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Evaluating the Transport Impacts of Transit
Oriented Developments (TODs)
Deepti Sadashiv Muley
B.E. (Civil), M. Tech (Transportation)
A thesis submitted for the degree of
Doctor of Philosophy
School of Urban Development
Faculty of Built Environment & Engineering
Queensland University of Technology
July 2011
Dedicated to my parents,
Aai and Baba
without your dreams, support and trust in me this was impossible
Deepti Muley Page i
Keywords
transit oriented developments (TODs), Australian TOD, TOD evaluation, transport impacts,
TOD users, TOD residents, travel characteristics, traffic generation, comparative analysis,
logistic regression, travel mode investigation, travel demand for TODs, travel demand
analysis, sustainable transport
Evaluating the transport impacts of TODs
Deepti Muley Page ii
Deepti Muley Page iii
Abstract
Sustainable transport has become a necessity instead of an option, to address the problems of
congestion and urban sprawl, whose effects include increased trip lengths and travel time. A
more sustainable form of development, known as Transit Oriented Development (TOD) is
presumed to offer sustainable travel choices with reduced need to travel to access daily
destinations, by providing a mixture of land uses together with good quality of public
transport service, infrastructure for walking and cycling. However, performance assessment
of these developments with respect to travel characteristics of their inhabitants is required.
This research proposes a five step methodology for evaluating the transport impacts of TODs.
The steps for TOD evaluation include pre–TOD assessment, traffic and travel data collection,
determination of traffic impacts, determination of travel impacts, and drawing outcomes.
Typically, TODs are comprised of various land uses; hence have various types of users.
Assessment of characteristics of all user groups is essential for obtaining an accurate picture
of transport impacts.
A case study TOD, Kelvin Grove Urban Village (KGUV), located 2km of north west of the
Brisbane central business district in Australia was selected for implementing the proposed
methodology and to evaluate the transport impacts of a TOD from an Australian perspective.
The outcomes of this analysis indicated that KGUV generated 27 to 48 percent less traffic
compared to standard published rates specified for homogeneous uses. Further, all user
groups of KGUV used more sustainable modes of transport compared to regional and
similarly located suburban users, with higher trip length for shopping and education trips.
Although the results from this case study development support the transport claims of
reduced traffic generation and sustainable travel choices by way of TODs, further
investigation is required, considering different styles, scales and locations of TODs. The
proposed methodology may be further refined by using results from new TODs and a
framework for TOD evaluation may be developed.
Evaluating the transport impacts of TODs
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Deepti Muley Page v
Contents
Keywords i
Abstract iii
Contents v
List of figures xi
List of tables xiii
Abbreviations xvii
Statement of original authorship xix
Acknowledgments xxi
Chapter 1 1Introduction 1
1.1 Overview 1
1.2 Background 1
1.3 Research hypothesis 3
1.4 Objectives 4
1.5 Scope of this research 5
1.6 Publications from this research 5
1.7 Thesis outline 5
1.8 Chapter close 7
Chapter 2 2Literature review 9
2.1 Introduction 9
2.2 Transit Oriented Developments (TODs) 9
2.2.1 The concept of TODs 9
2.2.2 Types of TODs 12
2.2.3 Australian perspective of TODs 12
2.2.4 Benefits of TODs 14
2.3 Data requirements for TOD evaluation 22
2.3.1 Background 22
2.3.2 Data need / data requirement 22
2.3.3 Types of travel surveys 23
2.3.4 Types of survey instruments 26
2.3.5 Design of survey instruments 28
Evaluating the transport impacts of TODs
Deepti Muley Page vi
2.3.6 Response rate 30
2.3.7 Data analysis 31
2.3.8 Summary of data collection 31
2.4 Travel data analysis 32
2.4.1 Background 32
2.4.2 Comparative and statistical analysis 33
2.4.3 Travel demand modelling 43
2.4.4 Summary of travel data analysis 53
2.5 Summary of literature review 56
2.5.1 Strengths of the literature 56
2.5.2 Gaps in the literature 57
2.6 Recommendations flowing from literature review 58
2.7 Chapter close 59
Chapter 3 3Methodology for evaluating transport impacts of transit
3oriented developments
61
3.1 Introduction 61
3.2 Measures for transport evaluation of TODs 61
3.3 Proposed new research methodology 62
3.3.1 Step I: Pre–TOD assessment 63
3.3.2 Step II: Traffic and travel data collection 65
3.3.3 Step III: Determination of traffic impacts 67
3.3.4 Step IV: Determination of travel impacts 68
3.3.5 Step V: Outcomes 70
3.4 Summary 71
3.5 Application 72
3.6 Chapter close 72
Chapter 4 4Selection of case study transit oriented development 73
4.1 Introduction to case study selection 73
4.2 Description of KGUV 73
4.3 Transport facilities at KGUV 74
4.4 Suitability of case study TOD 77
4.4.1 Background 77
4.4.2 Transit availability 78
4.4.3 Comfort and convenience 79
4.5 Transit availability for KGUV 79
Contents
Deepti Muley Page vii
4.5.1 Analysis background 79
4.5.2 Availability – Transit stops 81
4.5.3 Availability – Route segments / corridors 84
4.5.4 Availability – System 86
4.6 Interpretation of results 91
4.7 Summary 91
4.8 Chapter close 91
Chapter 5 5Data collection 93
5.1 Introduction to data collection 93
5.2 User groups at KGUV 93
5.2.1 Employees 93
5.2.2 Shoppers 94
5.2.3 Students 94
5.2.4 Residents 94
5.2.5 Recreational users 95
5.3 Cordon counts 96
5.4 Travel surveys 97
5.4.1 General methodology for conducting travel surveys 98
5.4.2 Selection of survey instrument 100
5.4.3 Design of questionnaire form 101
5.4.4 Reminder letters and incentives 105
5.4.5 Process of conducting surveys 105
5.4.6 Sample size and response rates 109
5.4.7 Sample bias 111
5.5 Lessons learned 111
5.6 Summary 112
5.7 Chapter close 113
Chapter 6 6Traffic generation at Kelvin Grove Urban Village 115
6.1 Introduction 115
6.2 Analysis of cordon data 115
6.3 Conditions of the survey period 118
6.4 Total traffic at KGUV 118
6.4.1 Traffic at the Village Centre 118
6.4.2 Traffic at whole of KGUV 120
6.5 Comparison of peak hourly traffic with published rates 123
Evaluating the transport impacts of TODs
Deepti Muley Page viii
6.5.1 ITE comparison 123
6.5.2 Australian sources (RTA) comparison 125
6.6 Interpretation of results 127
6.7 Summary 128
6.8 Chapter close 128
Chapter 7 7Characteristics of Kelvin Grove Urban Village users 129
7.1 Introduction 129
7.2 Determination of users’ characteristics 129
7.2.1 Demographic characteristics 130
7.2.2 Travel characteristics 130
7.3 Shoppers’ shopping trips at KGUV 131
7.3.1 Demographic characteristics 131
7.3.2 Travel characteristics 133
7.4 Employees’ work trips at KGUV 134
7.4.1 Demographic characteristics 134
7.4.2 Travel characteristics 138
7.5 Students’ education trips at KGUV 141
7.5.1 Demographic characteristics 141
7.5.2 Travel characteristics 144
7.6 Residents’ trips at KGUV 148
7.6.1 Demographic characteristics 148
7.6.2 Travel characteristics 151
7.7 Comparison of KGUV users’ characteristics 155
7.7.1 Comparison of demographic characteristics 155
7.7.2 Comparison of travel characteristics 157
7.8 Transport issues related to TOD from users’ perspective 158
7.9 Interpretation of TOD users’ characteristics 160
7.10 Summary 161
7.11 Chapter close 161
Chapter 8 8Comparative analysis of Kelvin Grove Urban Village’s users’ 8characteristics
163
8.1 Introduction 163
8.2 Basis for comparison 163
8.3 Comparison of shoppers’ shopping trips 164
8.3.1 Mode share comparison 164
Contents
Deepti Muley Page ix
8.3.2 Trip length comparison 165
8.4 Comparison of employees’ work trips 166
8.4.1 Mode share comparison 166
8.4.2 Trip length comparison 167
8.5 Comparison of students’ education trips 168
8.5.1 Mode share comparison 168
8.5.2 Trip length comparison 169
8.6 Comparison of residents’ trips 169
8.6.1 Household characteristics 169
8.6.2 Trip characteristics 170
8.6.3 Mode share comparison 171
8.6.4 Trip length comparison 172
8.7 Interpretation of results 173
8.8 Summary 175
8.9 Chapter close 175
Chapter 9 9Kelvin Grove Urban Village users’ travel demand analysis 177
9.1 Introduction 177
9.2 Analysis background 177
9.3 Analysis for shoppers’ shopping trips 182
9.4 Analysis for employees’ work trips 183
9.5 Analysis for students’ education trips 185
9.6 Analysis for residents’ first trip of the day 187
9.7 Interpretation of results 189
9.8 Summary 190
9.9 Chapter close 191
Chapter 10 10Models for travel modes of transit oriented development
10users
193
10.1 Introduction 193
10.2 Analysis background for model development 193
10.3 Model for shopping trips 195
10.4 Model for work trips 196
10.5 Model for education trips 198
10.6 Model for residents’ first trip of the day 199
10.7 Interpretation 201
10.8 Model application 201
Evaluating the transport impacts of TODs
Deepti Muley Page x
10.9 Summary 203
10.10 Chapter close 204
Chapter 11 11Conclusions and recommendations 205
11.1 Introduction 205
11.2 Conclusions from this research 205
11.2.1 Build up an understanding of the concept of TOD and
various aspects related to it with a detailed knowledge
on TOD evaluation
206
11.2.2 Develop a methodology for evaluating the transport
impacts of TODs
206
11.2.3 Demonstrate the methodology by implementing it on
an Australian case study TOD
209
11.2.4 Determine trip rates for various modes of transport for
a TOD and assess the travel demand of TOD users
210
11.2.5 Evaluate the transport impacts of TODs from an
Australian perspective by comparing the results with
characteristics of conventional development
211
11.3 Reflections from case study TOD 212
11.4 Applications of this research methodology 213
11.5 Contribution to knowledge 214
11.6 Limitations of this research 215
11.7 Areas of further research 216
11.8 Chapter close 217
References 219
Appendix A 233
Appendix B 235
Appendix C 237
Appendix D 239
Deepti Muley Page xi
List of figures
Figure 1.1 Greenhouse gas emissions 2
Figure 1.2 Layout of thesis 6
Figure 2.1 Path diagram of factors influencing changes in job accessibility and
commuting behaviour
42
Figure 2.2 The classic four–stage model 43
Figure 2.3 Idealised integrated urban modelling system 45
Figure 3.1 Measures for transport evaluation of TODs 62
Figure 3.2 Proposed new methodology for evaluating transport impacts of TODs 63
Figure 3.3 Step 1: Pre–TOD assessment 64
Figure 3.4 Step II: Traffic and travel data 66
Figure 3.5 Step III: Traffic impacts 68
Figure 3.6 Step IV: Travel impacts 69
Figure 3.7 Step V: Outcomes 71
Figure 4.1 Aerial overview of Kelvin Grove Urban Village (KGUV) 75
Figure 4.2 Regional map showing various offsite attractions considered in analysis 81
Figure 4.3 Buffers for bus stops for QUT route 391 intercampus shuttle service (ST3
& ST4)
89
Figure 4.4 Buffer for QUT KG Busway Station (ST1) 89
Figure 4.5 Buffers for bus stop at Kelvin Grove Road at Blamey Street (ST5) and
Normanby Busway Station (ST8) (inbound to CBD)
90
Figure 4.6 Buffers for bus stop at Kelvin Grove Road at Prospect Terrace (ST7) and
Normanby Busway Station (ST8) (outbound from CBD)
90
Figure 5.1 Overview of land uses and user groups at KGUV 95
Figure 5.2 Locations of cordon counts at KGUV 97
Figure 5.3 Steps involved in data collection process for a user group travel survey 99
Figure 5.4 Questionnaire details for non residential land use users 102
Figure 5.5 Questionnaire details for residential land use users 104
Figure 6.1 Process of cordon data analysis 116
Figure 6.2 Direction of through traffic at KGUV 117
Figure 7.1 Distribution of shoppers’ age groups in years 132
Figure 7.2 Frequency of shopping trips per week 132
Figure 7.3 Mode shares for shoppers’ at KGUV 133
Figure 7.4 Distribution of age group for employees’ at KGUV 135
Evaluating the transport impacts of TODs
Deepti Muley Page xii
Figure 7.5 Frequency of work trips at KGUV 136
Figure 7.6 Household size distribution at employees’ households 137
Figure 7.7 Mode shares for professional employees 138
Figure 7.8 Mode shares for retail shop employees 139
Figure 7.9 Distribution of age group for students at KGUV 142
Figure 7.10 Frequency of education trips at KGUV 143
Figure 7.11 Household size distribution at students’ households 144
Figure 7.12 Mode shares for school students 145
Figure 7.13 Mode shares for university students 146
Figure 7.14 Distribution of age group for residents’ at KGUV 149
Figure 7.15 Distribution of number of bedrooms in residents’ household 149
Figure 7.16 Distribution of household size at residents’ household 150
Figure 7.17 Mode shares for non student residents at KGUV 152
Figure 7.18 Mode shares for student residents at KGUV 153
Figure 8.1 Comparison of sustainable transport mode share 173
Figure 8.2 Comparison of overall average trip length 174
Figure 9.1 Sensitivity of typical shopper’s sustainable travel mode probability with
travel time saving
183
Figure 9.2 Sensitivity of typical employee’s sustainable travel mode probability with
age group
185
Figure 9.3 Sensitivity of typical student’s sustainable travel mode probability with
age group
187
Figure 9.4 Sensitivity of typical resident’s sustainable travel mode probability with
travel time saving
188
Deepti Muley Page xiii
List of tables
Table 1.1 Overview of publications from this research 5
Table 2.1 Characteristics of various existing TODs 11
Table 2.2 Application of four part TOD strategy to Australian cities 13
Table 2.3 An overview of household travel surveys 24
Table 2.4 Summary of different survey instruments 32
Table 2.5 Summary of studies on comparative and statistical analysis 54
Table 2.6 Summary of studies on travel demand modelling 55
Table 3.1 Measures for assessing suitability of a TOD 65
Table 4.1 Mixed land uses at KGUV 74
Table 4.2 Buses observing transit stops in KGUV 76
Table 4.3 Quality of service framework: Fixed – route (TRB, 2003, Exhibit 3–1) 77
Table 4.4 Fixed – route service frequency LOS (TRB, 2003, Exhibit 3 – 12) 82
Table 4.5 LOS for various trip destinations originating from KGUV (Kelvin Grove
Road bus stops)
82
Table 4.6 LOS for various trip destinations originating from KGUV (QUT KG
Busway Station)
83
Table 4.7 LOS for various trip origins where destination is KGUV (Kelvin Grove
Road bus stops)
83
Table 4.8 LOS for various trip origins where destination is KGUV (QUT KG
Busway Station)
83
Table 4.9 Fixed – route hours of service LOS (TRB, 2003, Exhibit 3 – 13) 84
Table 4.10 Hours of service LOS for different corridors with bus route numbers 85
Table 4.11 Fixed – route service coverage LOS (TRB, 2003, Exhibit 3 – 14) 86
Table 4.12 LOS for different bus stops 88
Table 5.1 Summary of travel surveys 109
Table 5.2 Sample sizes and response rates 111
Table 6.1 Time periods for analysis for Village Centre 119
Table 6.2 Traffic generated at Village Centre by mode on study day 119
Table 6.3 Car occupancy and directional distribution for Village Centre 120
Table 6.4 Directional distribution for Village Centre 120
Table 6.5 Time periods for analysis of whole KGUV traffic 121
Table 6.6 Through car traffic at KGUV by time of day 121
Table 6.7 Total traffic generated at KGUV by mode on study day 121
Evaluating the transport impacts of TODs
Deepti Muley Page xiv
Table 6.8 Car occupancy and directional distribution at KGUV 122
Table 6.9 Directional distribution at KGUV 122
Table 6.10 ITE comparison for traffic at the Village Centre 124
Table 6.11 ITE comparison for traffic at KGUV 125
Table 6.12 RTA comparison for traffic at the Village Centre 126
Table 6.13 RTA comparison for traffic at the KGUV 126
Table 7.1 Employment status of shoppers at KGUV 131
Table 7.2 Trip lengths by mode of transport for shoppers at KGUV 134
Table 7.3 Personal characteristics of employees’ at KGUV 135
Table 7.4 Vehicle ownership and licence availability at employees’ households 137
Table 7.5 Trip lengths by mode of transport for professional employees at KGUV 139
Table 7.6 Trip lengths by mode of transport for retail shop employees at KGUV 140
Table 7.7 Public transport trip details for employee trips at KGUV 141
Table 7.8 Personal characteristics of students at KGUV 142
Table 7.9 Vehicle ownership and licence availability at students’ households 144
Table 7.10 Trip lengths by mode of transport for school students at KGUV 146
Table 7.11 Trip lengths by mode of transport for university students at KGUV 146
Table 7.12 Public transport trip details for student trips at KGUV 147
Table 7.13 Personal characteristics of residents’ at KGUV 148
Table 7.14 Vehicle ownership and licence availability at residents’ households 150
Table 7.15 Number of trips for residents at KGUV 151
Table 7.16 Trip lengths by mode of transport for non student residents at KGUV 154
Table 7.17 Trip lengths by mode of transport for student residents at KGUV 154
Table 7.18 Activity frequency for non student residents at KGUV 154
Table 7.19 Activity frequency for student residents at KGUV 155
Table 7.20 Comparison of personal characteristics of KGUV users’ 156
Table 7.21 Comparison of household characteristics of KGUV users’ 157
Table 7.22 Comparison of travel characteristics of KGUV users’ 158
Table 8.1 Mode share comparison for shopping trips 165
Table 8.2 Comparison of average trip lengths for shopping trips 166
Table 8.3 Mode share comparison for work trips 167
Table 8.4 Comparison of average trip lengths for work trips 167
Table 8.5 Mode share comparison for education trips 168
Table 8.6 Comparison of average trip lengths for education trips 169
Table 8.7 Comparison of residents’ household characteristics 170
List of tables
Deepti Muley Page xv
Table 8.8 Comparison of residents’ average trips per person 171
Table 8.9 Mode share comparison for residents’ first trip of the day 171
Table 8.10 Comparison of average trip lengths for residents’ first trip of the day 172
Table 9.1 Details of coding system for travel mode investigation 180
Table 9.2 Travel mode analysis for shoppers’ shopping trips 182
Table 9.3 Travel mode analysis for employees’ work trips 184
Table 9.4 Travel mode analysis for students’ education trips 186
Table 9.5 Travel mode analysis for residents’ first trip of the day 188
Table 9.6 Significance of variables for travel mode determination for KGUV users 189
Table 9.7 Comparison of odds of KGUV users’ for choosing a sustainable mode of
transport
190
Table 10.1 Original model for shoppers’ shopping trips 195
Table 10.2 Revised model for shoppers’ shopping trips 196
Table 10.3 Original model for employees’ work trips 197
Table 10.4 Revised model for employees’ work trips 197
Table 10.5 Original model for students’ education trips 198
Table 10.6 Revised model for students’ education trips 199
Table 10.7 Original model for residents’ first trip of the day 200
Table 10.8 Revised model for residents’ first trip of the day 200
Evaluating the transport impacts of TODs
Deepti Muley Page xvi
Deepti Muley Page xvii
Abbreviations
Abbreviation Description
BINS Brisbane Inner North Suburbs
BISS Brisbane Inner South Suburbs
BRT Bus Rapid Transit
BSD Brisbane Statistical Division
CAPI Computer Assisted Personal Interview
CBD Central Business District
CI Creative Industries
ESD Ecological Sustainable Development
GFA Gross Floor Area
GIS Geographic Information System
GPS Global Positioning System
IHBI Institute of Health and Biomedical Innovation
KG Kelvin Grove
KGUV Kelvin Grove Urban Village
LA Licence Availability
LOS Level of Service
NRLU Non Residential Land Use
NRMA National Roads and Motorists’ Association
NSR Non Student Resident
QACI Queensland Academy of Creative Industries
QoS Quality of Service
QUT Queensland University of Technology
RACQ Royal Automobile Club of Queensland
RLU Residential Land Use
SEQ South East Queensland
SEQTS South East Queensland Travel Survey
SR Student Resident
TCQSM Transit Capacity and Quality of Service Manual
TOD Transit Oriented Development
Evaluating the transport impacts of TODs
Deepti Muley xviii
Abbreviation Description
TSA Transit Supportive Area
TTS Travel Time Saving
VC Village Centre
VKT Vehicle Kilometres Travelled
Deepti Muley Page xix
Statement of original authorship
The work contained in this thesis has not been previously submitted to meet requirements for
an award at this or any 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.
Signature:
Deepti Sadashiv Muley
Date: 21st July 2011
Evaluating the transport impacts of TODs
Deepti Muley xx
Deepti Muley Page xxi
Acknowledgments
First of all, I acknowledge my supervisors Dr. Jonathan Bunker and Prof. Luis Ferreira for
their guidance, knowledge and contribution which had enabled me to achieve this piece of
work.
I am grateful to the School of Urban Development for proving me the scholarship and
recourses for conducting this research specifically the data collection process.
My thanks for the excellent help obtained from research assistants during data collection
process, especially Mr. Daniel Buntine and Mr. William Dawson. I also thank Queensland
Transport for proving the dataset for comparison.
I am thankful to Mr. Mike Hyslop and Prof. Edward Chung for proving me a comfortable
working environment which has allowed me to manage my work and research commitments.
I thank my fellow postgraduate students, colleagues at PTV Asia Pacific, and all friends in
India and Australia for making this journey pleasant.
I acknowledge my parents (Aai and Baba), siblings (Trupti, Bhakti and Dhiraj) and brother in
law (Milind) for their constant love, faith and encouragement. This is equally yours as it is
mine. I appreciate Alok for his support in finalising this piece of work. Last but not the least;
I am grateful to Sumeet for being a support in Australia throughout three years of my
candidature.
Evaluating the transport impacts of TODs
Deepti Muley xxii
Deepti Muley Page 1
Chapter 1
Introduction
1.1 Overview
Urban development planners consider Transit Oriented Developments (TODs) as an
alternative to reduce urban sprawl and congestion. TODs are not only believed to provide
mobility choices and improve transit ridership but are also assumed to offer more lifestyle
options and social life by providing street level shops, parks and open spaces (Calthorpe,
1993; NIPC, 2001). This research explores the transport impacts of these communities by
studying the travel demand for various groups of users at a particular TOD.
The first section gives a brief background to this research and then states the research
hypothesis and the objectives of this research along with its scope. The subsequent section
briefs details about the publications from this research. The outline of this thesis is described
in the following section along with the chapter close which states the relevance or
relationship of this chapter with respect to the other chapters in the thesis.
1.2 Background
Transport demand is a derived demand, which arises from the activities which are performed
outside the home by an individual. A person is involved in many activities which lie outside
the home, hence one need to travel from one place to another. This travel can be performed
by two main modes of transport, either personalised mode or public mode. The personalised
modes of transport can be divided in two categories; motorised such as car and motorcycle,
and non motorised such as bicycle and walking. The use of non motorised modes of transport
is not feasible for covering long distances. Generally, the use of the public transport mode is
recommended by policy makers over motorised private modes of transport to control
pollution and congestion, while people mostly choose the motorised private mode of
transport (car) to maximise their flexibility of movement and very often to achieve travel time
savings. Due to a high proportion of urban populations opting for motorised private modes of
transport, some negative impacts are observed on the environment, as well as on human life.
This statement can be supported by the facts listed below:
Evaluating the transport impacts of TODs
Deepti Muley Page 2
Increasing load on infrastructure
Increased traffic on the road system may make the journey uncomfortable with undesirable
delays. Car ownership level is an indicator of the amount of vehicle usage and consequently
traffic on the roads. The trend in September 2007 (ABS, 2007) shows the increasing tendency
of Australians to own a vehicle when compared to the previous year. Increased car ownership
levels will further increase the tendency to drive to daily destinations and this tendency will
place extra load on existing transport infrastructure.
Travel characteristics
An estimate revealed that total travel in Australian urban areas has grown ten–fold over the
last 60 years, with private road vehicles having a major share of about 90% of the total
passenger task (BTRE, 2007). The projections forecast total kilometres travelled growing by
37% between 2005 and 2020. This increased travel will increase congestion and pollution on
the roads, leading to variability in journey time and increased vehicle operating costs. In the
same study, the social cost of congestion was estimated to rise from $9.4 billion in 2005 to
$20.4 billion in 2020.
Greenhouse gas emissions
Reducing greenhouse gas emissions to combat climate change is an important agenda for
policy makers. Australian statistics show that the transport sector emissions were 30.5%
higher in 2005 when compared to 1990 emissions (AGO, 2007).
Source: AGO (2007)
Figure 1.1 Greenhouse gas emissions
0
20
40
60
80
100
Transport as total
emissions
Share of road transport Share of passenger cars
Per
cen
tage (
%)
Introduction
Deepti Muley Page 3
Figure 1.1 explains the role of road transport in emissions for the year 2005. For Australia,
the transport sector is the third largest contributor to greenhouse gas emissions after
stationary energy sources and agriculture. The alternative fuels conversion programme and
travel demand management programmes, which promote the use of alternative fuels and
improve public transport and town planning, are key emission management measures (AGO,
2006).
Petrol prices
The Royal Automobile Club of Queensland (RACQ) and National Roads and Motorists’
Association (NRMA) found that an average Australian household spends 15 to 20 percent of
its net income directly on transport (BTA, 2007). Increasing petrol prices is one of the main
reasons as around 90 percent of trips are made by passenger cars. For the Brisbane
Metropolitan region, the average price of unleaded petrol has almost doubled in the past
seven years, from 60.9 cents per litre in June, 1999 to 122 cents per litre in June, 2007
(OESR, 2007).
To minimize the negative impacts of car use, more sustainable development is required. The
national greenhouse strategy supports integration of land use and transport planning which
includes promotion of development near public transport systems, incorporating higher
residential and commercial densities and appropriate mixed uses (including residential,
commercial, retail and other employment activities) (AGO, 1998). One proposed form,
Transit Oriented Development (TOD), is a mixed-use community within an average 600m
walking distance of a transit node and core commercial area. TODs mix residential, retail,
office, open space, and public uses in a walkable environment, making it convenient for
residents and employees to travel by transit, bicycle, foot, or car (Calthorpe, 1993). This
transport related land use strategy can be used in large urban and small communities in
coordination with bus, rail, and/or ferry transit systems (Parker et al., 2002). The past
research provides limited information regarding estimation of travel demand and evaluation
of these developments. This research aims to explore this area; The following section
presents the research hypothesis.
1.3 Research hypothesis
A TOD is often distinguished from conventional developments because of its atypical
development characteristics, such as high quality of transit service, pedestrian and cycling
Evaluating the transport impacts of TODs
Deepti Muley Page 4
facilities, higher density, moderated private vehicle infrastructure, improved accessibility to
and mixes of land uses. The travel characteristics of the people using TODs are assumed to
be sustainable or environmentally friendly. This statement gives rise to following research
hypothesis.
“Transit Oriented Developments (TODs) reduce car dependence and promote sustainable
modes of transport. Hence, TOD users will make more walking, cycling and public transport
trips as compared to their counterparts in conventional development, making TODs the more
sustainable form of development.”
This statement suggests that people may travel differently, showing a distinction in travel
behaviour when compared to conventional development. So the testing of this hypothesis
demands an assessment of:
How to determine the travel demand of a TOD?
What are the various aspects of a TOD’s travel demand?
How is the travel demand different from that of conventional development?
Why is a TOD’s travel demand different from that of conventional development?
What are the governing factors in determining the travel demand?
Answering these questions will help to understand and model the travel behaviour of these
communities, which will further help in planning them. This research aims at testing this
hypothesis and answering the associated questions in an Australian context, by conducting
data collection and studying the travel demand for a case study TOD.
1.4 Objectives
The objectives of this research can be formulated as below on the research hypothesis and the
questions that arose to answer it.
1. Build up an understanding of the concept of TOD and various aspects related to it
through gaining a detailed knowledge of TOD evaluation.
2. Develop a methodology for evaluating the transport impacts of TODs.
3. Demonstrate the methodology by implementing it on an Australian case study TOD.
4. Determine trip rates for various modes of transport for a TOD and assess the travel
demand of TOD users.
Introduction
Deepti Muley Page 5
5. Evaluate the transport impacts of TODs from an Australian perspective by comparing
the results with characteristics of conventional development similarly located in the
urban fabric.
1.5 Scope of this research
The development of TODs has various aspects including planning, designing, development or
implementation, and evaluation. This research looks at evaluating TODs. A TOD is assumed
to be more sustainable providing safety, affordability, sense of community and public health,
economic benefits to individuals, government and developers, and environmental benefits by
implementing sustainable practices in transport, infrastructure and energy benefits. This
research looks at the transport impacts of TODs. The scope of this research is limited to TOD
evaluation from a transport point of view. The transport evaluation in this research is limited
to studying the travel of these communities and assessing its sustainability.
1.6 Publications from this research
This research has resulted in one journal paper, three refereed conference papers, one book
chapter and one presentation at the United States’ Transportation Research Board National
Conference. A brief overview of the publications is given in Table 1.1 and a complete list of
publications is given in Appendix A.
Table 1.1 Overview of publications from this research
Sr. No. Authors Publication Comment
1. Muley et
al. (2007)
ATRF07
conference
Assessing suitability of the case study TOD
(Chapter 4)
2. Muley et
al. (2008)
ATRF08
conference
Procedure for conducting travel surveys and
preliminary results (Chapter 5 and Chapter 7)
3. Muley et
al. (2009)
QUT
conference Characteristics of TOD users (Chapter 7)
4. Muley et
al. (2009) ITS journal Travel demand analysis for TOD users
5. Muley et
al. (2009)
TRB conference
presentation
Travel demand analysis for TOD users (Chapter 8
and Chapter 9)
6. Muley et
al. (2009) Book chapter Characteristics of TOD users (Chapter 7)
1.7 Thesis outline
The research conducted on TOD transport evaluation is presented in this thesis through
eleven chapters. The initial chapters give an overview of the research followed by the case
Evaluating the transport impacts of TODs
Deepti Muley Page 6
study information and data analysis. The later chapters explain the analysis and the results
followed by conclusions and scope for future work. Figure 1.2 illustrates the relationships
between the thesis chapters and the following paragraphs present the detailed information
about each chapter.
Figure 1.2 Layout of thesis
Chapter One (this chapter) establishes the research context and sets the objectives of
this research.
1. Introduction (Hypothesis and Objectives)
2. Literature review
3. Methodology for evaluating
transport impacts of TODs
4. Selection of case
study TOD
5. Data collection
6. Traffic generation
at KGUV
7. Characteristics
of KGUV users
9. & 10. KGUV
users’ travel
demand analysis
8. Comparative
analysis of KGUV
users’ characteristics
Appendix D. TOD
users’ perceptions
11. Conclusions and
recommendations
Direct relation
Indirect relation
Introduction
Deepti Muley Page 7
Chapter Two presents a review of the past literature related to studies on TODs. The
review of literature mainly gives an overview of the TODs and states
the studies related to TOD evaluation.
Chapter Three describes the proposed new methodology in this research for assessing
transport impacts of TODs.
Chapter Four describes the case study development and the selection criteria followed
for analysing the suitability of the case study development as a TOD.
Chapter Five gives details of the data collection process followed for conducting the
cordon counts and travel surveys.
Chapter Six presents the analysis and results for the traffic generation analysis
undertaken for the case study TOD.
Chapter Seven provides an overview of the characteristics of various user groups
present at the TOD from the findings obtained from the preliminary
data analysis of the data obtained from the travel surveys.
Chapter Eight compares the characteristics of the TOD users with the characteristics
of regional and suburban users to determine the similarities and
differences and presents the findings from the comparative study.
Chapter Nine explains the analysis and the results for travel demand analysis
conducted for assessing the effect of various characteristics on the
travel modes of TOD users.
Chapter Ten develops the models for travel modes for various trips at a TOD.
Chapter Eleven discusses the results obtained from the case study and presents the
conclusions and recommendations from this research, and for future
research.
1.8 Chapter close
This chapter introduces the research, along with the research questions and objectives, which
were investigated during the research. This links with the literature to be studied (which is
presented in the next chapter) and provides base for the method of investigation presented in
Chapter 3.
Evaluating the transport impacts of TODs
Deepti Muley Page 8
Deepti Muley Page 9
Chapter 2
Literature review
2.1 Introduction
This chapter examines the literature illustrating the evaluation of transport aspects of Transit
Oriented Developments (TODs). The chapter is divided into four main sections. The first
section explains the concept of TODs. The perceived transport benefits of TODs and actual
(observed or estimated) benefits of TODs, which have been accomplished in practice, are
also reported. Travel demand analysis needs extensive data on demographics and travel
behaviour for all categories of people residing or visiting a TOD. The data requirements
necessitate various methods of data collection. The details of various travel surveys along
with different features of data collection process are examined briefly in the second section.
The third section provides insight into analysis and modelling of travel demand with specific
emphasis on areas having mixed land uses and higher densities. The closing section integrates
the various topics covered in the chapter and identifies the gaps as well as strengths of the
reviewed literature along with recommendations, followed by the chapter close.
2.2 Transit Oriented Developments (TODs)
2.2.1 The concept of TODs
Urban lifestyle and the population growth have changed the travel needs of inhabitants of
urban areas drastically, by increasing average trip lengths and usage of personalised modes of
transport, particularly the car. The consequences of these changes can be observed through
increased pollution and congestion levels on road networks, which in turn results in higher
travel times (BTRE, 2007). Transport emissions are one of the major contributors to the
damage of air quality (AGO, 2006). To counteract these ill effects and facilitate human life
with more sustainable and vibrant lifestyle options the concept of Transit Oriented
Development (TOD) has been utilised. Prior to the 1990’s TODs were aimed as profitable
real estate development, used to generate revenues for transit agencies and government, and
were evaluated purely on a financial basis rather than on sustainable transport principles.
Newly planned development is now generally accepted by planners as needing to reduce
overall vehicle use as well as to reduce concentration of urban travel patterns towards single
Central Business Districts (CBD). In simple terms, mixed use planned development around a
Evaluating the transport impacts of TODs
Deepti Muley Page 10
major public transport node is generally considered to be a transit oriented development. In
technical terms,
“Transit–Oriented Development (TOD) is moderate to higher–density development, located
within an easy walk of a major transit stop, generally with a mix of residential, employment
and shopping opportunities designed for pedestrians without excluding the car. TOD can be
new construction or redevelopment of one or more buildings whose design and orientation
facilitate transit use.” (Parker et al., 2002)
The term transit in the definition encapsulates a variety of modes and systems including
metro rail, heavy rail, suburban rail, commuter rail, light rail transit (LRT), streetcar, bus
rapid transit (BRT), and express bus (incorporating Dittmar and Ohland, 2004). The easy or
comfortable walking distance is affected by topography, climate, intervening arterial roads,
freeways and other transport corridors, and other physical features. An average distance of
600 m is used as comfortable walking distance in the American context (Calthorpe, 1993)
while a distance of 1km has in the past been used as comfortable walking distance for
Australian urban settings (Gilbert and Ginn, 2001). Generally, a buffer distance of 400 m for
local bus stops and 800m for premium bus stops is presently considered appropriate for
design (Queensland Transport, 1999; TRB, 2003).
Although TOD is characterised differently in different parts of the world; such as smart
growth, urban form, new urbanism, walkable communities, neo–traditional neighbourhood or
development, activity centres, new community design, transit village, and transit supportive
development, the benefits sought are the same. Planning and implementation of a TOD
involves multiple stakeholders including state and local government agencies, land owners,
funding agencies, developers, design professionals, investors, management agents, residents
and occupants, public interests and community interests (Dittmar and Ohland, 2004). Due to
involvement of so many entities the success of TOD becomes a complex phenomenon. This
research aims at assessing TOD’s success from a transport point of view by examining the
travel demand generated by TOD.
Literature review
Deepti Muley Page 11
Table 2.1 Characteristics of various existing TODs
TOD Type Land–use mix Minimum
housing density
Housing types Scale Regional
connectivity
Transit
modes
Transit
frequencies
Examples
Urban
downtown
Primary office
center, urban
entertainment,
multifamily
housing, retail
> 60 units/acre
(> 148 units/ha)
Multifamily
loft
High High Hub of
radial system
All modes < 10 minutes Printers row
(Chicago), LoDo
(Denver), South
Beach (San
Francisco)
Urban
neighbourhood
Residential,
retail, Class B
commercial
> 20 units/acre
(> 50 units/ha)
Multifamily
Loft
Townhome
Single family
Medium Medium
access to
downtown
subregional
circulation
Light–rail
Streetcar
Rapid bus
Local bus
10 minutes
peak
20 minutes
offpeak
Mockingbird
(Dallas), Fullerton
(Chicago), Barrio
Logan (San
Diego)
Suburban
centre
Primary office
centre, urban
entertainment,
multifamily
housing, retail
> 50 units/acre
(> 124 units/ha)
Multifamily
Loft
Townhome
High High access to
downtown
subregional
hub
Rail
Streetcar
Rapid bus
Local bus
Paratransit
10 minutes
peak
10-15 minutes
offpeak
Arlington County
(Virginia),
Addison Circle
(Dallas),Evanston
(Illinois)
Suburban
neighbourhood
Residential
neighbourhood,
retail, local
office
> 12 units/acre
(> 30 units/ha)
Multifamily
Townhome
Single family
Moderate Medium
access to
suburban
center
Access to
downtown
Light-rail
Rapid bus
Local bus
Paratransit
20 minutes
peak
30 minutes
offpeak
Crossings
(Mountain View,
CA), Ohlone–
Chynoweth (San
Jose, CA)
Neighbourhood
transit zone
Residential
neighbourhood,
retail
> 7 units/acre
(> 17 units/ha)
Townhome
Single family
Low
access to a
centre
Low Local bus
Paratransit
25-30 minutes
demand
responsive
Commuter
town centre
Retail center,
Residential
> 12 units/acre
(> 30 units/ha)
Multifamily
Townhome
Single family
Low Low access to
downtown
Commuter
rail
Rapid bus
Peak service
Demand
responsive
Prairie Crossing
(Illinois), Suisun
City (CA)
Source: Dittmar and Ohland (2004)
Evaluating the transport impacts of TODs
Deepti Muley Page 12
2.2.2 Types of TODs
Although the basic concept of planning and designing TODs remains the same, the outcomes
are distinct, with every TOD having specific features. The size of TOD is dependent on the
amount of land available and dedicated for this use. Urban TODs sustain much larger
commercial and office or employment area and higher density residential uses than those in
rural areas. Making soft conversions, urban TODs are generally constructed to a minimum
residential density of 30 dwelling units per net hectare and an average density of 45 dwelling
units per net hectare, while rural TODs are designed at minimum density of 17 dwelling units
per net hectare and an average density of 25 dwelling units per net hectare (Calthorpe, 1993;
Gatzlaff et al., 1999). In order to classify various TODs at different levels according to the
differences between places and destinations within the regions, and to identify the appropriate
performance measures and descriptive benchmarks, a TOD typology is defined as listed in
Table 2.1. The TODs are classified with respect to location, size, and transit type.
2.2.3 Australian perspective of TODs
The planners of the South East Queensland (SEQ) region have an increasing concern about
public transport because of the increased car use and decreased use of public transport over
the last 20 years (Rural and Regional Affairs and Transport References Committee, 2006).
The Queensland Government has identified potential TOD sites and provided principles of
TOD in SEQ (Queensland Government, 2005). The interim TOD working group focussed on
three major areas; principles of identifying TOD sites, governance agreements needed to
successfully deliver TODs and identification of specific TOD sites (James, 2005).
Bajracharya & Khan (2005) highlighted positive as well as negative aspects of TOD in SEQ
by developing a holistic theoretical model (conceptual framework). Gray and Bunker (2005)
analysed the connectivity of Kelvin Grove Urban Village (KGUV) by walking, cycling and
public transport; which is a new site for TOD in Brisbane. They used GIS based transport
analysis software called TLOS to integrate both temporal and spatial features of bus and train
services into a single analysis.
Apart from this, Mepham (2005) studied Brisbane rail as a mode of transit in Brisbane
evaluating the need for developing a model for TOD. The transit system of Brisbane was
studied in detail; describing the issues, factors for success, potential TOD sites and land use
policy associated with it. A four part strategy for successful TOD was outlined as a strategic
policy for centres, a strategic policy for rapid transit, a statutory base that requires
Literature review
Deepti Muley Page 13
implementation of necessary densities and design, and a public–private financing mechanism
to build rail linked to centres (Newman, 2005). The performance of major Australian cities is
shown in Table 2.2.
Table 2.2 Application of four part TOD strategy to Australian cities
City
Strategic
policy for
centres
Strategic policy
for rail transit
Statutory
process to
implement TOD
Public–private
funding
mechanism
Sydney Yes Weak. Present rail
mostly.
Yes in new areas;
possibly
elsewhere.
Possibly but not
yet.
Melbourne Yes, but
struggling.
Weak. Present rail
mostly.
Yes but not
strong.
No.
Brisbane Yes, but not
well defined.
Weak. Present rail
mostly. Busway
dominance.
No. No.
Perth Yes, but not
well defined.
Yes. No. No.
Adelaide Yes, but not
well defined.
Weak on rail. No. No.
Others: Canberra,
Hobart,
Newcastle….
Yes, but not
well defined.
No. No. No.
Source: Newman (2005)
Currie (2005) provided a critical look at the strengths and weakness of bus based transit
systems in relation to TOD through a review of the literature and an assessment of TOD
related developments. A small number of strengths were identified however these were
generally considered to be highly significant. These included cost effectiveness, flexibility,
complementarily or ubiquitousness and, for bus rapid transit, service frequency and transfers.
A large number of weaknesses have been identified. The lack of dedicated TOD development
staff in the bus industry, the noise/pollution impacts of buses and a poor track record of bus in
relation to TOD were the most significant weaknesses identified for bus services as a whole.
It was noted that the successful implementation of bus based TOD is a more difficult task
than related rail based TOD initiatives. It is noted that these comments were made prior to
realisation of any maturity of Australian BRT systems such as Brisbane’s Busway network.
A diagnostic tool that can assist TOD developers and decision–makers to quickly assess the
potential of developments and the likely travel behaviour produced by their design to rate the
residential travel performance was suggested by Burke and Brown (2005). The tool was
based on accessibility analysis techniques with the location and design of developments being
key issues relating to local area planning interventions.
Evaluating the transport impacts of TODs
Deepti Muley Page 14
Russell (2006) examined the opportunities for TOD along Perth’s then soon to be constructed
South West Metropolitan Rail line, using a combination of detailed site analysis, standardised
data sets and background information from previous research and government documents. To
conceptualise perceived opportunities, the calculations for vacant land availability within
each station pedestrian catchment (pedshed) were presented. The potential for development,
both under the present situation and with the changes proposed by TOD was determined. The
research highlighted several fundamental changes necessary if Perth is to encourage transit–
oriented development and experience the full benefits offered by its new transit system.
The draft 30 – year plan for greater Adelaide proposed the land use policies to manage the
population and economic growth while preserving the environment and protecting the
heritage of greater Adelaide (Government of South Australia, 2010). The plan was
underpinned by three objectives; namely maintain and improve liveability, increase
competitiveness, and drive sustainability and resilience to climate change. The outcomes of
the plan include creation of 14 new TODs and more than 20 sites that incorporate TOD
principles and design characteristics. The plan offered housing choices by keeping housing
affordable and liveable in these areas. The plan also aimed to protect 115,000 hectares of
environmentally significant land and 375,000 hectares of primary production land. The
growth for mining and defence industries was also supported. The plan created a network of
greenways, open space precincts and reduced water and energy consumption at new
dwellings. By implementing this, the plan proposed to have substantial social, economic, and
environmental benefits for greater Adelaide.
2.2.4 Benefits of TODs
2.2.4.1 Stated benefits of TODs
Planners, public officials and large–scale land developers increasingly promote mixed–use
and high density developments as an alternative to sprawl and congestion (Ewing et al., 2001;
Handy, 1996) and to address the increasing concerns about the environmental side effects of
the use of motor vehicles (Sun et al., 1998; Handy, 1996).
TOD mainly constitutes the development of retail and business amongst residential
development in a compact neighbourhood. This development offers a new range of
development patterns for households, businesses, towns and cities (Dittmar and Ohland,
2004). The planning and construction of TODs is performed by assuming benefits mainly
Literature review
Deepti Muley Page 15
related to lifestyle options, mobility choices, reduced travel needs, cleaner air quality, and
social life. The following summarises the benefits of TOD as stated in the literature:
TOD gives a balanced approach for development supporting economic development,
healthy environment and strong communities (Dittmar and Ohland, 2004).
Design of TOD enhances residents’ sense of community; affordable housing increases
the choices and reduces the infrastructure cost (Calthorpe, 1993; Gatzlaff et al., 1999;
Lund, 2006).
An important component of TOD is the clustering of activities due to a diverse mix of
land uses located closer together than found in undifferentiated low–density
development. One potential benefit of this clustering is the reduction of vehicular
traffic due to provision of high level of transit service and replacement of automobile
trips with walking and bicycling in addition to riding transit (Handy, 1996;
McCormack et al., 2001; Hess and Ong, 2002; Cervero, 2006; Lund, 2006).
As more customers walk, bicycle or use transit for visiting nearby shopping areas or
business centres, the number of parking spaces in those developments can be reduced.
This also warrants the reduced level of traffic in TODs (Steiner, 1998).
The high density neighbourhood supports a mixed level of retail and office uses so
these centres will satisfy many needs of the community resulting in reduced need to
drive to a town centre (Steiner, 1998).
Further, the people who live and work within such transit oriented developments will
make fewer and shorter automobile trips (Sun et al., 1998; Steiner, 1998) and will
choose to walk or bicycle or use transit more frequently as compared to other lower
density, single use residential areas (Steiner, 1998).
People will use sustainable modes of transport only if a competent transport system is
available for use; hence to plan the transit and other services in a TOD, travel demand needs
to be determined. Further, this demand needs to be assessed to verify claims on the benefits
of TODs. Before examining travel data collection and travel demand assessment matters, the
reported transport benefits of TODs are expanded in the following section.
2.2.4.2 Reported benefits of TODs
Many researchers have tried to investigate the link between various elements of TOD (mixed
land use, density, neighbourhood design) and the travel behaviour of people. Some studies
have found that the effect of these variables on travel behaviour is significant, while others
Evaluating the transport impacts of TODs
Deepti Muley Page 16
claimed the effect to be minimal (Badoe and Miller, 2000). The outcomes from some studies
are noted below:
Travel characteristics and travel behaviour
The presence of nearby commercial land uses is associated with short commuting distances
among the residents of a mixed–use neighbourhood (Cervero, 1996). The combination of
land use, transportation system improvement and demand management measures (LUTRAQ
alternative, Portland, Oregon) significantly reduced the need to own multiple automobiles
and the number of vehicle kilometres travelled reduced substantially, increasing walking,
bicycling and the use of transit (Rossi et al., 1993). TOD scenarios exhibited more efficient
pattern of trip making by increasing the number of walking and transit trips and reducing
VKT and slower growth in vehicle trip generation in a subregion of the Minneapolis – St.
Paul Metropolitan region (Dock and Swenson, 2003). In short, it was suggested that the built
environment itself influences individuals’ travel behaviour (Cao et al., 2009).
