanes, etc.

0 1 2 3 4 5

Have an aversion to a particular class of road user, and indicate your hostility by whatever means you can

0 1 2 3 4 5

Become impatient with a slow driver in the outer lane and overtake on the inside

0 1 2 3 4 5

Underestimate the speed of an oncoming vehicle when overtaking 0 1 2 3 4 5

Switch on one thing, such as the headlights, when you meant to switch on something else, such as the wipers

0 1 2 3 4 5

Brake too quickly on a slippery road, or steer the wrong way in a skid 0 1 2 3 4 5

Intending to drive to destination A, you ‘wake up’ to find yourself on the road to destination B, perhaps because the latter is your more usual destination

0 1 2 3 4 5

Drive even though you realise you may be over the legal blood-alcohol limit

0 1 2 3 4 5

Get involved in unofficial ‘races’ with other drivers 0 1 2 3 4 5

Realise that you have no clear recollection of the road along which you have just been travelling

0 1 2 3 4 5

Angered by another driver’s behaviour, you give chase with the intention of giving him/her a piece of your mind

0 1 2 3 4 5

440

7. Driving at Railway Level Crossings Please circle how frequently you drive through the following types of railway level crossings when you drive.

(Circle one number for each statement) N

ever

Onc

e a

year

Twic

e a

year

Mon

thly

Wee

kly

Dai

ly

Boom gates with flashing lights

0 1 2 3 4 5

Flashing lights only (no Boom Gate)

0 1 2 3 4 5

Only STOP or GIVE WAY sign

0 1 2 3 4 5

How often have you ever engaged in the following behaviours at a railway level crossing? On the scale of 0 ‘Not at all’ to 5 ‘Very often’, please circle the number which best sums up your answer. If you have never driven over any type of railway level crossing (i.e. with or without boom gates), please circle ‘Not Applicable’.

(Circle one number for each statement) I have: N

ot a

t all

Very

O

ften

Not

App

licab

le

Driven through a rail crossing when lights are flashing, but boom gates are yet to descend

0 1 2 3 4 5 n/a

Driven through a rail crossing when boom gates are descending

0 1 2 3 4 5 n/a

Driven through a rail crossing when booms gates are beginning to rise, but lights are still flashing

0 1 2 3 4 5 n/a

Rolling through a rail crossing without stopping, if no train is visible

0 1 2 3 4 5 n/a

Driven through a rail crossing after the first train has passed, without looking for a second

0 1 2 3 4 5 n/a

Failed to look for a train before crossing a rail crossing

0 1 2 3 4 5 n/a

Driven through a rail crossing when the train is visible, but still some distance away

0 1 2 3 4 5 n/a

Driven through a rail crossing when the train is close

0 1 2 3 4 5 n/a

Queued over a rail crossing

0 1 2 3 4 5 n/a

Driven around boom gates to cross a rail crossing

0 1 2 3 4 5 n/a

Tried to beat the train across the rail crossing

0 1 2 3 4 5 n/a

Sped on approach to a rail crossing

0 1 2 3 4 5 n/a

Driven through a rail crossing when the lights are flashing, but no train is visible

0 1 2 3 4 5 n/a

441

(Circle one number for each statement) I have: N

ot a

t all

Very

O

ften

Not

App

licab

le

Driven through a rail crossing when the lights are flashing, and the train is visible

0 1 2 3 4 5 n/a

Scanned on approach to a rail crossing, but failed to stop at it

0 1 2 3 4 5 n/a

Stopped on the yellow hatching road markings

0 1 2 3 4 5 n/a

Followed another vehicle across the rail crossing without looking for myself

0 1 2 3 4 5 n/a

Driven through a rail crossing when visibility is impaired

0 1 2 3 4 5 n/a

Driven through a rail crossing without realising it until after the vehicle is over the tracks

0 1 2 3 4 5 n/a

Seen police enforcing the road rules at a rail crossing

0 1 2 3 4 5 n/a

We would like to know the likelihood that you will engage in the following behaviours in the next 6 months of driving at railway level crossings. Even if you rarely drive through railway level crossings, please indicate how you would drive if you were to drive through a crossing. On the scale of 0 ‘Not at all likely’ to 5 ‘Very likely’, please circle the number which best describes your answer. (Circle one number for each statement) In the next 6 months of driving, it is likely I will: N

ot a

t all

likel

y

Very

lik

ely

Drive through a rail crossing when lights are flashing, but boom gates are yet to descend

0 1 2 3 4 5

Drive through a rail crossing when boom gates are descending

0 1 2 3 4 5

Drive through a rail crossing when booms gates are beginning to rise, but lights are still flashing

0 1 2 3 4 5

Roll through a rail crossing without stopping, if no train is visible

0 1 2 3 4 5

Drive through a rail crossing after the first train has passed, without looking for a second

0 1 2 3 4 5

Fail to look for a train before crossing a rail crossing

0 1 2 3 4 5

Drive through a rail crossing when the train is visible, but still some distance away

0 1 2 3 4 5

Drive through a rail crossing when the train is close

0 1 2 3 4 5

Queue over a rail crossing

0 1 2 3 4 5

Drive around boom gates to cross

0 1 2 3 4 5

Try to beat the train across the rail crossing

0 1 2 3 4 5

Speed on approach to a rail crossing 0 1 2 3 4 5

442

(Circle one number for each statement) In the next 6 months of driving, it is likely I will: N

ot a

t all

Like

ly

Very

Li

kely

Drive through a rail crossing when the lights are flashing, but no train is visible

0 1 2 3 4 5

Drive through a rail crossing when the lights are flashing, and the train is visible

0 1 2 3 4 5

Scan on approach to a rail crossing, but fail to stop at it

0 1 2 3 4 5

Stop on the yellow hatching road markings

0 1 2 3 4 5

Follow another vehicle across the rail crossing without looking for myself

0 1 2 3 4 5

Drive through a rail crossing when visibility is impaired

0 1 2 3 4 5

Drive through a rail crossing without realising until after the vehicle is over the tracks

0 1 2 3 4 5

See police enforcing the road rules at a rail crossing

0 1 2 3 4 5

Rate each of the following statements according to the degree they reflect your personal views. On the scale of ‘1’ ‘Strongly Disagree’ to ‘6’ ‘Strongly Agree’, please circle the number which best describes your answer.

(Circle one number for each statement)

Stro

ngly

A

gree

Stro

ngly

D

isag

ree

Your family generally obeys the rules at rail crossings 1 2 3 4 5

Your friends generally obey the rules at rail crossings 1 2 3 4 5

Other motorists generally obey the rules at rail crossings 1 2 3 4 5

Your family generally think it important to obey the rules at rail crossings 1 2 3 4 5

Your friends generally think it important to obey the rules at rail crossings 1 2 3 4 5

Other motorists generally think it important to obey the rules at rail crossings

1 2 3 4 5

It is generally safe to disobey the rules at rail crossings 1 2 3 4 5

It is generally possible to judge a train’s speed 1 2 3 4 5

It is generally safe to cross if you can’t see a train, even if the lights are flashing

1 2 3 4 5

It is generally safe to roll slowly through a crossing instead of stopping 1 2 3 4 5

Trains generally run to a regular timetable 1 2 3 4 5

Penalties need to be tougher for violating road rules at rail crossings 1 2 3 4 5

The main deterrent for breaking the rules at rail crossings is fear of getting caught

1 2 3 4 5

Generally it is more important to use common sense at rail crossings than strictly follow the road rules