Aditjandra et al. (2009) found that there is an association between changes in neighbourhood
characteristics and changes in travel behaviour, however in the UK this association is not as
strong as in the US, suggesting that land use planning in the UK may have less of an impact
on reducing private car travel, as compared to the US. It could however be interpreted that
urban fabrics in the UK are already less car orientated (or more TOD like) than those in the
US, so that any changes in travel behaviour in the UK under a TOD development may vary
less from the development norm than in the US.
Work trips
An American study found that the land–use environments of contemporary suburban work–
places appeared to have modest to moderate influence on commuting behaviour. The
existence of retail component within a suburban office building reduced vehicle–trip rates per
employee by about 8 percent. The variation of parking prices at the office buildings of
suburban activity centres with the number of occupants in a vehicle proved effective for
increasing carpools and vanpools (Cervero, 1991).
The compact, mixed–use, and pedestrian–oriented development form has the strongest effect
on access trips to rail stations, in particular inducing higher shares of access trips by foot and
bicycle (Cervero and Radisch, 1996).
Literature review
Deepti Muley Page 17
Non work trips
The residents of a neo–traditional neighbourhood (Rockridge, San Francisco Bay Area)
averaged around a 10 percent higher share of non–work trips by non–automobile modes than
the residents of conventional development (Lafayette, San Francisco Bay Area) with the
greatest difference for shop trips under one mile (1.6 km), 28 percent by foot and 66 percent
by car versus 6 percent by foot and 81 percent by car (Cervero and Radisch, 1996). The
Rockridge residents also averaged substantially higher rates of non–work walk trips per day,
matched by lower rates of daily car travel. This implied that walking substituted for
motorised travel, at the margin.
Similar to above results, Rajamani et al. (2003) showed that mixed uses promoted walking
and transit mode for non-work activities, while a large number of cul–de–sacs on local streets
discouraged walking in the Portland, Oregon metropolitan area. The individuals in higher
income households and those owning more vehicles exhibited a greater tendency to drive
alone to non–work activity sites than the individuals in lower income households. However,
no difference was observed in their propensity to choose among other modes. The older
individuals used carpool or vanpool more and used transit less than other young individuals
for non–work travel.
Vehicle kilometres travelled (VKT)
Miller and Ibrahim (1998) found that VKT per worker increased as one moved away from the
central core of the city and from other high density employment centres within the region;
this finding supported the claim of dense and compact urban form. The job–housing balance
or self containment showed little impact on commuting VKT while population density was
not strongly explaining the variations in VKT per worker across the urban area, once the
other urban structure variable was taken into account.
Further, Sun et al. (1998) qualitatively revealed that land use made a big difference in
household VKT and was important in determining household travel patterns, whereas its
impact on the number of daily trips was limited.
Similarly, McCormack et al. (2001) observed that residents of mixed land use study
neighbourhoods in Seattle, Washington travelled 28 percent fewer kilometres than residents
in adjacent areas and up to 120 percent fewer kilometres than residents in suburban areas.
This trend of lower travel distances held across different socioeconomic characteristics.
However, the differences in travel distances among the areas were not seen when travel time
Evaluating the transport impacts of TODs
Deepti Muley Page 18
was considered, because the inhabitants of mixed land use development travelled with slower
speeds by using slower modes travel (mainly transit and walking) when compared to
residents of other areas.
Home based non–work non–school trips (HB NWNS trips)
The relative share of land devoted to commercial service uses in the zone of a trip origin
increased the likelihood of making HB NWNS walking trips, whereas the relative share of
vacant land decreased the probability. The population density in the zone origin showed no
significant effect on HB NWNS walking trips (Zegras, 2004). This evidence supported the
claim of having increased walking trips with TOD.
Automobile ownership
The probability of owning an automobile was found to decrease by 31 percent as land use
changed from homogeneous to diverse (traditional neighbourhoods) indicating the strong
influence of mixed land use on automobile ownership. Walking and public transit were found
to be more suitable alternatives than private vehicle use due to mixed land uses (Cervero,
1996; Hess and Ong, 2002).
Car ownership was found to be a function of population density. Owning and keeping a car in
a very dense area was found to be more expensive and difficult than in less dense areas
(Giuliano and Dargay, 2006). This will result in reduced car ownership levels and increased
transit usage, supporting the TOD assumption.
Mode choice and density
Cervero and Gorham (1995) observed that walking and bicycling mode shares and respective
trip generation rates were considerably higher in transit neighbourhoods (consequently lower
drive alone mode shares and trip generation rates) than in car oriented neighbourhoods in the
San Francisco Bay Area and in Southern California. The transit neighbourhoods averaged
higher densities and had a more gridded street patterns compared to their counterparts with
car–oriented developments. Significant interaction was observed between neighbourhood
type and densities in both the areas.
The likelihood of non–auto commuting increased significantly with higher neighbourhood
densities and with the presence of shops and other non–residential activities found in the
immediate neighbourhood. It was observed that the relative proximity of mixed–use
development mattered greatly if the retail shops were within 100m of the dwelling unit. Then
Literature review
Deepti Muley Page 19
workers were more likely to commute by transit, foot or bicycle. However, beyond this
distance, mixed use activities induced auto–commuting (Cervero, 1996). Chatman (2003)
found that the residential density was correlated with transit and walking convenience for
commute but did not directly influence personal commercial VKT.
The daily travel distance was found to be inversely related to local population density (more
strongly in the US than in Great Britain) supporting the claim of high densities (Giuliano and
Dargay, 2006). A sensitivity analysis carried out for reviewing the development density
regulations at the Chunghsiao – Fuhsing station area in Taipei showed that enlarging the
upper bound of ratios of floor space to site space (RFS) can increase subway ridership, but at
a cost of reducing social equity and living environment (Lin and Gau, 2006). The RFS greater
than 70 percent has not shown significant increase in subway transit ridership. The living
environment and social equity aspect was considered along with economic efficiency of
transit ridership. It suggested that if cities use land use policies to offer options to drive less
and use transit and non–motorised modes more, many residents will do so (Cao et al., 2007;
Cao et al., 2009).
Intrazonal trips
Greenwald (2006) observed that intrazonal trips were shorter in distance and associated with
activities that were more quickly completed and are more likely to be completed by walking
or biking compared to private automobile. The mode and destination choice was found to be
influenced by elements of urban form. Street design and housing concentrations were found
to be less significant in keeping the trips intrazonal. On the contrary, variety and scale of
economic activity with sufficient scale (proximity and diversity of land use) played a
significant role in keeping trips intrazonal.
Parking requirements
Steiner (1998) stated that the reduction in parking and transport fees was misguided for TODs
because of higher attraction rates of shopping trips from adjacent suburbs and when the
demand for shopping centres specifically for Saturday peak loads was considered. This study
considered the parking requirements only for shopping centres and did not consider other
parking requirements, mainly being residential and office parking requirements.
Evaluating the transport impacts of TODs
Deepti Muley Page 20
Walking
While comparing the alternative approaches for exploring the link between urban form and
travel behaviour, Handy (1996) found that the difference between traditional and
conventional neighbourhoods was in the likelihood of walking to a store or other local
business; residents of traditional neighbourhoods, on average, made two or more times as
many walk trips to a store as residents of conventional neighbourhood.
Schlossberg and Brown (2004) concluded that the presence and location of pedestrian–hostile
streets had a significant, negative influence on the pedestrian environment surrounding transit
stops, often cutting off more–pedestrian–friendly environments from the transit stops. This
emphasised careful planning of street network and sidewalks, but this analysis has not
indicated the effect of pedestrian–hostile or pedestrian–friendly environments on travel
characteristics of people. Further, Khattak and Rodriguez (2005) suggested that the
households in the neo–traditional development substitute driving trips with walking trips.
Living in a TOD and transit use
The results of a survey done to assess the reasons for choosing to live in a TOD and
associated transit use showed that one third of the respondents reported access to transit as
one of the top reasons for living in a TOD. The surveyed residents were equally or more
likely to choose to live in a TOD because of lower housing cost or the quality of
neighbourhood. This indicated that TODs meet a range of needs. Further, the residents who
cited access to transit as one of the top three reasons for choosing transit to live in a TOD
were 13 to 40 times more likely to use transit than those who do not. In general, it was
observed that the TOD residents used transit at a relatively higher rate compared to the
population as a whole (Lund, 2006; Dill, 2008).
Deakin et al. (2004) reported that residents of TOD chose intentionally to live in a transit –
and pedestrian friendly area and own and use cars less often than average; the residents
included many non–students. Overall, the parking management and TOD were found to be
successful in supporting reduced car ownership and use in Berkeley, California. The evidence
for Taipei showed that land use diversity variables did not significantly increase metro
ridership. Hence it was concluded that the built environments consistent with TOD as
identified by Western studies were not always suitable for Taiwanese cities (Lin & Shin,
2008). On the other hand, Cervero and Day (2008) concluded that transit–oriented
Literature review
Deepti Muley Page 21
development holds considerable promise for placing rapidly suburbanising Chinese cities on
a more sustainable pathway.
Traffic generation
The Transit Cooperative Research Program (TCRP) Report 128 (Arrington & Cervero, 2008)
confirmed that TOD housing produced considerably less traffic than is generated by
conventional development. In other research and of particular interest to this study, Institute
of Transportation Engineers (ITE) trip generation and parking generation rates found to
overestimate automobile trips for TOD housing by approximately 50 percent (Arrington and
Sloop, 2009).
2.2.4.3 Stated benefits vs. reported benefits
In conclusion, the overall assessment of reported benefits of TODs shows positive results,
supporting the claimed benefits of TODs. The evidence of TOD was stronger in the US when
compared to UK and developing countries. Existing urban fabric forms in the UK and
developing countries might be more like TODs than those in the US. The studies examined
here were mainly concentrating on mode choice (walking and transit) and VKT. The findings
are briefly summarised below:
The presence of a nearby retail shopping centre was a major contributor in increasing
the number of walking trips for workers and residents of a TOD. In addition, a
significant amount of reduction in VKT and increase in transit trips was observed.
Some evidence of the reduced parking requirements was also found for office
buildings, residents of TOD, but the claim did not support commercial uses, while a
study for educational and recreational parking requirements was not found.
The presence of mixed uses and density was found to affect walking trips
substantially.
The scant research investigation on job–housing balance and travel times did not
show significant effect on travel. These variables need to be addressed properly to
comment on the contribution of self containment and effect or saving in travel time
due to TOD.
The trip lengths, travel costs for individual trip purpose needs to be studied further if
the transport sustainability claims of TODs made by proponents are to be supported.
Evaluating the transport impacts of TODs
Deepti Muley Page 22
2.3 Data requirements for TOD evaluation
2.3.1 Background
Travel demand evaluation provides an insight to the travel characteristics and in turn the
travel behaviour of people. To conduct travel demand evaluation precisely, the acquired data
needs to be accurate, reliable, based on real situations and representative of the practical
behaviour of people living in the area which is considered for assessment, and obtained with
limited budget and resources (Sharp and Murakami, 2005).
Generally for the purpose of data collection, a new Origin–Destination (O–D) survey is
proposed for a study area in two cases; when no O–D survey has been performed in the area
before and the existing models of another area cannot be applied successfully, and secondly,
when the models based on previous O–D surveys yield unsatisfactory results or some major
land use changes has been made which have altered the travel behaviour (Smith, 1979).
Typically, two types of transport surveys are used for travel demand determination; revealed
preference (RP) surveys and stated preference (SP) surveys. The revealed preference surveys
collect information regarding the actual travel behaviour and travel characteristics of
respondents. On the other hand, stated preference surveys gather responses or preferences of
the respondents based on the hypothetical travel options given to them. The evidence suggest
that the preferences derived from SP surveys are contingent on context. Also, it is extremely
difficult to identify core preferences based on SP surveys as the stable core preferences may
not exist prior to a choice (Fujii and Gӓrling, 2003).
The studies listed here are based on the revealed preference surveys which are proposed for
this research. The revealed preference surveys were proposed because this research aims at
assessing the actual travel behaviour of users of a functioning TOD. Also, it should be noted
that the revealed preference surveys do not suffer from the drawbacks mentioned before in
case of stated preference surveys. The following sections present an overview of revealed
preference travel surveys by describing their various aspects such as type of data to be
collected, types of travel surveys and survey instruments, their design, and response rates.
2.3.2 Data need / data requirement
The overall assessment of the travel demand literature shows that the data gathered include
attributes at the individual level and attributes at the zonal or network level. Typically, a
travel survey collects data at personal and household level. The responses of the travel survey
Literature review
Deepti Muley Page 23
are then aggregated to obtain the attributes at the zonal level. The various attributes of a
travel survey include:
2.3.2.1 Demographic and Socio–economic characteristics
The review of literature indicates that the demographic data and socioeconomic data are the
most widely used data sets in travel demand assessment. The demographic data set involves
individual or personal information about age, gender, employment status of each household
member and the details of households such as vehicle ownership, number of licensed drivers,
household size (including number of children), and location of residence. The socioeconomic
data set consists of details about household income, dwelling type, number and type of car
owned (Allen and Perincherry, 1996; Sun et al., 1998; Hess and Ong, 2002; Newmark et al.,
2004). These variables are mainly used to forecast trip generation.
2.3.2.2 Travel characteristics
The data related to travel of individuals is often collected by using a 24h activity travel diary
for a mid weekday. This diary gives detailed information of each trip (also called activity)
about purpose, frequency, duration, mode choice, number of persons accompanied, use of
household vehicle, travel cost and parking cost (if any) (Goldenberg, 1998; Hess and Ong,
2002; Rajamani et al., 2003; Newmark et al., 2004).
The questionnaire survey by Zakaria (1986) included some questions about the perceptions of
the quality of the public transport system. In addition to this, the data on land use patterns and
urban design factors (such as land use mix, street network (width), accessibility, residential
density and pedestrian connectivity) were used in many studies to address the mixed land use
and the urban form (e.g. Hess and Ong, 2002; Rajamani et al., 2003).
The data gathered is primarily used for estimating various attributes related to population
(such as car ownership level, classification of households) and travel characteristics (such as
VKT, mode choice, trip length, travel cost, etc). The same data is also used for modelling and
statistical analysis, economic development, land use planning and social service delivery. For
developing a travel demand model based on the four step modelling approach, demographic
data is mainly used for trip generation and travel data is used primarily in trip distribution and
mode choice. A separate data set is collected for calibrating the model.
2.3.3 Types of travel surveys
Transport planners traditionally use census data along with small sample surveys for
employment for travel demand modelling. But in order to validate and calibrate a travel
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Deepti Muley Page 24
demand model, a database is needed which will also used to forecast the travel characteristics
of people. In order to obtain more reliable data, different travel surveys need be planned for
different sets of people involved. Some examples are listed below:
2.3.3.1 Household travel survey
A household travel survey is the most common source for obtaining travel data for
developing travel demand models. The mail back survey technique is most commonly used
for household surveys.
Crevo et al. (1995) presented techniques, results and costs of a statewide mail–out or mail–
back survey for Vermont, USA. Postal codes were used to generate the sample instead of a
telephone directory. The response rate of 8 to 10 percent was obtained due to a lack of
advance contact and complexity of the survey.
Table 2.3 An overview of household travel surveys
Source Name of travel
survey
Location of
survey
Type of survey
instrument
Sample
size*
Response
rate
SEQTS
(2008)
South East
Queensland
Travel Survey
(SEQTS)
South East
Queensland,
Australia
Hand delivered and
hand collected survey
accompanied by
telephone and postal
reminders
5,671
households
52.7%
PARTS
(2010)
Perth and
Regions Travel
Survey
(PARTS)
Perth, Australia Personal delivery and
pick up methodology
10,947
households
–
TDC (2010) Household
Travel Survey
(HTS)
Sydney Greater
Metropolitan
Area, Australia
Face to face
interviews
14,409
households
66.0%
Sharp and
Murakami
(2005)
National
Household
Travel Survey
(NHTS)
United States of
America
Two stage telephone
interviews
26,000
households
41.0%
MOT
(2006)
New Zealand
Household
Travel Survey
(NZHTS)
New Zealand Personal interviews 5,367
households
74.9%
DOT (2005) National
Household
Travel Survey
(NHTS)
South Africa Face to face
interviews
45,346
households
86.6%
Anderson et
al. (2010)
National travel
Survey (NTS)
United
Kingdom
Computer-Assisted
Personal Interviewing
(CAPI)
8,384
households
62.0%
SIKA
(2007)
RES Sweden Telephone interviews
supported by journal
entries
41,225
persons
67.6%
Note: The time period for which the travel surveys are conducted varies.
Literature review
Deepti Muley Page 25
Various nationwide household travel surveys were conducted by different agencies or
government institutions to study travel behaviour of people living at various places. Table 2.3
shows an overview of the state wide household travel surveys conducted for Australia and
some other national household travel surveys.
2.3.3.2 Employee travel survey
Generally, the journey to work and other related trips are not usually modelled by collecting
the employee data separately due to the large expenses involved. So acquiring quality
employment data is a major problem in transport modelling. The employment data is
particularly important in trip generation and trip distribution steps of travel demand
modelling (Souleyrette et al., 2001).
Zakaria (1986) described the development and findings of a mail back questionnaire survey
sampled for 236,000 employees for Center City Philadelphia. The survey included questions
on trip characteristics, usage of highway and transit modes, socioeconomic characteristics
and place of residence and work for assessing travel behaviour of employees.
2.3.3.3 Students’ travel survey
McMillan (2007) examined the influence of urban form and non – urban form factors on
children’s travel mode to school. She conducted surveys to identify key variables influencing
the decision about a child’s trip to school. The survey was conducted for elementary schools
(grade 3 – 5), surveys were distributed to the children to bring home to their caregiver for
completion and the completed survey was collected back at the school via child.
No specific survey has been found which is designed to study the travel characteristics of
university or high school students.
2.3.3.4 Shoppers’ survey
The relationship between land use and shopping travel behaviour, for before and after the
introduction of fringe shopping malls in the Prague metropolitan area, Czech Republic was
examined by Newmark et al. (2004). The shoppers were surveyed by doing a personal
interview survey at four malls and trip frequency, shopping activity duration, and mode
shares were compared against age groups, gender, income, car ownership and household size.
With the provision of new fringe shopping centres, patrons made fewer, longer trips and tend
to shift travel mode from pedestrian to vehicle, particularly the private vehicle.
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Deepti Muley Page 26
2.3.4 Types of survey instruments
The previous section (Section 2.3.2) implies the extensive data requirements for travel
demand assessment purposes. Hence data collection is an inevitable part of the evaluation
process with the exception of a few cases where relevant data has already been collected for
some other purpose. Designing and conducting surveys is a critical aspect of this step.
Stopher & Greaves (2007) gave information about the various survey methods and
improvements or advancements in the survey methods from 1970’s to the present, listing the
changes in the data needs and survey methods. The standard method of conducting most
household travel surveys was found to be a diary, however the latest was GPS assisted trip
data collection.
Observational surveys, household self completion surveys, telephone surveys, intercept
surveys, household personal interview surveys, group surveys, in depth interviews, GPS
surveys, postal surveys and internet based household travel diary surveys were listed as
survey methods used for conducting a travel survey (Richardson et al., 1995). Characteristics
of four popular survey methods are discussed briefly based on the literature; GPS survey is
quoted as an emerging technique.
2.3.4.1 Home or personal interviews
Home or personal interviews produce more reliable and clearer data sets as the interviewer is
present at the time of obtaining responses and can help to better explain the survey questions.
However these surveys are time consuming and hence most expensive (Crevo et al., 1995;
Sharp and Murakami, 2005).
2.3.4.2 Telephone surveys
Telephone surveys are efficient and require fewer household contacts to obtain estimated
responses. Hence, it has been the most commonly used method in the United States in recent
years (Sharp and Murakami, 2005). Generally a random sample generation technique is used
for sample generation, but this technique does not guarantee a geographically representative
sample.
A computer assisted telephone interview (CATI) survey is sometimes used to increase the
response rate compared to postal surveys (Crevo et al., 1995; Stopher and Greaves, 2007).
One disadvantage of telephone surveys is that it is known that a significant numbers of trips
are omitted or are under reported at the time of telephone interview.
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Deepti Muley Page 27
2.3.4.3 Mail back surveys
Mail back surveys are mostly preferred due to their low cost and low administrative burden;
in addition mail back surveys can ensure representative samples by mailing to a greater
number of households (Crevo et al., 1995). Generally, postcard reminders and telephone calls
are used to increase the response rates. Pre–addressed, pre–paid envelopes are enclosed in the
survey envelopes so that respondents are not required to bear the expenses for returning back
the survey.
A self administered mail back survey was noted as a viable, cost–effective approach to gather
detailed information about household and personal travel information (Poorman and Stacey
1984; Crevo et al., 1995).
2.3.4.4 Internet based surveys
The internet based survey is a new cost–effective platform, becoming popular because of its
opportunity to deploy the important branching and data control features of the computer–
assisted telephone interview (CATI) instrument and the graphical features of the computer–
assisted self–interview (CASI) instrument.
For the surveyor, use of e–mail provides a convenient way to communicate with the
respondents and for the respondents this gives flexibility to complete the questionnaire at any
suitable time. When tested with other instruments, namely with conventional telephone and
mail administration technique for a household survey, the internet instrument had more trips
reported and lower non response rates (Alder et al., 2002).
In continuation to the earlier work, similar results were observed for the internet based
surveys by Abdel–Aty (2003) while performing an O–D travel survey for a toll plaza in
central Florida. An ordered Probit model was developed to explore the effects of individual
characteristics on the number of blank answers given. The individuals answered a specific
number of questions and the differences between mail and internet response were studied by
using a log–linear model.
Dillman (2006) listed criteria and principles of designing respondent friendly web
questionnaires. The major sources of errors were stated as coverage error, sampling error,
measurement error and non response error. Hence, this type of survey is suitable in the area
with web access and with a community having high computer usage.
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2.3.4.5 GPS surveys
GPS surveys have been proposed as a supplement or replacement for diaries, panel surveys,
continuous measurement surveys and data fusion. The advantage is that the respondents are
not required to fill any questionnaire survey forms; instead they have to carry a GPS with
them for each trip undertaken (Stopher & Greaves, 2007). But these surveys include a higher
cost for procurement of a GPS unit for each member of the household.
Mixed approaches are being commonly used to strike a balance between survey costs and
data–quality issues, also most often response and coverage.
2.3.5 Design of survey instruments
2.3.5.1 Steps in design
The design of a survey instrument involves methodological and design considerations, as
listed in Sharp and Murakami (2005). The steps involved in designing a survey instrument
are (Smith, 1979):
Decision of the purpose of the survey
Decision of the variables to be measured to fulfil the survey questions
Decision of suitable survey method to measure the variables adequately
Determination of coefficients of variation of the variables in the question
Decision of level of accuracy and confidence limit
Computation of sample size
The steps for conducting a travel survey generally include sample generation, questionnaire
delivery, reminder call or postcard, data collection and data entry (Goldenberg, 1998).
2.3.5.2 Selection of survey instrument
The method of data collection is largely dictated by the population coverage and sample
frame; other determinants include survey costs, response rates and data quality issues. The
method selection can also be influenced by the complexity and the length of survey and
timeliness needs (Sharp and Murakami, 2005).
Goldenberg (1998) examined methodological options and interactions between five options;
24–h versus 48–h recording period, shorter versus longer series of questions about each
activity recorded, three types of incentives, booklet versus log format of diaries and telephone
versus mail back survey. Chi–square tests were conducted to consider the effects of
methodological options on response rates, and the effects of different pre–test design factors
Literature review
Deepti Muley Page 29
on completion rate were assessed using a logistic regression model. The 24–h diary and
telephone surveys were found to have greater completion rates.
2.3.5.3 Design of questionnaire form
Design of questionnaire requires careful consideration of the data needs, the questions
included should give the variables needed for planning or modelling. Generally the questions
about demographics and socio–economic characteristics are asked at the end of the
questionnaire (Zakaria, 1986). An introductory letter is always useful in offering a good
understanding or overview of the survey. The questions should be objective in nature so that
they are easy to answer. Keeping the layout simple and giving the appropriate choices
(options) reduces confusion and helps to increase the responses. Pre–tests or pilot studies are
performed in most cases to assess the suitability of the method and discover the quality of
questions based on the responses received.
2.3.5.4 Pilot surveys
Pilot testing is one of the most important components of the survey procedure and also can be
one of the most neglected. Pilot surveys or pre–tests should be performed to guide adequacy
of the sampling frame, variability of parameters within the survey population, non–response
rates, method of data collection, question wording, layout of questionnaire, adequacy of the
questionnaire in general, efficiency of interviewer and administrator training, data entry,
editing and analysis procedure, cost and duration of survey and efficiency of survey
organisation. The size of a pilot survey is related to the size of the main survey; as a rule of
thumb 5 to 10 percent of budget is expected to be spent on pilot surveys (Richardson et al.,
1995).
2.3.5.5 Sample selection
Generally, use of area telephone directories was found to be prominent in the literature for
sample selection. The number of samples varies mainly according to the population size and
size of the area surveyed. Generally, a sample size of 2 to 5 percent of the population is used
in the literature.
Smith (1979) illustrated a procedure to estimate the required sample size from the local data
for conducting a home–interview origin–destination (O–D) survey based on the sample sizes
required to calibrate the travel demand models rather than the sample sizes required to
duplicate travel patterns. It was also noted that travel demand models can be developed from
a survey of less than 1,000 (900 – 1,200) households. The formula for computing sample size
is as given in Equation 2.1.
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Deepti Muley Page 30
𝑛 = 𝐶2𝑍2 𝐸2 Equation 2.1
where,
𝐶 = coefficient of variation
𝐸 = accuracy level expressed as a proportion rather than a percentage
𝑛 = number of samples and
𝑍 = normal variates corresponding to the confidence limit selected
In 1986, Zakaria used a cluster sampling procedure for distribution of survey questionnaire to
employees after considering practical, administrative and cost constraints. The sample size
was determined on the basis of specified levels of sampling error and confidence interval in
the survey results, using a relationship as shown in Equation 2.2. The sampling error was
high if the number of responses to a question was small.
ℎ = 𝑧2 𝑛 𝑝 ∙ 𝑞 1 2 Equation 2.2
where,
ℎ = specified (tolerable) sampling error
𝑧 = confidence interval or the multiple of standard errors corresponding to
the specified probability of obtaining the specified precision
𝑛 = sample size
𝑝 = probability that the population possesses certain characteristics
𝑞 = 1 − 𝑝
A new parcel based sampling strategy, called spatial sampling, was used to draw respondents
from a spatially delineated frame (Lee and Moudon, 2006).
2.3.6 Response rate
The response rate for a travel survey was found to vary depending upon the survey method,
follow up procedure and incentives offered. The response rate also fluctuated depending upon
the age, sex, occupation and income (Alder et al., 2002; Newmark et al., 2004; Abdel–Aty,
2003). The response rates were high for the surveys which offer some kind of incentives to
the respondents. Repeated reminders and resending of the questionnaire were found to
increase the response rates.
Literature review
Deepti Muley Page 31
Korimilli et al. (1998) estimated a linear regression model to predict response rates with a
sample of 35 transport surveys, using survey length, number of reminders, number of survey
stages, number of questions, geographic location of survey, method of survey administration,
type of survey instrument and presence of incentives as the design parameters.
2.3.7 Data analysis
Before analysing the data, the details collected by various survey methods for travel surveys
needs to be entered and organised in a format that is suitable for data analysis. Due to
advances in the computer technology, to assess the large amount of data collected, the data
processing is performed by computer using spreadsheet and database programs (such as
Microsoft Excel, Microsoft Access, Louts 1–2–3, dBase, FoxBASE/FoxPro). Zakaria (1986)
analysed the responses using a FORTRAN program with Contingency checks. Two types of
analysis are mainly made on the data; explanatory analysis and statistical analysis. The details
of these methods of data analysis are discussed in Section 2.4.
2.3.8 Summary of data collection
The data collection involves multiple aspects starting from selection of appropriate
methodology for conducting surveys through to obtaining a good response rate and
organising the collected data for analysis. A summary of different survey tools is given in
Table 2.4. The data collection method differs depending on the type of respondents. Personal
interviews were found to be the most efficient but most expensive tool for conducting the
data collection exercise. The variables required for analysis dominated the selection of survey
instrument. In addition, the pilot surveys or pre–tests were quoted as critical for improving
the quality of questions and response to the questions by a respondent.
The selection of sample was generally made randomly and should be such that it should
represent the population characteristics. Follow up mails, telephone calls and incentives were
highlighted as techniques to increase the response rates. The data collected then will be used
to perform statistical analysis or travel demand modelling. The techniques for data analysis
are explained in the following section.
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Table 2.4 Summary of different survey instruments
Author Survey
instrument Description Inherent bias
Crevo et al.
(1995); Sharp and
Murakami (2005)
Home or
personal
interviews
Time consuming and most
expensive technique.
Give good quality of data and
achieve higher response rates
Biased responses as the
respondent is being
observed by interviewer
Sharp and
Murakami (2005);
Crevo et al.
(1995); Stopher
and Greaves
(2007)
Telephone
surveys
Popular method in US, obtain
better response rates than mail back
surveys.
Significant shortfalls in trip
reporting, ethical barriers in
obtaining the telephone numbers,
selected sample does not ensure
population representation
Excludes people with no
telephone facility
Poorman and
Stacey (1984);
Crevo et al. (1995)
Mail back
surveys
Traditional method, higher non
response rates and underreporting
of trips than telephone surveys, can
ensure representative sample, non
response bias can not be evaluated
Excludes unstructured /
choice based questions,
restricts the format of
questionnaire design
Exclude young children
and illiterate population
Alder et al. (2002);
Abdel–Aty (2003);
Dillman (2006)
Internet
based surveys
Cost effective method, requires less
resources and good internet access
for respondents, can track
continuity and completeness of
reported trips, observes lower
response rates
Excludes non–computer
users, this will have an
age and underprivileged
bias
Stopher and
Greaves (2007)
GPS surveys Precise data can be collected for
multiple days.
Higher cost for procurement of
GPS unit and can face problems of
signal loss at some places.
Limits responses to
technically sound users,
geographical area bias
2.4 Travel data analysis
2.4.1 Background
TOD enables clustering of activities with a wide range of choices offered by a combination of
mixed land use, pedestrian friendly environment, good quality public transport service,
increased density and affordable housing. Looking from the transport point of view, the
presence of all these things at a place makes the travel behaviour different, so needs to be
studied or analysed specifically. Previous studies have tried to establish a link between these
variables and travel behaviour of people but fail to suggest a suitable methodology for overall
travel demand assessment and in turn the travel behaviour of people living in and using a
TOD. The previous studies are explained below for their methodology used and a summary
of all studies is presented in Section 2.4.4.
Literature review
Deepti Muley Page 33
2.4.2 Comparative and statistical analysis
Handy (1996) reviewed the alternative approaches to explore the link between urban form
and travel behaviour and listed strengths and weaknesses of each basic approach. The studies
were classified into five categories; namely simulation studies, aggregate analyses,
disaggregate analyses, choice models and activity based analyses. She found that most of the
studies fell into the first three classes.
2.4.2.1 Parking requirements
Transport and land use planners set parking requirements for TODs (both maximum and
minimum) to encourage transit use and avoid excess parking supply. Higgins (1993)
presented a method for setting parking requirements for office, commercial and industrial
developments in vicinity to transit stations and stops. The method was illustrated using 1991
employee transport survey data for the city of San Diego, California. Employee densities for
various land uses were used with other variables like mode share, number of visitors or
shoppers per employee and proportion of walk–in shoppers, their mode of travel, vehicle
occupancy and volume of shoppers in normal versus holiday periods. The maximum and
minimum parking demand was derived on the basis of employee mode shares. The parking
demand was observed to be sensitive to employee density.
Developers of TODs claims reduced parking requirements and transportation impact fees as a
result of reduced level of automobile usage at such places. To test this claim, Steiner (1998)
compared the parking requirements of six prototypical traditional shopping districts in the
Oakland–Berkeley subarea of the San Francisco Bay Area, California based on the trips
generated in each shopping centre. It was found that, the claim of reduced parking
requirements can not be supported wholeheartedly, if the Saturday peak loads are considered.
Deakin et al. (2004) presented findings of a study conducted in downtown Berkeley,
California, during autumn 2002 which focused on land use, parking supply and use, mode
choices, and housing and jobs development. The multiple roles of parking management and
efficacy of TOD in smaller cities were also highlighted. Four special studies were conducted
to understand specific transport issues which include surveys for workers, shoppers, residents
and analysis of an on–street parking occupancy and turnover. A separate survey of new
housing in downtown was carried out. Intercept method was used for conducting the workers
and shoppers surveys while 2000 census data were collected for the residents survey along
with supplementary interviews. Parking occupancy and turnover for each parking space were
checked out hourly between 9 am and 5 pm on weekdays in the study area.
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Deepti Muley Page 34
2.4.2.2 Car ownership
Hess and Ong (2002) quantified the effect of traditional neighbourhood land use patterns on
the automobile ownership using Portland, Oregon as a model. The probability of automobile
ownership was calculated by using Equation 2.3. An ordered logit regression was used to
model the decision to own zero, one, two, or three or more cars as dependant variable.
𝐴𝑢𝑡𝑜𝑚𝑜𝑏𝑖𝑙𝑒𝑜𝑤𝑛𝑒𝑟𝑠ℎ𝑖𝑝𝑖 = 𝑓 𝐻𝑖 ,𝑁𝑖 , 𝐿𝑖 Equation 2.3
where,
𝐻𝑖 = vector of household characteristics
𝑁𝑖 = vector of neighbourhood characteristics
𝐿𝑖 = vector of urban design characteristics
An international comparative analysis of relationships between car ownership, daily travel
and urban form using travel diary data from US and Great Britain (GB) was conducted by
Giuliano and Dargay (2006). Metropolitan size and population density were used as measures
of land use. A structural model including car ownership was estimated along with a reduced
form of model without car ownership. The total distance travelled by individual travel in
miles for the entire day by all modes was given by Equation 2.4. The results showed that
distance travelled was inversely related to residential density more strongly for non–work
travel than for work travel and for GB than US. Metropolitan size was found to have no
consistent influence on travel. The car ownership at the household level was represented by a
discrete variable and modelled using ordered probit specification.
𝑌𝐷 = 𝑓 𝑋𝐷 ,𝑇𝐷 , 𝐿𝐷 ,𝑢 Equation 2.4
where,
𝑌𝐷 = daily travel distance
𝑋𝐷 = attributes of the individual
𝑇𝐷 = transportation resources available to the individual
𝐿𝐷 = attributes of the residential location
𝑢 = unobserved factors
Literature review
Deepti Muley Page 35
2.4.2.3 Transit use
Messenger and Ewing (1996) described the variables affecting transit ridership in the
metropolitan Dade Country, Florida by using 1990 data. Five different variables were used to
model bus mode shares; socioeconomic variables, land – use variables, street network
variables, transit service variables and other (interaction) variables. Initially, the analysis of
bus mode share by place of residence was started with the estimation of a single equation
using stepwise regression analysis (ordinary least squares). Then a system of three equations
were used to model the bus mode share and the full – information maximum likelihood
(FIML) method was used to estimate the system of equations simultaneously. The bus mode
share by place of residence was found primarily to be dependent on automobile ownership
and secondarily on jobs–housing balance and service frequency, while the bus mode share by
place of work was dependant on the cost of parking, transit access to downtown, and overall
density through a web of interrelationships. The overall density had large indirect effect on
bus mode shares through car ownership and parking charges.
Lund (2006) reported the results of a survey of households (605 people) who moved to TODs
within past 5 years in the San Francisco Bay Area, Los Angeles or San Diego and studied the
factors that lead these households in TODs to move to a TOD and its implications on transit
use. Binary logistic regression analysis was used to predict the probability that a survey
respondent cited a particular factor as one of their household’s top three reasons for choosing
to live in a TOD.
The influence of built environment based on TOD on the level and temporal distribution of
metro ridership was examined using 46 metro stations in Taipei City, Taiwan, China. Two
regression models (one for weekday and another for weekend) were calibrated by using the
ordinary least–squares (OLS) method. The empirical results showed that daily ridership was
positively affected by the floor space area of the station areas, negatively affected by the
percentage of four–way intersections, and insignificantly affected by mixed land use. The
ridership dispersion in time was positively influenced by sidewalk length, negatively affected
by retail and service floor–space area, and insignificantly influenced by density (Lin and
Shin, 2008).
2.4.2.4 Travel mode
Binomial logit regression probability models were used to examine the likelihood of a child
walking or bicycling to school versus travel by private vehicle or neighbourhood carpool
(McMillan, 2007). The results of the analysis supported the hypothesis that urban form is
Evaluating the transport impacts of TODs
Deepti Muley Page 36
important, but not the sole factor that influences a caregiver’s decision about a child’s trip to
school. Other factors may be equally important such as neighbourhood safety, traffic safety,
household transportation options, caregiver attitudes, social or cultural norms and socio–
demographics.
The relationships between five urban form variables and walking in specific demographic
subgroups were assessed using stratified logistic models and controlling for participant
demographics (Kerr et al., 2007). The travel data for two day period from 3161 youths
between 5 and 18 years of age in Atlanta, US was considered for analysis. All five urban
form and recreation measures were related to walking among people from European ancestry,
but only land use mix and access to recreation spaces were significantly related to walking in
people not of European ancestry. There were more significant urban form physical activity
associations in high–income than in low–income households. More urban form variables
were related to walking in households with three or more cars than in households with no
cars. Living in mixed use–areas and having access to recreational space were related to youth
walking for transport in 11 of 13 population subgroups studied.
Cao et al. (2009) explored the relationship between the residential environment and non work
travel frequencies by auto, transit, and walk or bicycle modes controlling for residential self
selection by using the seemingly unrelated regression equations (SURE) model. The model
showed that neighbourhood characteristics were associated with individuals’ travel decisions,
especially non–motorized travel frequency. The mixed land uses tended to discourage auto
travel and facilitate the use of transit and non–motorized modes; the availability of transit
service and walking or biking infrastructures were important predictors for transit and non–
motorized travel; and walking or biking behaviour was also affected by the aesthetic quality
and social context of the built environment.
2.4.2.5 Travel behaviour
Khattak and Rodriguez (2005) examined differences in travel behaviour in a matched pair
neighbourhoods (one conventional and one neo–traditional) in Chapel Hill and Carrboro,
North Carolina. A detailed behavioural survey of 453 households and two–stage regression
models suggested that single–family households in the neo–traditional development made a
similar number of total trips, but significantly fewer automobile trips and fewer external trips,
and they travelled fewer miles, than households in the conventional neighbourhood, even
after controlling for demographic characteristics of the households and for resident self–
selection.
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Deepti Muley Page 37
A structural modelling approach was used to explore the relationships among changes in the
built environment, changes in auto ownership, and changes in travel behaviour (Cao et al.,
2007). The residential self selection was addressed in several ways. The study found that the
residential self–selection has significant direct and indirect impacts on travel behaviour. The
changes in the built environment have a statistically significant association with changes in
travel behaviour, controlling for current attitudes and changes in socio–demographics, and
taking multiple interactions into account. The influence on the built environment was found
not only to be statistically significant but also practically important.
2.4.2.6 Modelling vehicle availability
Generally, the emphasis of analysis is mainly on whether the residents walk or use transit or
how many residents drive their car. But instead of looking into this aspect we should study
whether the residents have any other competent option to their existing travel choices
(Handy, 1996). In order to undertake this type of study vehicle availability needs to be
considered and quality of service needs to be assessed subsequently.
An improved model for forecasting vehicle availability was described by incorporating transit
accessibility and land use indicators along with usual demographic variables using a two–
stage approach (Allen and Perincherry, 1996). A look up table was used to identify an initial
estimate of the proportion of the number of vehicles on the basis of the household’s size,
number of workers and income quartile in the first step. An incremental logit model as shown
in Equation 2.5 was applied to initial proportions to consider the effect of transit accessibility
and land use form. Equation 2.6 was used to calculate the utility function. The model was
validated using 1990 PUMS records for the Washington region. It was found that good transit
service and high development density were associated with lower vehicle ownership.
𝑝𝑓𝑣 =𝑝𝑖𝑣 × 𝑒𝑈𝑣
𝑝𝑖𝑣 × 𝑒𝑈𝑣 3+0
Equation 2.5
𝑈𝑣 = 𝑎𝑣 + 𝑏𝑣 × 𝑇𝑟𝑛𝐴𝑐𝑐 + 𝑐𝑣 ×𝑊𝑎𝑙𝑘𝐴𝑐𝑐 + 𝑑𝑣 × 𝐸𝑚𝑝𝑙𝐼𝑛𝑡 Equation 2.6
where,
𝑈𝑣 = utility of accessibility in the vehicle availability decision for vehicle
group 𝑣 (dimensionless)
𝑇𝑟𝑛𝐴𝑐𝑐 = number of employees within 𝑋 min by peak transit service
𝑊𝑎𝑙𝑘𝐴𝑐𝑐 = number of employees within 𝑌 min walk time
Evaluating the transport impacts of TODs
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𝐸𝑚𝑝𝑙𝐼𝑛𝑡 = proportion of the total region’s employment within 𝑍 mi
𝑝𝑓𝑣 = final percentage of households with 𝑣 vehicles available
𝑝𝑖𝑣 = initial percentage of households with 𝑣 vehicles available (based on
size, income and workers)
𝑎, 𝑏, 𝑐,𝑑 = coefficients that vary by vehicle availability group 𝑣
2.4.2.7 Land use
Travel Demand
Cervero (1991) studied the relationship between land use and various indicators of travel
demand for 83 office buildings at six different suburban activity centres across the United
States. Stepwise regression analyses was performed to identify the influence of project size,
density, land–use mixing, and parking facilities on three measures of transportation demand;
trip generation rates, work–trip mode splits, and automobile occupancy levels and the
strongest measures of travel demand in terms of land use factors were identified. It was
observed that the single–tenancy and mixed–use buildings were associated with low vehicle–
trip generation rates.
Travel characteristics
Cervero and Gorham (1995) compared commuting characteristics of transit–oriented and
auto–oriented suburban neighbourhoods in the San Francisco Bay Area and in Southern
California. Regression models were prepared to elaborate the relationship between
neighbourhood type, transit mode shares and generation rates. This study indicated the
importance of location of the TOD to make transit work. This research suggested that,
specifically, neighbourhood design affected the degree to which people drive alone to work
or the degree to which they walk and bicycle. But the effect of neighbourhood type on transit
commuting was less clear.
Household travel
The relative influence of socioeconomic and land use factors on households’ travel behaviour
was investigated based on the number of household daily trips and VKT (Sun et al., 1998).
The travel data from 1994 Portland Activity – Based Travel Survey and land use information
from (Portland, Oregon) Metro’s GIS data resource centre was used. The Analysis of
variance (ANOVA) process was used to investigate the significance of selected variable and
multi-linear regression analysis was conducted to study the complex relationship between
Literature review
Deepti Muley Page 39
various attributes of household travel and land use data, followed by sensitivity analysis for
analysing the elasticity of each variable.
Internal Capture
Ewing et al. (2001) studied 20 mixed–use communities in South Florida to determine the
effect of land use on internal capture rate of trips, using data from the Southeast Florida
Travel 2000 Survey. The internal capture rate was calculated by dividing the number of trip
ends for trips internal to the community with the total number of trip ends produced or
attracted by the community. The different land use measures included; size measure, density
measure, entropy measure, balance measure and accessibility measure. However, community
size and one accessibility measure were the only variables included in the final model,
concluding that the internal capture rates increased with community size and decreased with
accessibility to other regional trip attractions, while density and land use mix did not have
independent predictive powers. When tested for effect of retirement population, the
proportion of retirees did not prove significant after controlling for size and regional
accessibility.
Travel impacts
McCormack et al. (2001) empirically explored the transportation impacts of mixed land use
neighbourhoods using a two day travel diary data collected over three neighbourhoods of
greater Seattle, Washington area by comparing household location and commercial
establishment, trip stops, transit, pedestrian trips, number of trips, travel time, travel speed
and travel and socioeconomic characteristics. This data set was then compared with
countrywide identical household travel data. ANOVA technique was used to demonstrate the
variations in travel measures with household or socioeconomic categories.
Travel patterns
Coevering and Schwanen (2006) evaluated the impact of urban form on travel patterns by
considering the role of individual travellers and space–time context of cities in Europe,
Canada and the USA. The 1990 data was augmented with information on housing,
development history and socio–demographic situation and studied how these factors,
alongside land use and transport infrastructure are related to travel patterns. The analysis was
done using Ordinary Least Square Regression modelling in SPSS software.
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Individual travel behaviour (walking trips)
The influence of population density, relative share of commercial and service land uses, and
relative share of vacant land on individual’s propensity to make home–based, nonwork, non–
school (HB NWNS) walking trips was analysed for assessing the influence of land use on
travel behaviour in Santiago, Chile (Zegras, 2004). The number of trips by each travel mode
was chosen by an individual to maximise the function given in Equation 2.7 built on
microeconomic characteristics of travel behaviour, subject to constraint shown in Equation
2.8. Further, the number of HB NWNS trips was estimated using an ordered probit estimation
method.
𝑀𝑎𝑥𝑖𝑚𝑖𝑠𝑒,𝑈 𝑎,𝑤, 𝑏, 𝑥 Equation 2.7
subject to, 𝑦 = 𝑥 + 𝑎𝑝𝑎 + 𝑤𝑝𝑤 + 𝑏𝑝𝑏 Equation 2.8
where,
𝑈 = a utility function of benefits using time for each purpose
𝑎 = vector of number of auto trips for each purpose
𝑤 = vector of number of walk trips for each purpose
𝑏 = vector of number of bus trips for each purpose
𝑥 = composite variable of time spent on other activities
𝑝𝑖 = representative vector of time for each trip type in each mode 𝑖
𝑦 = total available time
2.4.2.8 Density
Mixed land–use and density
Tong and Wong (1997) described the land use and transport characteristics of a high density,
mixed land–use, linear urban development located along the northern shore of Hong Kong
Island using results of a home interview survey. Major comparisons were done for car
ownership and mode choice, trip rate and trip time, and self–containment. The degree of self
containment was measured by the percentage of trips made by residents which have both trip
ends within the study area. The development was shown to have four advantages; economy in
land utilization, less roads, commercially viable public transport, and high accessibility for
residents, in spite of a low private car ownership rate.
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Deepti Muley Page 41
Allowable development densities
TOD planning generally emphasizes development efficiency, but ignores two aspects of
sustainability: environment quality and social equity. Hence, to assist TOD planners in
reviewing the development density regulations around the subway station areas, a multi –
objective programming model was developed (Lin and Gau, 2006). Based on the concepts of
sustainability, three objectives were defined as maximising subway system ridership,
enhancing living environmental quality and maintaining social equity in land development.
The ratios of floor space to site space (RFS) for different land uses were considered as
decision variables and limitations on land use density, land use combinations and level of
facility services were considered as constraints. The model was applied to review the
development density regulations in the Chunghsiao – Fuhsing station area in Taipei and
sensitivity analysis was carried out.
2.4.2.9 Modelling accessibility
Mode accessibility plays a strong role in whether or not a trip is internalised in the originating
Transportation Analysis Zone (TAZ). Higher transit and walking times between origin and
destination reduce the likelihood of keeping a trip within a zone (Greenwald, 2006).
Waddell and Nourzad (2002) tested the effects of neighbourhood and regional accessibility
on residential location, controlling for housing and neighbourhood characteristics, and using
a spatially disaggregate model. The access measure for each location was given by Equation
2.9.
𝐴𝑎𝑖 = 𝐷𝑗 𝑒𝐿𝑎𝑖𝑗
𝐽
𝑗
Equation 2.9
where,
𝐷𝑗 = quantity of activity in location 𝑗
𝐿𝑎𝑖𝑗 = composite utility, or logsum, for vehicle ownership category 𝑎, from
location 𝑖 to 𝑗, scaled to a maximum value of 0 for the highest utility
interchange
2.4.2.10 Modelling other aspects of TOD
Sadownik and Jaccard (2001) used a spreadsheet model to evaluate aggregate energy–related
emissions in the year 2015, resulting from two alternative scenarios of urban growth
throughout China. The model focused on how energy demand, residential energy technology
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Deepti Muley Page 42
penetration and transportation mode choices were affected by factors of density and mix of
use in neighbourhood development.
Cervero (2006) presented examples of post–processing and direct (off–line) modelling, for
rail and transit–oriented land use proposals for greater Charlotte, the San Francisco Bay area
exurbs and south St. Louis County in California. These alternative approaches to traditional
four step modelling approach were used for scenario testing, which revealed that,
concentrating development near rail stations produced an appreciable ridership bonus.
The effects of residential relocation to Shanghai’s suburbs on job accessibility and
commuting focusing on the influences of proximity to metro rail services and neighbourhood
environments on commute behaviour and mode choice were examined by using data from
recent movers to three suburban neighbourhoods. The modelling was done using path
diagram shown in Figure 2.1. It was found that moving near a suburban rail station
significantly moderated the travel–consumption impacts of relocation, especially from the
central city to the outskirts. Notably, households that relocated in a neighbourhood served by
Shanghai’s metrorail system and lived within 1km of a metrorail station had substantially
higher access to jobs (and most likely other destinations as well) following the move than
similar households in otherwise comparable non–rail settings. Living near a suburban
metrorail station was also associated with commute–mode changes from Non–Motorised
Transport (NMT) and bus transit to rail commuting. The enhanced accessibility associated
with living in a rail–served community also correlated with reductions in the time spent
getting to and from work, controlling for other factors (Cervero and Day, 2008).
Source: Cervero and Day (2008)
Figure 2.1 Path diagram of factors influencing changes in job accessibility and commuting
behaviour
Δ Location Δ Job
Accessibility
Δ Commute
Mode
C
o
n
t
r
o
l
s
Δ Commute
Time
Literature review
Deepti Muley Page 43
2.4.3 Travel demand modelling
2.4.3.1 Four step modelling
The most common method of regional travel demand modelling is four step modelling
(Greenwald, 2006). A traditional four step model with interaction between trip generation,
trip distribution, modal split, and trip assignment is shown in Figure 2.2. The travel demand
analysis is usually done for various trip purposes. The trip purposes used for modelling the
SEQ region in Brisbane Strategic Transport Model (BSTM) (SKM, 2006) are as listed below:
Home Based Work – Blue collar (HBWB)
Home Based Work – White collar (HBWW)
Home Based Education – primary and secondary only (HBE)
Home Based Education – Tertiary only (HBET)
Home Based Shopping & personal business (HBS)
Home–based Other – including HBRec (HBO)
Work Based Work (WBW)
Other Non–Home Based – excluding WBW (ONHB)
Commercial Vehicles Medium (CVM)
Commercial Vehicles Heavy (CVH)
Source: Ortuzar and Willunsen (1994)
Figure 2.2 The classic four–stage model
Zone networks Base year data Future planning data
Database
Base year Future
Trip generation
Assignment
Evaluation
Distribution
Modal split
Output
Iter
atio
ns
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The traditional four step modelling approach has been proved to be a versatile modelling
approach for modelling areas or sites with conventional methods of development. A
traditional four step model assumes homogeneous land use, does not consider short trips; the
intra zonal trips are neglected and the trips by walk and bicycle are also not considered. Apart
from this, the land use and neighbourhood features (area characteristics) are not considered in
the modelling, making the model unable to internally predict the travel demand under
changed development conditions (specifically land use and neighbourhood characteristics).
These constraints of a traditional four step model make it unsuitable for modelling the travel
demand of special developments like TODs having mixed land use, high density
development located near transit station. Hence, the model should be updated to make it
suitable for analysing TODs. Some studies have attempted to address this issue; details of
them are provided in the following sub sections.
Demand models
Rossi et al. (1993) revised the travel demand models to include the variables representing
atypical land use patterns such as residential and employment density, heterogeneity and
quality of pedestrian environment to test the alternative land use patterns as a part of a project
“Making the Land Use, Transportation and Air Quality Connection” (LUTRAQ) for
Portland, Oregon.
The main four revised components of the model were auto ownership, destination choice
model, pre–mode choice model and mode choice model. The pre–mode choice model
estimated the percentage of trips using the walk or bicycle modes for each origin–destination
pair while the mode choice model determined the number of trips using auto mode and
transit. The auto ownership model included a variable measuring the quality of pedestrian
environment, called “pedestrian environment factor”, (PEF) based on ease of street crossing,
sidewalk continuity, local street characteristics and topography. The destination choice model
was formulated as a logit model.
Similar to the above work, the Auckland Strategic Planning model (ASP) was designed to
investigate strategic futures for Auckland, New Zealand over a 30 year planning horizon
(Winder, 1994). The model represented interaction between land use policies, transport
policies, infrastructure investment, and development controls and their impact upon urban
form and the transport system. A location model, a transport model, a regional demographic
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Deepti Muley Page 45
model, a model of regional employment growth and an evaluation module were composed in
the ASP model. The transport model was implemented in EMME/2.
Miller et al. (1999) described the analytical tools for integrated land use – transportation
planning by describing available integrated models, characteristics of an “ideal” integrated
model and steps that a planning organisation should take in order to support and expand such
modelling capability, to address the transportation – air quality relationship. Six levels of
modelling capability were described with a checklist of input and analytical requirements for
each level. The integrated land use – transportation models combined the travel demand
forecasting functions with the land use forecasting functions. The idealized integrated land
use – transportation system is as shown in Figure 2.3.
Source: Miller et al. (1999)
Figure 2.3 Idealised integrated urban modelling system
In another attempt to model the emerging requirements of land use and transport modelling,
Waddell (2002) modelled the metropolitan areas by developing a discrete model system
called “UrbanSim”. UrbanSim was designed specifically to address the policy analysis
requirements of metropolitan growth management, with particular emphasis on land use and
transportation interactions. The model was applied to Eugene – Springfield, Oregon region
and was validated using data from 1980 to 1994.
Demographics
Regional economics
Government policies
Transport system
Land use
Location choice
Auto ownership
Flows, times, etc. External impacts
Activity / Travel and
goods management
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Rodier et al. (2002) evaluated the effect of land use, transit and auto pricing policies in the
Sacramento, California (US), region by applying two urban models; the advanced travel
demand model, SACMET96 and the integrated land use and transport model, the Sacramento
MEPLAN model. Different scenarios were described for the light rail network to take into
account the effect of different policy measures. The land use, travel results and emissions of
both the models were compared to get some insights into heuristic policy strategies and to
determine strengths and weakness of each model.
The land use, transit policies and the addition of modest auto pricing policies were found to
reduce VKT and emissions by about 5 to 7 percent and by about 4 to 6 percent respectively in
a 20 year time horizon. The development taxes and land subsidy policies was found as not
sufficient to generate effective transit oriented land uses without strict growth controls
elsewhere in the region. Further, it was concluded that the parking pricing should not be
imposed in the areas served by light rail lines and in areas with increased densities with
promoted land subsidy policies.
The relationship between urban form, as shaped through transit – oriented urban design, and
transport demand was investigated by using regional travel demand forecasting model
technique (Dock and Swenson, 2003). The impact of urban transit oriented design on
suburban land use and trip patterns for Minneapolis – St. Paul Metropolitan region in the
USA was studied. The existing forecasting model was used with three enhancements for
addressing; shorter–distance trips and the effects of greater proximity to transit at the
transport analysis zone (TAZ) level, changes in travel within TOD which was caused by
changes in urban form, land use density and mix of uses, and interaction between a TOD with
adjacent development.
The traditional techniques of windowing and focusing zones were used to divide the regional
model into smaller pieces and to add more details of roads and transit routes. Transport
analysis zones were subdivided using a layered approach. Finely grained socioeconomic data
were given as basic inputs for trip generation. The trip distribution matrices were generated
using off line estimation techniques to facilitate the elasticity based adjustments. Intrazonal
and interzonal adjustment factors were applied and the model was validated without
considering the expansion of highways and transit networks. The model was applied to two
TOD scenarios (modifications to highways network and modification to transit network) and
the results were compared with conventional land use and TOD patterns. The main drawback
Literature review
Deepti Muley Page 47
of the study was the basic of off–line estimation techniques on extrapolated data relationships
from other studies.
Trip generation
Steiner (1998) studied the trip generation rates of six prototypical traditional shopping
districts in the Oakland–Berkeley subarea of the San Francisco Bay Area using the pedestrian
count, retail gross leasable area, automobile share and daily hours of operation. Peak hour
and average daily trip generation rates were calculated for weekday and weekend for
shopping centre as a whole and compared with the ITE trip generation rates. A customer
intercept survey was used along with the data on overall activity levels to estimate the
number of person trips and trip ends in the shopping area. It was observed that the trip
generation rates for shopping centres with food shops was almost double the average ITE, the
trip vehicle rates were also found to exceed the ITE average hourly rate.
Arrington and Sloop (2009) derived evidence on trip generation and parking from 17
residential TOD projects in four metropolitan areas located in the USA. The 24–hour TOD
vehicle trip rates were compared with ITE trip rates. Over a typical weekday period, the
surveyed housing projects averaged 44 percent fewer vehicle trips than that estimated by the
ITE report. The research also concluded that TOD households were twice as likely to not own
a car and own roughly half as many cars as comparable households not living in TODs.
Trip distribution
The trips can be classified mainly as interzonal trips and intrazonal trips. An interzonal trip
has different trip origin and trip destination zones, while an intrazonal trip has the same trip
origin and trip destination zone. In strategic modelling, very often, the intrazonal or short
trips are not considered or underestimated and the mode choice models informed by trip
distribution assign private mode to the majority of intrazonal trips (Cervero, 2006). Hence,
most of the studies found were focused on the study of interzonal trips.
The relationship between land use, destination choice and travel mode choice for intrazonal
trips was addressed using data from a 1994 Household Activity and Travel Diary Survey
(Greenwald, 2006). The travel mode choice and decision to internalize trips were measured
by using multinomial logit and binary logistic models. The explanatory variables influencing
trip making were grouped into four categories; namely standard demographic traits, costs of
trip making behaviour, schedule limitation and availability of alternative mode choices and
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land use measures. A modelling package EMME/2 was used to calculate door to door
network distances.
Mode choice
The influence of presence of retail activities on the commuting choices of residents was
explored using data from 1985 American Housing Survey. The effects of land–use
environments on mode choice were modelled using binomial discrete choice models of the
form shown in Equation 2.10. The neighbourhood densities showed a stronger influence than
mixed land–uses on all commuting mode choices except walking and cycling. For non–
motorised commuting, the presence or absence of shops was a better predictor than
residential densities (Cervero, 1996).
𝑝𝑟𝑜𝑏 𝑌𝑖 = 𝑘 =1
1 + 𝑒𝑥𝑝 − (𝑈𝑖|𝑌𝑖 = 𝑘) Equation 2.10
where,
𝑈𝑖 = 𝑏0 + 𝑏1𝑋1𝑖 + 𝑏2𝑋2𝑖 +⋯… . .…+ 𝑏𝑞𝑋𝑞𝑖
𝑌𝑖 = commuting mode 𝑘 for employed resident 𝑖
𝑋 = independent utility variable, 1…… . . 𝑞
In an another study, Cervero and Radisch (1996) studied the effects of new urbanism design
principles on non – work and commuting travel by comparing modal split between two
distinctly different neighbourhoods in the San Francisco Bay Area; Rockridge and Lafayette.
A dummy variable was used to represent the trips from the two fundamentally different built
environments. Two separate travel surveys were carried out for work and non work trips on
randomly selected households. The data for up to three non work trips per day were
considered instead of a whole travel diary. A Binomial logit model was developed to predict
the probability of using a non car mode for non work trips as a function of the type of
neighbourhood of respondents as well as other control variables. Another Binomial logit
model was developed for work trips to predict the probability of commuting by a non single
occupant vehicle (non–SOV). The results of the logit model were used to simulate mode
choice based on neighbourhood origin and number of vehicles per household.
Rajamani et al. (2003) investigated the impact of urban form on nonwork trip mode choice by
using the travel data from 1995 Portland Metropolitan Activity Survey conducted by Portland
Metro, which collected travel information from members of a sample of households over a
Literature review
Deepti Muley Page 49
two weekday period. A multinomial logit mode choice model was developed consisting of
drive–alone, shared–ride, transit, walk and bike as choice set. A geographic information
system (GIS) based method was used to develop urban form measures at the neighbourhood
level of each household. The effect of household and individual socio–demographics, level–
of–service of travel modes, and urban form measures on nonwork travel mode choice was
determined using a discrete choice methodology.
Kim et al. (2007) analysed the factors that influence mode choice for trips between home and
light rail stations using on–board passenger survey data describing St. Louis MetroLink riders
in the United States. A multinomial logit model (MNL) was used to model discrete mode
choices of drive and park, pick–up or drop off, bus and walk (Equation 2.11). The
coefficients in the model were estimated using the method of maximum likelihood.
It was found that crime at the station had an impact of mode choice in particular for female
transit riders. Further private vehicle availability, bus availability and convenience was
associated with choice of drive and park and bus mode respectively. The airline (straight–
line) distance between transit riders’ home and the station is considered but the actual
walking distance might be more, which was a drawback.
𝑃𝑛𝑖 =𝑒𝛽𝑖𝑥𝑛𝑖
𝑒𝛽𝑖′ 𝑥𝑛𝑖 ′𝐼𝑖 ′=1
Equation 2.11
where,
𝛽𝑖 = estimable mode specific constants
𝑥𝑛𝑖 = observable characteristics of the modes, trip makers and the
surroundings
Trip assignment
Miller and Ibrahim (1998) correlated energy consumption through private automobile usage
in terms of total number of vehicle kilometres travelled (VKT) by using EMME/2 road
network assignment procedure with particular focus to 24–hour home–based–work (HBW)
trips in greater Toronto area, Canada. The VKT for a given origin–destination (O–D) pair
was calculated by multiplying the equilibrium O–D travel distances over the road network by
the O–D flows for a given trip purpose. Trip geocodes were used to measure the average
straight–line distances for intrazonal trips. A linear regression of the form shown in Equation
2.12 was used to calculate the variation of HBW VKT with density and distance from CBD.
Evaluating the transport impacts of TODs
Deepti Muley Page 50
𝑉𝐾𝑇 = 𝑎 + 𝑏 × 𝐷𝐶𝐵𝐷 Equation 2.12
where,
𝑉𝐾𝑇 = average daily HBW VKT per worker produced by the residential
zone
𝐷𝐶𝐵𝐷 = straight line distance (km) from the zone to the Toronto CBD
2.4.3.2 Agent based simulation
Zhang and Levinson (2004) developed an agent–based travel demand model considering
interactions from three types of agents in the transportation system; node, arc, traveller. It
was found that simple rules of agent behaviours solved the complicated problems such as trip
distribution and trip assignment.
Agent–based models have a unique feature of explicitly modelling the goal, knowledge,
searching behaviour, and learning ability of related agents. Although agent–based modelling
technique provides flexible travel forecasting network that facilitates the prediction of
macroscopic travel patterns from microscopic agent behaviours, the studies on individual
travel behaviours are mandatory to be done. In order to assess the individual behaviour at the
microscopic level the corresponding attributes at the macroscopic level need to be measured.
The effect of land use regulations on travel behaviour was examined using agent-based
modelling. A simulation model for a hypothetical urban area loosely based on the Chicago,
Illinois, metropolitan area was used to study the impact of six land use regulation scenarios
on transit use and urban form. The results from the simulations showed that although the land
use regulations that were designed to increase the density near the transit station or in and
near the urban core were able to achieve the intended land use patterns, they did not increase
the transit mode share for the region in a significant manner. More detailed examination of
the output revealed that as long as the rules for mode choice, the distribution of employment,
and the transit network remained unchanged, land use regulations that affected residential
locations produced limited effects on transit use (Lu et al., 2008).
2.4.3.3 GIS based modelling
Modelling Accessibility
Grengs (2004) demonstrated a method to measure changes in transit accessibility on one
neighbourhood from Buffalo and another from Rochester, New York by developing a gravity
model using Geographic Information Systems (GIS). This method separated the combined
Literature review
Deepti Muley Page 51
spatial effect of shifts in land use patterns and transit service. The results were obtained in the
form of a dimensionless neighbourhood accessibility index (NAI) and were compared at two
points in time, 1990 and 1997. NAI was based on proportional coverage, frequency factor,
travel time and employment within walking distance of transit stop (Equation 2.13). A
variable was used to represent the share of accessibility change attributable to transit. The
accessibility was found to vary due to different causes, in Buffalo case it improved because of
changes in transit service while in Rochester study, accessibility improved because of
changes in land use. The analysis was mainly for transit dependant poor people who live in
inner–city neighbourhoods.
𝑁𝐴𝐼𝑘 = 𝐸𝑗
𝑛
𝑗=1
𝑇𝑘𝑗 −𝛽
Equation 2.13
where,
𝑁𝐴𝐼𝑘 = index indicating accessibility between census tract 𝑘 and the set of 𝑗
destination transit stops
𝐸𝑗 = number of jobs at transit stop 𝑗
𝑇𝑘𝑗 = travel time by transit between census tract 𝑘 and transit stop 𝑗
𝛽 = constant to represent distance decay
Modelling walkability
Understanding the opportunities for pedestrian movement is a key component in
understanding and evaluating TOD projects. In order to address this TOD – pedestrian link,
Schlossberg and Brown (2004) analysed twelve geographic information system (GIS) based
walkability measures to visualise and quantify the pedestrian environments at each site,
across eleven TOD sites in Portland, Oregon. The street networks were classified into
pedestrian – friendly and pedestrian – hostile categories. This refined street data were used to
identify the quantity of different street types, densities of good intersections and dead ends,
and the catchment areas to which pedestrians are likely able to reach. The comparative
analysis was extended to two specific sites (one positive and one negative example of
walkability) to demonstrate the usability of analysis technique. Access, connectivity, and
pedestrian choice (which can be derived using various elements of street network) were noted
as key elements in understanding the pedestrian environments.
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Deepti Muley Page 52
The neighbourhood walkability was quantified by using destinations, distance, density and
route as core constructs (Lee and Moudon, 2006). The four step analytical process comprised
of initial variable screening, variable grouping, variable prioritization and statistical
modelling. The environmental variables were measured using GIS. The likelihood of walking
moderately and sufficiently for health purpose relative to not walking was measured by two
multinomial logit models, using airline and network measures.
2.4.3.4 Activity based travel analysis
The activity based approach reveals the travel patterns in the context of a structure of
activities, of the individual or household, with a framework emphasising the importance of
time and space constraints (Kitamura, 1988).
Zhang (2005) presented an activity–based time–use analysis of the relationship between
urban form and nonwork travel, using data from 1991 Boston Activity–Travel Survey. The
role of spatial accessibility was tested as a composite measure of urban form in explaining
individuals’ nonwork activity participation, travel time and travel frequencies. All activities
taking place inside home were grouped into one category, outside home work related
activities were differentiated from nonwork activities and nonwork activities were divided
into six types as school, shopping, social, personal, pick up or drop off and other. Each of
these six types of activities was given two components; activity and travel associated with it.
A gravity model – based measure of spatial accessibility was adopted to characterise the
opportunities and constraints to individual’s activity participation in the urban form. The
analysis was performed in two parts; part one estimated a set of multinomial logit models of
activity participation in which activity duration and travel times were combined for each type
of nonwork activity and part two separated activity duration from travel and analysed travel
duration and activity – travel frequencies. The results showed varying effects of modifying
spatial accessibility on nonwork activity participation among different activity categories.
Activity based travel analysis technique is not further explored because the data collection
involves “in–depth” interviews, which are uneconomical and limits the sample size. The
interviews are mainly option–specific thus lacks spontaneity. Further, these models have
limited applications to a wide range of planning and policy problems (Kitamura, 1988).
2.4.3.5 Tour based modelling
The relative association between travel time, costs, and land use patterns where people live
and work and its impact on modal choice and trip chaining patterns was studied for Central
Literature review
Deepti Muley Page 53
Puget Sound (Seattle, Washington) region (Frank et al., 2008). A tour–based modelling
framework and highly detailed land use and travel data was used to understand how
community design influences travel choices. The findings suggested that Puget Sound area
residents made travel choice decisions based on several factors, the most important being
time. A number of land use variables were also found to be statistically significant for all tour
types modelled. The degree to which participants chain trips together into tours was also
highly correlated to the land use characteristics where residents live and work.
Lee et al. (2009) examined the effects of proximal residential density, roadway accessibility,
land use mix, and transit accessibility on individual tour–based travel behaviour across three
trip categories in detail. Cross–sectional activities were obtained from household activity
travel survey data from the Atlanta Metropolitan Region. Time durations allocated to
weekdays and weekends were also compared. The censoring and endogeneity between
activity categories and within individuals were captured using multiple equations Tobit
models. The analysis and modelling revealed that land–use characteristics such as net
residential density and the number of commercial parcels within a kilometre of a residence
were associated with differences in weekday and weekend time–use allocations. Household
type and structure were significant predictors across the three activity categories, but not for
overall travel times. The tour characteristics such as time–of–day and primary travel mode of
the tours also affected traveller’s out–of–home activity–tour time–use patterns.
2.4.4 Summary of travel data analysis
The studies reviewed on TOD evaluation can be categorised into two types; statistical
analysis and travel demand modelling. A summary table is given for studies on comparative
and statistical analysis and studies on travel demand analysis in Table 2.5 and Table 2.6
respectively.
The key observations from studies on travel demand analysis are noted below.
The statistical analysis studies illustrated the effect of car ownership, land use, density
and neighbourhood design impacts on travel characteristics of people. Regression
analysis and ANOVA techniques were found to be the most commonly used
techniques to analyse the effect of different variables on travel.
Analysis for mode shares was found to be the topic of interest for many researchers
followed by the calculation of trip generation rates. The issue of parking requirements
was also addressed by comparing the situation for different TODs.
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Table 2.5 Summary of studies on comparative and statistical analysis
Parameter Author/s Technique / method
Parking requirements
Higgins (1993) Setting minimum and maximum parking
requirements
Steiner (1998)
Deakin et al. (2004) Study of land use, parking supply and use, mode
choices, and housing and jobs development
Car ownership
Hess and Ong (2002) Ordered logit regression
Giuliano and Dargay
(2006)
Comparative analysis
Structured model for total distance travelled
Transit use
Messenger and Ewing
(1996)
Stepwise regression analysis
Full – information maximum likelihood
Lund (2006) Binary logistic regression analysis
Lin and Shin (2008) Regression models calibrated using Ordinary
Least squares method
Travel mode
McMillan (2007) Binomial logit regression probability models
Cao et al. (2009) Seemingly unrelated regression equations
(SURE) model
Walking Kerr et al. (2007) Stratified logistic models
Travel behaviour
Khattak and Rodriguez
(2005)
Determination of differences in matched pair
neighbourhoods
Two–stage regression models
Cao et al. (2007) Structural modelling approach
Modelling vehicle
availability
Allen and Perincherry,
(1996)
A two–stage approach; look up table and
incremental logit model
Travel demand Cervero (1991) Stepwise regression analyses
Travel characteristics
Cervero and Gorham
(1995)
Comparison of commuting characteristics of
transit–oriented and auto–oriented suburban
neighbourhoods
Regression models
Household travel Sun et al. (1998) The Analysis of variance (ANOVA), multilinear
regression analysis, sensitivity analysis
Internal Capture Ewing et al. (2001) Regression analysis
Travel impacts McCormack et al.
(2001)
Comparison
ANOVA technique to study variation
Travel patterns Coevering and
Schwanen (2006)
Ordinary Least Square Regression modelling
Individual travel
behaviour (walking
trips)
Zegras (2004) Maximisation technique
Ordered probit estimation
Mixed land–use and
density
Tong and Wong (1997) Comparative analysis
Allowable
development densities
Lin and Gau (2006) Maximisation and optimisation technique
sensitivity analysis
Accessibility Waddell and Nourzad
(2002)
Energy–related
emissions
Sadownik and Jaccard
(2001)
Spreadsheet model to evaluate aggregate energy–
related emissions
Scenario testing Cervero (2006) Post–processing and direct (off–line) modelling
Relocation impacts Cervero and Day (2008) Statistical modelling
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Deepti Muley Page 55
Table 2.6 Summary of studies on travel demand modelling
Modelling approach Author/s Method / Approach
Four step modelling
Rossi et al. (1993); Winder
(1994); Miller et al. (1999);
Waddell (2002); Rodier et
al. (2002); Dock and
Swenson (2003)
Travel demand models for scenario testing
Four step modelling: Trip
generation
Steiner (1998); Arrington
and Sloop (2009)
ITE comparison
Four step modelling: Trip
distribution (Internalisation
of trips)
Greenwald (2006) Multinomial logit and binary logistic
models
Four step modelling: Mode
choice
Cervero (1996) Binomial discrete choice models
Cervero and Radisch (1996) Binomial logit model
Rajamani et al. (2003) A geographic information system (GIS)
based and discrete choice method
Kim et al. (2007) A multinomial logit model (MNL),
Maximum likelihood method
Four step modelling: Trip
assignment
Miller and Ibrahim (1998) EMME/2 and linear regression equation
Agent based simulation Lu et al. (2008) A simulation model for a hypothetical
urban area
GIS based modelling
Grengs (2004) Gravity model to measure changes in
transit accessibility
Schlossberg and Brown
(2004)
GIS based walkability measures to
quantify the pedestrian environments
Lee and Moudon (2006) Quantification of neighbourhood
walkability, multinomial logit models
Activity based travel
analysis
Zhang (2005) Analysis of relationship between urban
form and nonwork travel
Kitamura (1988) A gravity model – based measure of
spatial accessibility
Tour based modelling
Frank et al. (2008) Investigation of impact of relative
associations between travel time, costs,
and land use patterns where people live
and work on modal choice and trip
chaining patterns
Lee et al. (2009) Effects of proximal residential density,
roadway accessibility, land use mix, and
transit accessibility on individual tour–
based travel behaviour
The two approaches, agent based simulation and activity based modelling, were
alternatives to four step modelling but they are not considered in detail because of the
limitations associated with them.
GIS modelling was performed to study the transit accessibility and walkability
indicators of TOD. This method is suitable to assess the accessibility and walkability
because it considers the geography of the area.
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The four step modelling procedure was found by researchers to be more suitable for
analysing the travel demand at TODs. But it was found that this technique requires
modifications in order to represent the trips made for various trip purposes.
The travel demand models developed for TOD evaluation consider land use variables
in addition to the conventional variables. The models were mostly developed to test
the hypothetical development alternatives. These models also included walk mode
and short trips.
Very little research has tried to address the details at any individual level in the four
step modelling process. Intrazonal trips were found to be important for modelling trip
distribution while use of discrete mode choice models (binomial and multinomial
logit models) found suitable for mode choice analysis. A software package EMME/2
was found to be the most commonly used software for four step modelling.
2.5 Summary of literature review
2.5.1 Strengths of the literature
Strong evidence supporting the benefits and in turn success of TODs was found from
a wide range of studies that were mainly conducted in the USA, whose cities have
similar urban development characteristics to Australian cities.
The papers dealing with the statistical analysis of various TOD variables shows the
contribution of these variables towards assessing the travel behaviour of residents at
TODs.
These analyses confirm the need of consideration of these aspects into travel demand
modelling in addition to the traditional demographic and socioeconomic variables.
The studies showing the comparison of the travel characteristics of people living in
TOD and outside TOD confirms the difference in travel behaviour of people living in
these two forms of communities.
Strong evidence was found claiming the increased mode share of walking trips and
reduced VKT for non work travel in TODs for residents. The car ownership level was
also found to decrease with an increase in density. These findings are in support of
TODs.
The four step modelling approach is found as a viable approach for studying travel
demand at various levels.
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Deepti Muley Page 57
2.5.2 Gaps in the literature
The theoretical background explains the relationship between travel and urban design. The
past studies provide an insight in to the relationship between TOD and travel behaviour of
people. Following are some of the gaps found while reviewing the literature.
The review of literature demonstrates many studies explaining the effects of various
indicators of TOD on travel characteristics of residents. However, more detailed study
of various trip purposes and trip characteristics of all users using these types of
developments should be carried out.
Another shortcoming is that the data used for developing the models is not directly
applicable or not solely collected for model development purposes, making the quality
of the results limited. In most of the cases, the developments emerged as TODs were
considered; whereas the travel data from a planned TOD needs to be considered for
improving the quality of the results.
Very few studies considered the special design characteristics of TODs when
preparing travel demand models.
The specifications of traffic generation rates for mixed land use developments and for
walking and bicycle trips were not given separately. This demands more research in
the area of traffic generation for TODs. This will also help in studying the overall
traffic impact of the TOD.
It was observed that TODs need to be assessed for the amount of self containment
with a proportion of internal trips with special consideration to trip length and mode
split. The number of trips staying within the TOD needs to be determined and
compared with respect to the number of trips going outside of the TOD for various
trip purposes.
Many studies considered car ownership and mode choice of people but failed to
analyse transit use with respect to the quality of service available, demanding more
research in vehicle availability and transit availability.
The various TODs have mainly been concentrated on the aspect of residential land
uses and some have dealt with the commercial, retail, official land uses, however
studies investigating the educational and recreational aspects of TODs are missing.
Most of the studies considered rail based TODs while not much evidence was found
for bus or Bus Rapid Transit (BRT) based TODs.
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None of the studies reviewed have focused on ways of quantifying the transport
related impacts of TODs from an Australian perspective to aid in planning and design
of TODs in this country.
2.6 Recommendations flowing from literature review
TODs are special developments which are designed to increase quality of life and reduce the
adverse environmental impacts. As the outcomes of TOD differ depending upon location,
size, type of land uses, density, accessibility, quality of public transport system and
pedestrian environment available; the travel characteristics of the people are also diverse
depending upon the type of community and its travel habits. Hence, each TOD needs to be
evaluated individually by giving consideration to the various design aspects of TOD and
travel habits of people living there. In order to do this, the literature review has identified the
following areas of research which warrant further investigations:
There is a need to develop a generalised (comprehensive) methodology for assessing
the travel demand of TODs. This study aims to develop this and also plans to test it
using a case study TOD.
For assessing travel demand the number of trips generated should be estimated
accurately. Previous studies (Steiner, 1998; Lin and Gau, 2006) have used ITE trip
generation rates for this purpose, but these trip rates are not necessarily directly
applicable as these assume homogeneous land uses. This research investigates the trip
generation rates for a TOD for various modes of transport.
The traditional travel demand models neglect the intrazonal trips. But in the case of
TOD, these require consideration as TOD has clustering of activities in a small place.
The trip lengths and mode choice for short trips need to be considered. The trip
distribution and mode choice steps should take into account these aspects.
The data for travel demand modelling should be collected for various categories of
users performing various activities at a TOD.
Comparison of trip characteristics for various trip purposes needs to be made for
checking the performance of a TOD as a whole system rather than considering
residents’ data only to confirm the claims of TOD.
The travel demand model should be developed considering the TOD design, location,
size, type of land uses, densities, accessibility, quality of public transport system and
pedestrian environment available. A multivariate analysis should be performed to
Literature review
Deepti Muley Page 59
investigate the relative importance of these variables and their effect on travel
demand.
The traditional four step modelling approach can not be applied directly for TOD
evaluation. Specifically, the procedure needs some advances or improvements in
order to make it suitable for travel demand analysis of TODs.
The trips should be modelled considering private car availability and transit
availability (quality of service), as the decision for mode choice may be driven by
these two factors.
The need for analysing TOD in a regional context should be noted. In order to assess
TOD at a higher level, detailed analysis of TOD at the zonal level should be
undertaken.
Activity based and tour based modelling techniques are emerging fields for travel
demand modelling which should be explored further for assessing their suitability.
Mixed results were obtained for the benefits of TODs for developing countries. Also,
stronger evidence was observed for studies in USA than in UK.
Travel at Australian TODs should be studied in order to gain understanding and
suitability of TODs from Australian perspective.
2.7 Chapter close
This chapter presented a review of literature, the strengths of the literature and the gaps in the
knowledge. These gaps provide direction for this research. The next chapter presents a
methodology for evaluating TODs, which will help to reduce the gaps in knowledge related
to TOD evaluation. The studies presented in the data requirements section offer base
information for the data collection process presented later in Chapter 5.
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Deepti Muley Page 60
Deepti Muley Page 61
Chapter 3
Methodology for evaluating transport impacts of transit oriented
developments
3.1 Introduction
To achieve the objectives of this research and assess the performance of Transit Oriented
Developments (TODs) from transport point of view, this chapter proposes an appropriate new
methodology for evaluating TODs. Importantly, this methodology discloses the key
characteristics of a TOD within its first stage. It is these characteristics that are used to state a
clear and concise definition of a TOD to be used in this thesis from this chapter onwards.
The proposed methodology will be further investigated using an appropriate case study TOD.
The observations made while undertaking TOD investigations will be useful for
implementing this methodology more generally for assessing various TODs. The following
section presents the measures used in evaluating transport impacts followed by a detailed
description of each step involved in the proposed methodology. Later a brief summary,
applications and chapter close are presented.
3.2 Measures for transport evaluation of TODs
The transport evaluation of TOD consists of determination of traffic generation and the
details of travel at a TOD. The various measures of transport performance of TODs have
been considered carefully based on literature review and are shown in Figure 3.1.
The performance measures include traffic generation, household characteristics and travel
characteristics. The performance of these measures needs to be determined for various groups
of TOD users to get a complete picture of travel at a TOD. Typically, travel characteristics of
all user groups should be assessed and the household characteristics and trip characteristics
for TOD residents should be evaluated. The new methodology is proposed in this research to
evaluate the transport impacts through these measures, which is presented in the following
section.
Evaluating the transport impacts of TODs
Deepti Muley Page 62
Figure 3.1 Measures for transport evaluation of TODs
3.3 Proposed new research methodology
This research proposes a new methodology to evaluate TODs from a transport perspective.
The conventional procedure does not take into account the effects of atypical development
characteristics of TODs for example, presence of mixed land uses and characteristics of
various user groups using these land uses etc. To consider these effects, this methodology
incorporates some additional steps to the existing procedure. The methodology for TOD
evaluation involves a stepwise approach. The first step of TOD evaluation is assessing a
development before considering it as a TOD and the last step is determination of
transportation impacts through the measures specified in previous section. Figure 3.2
represents the flow diagram explaining the major steps in TOD evaluation. The five step
approach to TOD evaluation include; pre–TOD assessment, traffic and travel data collection,
determination of traffic and travel impacts and obtaining outcomes. Following sections
explain the process involved in each step with detailed flow diagrams.
Transport impacts
assessment measures
Travel
characteristics
Household
characteristics
Household size
Number of
vehicles/household
Mode share
Trip lengths
Trip characteristics
Number of
bicycles/household
Number of
bedrooms/household
Traffic
generation
Methodology for evaluating transport impacts of TODs
Deepti Muley Page 63
Figure 3.2 Proposed new methodology for evaluating transport impacts of TODs
3.3.1 Step I: Pre–TOD assessment
The first step of pre–TOD assessment is selection of a development and confirming its
function as a TOD. The steps involved in pre–TOD assessment are shown in Figure 3.3. The
shaded box in the flow diagram indicates new or additional step for pre–TOD assessment.
Often a mixed use development or any developments around a transit station can be
presumed to have certain TOD like transport characteristics without substantiation. Although
having some similar characteristics, these developments may not really operate as TODs. To
ensure the function of development as a true TOD, the land use mix, parking provision for
cars, and infrastructure for sustainable modes of transport needs to be appropriate.
The land use mix at a TOD is important for integrating various activity nodes by providing
greater accessibility. These diverse land uses produce and attract various trip types.
Noticeably, at a TOD presence of trip attracting land uses is in greater proportion than the trip
producing land uses. Hence, TODs normally have higher trip attraction rate than trip
production rate for all modes of transport. To ensure the use of sustainable modes of transport
for these trips, the provision for walking, cycling and public transport is essential. The use of
public transport, walking, and cycling is widely regarded in the transport professional
community as being more sustainable in comparison to the use of other motorised modes of
transport, specifically the car.
Step II: Traffic and travel data
Step IV: Travel impacts
Step I: Pre-TOD assessment
Step III: Traffic impacts
Step V: Outcomes
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Figure 3.3 Step 1: Pre–TOD assessment
Awareness of parking infrastructure is also vital because the provision of large numbers of
parking spaces may attract, or produce, more car trips rendering the development less
pedestrian friendly, which defeats the purpose of a TOD. On the contrary, the provision of
fewer parking spaces may harm the development by distracting the attention of commercial
or retail development users. The optimum supply of parking infrastructure supports the
economic activities at a TOD and confines the avoidable use of car for transport, supporting
the more sustainable transport modes. The thresholds for parking supply can be obtained
from the standard guidelines followed by local government. Many local governments are now
stipulating thresholds for TODs or TOD like developments.
To evaluate a study development for TOD specification, data for three characteristics, land
use mix, parking provision for cars, and infrastructure for sustainable modes of transport,
should be gathered and evaluated to ensure correct anticipated provisions of a TOD rather
than assuming so and undertaking further steps of evaluation. If the selected development
does not exhibit appropriate TOD characteristics then another development form should be
selected and assessed for its suitability. Table 3.1 lists the measures used for assessing the
Yes
Selection of the development
Assess suitability of the development
Obtain land use mix and transport details
Is
suitable?
No
Proceed to step II
Represents new/additional steps
Step II: Traffic and travel data
Step IV: Travel impacts
Step I: Pre-TOD assessment
Step III: Traffic impacts
Step V: Outcomes
Methodology for evaluating transport impacts of TODs
Deepti Muley Page 65
suitability of a TOD, and in doing so, importantly disclosing the key characteristics of a
TOD.
Table 3.1 Measures for assessing suitability of a TOD
TOD attribute Measure of assessment
Land use mix Presence of diverse land uses, self containment
Road infrastructure Layout of road network, its quality and connectivity to
major transport corridors
Infrastructure for walking Provision of sidewalks, quality of sidewalks and secured
pedestrian crossings
Infrastructure for cycling Dedicated bicycle tracks or bicycle lanes, its connectivity to
bicycle network and secured parking for bicycles
Location of nearest public
transit node
Major public transport node accessible within easily
walkable distance (400m)
Quality of public transport Level of service for transit availability and comfort and
convenience
Parking supply Optimum parking supply
Based on the measures listed in Table 3.1, the following definition of a TOD is used from this
point onwards in this thesis:
A TOD is defined as a higher density mixed land use development located within easy
walkable distance of a major public transport node having excellent facilities for walking and
cycling without compromising infrastructure for private motorised modes of transport
particularly car. Basically, a TOD provides competent mobility choices for sustainable
modes of transport such as walk, cycle and public transport to promote its use and
discourage use of private motorised modes of transport.
3.3.2 Step II: Traffic and travel data collection
After selecting the study TOD, next step is gathering of traffic and travel data. This data is
essential to overview transport at a TOD, to study the trip rates for various modes of transport
and to obtain travel characteristics of various user groups. The steps for traffic and travel data
collection are given in Figure 3.4. The shaded boxes in the flow diagram indicate new or
additional steps for data collection.
First the existing data availability needs to be checked before proceeding to data collection. If
the data is available then the data should be checked for its suitability. Classified cordon
counts for all access points of the development should be available and the travel data for all
user groups at TOD should be available to proceed to further steps. If the data is not available
then it should be collected by a two–step process; cordon counts and travel surveys.
Evaluating the transport impacts of TODs
Deepti Muley Page 66
Figure 3.4 Step II: Traffic and travel data
Before starting the field studies, the observation of the traffic and user groups at a TOD
should be made to gain an understanding of the development. The cordon counts for 24 hour
or for the specified period of the day should be conducted at all the access locations of the
development for both directions (inbound and outbound). The counts should be conducted for
all modes of transport as study of traffic generation for sustainable modes of transport as well
as lesser sustainable modes of transport is important. This data will provide input to Step III.
To collect TOD users’ travel data, the travel surveys should be conducted for all TOD users,
including residents as well as visitors. The user groups can be obtained from the land use
used by a user. For example, the employee user group utilising commercial land use/s. For
Represents new/additional steps
Travel surveys for all TOD
user groups
Identification of TOD user groups
Input for step IV
Travel data
available
No
Yes
Input for step III
Multimodal cordon counts for TOD
and commercial / retail land use
Observation of traffic and
determination of access points
Cordon data
available
Yes
No
Cordon data TOD users’ travel data
Step II: Traffic and travel data
Step IV: Travel impacts
Step I: Pre-TOD assessment
Step III: Traffic impacts
Step V: Outcomes
Methodology for evaluating transport impacts of TODs
Deepti Muley Page 67
each user group, a suitable survey instrument should be selected as different users tend to
exhibit different characteristics that affect the response rate. The survey should be
implemented using a specifically designed questionnaire form. The selection of the
methodology and questions for the travel survey is based on the composition of the users,
such as age and occupation and the availability of the resources. Generally, TOD residents’
travel diaries for a typical working day and TOD visitors’ travel data for specific trip
purposes should be obtained along with the demographic characteristics. Before conducting
any full scale surveys, the survey process should be tested on a sample population to gain an
insight into respondents’ opinion about survey design. For a TOD, preferably the travel
surveys should be distributed to the whole population rather than undertaking sampling. The
travel data obtained through travel surveys provide input to Step IV.
3.3.3 Step III: Determination of traffic impacts
After obtaining traffic and travel data, the next step is Step III: Traffic impacts determination.
The steps involved in determining the traffic impacts are given in Figure 3.5. The shaded box
in the flow diagram indicates new or additional step for traffic impact determination. The first
task of analysing cordon counts is to determine the total trips generated at the TOD by
excluding the through traffic movements. Later the cordon data should be analysed to obtain
the trip rates by mode of transport and their characteristics. Further, the traffic generation
rates for complete development obtained from cordon counts should be compared with the
totals derived using the standard trip rates specified, for example, American context (ITE,
2008) and Australian context (RTA, 2002). The traffic generation rates should be also
compared with similar sized non – TOD development to determine any actual variation in
traffic generation. The results of these comparisons provide input for determining the
outcomes in Step V.
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Deepti Muley Page 68
Figure 3.5 Step III: Traffic impacts
3.3.4 Step IV: Determination of travel impacts
The travel impacts of a TOD are determined from analysis of travel data gathered from travel
surveys. The responses from travel surveys need to be compiled before proceeding to
analysis. The process of determination of travel impacts is shown in Figure 3.6. The shaded
box in the diagram indicates new or additional step for determination of travel impacts. Once
the compiled data set is obtained, the preliminary data analysis should be performed to obtain
various characteristics of the TOD users. These characteristics include personal, household,
and travel characteristics, and perceptions, issues, suggestions and comments about the
development. The personal characteristics include age group, occupation, employment status,
gender, driving licence availability, etc and the household characteristics contain household
size, vehicle ownership, bicycle ownership, type of dwelling and number of bedrooms in the
household. The travel characteristics can be explained with mode shares, trip lengths, parking
details such as parking location and parking fee and public transport details such as travel
time, number of routes used and access and egress walking times or distances. In addition the
perception and issues about public transport and about the development can be obtained.
Trip generation for all modes of transport
Traffic characteristics
Comparison with standard trip rates
and/or with non – TOD development
Input for Step V
Step II: Cordon data
Represents new/additional steps
Step II: Traffic and travel data
Step IV: Travel impacts
Step I: Pre-TOD assessment
Step III: Traffic impacts
Step V: Outcomes
Methodology for evaluating transport impacts of TODs
Deepti Muley Page 69
These results help to gain a better understanding about the travel at the TOD under
consideration.
Figure 3.6 Step IV: Travel impacts
Further, the travel and demographic characteristics should be compared with that of non –
TOD or conventional developments having some similar characteristics, such as size of the
development, distance from CBD or household characteristics. A regional comparison can
also be conducted by comparing travel data of TOD users with respective travel data for
entire region. The data for non – TOD or conventional developments can be obtained from
various data sources such as household travel surveys, census database etc. The preliminary
analysis of travel data provides a base for the travel demand analysis. The travel mode is a
Represents new/additional steps
Step II: Traffic and travel data
Step IV: Travel impacts
Step I: Pre-TOD assessment
Step III: Traffic impacts
Step V: Outcomes
Travel demand
analysis
Comparison with non –
TOD developments
Demographic
characteristics
Travel
characteristics
Perceptions
and issues
Characteristics of all user groups at TOD
Input to Step V
Step II: TOD users’ travel data
Data compilation and analysis
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Deepti Muley Page 70
prominent aspect of each trip hence essentially this aspect needs to be investigated. In
addition, trip distribution or internalisation can be studied depending on data availability. The
outcomes from step IV provide the travel impacts which are used to draw outcomes in Step
V.
3.3.5 Step V: Outcomes
The outcomes obtained from various steps of analysis are displayed in Figure 3.7. The shaded
boxes in the flow diagram indicate new or additional outcomes obtained from the proposed
methodology. The first step of TOD evaluation provides insight into selection process of a
development for TOD evaluation. The second step of data collection provides findings useful
for collecting traffic and travel data at TODs; these can be useful in planning and designing
of future data collection processes at TODs. The comparison of total traffic generation
indicates the performance of TOD with respect to the traffic at the conventional
developments (Step III) and the results from the comparative analysis reveal how the TOD
under consideration is performing in comparison to other conventional developments and at a
regional level in terms of transport and demographics (Step IV: travel impacts). The findings
for data collection, traffic and travel impacts are the outcomes obtained through TOD
evaluation. The positive differences in traffic generation as compared to standard trip rates
and in travel characteristics as compared to non – TOD developments for a TOD indicate
sustainable transport and encourage further investigation, and on the contrary the negative
differences calls for more stringent evaluations and reviewing of sustainable transport claims
of TODs made by transport planners. Further, the results from trip rates and travel demand
analysis provide guidance for planning future TODs.
Methodology for evaluating transport impacts of TODs
Deepti Muley Page 71
Figure 3.7 Step V: Outcomes
3.4 Summary
In evaluating a TOD for transport impacts, the starting point was selection of a case study
development, by observing the mix of land uses and sustainable transport infrastructure and
assessing its suitability, followed by collection of traffic and travel data for all the user groups
of the respective development from the classified multimodal cordon counts and travel
surveys. The characteristics obtained from the analysis of data helped to explain the travel
behaviour at a TOD. This also points towards the performance of a development which helps
in providing the evidence for TOD’s travel behaviour. The additional or new steps proposed
in this methodology aid in addressing the following gaps in the literature noted in Chapter 2.
A detailed study of various trip purposes and trip characteristics of all users was
missing. The additional steps suggested in this method provide a complete picture of
travel at TODs by studying all user groups at TOD under consideration.
Represents new/additional steps
Step II: Traffic and travel data
Step IV: Travel impacts
Step I: Pre-TOD assessment
Step III: Traffic impacts
Step V: Outcomes
Step II
Guidelines for
conducting travel
surveys
Traffic
impacts
Step III
Trip rates for
various modes
Step IV
Travel characteristics
for various user groups
Travel
impacts
Travel mode
assessment
Trip distribution/
internalisation
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Deepti Muley Page 72
Some data quality and reliability issues were observed for previous studies as mostly
the data was not collected solely for TOD evaluation, the pre–TOD assessment step of
this methodology ensures selection of a development with all essential characteristics
of a TOD and the data collection step ensures the quality and reliability of the results.