1 2 3 4 5

443

We would like to ask you some questions about your attitude towards railway level crossings. Please circle one number in each table. As a motorist, I believe that the design of rail crossings are:

Bad Good -3 -2 -1 0 1 2 3

Unsafe Safe

-3 -2 -1 0 1 2 3

As a motorist, I believe that the design of rail crossings are:

Confusing Easily Understood

-3 -2 -1 0 1 2 3

Difficult for

obeying road rules

Easy for obeying

road rules -3 -2 -1 0 1 2 3

As a motorist, I believe that road rules at rail crossings are:

Bad

Good

-3 -2 -1 0 1 2 3

Not Strict Enough

Too Strict

-3 -2 -1 0 1 2 3

As a motorist, I believe that road rules at rail crossings are:

Confusing

Easily Understood

-3 -2 -1 0 1 2 3

Not

Practical

Practical -3 -2 -1 0 1 2 3

Obeying the road rules at rail crossings is:

Not up to me

Up to me

-3 -2 -1 0 1 2 3

Out of my

control Under my

control -3 -2 -1 0 1 2 3

444

Obeying the road rules at rail crossings is:

Dependent on other

motorists

Dependent on me only

-3 -2 -1 0 1 2 3

Obeying the road rules at rail crossings is:

Dependent

on time constraints

Not dependent

on time constraints

-3 -2 -1 0 1 2 3

Other motorists influence my driving behaviour at rail crossings, making it:

Harder to obey road

rules

Easier to obey road

rules -3 -2 -1 0 1 2 3

More

confusing Less

confusing -3 -2 -1 0 1 2 3

More

stressful More

relaxing -3 -2 -1 0 1 2 3

When you are driving a car at railway level crossings, how often do each of the following occur? On the scale of 0 ‘Never’ to ‘5’ ‘Always’, please circle the number which best describes your answer.

(Circle one number for each statement) N

ever

Alw

ays

Blinding sun makes it difficult to see if the red flashing lights are activated

0 1 2 3 4 5

The design of the road makes it difficult to see if a train is approaching or at the rail crossing

0 1 2 3 4 5

Warning systems on the road approaching the crossing are not adequate to inform drivers there is a rail crossing ahead

0 1 2 3 4 5

Road surfaces are often poor and it is difficult to stop

0 1 2 3 4 5

Boom gates and/or flashing lights are often faulty

0 1 2 3 4 5

Other drivers do stupid things that put you in a dangerous situation

0 1 2 3 4 5

Intersections ahead of a rail crossing often cause your car to overhang the tracks

0 1 2 3 4 5

Difficult to hear an approaching train when the windows are up 0 1 2 3 4 5

445

(Circle one number for each statement) N

ever

Alw

ays

My car stalled on the tracks at a rail crossing

0 1 2 3 4 5

The following section tests your knowledge about railway level crossings. Circle the number that best describes your answer. When the twin red lights start flashing this means?

A train is coming but it is still safe to cross………….. You must stop and not enter the crossing ………….. Don’t know ………………………………………………

1 2 3

Boom gates usually stay down a lot longer than traffic lights take to change at an intersection

True ………………………………………………………False ……………........…………………………………. Don’t know ………………………………………………

1 2 3

Yellow hatching road markings means keep clear

True ………………………………………………………False …………………………………………………….. Don’t know ………………………………………………

1 2 3

There are fines for not stopping at railway crossings

True ………………………………………………………False …………………………………………………….. Don’t know ………………………………………………

1 2 3

How likely is it, that as a Driver you will be involved in a crash with a train at a railway level crossing?

Not at all Likely

Very Likely

0 1 2 3 4 5

We would like to be able to match this questionnaire with others at a later date. However, we wish to preserve your anonymity. One way to do this is to collect some information from you that will not identify you, but will allow us to match the questionnaires. Please assist us by providing:

First 3 letters of your mother’s maiden name (i.e. Surname before marriage): Your Date of Birth: Date Month Year

Thank you for completing this questionnaire. Please return this questionnaire in

the reply-paid envelope supplied.

446

ID Number: __ __ __ - __ Staff Use Only

Centre for Accident Research and Road Safety – Queensland

Drivers 60 + Years Post-Test Questionnaire

August 2006

Associate Professor Jeremy Davey Deputy Director Centre for Accident Research & Road Safety – Queensland Queensland University of Technology Telephone: 07 3864 4574 Email: j.davey@qut.edu.au

Angela Wallace PhD Scholar Centre for Accident Research & Road Safety – Queensland Queensland University of Technology Telephone: 0402 240 234 Email: angela.wallace@student.qut.edu.au

447

Driving Self-Assessment

The next few questions relate to your driving. We would like for you to answer these questions as truthfully as possible. (Circle one number for each statement) N

ot a

t al

l

Very

O

ften

Are your reactions to unexpected situations slower than they used to be?

0 1 2 3 4 5

Do you have trouble judging the distance of other vehicles, or changing focus from your instrument panel to the road?

0 1 2 3 4 5

Are you having more trouble adjusting to glare and/or night driving than you did previously?

0 1 2 3 4 5

Do you ever get surprised by pedestrians or other vehicles coming from your left or right while you are focusing straight ahead?

0 1 2 3 4 5

Do some traffic situations or other drivers upset you?

0 1 2 3 4 5

Do you have trouble driving through, or turning at busy intersections or roundabouts?

0 1 2 3 4 5

Do you feel uncomfortable driving in unfamiliar territory?

0 1 2 3 4 5

Do you find that you are easily distracted or that your thoughts wander while you are driving?

0 1 2 3 4 5

Do you have regular health and vision checks?

0 1 2 3 4 5

General Driving Behaviour No one is perfect. Even the best drivers make mistakes, do foolish things, or bend the rules at some time or another. Some of these behaviours are trivial, but some are potentially dangerous. For each item below you are asked to indicate HOW OFTEN, if at all, this kind of thing has happened to you. On the scale of 0 ‘Never’ to 5 ‘Nearly all the time’, please circle the number which best sums up your answer.

(Circle one number for each statement) How often do you…..

Nev

er

Har

dly

ever

Occ

asio

nally

Qui

te o

ften

Freq

uent

ly

Nea

rly a

ll th

e tim

e

Attempt to overtake someone that you hadn’t noticed to be signalling a right turn

0 1 2 3 4 5

Get into the wrong lane when approaching a roundabout or a junction

0 1 2 3 4 5

Miss ‘Stop’ or ‘Give Way’ signs and narrowly avoid colliding with traffic having right of way

0 1 2 3 4 5

448

(Circle one number for each statement) How often do you…..

Nev

er

Har

dly

ever

Occ

asio

nally

Qui

te o

ften

Freq

uent

ly

Nea

rly a

ll th

e tim

e

Misread the signs and exit from the roundabout on the wrong road

0 1 2 3 4 5

Fail to notice that pedestrians are crossing when turning into a side street from a main road

0 1 2 3 4 5

Drive especially close to the car in front as a signal to its driver to go faster or get out of the way

0 1 2 3 4 5

Forget where you left your car in the car park

0 1 2 3 4 5

Queuing to turn left onto a main road, you pay such close attention to the mainstream of traffic that you nearly hit the car in front

0 1 2 3 4 5

Hit something when reversing that you had not previously seen 0 1 2 3 4 5

Cross a junction knowing that the traffic lights have already turned against you

0 1 2 3 4 5

On turning left nearly hit a cyclist who has come up on your inside 0 1 2 3 4 5

Disregard the speed limits late at night or very early in the morning 0 1 2 3 4 5

Attempt to drive away from the traffic lights in third gear 0 1 2 3 4 5

Fail to check your rear-view mirror before pulling out, changing lanes, etc.