This methodology assesses the overall traffic generation and traffic impacts of TODs
by conducting traffic counts for various modes of transport and also provides various
trip rates useful for planning TODs.
Quality of transit service is an important aspect of TOD hence required more
investigation while travel demand analysis. This methodology considers quality of
transit service while selection TOD as well as while analysing the travel demand.
Previously most of the studies considered only residential land use at a TOD for
evaluation, this methodology proposes study of transport aspects of various land uses
located within a TOD including residential land uses, commercial, retail, official land
uses and educational land uses giving complete picture if travel at TODs.
3.5 Application
This chapter provides an overview of the methodology for evaluation of transport impacts of
a TOD. Although this methodology is broadly applicable for assessing various TODs, some
case study specific modifications may be required.
3.6 Chapter close
The procedures followed in this research forms a basis for all chapters in this thesis. Each
step or combination of two or three steps suggested in this methodology forms a chapter in
the thesis. The next chapter outlines the details of Step I: Pre–TOD assessment which
includes gathering of case study development information and evaluating whether it can be
considered as a TOD for further analysis.
Deepti Muley Page 73
Chapter 4
Selection of case study transit oriented development
4.1 Introduction to case study selection
A case study Transit Oriented Development (TOD) which has good transit service is essential
to conduct research on inhabitants’ travel behaviour and achieve the objectives of this
research. To ensure selection of a suitable case study TOD, the characteristics of the
development and public transit availability are used as indicators. The purpose of this chapter
is to introduce the case study TOD and demonstrate the selection process followed in this
research to test its suitability.
The first section gives an overview of the location and various land uses at the case study
TOD site. The following section continues with the description of the transport facilities. The
criterion used for assessing the suitability of the case study TOD site and the analysis for the
same are listed in the next section. Finally the interpretation of the results and a brief
summary of the case study TOD are provided.
A candidate case study TOD, the Kelvin Grove Urban Village (KGUV) in Brisbane,
Australia, was selected for investigation. KGUV was tested for its suitability as a case study
TOD by evaluating quality of service (QoS) indicators for public transport service in order to
better appreciate whether it has the appropriate transit availability to support its function as a
true TOD.
4.2 Description of KGUV
Kelvin Grove Urban Village (KGUV), designed as a sustainable and mixed use development,
is situated in the inner suburb of Kelvin Grove, approximately 2km northwest of Brisbane’s
Central Business District (CBD). The greater Brisbane metropolitan area had a population of
approximately 1.9 million at the time of writing. KGUV has been developed as a joint
venture between the Queensland Department of Housing and Queensland University of
Technology (QUT) based on the ecological sustainable development (ESD) principles.
KGUV spans over 16.57 Ha of land area and to the best of the authors’ knowledge is the first
of its kind of development in Australia. It is surrounded by inner city suburbs, Spring Hill,
Herston, Red Hill, Newmarket and Wilston. The mixed use development consists of
educational, residential, commercial, recreational, retail and office land uses. Young, single
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students and workers are expected to comprise the majority of the population. KGUV has a
university campus and a state government high school located on its northern boundary. This
site is expected to be fully developed by late 2009.
KGUV is an educational based mixed use development comprised of four distinct precincts;
the village centre, QUT Kelvin Grove campus extension, QUT’s Institute of Health and
Biomedical Innovation (IHBI), and QUT’s Creative Industries Precinct. The building height
varies between 4 and 7 stories. The details of mixed uses are as shown in Table 4.1.
Table 4.1 Mixed land uses at KGUV
Land use Size Description
Residential 35,668sqm Includes affordable accommodation, managed
accommodation for seniors, student accommodation,
investor apartments, townhouses (900 residential units
including 200 affordable housing units)
Education 14,770sqm QUT campus extension, Queensland Academy for Creative
Industries (QACI)
Retail Not Available Village centre; street level shops, catering for extended hour
demand
Commercial 3,878sqm Creative industries precinct, Institute of Health and
Biomedical Innovation (IHBI), health services and standard
commercial office facilities and tenancies with opportunity
to implement innovative structures
Lifestyle 6,897sqm La Boite theatre, Victoria Park golf course, network of
parks
Mixed 11,995sqm
26,014sqm
Mixed use area for residential, commercial and retail places
Mixed use area for education and commercial places
4.3 Transport facilities at KGUV
The main aspect of a TOD is the transport facilities, both public as well as private. KGUV is
well connected to arterial roads and has an internal street network forming a grid pattern, with
parks and open spaces. KGUV has sidewalks on both sides of all streets and through the
parks, and cycle lanes on road to encourage and support walking and cycling. BCC (2000)
stipulates car parking rates of minimum 3 spaces per 50m2 GFA at ground floor level and 1
space per 30m2 GFA above ground level for centre activities, 1 space for two staff and 1
space per 10 students at tertiary institutions and 1 space per 30m2 GFA for offices. In KGUV,
the number of car spaces is restricted to 1 space per 30m2 GFA for all non residential
development. These restricted parking facilities are provided at a TOD to discourage drivers
from driving their cars and to promote the use of sustainable modes of transport including
walking, cycling and use of public transport.
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Figure 4.1 shows an overview of KGUV, the public transport corridors and its proximate
transit stops (development has occurred on the site since the photograph was taken). In this
figure, the area bounded by red polygon represents KGUV, the dotted yellow lines show the
public transport corridors and the blue symbols indicate location of bus stops or busway
(BRT) stations catering for KGUV users. The major public transport corridors for KGUV run
along east and west flank with an intercampus shuttle bus service running through its centre.
The three transit corridors catering for residents and visitors at KGUV are:
The Inner Northern Busway located on the eastern flank of the QUT campus.
Kelvin Grove Road located on the western flank of KGUV. Many of its services leave
the Busway at Normanby immediately to the south.
The QUT intercampus shuttle service operating along Musk Avenue within QUT KG
campus/KGUV and runs express to QUT Gardens Point (City) campus 3.5km to the
south.
Figure 4.1 Aerial overview of Kelvin Grove Urban Village (KGUV)
KGUV is close to two major busway (BRT) stations (ST1 and ST8), four express bus stops
(ST3, ST4, ST5, and ST7) and two local bus stops (ST2 and ST6); the locations of bus stops
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can be seen in Figure 4.1. KGUV is served by 16 bus services including nine express and
very high frequency services. Table 4.2 identifies the stops and describes the bus routes
observing them. (The details of public transport services were obtained from the TransLink
Transit Authority’s public transport information website, www.translink.com.au.)
Table 4.2 Buses observing transit stops in KGUV
Stop Location Routes Destination
ST1 QUT KG Busway
Station on Inner
Northern Busway
330e, 333b, 340e, 376e,
392e, 393l, 680l
Inbound to CBD and
outbound to northern suburbs
ST2 Victoria Park Road 361a, 364a
ST3 Start of Musk Avenue 391e QUT Gardens point
(intercampus shuttle service)
ST4 Middle of Musk
Avenue (Near IHBI)
391e, QUT Gardens point
(intercampus shuttle service)
ST5 Kelvin Grove Road at
Blamey street
344cp, 345b, 351r, 357e,
359e, 390a
Inbound to CBD
ST6 Kelvin Grove Road at
School street
390a, 364a
ST7 Kelvin Grove Road at
Prospect terrace
344cp, 345b, 351r, 357e,
359e, 390a
Outbound to northern suburbs
ST8 Normanby Busway
Station on Inner
Northern Busway
330e, 333b, 340e, 344cp,
345b, 351r, 357e, 359e,
376e, 390a, 393l
Inbound to CBD and
outbound to northern suburbs
Note: The suffix to the bus numbers indicates type of bus service. Where e = express service, b = buz
service, l = link service, a = all stop service, cp = city precinct service, and r = rocket service
There are six main types of services operating on these corridors;
Buz (routes 333, 345), which are very high frequency buses with limited stops (10
minutes – 15 minutes, early morning till late night).
Express buses (routes 330, 340, 357, 359, 376), which are high frequency buses with
limited stops (10 minutes – 15 minutes).
City precincts (route 344), which stop at limited stops and run only at peak time.
Rocket buses (route 351), which stop only at few specific stops and run only at peak
times serving commuter markets.
All-stops buses (routes 361, 364), which stop at all bus stops on the specified route
across the day.
Links (route 393, 680), which are linked to other modes of transport such as train and
/ or ferry at transfer stations (early morning till evening).
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Special purpose QUT intercampus shuttle service (routes 391, 392), which are only
available to QUT students and staff members (morning till night).
Generally TOD developments are planned around a major transit node. As the distance from
the node increases the density often decreases. However, KGUV does not have a major
transit node at its centre; rather, transit services are available along the corridors mentioned
above. Although the three main bus corridors suggest good public transport coverage, the
Quality of Service (QoS) needs to be assessed further. The analysis for the availability
criteria of the QoS framework for KGUV is presented in the following section.
4.4 Suitability of case study TOD
4.4.1 Background
The suitability of the case study TOD will be analysed using the Quality of Service (QoS)
framework described in the Transit Capacity and Quality of Service Manual (TCQSM) (TRB,
2003). A QoS framework is a tool for assessing effectiveness or usefulness of transit systems.
The QoS for a transit facility is the measure of the performance of the system within a
particular area from the passenger’s point of view. The performance measures used for
evaluating the QoS can be qualitative or quantitative in nature. The QoS framework given in
the TCQSM (TRB, 2003) is shown in Table 4.3. The analysis for QoS for a transit facility is
based on the type of service provided for the transit system; either fixed-route service or
demand-responsive service. The QoS framework is divided into “Availability” and “Comfort
and Convenience” measures. A primary measure of QoS is assigned to each of these
attributes, at three levels of scale; for individual transit stops, for route segments / corridors,
and for the whole system. The QoS is determined by calculating the level of service (LOS)
separately for each parameter. The LOS is graded on a scale from A to F, with “A” being best
and “F” being the worst result. The various performance measures used are listed in Table
4.3.
Table 4.3 Quality of service framework: Fixed – route (TRB, 2003, Exhibit 3–1)
Service measures
Stop Corridor
(Route segment)
System
Availability Frequency Hours of service Service coverage
Comfort and
convenience
Passenger load Reliability Transit – Auto travel time
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4.4.2 Transit availability
“Availability” estimates how accessible the service is to the passengers, whenever required,
without considerable waiting time and walking distance (TRB, 2003). The transit availability
is measured from supply side without considering the public transport demand.
For transit stops, availability is measured by service frequency of the transit service. The
service frequency is calculated by considering various destinations from a particular transit
stop. Several routes serving the same destination and available from a particular transit stop
can be combined for analysis. According to the TCQSM (TRB, 2003), buses serving the
same destination and arriving at a stop within three minutes of each other are counted as one
bus for calculation of service frequency. Bus services to different destinations from a
particular transit stop should not be combined for calculation of service frequency. The LOS
is determined using values of average headway or average frequency.
For route segments or corridors, availability is measured by service span (hours of service)
for a transit service between two locations during the entire day. This is to check whether
service is provided when it is needed. Service span is calculated by route rather than by trip.
When the service is provided at least once in an hour then an extra hour is added to the
calculated day service span. This additional one hour takes into account the last hour of the
service provided.
For the system, availability is measured by the system area served, which is the percentage of
the inhabited area within walking distance of a transit stop. This analysis can be performed in
two ways; either using a Geographical Information System (GIS) or a manual method. The
GIS method involves drawing circular buffers of 400m (for a bus stop) and 800m (for a
premium node such as a busway or railway station) around the transit stop or station. These
buffers of 400m and 800m represent the normal walking distance for the passengers using the
transit service; corresponding to 5 minutes and 10 minutes walk respectively. In the manual
method, the actual transit service radius is calculated by using a street connectivity factor,
grade factor, population factor and a pedestrian crossing factor. A physical (actual) map is
observed for determination of values of these factors. Alternatively, actual walk buffers may
be determined.
All three measures are useful for evaluating the availability of transit service to the potential
passengers within a case study area such as KGUV. Evaluation using these measures has
been performed to determine the LOS for transit availability at KGUV.
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Deepti Muley Page 79
4.4.3 Comfort and convenience
“Comfort and convenience” is the standard or quality of service provided; including comfort
onboard, reliability for the service, and comparative journey time (TRB, 2003).
For transit stops, comfort and convenience is measured by passenger loads onboard,
determined by calculating the load factor and / or standing passenger area during the study
period/s. These require knowledge of the characteristics of the buses observing the stop and
the number of passengers onboard.
For route segments or corridors, comfort and convenience is measured by reliability; either
on-time performance, or headway adherence. On time performance measures the percentage
of buses maintaining their time within 5 minutes of their schedule along the route. Buses
arriving early are not counted as on time. Headway adherence is used to determine the
reliability of transit services operating at headways of ten minutes or less, and is based on the
coefficient of variation of headways.
For the system, comfort and convenience is measured by the travel time difference between a
door to door trip made by car and the same trip made by using transit. For car, the travel time
includes in vehicle time, parking time, walking time from parking to destination and for
transit, the travel time includes the time taken by the user to reach bus stop, waiting time,
time on the bus, walking time to the destination and the transfer time if applicable. For a
system a basket of trips may be used during a given day or across a number of days.
All three measures are useful for evaluating the comfort and convenience of transit service to
the potential passengers within a case study area such as KGUV. But the evaluation using
these measures is more data intensive and requires skilled personnel; hence for time and
expense reasons, the comfort and convenience analysis was not performed for assessing
suitability of KGUV. However, it is reported that Brisbane buses services overall offered
better performance in terms of on time running and comfort for its users (TransLink, 2010).
4.5 Transit availability for KGUV
4.5.1 Analysis background
A TOD is a compact area with increased population density and employment opportunities
and some people may live and work in the TOD, however others will live there and work
offsite, while others still will live off site and visit the TOD for work, shopping, education or
recreational purpose. This is expected to be the case for KGUV.
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A readily available transit system will enhance transit mode share for trips between the site
and off site locations. So the analysis for determining QoS for a development should consider
it both as an important destination and origin for trips. Thus, all analyses have been
performed for both directions.
As discussed earlier, KGUV has three fixed route transit corridors; the analysis has been
performed separately for each corridor. The buses passing by a transit stop but not observing
it were not considered in the analysis. Analyses have been performed for four distinct time
periods corresponding to the following ticket validity periods set by the transit planning and
delivery agency, TransLink:
morning peak between 7am and 9am,
off – peak day time between 9am and 3pm,
evening peak between 3pm and 7pm, and
off – peak evening time between 7pm and 10pm.
The extended peak hour for evening peak is considered to be four hours rather than normal
two hours. Services operating during the early morning before 7am and late evening after
10pm were not considered in the analysis. The schedules for weekend days, public holidays
and NightLink services (operating only on Friday and Saturday nights) have not been
included.
The local bus stop at School Street on Kelvin Grove Road was not considered separately in
the analysis, because all the buses observing these stops also observe the bus stop on Kelvin
Grove Road at Blamey Street. The bus stop at Victoria Park Road was also not considered
separately for the same reason. This stop serves only route 364 operating from Herston to the
CBD in the evening and on weekends. No direct bus route was found to and from the north-
east side of KGUV so no destination was considered in that direction.
The analysis for transit frequency and hours of service was performed considering KGUV as
a destination for various origins located to the north and south of KGUV, and vice versa.
Figure 4.2 shows the location of KGUV with respect to the Brisbane CBD and various
northern suburbs. For this analysis, Kelvin Grove Road and QUT KG Busway Station were
considered separately. The Normanby Busway Station was not considered separately because
this would have given an optimistic picture of the LOS for transit frequency and hours of
service. All the buses served Normanby Busway Station and then split their routes to Kelvin
Grove Road or to QUT KG Busway Station. In order to avoid double counting, Normanby
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Busway Station was not considered in analysis. On the contrary, the Normanby Busway
Station was considered for calculation of transit supportive area (TSA) to determine the
proportion of area served by Normanby Busway Station. In this case, even if the TSA overlap
is observed it will not be double counted.
Source: www.whereis.com
Figure 4.2 Regional map showing various offsite attractions considered in analysis
4.5.2 Availability – Transit stops
Table 4.4 defines the fixed route service frequency LOS ranges according to the TCQSM
(TRB, 2003). UK traffic identifies 12 – 15 minutes time interval for a random arrival for a
passenger (Balcombe et al., 2004) while US guidelines denote 10 – 14 minutes time interval
for a random arrival (TRB, 2003). This difference might be because of difference in attitudes
between public transport users. For cultural reasons Australian users are more likely to
behave as users from the United States of America; hence the guidelines provided by TRB
(2003) are used in this study.
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Table 4.4 Fixed – route service frequency LOS (TRB, 2003, Exhibit 3 – 12)
LOS Average headway
(min)
veh / h Comments
A < 10 > 6 Passengers do not need schedule
B 10 – 14 5 – 6 Frequent service, passengers consult schedules
C 15 – 20 3 – 4 Maximum desirable time to wait if bus / train missed
D 21 – 30 2 Service unattractive to choice riders
E 31 – 60 1 Service available during the hour
F > 60 < 1 Service unattractive to all riders
For those living at KGUV various destinations were analysed. The popular destinations for
residents such as Brisbane CBD and Cultural Centre (both to the south of the study area), and
QUT Gardens Point (City) campus were considered. It is noted that busway stations in the
CBD and Cultural Centre are the key interchange hubs for regional transport, both bus and
rail. For reference, a major destination along each corridor to the north of KGUV was
considered; Carseldine, Aspley, Chermside and Everton Park. The locations of the various
destinations are shown in Figure 4.2. The timetables, obtained from the TransLink website
(www.translink.com.au), for all bus routes serving the same destinations from the same
transit stop were compiled to calculate the service frequencies. Average frequency was
calculated by dividing the total number of buses in the time period by the number of hours.
Average headway was calculated by taking the inverse of average frequency and then
converting to minutes. When combined, certain buses arriving at the transit stop within three
minutes of each other were counted as one service. LOS results obtained and the total
numbers of bus services noted during the analysis period of 7.00am to 10.00pm are shown in
Table 4.5 and Table 4.6. These are not the daily totals.
Table 4.5 LOS for various trip destinations originating from KGUV (Kelvin Grove Road bus
stops)
Time Period Gardens
Point
CBD
(1)
Cultural
Centre (1)
Aspley
(1)
Everton
Park (1)
Carseldine
7am to 9am D/C A A C F C
9am to 3pm C A B/C C E C
3pm to 7pm C A C B C E
7pm to 10pm E B C B/C No service E
Total No of
Services 48 145 67 68 23 34
Note: (1) – Indicates results for Kelvin Grove Road bus stops
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Table 4.6 LOS for various trip destinations originating from KGUV (QUT KG Busway Station)
Time Period CBD (2) Cultural Centre (2) Chermside (2)
7am to 9am A A A
9am to 3pm A A A
3pm to 7pm A A A
7pm to 10pm A A B/C
Total No of services 177 121 127
Note: (2) – Indicates results for QUT KG Busway Station
The same procedure was then repeated for those visiting KGUV by considering the locations
mentioned above as origin of trips and KGUV as a destination. The results obtained and the
total numbers of bus services noted during the analysis period of 7.00am to 10.00pm are
listed in Table 4.7 and Table 4.8.
Table 4.7 LOS for various trip origins where destination is KGUV (Kelvin Grove Road bus
stops)
Time Period Gardens
Point
CBD
(1)
Cultural
Centre (1)
Aspley (1) Everton
Park (1)
Carseldine
7am to 9am C A C A A E
9am to 3pm C A C C E C
3pm to 7pm C A B C E D
7pm to 10pm E B/C C B/C No service E
Total No of
services 48 145 67 66 23 33
Note: (1) – Indicates results for Kelvin Grove Road bus stops
Table 4.8 LOS for various trip origins where destination is KGUV (QUT KG Busway Station)
Time Period CBD (2) Cultural Centre (2) Chermside (2)
7am to 9am A A A
9am to 3pm A A A
3pm to 7pm A A A
7pm to 10pm A A A
Total No of services 171 119 127
Note: (2) – Indicates results for QUT KG Busway Station
The following points are observed from the LOS results from the point of view of residents of
KGUV who are living in KGUV and going outside KGUV for various purposes:
Transit is a very good option for KGUV residents commuting to the CBD and
Cultural Centre (and connecting to/from other transit services at these locations) and
to Chermside.
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Transit seems to be a fair to poor option for KGUV residents commuting to the other
outlying suburbs Aspley, Everton Park and Carseldine in the morning and home in the
evening.
Transit remains a poor option at night for KGUV residents except to or from the
CBD, Cultural Centre, and Chermside, where it is good.
The following points are observed from the LOS results from the point of view of visitors to
KGUV for various purposes:
Transit is a very good option for visitors from the CBD and Cultural Centre (and
those connecting to/from other transit services at these locations).
Transit is a good option for visitors from Aspley in the peak periods and fair in the off
peak period.
Transit is a fair to good option for visitors from Everton Park in the peak periods and
poor in the off peak period.
Transit is a very good option for visitors from or to Chermside throughout the day.
Transit is a poor option for visitors from Carseldine except during the outbound
morning peak period and daytime off peak period when it is fair.
Transit is a poor option for the visitors going back to the outlying suburbs at night,
except Aspley and Chermside.
Transit offers a fair option for students and staff members using the 391 QUT
intercampus shuttle service during the day but a poor option in the evening.
4.5.3 Availability – Route segments / corridors
Table 4.9 defines the fixed route hours of service LOS ranges according to the TCQSM
(TRB, 2003).
Table 4.9 Fixed – route hours of service LOS (TRB, 2003, Exhibit 3 – 13)
LOS Hours of service Comments
A 19 – 24 Night or “owl” service provided
B 17 – 18 Late evening service provided
C 14 – 16 Early evening service provided
D 12 – 13 Daytime service provided
E 4 – 11 Peak hour service only or limited midday service
F 0 – 3 Very limited or no service
For calculation of hours of service with respect to route segments the same destinations were
considered as above. The hours of service were calculated for a round trip; originating from a
bus stop and terminating at the same bus stop. For example, if we consider Kelvin Grove
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Deepti Muley Page 85
Road as the origin and the CBD as the destination, then the round trip from Kelvin Grove
Road – CBD – Kelvin Grove Road was considered. The hours of service were calculated by
subtracting the time of last service departing from CBD to Kelvin Grove Road from the time
of departure of first service from Kelvin Grove Road. Although suggested by the TCQSM
(TRB, 2003), an extra hour has not been added to calculate the hours of service because it
was considered to give an overly optimistic picture of this LOS measure. The results for
hours of service for various round trips with the numbers of bus routes are listed in Table
4.10.
Table 4.10 Hours of service LOS for different corridors with bus route numbers
Route Segment (Round trip) Bus routes LOS
QUT Kelvin Grove Campus – QUT Gardens Point
Campus – QUT Kelvin Grove Campus 391
D
QUT Gardens Point Campus – QUT Kelvin Grove
Campus – QUT Gardens Point Campus C
Kelvin Grove Road – CBD – Kelvin Grove Road 344, 345, 351,
357, 359, 390
B
CBD – Kelvin Grove Road – CBD B
QUT KG Busway station – CBD – QUT KG Busway
Station 330, 333, 340,
376, 393, 680
A
CBD – QUT KG Busway Station – CBD B
Kelvin Grove Road – Cultural Centre – Kelvin Grove
Road 345 B
Cultural Centre – Kelvin Grove Road – Cultural Centre C
QUT KG Busway Station – Cultural Centre – QUT KG
Busway Station 330, 333, 340
A
Cultural Centre – QUT KG Busway Station – Cultural
Centre B
Kelvin Grove Road – Aspley – Kelvin Grove Road 345
C
Aspley – Kelvin Grove Road – Aspley B
QUT KG Busway Station – Chermside – QUT KG
Busway Station 330, 333, 340,
680
B
Chermside – QUT KG Busway Station – Chermside A
Kelvin Grove Road – Everton park – Kelvin Grove Road 351, 357, 359
E
Everton park - Kelvin Grove Road – Everton park E
QUT KG Busway Station – Carseldine – QUT KG
Busway Station 340, 392 C
Carseldine – QUT KG Busway Station – Carseldine B
The following points are observed from the LOS results for hours of service for the round trip
undertaken by residents of KGUV:
Transit is a good option for KGUV residents commuting to the CBD and Cultural
Centre throughout the day (and night and commuting to or from other services at
these locations).
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Transit is a good to fair option for KGUV residents commuting to outlying suburbs,
except for Everton Park with limited evening service.
Transit is a limited option for KGUV students and staff members commuting to GP
Campus with service limited to the daytime and early evening.
The following points are observed from the LOS results for hours of service for the round trip
undertaken by visitors to KGUV:
Transit is a good option for visitors coming to KGUV from the CBD and Cultural
Centre across the day (and night and commuting to or from other services at these
locations).
Transit is a fair to good option for visitors coming to KGUV from outlying suburbs
throughout the day, except from Everton Park with no late evening service provided.
Transit is a limited option for students and staff members visiting to KGUV with
service limited to the daytime and early evening.
4.5.4 Availability – System
Table 4.11 defines the fixed route service coverage LOS ranges according to the TCQSM
(TRB, 2003).
Table 4.11 Fixed – route service coverage LOS (TRB, 2003, Exhibit 3 – 14)
LOS % TSA covered Comments
A 90.0 – 100.0% Virtually all major origins & destinations served
B 80.0 – 89.9% Most major origins & destinations served
C 70.0 – 79.9% About ¾ of higher – density areas served
D 60.0 – 69.9% About two – thirds of higher – density areas served
E 50.0 – 50.9% At least ½ of the higher – density areas served
F < 50% Less than ½ of higher – density areas served
Transit supportive area (TSA) is the portion of the area being analysed having a household
density of at least 7.5 units per gross hectare or an employment density of at least ten jobs per
gross hectare. All 16.57 Ha area of KGUV is TSA. The system availability was calculated
using the MapInfo Professional 8.5. The availability for system was analysed for the
following distinct service coverage areas:
for bus stops on Kelvin Grove Road (separately for stops in both directions) and
Normanby Busway Station,
the QUT KG Busway Station, and
the area covered by the QUT intercampus shuttle service (Figure 4.1).
Selection of case study TOD
Deepti Muley Page 87
The image of KGUV was obtained from the Google Earth. A GPS survey was carried out to
determine the exact latitude and longitude of bus stops (accurate position of bus stops) at
KGUV and data for four distinct points at each end was collected for the image registration in
MapInfo 8.5. A GPS, Garmin 76S was used for data collection. The data for inbound and
outbound bus stops was noted separately. Buffers were drawn around the bus stops with
specific radius. The radius of the buffers was calculated using Equation 4.1 (TRB, 2003).
𝑟 = 𝑟0𝑓𝑠𝑐𝑓𝑔𝑓𝑝𝑜𝑝 𝑓𝑝𝑥 Equation 4.1
where,
𝑟 = radius of the buffer
𝑟0 = the ideal transit stop service radius
𝑓𝑠𝑐 = the street connectivity factor
𝑓𝑔 = the grade factor
𝑓𝑝𝑜𝑝 = the population factor
𝑓𝑝𝑥 = the pedestrian crossing factor
The value of 𝑟0 is 400m for a bus stop and 800m for a busway station or rail station. The
value of 𝑓𝑠𝑐 the street connectivity factor was universally equal to 1.0 due to the grid street
layout. The 𝑓𝑝𝑜𝑝 was universally 1.0 as most of the residents are young singles and 75 % of
them are ages under 45 (The Hornery Institute and Hassell, 2004). The 𝑓𝑝𝑥 was universally
equal to 1.0 as all pedestrian crossing delays were less than 30 seconds. The grade factor (𝑓𝑔)
was calculated by taking the average grade for various walking distances placed at extreme
ends of KGUV to the related bus stops. Then after image registration and establishing exact
location of bus stops, the circular buffers of the radius obtained by calculation were drawn for
all cases by considering the respective bus stops. The buffers for the bus stops are shown in
the Figure 4.3 to Figure 4.6. Table 4.12 lists the radius of buffer, percentage of TSA covered
and LOS for system availability.
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Table 4.12 LOS for different bus stops
Transit service Buffer
radius (m)
% TSA on
KGUV covered
LOS
QUT intercampus shuttle service 400 100 A
QUT KG Busway Station 760 100 A
Kelvin Grove Road Stop at Blamey Street
and Normanby Busway Station
400 or 760 96 A
Kelvin Grove Road Stop at Prospect
Terrace and Normanby Busway Station
400 or 760 95 A
The following points are observed from the LOS results of service coverage area from the
point of view of both residents of, and visitors to, KGUV:
QUT KG Busway Station provides excellent coverage to the whole study area,
considering that it provides a premium service that people are prepared to walk
further to access, as reflected by the 760m radial catchment. This caters for
passengers to or from the northern and northeast suburbs including Chermside and
Carseldine, the CBD and Cultural Centre.
The two QUT 391 intercampus shuttle service bus stops located on Musk Avenue
provide excellent coverage to the study area, which is to be expected given that they
are on the main street spine of KGUV. These cater for passengers to or from QUT’s
Gardens Point (City) campus.
The bus stops on Kelvin Grove Road inbound and outbound to the CBD and
Normanby Busway Station provide excellent coverage to the study area. These cater
for passengers to or from the northern and northwest suburbs including Aspley and
Everton Park, the CBD and Cultural Centre.
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Deepti Muley Page 89
Figure 4.3 Buffers for bus stops for QUT route 391 intercampus shuttle service (ST3 & ST4)
Figure 4.4 Buffer for QUT KG Busway Station (ST1)
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Figure 4.5 Buffers for bus stop at Kelvin Grove Road at Blamey Street (ST5) and Normanby
Busway Station (ST8) (inbound to CBD)
Figure 4.6 Buffers for bus stop at Kelvin Grove Road at Prospect Terrace (ST7) and Normanby
Busway Station (ST8) (outbound from CBD)
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Deepti Muley Page 91
4.6 Interpretation of results
The following observations can be drawn from the Transit QoS Availability analysis
undertaken for KGUV:
Transit availability to the Brisbane CBD and Cultural Centre (CBD is to the south of
KGUV) is very good in terms of frequency and hours of service for KGUV residents
and visitors.
Transit is a fair option for students and staff members using QUT intercampus shuttle
service in terms frequency in both directions however hours of services are limited to
the daytime and early evening only.
Transit remains a good to fair option for KGUV residents travelling to certain
outlying suburbs in terms of frequency and hours of service,
Transit is a good option for visitors coming from certain outlying suburbs but for
others it offers fair to poor transit availability and hours of service.
All the bus stops, and both busway stations, offer very good transit service coverage
for KGUV residents and visitors.
The overall observation of results shows that KGUV has good transit availability and
therefore as a TOD represents a worthy case study. The QoS determination for transit
availability was effective and useful for this desktop study, however, comfort and
convenience analysis would require a substantial field data base, which reduces the method’s
effectiveness. This aspect is an area proposed for future research.
4.7 Summary
The composition of mixed land uses at KGUV suggests that the site has appropriately placed
land uses for self containment and reduced parking facilities to restrict car use. And the
analysis for QoS for transit availability indicates that KGUV has overall good public transit
availability to or from various origins and destinations. Due to these qualifying characteristics
KGUV was selected as the case study TOD site for further research.
4.8 Chapter close
This chapter provided details about the case study TOD site and the criterion for assessing its
suitability which completes the first step of TOD evaluation, pre–TOD assessment. Next
chapter provides the details of data collection process undertaken to collect the transport data
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Deepti Muley Page 92
for the case study TOD, KGUV. The details of KGUV will also be useful for the traffic
generation calculations displayed in Chapter 6.
Deepti Muley Page 93
Chapter 5
Data collection
5.1 Introduction to data collection
The main objective of this chapter is to document the procedure followed for gathering traffic
and travel data for Kelvin Grove Urban Village (KGUV) and list the findings flowing from it.
As an initial step, the availability of existing datasets was checked. No dataset was found
explaining traffic and travel at KGUV. KGUV was a newly planned development hence the
data was required to be collected by conducting surveys. Before collecting the data, an
observational study was conducted to gain an understanding of the traffic and users at
KGUV. The next section presents an overview of the users using various land uses at KGUV
followed by the details of cordon counts conducted for gathering traffic data. Later the
methodology used for conducting the travel surveys is explained with the help of procedural
details along with sample sizes and response rates. In the last section, the lessons learned
from the data collection process are reported, followed by a brief summary and chapter close.
5.2 User groups at KGUV
In case of a Transit Oriented Development (TOD), the diverse mix of land uses provides
space for various categories of people to interact in a relatively small area. Various people
interacting at KGUV include residents, students, shoppers, employees and recreational users.
Mainly, the people residing in the TOD are termed as „residents‟ and people using the TOD
but residing outside the TOD boundary are termed as „visitors‟. For the purpose of this
research, the user groups were denoted based on the user characteristics and land uses they
use. In general, more than one user group was assigned for one land use. For example, two
user groups were specified for the users of the educational land use; namely students and
employees. The users of residential land use are termed as Residential Land Use (RLU) users
and Non Residential Land Use (NRLU) users. The following subsections note the overview
of land use and the users using the respective land uses.
5.2.1 Employees
The commercial land use at KGUV is comprised of a centrally located shopping centre, and
office land use comprising education related and private sector employment. The shopping
centre has retail outlets serving the KGUV users. The office land use has a separate block for
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private companies and combined research facilities for the university (QUT). The office
spaces for private companies were under development so were not considered in this
research. The persons working at commercial and education land use were termed as
employees, who comprised the “employee” group for data collection.
The employees at KGUV were divided into two groups depending on the type of employment
they were engaged in; professional employees working at the university campus extension
and retail shop employees working at the centrally located shopping centre at KGUV. The
research students working at the research facility of the university were included in the
professional employee group because these two groups possessed similar characteristics such
as type of work, access to facilities and services, etc.
5.2.2 Shoppers
KGUV has a centrally located small local service function shopping centre. The shopping
centre has speciality shops and retail outlets. The shopping centre serves the KGUV and area
located within close proximity of KGUV. Anyone who shops at this shopping centre is
termed as a shopper at KGUV and these users formed the “shoppers” group for the purpose
of data collection. This group mainly consisted of employees, students at KGUV and some
people residing within close vicinity of KGUV.
5.2.3 Students
KGUV has a major share of education land use attracting a significant proportion of students.
It has a university campus extension and a high school for specialised education, which is a
separate entity from Kelvin Grove State College, which is located just on the north boundary
of the study area. The students undertaking studies at these facilities formed the “students”
group for analysis.
Similar to the employee user group, the students at KGUV were also divided into two groups
depending on the type of education; school students and university students. The students
studying at the high school formed school students group and the students studying at the
university formed the university students group.
5.2.4 Residents
The residential land use at KGUV is comprised of affordable apartments (public housing),
student accommodation, apartments and townhouses. Majority of the apartments are one
bedroom or two bedroom apartments. Most of the affordable housing apartments are
Data collection
Deepti Muley Page 95
occupied by single parent or young couples and the apartments are rented or owned by young
students or small sized families. The student accommodation was occupied by full time
students studying at universities located nearby KGUV. The persons living in these
apartments formed the “residents” group.
These diverse residential developments at KGUV provided living opportunities for people
representing various household characteristics. The residents at KGUV comprised of students
studying at the university, young couples and senior citizens possessing different personal
characteristics. The Student Residents (SR), living in student accommodation, and Non
Student Residents (NSR), living in other apartments, were the two groups of residents.
5.2.5 Recreational users
The recreational land use at KGUV consists of a theatre, an activity centre, parks and open
spaces. The various KGUV users, residents at KGUV and visitors use these facilities. These
users are termed as “recreational” users.
Figure 5.1 gives an overview of the various land uses and user groups at KGUV, which are
considered for further investigation.
Figure 5.1 Overview of land uses and user groups at KGUV
TOD Land uses Users User groups
Kelvin Grove
Urban Village
Residential
Commercial
or retail
Education
Students
University students
Non student residents
Employees
Shoppers
Residents
Professional employees
Retail shop employees
Shoppers
School students
Student residents
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5.3 Cordon counts
Cordon counts were conducted to collect the traffic data for KGUV and determine the traffic
characteristics of this stated TOD. The locations for the cordon counts were selected from the
observational study conducted for KGUV. The access points were primarily considered as the
locations of cordon counts. In total 18 access points were targeted to gather the traffic data
for KGUV. Eight locations were designated for conducting counts at 18 access points. Thus
the recorder or observer standing at one location could collect traffic data for more than one
access point. Figure 5.2 shows the locations of the cordon counts for KGUV.
The traffic counts were not conducted for 24 hours due to a lack of resources; instead three
distinct time periods, morning peak (7:45am to 9:30am or 7:15am to 9:30am), midday peak
(11:30am to 1:30pm), and evening peak (3pm to 6pm) were chosen for traffic data collection.
From the many observations done at various times of the day, it was noticed that much of the
travel activity occurred during this time period. The midday peak was intentionally
considered to determine the details of activities during the typical lunch hour period. The
counts were conducted for a typical weekday during the school term. The cordon count for
AM peak period was undertaken on Wednesday morning and all other counts (midday peak
and PM peak) were undertaken on Thursday.
The bi–directional counts were undertaken for all modes of transport; car, buses, pedestrians,
motorcycles and bicycles, to capture vehicle and pedestrian movements inbound and
outbound of KGUV. The car occupancy was noted along with the number plates. The string
of first four characters of the number plate of each vehicle was noted. This data was later
cross matched to determine the amount of through traffic. The data was recorded in 15
minutes intervals for all three time periods. A sample of the form used for recording the
cordon data is included in Appendix B.
In addition to the above mentioned cordon count, a separate count was done for the centrally
located shopping centre. The shopping centre was considered separately because it was the
highest trip attractor, especially for the intra-zonal trips due to the retail mix. Hence it was
important to gain an understanding about the pedestrians as well as vehicular activities for the
shopping centre, as these could be different from the conventional shopping centre. The
shopping centre counts were conducted at two locations for four access points. The number
of cars, pedestrians, motorcycles and bicycles were noted. The car occupancy was noted but
the data for number plates were not collected. The bi–directional counts were conducted for
Data collection
Deepti Muley Page 97
the same three time periods which were used for cordon counts were used for collecting the
traffic data for the shopping centre.
Represents the locations of cordon counts for whole of KGUV
Represents the locations of counts for Village Centre
Figure 5.2 Locations of cordon counts at KGUV
5.4 Travel surveys
Revealed preference travel surveys were carried out in phases for all groups of KGUV users
(except recreational users) mentioned in Section 5.2; namely one for shoppers, one for
employees (professional employees and retail shop employees), one for students (school
students and university students) and one for residents (student residents and non student
residents). It is acknowledged that there can be some overlap of users using multiple
developments and responding to the same survey. No method was used to cross check
responses from the same person for multiple questionnaires as this might have confused the
respondents. An overview of the general methodology is outlined in the following section
followed, by critical issues and observations made while conducting each survey in
subsequent sections.
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5.4.1 General methodology for conducting travel surveys
The step by step procedure followed in conducting the travel surveys for each user group is
demonstrated in Figure 5.3 with the help of a flowchart. The first step for travel data
collection involves selection of variables about which the information needs to be collected
from the travel survey. The variables were selected from existing literature and practical
conditions of the study area. The selection of survey instrument and the design of survey
questions were governed by these chosen variables. Sample size and the required target
response rate were the main factors determining the type of survey instrument to be used.
Selection of an acceptable response rate was crucial as it controlled the decision of whether to
stop or continue the main surveys. After this step, the questionnaire survey form was
designed in such a way that all questions were easy to understand by a lay person. Use of
technical terms was avoided while designing the questionnaire form to simplify the questions.
The survey questions and survey technique were tested for suitability, before conducting the
main travel surveys, by conducting pilot studies on some respondents. Responses to the
questions implied suitability of the survey technique and layout of the questions. Some
changes to the survey instrument or the survey questions were required at this stage in order
to improve the response rate and quality of responses for main surveys. The descriptions of
the changes made are noted in Section 5.4.5.
After updating the questionnaire from the pilot study, the main survey was conducted for
people selected randomly in the user group under study. Once the required response rate was
achieved, the surveys were discontinued and all collected responses were compiled and
combined together for preliminary data analysis. In subsequent stages of the research this
travel data can be used for travel demand modelling. This process was repeated for all user
groups at KGUV.
Data collection
Deepti Muley Page 99
Figure 5.3 Steps involved in data collection process for a user group travel survey
No
No
Yes
Yes
Yes
No
Update
questions?
Main surveys
Organize responses and
prepare database
Preliminary data analysis
Collect more survey data
Update questions
Update survey
method?
Is response rate
acceptable?
Variables
selection
Literature
review
Investigation of various
survey instruments
Selection of appropriate
survey instrument
Design questionnaire form
Pilot study
Decision of
sample size and
response rate
Review responses
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5.4.2 Selection of survey instrument
The selection of survey instrument depends on the characteristics of respondents. After
reviewing the available survey techniques it was found that, due to the diverse mix of people,
a single methodology was not suitable for all users at KGUV. Hence a combination of
various survey techniques, such as mail back survey, internet based survey, personal
interview and intercept survey, was used to collect data related to KGUV travel. An
appropriate survey technique for each group of user was selected considering the data
collection time and cost involved in conducting the surveys. A computer assisted personal
interview (CAPI) survey was selected for collecting the travel data for shoppers. CAPI
surveys were favoured because survey techniques involving computer assisted methods, new
technologies and face–to–face techniques shown elsewhere to provide a higher response rates
(Korimilli et al., 1998). Shoppers were also given an option of taking the survey forms with
them with a reply paid envelope.
Different survey methods were adopted for the two types of employee groups. An internet
based survey was chosen for the professional employees because it is less time consuming,
more cost effective, and flexible for respondents. All of the professional employees also had
work email addresses and good access to internet at their work places. By contrast, the
employees at the shopping centre were surveyed using a CAPI survey. This method was
chosen as these employees did not necessarily have access to internet at their workplace.
Their working hours were variable; hence it was necessary to book times for meetings and to
contact them personally.
The two student groups at KGUV were also surveyed using separate survey instruments. An
internet based survey technique was used for the university students. This was done for
similar reasons that applied to professional employees. A mail back survey technique was
chosen for the school students. This method was selected particularly because it was
important to obtain the responses with written consent from parents of students who were
mostly under 18 years of age (adulthood).
Similar to other user groups, two different methods were used to survey the residents. In case
of residents the methods used for other user groups were not appropriate as it was not
possible to obtain either contact email addresses of the residents or any personal contact
details like telephone number, names of persons. These details were not available due to
Privacy law. A modified mail based method was used to gather data related to NSR‟s travel.
The respondents were given a choice of mail back, internet based surveys, telephone and
Data collection
Deepti Muley Page 101
personal interviews. An introductory letter was posted to NSRs requesting the preferred
method and the surveys were conducted based on the method chosen by the respondents.
Similar to NSRs, the SRs were given an option of mail back survey or internet based survey.
A notice was placed on the notice boards of the student accommodation with a web link for
the survey for the SRs at KGUV asking for response. If mail back survey was chosen then the
questionnaire survey was posted to the respondent.
To acknowledge the participants for their time and input some incentives were given to
certain user groups. The details of the same are noted in Section 5.4.4.
5.4.3 Design of questionnaire form
A separate questionnaire form was designed for the users of the non residential land use and
the users of the residential land use. Ethical clearance was obtained from the Research Ethics
Unit of the QUT to ensure the appropriate design of questionnaire forms. The following
sections explain details of each questionnaire form.
5.4.3.1 Questionnaire for non residential land use users
A separate questionnaire survey was designed for each category of user. The users of non
residential land use were asked about their travel details for the specific trip to KGUV by
their most usual way, along with personal and household details. Most of the questions were
multiple choice and stated in a layperson‟s language. While specifying possible responses,
broad ranges were given instead of asking overly specific questions. For example, the age
group of a respondent was asked instead of exact age. The respondents were given five
choices of age groups to choose from (0–18years, 18years–30years, 30years–45years,
45years–65years and 65years and above). The school students‟ survey was designed as a
short survey keeping in mind the age of the respondents. Some questions were excluded from
the general questionnaire form. A copy of the final questionnaire form can be obtained from
Muley et al. (2008). The questions in the questionnaire form asked information on the
following aspects:
Mode of travel to work, mode choices (if available), mode specific questions (like
parking fees, parking place, boarding and alighting stop location, public transport
route number, transfer location, walking time to and from the stop) and reasons for
choosing the selected mode of travel
Perceptions of existing public transport at KGUV and any improvements the
respondent considers are required to improve existing public transport
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Perceptions of KGUV
Personal and household information (age, gender, occupation, vehicle ownership,
number of driving license holders, size of household, etc.)
Figure 5.4 Questionnaire details for non residential land use users
Yes
Introduction to travel survey
Usual mode of travel to KGUV
Private
vehicle Bicycle
Public
bus Train Ferry Taxi
Walk
only Other
Parking details Public transport details Specify
Other option
available? Specify options
Perception about public transport at KGUV
Activity purpose
(if not travelling home)
Perception about KGUV
Comments
Personal and household information
No
Frequency of travel to KGUV
Data collection
Deepti Muley Page 103
The flow of the survey questions is shown in Figure 5.4. The answer to the first question
asking the usual mode of travel to KGUV was compulsory. The respondent who did not
answer this question was directed towards the last page. Skip logic was used to direct
respondents to direct them to provide their travel details based on their mode of transport.
All the questionnaire forms were designed using the popular American web based survey
design site called www.surveymonkey.com. It was estimated that each respondent would take
approximately 10 to 15 minutes to complete a survey. Although a separate questionnaire
survey was designed for each group of visitors, all questionnaire forms essentially collected
the same travel data.
5.4.3.2 Questionnaire for residents
A questionnaire form was designed for both resident groups. The residents were asked to
record the travel details for all trip purposes for the whole day. This was necessary as the
residents were the only trip producers in KGUV. Although different survey techniques were
offered to the residents, the design of questionnaire for all the survey techniques was similar
in order to obtain the same data set for all the respondents. The same online survey tool was
used to design this questionnaire survey. A working Wednesday was chosen as the travel day
for the residents.
The questionnaire was composed of two forms; household information form and travel diary.
A copy of questionnaire form can be obtained from Muley et al. (2009). The household
information form collected details about the household characteristics such as type of
household, number of bedrooms in the household, household size, vehicle and bicycle
ownership, number of valid driving licence holders and street name.
A travel diary was designed to collect travel data related to residents‟ travel and personal
information. The travel diary was divided into three main sections. The first section asked
questions related to personal information like age, gender, occupation, employment status and
driving licence availability. The second section included questions related to each trip made
on the travel day. The travel details for each trip were similar to those listed for non resident
users‟ travel data. An option for filling maximum 10 trips was given to the residents, in case a
resident made more than 10 trips, a separate travel diary was provided upon request for
additional trips. The details of each trip included origin and destination of trip, start time and
end time of trip, mode specific questions and purpose of making the trip. The third and last
section asked questions related to the perceptions about public transport at KGUV,
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perceptions about KGUV and any special comments. An overview of the survey form design
for the users of residential land use is given in Figure 5.5. It was estimated that each
respondent would take no more than 20 minutes to complete the questionnaire form.
Figure 5.5 Questionnaire details for residential land use users
Frequency of activities at KGUV
Perception about public transport KGUV
Comments
Perception about KGUV
Travelled more?
No
Yes
Mode choice/s (if available)
Trip purpose
Mode of travel for the trip
Private
vehicle Bicycle
Public
bus Train Ferry Taxi
Walk
only Other
Parking details Public transport details Specify
Time and location of origin and destination of the trip
Introduction to travel survey
Personal information
Household information
Travelled on
assigned travel day?
Frequency
of travel
No
Yes
Data collection
Deepti Muley Page 105
5.4.4 Reminder letters and incentives
All user groups were posted a reminder letter after two weeks of initial contact. If the number
of responses was less than the targeted sample size then a second reminder letter was sent
after four weeks of first reminder letter. The reminder letters emphasized the importance of
residents‟ response and participation, confidentiality and future benefits.
In previous research (Murakami & Watterson, 1992 and Tooley, 1996) it was found that use
of pre–incentives helped to achieve a better response rates, hence the participants of the
survey were given some small gifts to appreciate their participation and time. The
nonmonetary incentives were given to the respondents instead of monetary incentives. All the
respondents of CAPI surveys (shoppers and retail shop employees) were given a pocket pal
guide, providing information about the bus timetables obtained from the region‟s TransLink
Transit Authority as a reward for participating in the survey.
The NSR were posted a free coffee voucher of value $3.50 as a reward for participation in
advance with the questionnaire removing the ambiguity associated with the lucky draw prizes
in order to improve response rates. On the other hand, the participants of the SR survey were
given a book shop voucher of $5 for their contribution. No incentive was given to the
respondents of the internet based surveys (professional employees & university students) and
respondents of school students‟ survey because these was no individual contact made with
these users and also their personal contact details was not known to deliver the incentives due
to anonymity of the collected responses.
5.4.5 Process of conducting surveys
As stated in Section 5.4.2 the various KGUV users were surveyed using different survey
techniques, as the users had different characteristics. All the user groups were given a
covering participant information sheet that provided some information about the research,
contacts of research team, and the Research Ethics Committee at QUT for reporting any
issues or concerns about the project or questionnaire form. The procedure for conducting the
travel surveys for each user group was distinct; the procedural details for each user group are
listed in following subsections.
5.4.5.1 Shoppers’ survey
Initially, a CAPI survey was selected for shoppers. Pilot surveys were conducted to test the
selected methodology. A full questionnaire survey (with similar questions as noted in
employee survey (Muley et al., 2008), which took 10 to 15 minutes to complete, was offered
Evaluating the transport impacts of TODs
Deepti Muley Page 106
to shoppers, but it was found that they were always in a rush. Only two shoppers out 15
agreed to participate in the survey, but after talking to them it was found that these two
shoppers visited KGUV for the first time and they were not frequent visitors. Some people
were reluctant to stop and hear about the survey. Hence the size of questionnaire was required
to be reduced significantly for this group of subjects.
Only five questions were retained for the shoppers‟ survey, seeking information about their
shopping trip. The answers were noted down in a tabular format (Appendix B), so as to
reassure the shoppers that the survey would not take more than 1 or 2 minutes. Completing
the responses in the table made the survey process faster and easier for interviewers as well.
A good response rate of 72.7 percent was subsequently gained when this revised
methodology was tested. Considering the good response rate and time required to conduct the
survey, this methodology was used for the final survey.
5.4.5.2 Professional employee survey
To test the internet based survey technique for professional employees, a pilot survey was
sent to 30 professional employees selected at random. The employees were working at the
educational land use and the survey was sent to their work email addresses. A response rate
of 30 percent was obtained and the comments about the layout and design of the survey
questionnaire indicated that there was no need for any changes. Hence the same methodology
was adopted for conducting the main travel surveys. The participant information sheet was
attached to the email and displayed as a first page of the questionnaire form.
5.4.5.3 Retail shop employee survey
Initially, the questionnaire form that was used for professional employee survey was used for
conducting the main travel survey for employees at the retail shops. Before conducting the
main travel survey, the survey methodology was tested on only two employees and the
questionnaire form was shown to the owners or managers of all the shops to obtain
permission to conduct the surveys. These two surveys took around 10 to 15 minutes to
complete as expected. More employees were not surveyed for the pilot study as this is a small
scale shopping centre with about 125 employees. It was then decided to proceed directly with
the main survey.
When the main surveys commenced and the survey was conducted for all the employees, it
was observed that completion of survey form for some respondents took almost 30 to 45
minutes as the respondents were serving customers while the interview was in progress. After
Data collection
Deepti Muley Page 107
considering the time constraints on the respondents, some questions were removed as they
took more time to answer, these being the rating scale questions related to the perceptions
about public transport and KGUV and the reasons for choosing main mode of travel. The
survey methodology was also modified to “pen and paper” based interviews instead of using
“CAPI surveys”. This eliminated the time required to set up the laptop and gave flexibility to
respondents (some respondents preferred to fill the survey on their own). This accelerated the
data collection process and it took approximately 5 to 7 minutes to complete a questionnaire
survey.
5.4.5.4 School students’ survey
A mail back survey was used for school students‟ survey. Pilot surveys were not carried out
for school students due to the limited number of students. The questionnaire form used for
professional employees was used as a base for preparing the questionnaire form for school
students. The professional employee survey questionnaire was shortened keeping in mind
school students‟ age group. The questions related to the reasons for choosing the usual mode
of transport were omitted from the base questionnaire. Some questions related to personal
information like age, occupation, industry, and work postcode were also removed from base
questionnaire. A copy of the questionnaire can be found at (Muley et al., 2010). The survey
method and questionnaire form was approved by the School Principal before delivering it to
the students. The survey forms and parent consent sheets were handed over to the school
students at the school and the responses were collected via reply paid envelope provided with
the questionnaire form. The students were required to complete the questionnaire form and
return it using the reply paid envelopes provided with the signed parent consent form. The
responses without parent consent form were not considered for analysis. It was estimated that
a school student would take approximately 10 minutes to complete the survey.
5.4.5.5 University students’ survey
The same survey used for professional employees was utilised for university students. Hence,
a separate pilot survey was not conducted for the university students. The web link for an
internet based survey was placed at a university webpage, which the students were accessing
frequently. This web link appeared on each student‟s access page for two weeks. This method
was chosen because it was the best way to contact students without obtaining their personal
contact details. Similar to the professional employees, the participant information sheet was
displayed as the questionnaire cover page.
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5.4.5.6 Non student residents’ survey
At the first stage, the NSRs were asked for their consent to participate and the method of
conducting the survey was described by way of letter box drop. In this letter, the residents
were given a choice of mail back survey, internet based survey, telephone interview or
personal interview. The residents were asked to register their interest with the number of
people living in the household by contacting any of the team members personally, through
email or telephone. After registration, the surveys were scheduled as per the chosen survey
technique and the place and time were decided as per the respondents‟ convenience. To test
this method, a pilot survey was undertaken for 42 households living in one affordable
housing block. No responses were obtained after posting the introductory letter. To modify
the process, discussions were arranged with the mangers of the residential apartments and the
method was modified to mail back questionnaire technique. The residents were also given a
choice of internet based surveys by providing a web link to enter into the survey.
Each household was mailed two travel diaries, a household information form, a free coffee
coupon and an introductory letter with instructions. The residents were requested to ask for
more travel diaries in case the number of people in the household was more than two, as one
travel diary was required for filling travel details of one person. The responses were collected
through a reply paid envelop provided with the questionnaire form. A response rate of 16
percent was obtained for the pilot survey undertaken for 48 households, so this method was
retained for the main surveys. The actual time required for filling the survey was not known.
The responses to the questions indicated that no change was required in layout of the
questions and language of the questions.
5.4.5.7 Student residents’ survey
The questionnaire form used for non student residents‟ survey was used for student residents‟
survey. Before conducting the main survey, the methodology for the SRs‟ survey was tested
on three students residing in the student accommodation. On average 15 minutes were
required to complete one questionnaire form. For the main survey, a notice was posted on the
notice boards of the student accommodation to gather interest in the survey and study.
However, only two responses were obtained for the internet based survey. In order to obtain
more responses, the methodology was modified to “intercept surveys”. Although the
questionnaire form was the same, this method obtained good response rates for the pilot
surveys because of personal contacts. Typically the intercept surveys were carried out during
late afternoon or early evening time periods. These time periods were chosen because the
Data collection
Deepti Muley Page 109
students returned home from university or came out for the evening activity and it was
noticed that they had some time to stop and answer the questions. If the students were in
hurry and were interested in participating in the survey then they were given the
questionnaire form, book voucher and a reply paid envelope. It was observed that the students
took 10 to 15 minutes of time to complete the questionnaire.
Table 5.1 presents summary of the travel surveys used for collecting travel data for various
groups of KGUV users. It should be noted that the details of the final surveys are listed.
Table 5.1 Summary of travel surveys
User group Survey
period
Survey
instrument
Type of travel
data
Survey
completion time
Professional
employees
March &
April 2008
Internet based Usual trip to work 10 to 15 minutes
Retail shop
employees
March &
April 2008
Pen & paper
based interviews
Usual trip to work 5 to 7 minutes
University
students
August 2008 Internet based Usual trip to
university
10 to 15 minutes
School students October 2008 Mail back surveys Usual trip to
school
10 minutes
Shoppers May 2008 Intercept surveys Specific trip on
survey day
1 to 2 minutes
Student
residents
March 2009 Intercept surveys Travel diary for a
weekday
10 to 15 minutes
Non student
residents
August &
September
2008
Mail back surveys Travel diary for a
weekday
20 minutes
5.4.6 Sample size and response rates
Determination of sample size is an important step before conducting any kind of survey.
Sample size is selected in such a way to represent the average household characteristics of the
study area. In the case of a TOD, the size is usually small; hence the travel surveys should be
given to all people using the case study TOD. This offers the sample size as 100 percent of
the population size. So there was no requirement to obtain the sample size using a specified
method. This decision sought to minimise coverage error and sampling error.
For KGUV, only people interacting within the KGUV boundary were targeted for data
collection. The sample size was kept as 100 percent of the population size to reduce coverage
and sampling error. For the shoppers‟ survey, best efforts were made to survey the entire
population; however some error of about 5 percent is estimated.
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The number of responses and in turn the response rate both depend on the sample size.
Reasonable numbers of responses were obtained from the main travel surveys after
modifying the methodologies. Reminder letters were used only in the cases of non student
residents and professional employees. The reminder letters obtained additional 14 and 17
responses for non student residents and professional employees respectively. In general, a
minimum 30 responses or a response rate of 10 percent (whichever is more) was desired for
the travel survey of each user group.
The response rate for each survey was determined by dividing the number of complete
(usable) responses received by the total number of surveys sent (which is equivalent to the
size of population). For the shoppers‟ survey, instead the population size, the number of
KGUV shoppers approached for participation is taken as the number of survey sent. The
employees of educational land use showed a response rate of about 10 percent while the
response rate for employees of the retail shopping centre was approximately 31.2 percent.
The response rate for shoppers‟ survey was about 67.6 percent. Responses obtained from the
school students and university students were 20 percent and 15 percent respectively. While
for the student residents observed response rate was 11 percent and for non student residents
it was 8 percent. Although the response rate for NSR‟s survey was lower, the data collection
was stopped because of the limitations on the process of conducting the survey. For all other
surveys, the numbers of responses obtained were considered sufficient for travel
characteristics determination; hence the surveys were ceased at this point.
The mail back survey for residents showed the lowest response rate of 8 percent. This may be
due to large number of international students and young adults living in single family
households. Over-surveying of the residents can also be an important factor in lower response
rates as the residents had reportedly filled nine different surveys, not related to this research,
over a six months time period. It was also found that incentives did not help much in
obtaining a better response rate for the residents. The internet based surveys obtained
relatively lower response rates, around 10 to 15 percent compared to personal interviews (30
to 60 percent). Personal interviews, in our circumstances, are regarded as the best approach to
achieve a high response rate. A summary of survey instruments adopted and the final
response rates obtained after the surveys is given in Table 5.2.
Data collection
Deepti Muley Page 111
Table 5.2 Sample sizes and response rates
User
group
TOD user
group
Survey
instrument
Sample
size
Response
rate
Reason
Shoppers Shoppers Intercept
surveys 117 68%
Responses collected
in a tabular format
Employees
Professional
employees
Internet based 125 10%
Lack of direct contact
with respondents
Retail shop
employees
Personal
interviews 39 31%
Personal contact and
shorter questionnaire
Students
School
students
Mail back
surveys 28 20%
Use of shorter
questionnaire
University
students
Internet based 89 15%
No direct contact
with respondents
Residents
Student
residents
Intercept or
personal
interviews
51 11% Over surveying of
respondents by other
studies Non student
residents
Mail back
surveys 34 8%
Note: Sample size indicated number of usable responses
5.4.7 Sample bias
Generally, bias in the sample composition is inherited from the survey sample. Sampling bias
is caused due to unrepresentative sample, measurement error, sampling error and survey bias
(StatTrek, 2009). For all the surveys, the sample sizes were kept as close as possible to 100
percent of the population. Further, most suitable survey instruments were used for each user
group. In this case, there was no sampling technique applied, the survey was conducted under
the best possible environment for respondents, and the sample size was highest for maximum
coverage. Overall, it is believed that these survey responses contain minimal bias, although
any bias in the residents‟ data could be reduced if a larger sample size could be obtained.
However, the discussion above demonstrates the difficulty of achieving this.
5.5 Lessons learned
The observations and experiences from conducting the travel surveys at a stated TOD are
summarised below:
Multiple choice questions were quicker for respondents to answer, compared to
questions related to perceptions, which are usually designed on a rating scale.
The shoppers‟ survey had to be designed as a quick response survey in order to
maximise responses. A good response rate is likely to be obtained for a questionnaire
of one A4 page sheet or questions listed in the form of a table.
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A pen and paper based survey was preferred over a computer based interview (CAPI)
for employees at retail shops. A higher response rate was obtained for personal
interview surveys compared to mail back survey forms.
An internet based survey was suitable for professional employees and university
students, as in both cases these groups had good internet access. This method offers
flexibility to these respondents, which improves response rates.
Similar to the observation made by Shih and Fan (2008), the newest internet based
survey technique obtained the lowest response rate for professional employees when
compared to other survey techniques, which include personal contact. The response
rate for internet based surveys was found to be dependant mainly on the age, and the
profession of the respondents.
Personal contact was the best way to approach to the respondents, although this
approach is time consuming and somewhat expensive.
The questionnaire for the school students needed to be short and simple with more
multiple choice questions, which are easy to answer. The mail back survey technique
is suitable for school students.
Respondents prefer a short survey over a lengthy survey, as was seen for the retail
shop employee survey.
For a TOD, ideally the entire population should be surveyed. If this is not possible for
a specific user group then maximum possible number of TOD users should be
contacted to achieve a sample size as close to 100 percent as practicable.
Although the mail back survey technique is most commonly used for a residents‟
survey, intercept surveys can yield better response rates and good quality data.
5.6 Summary
KGUV incorporates four prominent user groups; employees, shoppers, students and
residents. Due to various activities of these users, KGUV experiences traffic movements. The
cordon counts gathered traffic data for all modes of transport for KGUV. The diverse mix of
users required different survey instruments; hence various survey techniques were adopted to
collect travel data for all user groups. Variations were required to achieve better response
rates. An internet based survey was used for professional employees and university students,
personal interviews for retail shop employees, an intercept survey for shoppers and SRs and a
Data collection
Deepti Muley Page 113
mail back survey was used for NSRs and school students. The responses obtained from the
travel surveys yield information about transport at a TOD.
5.7 Chapter close
This chapter presented the methodology used for obtaining transport data, which completes
Step II of the TOD evaluation. The findings of this chapter provide the base for Step V of the
TOD evaluation. The subsequent chapters use traffic and travel data collected using
procedures described in this chapter for analysing TODs from a transport perspective. The
next chapter presents analysis and results for the traffic data analysis in terms of the traffic
generation. Chapters 7, 9 and 10 present the analysis and results obtained from travel survey
data analysis.
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Deepti Muley Page 115
Chapter 6
Traffic generation at Kelvin Grove Urban Village
6.1 Introduction
A limited number of studies have been undertaken to study the traffic impacts of Transit
Oriented Developments (TODs) or mixed use developments. This chapter provides a detailed
overview of the traffic generated at the case study TOD, Kelvin Grove Urban Village
(KGUV), based on the cordon count data collected for KGUV and its centrally located
shopping centre known as Village Centre (VC). The traffic generation is compared with the
combined standard trip rates provided for homogeneous land uses because the data for similar
sized non – TOD developments or guidelines for trip rates for mixed land uses was not
available.
The first section of this chapter provides the methodological details of the cordon data
analysis. The following section presents an overview of the traffic at KGUV with the help of
car occupancy, hourly volumes and directional distribution for various modes of transport.
The comparison of peak period traffic with the trip rates from ITE (ITE, 2008) and Australian
sources (RTA, 2002) is undertaken in the following section. The final section presents a
summary of the chapter with a chapter close.
6.2 Analysis of cordon data
The details of the data collection were provided in Section 5.3. The analysis of cordon data
involved a stepwise approach, which is explained with the help of a flowchart in Figure 6.1.
The first step of analysis was to determine the amount of through traffic and eliminate it from
the recorded traffic volume. This task was performed only for the KGUV cordon counts and
not for the VC counts; all other tasks were similar for both the analysis. The possible
directions of the through traffic movements are shown in Figure 6.2. The through traffic
consists of the traffic that uses the road network at KGUV but does not stay in KGUV. Those
cars travelling in each direction were hence removed from recorded data for each time period.
There were six pairs of access cordon points, which were used by through cars. The through
traffic was only determined for cars. Through movements for all other modes of transport
were not determined because their data were not recorded with any identification number, as
in case of number plates for cars, to identify the through movements. It should also be noted
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that KGUV has only one bus service running through it; the frequency of this bus service was
known hence number of buses were not counted during cordon counts. All other public
transport services were running on the edge of KGUV, hence these services did not use the
road network at KGUV hence these buses were not counted during cordon counts. However,
the frequency of buses can be used as a measure to determine the public transport traffic.
Figure 6.1 Process of cordon data analysis
The traffic data obtained after removal of through traffic was used for further analysis. The
calculations were undertaken for all modes of transport; cars, motorcycles bicycles, and
pedestrians. The person movements were also determined by calculating the total number of
persons travelling in or out of KGUV using various modes of transport excluding buses.
Initially, the total vehicle traffic as well as person traffic was determined for each 15 minute
time interval, which was then added to obtain the total traffic for each analysis period. The
directional distribution for each mode of transport was calculated by dividing the total traffic
in a particular direction by the total traffic in both the directions of transport (inbound and
outbound to KGUV). Later, the car person occupancy was obtained by dividing total number
of people in the car by total number of cars in both directions.
The hourly volumes for each mode of transport were determined by adding the traffic
volumes for four consecutive time intervals during the specified time period. The hourly
volumes observing the highest person movements in the respective time periods were termed
as the peak hour in each time period. The vehicle or person movements occurring during this
time period represented the maximum hourly volumes for each mode of transport. Using a
Cordon count data
Eliminate through traffic
Obtain total traffic volumes
Determine traffic flow attributes
Compare with ITE (USA) trip rates
Compare with RTA (Australian) trip rates
Determine differences in traffic volume
Traffic generation at KGUV
Deepti Muley Page 117
similar rationale, the minimum traffic volumes and the corresponding time period were
determined for all three time periods.
Source: Department of Housing (2008)
Figure 6.2 Direction of through traffic at KGUV
After determination of the maximum hourly traffic volumes, the respective values for AM
and PM peak period were compared with that of peak hourly volumes derived from the
average rates and regression equations provided in ITE (2008) for the United States of
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America and the maximum of peak period traffic from the Australian sources (RTA, 2002).
This task pointed out the differences in the traffic volumes between the actual counts and the
standard practice. If the actual counts indicate less traffic than the standard guidelines then
the case study TOD exemplifies the claim of reduced traffic at TODs and the opposite trend
indicates that the TOD principles are not sufficient in reducing the traffic generation at this
particular development. On the other hand, if the amounts of observed and calculated traffic
are similar then the case study TOD does not display any change in traffic due to its stated
qualities. The following section presents the results of the analysis for the shopping centre
counts as well as cordon counts conducted for KGUV.
6.3 Conditions of the survey period
As stated before, KGUV includes educational land uses on its periphery. Hence, it was
necessary to conduct the cordon counts when the university and schools were open.
Considering the school and university semester timetables, the counts were undertaken during
September 2008 on a typical weekday. A typical weekday was chosen as a weekday during
the school term, falling in the middle of the week. Monday and Friday are not considered as
typical weekdays because members of the community tend to take leave on these days more
so than midweek days. More specifically, the Village Centre counts were undertaken on
Thursday for three time periods. For the whole KGUV, the AM peak period counts were
undertaken on Wednesday morning and midday peak and PM peak period counts were
undertaken on Thursday. The cordon counts were not conducted for 24 hours; instead typical
peak periods were identified by observation of traffic at KGUV.
6.4 Total traffic at KGUV
This section presents the overview of the traffic at KGUV for all modes of transport and its
attributes. First the details for the VC counts are presented, followed by the results from the
cordon counts for whole KGUV.
6.4.1 Traffic at the Village Centre
A detailed analysis was conducted for the counts undertaken for the VC. The details of the
time periods are listed in Table 6.1. The maximum hourly person movements were observed
between 8:00am to 9:00am, 11:45am to 12:45pm, and 16:45pm to 17:45pm.
Traffic generation at KGUV
Deepti Muley Page 119
Table 6.1 Time periods for analysis for Village Centre
Details AM peak Midday peak PM peak
Time period 7:15 to 9:30 11:30 to 13:30 15:00 to 18:00
Peak hour 8:00 to 9:00 11:45 to 12:45 16:45 to 17:45
Lowest volume hour 7:15 to 8:15 12:30 to 13:30 15:00 to 16:00
The traffic for VC consists of the traffic generated by the mixed uses, which include
residential apartments, commercial and retail shops. The share of the residential traffic is
significant in the case of the vehicular traffic (specifically car) because there is only one
access point for the car park entry, for the shoppers as well as residents. Pedestrians have
separate access points for entering the residential apartments, and shoppers have other access
points. So there numbers are not included in the count. The results for the total volume,
minimum, and maximum hourly volumes are displayed in Table 6.2.
Table 6.2 Traffic generated at Village Centre by mode on study day
Mode of
transport Traffic volume AM peak Midday peak PM peak
Cars
Total volume 245 246 538
Max hourly volume 117 129 211
Min hourly volume 108 134 140
Motorcycles
Total volume 4 2 6
Max hourly volume 4 0 2
Min hourly volume 0 1 0
Bicycles
Total volume 5 0 28
Max hourly volume 2 0 18
Min hourly volume 2 0 4
Walk only
(Pedestrians)
Total volume 769 1411 1753
Max hourly volume 431 799 599
Min hourly volume 248 634 560
Total persons
Total volume 1081 1708 2440
Max hourly volume 581 955 881
Min hourly volume 385 795 737
It can be observed that the VC had very minimal bicycle as well as motorcycle traffic for all
three time periods. The AM peak observed the least pedestrian movements while the midday
peak showed the highest total person movements as well as pedestrian movements. This
indicated that the users of non residential land uses visited the shopping centre during the
midday peak period for brief activities. The car traffic was less in the AM peak and more in
the PM peak, indicating that people used the car for home based or work based shopping trips
not originating from KGUV.
The PM peak experienced the maximum hourly volume for cars (211 cars/hour) and bicycles
(18 bicycles/hour) while the AM peak observed the maximum hourly volume for motorcycles
Evaluating the transport impacts of TODs
Deepti Muley Page 120
(6 motorcycles/hour). The midday peak showed the maximum hourly volumes for pedestrians
(799 pedestrians per hour) and subsequently total person movements (955 persons/hour).
Table 6.3 and Table 6.4 present the outcomes for the car occupancy and directional
distribution calculations for cars and other modes of transport respectively. The car
occupancy was almost same for all three time periods; the AM peak had highest car
occupancy rate while the midday peak had highest proportion of single occupant cars at the
VC. The directional distribution for car also indicates close to a 50/50 distribution of traffic
during all three periods, highlighting an association with short stay visits.
Table 6.3 Car occupancy and directional distribution for Village Centre
Time period AM peak Midday peak PM Peak
Car person occupancy 1.24 1.20 1.21
Cars In (%) 53.47 48.78 51.49
Cars Out (%) 46.53 51.22 48.51
The pedestrians entering the VC were predominant in the AM peak and midday peak while a
50/50 distribution was observed for the PM peak. The midday peak was dominated by the
lunch hour activities undertaken by various users of KGUV or facilities located on the edge
of KGUV. The motorcycles also had an even directional distribution except during the PM
peak, where the motorcycles coming in were in large number compared to the other
directional movement, although numbers are too small for this observation to be statistically
significant. The bicycles observed 50/50 directional distribution for PM peak and more
bicycles were going out of VC as compared to the bicycles coming into the VC during AM
peak.
Table 6.4 Directional distribution for Village Centre
Time period Pedestrians Bicycles Motorcycles*
% In % Out % In % Out % In % Out
AM Peak 54.0 46.0 40.0 60.0 50.0 50.0
Midday peak 57.1 42.9 NA NA 50.0 50.0
PM peak 49.6 50.4 50.0 50.0 66.7 33.3
* Note: Small sample sizes
6.4.2 Traffic at whole of KGUV
The analysis of cordon data was performed for three time periods; a specific 60 minute peak
hour and lowest traffic hour were noted for each time period (Table 6.5). The analysis
includes the traffic observed at all cordons to KGUV. The traffic volumes for the whole
KGUV were determined instead of individual land uses. The various land uses at a TOD
Traffic generation at KGUV
Deepti Muley Page 121
interact with each other; hence the traffic generated by the whole TOD should be assessed
instead of traffic generated by individual land uses for studying its traffic generation.
Table 6.5 Time periods for analysis of whole KGUV traffic
Details AM peak Midday peak PM peak
Time period 7:45 to 9:30 11:30 to 1:30 3:00 to 6:00
Peak hour 8:00 to 9:00 12:00 to 1:00 4:15 to 5:15
Lowest volume hour 7:45 to 8:45 12:30 to 1:30 3:00 to 4:00
During the survey KGUV experienced a considerable amount of through traffic on its street
network. Not eliminating through traffic would have provided a false impression of traffic
generated by KGUV. Table 6.6 gives the proportion of through cars for each time period. The
highest proportion of through traffic was observed for midday peak period. The through
traffic was mainly accessing the university campus located on the northern boundary of the
KGUV.
Table 6.6 Through car traffic at KGUV by time of day
Time period Observed volumes Without through traffic % Through traffic
AM peak 2182 1784 18.2
Midday peak 2217 1712 22.8
PM peak 3552 2773 21.9
Table 6.7 Total traffic generated at KGUV by mode on study day
Mode of
transport Traffic volume AM peak Midday peak PM peak
Cars
Total volume 1784 1712 2773
Max hourly volume 1078 856 1051
Min hourly volume 987 863 809
Motorcycles
Total volume 36 55 89
Max hourly volume 24 22 32
Min hourly volume 16 27 25
Bicycles
Total volume 73 51 69
Max hourly volume 41 26 18
Min hourly volume 50 30 18
Walk only
(Pedestrians)
Total volume 2067 2516 3681
Max hourly volume 1321 1273 1375
Min hourly volume 1220 1238 1209
Total persons
Total volume 4385 4821 7402
Max hourly volume 2695 2443 2774
Min hourly volume 2485 2391 2262
The cordon data analysis determined traffic characteristics of various modes of transport. The
details of hourly traffic are explained in Table 6.7. The total person volumes for whole
KGUV had the major share by pedestrian mode followed by car mode. A comparison of the
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Deepti Muley Page 122
total volumes indicates that KGUV had very little bicycle and motorcycle traffic throughout
the three time periods on the survey day. For cars, the maximum hourly volume occurred
during the AM peak, closely followed by the PM peak hour, while the minimum peak hourly
volume occurred during the midday peak period. The PM peak experienced the minimum as
well as maximum hourly flow of pedestrians; it also had the highest hourly volume in
comparison to its counterparts.
The person occupancy for cars shows maximum occupancy later in the day and PM peak and
slightly lower occupancy for the AM peak period (Table 6.8), which is likely reflection of a
greater proportion of commute trips during this early time of day. The car person occupancy
values are very similar to those for the shopping centre. A balanced distribution was observed
for cars except during the midday and evening peaks, while for the AM peak period the
proportion of cars entering KGUV was higher than the cars travelling out of KGUV again
likely reflection of commute car traffic entering in a more concentrated fashion in the
morning.
Table 6.8 Car occupancy and directional distribution at KGUV
Time period AM peak Midday peak PM Peak
Car person occupancy 1.24 1.28 1.28
Cars % In 56.28 50.00 48.25
Cars % Out 43.72 50.00 51.75
The directional distribution for pedestrians, bicycles and motorcycles is shown in Table 6.9.
The highest proportion of inbound movements was observed in the AM peak for all these
modes, which is again likely reflection of a marked inbound commute during this morning
peak period. For pedestrians slightly more entered than exited during the midday peak, likely
the reflection of an outbound lag. Pedestrian activity was relatively evenly split during the
PM peak. Bicycle entry was also predominant during the AM peak, while exiting was
predominant during the PM peak, but interestingly also during the midday peak. Similar to
pedestrians, slightly more motorcycles entered during midday peak and exited during PM
peak possibly due to similar reason.
Table 6.9 Directional distribution at KGUV
Time period Pedestrians Bicycles Motorcycles
% In % Out % In % Out % In % Out
AM Peak 61.1 38.9 60.3 39.7 63.9 36.1
Midday peak 55.7 44.3 35.3 64.7 52.7 47.3
PM peak 49.0 51.0 36.2 63.8 43.8 56.2
Traffic generation at KGUV
Deepti Muley Page 123
6.5 Comparison of peak hourly traffic with published rates
This comparison was undertaken for car trip ends only, as guidelines for other modes of
transport are not available. The observed maximum AM peak and PM peak hourly rates were
compared with the ITE trip generation rates for the United States (ITE, 2008). The Maximum
peak hourly rate amongst all three time periods was compared with the RTA traffic
generation rates for Australia (RTA, 2002).
For comparison of the VC traffic counts alone, the traffic generated by residential apartments
and the shopping centre were considered because they have a common access point for
vehicular traffic, hence it was not possible to distinguish the traffic individually for the
distinct land use. This statement is not applicable for the pedestrian traffic because the
residents had separate access points if they were on foot.
The total trip rates for KGUV estimated using the ITE and RTA rates respectively were
determined by calculating the car trip generation for all individual land uses at KGUV and
adding them together. These totals were compared with that obtained from the cordon count
analysis. The detailed calculations for ITE and RTA comparisons and their outcomes are
presented in Appendix C.
6.5.1 ITE comparison
6.5.1.1 ITE trip rates
The trip generation guidelines (ITE, 2008) provided trip rates for various land uses using an
independent variable. The trip rates were based on mainly the studies conducted in various
parts of the United States. The following points provide some information about ITE trip
rates.
Generally, the sites were surveyed between the 1960s and the 2000s. Some of the data
collected was few decades ago which might represent different traffic generation
characteristics than the current characteristics.
The regression equations are provided when three criteria are met; results for
minimum 4 studies were available, R2 value is greater than 0.5 and number of trips
increase as the value of independent variable increases.
The number of studies conducted for each land use type also varied greatly,
specifically for the land uses used in this case from 4 to 173 (Appendix C). This large
variation in number of studies raises data quality issues when the trip rates were
Evaluating the transport impacts of TODs
Deepti Muley Page 124
combined to obtain whole traffic generation in case of TODs. Further, the land uses
which contained fewer studies required caution while using the trip rates.
The sites were surveyed using various data collection techniques, which affects the
quality of the data collected.
No information was provided about the time of year during which the various studies
were conducted. So these trip rates do not account for seasonal variations if any.
The trip rates are based on one independent variable. The trip rates do not consider the
variation in other independent variables or multiple independent variables, which
could affect the vehicular traffic generation.
Although some shortcomings or data quality issues were found for the ITE trip rates; these
rates were used for comparison because they are widely used as guidelines in various parts of
the world. The following section presents the comparison for Village Centre and the whole of
KGUV.
6.5.1.2 Comparison of shopping centre traffic volumes
Table 6.10 shows the comparison of AM peak and PM peak traffic volumes with the ITE trip
generation rates obtained with the help of average trip rate and regression equation provided
in ITE, 2008. For determination of the peak period traffic, the trip rates for the peak period of
the generator were considered because the traffic volumes from peak periods of the Village
Centre were used for comparison.
Table 6.10 ITE comparison for traffic at the Village Centre
Description AM peak PM peak
Actual count 117 211
Volume based on ITE (average trip rate) 525 624
Volume based on ITE (Regression equation) 530 613
Difference with respect to ITE average trip rates -78% -66%
Difference with respect to ITE regression equation -78% -66%
Note: All volumes are in veh/h
From the percentage differences, it can be concluded that the VC generated in excess of 66
percent less traffic than estimated by ITE guidelines. Overall, the shopping centre shows
reduced vehicular traffic for the centrally located shopping centre (VC) at KGUV based on
ITE guidelines. One reason for that was many shoppers accessed the VC on foot as an
intrazonal trip for KGUV, or the surrounding land uses including QUT Kelvin Grove Campus
and the State School.
Traffic generation at KGUV
Deepti Muley Page 125
6.5.1.3 Comparison of total traffic volumes at KGUV
The comparison of the maximum hourly traffic for the AM and PM peak hours at KGUV
respectively is presented with the ITE trip rates in Table 6.11. For calculation of trips
generated under ITE guidelines, the trip rates for the peak hour of the adjacent street were
used. Where the data was not available the rates for the peak hour of the generator were used.
Commonly, the peak hour of the generator was not used because the peak hour traffic of
KGUV as a whole was used for comparison and all land uses at KGUV might not have the
same peak hour for traffic generation as the whole KGUV, this implied use of peak hour of
adjacent traffic was more appropriate. In addition, the regression equations for the AM and
PM peak hour for the high school were not provided so the value obtained by the average
rates were used for determination of total traffic generated by regression equation. A similar
case was observed for the AM peak of the adjacent street traffic for the shopping centre. A
specific category for the student accommodation was not found so the trip rates were
calculated based on the guidelines for similar land use of Senior Adult Housing–Attached.
Table 6.11 ITE comparison for traffic at KGUV
Description AM peak PM peak
Actual count 1078 1051
Volume based on ITE (average trip rate) 1462 1813
Volume based on ITE (Regression equation) 1513 2296
Difference with respect to ITE average trip rates -26% -42%
Difference with respect to ITE regression equation -29% -54%
Note: All volumes are in veh/h
The ITE trip rate comparison clearly showed that actual traffic generated at the KGUV is less
than that of the ITE guidelines. Reductions of 26 percent and 42 percent were observed when
compared with the average trip rates for the AM and PM peak respectively. The same
reduction increased to 29 to 54 percent when the regression equations were used.
6.5.2 Australian sources (RTA) comparison
6.5.2.1 RTA trip rates
Similar to ITE (2008), the RTA (2002) presents trip rates for various land uses. The trip rates
were derived from Road and Traffic Authority’s (RTA) Land Use Traffic Generation – Data
and Analysis reports. These rates are based on the studies conducted by RTA, New South
Wales (NSW), Australia. These trip rates are widely used in Australia by various government
authorities. Hence, to compare the traffic generation at KGUV with local guidelines RTA
(2002) trip rates were used despite of the following observations.
Evaluating the transport impacts of TODs
Deepti Muley Page 126
Generally, RTA (2002) provides average trip rates. In cases where it provides
regression equations, the equations use one or more independent variables to
determine trip rate.
The land uses covered in RTA (2002) were not as extensive as ITE (2008).
Site by site variations from the average values were not considered while determining
the trip rates.
6.5.2.2 Comparison of shopping centre traffic volumes
The peak hour traffic generation rate provided by RTA (2002) varied depending on size of
the shopping centre and the day of the week. The peak hour generation rate for Thursday was
considered for comparison as the VC counts were undertaken on Thursday. The comparison
of PM peak traffic showed that the RTA guidelines overestimated the vehicular (specifically
car) traffic generation by almost 63 percent. The PM peak traffic was considered for
comparison because RTA (2002) provided guidelines for peak period traffic and in the case
of the Village Centre the maximum car traffic was observed in the PM peak when compared
with AM peak and midday peak traffic volumes. This is in line with the general findings
where peak weekday traffic is observed for Thursday evening (RTA, 2002).
Table 6.12 RTA comparison for traffic at the Village Centre
Description PM peak
Actual count 211
Maximum Volume based on RTA guidelines 571
Difference with respect to RTA regression equation -63%
Note: All volumes are in veh/h
6.5.2.3 Comparison of total traffic volumes at KGUV
The RTA guidelines provided various rates for traffic generation by different land uses. The
maximum rate was used for each land use. The most similar available rate for student
accommodation was considered as that of Aged or Disabled persons. The maximum value
corresponded to the resident funded developments. The RTA (2002) did not provide any trip
rates for the educational land use. Hence, the rate provided by ITE (2008) was used.
Table 6.13 RTA comparison for traffic at the KGUV
Description AM peak
Actual count 1078
Volume based on RTA guidelines 1857
Difference with respect to regression equation -42%
Note: All volumes are in veh/h
Traffic generation at KGUV
Deepti Muley Page 127
The comparison of the peak hour counts and the RTA trip rates indicated that similar to the
ITE rates, the RTA rates also overestimated traffic generation (by 42 percent) when
compared to the actual measured traffic at KGUV.
6.6 Interpretation of results
The maximum person movement was seen for the PM peak for KGUV as a whole and
midday peak in case of the shopping centre. The Village Centre as well as KGUV as a whole
had significant pedestrian activity, with a higher mode share, more than any other mode of
transport. This indicates that this TOD’s users performed more walking trips and accessed the
development by walking; which accords with Steiner (2008). Similar observations were made
for residents at a traditional neighbourhood (Handy, 1996).
The comparison for the shopping centre showed reduced traffic generation by more than 63
percent when compared with the ITE (2008) and RTA (2002) guidelines for determining the
trip rates. When KGUV as a whole was considered, a reduction of about 42 percent was
observed for peak period traffic (RTA comparison) and on average a reduction of about 27 to
48 percent was observed for the AM and PM peak hours respectively (ITE comparison).
These finding are in line with the results found by Arrington and Sloop (2009) who observed
TOD housing producing 50 percent fewer trips than the conventional development. These
reductions support the presumption that a TOD reduced traffic generation due to its atypical
development characteristics.
The sensitivity analysis of actual traffic counts showed that an increase of 72.3 percent in
actual traffic count matched the traffic obtained by RTA peak period trip rates. While an
increment of 35.6 percent to 40.4 percent was required to match the AM peak traffic volume
by ITE average trip rates and by regression equations respectively. Similarly, an increase of
72.5 percent and 118.5 percent was required to equal the PM peak traffic counts by ITE
average trip rates and by regression equations respectively.
The findings from this analysis also emphasize the need for a special land use category for
TODs, which may need to address the mix, proximity and therefore interaction in
accessibility between land uses. The specifications for traffic generation at TODs should not
only address car traffic but also pedestrian and bicycle traffic as provision of infrastructure
for these facilities including parking facilities depend on the traffic generation. Some research
Evaluating the transport impacts of TODs
Deepti Muley Page 128
on mixture of land uses at TODs should be conducted to determine the level of proper land
use mix and proportion of self containment of trips.
6.7 Summary
In summary, an analysis of cordon data indicated that the Village Centre of KGUV as well as
KGUV as a whole, exhibit a decrease in traffic generation in excess of 27 to 48 percent and
42 percent respectively when compared to the ITE (2008) and RTA (2002) rates provided for
homogeneous land uses. This shows that the particular investigated TOD generated less
traffic, likely because of mixed use developments and provision of public transport service. It
is recognised that similar studies at a range of TODs in Australia will be required to
investigate this claim in depth and use those results as a practice or norm while designing
new TODs.
The use of ITE (2008) showed that the manual does not have specific trip rates for student
accommodation. In the case of the RTA manual, no guidelines were found for determination
of trip generation by education land uses as well as student accommodation. TODs should be
assessed as a special land use category and land use mix at a TOD should be assessed for self
containment. Further, no specifications for other means of transport (pedestrians, bicycle, and
motorcycle) were given in either. These aspects ought to be investigated through further
research.
6.8 Chapter close
This chapter provided a detailed overview of traffic characteristics at the TOD, KGUV. This
completes Step III of TOD evaluation which deals with traffic impact determination and
provides input to Step V. The findings from this chapter provide some of the basis for the
conclusions and recommendations in Chapter 11. This chapter contributes to the knowledge
in the area of trip generation at a TOD, which was highlighted as a gap in the literature
review (Chapter 2).
Deepti Muley Page 129
Chapter 7
Characteristics of Kelvin Grove Urban Village users
7.1 Introduction
A Transit Oriented Development (TOD) can be better explained in terms of its travel demand
by identifying characteristics of various trips undertaken by its different users. This can be
explained by analysing demographic and travel characteristics. This chapter presents the
analysis of demographic and travel characteristics of each group of TOD users to better
explain Kelvin Grove Urban Village (KGUV) in terms of its transport activity.
The next section presents details of the analysis performed on responses obtained from the
travel surveys of KGUV users to determine the desired characteristics. The later sections
describe the demographic as well as travel characteristics of each group of KGUV user. First
the characteristics of the shopping trips undertaken by shoppers are explained, followed by
the characteristics of work trips undertaken by employees, education trips undertaken by
students, and trips undertaken by the residents. An interpretation of the characteristics is then
given by comparing characteristics of various groups of KGUV users. A brief summary of
the chapter and chapter close are then offered.
7.2 Determination of users’ characteristics
All responses collected from various KGUV users were entered into the online survey tool
used for travel data collection. These responses were downloaded from the website in
Microsoft Excel format and then the responses in the Microsoft Excel sheet were rearranged
in a convenient format for analysis. The formatted sheets were then analysed using Microsoft
Excel‟s functions to obtain the characteristics of KGUV users. Responses from the travel
surveys for each category of KGUV user were compiled separately. The responses from the
pilot studies were combined with responses obtained from the full scale surveys before
conducting the analysis. This study only conducts preliminary data analysis to determine
users‟ characteristics. A detailed cross tabulation analysis would provide better information
about KGUV users‟ characteristics but due to limited number of datasets, this was not
performed. However, the detailed cross tabulation analysis should be undertaken for a case
study site with larger dataset. The analysis of the travel survey data mainly provided two
Evaluating the transport impacts of TODs
Deepti Muley Page 130
types of characteristics; demographic and travel characteristics. A brief description of the
preliminary analysis undertaken is provided in the following subsections.
7.2.1 Demographic characteristics
The demographic characteristics describe the structure or profile of users based on the
personal and household characteristics. The personal characteristics of KGUV users are
explained with the help of gender, age distribution, employment status, frequency of visiting
KGUV, share of valid driver‟s licence holders, and average working hours at KGUV. In
addition to the personal characteristics, the household characteristics are described using
household size (adults and children), number of vehicles in the household and number of
valid driver‟s license holders in the household. These characteristics were directly obtained
from the responses of the travel surveys.
7.2.2 Travel characteristics
To gain an overview of the travel at KGUV, the travel characteristics of various user groups
were determined. The travel characteristics of KGUV users are explained with the help of
mode shares, trip lengths by mode of transport, parking details such as location and parking
fee, and public transport characteristics such as number of legs of the journey, transfer
locations, and access and egress times. The proportions of choice riders and captive riders are
also explained.
The mode shares and mode choices, parking details and public transport characteristics were
obtained directly from the responses. All three modes of public transport, namely public bus,
train and ferry were combined and denoted as a public transport mode. The share of
sustainable modes of transport was also determined by combining the mode shares for public
transport, bicycle and walk only as these modes are termed as more sustainable means of
transport than their other counterparts, specifically car. The trip length for each trip was
calculated with the help of the “home suburbs” specified using “Google Maps”
(www.maps.google.com.au). Trip length was the actual road distance travelled by car from
home suburb centroid to centrally located Village Centre for the employee, students and
shoppers‟ trip. The trip lengths for residents‟ trips were calculated considering the Village
Centre as an origin and the trip end suburb centroid as the destination. The trip length details
were derived from these calculated values.
The following sections provide details of demographic and travel characteristics of each user
group.
Characteristics of KGUV users
Deepti Muley Page 131
7.3 Shoppers’ shopping trips at KGUV
The shopping centre (also known as Village Centre) has some street level family owned
businesses; this gives the feeling of a village atmosphere and made the shopping centre more
attractive to shoppers. A data set for 117 respondents to the quick interview shoppers‟ survey
was used for analysis. The responses from the shoppers‟ pilot survey were not included in the
analysis as they represented weekend data.
7.3.1 Demographic characteristics
The demographic characteristics of the shoppers are explained with the help of employment
status and age group distribution. The frequency of shoppers‟ shopping trips to the Village
Centre was also analysed to explain the rate of the recurrence of shopping trips. The
proportion of shoppers belonging to each category of employment status is shown in Table
7.1. It can be seen that more than 60 percent of respondents were students, and others were
residents or visitors coming from the suburbs located in close vicinity to KGUV. The
students of the educational land use on KGUV, and from the school and university campuses
located on the boundary of KGUV constituted a significant proportion of the shoppers.
Table 7.1 Employment status of shoppers at KGUV
Description Proportion of shoppers
Employed 23.9
Student 63.2
Homemaker 3.4
Retired 3.4
Not available 6.0
Note: All values are presented in percentage
Figure 7.1 illustrates the distribution of age groups for shoppers at KGUV. It can be seen that
73 percent of respondents were young adults (between 18 and 45 years) and very few
respondents were above 65 years of age. The proportion of students seemed to have a great
influence on the distribution of age groups.
The distribution of frequency of shopping trips for shoppers at KGUV is shown in Figure 7.2.
A zero frequency of shopping trip includes the respondents who visited KGUV for the first or
second time. A frequency of 2.5 indicates that on an average the shopper visited the shopping
centre two to three times a week. More than 50 percent of respondents visited the shopping
centre once to thrice in a week. Remaining respondents visited the shopping centre more than
three times in a week. This indicates that students use the shopping centre for their
convenience (day to day) shopping. It was also observed that the shoppers who were
Evaluating the transport impacts of TODs
Deepti Muley Page 132
residents of KGUV made 3.5 trips a week; likely due to close proximity of the shopping
centre. These trips contributed to internal trips which are mostly made by walking.
Figure 7.1 Distribution of shoppers’ age groups in years
Figure 7.2 Frequency of shopping trips per week
0 - 18
14%
18 - 30
53%
30 - 45
20%
45 - 65
11%
> 65
2%
0
5
10
15
20
0 0.5 1 2 2.5 3 3.5 4 5 6 7
Per
cen
tage o
f sh
op
per
s (%
)
Frequency of shoppers' shopping trips per week
Characteristics of KGUV users
Deepti Muley Page 133
7.3.2 Travel characteristics
7.3.2.1 Mode shares
Mode share is an important variable to be considered when assessing travel characteristics.
The classified distribution of mode shares for shoppers at KGUV is shown in Figure 7.3. The
public transport trips include trips made by public bus and train; no ferry trips were observed
as there is no ferry terminal nearby KGUV. Only 27 percent of shopping trips were
undertaken by car, while very few shoppers used bicycle and motorcycle. It may be
postulated that the mixed land uses promoted walking trips (43 percent). The mode share
distribution indicates that more than 70 percent of the shoppers travelled by one of the more
sustainable modes of transport, including public transport, walking and cycling. This is a
good indication of success of this TOD in terms of sustainable transport mode share.