0 1 2 3 4 5

Have an aversion to a particular class of road user, and indicate your hostility by whatever means you can

0 1 2 3 4 5

Become impatient with a slow driver in the outer lane and overtake on the inside

0 1 2 3 4 5

Underestimate the speed of an oncoming vehicle when overtaking 0 1 2 3 4 5

Switch on one thing, such as the headlights, when you meant to switch on something else, such as the wipers

0 1 2 3 4 5

Brake too quickly on a slippery road, or steer the wrong way in a skid 0 1 2 3 4 5

Intending to drive to destination A, you ‘wake up’ to find yourself on the road to destination B, perhaps because the latter is your more usual destination

0 1 2 3 4 5

Drive even though you realise you may be over the legal blood-alcohol limit

0 1 2 3 4 5

Get involved in unofficial ‘races’ with other drivers 0 1 2 3 4 5

Realise that you have no clear recollection of the road along which you have just been travelling

0 1 2 3 4 5

Angered by another driver’s behaviour, you give chase with the intention of giving him/her a piece of your mind

0 1 2 3 4 5

449

Driving at Railway Level Crossings During the past month, how often have you ever engaged in the following behaviours at a railway level crossing? On the scale of 0 ‘Not at all’ to 5 ‘Very often’, please circle the number which best sums up your answer. If you have not driven through any type of railway level crossing during the past month, (i.e. with or without boom gates), please circle ‘Not Applicable’.

(Circle one number for each statement) I have: N

ot a

t all

Very

O

ften

Not

App

licab

le

Driven through a rail crossing when lights are flashing, but boom gates are yet to descend

0 1 2 3 4 5 n/a

Driven through a rail crossing when boom gates are descending

0 1 2 3 4 5 n/a

Driven through a rail crossing when booms gates are beginning to rise, but lights are still flashing

0 1 2 3 4 5 n/a

Rolling through a rail crossing without stopping, if no train is visible

0 1 2 3 4 5 n/a

Driven through a rail crossing after the first train has passed, without looking for a second

0 1 2 3 4 5 n/a

Failed to look for a train before crossing a rail crossing

0 1 2 3 4 5 n/a

Driven through a rail crossing when the train is visible, but still some distance away

0 1 2 3 4 5 n/a

Driven through a rail crossing when the train is close

0 1 2 3 4 5 n/a

Queued over a rail crossing

0 1 2 3 4 5 n/a

Driven around boom gates to cross a rail crossing

0 1 2 3 4 5 n/a

Tried to beat the train across the rail crossing

0 1 2 3 4 5 n/a

Sped on approach to a rail crossing

0 1 2 3 4 5 n/a

Driven through a rail crossing when the lights are flashing, but no train is visible

0 1 2 3 4 5 n/a

Driven through a rail crossing when the lights are flashing, and the train is visible

0 1 2 3 4 5 n/a

Scanned on approach to a rail crossing, but failed to stop at it

0 1 2 3 4 5 n/a

Stopped on the yellow hatching road markings

0 1 2 3 4 5 n/a

Followed another vehicle across the rail crossing without looking for myself

0 1 2 3 4 5 n/a

Driven through a rail crossing when visibility is impaired

0 1 2 3 4 5 n/a

Driven through a rail crossing without realising it until after the vehicle is over the tracks

0 1 2 3 4 5 n/a

Seen police enforcing the road rules at a rail crossing

0 1 2 3 4 5 n/a

450

We would like to know the likelihood that you will engage in the following behaviours in the next 6 months of driving at railway level crossings. Even if you rarely drive through railway level crossings, please indicate how you would drive if you were to drive through a crossing. On the scale of 0 ‘Not at all likely’ to 5 ‘Very likely’, please circle the number which best describes your answer. (Circle one number for each statement) In the next 6 months of driving, it is likely I will: N

ot a

t all

likel

y

Very

lik

ely

Drive through a rail crossing when lights are flashing, but boom gates are yet to descend

0 1 2 3 4 5

Drive through a rail crossing when boom gates are descending

0 1 2 3 4 5

Drive through a rail crossing when booms gates are beginning to rise, but lights are still flashing

0 1 2 3 4 5

Roll through a rail crossing without stopping, if no train is visible

0 1 2 3 4 5

Drive through a rail crossing after the first train has passed, without looking for a second

0 1 2 3 4 5

Fail to look for a train before crossing a rail crossing

0 1 2 3 4 5

Drive through a rail crossing when the train is visible, but still some distance away

0 1 2 3 4 5

Drive through a rail crossing when the train is close

0 1 2 3 4 5

Queue over a rail crossing

0 1 2 3 4 5

Drive around boom gates to cross

0 1 2 3 4 5

Try to beat the train across the rail crossing

0 1 2 3 4 5

Speed on approach to a rail crossing

0 1 2 3 4 5

Drive through a rail crossing when the lights are flashing, but no train is visible

0 1 2 3 4 5

Drive through a rail crossing when the lights are flashing, and the train is visible

0 1 2 3 4 5

Scan on approach to a rail crossing, but fail to stop at it

0 1 2 3 4 5

Stop on the yellow hatching road markings

0 1 2 3 4 5

Follow another vehicle across the rail crossing without looking for myself

0 1 2 3 4 5

Drive through a rail crossing when visibility is impaired

0 1 2 3 4 5

Drive through a rail crossing without realising until after the vehicle is over the tracks

0 1 2 3 4 5

See police enforcing the road rules at a rail crossing

0 1 2 3 4 5

451

Rate each of the following statements according to the degree they reflect your personal views. On the scale of ‘1’ ‘Strongly Disagree’ to ‘6’ ‘Strongly Agree’, please circle the number which best describes your answer. (Circle one number for each statement)

Stro

ngly

D

isag

ree

Stro

ngly

A

gree

Your family generally obeys the rules at rail crossings 1 2 3 4 5 6

Your friends generally obey the rules at rail crossings 1 2 3 4 5 6

Other motorists generally obey the rules at rail crossings 1 2 3 4 5 6

Your family generally think it important to obey the rules at rail crossings 1 2 3 4 5 6

Your friends generally think it important to obey the rules at rail crossings 1 2 3 4 5 6

Other motorists generally think it important to obey the rules at rail crossings

1 2 3 4 5 6

It is generally safe to disobey the rules at rail crossings 1 2 3 4 5 6

It is generally possible to judge a train’s speed 1 2 3 4 5 6

It is generally safe to cross if you can’t see a train, even if the lights are flashing

1 2 3 4 5 6

It is generally safe to roll slowly through a crossing instead of stopping 1 2 3 4 5 6

Trains generally run to a regular timetable 1 2 3 4 5 6

Penalties need to be tougher for violating road rules at rail crossings 1 2 3 4 5 6

The main deterrent for breaking the rules at rail crossings is fear of getting caught

1 2 3 4 5 6

Generally it is more important to use common sense at rail crossings than strictly follow the road rules

1 2 3 4 5 6

We would like to ask you some questions about your attitude towards railway level crossings. Please circle one number in each table. As a motorist, I believe that the design of rail crossings are:

Bad Good -3 -2 -1 0 1 2 3

Unsafe Safe

-3 -2 -1 0 1 2 3

As a motorist, I believe that the design of rail crossings are:

Confusing Easily Understood

-3 -2 -1 0 1 2 3

452

As a motorist, I believe that the design of rail crossings are:

Difficult for obeying

road rules

Easy for obeying

road rules -3 -2 -1 0 1 2 3

As a motorist, I believe that road rules at rail crossings are:

Bad

Good

-3 -2 -1 0 1 2 3

Not Strict Enough

Too Strict

-3 -2 -1 0 1 2 3

As a motorist, I believe that road rules at rail crossings are:

Confusing

Easily Understood

-3 -2 -1 0 1 2 3

Not

Practical

Practical -3 -2 -1 0 1 2 3

Obeying the road rules at rail crossings is:

Not up to me

Up to me

-3 -2 -1 0 1 2 3

Out of my

control Under my

control -3 -2 -1 0 1 2 3

Obeying the road rules at rail crossings is:

Dependent on other

motorists

Dependent on me only

-3 -2 -1 0 1 2 3

453

Obeying the road rules at rail crossings is:

Dependent

on time constraints

Not dependent

on time constraints

-3 -2 -1 0 1 2 3

Other motorists influence my driving behaviour at rail crossings, making it:

Harder to obey road

rules

Easier to obey road

rules -3 -2 -1 0 1 2 3

More

confusing Less

confusing -3 -2 -1 0 1 2 3

More

stressful More

relaxing -3 -2 -1 0 1 2 3

When you are driving a car at railway level crossings, how often do each of the following occur? On the scale of 0 ‘Never’ to ‘5’ ‘Always’, please circle the number which best describes your answer.

(Circle one number for each statement) N

ever

Alw

ays

Blinding sun makes it difficult to see if the red flashing lights are activated

0 1 2 3 4 5

The design of the road makes it difficult to see if a train is approaching or at the rail crossing

0 1 2 3 4 5

Warning systems on the road approaching the crossing are not adequate to inform drivers there is a rail crossing ahead

0 1 2 3 4 5

Road surfaces are often poor and it is difficult to stop

0 1 2 3 4 5

Boom gates and/or flashing lights are often faulty

0 1 2 3 4 5

Other drivers do stupid things that put you in a dangerous situation

0 1 2 3 4 5

Intersections ahead of a rail crossing often cause your car to overhang the tracks

0 1 2 3 4 5

Difficult to hear an approaching train when the windows are up 0 1 2 3 4 5 My car stalled on the tracks at a rail crossing

0 1 2 3 4 5

454

The following section tests your knowledge about railway level crossings. Circle the number that best describes your answer. When the twin red lights start flashing this means?

A train is coming but it is still safe to cross………….. You must stop and not enter the crossing ………….. Don’t know ………………………………………………

1 2 3

Boom gates usually stay down a lot longer than traffic lights take to change at an intersection

True ………………………………………………………False ……………........…………………………………. Don’t know ………………………………………………

1 2 3

Yellow hatching road markings means keep clear

True ………………………………………………………False …………………………………………………….. Don’t know ………………………………………………

1 2 3

There are fines for not stopping at railway crossings

True ………………………………………………………False …………………………………………………….. Don’t know ………………………………………………

1 2 3

How likely is it, that as a Driver you will be involved in a crash with a train at a railway level crossing?

Not at all Likely

Very Likely

0 1 2 3 4 5

Road Safety Radio Message We would now like to ask you some questions about the road safety advertisement that you listened to recently for this project when telephoned by one of our researchers.

Which road safety message did you listen to on the telephone?

Vision and driving ………...……………………….…… Driving at railway level crossing …...…………………

1 2

Can you remember any slogans from the road safety advertisement that you listened to on the telephone? If YES, which slogan do you recall?

No ..………………………………………………….…… Yes .....…………………………………………………… Slogan: ___________________________________________

1 2

Can you remember any important information mentioned in the advertisement? If YES, what important information do you recall?

No ..………………………………………………….…… Yes .....…………………………………………………… Important information: ___________________________________________

1 2

455

How likely is it, that your driving behaviour has been affected by listening to this road safety advertisement on the telephone?

Not at all

likely Very likely

0 1 2 3 4 5

Thank you for completing this questionnaire. Please return this questionnaire in

the reply-paid envelope supplied.

456

Company: ______________ (Staff Use Only)

ID Number: ____ ____ ____

Centre for Accident Research and Road Safety – Queensland

Heavy Vehicle Drivers Pre-Test Questionnaire

July 2006

Associate Professor Jeremy Davey Deputy Director Centre for Accident Research & Road Safety – Queensland Queensland University of Technology Telephone: 07 3864 4574 Email: j.davey@qut.edu.au

Angela Wallace PhD Scholar Centre for Accident Research & Road Safety – Queensland Queensland University of Technology Telephone: 0402 240 234 Email: angela.wallace@student.qut.edu.au

457

1. Personal Information We would like to start by asking you some questions about yourself. Please circle the number that best describes you. a) What is your gender? Male………………………………………………….……

Female…………………………………………………… 1 2

b) What is your age? _________ Years

c) What is your employment status? Full-time ………….. ...………………………..…………

Part-time / Casual ……… …………………..….……… Non-employee (agency, sub-contractor or haulier) …

1 2 3

d) What type of employment do you have with the company?

Company employee ………….. ...…………………..… Sub-contractor ………………………….……............... Agency employee ………………………………………

1 2 3

e) Do you work shift work? If YES – please circle your working hours.

Yes ………………………………………………………. No ………………………………………………………... Night and shift work …………………….....……...…… Shift work, no night work …..……………..…………… Night work, no shift work ….....………………………..

1 2

1 2 3

f) What is the postcode where you live?

g) What type of Licence do you hold? Class HR (Heavy Rigid Truck) ………….. ...………… Class HC (Heavy Combination) …………...…………. Class MC (B-double, Road Train) ……......................

1 2 3

h) How many years have you held your Heavy Vehicle Licence for?

_________ Years

2. Driving Patterns We would like to ask you some questions about how often you drive a Truck. a) How often do you drive a Truck, assuming an average week?

Every day of the week ………………………………… 4 – 6 days a week.....………….………………………. 2 – 3 days a week..……………………………………. At least 1 day a week .………………………………… Less than 1 day a week ……….……………………....

1 2 3 4 5

b) How many hours do you drive a Truck each week?

1 – 40 hrs ……………………………….....……....…… 41 – 50 hrs……. …………….……..…………………… 51 – 60 hrs.……..…...………………….………………. 61 – 72 hrs ..………………………………………..…… More than 72 hrs ………………………………………..

1 2 3 4 5

458

c) How many kilometres do you drive a Truck in an average week?

0 – 999 ………....…………..…………………………… 1000 – 1999….…………………………………………. 2000 – 2999 …..…….………………………………….. 3000 – 3999 ……..……….…………………………….. 4000 – 4999 ……………………………………………. 5000 or more .…….……………………………………..

1 2 3 4 5 6

3. Road Crashes We would now like to ask you some questions about any road crashes you have been involved in as Truck Driver in the past 3 years. By crash, we mean any collision that involved either injury to another person or yourself, damage to property, damage to another vehicle, or damage to the vehicle you were driving. Please mention only those crashes that you were involved in whilst driving a Truck. a) How many crashes have you been involved in as a Truck Driver in the past 3 years?

__________

If you have not been in a crash as a Truck Driver in the past 3 years, please skip to Question 4. b) Of the crashes that you have been involved in as a Truck Driver, how many resulted in: Damage only ______________ Slight injury (to any person)

______________

Serious injury (to any person)

______________

Fatality (of any person)

______________

c) Did any of the road crashes occur at a railway level crossing? Yes ……………………………………...… No ……………………………………........