Figure 7.3 Mode shares for shoppers’ at KGUV
7.3.2.2 Trip lengths
The trip lengths for each shopping trip were calculated and the average trip lengths are
tabulated in Table 7.2. The overall average trip length for shoppers at KGUV was calculated
to be 7.6km. The minimum trip length was theoretically 0km for internal trips and maximum
trip length was 94.5km. The internal trips originated at KGUV and terminated at the Village
Centre so a 0km trip length was assigned for these trips. About 17 percent of trips were
internal trips and 83 percent trips were external or generated from the non residential land
Car
27%
Public
transport
24%
Walk only
43%
Bicycle
4%
Motorcycle
2%
Evaluating the transport impacts of TODs
Deepti Muley Page 134
uses. From the trip length values it was observed that most trips were from suburbs located in
close proximity to KGUV.
Table 7.2 Trip lengths by mode of transport for shoppers at KGUV
Mode of transport Trip length (km)
Minimum Average Maximum
Car 0.9 9.4 69.4
Public transport 0.9 15.7 94.5
Walk only 0.0 1.0 5.1
Bicycle 0.0 2.0 4.1
Motorcycle 1.9 3.7 5.5
Combined (Overall) 0.0 6.9 94.5
7.3.2.3 Time of day
The shopping activity was distributed all over the day. Shoppers visited the shopping centre
mainly between 7.30am to 9am, 11.30am to 1pm and 3.30pm to 6pm. The shopping activity
was very much dependant on the working hours of students and employees at KGUV.
7.4 Employees’ work trips at KGUV
KGUV has a university campus extension and a research centre which contains employment
for professionals (professional employees), and also has employees working at the centrally
located shopping centre (retail shop employees). A data set of 125 responses obtained from
the internet based survey was used for analysing professional employees and their travel. The
personal interviews carried out for retail shop employees collected travel data of 39
employees. This dataset was used to determine the characteristics of the retail shop
employees. These two datasets incorporated responses from the pilot surveys.
7.4.1 Demographic characteristics
The demographic characteristics of employees are explained with the help of personal
characteristics such as gender, employment status, proportion of licence holders, and
frequency of visiting KGUV and the household characteristics are explained with the help of
household size, vehicle ownership, and number of valid driver‟s licence holders in the
household.
7.4.1.1 Personal characteristics
Table 7.3 lists the personal characteristics of KGUV employees. The proportion of female
respondents was higher than that of male respondents for both employee groups. The retail
shops provided an employment opportunity to students studying at KGUV or at the adjacent
Characteristics of KGUV users
Deepti Muley Page 135
university. These work trips undertaken by students were most likely combined with their
educational trip, hence can be counted as intra-zonal trips. The proportion of driver‟s licence
holders was high for both categories of employees, but particularly so for the professional
employees.
Table 7.3 Personal characteristics of employees’ at KGUV
Characteristic Professional employees Retail shop employees
Male 37.5 38.5
Female 62.5 61.5
Valid driver‟s licence holders 92.4 84.6
Employed full time 62.3 53.8
Employed part time 11.5 7.7
Employed part time & student full time* 10.7 25.6
Self employed – 12.8
Research students full time* 14.8 –
Employed full time & student part time 0.8 –
*Research students are treated as employees
Note: All values are presented in percentage
The distribution of age groups (in Figure 7.4) shows that the retail shop employees have more
young workers. The difference in age groups can be related to the type of work as the retail
shop employees perform mostly hospitality or customer service and professional employees
do research and education oriented work.
Figure 7.4 Distribution of age group for employees’ at KGUV
0
10
20
30
40
50
60
70
0-18 18-30 30-45 45-65
Per
cen
tag
e o
f em
plo
yees
(%
)
Age group (years)
Professional employees
Retail shop employees
Evaluating the transport impacts of TODs
Deepti Muley Page 136
Figure 7.5 compares the frequency of employees‟ work trips per week. Almost 31 percent of
retail shop employees worked on weekends while professional employees worked typically
five days per week.
Figure 7.5 Frequency of work trips at KGUV
7.4.1.2 Household characteristics
The variation in the household size for the professional employee households and the retail
shop employee households is presented in Figure 7.6. The size of a retail shop employee
household varied from one to eight with an average size of 3.4 persons in the household. The
average household size for the professional employee‟s household was 2.6 (lesser than its
counterpart) with a minimum of one and maximum of six. As the household size increased
the proportion of professional employee households decreased. With an increase in the size of
household, the proportion of retail shop employee households did not vary greatly.
Table 7.4 shows an overview of the vehicle and bicycle ownership along with the number of
valid driver‟s licence holders in an employee‟s household. Although the retail shop
employees had higher proportion of households with no car; a higher proportion of them
possessed more cars per household, hence they had higher number of valid driver‟s licence
holders in their households. The majority of households in both employee groups did not own
0
10
20
30
40
50
60
1 or 2 3 4 5 > 5
Per
cen
tag
e o
f em
plo
yees
(%
)
Frequency of work trip per week
Professional employees
Retail shop employees
Characteristics of KGUV users
Deepti Muley Page 137
a motorcycle. The households of professional employees had higher bicycle ownership than
the retail shop employees.
Figure 7.6 Household size distribution at employees’ households
Table 7.4 Vehicle ownership and licence availability at employees’ households
Parameter Quantity Professional employees Retail shop employees
Cars
0 4.2 20.5
1 55.9 20.5
2 23.7 38.5
> = 3 16.1 20.5
Bicycles
0 32.4 51.3
1 22.5 20.5
2 17.6 17.9
>= 3 27.5 10.3
Motorcycles
0 86.9 94.9
1 10.7 2.6
2 2.4 2.6
Valid driver‟s licence
holders
0 1.7 5.4
1 22.9 5.4
2 53.4 48.6
>=3 22.0 40.5
Note: All values are presented in percentage
0
5
10
15
20
25
30
35
40
45
1 2 3 4 5 >= 6
Per
cen
tag
e o
f em
plo
yee
ho
use
ho
lds
(%)
Household size
Professional employees
Retail shop employees
Evaluating the transport impacts of TODs
Deepti Muley Page 138
7.4.2 Travel characteristics
7.4.2.1 Mode shares
The mode share distribution for the professional employees at KGUV is presented in Figure
7.7. The professional employees travelled by many modes of transport with 50 percent by
car, about one third by public transport, and almost 20 percent by walking or cycling. The
professional employees travelled typically during peak hours when public transport provision
is most frequent, which might be a reason for its higher public transport mode share. Cycling
was also a high mode share for this group, which is attributed to good quality access and trip-
end facilities (such as bikeways, showers, cycle lockers, etc). The greater bicycle ownership
also contributed to a high mode share. Most of the professional employees had another choice
for making their work trips; about less than a third professional employees were captive
riders. Overall, about 50 percent of professional employees travelled by the more sustainable
modes of transport.
Figure 7.7 Mode shares for professional employees
Figure 7.8 shows the distributions of mode shares for work travel for employees working at
the retail shops at KGUV. In contrast to the professional employees, the retail shop
employees travelled by only three modes of transport; car, public transport and walk only,
with car comprising 60 percent of mode share. This higher mode share for car travel is
attributed to odd (late night or early morning) working hours and less frequent public
transport or no public transport during off peak times (see Muley et al. (2007)) and
Car
50%
Public
transport
28%
Walk only
10%
Bicycle
10%
Other
2%
Characteristics of KGUV users
Deepti Muley Page 139
availability of free parking spaces. This infers that a lower proportion of retail shop
employees (about 40 percent) travelled by sustainable modes as compared to the professional
employees. Almost 50 percent of the retail shop employees had another choice for their work
travel while others did not have any other choice than their chosen mode.
Figure 7.8 Mode shares for retail shop employees
7.4.2.2 Trip lengths
The maximum, minimum and average trip lengths for employees at KGUV are listed in Table
7.5 and Table 7.6. Though the average trip length for both types of employees is similar, the
maximum trip length varies almost two fold. The longer trip lengths for professional
employees are attributed to the presence of specialised educational facilities (including a
university extension comprising a research centre).
Table 7.5 Trip lengths by mode of transport for professional employees at KGUV
Mode of transport Trip lengths (km)
Minimum Average Maximum
Car 0.9 14.0 44.3
Public transport 1.3 16.9 93.8
Walk only 0.9 3.0 6.5
Bicycle 2.9 5.8 8.7
Other 3.8 5.2 6.5
Combined (overall) 0.9 12.7 93.8
Car
59%Public
transport
20%
Walk only
21%
Evaluating the transport impacts of TODs
Deepti Muley Page 140
Table 7.6 Trip lengths by mode of transport for retail shop employees at KGUV
Mode of transport Trip lengths (km)
Minimum Average Maximum
Car 0.9 15.8 53.1
Public transport 3.2 8.5 19.1
Walk only 0.0 0.9 2.6
Combined (overall) 0.0 11.3 53.1
7.4.2.3 Time of day
The working hours for professional employees were mostly between 8am and 6pm. These
employees sometimes worked in the evenings depending upon the class times. The average of
working hours for professional employees was 8.4 hours. For the retails shop employees, the
working hours varied greatly from early in the morning (5am) to late in the evening
(9.30pm). The average of working hours was 8.2 hours.
7.4.2.4 Parking characteristics
The majority of the professional employees who drove parked their car at their workplace.
The rest of the employees first searched for a free parking space on street. If not available
then they parked at paid parking. Around 50 percent of car drivers paid less than $10 per day
as a parking fee and others parked for free.
Mostly, the retail shop employees who drove parked their car at their workplace and they had
free parking available. The availability of the free parking was a strong factor in choosing car
as a mode of travel.
7.4.2.5 Public transport characteristics
Table 7.7 provides the public transport trip details for employees at KGUV. None of the
employees performed three legged journey by public transport. The majority of employees
who took public transport had two legs to their trip (77 percent and 75 percent). Roma street
public bus / train station and the Cultural Centre Busway Station were the most popular
transfer locations. Mostly, the employees walked for 5 to 10 minutes to reach the bus stop.
The employees walked for about 10 minutes to reach to their workplace. These distances are
standard walking distances for premium services (TRB, 2003 and Queensland Transport,
1999). The employees who walked more than 15 minutes to reach KGUV accessed the site
from the Roma Street train station located 1.9km from KGUV, on the western edge of the
Brisbane Central Business District.
Characteristics of KGUV users
Deepti Muley Page 141
Table 7.7 Public transport trip details for employee trips at KGUV
Description Specification Professional employees Retail shop employees
No of legs 1 22.9 25.0
2 77.1 75.0
Access time
< 5 minutes 28.6 25.0
5 – 10 minutes 45.7 62.5
10 – 15 minutes 8.6 12.5
> 15 minutes 17.1 0.0
Egress time
< 5 minutes 35.3 62.5
5 – 10 minutes 32.4 37.5
10 – 15 minutes 11.8 0.0
> 15 minutes 7.0 0.0
Note: All values are presented in percentage
7.5 Students’ education trips at KGUV
KGUV has a university campus extension and a specialised government sponsored high
school for students. The data collected from both the users (school students and university
students) was used for analysis. The university students‟ dataset contained 89 responses and
the school students‟ dataset had 28 responses, which were used to explain the characteristics
of students at KGUV. The characteristics of both the groups were determined separately and
then compared with the other group. There were no pilot surveys conducted hence no
responses available from the pilot surveys in case of students surveys.
7.5.1 Demographic characteristics
The students‟ demography was described using the same parameters used for the explaining
the demographic characteristics of employees in previous section (Section 7.4.1).
7.5.1.1 Personal characteristics
Table 7.8 indicates the gender distribution, employment status distribution and the proportion
of valid driver‟s licence holders for school students and the university students. It can be seen
that for both student groups the proportion of female student respondents is almost double
and triple for school students and university students respectively. Only few university
students were undertaking their studies as part time but others were full time students. More
than 60 percent of students had some casual or part time employment in addition to their full
time university commitments. Most of the school students were at school full time and few
students undertook part time employment. As the school students were below 18 years of age,
they did not possess an open driver‟s licence. But a significant proportion of university
students possessed a valid driver‟s licence.
Evaluating the transport impacts of TODs
Deepti Muley Page 142
Table 7.8 Personal characteristics of students at KGUV
Characteristic School students University students
Male 32.1 21.3
Female 67.9 78.7
Full time students 85.7 30.7
Full time students & employed part time 14.3 63.6
Part time students NA 5.7
Valid driver‟s licence holders NA 78.7
Note: All values are presented in percentage
Figure 7.9 compares the distribution of age groups for the students at KGUV. All the school
students were in the age group of 0 to 18 years. Almost 85 percent of the university students
were in the age group of 18 years to 30 years. This high proportion of young students is
related to the type of university precinct (Creative Industries).
Figure 7.9 Distribution of age group for students at KGUV
The comparison of frequency of education trip (Figure 7.10) indicates that the school students
visited school more consistently, that is five times a week. While the frequency of trip varies
for university students, a major proportion of students visit university three or four times a
week. This may be due to the diverse arrangement of university educational commitments.
0
20
40
60
80
100
0-18 18-30 30-45 45-65 >65
Per
cen
tag
e o
f st
ud
ents
(%
)
Age group (years)
School students
University students
Characteristics of KGUV users
Deepti Muley Page 143
Figure 7.10 Frequency of education trips at KGUV
7.5.1.2 Household characteristics
The household size variation for school and university students‟ households is shown in the
Figure 7.11. The average household size for school students was four persons with a
minimum of two and maximum of six persons in a household. Similarly, for university
students, the average household size was 3.6 persons but with a minimum of one and
maximum of six persons in the household. Two university students were living in a shared
accommodation, which had a household size greater than six. These responses were omitted
before plotting the graph as these were classed as outliers.
Table 7.9 lists the vehicle ownership and the proportion of the driver‟s licence holders in each
category for school students and university students‟ households. A significant proportion of
households possessed two or more than two cars in both the cases. More than 25 percent and
35 percent of school students‟ and university students‟ households respectively did not own
any bicycle. The 43.5 percent of school students‟ households possessed three or more than
three bicycles; this might be because these households use bicycles for recreational purposes
but not for commute purposes. Only small proportion of students‟ households owned a
motorcycle. In case of school students, the proportion of two driver‟s licence holders was
highest as compared to other groups. For the university students, the proportion of three or
more than three driver‟s licence holders was highest.
0
20
40
60
80
100
1 or 2 3 4 5 > 5
Per
cen
tag
e o
f st
ud
ents
(%
)
Frequency of education trip per week
School students
University students
Evaluating the transport impacts of TODs
Deepti Muley Page 144
Figure 7.11 Household size distribution at students’ households
Table 7.9 Vehicle ownership and licence availability at students’ households
Parameter Quantity School students University students
Cars
0 0.0 9.2
1 11.1 25.3
2 51.9 33.3
> = 3 37.0 32.2
Bicycles
0 26.1 36.8
1 13.0 32.9
2 17.4 15.8
>= 3 43.5 14.5
Motorcycles 0 87.0 93.2
1 13.0 6.8
Valid driver‟s licence
holders
0 – 2.2
1 10.7 10.1
2 57.1 25.8
>=3 32.1 61.8
Note: All values are presented in percentage
7.5.2 Travel characteristics
The students‟ education travel is explained with the help of mode share, and trip lengths. The
average working hours, and time of day of travel are also noted in the following subsections.
7.5.2.1 Mode shares
The mode shares for school students are presented in Figure 7.12. The school students
travelled by only two modes of transport; public transport and car. Almost 86 percent of
students used public transport for their trip to school and remaining 14 percent students were
0
5
10
15
20
25
30
35
40
1 2 3 4 5 6
Per
cen
tage
of
stu
den
ts h
ou
seh
old
s
(%)
Household size
School students
University Students
Characteristics of KGUV users
Deepti Muley Page 145
dropped at school by car. The share of public transport represented the high share of use of
sustainable modes of transport. This student group did not have valid driver‟s licences hence
by and large had no access to car as a driver. Due to this, this group of KGUV users can be
termed as „captive riders‟. The responses to the mode choice questions indicated that 69.2
percent of respondents were choice riders and 30.8 percent of them were captive riders who
did not have any other option to travel to KGUV. There were no walk only and bicycle trips
for school students; these two modes were not considered any further as an alternative option
for journey to school. (It is noted that QACI is an elite school, to which students will travel
from across Brisbane.)
Figure 7.12 Mode shares for school students
Figure 7.13 shows the mode shares for university students. Similar to retail shop employees,
the university students travelled by only three modes of transport; walk only, car or public
transport. Similar to school students, the university students exhibited more use of the more
sustainable modes of transport (84 percent). Similar to school students, no bicycle trip was
recorded for university students. Almost two-thirds of students travelled to university by
public transport and only 16 percent arrived by private car. The higher public transport mode
share may be attributed to the student having no driver‟s licence or to the cost involved in
using a car. In the case of university students, 53.9 percent students had another option for
travelling to KGUV and 46.1 percent students did not have any other option to perform their
education trip.
Car
14%
Public
transport
86%
Evaluating the transport impacts of TODs
Deepti Muley Page 146
Figure 7.13 Mode shares for university students
7.5.2.2 Trip lengths
Table 7.10 and Table 7.11 tabulate the details of trip lengths for school students and
university students‟ education trip at KGUV respectively. The minimum trip length for both
groups of students was the same (0.9km). However, the maximum trip lengths varied greatly,
with for university students nearly twice of that for school students. In spite of having a lower
value of maximum trip length, school students showed slightly higher value of average trip
length compared to the university students.
Table 7.10 Trip lengths by mode of transport for school students at KGUV
Mode of transport Trip length (km)
Minimum Average Maximum
Car 0.9 14.0 38.4
Public transport 2.9 20.9 44.3
Combined (overall) 0.9 19.9 44.3
Table 7.11 Trip lengths by mode of transport for university students at KGUV
Mode of transport Trip length (km)
Minimum Average Maximum
Car 3.6 16.7 42.6
Public transport 0.9 18.3 73.2
Walk only 0.9 1.6 3.9
Combined (overall) 0.9 16.3 73.2
Car
16%
Public
transport
75%
Walk only
9%
Characteristics of KGUV users
Deepti Muley Page 147
7.5.2.3 Time of day
The school students attended the school typically between 8am and 4pm. The average stay of
the students at their school was 7.3 hours. Some university students were engaged in the
evening classes hence they stayed until late. On an average, a student was at university for
7.8 hours.
7.5.2.4 Parking characteristics
The school students at the university were dropped off at the school, hence their vehicle was
not parked, and hence they did not pay any parking fee. The university students who travelled
by car parked their vehicle on street or at the university parking facility. Most of them paid a
parking fee of less than ten dollars a day if they could not find a free car parking space.
7.5.2.5 Public transport characteristics
The most popular public transport transfer locations for school students were Roma Street
public bus / train station or Cultural Centre Busway Station, while the King George Square
Busway Station and Roma Street public bus / train station were the main transfer stations for
university students. Some students had suburban train stations as their transfer locations.
Table 7.12 indicates the distribution of access and egress time and number of legs for the
journey. The university students observed highest egress time of more than 15 minutes when
they accessed KGUV through the Roma Street station. Although few three leg journeys were
observed; again majority was two legs for public transport trips suggesting interchange
locations and their facilities are important.
Table 7.12 Public transport trip details for student trips at KGUV
Description Specification School students University students
No of legs
1 12.5 9.7
2 75.0 79.0
3 12.5 11.3
Access time
< 5 minutes 29.2 41.3
5 – 10 minutes 41.7 33.3
10 – 15 minutes 25.0 15.9
> 15 minutes 4.2 9.5
Egress time
< 5 minutes 20.8 53.7
5 – 10 minutes 70.8 13.0
10 – 15 minutes 8.3 14.8
> 15 minutes – 18.5
Note: All values are presented in percentage
Evaluating the transport impacts of TODs
Deepti Muley Page 148
7.6 Residents’ trips at KGUV
KGUV has two types of residents; non student residents, who lived in the privately owned or
rented apartments, and student residents who lived in the managed student accommodation. It
should be noted that there were some university students renting the apartments in the Village
Centre. A dataset of 34 responses for non student residents‟ and 51 responses for student
residents‟ survey was used for analysis. These responses included the responses from the
pilot surveys as well as from the different phases of travel surveys.
7.6.1 Demographic characteristics
To assess the residents‟ demographic characteristics, the same parameters were used as per
previous user groups. In addition to those, the household characteristics are described by the
type of dwelling and the number of bedrooms in that dwelling.
7.6.1.1 Personal characteristics
Table 7.13 lists the demographic characteristics of the residents at KGUV. The proportion of
female respondents was a little higher than male respondents in the case of student residents,
while for non student residents a balanced distribution was obtained. The majority of
residents in both user groups were employed full time or studying full time. Only six percent
of the non student residents were unemployed, and these were mostly retired persons. A
significant proportion of non student residents (88.2 percent) possessed a valid driver‟s
licence. The variation in the distribution of the age groups is shown in Figure 7.14. Around
90 percent of student residents were young adults (age group 18 years to 30 years), which is
obvious. The non student residents contained some older residents when compared with
student residents.
Table 7.13 Personal characteristics of residents’ at KGUV
Characteristic Non student residents Student residents
Male 50.0 43.1
Female 50.0 56.9
Valid driver‟s licence holders 88.2 68.6
Employed full time 38.2 –
Students full time & employed part time 8.8 23.5
Employed full time & student part time 2.9 –
Employed full time & student full time 2.9 –
Students full time 35.3 76.5
Employed part time 5.9 –
Retired / unemployed 5.9 –
Note: All values are presented in percentage
Characteristics of KGUV users
Deepti Muley Page 149
Figure 7.14 Distribution of age group for residents’ at KGUV
7.6.1.2 Household characteristics
The apartments at KGUV were typically one or two bedroom while the students were living
in shared units having 1 to 6 bedrooms. Figure 7.15 shows the variation in the number of
bedrooms in a household. The variation in the household size is displayed in Figure 7.16.
There was a drop in one person households compared to the number of bedrooms. This was
largely because of a couple or family of two persons living in a one bedroom apartment.
Figure 7.15 Distribution of number of bedrooms in residents’ household
0
10
20
30
40
50
60
70
80
90
0-18 18-30 30-45 45-65
Per
cen
tag
e o
f re
sid
ents
(%
)
Age group (years)
Non student residents
Student residents
0
20
40
60
80
1 2 3 > 3
Per
cen
tag
e o
f re
sid
ent
hou
seh
old
s (%
)
Number of bedrooms in residents' household
Non student residents
Student residents
Evaluating the transport impacts of TODs
Deepti Muley Page 150
Figure 7.16 Distribution of household size at residents’ household
Table 7.14 Vehicle ownership and licence availability at residents’ households
Parameter Quantity Non student residents Student residents
Cars
0 20.6 41.2
1 55.9 47.1
2 20.6 7.8
> = 3 2.9 3.9
Bicycles
0 82.4 88.2
1 14.7 9.8
2 2.9 2.0
Motorcycles 0 100.0 98.0
1 – 2.0
Valid driver‟s licence
holders
0 5.9 7.0
1 52.9 14.0
2 38.2 34.9
3 – 20.9
> 3 2.9 23.3
Note: All values are presented in percentage
Vehicle ownership is an important household characteristic describing private vehicle
dependency of residents. The distribution of vehicle ownership for KGUV residents is given
in Table 7.14. KGUV residents had lower car ownership compared to a high driver‟s licence
possession. Around one fifth of households did not have a car. A large proportion of KGUV
households did not possess either a bicycle or a motorcycle, indicating very low vehicle
0
5
10
15
20
25
30
35
40
45
50
1 2 3 > 3
Per
cen
tag
e o
f re
sid
ents
ho
use
ho
lds
(%)
Household size
Non student residents
Student residents
Characteristics of KGUV users
Deepti Muley Page 151
ownership, which can result in low private vehicle usage. During the interviews, some of the
respondents pointed out that they did not have a car or bicycle because they did not need it.
Most of the student residents left their car at home. They believed that attractions were
sufficiently close to each other that they did not require a vehicle for transport. This is argued
by many transport professionals to be one of the biggest advantages of TODs.
7.6.2 Travel characteristics
The travel diaries for a complete day were collected from the residents‟ travel surveys. The
complete trip details were determined for the trip making characteristics. Later, the first trip
of the day was chosen for evaluating the travel characteristics as this trip was originating
from KGUV and was of most interest for the purpose of this study. The parking
characteristics and public transport characteristics are not listed for residents travel; instead
an insight into the internalisation of trips is provided.
In the case of non student residents only one person did not travel on the assigned travel day,
while in case of student residents three students did not travel on the assigned travel day. A
retired person who did not travel on the assigned day mostly travelled on the pension day and
sometimes for visiting a doctor. The students did not travel because they did not have any
academic engagements on that day. A set of 33 and 48 travel diaries were analysed for non
student residents and student residents respectively.
7.6.2.1 Trip characteristics
Table 7.15 lists the minimum, average and maximum number of trips made by the residents
at KGUV. The minimum number of trips was 0 as the respondents did not perform any trip
on the assigned travel day. A non student resident made more trips than a student resident,
partly because of the various activities required to perform by a household (pick up and drop
off formed a major share of this). On a typical weekday, the residents mostly travelled for
work or education during the day and in addition to a return trip home for shopping or
recreation during the evening. The evening shopping trip was mostly on foot.
Table 7.15 Number of trips for residents at KGUV
Description Number of trips per person
Minimum Average Maximum
Non student residents 0.0 2.9 6.0
Student residents 0.0 2.4 4.0
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Deepti Muley Page 152
7.6.2.2 Mode shares
Mode share was assigned based on the first trip of the day. The travel mode share for
residents living in apartments at KGUV is shown in Figure 7.17. Similar to the university
students and retail shop employees, the residents at KGUV typically travelled by either car,
public transport or walk only. No train, ferry or bicycle trips were reported; the reason for no
train or ferry trips was that KGUV does not have a train station or a ferry terminal within
easily walkable distance. The mode shares indicated that 63 percent of the residents travelled
by the more sustainable modes of transport. More than 50 percent (54.5 percent) of the
residents had another mode choice for performing this trip.
Figure 7.17 Mode shares for non student residents at KGUV
Figure 7.18 shows a pie chart showing the distribution of mode shares for the students living
in student accommodation for their first trip of the day. It can be noted that these residents
travelled by only three modes of transport; car, walk only and public transport (specifically
public bus as no train or ferry trips were reported) similar to non student residents. No
resident used a bicycle or taxi for arriving at their desired destination. The reason for there
being no bicycle trips is postulated to be due to the limited bicycle connections available to
more remote areas, heavy traffic around the area, and the hilly terrain of the area.
Public bus was the most preferred mode with a share of 48 percent. Most of the residents
(More than 90 percent) used the modes of transport labelled as sustainable (which include
Car
37%
Public
transport
36%
Walk only
27%
Characteristics of KGUV users
Deepti Muley Page 153
walk only and public transport) and only eight percent residents‟ used car as their mode of
transport. When asked about mode choices, around 77 percent of residents did not perceive
that they had any choice other than their chosen mode of transport; we therefore consider
them to be captive users.
Figure 7.18 Mode shares for student residents at KGUV
7.6.2.3 Trip lengths
The minimum, average and maximum trip lengths for residents at KGUV were calculated for
the first trip listed in the travel diary. The details are listed in Table 7.16 and Table 7.17 for
non student residents and student residents respectively. Theoretically, the minimum trip
length was zero. This is a trip undertaken by a student to the university or a shopping trip
undertaken by walking. For non student residents, the maximum trip length was observed as
94.4km for a non student resident by car and the overall average trip length was 6.1km.
Similarly, the maximum trip length for student residents was observed by car as 40.7km and
overall average trip length was 3.1km. The overall average trip lengths indicate that the trips
originating from KGUV were distributed to a relatively smaller area specifically for student
residents. The residents used car for accessing destinations located away from KGUV. This
also points out that the mixed uses can help in containing the trips over a smaller area.
Car
8%
Public transport
48%
Walk only
44%
Evaluating the transport impacts of TODs
Deepti Muley Page 154
Table 7.16 Trip lengths by mode of transport for non student residents at KGUV
Mode of transport Trip lengths (km)
Minimum Average Maximum
Car 0.9 12.8 94.4
Public transport 2.9 3.4 6.3
Walk only 0.0 0.9 4.8
Combined (overall) 0.0 6.1 94.4
Table 7.17 Trip lengths by mode of transport for student residents at KGUV
Mode of transport Trip lengths (km)
Minimum Average Maximum
Car 2.2 15.8 40.7
Public transport 2.1 3.7 10.1
Walk only 0.0 0.2 1.6
Combined (overall) 0.0 3.1 40.7
7.6.2.4 Internalisation of trips
The amount of internal trips is an important factor of a TOD travel. In order to gain an
understanding about the various activities performed by the residents at the KGUV, the
different activities were noted with their frequency. Table 7.18 represents the percentage of
activities and their frequency for non student residents. Table 7.19 lists the details for the
student residents‟ trips. Shopping at the Village Centre was the most popular activity for the
residents at KGUV. Other activities undertaken by residents were visiting cafes and farmers
market on Saturday. Also some students used the newly opened activity centre. Further, the
more than 60 percent of student residents at KGUV never made an education trip to KGUV
because they were studying at the campuses located outside KGUV specifically the QUT
campus located on the edge of KGUV or in the Brisbane CBD.
Table 7.18 Activity frequency for non student residents at KGUV
Activity Shopping Visiting parks Education trip Travel to work
Once a week 15.6 16.7 3.4 6.9
Twice a week 15.6 3.3 10.3 –
Thrice a week 28.1 – – 6.9
Four times a week 12.5 – 6.9 –
Five times a week 9.4 3.3 13.8 17.2
More than five
times a week
15.6 6.7 3.4 –
Never – 30.0 31.0 20.7
Once a fortnight – 6.7 3.4 –
Once a month – 26.7 – –
Not Applicable 3.1 6.7 27.6 48.3
Note: All values are presented in percentage
Characteristics of KGUV users
Deepti Muley Page 155
Table 7.19 Activity frequency for student residents at KGUV
Activity Shopping Visiting parks Education trip Travel to work
Once a week 14.0 14.0 2.0 2.0
Twice a week 22.0 2.0 4.0 –
Thrice a week 30.0 2.0 6.0 –
Four times a week 8.0 – 2.0 –
Five times a week 10.0 – 6.0 10.0
More than five
times a week
14.0 4.0 4.0 4.0
Never 2.0 48.0 62.0 48.0
Once a fortnight – 12.0 – 10.0
Once a month – 16.0 14.0 10.0
Not Applicable – 2.0 – 16.0
Note: All values are presented in percentage
7.7 Comparison of KGUV users’ characteristics
The characteristics of KGUV users can be further explored by comparing the characteristics
of each user group. For this purpose, the characteristics of the main user groups were
determined by combining the characteristics of the subgroups in that user group. For
example, the characteristics of professional employees and retail shop employees were
combined to obtain the characteristics of the employee user group. Table 7.20 and Table 7.21
represent a comparison of personal and household characteristics respectively. A comparison
of travel characteristics is shown in Table 7.22.
7.7.1 Comparison of demographic characteristics
7.7.1.1 Comparison of personal characteristics
The comparison of the personal characteristics of KGUV users (Table 7.20) indicates most of
the KGUV users were between 18 to 30 years of age. The proportion of female respondents
was more for users of Non Residential Land Use (NRLU) while the Residential Land Use
(RLU) exhibited balanced distribution. Most of the employees in all user groups were
employed full time or were full time students. Amongst all user groups, employees at KGUV
visited the development more frequently than any other NRLU user group. Further, the
highest proportion of employees possessed a valid driver‟s licence, followed by residents and
students; with students having higher proportion of users who do not possess a valid driver‟s
licence.
Evaluating the transport impacts of TODs
Deepti Muley Page 156
Table 7.20 Comparison of personal characteristics of KGUV users’
Characteristic Description Shoppers Employees Students Residents
Age group
0 to 18 years 14.0 0.6 30.8 7.1
18 to 30 years 53.0 45.3 64.1 74.1
30 to 45 years 20.0 37.1 2.6 11.8
45 to 65 years 11.0 17.0 1.7 7.1
65 years and above 2.0 0.0 0.9 0.0
Gender Female NA 62.3 76.1 54.1
Male NA 37.7 23.9 45.9
Employment
status
Employed full time
or Student full time
87.1*
71.4 47.4 78.0
Self employed 3.1 NA 0.0
Employed part time
and student full time 14.3 48.3 18.3
Employed part time /
student part time 10.6 4.3 0.0
Employed full time
and student part time 0.6 0.0 1.2
Homemaker / retired 6.8 0.0 NA 2.4
Other 6.0 0.0 0.0 1.2
Frequency of
trip (per week)
Rarely 6.0 NA NA NA
Once or twice 38.5 7.9 6.8 NA
Two to three times 4.3 – – NA
Three times 17.9 7.3 30.8 NA
Three to four times 0.9 – – NA
Four times 12.8 14.6 20.5 NA
Five times 9.4 51.2 33.3 NA
More than five times 10.3 18.9 8.5 NA
% valid driver‟s licence holders NA 90.5 59.8 76.5
Note: All values are given in percentage
*indicates combined value for employees and students
7.7.1.2 Comparison of household characteristics
The comparison of household characteristics was made for employees, students and residents
at KGUV. Shoppers were not considered in this comparison as shopper‟s household data was
not available (Table 7.21). The household size comparison shows that the student households
had higher household size than the employee and residents households. Most of the KGUV
employees‟ and residents‟ households owned one car but the students‟ households possessed
more cars (two or more). All three user households had higher proportion of households with
no bicycles and motorcycles. The student households possessed higher proportion of driver‟s
licence holders which supports the higher car ownership.
Characteristics of KGUV users
Deepti Muley Page 157
Table 7.21 Comparison of household characteristics of KGUV users’
Characteristic Description Employees Students Residents
Household size
1 11.5 1.8 24.3
2 38.9 13.2 41.9
3 24.8 33.3 18.9
4 14.0 25.4 1.4
5 7.6 18.4 13.5
> = 6 3.2 7.9 0.0
Car ownership
0 8.3 7.0 32.9
1 47.1 21.9 50.6
2 27.4 37.7 12.9
> = 3 17.2 33.3 3.5
Bicycle ownership
0 37.6 34.3 85.9
1 22.0 28.3 11.8
2 17.7 16.2 2.4
> = 3 22.7 21.2 0.0
Motorcycle
ownership
0 89.4 91.8 98.8
1 8.1 8.2 1.2
2 2.4 0.0 0.0
Driver‟s licence
holders
0 2.6 1.7 6.5
1 18.7 10.3 31.2
2 52.3 33.3 36.4
> = 3 26.5 54.7 26.0
Note: Values are given in percentage and trip lengths are in km
Indicates minimum values for each user group
Indicates maximum values for each user group
7.7.2 Comparison of travel characteristics
The comparison of travel characteristics of the KGUV users (Table 7.22) shows that
employees at KGUV have highest car mode share while students have the lowest car mode
share. This might be due to high access to car for travelling to KGUV for employees at
KGUV. Although the KGUV students have lower maximum trip length, they had highest
average trip length by when travelled by car. Comparing the public transport trip details, the
students used more public transport for undertaking education trips while least proportion of
shoppers used public transport for their shopping trips. The residents of KGUV have lowest
value of maximum trip length and average trip length when travelled by public transport.
Similar to car trips, KGUV students had maximum average trip length for travel by public
transport. When walk only trips were compared shoppers showed highest mode share because
of internalisation and students indicated lowest walk trip mode share might be because of
higher trip lengths. The residents exhibited lowest average trip length for walk trips while
employees indicated the highest average trip length. When the bicycle mode share was
Evaluating the transport impacts of TODs
Deepti Muley Page 158
compared two groups of KGUV users; shoppers and employees used bicycle with employees
showing maximum values of bicycle mode share and shoppers having the minimum values.
Table 7.22 Comparison of travel characteristics of KGUV users’
Mode Characteristic Shoppers Employees Students Residents
Car
Mode share 27.4 52.4 15.4 19.8
Minimum tip length 0.9 0.9 0.9 0.9
Maximum trip length 69.4 53.1 42.6 94.4
Average trip length 9.4 14.6 16.0 13.6
Public transport
Mode share 23.9 26.2 77.8 43.2
Minimum trip length 0.9 1.3 0.9 2.1
Maximum trip length 94.5 93.8 73.2 10.1
Average trip length 15.7 15.4 19.0 3.6
Walk only
Mode share 42.7 12.8 6.8 37
Minimum trip length 0 0 0.9 0
Maximum trip length 5.1 6.5 3.9 4.8
Average trip length 1.0 2.2 1.6 0.4
Bicycle
Mode share 4.3 7.3 0 0
Minimum trip length 0 2.9 – –
Maximum trip length 4.1 8.7 – –
Average trip length 2.0 5.8 – –
Other
Mode share 1.7 1.2 0 0
Minimum trip length 1.9 3.8 – –
Maximum trip length 5.5 6.5 – –
Average trip length 3.7 5.2 – –
Note: Mode shares are given in percentage and trip lengths are in km
Indicates minimum values for all user groups
Indicates maximum values for all user groups
7.8 Transport issues related to TOD from users’ perspective
The respondents were asked to rate the public transport and highlight any transport related
issues. A detailed overview of the results of perception analysis is given in Appendix D.
Some key issues about the transport facilities at KGUV for all respondents were noted as
below.
Respondents placed a strong emphasis on the frequency and reliability of public
transport service to afford a mode shift from car to public transport. The travel time
difference and the absence of a direct public transport link1 were also pointed out as
the main reasons for using personalised modes of transport. This indicated that for a
TOD to be successful from a transport point of view, a good quality direct public
1 Brisbane has a hub and spoke public transport network with most services intersecting at the CBD. One often
needs to change service, particularly for public buses, to access a destination on the other side of the CBD. The
imposition of a seat change has been reported to make public transport less attractive.
Characteristics of KGUV users
Deepti Muley Page 159
transport service is required from various destinations, not only from the CBD. Some
respondents preferred train over public bus because of its on time performance or
reliability.
As this area lies near the CBD, some respondents suggested having a “loop service”
(with minimum or no cost) running at a 15 minute interval from the CBD to KGUV
which also connects the nearby Roma street railway station to make KGUV more
attractive. (It is noted that, after this survey, TransLink implemented a high frequency
bus service, Route 66, along the busway system between Kelvin Grove busway
station to the east of KGUV, through the CBD via Roma Street railway station and on
to the inner southeast suburb of Woolloongabba. A service called 933 between Roma
Street train / public bus station and QUT KG Busway Station now augments this in
the peak.)
A professional employee also suggested having strictly enforced parking restrictions
on the local streets in KGUV with increased parking cost at the work place and
incentives for employees who travel by sustainable modes of transport from the
organisations to promote the more sustainable modes of transport.
The responses also indicated that on time performance and actual arrival of the public
bus at scheduled time or some information which would provide them an idea about
the public bus arrival were highlighted as important factors. This was linked to the
overcrowding and passenger discontent, as some passengers were unable to board the
first available public bus due to high demand. This issue was prominent in peak times
when visitors travelled to KGUV and also back home after finishing their activity at
KGUV. Access to the real time mobile information and linked information with the
nearby major train stations or busway stations was suggested as a solution for this.
A perceived lack of public transport in outlying suburbs was also a concern and
reason for using personalised modes of transport.
Some respondents perceived the cost of the public transport as expensive. Most of the
students were quite happy with the cost as they travelled on a concession fare which is
half the price of a full fare.
Few respondents suggested having route maps at the bus stop and signage on the
public buses to guide users. There are route maps in the city area but these may need
to be extended to the outer suburbs or the places of public interest as this may aid
users to provide more information about the bus routes.
Evaluating the transport impacts of TODs
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Some respondents also highlighted that they have infrequent or inadequate services
during off peak times and due to work commitments it was not always possible to
travel during peak times. The frequency of services during off peak hours to some
areas demanded more attention. This indicates need for public transport service to or
from various destinations for complete day not only in peak periods.
The accessibility to the Roma Street station and Normanby Busway Station was stated
as a major concern. The respondents were ready to walk for about 2km provided they
have good infrastructure for walking as this will also yield them health benefits. This
implies that connections to Normanby Busway and Roma Street station could be
enhanced.
The respondents demanded better signage and better sun / rain protection at public
bus stops. Public bus stops should not only look aesthetically good but also need to
perform better in terms of form and function. Decent shelter and accurate timetables
at the public transport stops were the amenities requested by a user.
The respondents requested bicycle lanes and protected bicycle parking facilities at the
KGUV in addition to their workplace. Wider footpaths were suggested for
accommodating bicycles to avoid collision. Demand for bicycle facilities indicated
that a good bicycle path is needed not only in KGUV but also from home to the
workplace. Riding a bicycle on Brisbane‟s roads was described as “scary”.
A pedestrian friendly sequencing of signals (or scramble crossing) and pedestrian
priority at intersections were suggested for improving the pedestrian facilities at
KGUV.
The presence and quality of bicycle ways in Brisbane was stated by a professional
employee as generally poor. It was stated that presence of a strong network of bicycle
ways throughout inner city suburbs and the CBD may encourage more people to
consider cycling to / from work (which maintains fitness and is sustainable).
7.9 Interpretation of TOD users’ characteristics
A comparison of various user characteristics was undertaken to determine the similarities or
differences. The proportion of female respondents was high for employees and students while
for residents a balanced distribution was obtained. The household size for student households
was more than the employee and residents‟ households. When the vehicle ownership was
compared for the subgroups it was found that the retails shop employees‟ and school
Characteristics of KGUV users
Deepti Muley Page 161
students‟ households had higher vehicle ownership as compared to their counterparts. Very
few households in each user group possessed a motorcycle. The comparison of bicycle
ownership showed that the households of non TOD resident user groups (employees‟ and
students‟ households) owned more bicycles than the KGUV resident households.
The mode share comparison suggested that a significant proportion of each user group
travelled by sustainable modes of transport; the lowest proportion was for employees (46
percent) and highest was for residents (80 percent). The comparison for average trip lengths
for various modes of transport showed that the KGUV residents used car for covering longer
trip lengths while the other user groups (students, employees and shoppers) preferred public
transport for longer trips as opposed to use of a car.
7.10 Summary
The preliminary analysis of travel data provided the various user characteristics as the
outcome. The mode share plots for KGUV users demonstrated encouraging mode share
values for the more sustainable modes of transport. Fewer cycling trips were reported by
KGUV users, possibly due to relatively treacherous cycling conditions on certain Brisbane
arterial roads and hilly topography around KGUV. Despite some steep terrain, walk trips
were considerable due to the complementary land uses placed together and attractive walk
paths constructed at KGUV. The share of public transport was significant, which is an
encouraging result. The comparison of overall average trip length for all user groups
indicated that of all users the KGUV residents had the lowest average trip lengths. The
students travelled farther to access the specialised education facilities.
The household characteristics of residents of KGUV indicated that KGUV residents had
lower car ownership compared to high valid driver‟s licence possession. The KGUV
residents also reported low bicycle and motorcycle ownership. This might be due to the
proximity of mixed land uses and availability of good public transport service. These
characteristics of KGUV users need to be investigated further by performing comparative
analysis and travel demand analysis for evaluating travel for the users of KGUV.
7.11 Chapter close
This chapter provided an insight into demographic as well as travel characteristics of various
KGUV users, which partly completes Step IV of TOD evaluation. These characteristics will
be used as a reference for further data analysis. Although the characteristics for each
Evaluating the transport impacts of TODs
Deepti Muley Page 162
subgroup of KGUV were explained separately in this chapter, considering the number of
responses and diversity of the user groups, further analysis was conducted for the combined
datasets. The combined trips undertaken by shoppers, employees and students were used for
further analysis. The next chapter presents the comparative analysis of KGUV users‟
characteristics with regional and similarly located suburban characteristics. The personal and
travel characteristics obtained in this chapter will also be used for the travel demand analysis
of KGUV users in Chapter 9 and Chapter 10 respectively.
Deepti Muley Page 163
Chapter 8
Comparative analysis of Kelvin Grove Urban Village’s users’
characteristics
8.1 Introduction
To assess the travel impacts of Kelvin Grove Urban Village (KGUV) with respect to other
non Transit Oriented Developments (TOD), the various characteristics need to be compared
with non TOD (or conventional) developments to determine the variations. This chapter
explains the differences in characteristics of KGUV users with respect to the Brisbane
regional and similarly located suburban users’ characteristics. The following section
describes details of these comparisons and later sections present the comparison of
characteristics of each group of KGUV users with their counterparts. The comparison of
travel characteristics for all KGUV user groups is demonstrated by comparing the mode
shares and trip lengths with regional and suburban values and a comparison of KGUV
residents’ household characteristics, with regional and suburban household characteristics is
also presented. Finally, an interpretation of the results from the comparative study, summary
and chapter close are provided.
8.2 Basis for comparison
The comparative analysis was mainly conducted using the travel characteristics of KGUV
users; namely mode shares and average trip lengths. In addition, the residents’ household
characteristics were compared. Basically, this comparison needs data from non TOD
developments. In this case, data from a specific non TOD development with similar size was
not available; hence the data availability for various suburbs located at similar distance from
the CBD was checked but due to insufficient datasets, the comparisons were undertaken by
grouping data from inner city suburbs. Firstly, a regional comparison was made to determine
the variation of KGUV travel with respect to Brisbane as a whole (known as the Brisbane
Statistical Division (BSD)). Secondly, suburban comparisons were conducted. A group of
suburbs located within similarly close proximity to Brisbane CBD as KGUV were considered
for comparison. The suburban comparison consisted of two groups of suburbs, Brisbane Inner
North Suburbs (BINS) and Brisbane Inner South Suburbs (BISS). BINS and BISS are typical
cases of suburbs located within close vicinity of an Australian capital city CBD. Note that
Evaluating the transport impacts of TODs
Deepti Muley Page 164
BISS travellers often need to cross the Brisbane River, which has a limited number of
crossings or bridges and is a natural barrier.
The travel data for various groups of KGUV users was obtained from preliminary analysis
(Chapter 7). Specifically four major trip types were considered, namely shopping trips (by
shoppers), work trips (by employees), education trips (by students), and residents’ trips. The
travel data for BSD and its inner suburbs (BINS and BISS) was obtained from the South East
Queensland Travel Surveys (SEQTS) conducted during 2006–08 (SEQTS, 2009).
Specifically, the SEQTS survey for Brisbane statistical division undertaken in 2006, known
as BSEQTS06, was used for analysis in this chapter. The BSEQTS06 collected travel data
and household data using a self–completion questionnaire which was hand-delivered to, and
hand-collected from, the survey households (SEQTS, 2008).
The BSEQTS06 obtained 4178 travel diaries from 1564 households and this data was
analysed using Microsoft Access®. The travel characteristics for shopping trips, work trips
and education trips were determined by considering the trips terminating at these suburbs,
while the characteristics for residents’ were obtained by considering their location of the
household. The mode shares were obtained by considering the “LINKMODE” assigned to
each trip. LINKMODE was the priority mode assigned for each trip after considering distinct
modes for each leg of the journey. The average trip lengths were determined by considering
the “QT_CUMDIST” for each trip. QT_CUMDIST was the total network distance travelled
for each trip noted in km. In case of BISS and BSD work trips, few interstate trips having
very large trip lengths were observed, so to avoid a false impression, these values were
termed as outliers and were removed from analysis. The absolute values were used instead of
the weighted values from SEQTS.
The following sections present comparison for each trip type at KGUV, BSD, BINS and
BISS as explained before. It should be noted that the public transport mode share and average
trip length comprised of trips undertaken by public bus, train, ferry and school bus.
8.3 Comparison of shoppers’ shopping trips
8.3.1 Mode share comparison
When comparing users’ shopping trip mode shares to KGUV with those of BSD, BINS, and
BISS, Table 8.1 shows that the car mode share for shoppers visiting KGUV was one third of
BSD and BISS, and one half of BINS. Significantly higher public transport and walking
Comparative analysis of KGUV’s users’ characteristics
Deepti Muley Page 165
mode shares for shopping trips were observed at KGUV compared to BSD, BINS, and BISS.
A bicycle mode share of 4.3 percent was observed for KGUV shopping trips compared to 0.6
percent for BSD, zero for BINS and 1.7 percent for BISS. Similar to BISS shoppers, none of
the KGUV shopping trips was by taxi as opposed to 0.2 percent for BSD and 0.4 percent for
BINS.
Overall, shoppers visiting KGUV used more sustainable modes of transport (70.9 percent)
compared to BSD shoppers (14.7 percent), BINS shoppers (42.2 percent) and BISS shoppers
(11.1 percent). KGUV has a small scale shopping centre, which caters for the daily needs of
its inhabitants and apparently some of the population of the immediate surrounding area. The
higher walking and cycling mode shares may be attributable to the various mixed land uses
placed together, which in theory induces more intrazonal trips, which are mostly undertaken
by walking.
Table 8.1 Mode share comparison for shopping trips
Mode of
transport KGUV
Brisbane
statistical division
Brisbane inner
north suburbs
Brisbane inner
south suburbs
Public transport 23.9 4.7 12.5 3.4
Walk only 42.7 9.4 29.7 6.0
Bicycle 4.3 0.6 0.0 1.7
Subtotal labelled
as “sustainable” 70.9 14.7 42.2 11.1
Car 27.4 84.2 54.7 88.0
Taxi 0.0 0.2 0.4 0.0
Other 1.7 0.9 2.6 0.9
Note: Mode share values are in percentage
8.3.2 Trip length comparison
Table 8.2 lists comparison of average trip lengths by modes of transport for KGUV, BSD,
BINS and BISS shoppers. The overall average trip length was KGUV and BSD (6.9km and
7.0km) shoppers were same, this was slightly higher than average trip length for BINS
shoppers (6.4km) and considerably higher than BISS shoppers (4.8km). The KGUV shoppers
exhibited higher average trip length by car when compared to its counterparts. While the
average trip length by public transport and bicycle did not show much variation. Noticeably,
KGUV shoppers on an average walked longer (1km) than BSD (0.9km), BINS (0.6km), and
BISS (0.5km) shoppers.
Evaluating the transport impacts of TODs
Deepti Muley Page 166
Table 8.2 Comparison of average trip lengths for shopping trips
Mode of
transport KGUV
Brisbane
statistical division
Brisbane inner
north suburbs
Brisbane inner
south suburbs
Public transport 15.7 15.6 16.7 18.4
Walk only 1.0 0.9 0.6 0.5
Bicycle 2.0 2.6 – 1.9
Car 9.4 7.3 7.2 4.6
Taxi – 5.7 11.7 –
Other 0.0 4.2 5.2 6.0
Overall 6.9 7.0 6.4 4.8
Note: Average trip lengths are in km
8.4 Comparison of employees’ work trips
8.4.1 Mode share comparison
The mode shares for work trips by employees at KGUV were compared with those of BSD
and the inner suburbs of Brisbane; BINS and BISS (Table 8.3). The comparison shows a
considerably smaller car mode share for KGUV employees, 29 percent and 22.8 percent less
for KGUV employees compared to BSD and BISS employees respectively. Their mode share
is a more modest 5.2 percent less when compared to BINS employees. When public transport
trips are compared, there is not much difference for BINS and KGUV employees; however
these two values are both considerably higher than the BSD and BISS public transport mode
share of 10.2 percent and 13.6 percent respectively. A similar trend exists for walk only mode
shares. The employees of BINS and KGUV likely exhibited higher mode shares than BSD
for public transport because of good quality services to the CBD and surrounding suburbs,
particularly during peak times. A high mode share for bicycle for KGUV employees may be
attributable to good quality access and end-of-trip facilities (such as showers and cycle
lockers). The higher car mode share in case of BISS as compared to BINS may be attributed
to the strict parking restrictions imposed for inner city and northern suburbs.
Overall, KGUV employees demonstrated more sustainable travel choices (46.3 percent)
compared to BSD employees (17.6 percent) and BISS employees (23.4 percent) and a little
improvement in use of sustainable modes of transport than BINS employees (41.6 percent)
for their work trips.
Comparative analysis of KGUV’s users’ characteristics
Deepti Muley Page 167
Table 8.3 Mode share comparison for work trips
Mode of
transport KGUV
Brisbane
statistical division
Brisbane inner
north suburbs
Brisbane inner
south suburbs
Public transport 26.2 10.2 25.6 13.6
Walk only 12.8 6.2 14.5 8.0
Bicycle 7.3 1.2 1.5 1.8
Subtotal labelled
as “sustainable” 46.3 17.6 41.6 23.4
Car 52.4 81.4 57.6 75.2
Taxi 0.0 0.3 0.6 0.9
Other 1.2 0.6 0.2 0.4
Note: Mode share values are in percentage
8.4.2 Trip length comparison
The comparison of average trip lengths for KGUV employees work trips (Table 8.4) indicate
that overall KGUV had lower average trip length (12.3km) than BSD (14.7km), BINS
(20.7km), and BISS (18.0km) employees. The average trip length for car trips was same for
KGUV and BSD employees which was about 40 percent lesser than average trip lengths for
BINS and BISS employees. Similarly, KGUV employees have almost 40 percent, 50 percent,
and 35 percent less average trip length when travelled by public transport compared with
BSD, BINS, and BISS employees respectively. Despite lower average trip length by public
transport and car, KGUV employees walked longer to reach their workplace than BSD, BINS
and BISS employees. Similarly, KGUV employees had higher average trip length for work
trips by bicycle when compared with BSD and BINS employee trips, but lesser when
compared with BISS employee bicycle trips.
Table 8.4 Comparison of average trip lengths for work trips
Mode of
transport KGUV
Brisbane
statistical division
Brisbane inner
north suburbs
Brisbane inner
south suburbs
Public transport 15.4 25.9 29.7 23.6
Walk only 2.2 0.7 0.6 0.7
Bicycle 5.8 5.2 4.6 6.7
Car 14.6 14.6 22.4 19.3
Taxi – 6.4 3.8 3.1
Other – 13.3 18.3 21.5
Overall 12.3 14.7 20.7 18.0
Note: Average trip lengths are in km
Evaluating the transport impacts of TODs
Deepti Muley Page 168
8.5 Comparison of students’ education trips
8.5.1 Mode share comparison
When comparing user students’ education trip mode shares to KGUV with those of BSD,
BINS and BISS, Table 8.5 shows that only 15.4 percent of KGUV students used a car for
their education trip compared to 58.4 percent for BSD, 40 percent for BINS, and 54.6 for
BISS. These equate to shares 52.9 percent, 28.3 percent and 43.5 percent higher shares for
public transport than those of BSD, BINS and BISS respectively. This may be attributed to
the high quality of public transport facilities for access to KGUV.
Although the public transport mode share is particularly high, KGUV students had a lower
walking mode share for education trips compared to its counterparts, perhaps due to the hilly
terrain and highly trafficked roadways, such as Kelvin Grove Arterial Road on the western
flank. Further, and perhaps for similar reasons, no bicycle mode shares for education trips to
KGUV were reported, in this case consistent with BINS. Although dedicated bicycle facilities
are located nearby, including a segregated facility running alongside the M3 motorway to the
south east, students may be deterred from cycling due to highly trafficked crossings such as
Kelvin Grove Arterial Road, as well as missing links. Long travel distances may also be
playing a part in these results. The bicycle mode share for BSD students indicate that the
students studying in education institutions located in outer suburbs used bicycle for their trip
to school.
Table 8.5 Mode share comparison for education trips
Mode of
transport KGUV
Brisbane statistical
division
Brisbane inner
north suburbs
Brisbane inner
south suburbs
Public transport 77.8 24.9 49.5 34.3
Walk only 6.8 13.8 9.5 11.1
Bicycle 0.0 2.9 0.0 0.0
Subtotal labelled
as “sustainable” 84.6 41.6 59.0 45.4
Car 15.4 58.4 40 54.6
Taxi 0.0 0.1 0.0 0.0
Other 0.0 0.0 1.1 0.0
Note: Mode share values are in percentage
When mode shares for all sustainable modes of transport were combined and compared,
KGUV user students showed a significant difference in mode shares for their education trips
at 84.6 percent, compared to 41.6 percent, 59 percent and 45.4 percent for BSD, BINS, and
BISS respectively.
Comparative analysis of KGUV’s users’ characteristics
Deepti Muley Page 169
8.5.2 Trip length comparison
When comparing the average trip lengths by mode of transport for education trips at KGUV,
BSD, BINS and BISS students (Table 8.6) shows that KGUV students have a higher overall
average trip length (17.2km) than BSD (7.0km), BINS (14.7km), and BISS (7.0km) students.
This was almost 2.5 times higher than BSD and BISS students and 17 percent more than
BINS students. The average trip length for car trips was more than two fold and three fold for
BINS, and BSD and BISS students respectively. Similar to shopping and work trips, students
at KGUV walked longer distances than students of its counterparts. The students at KGUV
travelled on an average 19km by public transport, which was more than BSD (13.3km) and
BISS (12.4km) student trips and less than BINS (23.8km) education trips.
Table 8.6 Comparison of average trip lengths for education trips
Mode of
transport KGUV
Brisbane
statistical division
Brisbane inner
north suburbs
Brisbane inner
south suburbs
Public transport 19.0 13.3 23.8 12.4
Walk only 1.6 1.1 0.9 0.9
Bicycle – 2.9 – –
Car 16.0 5.9 7.1 4.9
Taxi – 18.1 – –
Other – – 5.0 –
Overall 17.2 7.0 14.7 7.0
Note: Average trip lengths are in km
8.6 Comparison of residents’ trips
8.6.1 Household characteristics
Table 8.7 represents a comparison of KGUV residents’ household characteristics with the
residents of BSD, BINS and BISS. It should be noted that characteristics of KGUV student
residents were not included in the analysis as it is argued that the characteristic of students’
accommodation are different than of typical conventional households due to difference in
demographic characteristics. So in order to obtain a fair comparison, the data for this user
group was not included. The comparison of household size suggests that KGUV residents
have slightly lower household size than residents of BINS and BISS and considerably smaller
household size than BSD households. This is likely due to the higher number of single and
double bedroom apartments at KGUV.
KGUV has a lesser average number of bedrooms per household, indicating these apartments
are highly attractive to small family households; typically to young adults and families with
no children. However, a TOD should also cater for large families as they tend to drive more
Evaluating the transport impacts of TODs
Deepti Muley Page 170
for children’s activities, and living in such an environment may help to reduce the number of
vehicle trips by a household considerably.
Table 8.7 Comparison of residents’ household characteristics
Household
characteristics KGUV
Brisbane
statistical
division
Brisbane inner
north suburbs
Brisbane inner
south suburbs
Average household size 2.0 3.4 2.3 2.2
Average of number of
bedrooms 1.4 3.5 2.8 2.5
Average motor vehicles
per household 1.1 2.0 1.5 1.3
Average bicycles per
household 0.2 1.8 1.2 1.0
Note: These are Non Student Residents’ (NSRs) characteristics only
Transport professionals often postulate that TOD residents have low vehicle ownership; this
is true in case of KGUV, with 1.1 vehicles per household in comparison to 2.0, 1.5, and 1.3
vehicles per household for BSD, BINS and BISS respectively. This finding was similar to the
findings of previous studies (Deakin et al., 2004 and Giuliano and Dargay, 2006). When
asked about vehicle ownership while conducting the surveys; the respondents indicated that
they do not own a car because they do not need one. One respondent said, “Everything is so
close here. I can access everything by walk and I like it very much”. The low vehicle
ownership requires less parking infrastructure, and as such this aspect of TOD needs to be
studied separately for appropriate parking arrangements, as does making these developments
more pedestrian friendly.
Transport professionals also often postulate that TOD residents have higher walking and
cycling trip rates, which means that they ought to have higher bicycle ownership. However,
KGUV residents exhibit an opposite trend, having only 0.2 bicycles per household in
comparison to 1.8, 1.2 and 1.0 bicycles per household by BSD, BINS and BISS households.
This may attributable to the reasons cited above.
8.6.2 Trip characteristics
When the average trip characteristics for KGUV residents and BSD, BINS and BISS
residents were compared (Table 8.8), it was found that on an average KGUV residents
undertook fewer trips on the given travel day (2.6 trips/person) compared to BSD (3.1
trips/person), BINS (3.6 trips/person) and BISS (3.5 trips/person) residents. The minimum
trips were the same (zero) as there were few respondents in each category who did not travel
on the assigned travel day. KGUV residents made a quarter and one third fewer trips when
Comparative analysis of KGUV’s users’ characteristics
Deepti Muley Page 171
compared with BSD and BISS, and BINS residents respectively. Previous research suggested
that TOD residents make fewer car trips and more walk trips. Car trips are stated to be
replaced by walk trips, but making same number of trips as for residents living in
conventional development (Sun et al., 1998). However KGUV residents made less number of
trips exhibiting an opposite trend.
Table 8.8 Comparison of residents’ average trips per person
Description KGUV Brisbane
statistical division
Brisbane inner
north suburbs
Brisbane inner
south suburbs
Minimum trips by
a person 0 0 0 0
Maximum trips by
a person 6 23 18 23
Average trips per
person 2.6 3.1 3.6 3.5
8.6.3 Mode share comparison
The mode shares for residents’ first trip of the day were compared to each other. Table 8.9
lists the details of mode shares for KGUV, BSD, BINS and BISS residents. Only 20 percent
of KGUV residents used the car, compared to around 70 percent for other inner suburban
residents. KGUV residents used public transport at around twice and four fold the rate
compared to other inner suburban residents and BSD residents respectively. Similarly,
KGUV residents walked at around four fold the rate compared to other inner suburban
residents. A small proportion of the BSD, BINS and BISS residents used bicycle for their
travel while none of the KGUV resident used bicycle. Noteworthy, taxi was not a popular
mode for the first trip of the day in any group of Brisbane or inner suburban residents.
Table 8.9 Mode share comparison for residents’ first trip of the day
Mode of
transport KGUV
Brisbane
statistical division
Brisbane inner
north suburbs
Brisbane inner
south suburbs
Public transport 43.2 11.7 17.6 19.2
Walk only 37.0 7.4 7.9 8.8
Bicycle 0.0 1.5 0.9 2.4
Subtotal labelled
as “sustainable” 80.2 20.6 26.4 30.4
Car 19.8 78.5 71.8 68.2
Taxi 0.0 0.2 0.9 0.4
Other 0.0 0.7 0.9 0.8
Note: Mode share values are in percentage
Overall, 80.2 percent of KGUV residents used sustainable modes of transport for making
their first trip of the day. On the other hand, only 20.6 percent, 26.4 percent and 30.4 percent
Evaluating the transport impacts of TODs
Deepti Muley Page 172
residents of BSD, BINS and BISS used such modes for making their first trip of the day
respectively. The relatively high mode share for KGUV residents was due to the fact that
many residents were living in KGUV and working / studying not too far from their place of
residence. Further, they have high quality public transport facilities close by and services to /
from various destinations. This outcome was in line with the previous findings from past
studies (Cervero and Gorham, 1995; Cervero, 1996; and Hess and Ong, 2002). This
demonstrates that KGUV has a greater tendency towards using modes of transport labelled as
sustainable; supporting the hypothesis that TOD development will help to reduce transport
congestion in urban areas over traditional development of the same size.
8.6.4 Trip length comparison
When the average trip lengths for KGUV, BSD, BINS and BISS residents’ first trip were
compared (Table 8.10), it was found that overall KGUV residents travelled within close
vicinity as compared to BSD, BINS and BISS residents. The average trip length by public
transport for KGUV residents was one sixth and one half of the corresponding trip lengths for
BSD, and BINS and BISS residents respectively. Similarly, the residents at KGUV had very
short walking trips (0.4km) compared to BSD (1.2km), BINS (1.5km), and BISS (1.0km)
residents. The residents of BINS had lower bicycle average trip length than BSD and BISS
residents possibly due to the hilly conditions noted before. The average trip lengths by car
suggests a similar value for KGUV and BSD residents, this was lower than BINS residents’
and higher than BISS residents’ average car trip lengths. The higher trip length for car trips
was in contradiction to the claim of TOD residents’ shorter car trips made by (Sun et al.,
1998 and Steiner, 1998). It should be noted that local factors will specifically impact the trip
lengths.
Table 8.10 Comparison of average trip lengths for residents’ first trip of the day
Mode of
transport KGUV
Brisbane
statistical division
Brisbane inner
north suburbs
Brisbane inner
south suburbs
Public transport 3.6 24.7 7.0 8.5
Walk only 0.4 1.2 1.5 1.0
Bicycle – 4.6 2.2 5.5
Car 13.6 12.3 16.1 7.7
Taxi – 13.5 8.2 5.8
Other – 9.2 2.9 2.9
Overall 4.4 12.8 13.0 7.1
Note: Average trip lengths are in km
Comparative analysis of KGUV’s users’ characteristics
Deepti Muley Page 173
8.7 Interpretation of results
The results of a comparative analysis indicated that TODs are transport efficient in practice
when compared with the other non TOD areas. On an average, KGUV residents made fewer
trips than BSD, BISS and BINS residents. Due to presence of mixed land uses the KGUV
residents possessed fewer motor vehicles (1.1) per household in comparison to its other
counterparts (BSD at 2.0, BINS at 1.5 and BISS at 1.3). This reduces the dependence on
motor vehicle and ultimately restrains its usage.
The comparison shows that all user groups of KGUV used more sustainable modes of
transport compared to those of BSD, BINS and BISS (Figure 8.1). This outcome provides
evidence to support the presumption that TOD is a more sustainable form of development
than traditional development, with respect to travel modes of users. This high proportion of
sustainable mode usage resulted in more environment friendly practices supporting
transportation claims of TODs and making them more transportation efficient. A good public
transport connection to or from various destinations at KGUV attracted higher public
transport mode shares and subsequently reduced car usage. Clustering of activities was
correlated with more walking trips; specifically for shopping and recreational trips.
Figure 8.1 Comparison of sustainable transport mode share
In general, the mode shares of BISS and BSD were very similar when the trips by Non
Residential Land Use (NRLU) users were considered while mode shares for BINS and
0
10
20
30
40
50
60
70
80
90
Shopping trips Work trips Education trips Residents’ first
trip
Su
sta
inab
le t
ran
sport
mod
e sh
are
(%
)
Trip type
KGUV
BSD
BINS
BISS
Evaluating the transport impacts of TODs
Deepti Muley Page 174
KGUV were similar with KGUV showing more sustainable travel choices. This improvement
in the mode shares can be attributed to the atypical TOD development characteristics mainly
the quality of public transport service. BINS has better connections to CBD than BISS or
BSD overall.
The comparison of overall average trip length for KGUV, BSD, BINS and BISS users
(Figure 8.2) indicates that KGUV shoppers and students have greater average trip lengths and
KGUV employees and residents have lower average trip lengths as compared to its
counterparts in BSD, BINS and BISS. The higher trip length can be attributed to the
specialised education facilities at KGUV and the shopping trips attracted because of these
specialised facilities. The reduction in average trip length for residents shows that the TOD
residents travel fewer kilometres, this was similar to the finding determined by McCormack
et al. (2001).
Figure 8.2 Comparison of overall average trip length
When the average trip lengths by car and public transport were compared for all trips
undertaken by NRLU users, it was found that for all user groups at KGUV as well as BSD,
BINS and BISS, the average trip lengths by public transport were higher than average trip
lengths by car indicating that public transport users travelled longer distances. When a similar
comparison was made for residents, those of KGUV and BINS used car for travelling farther
0
5
10
15
20
25
Shopping trips Work trips Education trips Residents’ first trip
Over
all
aver
age
trip
len
gth
(k
m)
Trip type
KGUV
BSD
BINS
BISS
Comparative analysis of KGUV’s users’ characteristics
Deepti Muley Page 175
and used public transport for accessing destinations located closer to their residence. This
may be because of availability of good public transport service to the destinations located
closer to their homes and strict parking conditions in those areas. On the contrary, the
residents of BSD and BISS exhibited an opposite trend than KGUV and BINS residents. This
might be possibly due to the similar factors as of KGUV and BINS residents but exhibiting
an opposite trend.
8.8 Summary
The comparison of residents’ household characteristics showed that KGUV residents have
low vehicle and bicycle ownership as compared to BSD, BINS and BISS residents. The
outcomes of comparative analysis indicate that KGUV users use more sustainable modes of
transport than BSD, BINS and BISS users. The overall average trip length for shopping and
education trips at KGUV shows higher overall trip lengths than its counterparts. On the other
hand, shorter trips were observed for KGUV residents and employees when compared with
their respective counterparts. These results provide a means of comparing transport
performance of KGUV with respect to non TOD conventional developments, indicating the
travel impacts of one kind of TOD. The outcomes from this comparative analysis should,
however, be applied with caution while planning future TODs, as each TOD has its own
location, geographic, demographic, socio–economic and built form characteristics. No two
TODs are exactly alike. The finding that sustainable travel choices are made at this site,
however, supports the notion of development of future TODs from KGUV users’ perspective.
Although this study supports TOD planning, travel data for more case studies at various
scales and characteristics should be examined. In the Australian context, this task will
become easier as more TODs are developed and are able to be studied by transport
researchers using standardised techniques.
8.9 Chapter close
This chapter provided a comparison of travel characteristics of all TOD users, and household
characteristics of residents, with corresponding regional and suburban characteristics. The
results of this chapter provided the travel impacts of KGUV; these outcomes will provide
some information influencing the conclusions in Chapter 11. This partly completes the fourth
step of TOD evaluation. To study this outcome in detail and make use of it for further
planning purposes, it is also important to assess the travel demand at KGUV, which is next
Evaluating the transport impacts of TODs
Deepti Muley Page 176
sub step in TOD evaluation. Chapter 9 and Chapter 10 undertake this task and explore the
travel at KGUV by conducting the travel demand analysis.
Deepti Muley Page 177
Chapter 9
Kelvin Grove Urban Village users’ travel demand analysis
9.1 Introduction
After assessing the travel demand of a Transit Oriented Development (TOD) with respect to
other non TOD areas, the next step is to carry out travel demand analysis for detailed travel
demand investigation. The issue of mode choice is probably the single most important
element in transport planning and policy making. It affects the general efficiency of urban
transport (Ortuzar and Willumsen, 1994). Hence, an investigation into the travel modes was
conducted for all user groups at Kelvin Grove Urban Village (KGUV) to determine the
influence of various characteristics on the travel modes of TOD users. For mode choice
analysis, two types of models can be developed, models based on aggregate demand and
models based on disaggregate demand. For analysing the travel modes of KGUV users,
disaggregate demand was used as this will enable more realistic models to be developed
(Ortuzar and Willumsen, 1990). This chapter presents the analysis and results for the same.
The following section provides the background and details used for analysing KGUV users‟
travel modes. Later the equations for travel mode determination are provided for individual
TOD user groups; namely for shoppers‟ shopping trips, employees‟ work trips, students‟
education trips and residents‟ first trip of the day. An interpretation of the analysis is provided
in the next section. At the end, a summary and chapter close are presented.
9.2 Analysis background
The investigation into travel modes was made using a logistic regression technique. Logistic
regression is a widely used technique for performing statistical analysis of survey data. This
consists of fitting a linear logistic model to an observed data set in order to measure the
relationship between the outcome variable and one or more explanatory variables. Logistic
regression was used as it is more flexible than other techniques. Also, it does not assume
distributions of the predictor variables, hence the variables do not have to be normally
distributed, linearly related or of equal variance within each group. The predictors in the
logistic regression analysis can be a mix of continuous, discrete or dichotomous variables.
Unlike multiple regression analysis, logistic regression cannot produce negative predicted
probabilities (Tabachnick and Fidell, 2007). Further, logistic regression can be used to
Evaluating the transport impacts of TODs
Deepti Muley Page 178
determine the effect of size of the independent variables on the dependent variable; to rank
the relative importance of independents; to assess interaction effects; and to understand the
impact of covariate control variables. The impact of predictor variables is usually explained
in terms of odds ratios.
Binary logistic regression was used to predict the probability of a KGUV user using
sustainable modes of transport. Firstly KGUV users‟ travel modes were divided into two
categories; sustainable transport modes (walking, cycling and public transport) and the
private car. Separate logistic regression analysis was carried out for each trip purpose to
determine the probability of a KGUV user choosing a sustainable transport mode. The
general form of logistic regression is shown in Equation 9.1 and Equation 9.2.
Logistic regression function,
𝑝(𝑦) =𝑒𝑧
1 + 𝑒𝑧 Equation 9.1
The linear regression equation,
𝑍 = 𝑎0 + 𝑎1 × 𝑥1 + 𝑎2 × 𝑥2 + 𝑎3 × 𝑥3 + ⋯ Equation 9.2
where,
𝑝(𝑦) = Probability of predicting y, in this case use of a sustainable
transport mode, using independent variables via Z
𝑎0 = Regression constant
𝑎1,𝑎2,… = Regression coefficients
𝑥1, 𝑥2,… = Independent variables
𝑍 = Linear function of independent variables
The independent variables were obtained from the travel surveys. The travel survey mainly
gathered information on respondents‟ trip characteristics, household characteristics, personal
characteristics and perception about KGUV and transport at KGUV. The perception based
variables were discarded from travel demand analysis and analysed separately (Appendix D)
as they are mainly used for qualitative analysis, whereas travel demand analysis is
quantitative analysis. Amongst the household characteristics, vehicle availability is the most
important and governing variable. Initially to represent vehicle availability for an individual
trip, the variable named car availability was used. To represent the trip characteristics trip
KGUV users’ travel demand analysis
Deepti Muley Page 179
length by car, LOS for public transport and travel time saving of car over public transport
were used, which incorporate the characteristics of public as well as private modes of
transport.
From the observations, it was noted that, in this case, personal characteristics were the
governing factors in determining the travel mode. Hence all variables related to personal
characteristics were considered in the analysis. This inclusion of personal variables also fills a
knowledge gap, as previously no study was found demonstrating the influence of personal
characteristics on choice of travel mode at the disaggregate level.
Based on the above arguments, the probability of choosing sustainable modes of transport
was calculated as a function of personal characteristics, such as age group, occupation,
employment status, type of student, frequency of shopping trip and gender, and transit
characteristics such as (Quality or) Level of Service (LOS), trip length, and travel time saving
(TTS). The LOS for frequency of public transport service was included in the analysis
because it was rated as the most important parameter for public transport service by KGUV
users (Appendix D). Each variable was coded using the coding system specified in Table 9.1.
Actual values of the continuous variables including trip length and travel time saving were
used rather than coded values.
The personal characteristics of the users and the trip length values were obtained from the
travel survey analysis illustrated in Chapter 7. The travel time saving was the time saving if a
user travelled by public transport. It was calculated as the difference between travel time by
car and the travel time by public transport. The car travel time was the time required to travel
the specified trip length by car (Given by Google Maps, www.maps.google.com.au). The
public transport travel time was calculated using the journey planner on the TransLink
Transit Authority‟s public transport information website www.translink.com.au. The origin
location was the home suburb and for convenience the destination was given as the QUT
Kelvin Grove Busway Station for trips by visitor groups and vice versa for the residents‟
trips. The option of fastest travel time for a weekday was considered in travel time
calculations. An additional travel time of 10 minutes and 15 minutes was added to the
estimated travel time for car and public transport respectively to take into account the access
times. In the TransLink journey planner, the options of arrive before start time and leave
after start time were selected for visitors and residents of KGUV respectively. The travel
time saving was calculated by subtracting public transport travel time from car travel time.
Evaluating the transport impacts of TODs
Deepti Muley Page 180
The travel time saving for the cases in which no public transport option was available was
coded as missing values.
Table 9.1 Details of coding system for travel mode investigation
Variable Coding
Mode of transport Sustainable mode of transport = 1
Private car mode = 0
Age Group (AG)
0 to 18 years = 0
18 years to 30 years = 1
30 years to 45 years = 2
45 years to 65 years = 3
65 years and above = 4
Occupation (OCCU)
Full time worker = 0
Student = 1
Part time worker = 2
Part time student = 3
Homemaker / retired = 4
Employment Status (ES)
Employed full time = 0
Employed part time & student full time = 1
Self employed = 2
Employed part time = 3
Student part time & employed part time = 4
Unemployed / retired = 5
Gender (GDR) Female = 0
Male = 1
Type of Student (TS)
Full time student = 0
Full time student & Casual employment = 1
Student part time = 2
Licence Availability (LA) Licence available = 1
Licence not available = 0
Car Availability (CA) Car available = 1
Car not available = 0
Level of Service (LOS)
LOS A = 6
LOS B = 5
LOS C = 4
LOS D = 3
LOS E = 2
LOS F = 1
No service = 0
The LOS for public transport frequency of service was determined using the procedure stated
in the TCQSM (TRB, 2003). The LOS was determined for each journey undertaken by a
KGUV user by public transport. It was observed before that the KGUV users had multiple
legs to their journeys. The LOS for each leg was determined and the minimum LOS was
assigned for the whole journey. The public transport frequency calculations were made for
KGUV users’ travel demand analysis
Deepti Muley Page 181
the AM peak period of 7am to 9am as most of the trips were undertaken during this time
period.
The final regression model was obtained iteratively for trip undertaken by each user group.
Iterations were terminated when the change in parameter estimates was less than 0.001. To
test the goodness – of – fit of models, the Omnibus test for model coefficients was performed
to obtain the model Chi-square (2) from the log–likelihood technique. The statistically
significant difference between the full model and constant only model should be at a level of
at least p < 0.05 (Tabachnick and Fidell, 2007).
The effect of multicollinearity of independent variables was assessed from the value of the
coefficient and standard error. If a variable had high degree of multicollinearity then the
regression coefficients become unstable and the standard errors for the coefficients become
wildly inflated (UCLA, 2010). Any such variable with a high degree of multicollinearity was
removed from analysis. The importance of the individual variables was assessed using the
Wald statistics and the significance (p) value. Wald statistics should be significantly different
from zero to assume that the predictor is making a significant contribution in predicting the
outcome (Field, 2009). On the contrary, the significance value closer to the zero is desired for
a variable to be noted as significant in predicting the outcome. The variable with the value of
least significance is the most significant in predicting the outcome (Hair et al., 2006 and
Warner, 2008).
The outcomes from the logistic regression are explained with the help of odds ratios. Odds
are defined as the ratio of the probability of an event occurring to the probability of the event
not occurring (Hair et al., 2006). The odds ratio is defined as the change in odds of one of the
categories of outcome when the value of a predictor variable increases by one unit
(Tabachnick and Fidell, 2007).
The statistical software package “Statistical Package for Social Scientists (SPSS)” was used
for conducting the logistic regression analysis (Kinnear and Gray, 2009). The initial
regression analysis was undertaken considering all variables. The variable “Car Availability”
was found to have high degree of multicollinearity hence was removed from final analysis.
The values of regression coefficient and standard error can be observed from Muley et al.
(2009). The following section presents the final binary Logistic regression models developed
for shoppers, employees, students and residents at KGUV.
Evaluating the transport impacts of TODs
Deepti Muley Page 182
9.3 Analysis for shoppers’ shopping trips
The analysis of travel modes used for shopping trips by shoppers at KGUV was conducted
using LOS, trip length, travel time saving, frequency of shopping trip, age group and
occupation as independent variables, with 117 cases. Equation 9.3 shows the linear regression
equation and Table 9.2 lists the detailed results of logistic regression analysis. The final
model was statistically significant at a significance of 0.001 with 2 of 30.930 predicting 79.8
percent cases correctly. The values of regression coefficient and standard error indicated no
variable with high degree of multicollinearity. The comparison of Wald statistic and
significance values identified occupation as the least significant variable and age group as the
most significant variable in determining the travel mode of a shopper.
𝑍 = 1.168 + 0.155 × 𝐿𝑂𝑆 + 0.042 × 𝑇𝐿 + 0.069 × 𝑇𝑇𝑆 + 0.353 × 𝐹𝑅𝑄
− 1.122 × 𝐴𝐺 − 0.104 × 𝑂𝐶𝐶𝑈 Equation 9.3
The odds ratio for frequency of shopping trips (1.423) showed the highest odds of increased
use of a sustainable mode of transport with a unit increase in frequency of shopping trip. This
is justified because many employees and students visit the shopping centre often by walking
as this is their intrazonal trip within a short walking distance. In contrast, the odds ratio for
age group indicated that a unit increase in age group reduces the odds of using of a
sustainable mode of transport by 0.326. A unit increase in the LOS score, trip length and
travel time saving increase the odds of choosing walking, cycling or public transport by
1.167, 1.043 and 1.072 respectively. This isn‟t a fair comparison for trip length and travel
time because they exercise much greater variability i.e. across broader scales.
Table 9.2 Travel mode analysis for shoppers’ shopping trips
Variable Coefficient
(B)
Standard
error
Wald
statistic
Significance
(p)
Odds ratio
Exp(B)
Level of service (LOS) 0.155 0.308 0.253 0.615 1.167 Trip length (TL) 0.042 0.030 1.926 0.185 1.043 Travel time saving (TTS) 0.069 0.034 4.169 0.046 1.072 Frequency of shopping
trip (FRQ)
0.353 0.154 5.258 0.023 1.423
Age group (AG) -1.122 0.318 12.432 0.000 0.326 Occupation (OCCU) -0.104 0.258 0.162 0.688 0.902 Constant 1.168 1.826 0.410 0.522 3.217
No of cases = 117
% cases correctly predicted = 79.8 (criterion, if estimated probability > 0.500, the predicted mode is
sustainable mode of transport)
2model = 30.930, degree of freedom = 6, significance = 0.001
2critical = 22.458
KGUV users’ travel demand analysis
Deepti Muley Page 183
Similar to age group, the unit increase in occupation reduces the odds of a using sustainable
mode of transport by 0.902. The odds ratio for occupation also indicates better odds of
choosing a sustainable mode for students and full time employees. The retired persons and
homemakers have a greater tendency to drive to the shopping centre.
Figure 9.1 plots the changes in the probability of selecting sustainable mode with the travel
time saving. The probabilities were calculated for a range of travel time saving (50 minutes to
-50 minutes) for a typical shopper who is full time employed (18 to 30 years of age) and has a
LOS of five with trip length of 7km visiting shopping centre three times a week. As the car
travel time becomes more than the public transport travel time the probability of choosing
public transport, walk or cycle increases. This is obvious because the shoppers aim at travel
time savings. For zero travel time saving the probability is 0.89 which is quite impressive.
Figure 9.1 Sensitivity of typical shopper’s sustainable travel mode probability with travel time
saving
9.4 Analysis for employees’ work trips
The logistic regression for work trips by employees at KGUV was undertaken for a combined
data set of retail shop employees and professional employees with 164 cases using LOS, trip
length, travel time saving, age group, employment status and gender as independent
variables. Unlike shoppers shopping trips analysis, the frequency of work trips was not
considered because the employment status indirectly considered the frequency of travel. An
additional variable, gender of employees, was used for this analysis. The travel mode was
0
0.2
0.4
0.6
0.8
1
-60 -40 -20 0 20 40 60
Pro
bab
ilit
y
Travel time saving (minutes)
Evaluating the transport impacts of TODs
Deepti Muley Page 184
used as a dependent variable. The results of Wald statistic and significance values indicated
that age group was statistically the most significant variable governing the travel mode; while
LOS was statistically the least significant. The values of regression coefficient and standard
error indicated no variable with high degree of multicollinearity. The model predicted 69.1
percent of cases correctly. The 2 model was 26.391 at a significance level of 0.001. The
details of the analysis are given in Table 9.3. Equation 9.4 notes the linear regression
equation obtained from the logistic regression analysis.
𝑍 = 1.842 + 0.012 × 𝐿𝑂𝑆 − 0.018 × 𝑇𝐿 + 0.014 × 𝑇𝑇𝑆 − 0.759
× 𝐴𝐺 − 0.602 × 𝐸𝑆 + 0.375 × 𝐺𝐷𝑅 Equation 9.4
Table 9.3 Travel mode analysis for employees’ work trips
Variable Coefficient
(B)
Standard
error
Wald
statistic
Significance
(p)
Odds ratio
Exp(B)
Level of service (LOS) 0.012 0.183 0.004 0.948 1.012 Trip length (TL) -0.018 0.017 1.103 0.294 0.982 Travel time saving (TTS) 0.014 0.017 0.659 0.417 1.014 Age group (AG) -0.759 0.256 8.766 0.003 0.468 Employment status (ES) -0.602 0.214 7.952 0.005 0.547 Gender (GDR) 0.375 0.379 0.976 0.323 1.455 Constant 1.842 1.218 2.286 0.131 6.309
No of cases = 164
% cases correctly predicted = 69.1 (criterion, if estimated probability > 0.500, the predicted mode is
sustainable mode of transport)
2model = 26.391, degrees of freedom = 6, significance = 0.001
2critical = 22.458
The details of analysis indicate that a male employee at KGUV has 1.455 times higher odds
of choosing a sustainable mode of transport than their female counterpart. It should be noted
that the proportion of female employees was higher than male employees. The higher odds
might be attributable to convenience and personal security strengths associated with the car.
The odds of choosing sustainable mode of transport reduce by 0.468 times as the age group
of a KGUV employee increases. This variation is shown in Figure 9.2. The model was
evaluated for a typical full time male employee who has LOS of five for public transport and
has a trip length of 12km with 20 minutes higher travel time by public transport. The highest
probability of choosing a sustainable transport mode was 0.85 for an employee 18 years or
younger. This probability was decreased to 0.22 for an employee 65 years or older. This may
be attributable to better availability of parking spaces for more senior employees. Overall, the
odds of choosing sustainable modes of transport increased with an increase in LOS, travel
KGUV users’ travel demand analysis
Deepti Muley Page 185
time saving, and gender, while the odds decreased with an increase in trip length, age group
and employment status. Generally, the odds ratios followed the trend because public transport
use increases with an increase in public transport availability, and the use of public transport,
walking and cycling reduces as trip length increases. For an employee, use of sustainable
transport modes depends upon frequency of travel and parking space availability.
Figure 9.2 Sensitivity of typical employee’s sustainable travel mode probability with age group
9.5 Analysis for students’ education trips
The travel mode analysis for education trips for students at KGUV was undertaken using
school students‟ and university students‟ data (117 cases). A variable termed „licence
availability (LA)‟ was introduced to better distinguish between two groups of students. In
addition, transit characteristics such as LOS and travel time saving, and personal
characteristics such as LA, age group, gender and type of student were utilised in the logistic
regression analysis. Trip length was not included in the model because inclusion of trip
length provided a 2
model of 12.321 at a degree of freedom 7 at a significance level of 0.090.
This significance was greater than 0.05 hence the model did not fit as well statistically. So to
obtain a statistically fit model the least significant variable, trip length, was removed and
logistic regression analysis was again conducted.
The results of the analysis revealed that type of student was statistically the most significant
variable, while LOS was statistically the least significant variable. This might be because the
y = -0.1625x + 1.0388
R² = 0.9954
0
0.2
0.4
0.6
0.8
1
0 - 18 18 - 30 30 - 45 45 - 65 65 & above
Pro
ba
bil
ity
Age group (years)
Evaluating the transport impacts of TODs
Deepti Muley Page 186
students were mostly captive riders. The values of regression coefficient and standard error
indicated no variable with high degree of multicollinearity. The analysis predicted 87.2
percent cases correctly. The model has a significance value of 0.055 which is very close to
the desired level of significance (p < 0.05) with 2 of 12.311. Although the significance and
2
model values were at par of desired values, the model was used due to data constraints. The
data for education trips was biased with a high proportion of public transport trips. Equation
9.5 shows the linear regression equation for logistic regression and Table 9.4 explains the
details of analysis.
𝑍 = 3.263 − 0.054 × 𝐿𝑂𝑆 + 0.015 × 𝑇𝑇𝑆 − 1.934 × 𝐿𝐴 − 0.446 × 𝐴𝐺
+ 0.753 × 𝐺𝐷𝑅 + 1.362 × 𝑇𝑆 Equation 9.5
Table 9.4 Travel mode analysis for students’ education trips
Variable Coefficient
(B)
Standard
error
Wald
statistic
Significance
(p)
Odds ratio
Exp(B)
Level of service (LOS) -0.054 0.255 0.045 0.832 0.947 Travel time saving (TTS) 0.015 0.032 0.218 0.641 1.015 Licence availability (LA) -1.934 0.903 4.586 0.036 0.145 Age group (AG) -0.446 0.652 0.468 0.494 0.640 Gender (GDR) 0.753 0.826 0.830 0.362 2.123 Type of student (TS) 1.362 0.630 4.677 0.033 3.904 Constant 3.263 1.715 3.620 0.057 26.131
No of cases = 117
% cases correctly predicted = 87.2 (criterion, if estimated probability > 0.500, the predicted mode is
sustainable mode of transport)
2model = 12.311, degree of freedom = 6, significance = 0.055
2critical = 12.396
The odds ratios from the analysis indicated that the odds of choosing a sustainable mode of
transport highly depend on the type of student; the odds of a student choosing public
transport are 3.904 times higher for a unit change in type of student. This means under similar
conditions a full time student having casual employment had 3.904 times higher odds of
choosing sustainable modes of transport in comparison to a full time student. The LA had
0.145 odds of reducing public transport or walk only travel mode choice. The travel time
saving increases the odds of using a sustainable mode by 1.015 with a unit increase in travel
time saving. Similar to employees, and perhaps for similar reasons, male students had higher
odds (2.123) of choosing a sustainable transport mode. The LOS, LA and age group reduce
the odds of using a sustainable transportation mode. The reason for LOS results exhibiting an
KGUV users’ travel demand analysis
Deepti Muley Page 187
opposite trend to the general trend may be that a large proportion of students used public
transport and this provided less knowledge about car trips made by a student to the university.
The regression equation was evaluated for a typical female full time student who has a valid
driver‟s licence, has a LOS of 4, and needs an extra 20 minutes if travelling by public
transport instead of car. Figure 9.3 represents the sensitivity of age group for probability of a
student choosing a sustainable mode of transport for that student. The probability shows a
reduction (0.69 – 0.27) as age group increases.
Figure 9.3 Sensitivity of typical student’s sustainable travel mode probability with age group
9.6 Analysis for residents’ first trip of the day
A binary logistic regression was performed on KGUV residents‟ data to predict the
probability of using sustainable modes of transport with six predictor variables; age group,
employment status, gender, LOS, trip length and travel time saving with 81 cases. Table 9.5
reveals the results of analysis and Equation 9.6 presents the regression equation. The values
of regression coefficient and standard error indicated no variable with high degree of
multicollinearity. The model predicted almost 89 percent of cases correctly. The 2 was
41.558 with a significance of 0.001. The values for Wald statistic and significance indicated
age group as the most significant variable in determining the travel mode while employment
status and LOS as the least influential variable. The residents had a good quality of service to
y = -0.1055x + 0.7986
R² = 0.9995
0
0.2
0.4
0.6
0.8
1
0 - 18 18 - 30 30 - 45 45 - 65 65 & above
Pro
bab
ilit
y
Age group (years)
Evaluating the transport impacts of TODs
Deepti Muley Page 188
several destinations (See Chapter 4) hence the availability of public transport was not a
concern for this group of users.
𝑍 = 4.590 + 0.498 × 𝐿𝑂𝑆 − 0.432 × 𝑇𝐿 + 0.115 × 𝑇𝑇𝑆 − 1.578 × 𝐴𝐺
− 0.461 × 𝐸𝑆 − 2.166 × 𝐺𝐷𝑅 Equation 9.6
Table 9.5 Travel mode analysis for residents’ first trip of the day
Variable Coefficient
(B)
Standard
error
Wald
statistic
Significance
(p)
Odds ratio
Exp(B)
Level of service (LOS) 0.498 0.527 0.893 0.345 1.646
Trip length (TL) -0.432 0.225 3.674 0.059 0.649
Travel time saving (TTS) 0.115 0.066 3.071 0.085 1.122
Age group (AG) -1.578 0.646 5.973 0.019 0.206
Employment status (ES) -0.461 0.492 0.881 0.348 0.630
Gender (GDR) -2.166 1.207 3.222 0.079 0.115
Constant 4.590 3.598 1.628 0.202 98.532
No of cases = 81
% cases correctly predicted = 88.9 (criterion, if estimated probability > 0.500, the predicted mode is
sustainable mode of transport)
2model = 41.558, degree of freedom = 6, significance = 0.001
2critical = 22.458
Figure 9.4 Sensitivity of typical resident’s sustainable travel mode probability with travel time
saving
The odds ratios indicate that the LOS variable has highest odds of increasing the use of
sustainable modes of transport (1.646). Unlike other users, male residents tend to use car
0
0.2
0.4
0.6
0.8
1
-60 -40 -20 0 20 40 60
Pro
bab
ilit
y
Travel time saving (minutes)
KGUV users’ travel demand analysis
Deepti Muley Page 189
more than their female counterparts. The odds of a male resident using sustainable modes of
transport were less by 0.115 than a female resident. Generally, the odds of a resident using a
sustainable mode of transport increased with unit increase in LOS, and travel time saving. On
the contrary, the odds decreased with unit increase in trip length, age group, employment
status and gender. As the residents travelled longer distances the probability of choosing
public transport decreased and as the travel time saving increased the probability of using
public transport.
Figure 9.4 shows the variation of probability of a resident using sustainable modes of
transport with the variation in the travel time saving. The plot was drawn for a female full
time employee resident having an age group of 18 to 30 years with LOS of five, and a trip
length of 5km. The model indicates that the elder residents used car, likely due to the
availability of car and parking space at the destination. The full time employees and students
used more sustainable modes of transport as they were working in the CBD (where they have
good public transport service) or their university was within walkable distance of KGUV.
9.7 Interpretation of results
Table 9.6 presents the values of Wald statistic, and in parentheses significance, of different
independent variables for various user groups at KGUV. The comparison indicates that LOS
is least significant for travel mode determination, while age group is most significant except
for students‟ education trips. The trip length was significant only for residents‟ trips and
travel time saving was significant only for the shoppers‟ trips and residents‟ trips. The
employment status was significant in case of students and employee trips.
Table 9.6 Significance of variables for travel mode determination for KGUV users
Variable Shoppers trips Employee trips Students trips Residents
first trip
LOS 0.253 (0.615) 0.004 (0.948) 0.045 (0.832) 0.893 (0.345)
Trip length 1.926 (0.165) 1.103 (0.294) NA 3.674 (0.059)
Travel time saving 4.169 (0.041) 0.659 (0.417) 0.218 (0.641) 3.071 (0.085)
Age group 12.432 (0.000) 8.766 (0.003) 0.468 (0.494) 5.973 (0.019)
Employment status/
Occupation/Type of
student
0.162 (0.688) 7.952 (0.005) 4.677 (0.033) 0.881 (0.348)
Gender NA 0.976 (0.323) 0.830 (0.362) 3.222 (0.079)
Frequency of trip 5.258 (0.022) NA NA NA
Licence availability NA NA 4.586 (0.036) NA
Note: NA is Not Applicable
Evaluating the transport impacts of TODs
Deepti Muley Page 190
When the odds of different user groups were compared (Table 9.7), it was observed that all
TOD users except shoppers showed lesser odds of choosing public transport for greater trip
lengths. The most distinct was the residents‟ first trip. For all user groups, the increase in
travel time saving marginally increased the odds of using sustainable modes of transport, as is
to be expected. The increase in LOS increased the odds of using public transport and walk for
all users except for student trips where the odds ratio was subtly less than unity. The increase
in odds with LOS was most distinct for the residents‟ first trip.
The older KGUV users showed higher odds of using car compared to younger KGUV users.