1 2

d) Thinking about your most recent crash whilst driving a Truck, what type of collision was it?

(Circle one number on each line)

Yes No

Head on collision with another vehicle

1 0

Rear-end collision with another vehicle

1 0

Angular (i.e. side-on) collision with another vehicle

1 0

Collision with a pedestrian

1 0

Collision with another object (e.g. parked car, animal, tree)

1 0

Overturned vehicle

1 0

Other 1 0

459

e) Was the crash your fault?

Not at all Partially Fully (Circle one number only) 0 1 2

f) How true were the following statements at the time of the crash? (Circle one number on each line) N

ot T

rue

Som

ewha

t Tru

e

Very

Tru

e

I had been drinking alcohol before driving

0 1 2

I felt tired

0 1 2

I was driving too fast for the conditions

0 1 2

I was talking to a passenger in the vehicle

0 1 2

I was listening to music or the CB radio

0 1 2

I was talking on a mobile phone

0 1 2

I was trying to pick up something from the seat or floor

0 1 2

I was adjusting the radio/cassette/CD or fan/air conditioning

0 1 2

I was checking the instruments (e.g. fuel gauge or speedometer)

0 1 2

I was daydreaming

0 1 2

I was talking on the CB radio

0 1 2

I was distracted by something inside the vehicle

0 1 2

I was distracted by something outside the vehicle

0 1 2

I couldn’t see the other vehicle / object

0 1 2

4. General Driving Behaviour The following are statements that may describe your driving behaviour whilst driving a Truck. On the scale of ‘1’ ‘Never’ to ‘6’ ‘Nearly all the time’ please circle the number which best sums up your answer.

(Circle one number on each line) How often do you…..

Nev

er

Har

dly

ever

Occ

asio

nally

Qui

te o

ften

Freq

uent

ly

Nea

rly a

ll th

e tim

e

Attempt to overtake someone in front of you that you hadn’t noticed to be turning in front of you

1 2 3 4 5 6

Stay in a lane that you know will be closed ahead until the last minute before forcing your way into another lane

1 2 3 4 5 6

460

(Circle one number on each line) How often do you…..

Nev

er

Har

dly

ever

Occ

asio

nally

Qui

te o

ften

Freq

uent

ly

Nea

rly a

ll th

e tim

e

Miss ‘Stop’ or ‘Give Way’ signs 1 2 3 4 5 6

Intentionally disobey a ‘Stop’ or ‘Give Way’ sign 1 2 3 4 5 6

Pull out of a junction so far that you disrupt the flow of traffic 1 2 3 4 5 6

Fail to notice that pedestrians are crossing in your path of traffic 1 2 3 4 5 6

Drive especially close to the car in front as a signal to its driver to go faster or get out of the way

1 2 3 4 5 6

Sound your horn to indicate your annoyance to another driver 1 2 3 4 5 6

Queuing to enter a main road, you pay such close attention to the mainstream of traffic that you nearly hit the car in front

1 2 3 4 5 6

Cross a junction knowing that the traffic lights have already turned against you

1 2 3 4 5 6

Whilst turning nearly hit a cyclist who has come up on your inside 1 2 3 4 5 6

Intentionally disregard the speed limit on a highway/freeway 1 2 3 4 5 6

Exceed the speed limit on a highway/freeway without realising 1 2 3 4 5 6

Fail to check your rear-view mirror before pulling out, changing lanes, etc

1 2 3 4 5 6

Become angered by a certain type of driver and indicate your hostility by whatever means you can

1 2 3 4 5 6

Become impatient with a slow driver ahead and overtake on the inside

1 2 3 4 5 6

When overtaking underestimate the speed of an oncoming vehicle 1 2 3 4 5 6

Race away from the traffic lights with the intention of beating the driver next to you

1 2 3 4 5 6

Skid while braking or cornering on a slippery road 1 2 3 4 5 6

Drive even though you suspect you may be over the legal blood-alcohol limit

1

2

3

4

5

6

Intentionally disregard the speed limit on a residential road 1 2 3 4 5 6

Exceed the speed limit on a residential road without realising 1 2 3 4 5 6

Become angered by another driver and give chase 1 2 3 4 5 6

Hit something while manoeuvring (including parking and reversing) 1 2 3 4 5 6

461

(Circle one number on each line) How often do you…..

Nev

er

Har

dly

ever

Occ

asio

nally

Qui

te o

ften

Freq

uent

ly

Nea

rly a

ll th

e tim

e

Come close to hitting something while manoeuvring (including parking and reversing)

1 2 3 4 5 6

Drive while under time pressure 1 2 3 4 5 6

Find your attention being distracted from the road 1 2 3 4 5 6

Drive while tired 1 2 3 4 5 6

Have difficulty driving because of tiredness or fatigue 1 2 3 4 5 6

Find yourself nodding off while driving for work 1 2 3 4 5 6

Lose concentration while driving 1 2 3 4 5 6

Not wear your seatbelt 1 2 3 4 5 6

Remove your seatbelt for some reason while driving 1 2 3 4 5 6

Drive while using a ‘hand-held’ mobile phone 1 2 3 4 5 6

Drive while using a ‘hands-free’ mobile phone 1 2 3 4 5 6

Check the tyre pressure and fluid levels of your vehicle 1 2 3 4 5 6

Do paperwork or other administration whilst driving 1 2 3 4 5 6

Save the time during the day by driving quicker between places 1 2 3 4 5 6

Have one or two alcoholic drinks before driving 1 2 3 4 5 6

Smoke a cigarette whilst driving 1 2 3 4 5 6

Eat food whilst driving 1 2 3 4 5 6

5. Driving at Railway Level Crossings a) Please circle how frequently you go through the following types of railway crossing whilst driving a Truck.

(Please only circle one number for each statement)

Dai

ly

Wee

kly

Mon

thly

Year

ly

Nev

er

Boom gates with flashing lights

1 2 3 4 0

Flashing lights only (no Boom Gate)

1 2 3 4 0

Only STOP or GIVE WAY sign

1 2 3 4 0

462

b) How often have you engaged in the following behaviours whilst driving a Truck at a railway level crossing? On the scale of ‘1’ ‘Not at all’ to ‘5’ ‘Very often’, please circle the number which best sums up how much you agree with the following statements

(Circle one number on each line)

Not

at a

ll

Very

Ofte

n

Driven through a crossing when lights are flashing, but boom gates are yet to descend

1 2 3 4 5

Driven through a crossing when boom gates are descending

1 2 3 4 5

Driven through a crossing when booms gates are begin to rise, but lights are still flashing

1 2 3 4 5

Rolling through a crossing without stopping, if no train is visible

1 2 3 4 5

Driven through a crossing after the first train has passed, without looking for a second

1 2 3 4 5

Failed to look for a train before crossing

1 2 3 4 5

Driven through a crossing when the train is visible, but still some distance away

1 2 3 4 5

Driven through a crossing when the train is close

1 2 3 4 5

Queued over a crossing

1 2 3 4 5

Driven around boom gates to cross

1 2 3 4 5

Tried to beat the train across the crossing

1 2 3 4 5

Sped on approach to a crossing

1 2 3 4 5

Scanned on approach to a crossing, but failed to stop at it

1 2 3 4 5

Driven through a crossing when the lights are flashing, but no train is visible

1 2 3 4 5

Driven through a crossing when the lights are flashing, and the train is visible

1 2 3 4 5

Stopped on the yellow hatching road markings

1 2 3 4 5

Followed another vehicle across the crossing without looking for yourself

1 2 3 4 5

Driven through a crossing when visibility is impaired 1 2 3 4 5

Driven through a crossing without realising it until after the vehicle is over the tracks

1 2 3 4 5

463

c) We would like to know the likelihood that you will engage in the following behaviours in the next 6 months of driving at railway level crossings. Even if you rarely drive through railway level crossings, please indicate how you would drive if you were to drive through a crossing. On the scale of ‘1’ ‘Not at all’ to ‘5’ ‘Very often’, please circle the number which best sums up how much you agree with the following statements.

(Circle one number on each line) In the next 6 months of driving, it is likely I will:

Not

at a

ll Li

kely

Very

Lik

ely

Drive through a crossing when lights are flashing, but boom gates are yet to descend

1 2 3 4 5

Drive through a crossing when boom gates are descending

1 2 3 4 5

Drive through a crossing when booms gates are begin to rise, but lights are still flashing

1 2 3 4 5

Roll through a crossing without stopping, if no train is visible

1 2 3 4 5

Drive through a crossing after the first train has passed, without looking for a second

1 2 3 4 5

Fail to look for a train before crossing

1 2 3 4 5

Drive through a crossing when the train is visible, but still some distance away

1 2 3 4 5

Drive through a crossing when the train is close

1 2 3 4 5

Queue over a crossing

1 2 3 4 5

Drive around boom gates to cross

1 2 3 4 5

Try to beat the train across the crossing

1 2 3 4 5

Speed on approach to a crossing

1 2 3 4 5

Scan on approach to a crossing, but failed to stop at it

1 2 3 4 5

Drive through a crossing when the lights are flashing, but no train is visible

1 2 3 4 5

Drive through a crossing when the lights are flashing, and the train is visible

1 2 3 4 5

Stop on the yellow hatching road markings

1 2 3 4 5

Follow another vehicle across the crossing without looking for yourself

1 2 3 4 5

Drive through a crossing when visibility is impaired 1 2 3 4 5

Drive through a crossing without realising until after the vehicle is over the tracks

1 2 3 4 5

464

d) Rate each of the following statements according to the degree they reflect your personal opinion. On the scale of ‘1’ ‘Not at all’ to ‘5’ ‘Very often’, please circle the number which best sums up how much you agree or disagree with the following statements. (Please only circle one number for each statement)

Stro

ngly

A

gree

Stro

ngly

D

isag

ree

Your colleagues generally obey the rules at level crossings

1 2 3 4 5

Your friends generally obey the rules at level crossings

1 2 3 4 5

Other motorists generally obey the rules at level crossings

1 2 3 4 5

Your colleagues generally think it important to obey the rules at level crossings

1 2 3 4 5

Your friends generally think it important to obey the rules at level crossings

1 2 3 4 5

Other motorists generally think it important to obey the rules at level crossings

1 2 3 4 5

It is generally safe to disobey the rules at level crossings

1 2 3 4 5

It is generally possible to judge a train’s speed

1 2 3 4 5

It is generally safe to cross if you can’t see a train, even if the lights are flashing

1 2 3 4 5

It is generally safe to roll slowly through a crossing instead of stopping

1 2 3 4 5

Trains generally run to a regular timetable

1 2 3 4 5

Penalties need to be tougher for violating road rules at level crossings

1 2 3 4 5

The main deterrent for breaking the rules at level crossings is fear of getting caught

1 2 3 4 5

Generally it is more important to use common sense at level crossings than strictly follow the road rules

1 2 3 4 5

Your colleagues generally obey the road rules at level crossings

1 2 3 4 5

Your friends generally obey the rules at level crossings

1 2 3 4 5

Other motorists generally obey the rules at level crossings

1 2 3 4 5

Your colleagues generally think it important to obey the rules at level crossings

1 2 3 4 5

465

e) We would like to ask you some questions about your attitude towards railway level crossings. Please circle one number in each table. As a truck driver, I believe that the design of railway level crossings is:

Bad Good

-3 -2 -1 0 1 2 3

Unsafe Safe

-3 -2 -1 0 1 2 3

Confusing Easily

Understood -3 -2 -1 0 1 2 3

Difficult for

obeying road rules

Easy for obeying

road rules -3 -2 -1 0 1 2 3

As a truck driver, I believe that road rules at railway level crossings are:

Bad Good

-3 -2 -1 0 1 2 3

Not Strict Enough

Too Strict

-3 -2 -1 0 1 2 3

Confusing Easily

Understood -3 -2 -1 0 1 2 3

Not

Practical Practical

-3 -2 -1 0 1 2 3

Obeying the road rules at railway level crossings is:

Not up to me

Up to me

-3 -2 -1 0 1 2 3

Out of my

control Under my

control -3 -2 -1 0 1 2 3

466

Obeying the road rules at railway level crossings is:

Dependent on other

motorists

Dependent on me only

-3 -2 -1 0 1 2 3

Dependent

on time constraints

Not dependent

on time constraints

-3 -2 -1 0 1 2 3

Other motorists influence my ability to obey road rules at railway level crossings, making it:

Harder Easier -3 -2 -1 0 1 2 3

More

confusing Less

confusing -3 -2 -1 0 1 2 3

More

stressful More

relaxing -3 -2 -1 0 1 2 3

f) When you are driving a Truck at railway level crossings, how often do each of the following occur?

(Please only circle one number for each statement) N

ever

Alm

ost N

ever

Som

etim

es

Alm

ost A

lway

s

Alw

ays

Blinding sun makes it difficult to see if the red flashing lights are activated

1 2 3 4 5

The design of the road is an ‘S’ bend and it is difficult to see if a train is approaching or at the railway crossing

1 2 3 4 5

The height of the truck’s cabin makes it difficult to see a train or the warning systems

1 2 3 4 5

Warning systems on the road approaching the crossing are not adequate to inform trucks there is a railway crossing ahead

1 2 3 4 5

The mass of the truck makes it difficult to brake in time to stop at a railway crossing

1 2 3 4 5

Road surface is often poor and it is difficult to stop

1 2 3 4 5

Boom gates and/or flashing lights are often faulty

1 2 3 4 5

467

(Please only circle one number for each statement) N

ever

Alm

ost N

ever

Som

etim

es

Alm

ost A

lway

s

Alw

ays

Other drivers (such as cars) do stupid things that put you in a dangerous situation

1 2 3 4 5

Intersections ahead of a railway crossing often cause your truck to overhang the tracks

1 2 3 4 5

Noise from the truck’s engine is too loud to hear an approaching train

1 2 3 4 5

When you have to stop at a railway crossing, it takes a long time before your truck is able to get over the crossing

1 2 3 4 5

All ‘Stop’ signs should be changed to ‘Give Way’ signs at railway crossings as these are easier for trucks to get through the crossing

1 2 3 4 5

My truck has stalled on the tracks at a railway crossing 1 2 3 4 5 g) The following section tests your knowledge about railway level crossings. Circle the number that best describes your answer. When the twin red lights start flashing this means?

A train is coming but it is still safe to cross………….. You must stop and not enter the crossing ………….. Don’t know ………………………………………………

1 2 3

Boom gates usually stay down a lot longer than traffic lights take to change at an intersection

True ………………………………………………………False ……………........…………………………………. Don’t know ………………………………………………

1 2 3

Yellow hatching road markings means keep clear

True ………………………………………………………False …………………………………………………….. Don’t know ………………………………………………

1 2 3

There are fines for not stopping at railway crossings

True ………………………………………………………False …………………………………………………….. Don’t know ………………………………………………

1 2 3

h) How likely is it that whilst driving a Truck that you will be involved in a crash with a train at a railway level crossing?

Not at all Likely

Very Likely

1 2 3 4 5

468

Finally, we would like to be able to match this questionnaire with others at a later date. However, we wish to preserve your anonymity. One way to do this is to collect some information from you that will not identify you, but will allow us to match the next questionnaire. Please assist us by providing: The month of your birth: __________ (eg. May) The year of your birth: __________ (eg. 1966) First 3 letters of your surname: _______________ (eg. Smi for Smith)

Thank you for completing this questionnaire.

469

Company: ______________ (Staff Use Only)

ID Number: ____ ____ ____

Centre for Accident Research and Road Safety – Queensland

Heavy Vehicle Drivers Post-Test Questionnaire

August 2006

Associate Professor Jeremy Davey Deputy Director Centre for Accident Research & Road Safety – Queensland Queensland University of Technology Telephone: 07 3864 4574 Email: j.davey@qut.edu.au

Angela Wallace PhD Scholar Centre for Accident Research & Road Safety – Queensland Queensland University of Technology Telephone: 0402 240 234 Email: angela.wallace@student.qut.edu.au

General Driving Behaviour

470

The following are statements that may describe your driving behaviour whilst driving a Truck. On the scale of ‘1’ ‘Never’ to ‘6’ ‘Nearly all the time’ please circle the number which best sums up your answer.

(Circle one number on each line) How often do you…..

Nev

er

Har

dly

ever

Occ

asio

nally

Qui

te o

ften

Freq

uent

ly

Nea

rly a

ll th

e tim

e

Attempt to overtake someone in front of you that you hadn’t noticed to be turning in front of you

1 2 3 4 5 6

Stay in a lane that you know will be closed ahead until the last minute before forcing your way into another lane

1 2 3 4 5 6

Miss ‘Stop’ or ‘Give Way’ signs 1 2 3 4 5 6

Intentionally disobey a ‘Stop’ or ‘Give Way’ sign 1 2 3 4 5 6

Pull out of a junction so far that you disrupt the flow of traffic 1 2 3 4 5 6

Fail to notice that pedestrians are crossing in your path of traffic 1 2 3 4 5 6

Drive especially close to the car in front as a signal to its driver to go faster or get out of the way

1 2 3 4 5 6

Sound your horn to indicate your annoyance to another driver 1 2 3 4 5 6

Queuing to enter a main road, you pay such close attention to the mainstream of traffic that you nearly hit the car in front

1 2 3 4 5 6

Cross a junction knowing that the traffic lights have already turned against you

1 2 3 4 5 6

Whilst turning nearly hit a cyclist who has come up on your inside 1 2 3 4 5 6

Intentionally disregard the speed limit on a highway/freeway 1 2 3 4 5 6

Exceed the speed limit on a highway/freeway without realising 1 2 3 4 5 6

Fail to check your rear-view mirror before pulling out, changing lanes, etc

1 2 3 4 5 6

Become angered by a certain type of driver and indicate your hostility by whatever means you can

1 2 3 4 5 6

Become impatient with a slow driver ahead and overtake on the inside

1 2 3 4 5 6

When overtaking underestimate the speed of an oncoming vehicle 1 2 3 4 5 6

Race away from the traffic lights with the intention of beating the driver next to you

1 2 3 4 5 6

Skid while braking or cornering on a slippery road 1 2 3 4 5 6

471

(Circle one number on each line) How often do you…..

Nev

er

Har

dly

ever

Occ

asio

nally

Qui

te o

ften

Freq

uent

ly

Nea

rly a

ll th

e tim

e

Drive even though you suspect you may be over the legal blood-alcohol limit

1

2

3

4

5

6

Intentionally disregard the speed limit on a residential road 1 2 3 4 5 6

Exceed the speed limit on a residential road without realising 1 2 3 4 5 6

Become angered by another driver and give chase 1 2 3 4 5 6

Hit something while manoeuvring (including parking and reversing) 1 2 3 4 5 6

Come close to hitting something while manoeuvring (including parking and reversing)

1 2 3 4 5 6

Drive while under time pressure 1 2 3 4 5 6

Find your attention being distracted from the road 1 2 3 4 5 6

Drive while tired 1 2 3 4 5 6

Have difficulty driving because of tiredness or fatigue 1 2 3 4 5 6

Find yourself nodding off while driving for work 1 2 3 4 5 6

Lose concentration while driving 1 2 3 4 5 6

Not wear your seatbelt 1 2 3 4 5 6

Remove your seatbelt for some reason while driving 1 2 3 4 5 6

Drive while using a ‘hand-held’ mobile phone 1 2 3 4 5 6

Drive while using a ‘hands-free’ mobile phone 1 2 3 4 5 6

Check the tyre pressure and fluid levels of your vehicle 1 2 3 4 5 6

Do paperwork or other administration whilst driving 1 2 3 4 5 6

Save the time during the day by driving quicker between places 1 2 3 4 5 6

Have one or two alcoholic drinks before driving 1 2 3 4 5 6

Smoke a cigarette whilst driving 1 2 3 4 5 6

Eat food whilst driving 1 2 3 4 5 6

472

Driving at Railway Level Crossings How often have you engaged in the following behaviours whilst driving a Truck at a railway level crossing in the past 2-3 weeks? On the scale of ‘1’ ‘Not at all’ to ‘5’ ‘Very often’, please circle the number which best sums up how much you agree with the following statements

(Circle one number on each line)

Not

at a

ll

Very

Ofte

n

Driven through a crossing when lights are flashing, but boom gates are yet to descend

1 2 3 4 5

Driven through a crossing when boom gates are descending

1 2 3 4 5

Driven through a crossing when booms gates are begin to rise, but lights are still flashing

1 2 3 4 5

Rolling through a crossing without stopping, if no train is visible

1 2 3 4 5

Driven through a crossing after the first train has passed, without looking for a second

1 2 3 4 5

Failed to look for a train before crossing

1 2 3 4 5

Driven through a crossing when the train is visible, but still some distance away

1 2 3 4 5

Driven through a crossing when the train is close

1 2 3 4 5

Queued over a crossing

1 2 3 4 5

Driven around boom gates to cross

1 2 3 4 5

Tried to beat the train across the crossing

1 2 3 4 5

Sped on approach to a crossing

1 2 3 4 5

Scanned on approach to a crossing, but failed to stop at it

1 2 3 4 5

Driven through a crossing when the lights are flashing, but no train is visible

1 2 3 4 5

Driven through a crossing when the lights are flashing, and the train is visible

1 2 3 4 5

Stopped on the yellow hatching road markings

1 2 3 4 5

Followed another vehicle across the crossing without looking for yourself

1 2 3 4 5

Driven through a crossing when visibility is impaired 1 2 3 4 5

Driven through a crossing without realising it until after the vehicle is over the tracks

1 2 3 4 5

473

We would like to know the likelihood that you will engage in the following behaviours in the next 6 months of driving at railway level crossings. Even if you rarely drive through railway level crossings, please indicate how you would drive if you were to drive through a crossing. On the scale of ‘1’ ‘Not at all’ to ‘5’ ‘Very often’, please circle the number which best sums up how much you agree with the following statements.

(Circle one number on each line) In the next 6 months of driving, it is likely I will:

Not

at a

ll Li

kely

Very

Lik

ely

Drive through a crossing when lights are flashing, but boom gates are yet to descend

1 2 3 4 5

Drive through a crossing when boom gates are descending

1 2 3 4 5

Drive through a crossing when booms gates are begin to rise, but lights are still flashing

1 2 3 4 5

Roll through a crossing without stopping, if no train is visible

1 2 3 4 5

Drive through a crossing after the first train has passed, without looking for a second

1 2 3 4 5

Fail to look for a train before crossing

1 2 3 4 5

Drive through a crossing when the train is visible, but still some distance away

1 2 3 4 5

Drive through a crossing when the train is close

1 2 3 4 5

Queue over a crossing

1 2 3 4 5

Drive around boom gates to cross

1 2 3 4 5

Try to beat the train across the crossing

1 2 3 4 5

Speed on approach to a crossing

1 2 3 4 5

Scan on approach to a crossing, but failed to stop at it

1 2 3 4 5

Drive through a crossing when the lights are flashing, but no train is visible

1 2 3 4 5

Drive through a crossing when the lights are flashing, and the train is visible

1 2 3 4 5

Stop on the yellow hatching road markings

1 2 3 4 5

Follow another vehicle across the crossing without looking for yourself

1 2 3 4 5

Drive through a crossing when visibility is impaired 1 2 3 4 5

Drive through a crossing without realising until after the vehicle is over the tracks

1 2 3 4 5

474

Rate each of the following statements according to the degree they reflect your personal opinion. On the scale of ‘1’ ‘Not at all’ to ‘5’ ‘Very often’, please circle the number which best sums up how much you agree or disagree with the following statements. (Please only circle one number for each statement)

Stro

ngly

A

gree

Stro

ngly

D

isag

ree

Your colleagues generally obey the rules at level crossings

1 2 3 4 5

Your friends generally obey the rules at level crossings

1 2 3 4 5

Other motorists generally obey the rules at level crossings

1 2 3 4 5

Your colleagues generally think it important to obey the rules at level crossings

1 2 3 4 5

Your friends generally think it important to obey the rules at level crossings

1 2 3 4 5

Other motorists generally think it important to obey the rules at level crossings

1 2 3 4 5

It is generally safe to disobey the rules at level crossings

1 2 3 4 5

It is generally possible to judge a train’s speed

1 2 3 4 5

It is generally safe to cross if you can’t see a train, even if the lights are flashing

1 2 3 4 5

It is generally safe to roll slowly through a crossing instead of stopping

1 2 3 4 5

Trains generally run to a regular timetable

1 2 3 4 5

Penalties need to be tougher for violating road rules at level crossings

1 2 3 4 5

The main deterrent for breaking the rules at level crossings is fear of getting caught

1 2 3 4 5

Generally it is more important to use common sense at level crossings than strictly follow the road rules

1 2 3 4 5

475

We would like to ask you some questions about your attitude towards railway level crossings. Please circle one number in each table. As a truck driver, I believe that the design of railway level crossings is:

Bad Good

-3 -2 -1 0 1 2 3

Unsafe Safe

-3 -2 -1 0 1 2 3

Confusing Easily

Understood -3 -2 -1 0 1 2 3

Difficult for

obeying road rules

Easy for obeying

road rules -3 -2 -1 0 1 2 3

As a truck driver, I believe that road rules at railway level crossings are:

Bad Good

-3 -2 -1 0 1 2 3

Not Strict Enough

Too Strict

-3 -2 -1 0 1 2 3

Confusing Easily

Understood -3 -2 -1 0 1 2 3

Not

Practical Practical

-3 -2 -1 0 1 2 3

Obeying the road rules at railway level crossings is:

Not up to me

Up to me

-3 -2 -1 0 1 2 3

Out of my

control Under my

control -3 -2 -1 0 1 2 3

476

Obeying the road rules at railway level crossings is:

Dependent on other

motorists

Dependent on me only

-3 -2 -1 0 1 2 3

Dependent

on time constraints

Not dependent

on time constraints

-3 -2 -1 0 1 2 3

Other motorists influence my ability to obey road rules at railway level crossings, making it:

Harder Easier -3 -2 -1 0 1 2 3

More

confusing Less

confusing -3 -2 -1 0 1 2 3

More

stressful More

relaxing -3 -2 -1 0 1 2 3

When you are driving a Truck at railway level crossings, how often do each of the following occur?

(Please only circle one number for each statement) N

ever

Alm

ost N

ever

Som

etim

es

Alm

ost A

lway

s

Alw

ays

Blinding sun makes it difficult to see if the red flashing lights are activated

1 2 3 4 5

The design of the road is an ‘S’ bend and it is difficult to see if a train is approaching or at the railway crossing

1 2 3 4 5

The height of the truck’s cabin makes it difficult to see a train or the warning systems

1 2 3 4 5

Warning systems on the road approaching the crossing are not adequate to inform trucks there is a railway crossing ahead

1 2 3 4 5

The mass of the truck makes it difficult to brake in time to stop at a railway crossing

1 2 3 4 5

Road surface is often poor and it is difficult to stop

1 2 3 4 5

Boom gates and/or flashing lights are often faulty 1 2 3 4 5

477

(Please only circle one number for each statement) N

ever

Alm

ost N

ever

Som

etim

es

Alm

ost A

lway

s

Alw

ays

Other drivers (such as cars) do stupid things that put you in a dangerous situation

1 2 3 4 5

Intersections ahead of a railway crossing often cause your truck to overhang the tracks

1 2 3 4 5

Noise from the truck’s engine is too loud to hear an approaching train

1 2 3 4 5

When you have to stop at a railway crossing, it takes a long time before your truck is able to get over the crossing

1 2 3 4 5

All ‘Stop’ signs should be changed to ‘Give Way’ signs at railway crossings as these are easier for trucks to get through the crossing

1 2 3 4 5

My truck has stalled on the tracks at a railway crossing 1 2 3 4 5 The following section tests your knowledge about railway level crossings. Circle the number that best describes your answer. When the twin red lights start flashing this means?

A train is coming but it is still safe to cross………….. You must stop and not enter the crossing ………….. Don’t know ………………………………………………

1 2 3

Boom gates usually stay down a lot longer than traffic lights take to change at an intersection

True ………………………………………………………False ……………........…………………………………. Don’t know ………………………………………………

1 2 3

Yellow hatching road markings means keep clear

True ………………………………………………………False …………………………………………………….. Don’t know ………………………………………………

1 2 3

There are fines for not stopping at railway crossings

True ………………………………………………………False …………………………………………………….. Don’t know ………………………………………………

1 2 3

How likely is it that whilst driving a Truck that you will be involved in a crash with a train at a railway level crossing?

Not at all Likely

Very Likely

1 2 3 4 5

478

Road Safety Radio Message We would now like to ask you to cast your mind back to the audio message played to you several months ago by one of our researchers. The following questions refer to this road safety message.

Which road safety message did you listen to on the telephone?

Fatigue and driving ………...……………………….……Driving at railway level crossings ...………………...…

1 2

Can you remember any slogans from the road safety advertisement that you listened to? If YES, which slogan do you recall?

No ..………………………………………………….…… Yes .....…………………………………………………… Slogan: ___________________________________________

1 2

Can you remember any important information mentioned in the advertisement? If YES, what important information do you recall?

No ..………………………………………………….…… Yes .....…………………………………………………… Important information: ___________________________________________

1 2

How likely is it, that your driving behaviour has been affected by listening to this road safety advertisement for heavy vehicle driving?

Not at all

likely Very likely

0 1 2 3 4 5

Thank you for completing this questionnaire. Please return this questionnaire in

the reply-paid envelope supplied.