This trend was consistent for all user groups. Considering employment status as an indication
of frequency of travel, the most frequent travellers in each group exhibited higher probability
of using sustainable modes compared to less frequent travellers, except for students‟
education trips. This is in line with the trend because the students having part time
employment with full time studies tend to use cars more to manage time and commitments.
The female residents showed higher tendency of using sustainable mode of transport as
opposed to the visitor groups, namely employees and students, where males did so. The
higher use of car by female is justified by previous research (Cervero and Radisch, 1996) but
the female residents at KGUV present an opposite trend.
Table 9.7 Comparison of odds of KGUV users’ for choosing a sustainable mode of transport
Variable Shoppers
trips
Employee
trips
Students
trips
Residents
first trip
LOS 1.167 1.043 0.947 1.646
Trip length 1.043 0.983 NA 0.649
Travel time saving 1.072 1.015 1.015 1.122
Age group 0.326 0.437 0.640 0.206
Employment status /
Occupation / Type of student 0.902 0.632 3.904 0.630
Gender NA 1.582 2.123 0.115
Frequency of trip 1.423 NA NA NA
Licence availability NA NA 0.145 NA
Note: NA is Not Applicable
9.8 Summary
The binary logistic models revealed that personal and transit characteristics have an impact
on the decision of mode selection. Overall, age group was the most significant variable for
determining the sustainable travel mode in case of shoppers, employee and residents‟ trips
while type of student and driver‟s licence availability was the most significant in case of
KGUV users’ travel demand analysis
Deepti Muley Page 191
determining mode for students trips. The inclusion of LOS in the travel mode analysis
illustrates the effect of LOS on the travel mode selection. This adds to the existing state of
knowledge as this was noted as the least explored area in Section 2.5.2.
The logistic regression analysis presented in this chapter is useful to explain mode shares and
also in predicting mode shift. For example, application of the equations presented include
predicting mode shift to public transport, assessing the effect of sprawl on travel mode
(increased trip lengths), and determining the effect of aging population on the travel mode. It
is also important to note that the results from the statistical analysis may be applicable in
mode choice estimation in the strategic four step modelling process for certain TOD types.
9.9 Chapter close
This chapter presented the equations to assess the influence of personal and transit
characteristics on travel modes of a KGUV user. The following chapter (Chapter 10) derives
the models for travel modes of travel at TODs based on the equations presented in this
chapter. The outcomes presented in this chapter also contribute to Chapter 11.
Evaluating the transport impacts of TODs
Deepti Muley Page 192
Deepti Muley Page 193
Chapter 10
Models for travel modes of transit oriented development users
10.1 Introduction
In Chapter 9, we have seen the effect of personal and transit characteristics on selection of
travel modes of Kelvin Grove Urban Village (KGUV) users and the variation caused in the
probability of choosing sustainable modes of transport. These equations need to be simplified
for application into planning of Transit Oriented Developments (TODs) as it is not always
possible to predict all the variables for a newly planned development. This chapter presents
models that in the absence of site specific data or local data may be used to forecast the
probability of choosing a sustainable mode of transport for TOD users, which are derived
from the equations presented in Chapter 9. First a brief background for model development is
provided, followed by the models for each user group’s travel; namely shoppers’ shopping
trips, employees’ work trips, students’ education trips and residents’ first trips of the day.
Later a brief discussion about the models and their application is presented with a brief
summary and chapter close.
10.2 Analysis background for model development
The models predicting the probability of choosing sustainable mode were developed by
simplifying the equations produced in the previous chapter. In order to simplify a model,
variables which were not evidently significant in determining the sustainable travel mode
were removed from the equation, and the final model was obtained using the significant
variables. The Wald statistic was used to determine the significance of each variable (Hair et
al., 2006).
The Wald statistic is analogous to the t–statistic in linear regression. A higher value of Wald
statistic (significantly different than zero) denotes greater significance and a value closer to
zero represents least significance. The Wald statistic is the value of regression coefficient
divided by its associated standard error (Field, 2009). Mathematically,
𝑊𝑎𝑙𝑑 =𝐵
𝑆𝐸𝐵 Equation 10.1
Evaluating the transport impacts of TODs
Deepti Muley Page 194
where,
𝑊𝑎𝑙𝑑 = Wald statistic for a variable
𝐵 = Regression coefficient of the variable
𝑆𝐸𝐵 = Standard error of the variable
The Wald statistic follows a 2 distribution. The value of each Wald statistic is compared
with a 2 distribution with a degree of freedom of one (Bewick et al., 2005). If the
2 value of
the distribution is less than the observed value (Wald statistic) then the variable is significant
in predicting the travel mode of a TOD user. Only significant variables were retained for
inclusion in the final model development. The significance level indicates the percentage of
confidence level, that is, a significance of 0.05 indicates 5 percent confidence level. The 2
values of significance greater than 0.25 are not available (Tabachnick and Fidell, 2007),
hence the variables having significance in their Wald statistics in excess of 0.25 were denoted
as the least significant variables in predicting the travel mode.
After removing the least significant variables, logistic regression analysis was performed and
the model performance was noted. The final model was tested by assessing the proportion of
cases predicted correctly and the significance of the 2 obtained from the Omnibus test for
model coefficients. The significance of the Wald statistic of the variables in the final model
was also noted. The linear equation obtained from the final model (Equation 10.3) provided a
measure of individual’s preference for travel by sustainable modes of transport, which was
further used in determining the probabilities of using the sustainable modes of transport p(y)
using Equation 10.2. The details of model development for each user groups are explained in
following sections.
𝑝 𝑦 =𝑒𝑍
1 + 𝑒𝑍 Equation 10.2
𝑍 = 𝑎0 + 𝑎1 × 𝑏1 + 𝑎2 × 𝑏2 + 𝑎3 × 𝑏3 + ⋯ Equation 10.3
where,
𝑍 = Linear function of independent variables
𝑎0 = Regression constant
𝑎1, 𝑎2, 𝑎3 … = Regression coefficients
Models for travel modes of TOD users
Deepti Muley Page 195
𝑥1, 𝑥2, 𝑥3 … = Independent variables
𝑝 𝑦 = Probability of predicting y, in this case use of a sustainable
transport mode, using independent variables via Z
Similar to Chapter 9, the effect of each variable in the final model is described using odds
ratios. The odds ratios are exponentiated coefficients which will not have negative values. An
odds ratio above 1.0 reflects a positive relationship and a value less than 1.0 reflects a
negative relationship (Hair et al., 2006).
10.3 Model for shopping trips
Table 10.1 presents the details of original travel mode analysis and the χ2
critical for each
variable. The original equation considered Level (Quality) of Service (LOS), trip length,
travel time saving, frequency of shopping trip, age group and occupation as independent
variables for predicting probability of choosing sustainable mode of transport. The Wald
statistic indicated that LOS variable and occupation are the least significant variables
(Significance > 0.25) hence these variables were removed from the final model. Further,
comparison of Wald statistic implies that trip length, travel time saving, frequency of
shopping trips and age group are significant in predicting the probability of choosing
sustainable travel mode. Hence, the final regression model was developed using four
independent variables. The model form is given in Equation 10.4 and the details of the model
are given in Table 10.2.
Table 10.1 Original model for shoppers’ shopping trips
Variable Coefficient
(B)
Standard
error
Wald
statistic
Significance
(p)
2critical
Level of service (LOS) 0.155 0.308 0.253 0.615 NA
Trip length (TL) 0.042 0.030 1.926 0.185 1.922
Travel time saving (TTS) 0.069 0.034 4.169 0.046 4.030
Frequency of shopping
trip (FRQ)
0.353 0.154 5.258 0.023 5.238
Age group (AG) -1.122 0.318 12.432 0.001 10.828
Occupation (OCCU) -0.104 0.258 0.162 0.688 NA
Constant 1.168 1.826 0.410 0.522 –
No of cases = 117
% cases correctly predicted = 79.8 (criterion, if estimated probability > 0.500, the predicted mode is
sustainable mode of transport)
2 model = 30.930, degree of freedom = 6, significance = 0.001
2critical = 22.458
Evaluating the transport impacts of TODs
Deepti Muley Page 196
The final form of the model predicted 76.9 percent cases correctly and was statistically
significant at 0.001 with 2 of 30.380. Although the percentage of cases correctly predicted
reduced slightly the final model form was acceptable because the χ2
model was considerably
higher than its critical value.
𝑍 = 1.964 + 0.040 × TL + 0.075 × 𝑇𝑇𝑆 + 0.311 × 𝐹𝑅𝑄 − 1.043 × 𝐴𝐺 Equation 10.4
The odds of the final model for shoppers’ shopping trips indicates that a unit increase in trip
length, travel time saving and frequency of shopping trips increased the odds of choosing
sustainable modes of transport by 1.041, 1.078 and 1.365 respectively. While the odds of
choosing sustainable modes of transport decreases by 0.352 with a unit increase in age group.
These odds exhibit a similar trend to the original model, indicating removal of a variable
changes the magnitude of odds but does not affect the inclination of variables.
Table 10.2 Revised model for shoppers’ shopping trips
Variable Coefficient
(B)
Standard
error
Wald
statistic
Significance
(p)
Odds ratio
Exp(B)
Trip length (TL) 0.040 0.024 2.755 0.097 1.041
Travel time saving
(TTS)
0.075 0.029 6.654 0.010 1.078
Frequency of
shopping trip (FRQ)
0.311 0.143 4.740 0.029 1.365
Age group (AG) -1.043 0.284 13.489 0.001 0.352
Constant 1.964 0.570 11.867 0.001 7.130
No of cases = 117
% cases correctly predicted = 76.9 (criterion, if estimated probability > 0.500, the predicted mode is
sustainable mode of transport)
2 model = 30.380, degree of freedom = 4, significance = 0.001
2critical = 16.266
10.4 Model for work trips
The analysis for work trips for employees was undertaken similar to the previously explained
case. The sustainable travel mode equation containing LOS, trip length, travel time saving,
age group, employment status and gender as independent variables was used for analysis. The
details of the original analysis are listed in Table 10.3 with χ2
critical values for each variable.
The significance of the Wald statistic for LOS, trip length, travel time saving and gender
indicate that these variables are not significant in predicting the probability of choosing a
sustainable travel mode hence should be removed from the final model. Further, the
comparison of Wald statistic with χ2
critical shows that age group and employment status are
important for predicting the probability of choosing sustainable travel mode so these
Models for travel modes of TOD users
Deepti Muley Page 197
variables were considered in final analysis and logistic regression was performed. The results
of logistic regression are listed in Table 10.4 and the regression equation is presented as
Equation 10.5. The final mode model form contained only two variables and predicted 64.2
percent of cases correctly. The 2
model was 21.043 at a significance level of 0.001. Although a
drop in the percent of cases predicted correctly was observed and the model was acceptable
because 2
model was greater than 2
critical.
Table 10.3 Original model for employees’ work trips
Variable Coefficient
(B)
Standard
error
Wald
statistic
Significance
(p)
2critical
Level of service (LOS) 0.012 0.183 0.004 0.948 NA Trip length (TL) -0.018 0.017 1.103 0.294 NA Travel time saving
(TTS)
0.014 0.017 0.659 0.417 NA
Age group (AG) -0.759 0.256 8.766 0.003 0.468 Employment status
(ES)
-0.602 0.214 7.952 0.005 0.547
Gender (GDR) 0.375 0.379 0.976 0.323 NA Constant 1.842 1.218 2.286 0.131 –
No of cases = 164
% cases correctly predicted = 69.1 (criterion, if estimated probability > 0.500, the predicted mode is
sustainable mode of transport)
2model = 26.391, degrees of freedom = 6, significance = 0.001
2critical = 22.458
The odds ratios imply that a unit increase in the age group of an employee decreases the use
of sustainable modes of transport by 0.459. A full time employee exhibited higher odds of
using sustainable modes of transport than a part time employee. In accordance with the
shoppers’ trends in odds ratio, the trend for odds ratios were similar in case of the original
and final model, although the magnitude varied slightly.
Table 10.4 Revised model for employees’ work trips
Variable Coefficient
(B)
Standard
error
Wald
statistic
Significance
(p)
Odds ratio
Exp(B)
Age group (AG) -0.779 0.236 10.926 0.001 0.459
Employment status
(ES)
-0.545 0.201 7.343 0.007 0.580
Constant 1.452 0.440 10.889 0.001 4.272
No of cases = 164
% cases correctly predicted = 64.2 (criterion, if estimated probability > 0.500, the predicted mode is
sustainable mode of transport)
2 model = 21.043, degrees of freedom = 2, significance = 0.001
2critical = 13.816
Evaluating the transport impacts of TODs
Deepti Muley Page 198
𝑍 = 1.452 − 0.779 × 𝐴𝐺 − 0.545 × ES Equation 10.5
10.5 Model for education trips
The details of original logistic regression for student’s education trips using LOS, travel time
saving, licence availability, age group, gender and type of student is shown in Table 10.5.
The significance of the Wald statistic indicates that LOS, travel time saving, age group and
gender are not significant in predicting the probability of choosing a sustainable travel mode.
Comparison of the Wald statistic with χ2critical shows that licence availability and type of
student are the only two variables predicting the probability of choosing a sustainable mode.
Hence, the final model contained these two variables for predicting the probability of
choosing a sustainable travel mode. The details of logistic regression of these two variables
are shown in Table 10.6 and the final model form is given by Equation 10.6.
Table 10.5 Original model for students’ education trips
Variable Coefficient
(B)
Standard
error
Wald
statistic
Significance
(p)
2critical
Level of service
(LOS)
-0.054 0.255 0.045 0.832 NA
Travel time saving
(TTS)
0.015 0.032 0.218 0.641 NA
Licence availability
(LA)
-1.934 0.903 4.586 0.036 4.502
Age group (AG) -0.446 0.652 0.468 0.494 NA
Gender (GDR) 0.753 0.826 0.830 0.362 NA
Type of student (TS) 1.362 0.630 4.677 0.033 4.644
Constant 3.263 1.715 3.620 0.057 –
No of cases = 117
% cases correctly predicted = 87.2 (criterion, if estimated probability > 0.500, the predicted mode is
sustainable mode of transport)
2 model = 12.311, degree of freedom = 6, significance = 0.055
2critical = 12.3967
The final model form for student’s education trips was significant with significance level of
0.014 and χ2
model of 8.484. The model predicted 85.3 percent cases correctly, the percent of
cases predicted reduced compared to the original model. In this case, although the χ2
model and
χ2
critical are very close, the final model was accepted because the significance level was
reduced from original value increasing the model fit and data constraints noted earlier.
The odds ratios indicate that a unit increase in type of student increases the odds of a student
using sustainable modes of transport by 2.958. If a student possesses a valid driver’s licence
Models for travel modes of TOD users
Deepti Muley Page 199
then odds of the student using public transport or walking to school or university decreases
by 0.170 as compared to a student who does not have valid driver’s licence.
𝑍 = 2.414 − 1.775 × LA + 1.085 × TS Equation 10.6
Table 10.6 Revised model for students’ education trips
Variable Coefficient
(B)
Standard
error
Wald
statistic
Significance
(p)
Odds ratio
Exp(B)
Licence
availability (LA)
-1.775 0.724 6.009 0.014 0.170
Type of student
(TS)
1.085 0.558 3.784 0.052 2.958
Constant 2.414 0.605 15.903 0.001 11.179
No of cases = 117
% cases correctly predicted = 85.3 (criterion, if estimated probability > 0.500, the predicted mode is
sustainable mode of transport)
2 model = 8.484, degree of freedom = 2, significance = 0.014
2critical = 8.721
10.6 Model for residents’ first trip of the day
The model for the travel mode of the first trip of the residents’ mode was determined using
details shown in Table 10.7. The initial model contained LOS, trip length, travel time saving,
age group, employment status and gender as independent variables. The Wald statistic
significance of LOS and employment status was greater than 0.25 hence these variables were
removed from the final analysis. In addition, the comparison of Wald statistic of each
variable with the corresponding 2
critical indicates that trip length, travel time saving, age
group and gender were significant variables. Hence, these four variables were retained for
further analysis. The final model form of the linear regression equation is given in Equation
10.7 and the statistical details are presented in Table 10.8.
The final model predicted 90.1 percent cases correctly with a χ2 of 39.894 at a significance of
0.001. The percent cases predicted correctly slightly increased from the original model.
Although χ2
model reduced slightly, when compared with the final degree of freedom at a given
significance, it was higher than the critical value. So the final model form was acceptable.
Evaluating the transport impacts of TODs
Deepti Muley Page 200
Table 10.7 Original model for residents’ first trip of the day
Variable Coefficient
(B)
Standard
error
Wald
statistic
Significance
(p)
2critical
Level of service (LOS) 0.498 0.527 0.893 0.345 NA
Trip length (TL) -0.432 0.225 3.674 0.059 3.636
Travel time saving
(TTS)
0.115 0.066 3.071 0.085 3.045
Age group (AG) -1.578 0.646 5.973 0.019 5.667
Employment status
(ES)
-0.461 0.492 0.881 0.348 NA
Gender (GDR) -2.166 1.207 3.222 0.079 3.182
Constant 4.590 3.598 1.628 0.202 –
No of cases = 81
% cases correctly predicted = 88.9 (criterion, if estimated probability > 0.500, the predicted mode is
sustainable mode of transport)
2
model = 41.558, degree of freedom = 6, significance = 0.001
2critical = 22.458
The odds ratios for final model indicates that the odds of a resident choosing sustainable
modes of transport increases with a unit increase in travel time saving (1.146) and decreases
with a unit increase in trip length and age group by 0.662 and 0.215 respectively. The odds of
0.154 for gender show that female residents have more tendency of using sustainable modes
of transport than their male counterparts. These trends were similar to that observed in the
original model.
𝑍 = 6.999 − 0.413 × 𝑇𝐿 + 0.136 × 𝑇𝑇𝑆 − 1.538 × 𝐴𝐺 − 1.871 × 𝐺𝐷𝑅 Equation 10.7
Table 10.8 Revised model for residents’ first trip of the day
Variable Coefficient
(B)
Standard
error
Wald
statistic
Significance
(p)
Odds ratio
Exp(B)
Trip length (TL) -0.413 0.204 4.096 0.043 0.662
Travel time
saving (TTS)
0.136 0.064 4.478 0.034 1.146
Age group (AG) -1.538 0.628 6.007 0.014 0.215
Gender (GDR) -1.871 1.114 2.820 0.093 0.154
Constant 6.999 1.916 13.345 0.001 1095.764
No of cases = 81
% cases correctly predicted = 90.1 (criterion, if estimated probability > 0.500, the predicted mode is
sustainable mode of transport)
2 model = 39.894, degree of freedom = 4, significance = 0.001
2critical = 18.467
Models for travel modes of TOD users
Deepti Muley Page 201
10.7 Interpretation
The models for travel modes derived by removing the least significant variables indicate that
the χ2
model reduces from each original model, but the reduction in the degrees of freedom
increases the model fit. Similarly, the percentage of cases predicted correctly observes a
small reduction. This small reduction in percentage of cases predicted correctly indicated the
piecemeal influence of removal of variables on the overall model. The odds ratios also
exhibit a similar trend with a slight variation in the odds. The significance value for model fit
remained constant in the case of all models, except for education trips, where the significance
was decreased significantly from the original model increasing the model fit.
The final models indicate that the travel mode of shoppers and residents trips was dependant
on personal as well as travel characteristics, while the travel mode of employees and students
was determined from their personal characteristics only. The LOS variable was not retained
for any user group, but the effect if transit service was taken into account through travel time
saving for residents and shoppers; travel time savings was noted as an important variable
when asked about perceptions (Appendix D). Although employees and students noted travel
time saving as a prominent variable, statistically it was least significant, hence was removed
from the final model. The models developed based on KGUV users’ travel data can be used
for planning future TODs, which can further help in planning the infrastructure for
sustainable modes of transport. A brief overview of the procedure for model application is
noted in the following section.
10.8 Model application
As stated earlier, in the absence of site specific or local data, the models proposed in this
research may be used to forecast travel and planning of a future TOD. However, these models
first need to be generalised by considering travel data from various forms of TODs and
general models for all trips undertaken at TODs need to be developed. It should be noted that
the general equation is obtained from the raw travel data of various TOD sites. Before model
development; the variables in the raw data should be normalised, i.e. they should be
transformed to a normal distribution with unit standard deviation and a zero mean. This step
is necessary as the characteristics of each case study TOD varies (for example, scale of
TODs), which is not explained by the variables under consideration. From the normalised
data, the generalised (global) equation for each trip type is obtained of the form given in
Equation 10.8. This equation provides basis for estimating mode split.
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𝑍 = 𝑏0 + 𝑏1𝑥1 + 𝑏2𝑥2 Equation 10.8
where,
𝑍 = Linear function of independent variables
𝑏0 = Global regression constant
𝑏1,𝑏2 = Global regression coefficients
𝑥1, 𝑥2 = Independent variables
This following section illustrates a methodology which may be adopted in applying the
general model for a specific trip type for TOD planning. The similar procedure should be
adopted for all trip types. However, it must be noted that this methodology has not been
tested through this research; such testing is a proposed topic for future research. The
methodology for application of a general equation given, as noted by Bruton (1985), consists
of five steps as explained below:
1) Assume the mean (µ1, µ2,...) and standard deviation (σ1, σ2,...) for each variable for the
development under consideration by using the values from past travel data and
applying the experience and considering the proposed characteristics of the users.
2) Derive the appropriate equation coefficients from the global regression constants
applicable for the subject development. The new regression coefficients, applicable
for subject development, can be derived by dividing global regression coefficients by
the standard deviation of the respective variable. The new regression coefficients can
be derived from the equation below:
𝑏1′ =
𝑏1
𝜎1, 𝑏2
′ =𝑏2
𝜎2, … Equation 10.9
3) Determine the value of 𝑍
The value of linear regression function (𝑍) can be calculated by inputting the new
regression coefficients into Equation 10.8. The new form of the equation is given by
Equation 10.10. It should be noted that the global regression constant is dropped as it
has no relevance for the TOD under consideration. The calibration of the equation is
achieved using a scaling factor while determining the probability.
𝑍 = 𝑏1′ μ1 + 𝑏2
′ μ2 + ⋯ Equation 10.10
Models for travel modes of TOD users
Deepti Muley Page 203
4) Apply 𝑍Z and scaling factor (𝑥) to determine the probability of choosing sustainable
travel mode
The value of scaling factor, ranging from 0 to 1, is obtained considering the variables
under consideration and from raw data or proposed modal split. The scaling factor can
be derived from Equation 10.12. The probability of a TOD user using sustainable
modes of transport can therefore be obtained using Equation 10.11.
𝑝(𝑦) =𝑒𝑥+𝑍
1 + 𝑒𝑥+𝑍 Equation 10.11
𝑥 = − 𝑏𝑖′𝜇𝑖
𝑖=𝑛
𝑖=0
+ 𝑙𝑜𝑔𝑒
𝑎2
𝑎1 Equation 10.12
where,
𝑏𝑖′ = Coefficients for the variables under consideration
𝜇i = Mean of the variable under consideration
𝑛 = Number of variables under consideration
𝑎2
𝑎1
= Ratio of more sustainable mode users to less sustainable mode
users
5) Determine the mode split using the projected population for the subject development
The mode split for the subject development can be determined by multiplying
probability obtained by applying Equation 10.11 with the population of the respective
user group undertaking the respective trip type.
10.9 Summary
The models developed in this chapter allow the probability of TOD users choosing a
sustainable mode of transport. In the absence of site specific or local data, these model forms
may be used in forecasting travel patterns of new greenfield or brownfield TODs. It must be
stressed that the models presented in this study are based on data from only one development,
so these models need to be refined by observing travel data from other TODs, as each TOD
has its own characteristics due to location, geography, mix of land uses and socio – economic
characteristics.
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10.10 Chapter close
This chapter exhibited the models for travel modes a TOD; these models complete the fourth
step of TOD evaluation. The input from this step completes the fifth step of determining the
outcomes. The developments in this chapter provide base for conclusions of this research
which are presented in the following chapter (Chapter 11).
Deepti Muley Page 205
Chapter 11
Conclusions and recommendations
11.1 Introduction
This research was aimed at developing a universal methodology for evaluating the transport
impacts of Transit Oriented Developments (TODs). This methodology was elaborated upon,
using a case study TOD. This chapter presents the conclusions and recommendations from
this research. Firstly, the main conclusions are provided followed by reflections from case
study TOD and applications of this research. The contribution to knowledge, limitations of
this research and areas of further research are noted in the following sections. Finally, the
chapter close is provided, which also provides closure to this thesis.
11.2 Conclusions from this research
Based on the research conducted to achieve the objectives, this thesis maintains that TODs
are special property developments; hence from a transport perspective needs to be evaluated
differently from conventional developments. These developments should be assessed as a
complete system considering characteristics of residential as well as non residential land uses
and their users. Simply evaluating the transport at the residential land use of a TOD, for
assessing its performance, can lead to only a partial impression of transport at TODs.
The residential land use at TODs should be designed to cater for small as well as larger
family households. Further, instead of strictly developing TODs around a major public
transport node, the public transport nodes might be able to be located on the edge / boundary
of the TOD while still obtaining transport efficiency. The underlying principles for public
transport design should be that the TODs should not only have good quality of public
transport service to and from key centres such as Central Business Districts (CBD) but also to
and from various surrounding key origins and destinations to serve the transport demand of
residents as well as visitors of TODs. This is important because the travel mode is governed
by personal characteristics as well as on available public transport service, rather than the
development characteristics only.
To illustrate the above mentioned aspects, the key conclusions from this research are noted
below, by linking them with the aims and objectives of this research, which were set out in
Chapter 1. This is explained with the work reported in respective chapters to achieve these
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objectives. The work reported in some chapters helped to achieve multiple objectives, so
these chapters may appear more than once in following sections.
11.2.1 Build up an understanding of the concept of TOD and various aspects related to
it with a detailed knowledge on TOD evaluation
Chapter 2: An extensive review of the literature was carried out to study the existing state of
knowledge about TOD and studies conducted on TOD evaluation from a transport point of
view. The review suggested that in the main, TODs were evaluated based on data from
residential land use only. But this is not completely valid because, due to presence of mixed
land uses, TODs attract higher trips by different users. Hence a study of data for users of non
residential land uses was noted as the least explored area, and proposed for action in this
research.
11.2.2 Develop a methodology for evaluating the transport impacts of TODs
Chapter 3: This chapter presented the detailed methodology for TOD evaluation. The
proposed five step methodology was comprised of some new or additional steps for TOD
evolution in addition to the standard practice of the literature. The main steps were pre–TOD
assessment, traffic and travel data collection, obtaining traffic impacts, determining the travel
impacts and drawing the outcomes.
The following sections explain key features for implementing this methodology for
evaluating a TOD.
Chapter 4: The pre–TOD assessment of the development for assessing its suitability as a
TOD should be made by studying the development characteristics and evaluating the quality
of service (QoS) for public transport service to and from various origins and destinations. The
framework provided by TRB (2003) can be used to determine the QoS of public transport
service, while universal frameworks for assessing walking and cycling infrastructure, as well
as the mix of land uses, remains to be developed.
Chapter 5: A cordon count should be conducted at all access points to obtain the classified
traffic counts. The travel data collection is an extensive and expensive process. The detailed
procedure for travel data for each TOD user group was explained in this chapter. Different
survey instruments should be employed to gather travel data for the various groups of TOD
users, depending on their demographic profile or characteristics. The questionnaire form
should be designed as a short form using a simple language so that it is easily understood by a
layperson; the questions can be simplified by providing multiple choices for answer. Pilot
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Deepti Muley Page 207
surveys are an integral part of travel data collection, as these test and refine the survey
process. Incentives for those surveyed helped to increase the response rates, but to a limited
extent. Importantly, the sample size for a TOD should ideally be 100 percent of its
population, making it a census. However, this becomes more challenging over time.
Chapter 6: For determination of traffic impacts, a comparison of published trip rates is more
appropriate over the comparison of the trip rates obtained by household travel surveys. The
data collected for determining published trip rates is mainly collected for impact assessment
purposes, while the data from the travel surveys is mainly collected for strategic modelling
purposes. Further, the purpose of data collection governs the details and the extent of data
collected. The trip rates from travel surveys are on the sample basis while the published trip
rates are based on the population data. Hence this research proposes use of published rates for
determination of traffic impacts.
The traffic impacts of a TOD can be obtained from analysis of the cordon data. It is not
reasonable to expect that a road network at a TOD in an open environment will only be
carrying the traffic generated by the users of development. Hence, it is important to eliminate
through traffic from observed traffic so as to determine the exact traffic generated by the
development. The peak hour/s for a TOD should be identified based on the maximum person
movements rather than maximum vehicle movements. This is because TODs tend to attract
large numbers of pedestrian, bicycle and public transport trips. The details such as directional
distribution, and car occupancy for all modes of transport; cars, motorcycles, pedestrians,
bicycles, and public transport should be determined to gain a full appreciation of traffic
movement at the TOD. The total traffic generated, considering this as a conventional
development, can be determined from combining the trip rates specified for each land use by
the standard guidelines (such as ITE, 2008; RTA, 2002). Admittedly, data for some land uses
can be hard to come by. Trip rates should be compared to the observed maximum trip rate to
determine differences, and hence the traffic differences of the TOD.
It was found that only car traffic generation can be compared with standard guidelines to
determine traffic impacts. No trip rates for pedestrian or bicycle trip generation are specified
in standard literature. A study of this aspect is necessary for a TOD, as this governs the
provision of infrastructure for these modes. Further, TODs should be assessed as a special
land use category and land use mix and proportion of self containment of trips at a TOD
should be assessed.
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Chapter 7: The requirement for determination of travel impacts is made by obtaining
datasets for comparison; specifically for TOD users and non TOD users. The dataset for TOD
users can be obtained from analysis of the travel data. The compiled dataset for each group of
TOD users should be analysed to obtain the demographic and travel characteristics. The
demographic characteristics sought to include personal characteristics such as age group,
gender, frequency of visiting TOD, employment status, driver’s licence availability and
household characteristics such as household size, number of bedrooms in the household,
vehicle and bicycle ownership, number of valid driver’s licence holders. The travel
characteristics include mode shares, trip lengths, parking details such as parking location and
parking fee, and public transport trip details such as access and egress times, number of
transfers and transfer locations. Additionally, the trip making characteristics such as number
of trips per person and internalisation of travel activities should be obtained for TOD
residents.
Chapter 8: The dataset for non TOD users can be obtained from secondary sources or by
conducting a separate travel survey. The decision to conduct a separate travel survey depends
on the time and resources available. Typically, the dataset for non TOD users can be obtained
from a development which has similar size and distance from the CBD as does the TOD
development. A regional comparison should also be made to determine any variation.
After obtaining the datasets for comparison, the travel characteristics of users of Non
Residential Land Uses (NRLU) should be compared and the travel as well as household
characteristics of the users of Residential Land Uses (RLU) should be compared. The travel
characteristics can include (but not limited to) mode share, trip length, access distances,
parking characteristics and trip making characteristics (for residents). The household
characteristics can include (but not limited to) motor vehicle ownership, bicycle ownership
and household size.
Chapter 9 and Chapter 10: Travel demand analysis for investigating travel at a TOD should
be primarily undertaken for assessing the travel modes of TOD users. This can further be
extended for studying the distribution of trips. Further, a detailed analysis should be
conducted to investigate the travel modes and distribution of trips, to develop models for
travel modes and trip distribution.
Chapters 5, 6, 8, 9, 10: The findings from the data collection provide guidelines for
conducting data collection at a TOD. The results from the investigation carried out for
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Deepti Muley Page 209
determining traffic impacts indicate variation in traffic generation as well as trip rates for
various modes such as cars, pedestrian, bicycles, public transport and motorcycles. The
comparative analysis of users’ characteristics provide an appreciation of the variation in
travel characteristics of TOD users and household characteristics of TOD residents, which
provide detail on the travel impacts of a TOD. Further, the investigation into travel modes
provides more information about travel demand at TODs. These outputs complete the
analysis for obtaining the final outcomes.
11.2.3 Demonstrate the methodology by implementing it on an Australian case study
TOD
For implementing the methodology developed in this study, a fully planned development,
Kelvin Grove Urban Village (KGUV), located 3km northwest of Brisbane’s CBD, in
Australia, was used as a case study TOD. The selected development was assessed for its
suitability as a TOD by studying the development characteristics and evaluating the QoS for
transit availability for various origins and destinations. The development was reported to
have a mix of land uses, good quality of public transport service, walking and cycling
infrastructure. This was the first step of TOD evaluation. (Chapter 4)
The traffic data was collected by conducting the classified cordon counts for KGUV as a
whole, and of the centrally located shopping centre. The travel data was collected by
conducting travel surveys. The professional employees and university students who had good
internet access were surveyed using internet based surveys. Retail shop employees and
shoppers who may not have internet access or office email were surveyed using the personal
interview technique. For these two groups, initially CAPI surveys were planned but the
survey instrument was modified to pen and paper form for convenience and improved
uptake. The non student residents and school students were surveyed using a mail back
survey technique and the student residents were surveyed using intercept surveys. This
process completed the second step of TOD evaluation being traffic and travel data collection.
(Chapter 5)
The traffic impacts of KGUV were determined by following the procedure mentioned in the
third step of TOD evaluation. The maximum trip rate for car was compared with the total
traffic volumes derived from guidelines specified in ITE (2008) and RTA (2002). A similar
procedure was repeated for the cordon counts conducted for centrally located shopping
centre. (Chapter 6)
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The travel data from shoppers, employees, students and residents at KGUV was collected to
compile datasets for comparison of KGUV (which is a TOD in this case). The travel impacts
were determined by comparing the characteristics of KGUV users with those of respective
user groups from regional (Brisbane Statistical Division, BSD) and suburban (Brisbane Inner
North Suburbs, BINS and Brisbane Inner South Suburbs, BISS) users’ characteristics.
(Chapter 7 and Chapter 8)
Travel demand analysis for investigating the travel modes of TOD users was undertaken, as
travel mode is a prominent aspect of a trip. The effect of personal characteristics such as
employment status, type of student, gender, age group, frequency of trip and travel
characteristics such as trip length, LOS and travel time savings were evaluated using the
logistic regression technique. Separate equations were developed for each TOD user group
predicting the probability of choosing sustainable modes of transport. Further, the
investigation into the travel modes of TOD users was undertaken and models were developed
for determining the travel modes of different trips at KGUV. (Chapter 9 and Chapter 10)
The findings from analysis (Chapter 6, 8, 9, and 10) provided transport impacts and
completed the evaluation of KGUV.
11.2.4 Determine trip rates for various modes of transport for a TOD and assess the
travel demand of TOD users
Chapter 6: Analysis of cordon data provided the peak periods for person movements. The
trips undertaken during these time periods can be used as trip rates. It was observed that on
given survey day the maximum vehicle trips were observed for PM peak for shopping centre
(211 veh/h) and 1078 veh/h for AM peak when KGUV was considered as a whole. The
highest pedestrian movement was 799 ped/h for midday peak and 1375 ped/h for PM peak
for the shopping centre and the entire KGUV respectively.
Chapter 9: The travel demand for each group was assessed by conducting binary logistic
regression analysis for travel modes. The travel modes were divided into two parts;
sustainable modes (walk only, bicycle, and public transport) and less sustainable modes (car,
motorcycle, and taxi). The results indicated that overall age group was the most significant
variable for determining the sustainable mode choice in case of shoppers, employee and
residents’ trips, while type of student and driver’s licence availability were the most
significant in case of determining mode for students trips.
Conclusions and recommendations
Deepti Muley Page 211
Chapter 10: Travel demand assessment was continued for determining the models for travel
mode. The models were developed for trips by all user groups at KGUV.
11.2.5 Evaluate the transport impacts of TODs from an Australian perspective by
comparing the results with characteristics of conventional development
The results obtained from an Australian case study TOD, KGUV, helped to determine the
transport impacts of TODs from an Australian perspective.
Chapter 6: The impacts on the traffic generation at the TOD were determined from the
differences between total traffic generation at KGUV and ITE (2008) and RTA (2002) trip
rates. The comparison for the shopping centre showed reduced traffic generation by more
than 63 percent when compared with the ITE (2008) and RTA (2002) guidelines. When
KGUV as a whole was considered, a reduction of about 42 percent was observed for peak
period traffic (RTA, 2002 comparison) and a reduction of about 27 to 48 percent was
observed for the AM and PM peak hours respectively (ITE, 2008 comparison).
Chapter 8: The travel impacts of the TOD were obtained by comparing the characteristics of
KGUV users with Brisbane Statistical Division (BSD), Brisbane Inner North Suburbs
(BINS), and Brisbane Inner South Suburbs (BISS) users’ characteristics. The outcomes of
comparative analysis indicated that the KGUV users used more sustainable modes of
transport than BSD, BINS and BISS users. While the overall average trip length for shopping
and education trips at KGUV shows higher trip lengths than its counterparts. Shorter trips
were observed for KGUV residents and employees. The KGUV residents exhibited a trend of
undertaking fewer trips and possessing fewer motor vehicles than BSD, BINS and BISS
residents showing reduced dependence on the car. The residents also had smaller household
size and a smaller number of bicycles per household than their counterparts.
Although KGUV was not fully developed when the data collection was undertaken, the
results will be still applicable in the future. The further development of KGUV will
incorporate additional users from office and residential land use and these users are more
likely to possess similar travel characteristics to those of existing KGUV users due to
availability of infrastructure for more sustainable modes of transport and the development’s
proximity to the CBD.
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Deepti Muley Page 212
11.3 Reflections from case study TOD
The application of the proposed methodology to an Australian case study provided some
insights into TOD implementation. It was observed that some aspects of case study TOD
were more successful than others. The key indications observed from case study TOD which
can be useful for TOD planners are as noted below:
For successful implementation of TOD, good quality of public transport service is
required from key origins and destinations. Provision of competent public transport
service leads to increased public transport usage (as observed from increased mode
shares).
The topography of the surrounding area governed the bicycle ownership and in turn
the bicycle usage for TOD residents. Further good connections to various origins and
destinations across Brisbane (for example bicycle tracks, dedicated bicycle facilities)
are desired for promoting bicycle usage for all TOD users. Trip end facilities have
also shown considerable influence on the bicycle mode share in case of users of non
residential land uses.
The public transport node may be located at the centre or on the edge of development
but should be easily accessible to encourage use of public transport. Good
accessibility is important rather than its location.
The provision of specialised facilities at case study TOD exhibited higher trip lengths
for the concerned TOD users as compared to the users of conventional land uses.
The comparison of trip characteristics indicated that the case study TOD residents
made fewer trips and the trips were contained in a nearby area of TOD. TOD residents
used car for making longer trips.
The provision of limited parking facilities restricted the car trips which have
ultimately increased use of more sustainable modes of transport.
It is necessary to provide some form of shelter at the stops and for sidewalks
considering the local climatic conditions.
Inclusion of education land use and provision of supporting infrastructure generated
more local walking trips. On the negative side the area was very quiet during
university holidays making it less attractive.
The careful placement of various land uses such as parks and open spaces,
recreational facilities made the TOD environment more vibrant. Presence of centrally
located shopping centre kept trips intrazonal undertaken by walk. The scale of
Conclusions and recommendations
Deepti Muley Page 213
shopping centre is vital in keeping shopping and conveyance trips intrazonal and
attracting interzonal trips.
The various land uses should be carefully mixed together to ensure proper functioning
of each land use especially care should be exercised while locating residential land
use. Careful mixing of various land uses also reduce the total vehicular traffic
generation.
The TOD should have houses/apartments with more number of bedrooms per
household. Restricting the development to one or two bedroom apartments attracts
young couples, single family households or families with no children but deters large
family households who tend to drive more due to children’s activities.
Adjoining areas may also benefit from the TOD’s atypical development
characteristics. So careful planning of land uses should be done to serve this
additional demand due to adjoining land uses.
The affordability is a vital consideration when deciding the pricing for the facilities.
Excessive pricing discourage an average person from using the facilities at a TOD.
11.4 Applications of this research methodology
The research methodology developed in this study is applicable to any TOD irrespective of
its size and location. The application of this methodology for various TOD assessments can
lead to a framework for TOD evaluation. Further, the outcomes obtained from TOD
evaluation can be used for designing new TODs and also can be used to assess performance
of existing TODs.
To apply this methodology to another TOD some site specific modifications may be required.
The following points provide some possible alterations.
In the case of a TOD served by more than one mode of public transport, a step for
determination of the combined quality of service for public transport needs to be
added. This should be an additional step for pre–TOD assessment.
An additional step of sample size determination may be required for travel data
collection in case of large scale TODs or where time and resources are limited.
If the public transport station is at the centre of a TOD then the public transport
demand also needs to be determined for the calculation of trip rates. The public
transport demand for a TOD should be determined based on the type of station. If the
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Deepti Muley Page 214
public transport station is a major interchange or the station is also serving to an
adjacent development then an additional step is required in Step III of TOD
evaluation to determine the accurate demand generated by the TOD.
Generally, for a TOD the centrally placed land use is the highest trip generator which
attracts trips from various land uses. A TOD which has other land use, such as an
activity centre, as the major trip attractor then the traffic generation of that particular
land use should be determined, in addition to the whole TOD traffic generation.
11.5 Contribution to knowledge
The main contribution from this research is the development of a comprehensive
methodology for TOD evaluation and an insight into the travel characteristics of TODs
considering both residential and non residential land uses. The key features achieved while
studying these two aspects are as noted below:
This research proposes a comprehensive methodology for evaluating the transport
impacts of TODs which considers various user groups of TODs with the total traffic
generation considering the TOD as a whole.
The previous studies considered in the main only residents’ travel data, and traffic
generated by TOD residents. This study focuses on the users of non residential land
use, which has been a neglected focus, and improves understanding about users of
these land uses. Thus a complete picture of travel at TODs may be provided. The
outcomes of this research will help planners while designing various land uses at
TODs.
This research provides trip rates for various modes of transport and assesses the traffic
impacts of TOD as a whole. These will help transport engineers and planners, and
policy makers to set appropriate targets of traffic reduction at TODs.
This research used an Australian case study for demonstrating the research
methodology, which provides insight into TOD evaluation from an Australian
perspective. This fills a major gap in knowledge as only a limited amount of previous
study explaining transport impacts of TODs from Australian perspective were found.
The Australian perspective will provide input to stakeholders for setting up more
TODs and develop activity centres.
Conclusions and recommendations
Deepti Muley Page 215
This study collects the travel data and perceptions of all user groups at a TOD, which
can be used by transport engineers and planners for further planning of TODs and
extending the research in identified areas.
The travel mode analysis was conducted to study how personal and transit
characteristics affects the sustainable mode choice at a disaggregate level. These
findings can be used for assessing the mode shifts for various groups of TOD users.
These results will aid planners while planning transport for TODs.
The results from this research provide evidence to support claims in the literature of
the travel efficiency of TODs by comparing South East Queensland Travel Survey
(SEQTS) dataset with case study data set. The outcome will help local government to
support TOD implementation locally.
Review of the research suggested that most focus was on the rail based TODs; very
little evidences were found for bus or Bus Rapid Transit (BRT) based TODs. The case
study TOD considered a bus or BRT based TOD and assessed its performance.
11.6 Limitations of this research
Although the scope of this research was limited to evaluating the transport impacts of TODs,
some limitations to the methodology applied for assessing KGUV were noted. These
limitations are listed below.
One major limitation of this study is it does not separate the intrazonal and inter zonal
trips. This means the travel surveys do not consider the multiple activities undertaken
by the visitors within KGUV, which give rise to intrazonal trips.
Flowing from the previous point, there is a possibility of double counting of trips, for
example, the trip by a resident is separately noted as a shopping trip when the
shoppers’ survey was conducted, but it may also be considered as a trip in the
residents’ surveys. Correlation was not established between the different surveys.
This methodology was demonstrated through one TOD only; the outcomes may be
used as indicators for planning future TODs, but needs refinement through more
studies for them to be used in general application.
The traffic counts conducted for KGUV were for a limited number of hours of the
day. Full cordon surveys for 24 hour period were not conducted for determining the
total traffic generation. Hence, this study provided trip rates for the peak periods only.
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Deepti Muley Page 216
The models for determining travel mode of TODs include the complete dataset. The
mode choice models were not developed by removing the responses obtained from
captive users.
11.7 Areas of further research
While conducting this research for evaluating TODs from a transport pint of view, several
research areas were observed. These areas are listed below, which provide directions for
further future research. Points 1 to 10 denote areas of future research related to application of
generalised methodology developed in this research and points 11 to 17 present areas of
future research identified for gaining more understanding of travel at TODs and are not
covered in this research.
1. The methodology proposed in this research should be refined by applying it to various
TODs and a framework for evaluating TODs should be developed based on the
experiences of more TODs to reach more generalised conclusions.
2. This research provides an insight into travel behaviour of TOD residents and visitors.
To test the methodology proposed in this research and verify the TOD performance,
various TODs should be assessed. The performances for TODs of different scale
which are located at various distances from CBDs need to be considered.
3. In addition to the comparison of traffic generation with the standard trip rates, the
traffic generation at a TOD should also be compared with a similar sized non TOD
development to determine actual variation in traffic flow.
4. The ITE (2008) and RTA (2002) trip generation guidelines do not have trip rates for
student accommodation, and in addition the RTA guidelines do not have any
specifications for education land uses. So these areas need to be explored in future for
a better understanding under Australian conditions.
5. The various land uses at TODs do not act as stand alone. Hence, to obtain accurate
traffic generation, standard guidelines for total traffic reduction due to a TOD need to
be set. Every TOD is different; hence the standards for total traffic reduction will be
more appropriate than obtaining trip rates for individual land uses. The parking
requirements need to be studied separately, as this will be affected due to reduced
traffic generation.
6. The quality of service for comfort and convenience should be included in the
selection criteria for a TOD, along with criteria for walking and cycling infrastructure.
Conclusions and recommendations
Deepti Muley Page 217
7. The travel of TOD users can be compared with the households having similar
demographic characteristics. Further comparison of Journey to Work (JTW) data by
suburbs can be conducted to study the work travel at a TOD.
8. A suitable method for assessing statistical significance of the comparative analysis
needs to be developed to strengthen the outcomes.
9. More residents’ data should be collected and explored by considering various trip
types.
10. The personal characteristics are not considered as measure for transport evaluation
although they found to have influence on the travel demand. Hence for future TOD
evaluation, the personal characteristics should be included as the measures of
transport evaluation.
11. This research does not investigate the characteristics of intrazonal trips and
multipurpose trips undertaken by various TOD user groups. So these aspects need to
be studied in detail for understanding intrazonal trip making patterns at TODs. This
will lead to study distribution of trips at a TOD.
12. To gain better understanding study of the travel at a TOD in detail, cross tabulation
analysis of the travel data for each user group at a TOD should be undertaken.
13. Before and after studies should be conducted for assessing the transport impacts of
TOD development.
14. This study considers personal and transit characteristics, but does not consider the
effect of neighbourhood characteristics. The effect of neighbourhood characteristics
on travel of TOD users should be studied in future research while assessing TODs.
15. Appropriate travel demand models for TODs including mode specific mode choice
models for travel at TODs should be developed.
16. The effect of location of TOD in terms of its distance from CBD and other key centres
should be assessed.
17. Various TODs should be studied to determine the amount of self containment such
that guidelines for placing appropriate land uses together may be developed.
11.8 Chapter close
This chapter documented the main conclusions of the study along with applications of this
research and future areas for investigation. This completes documentation of all aspects of
this research.
Evaluating the transport impacts of TODs
Deepti Muley Page 218
Deepti Muley Page 219
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Appendix A
List of publications
Refereed journal paper
Muley, D. S., Bunker, J. M. and Ferreira, L. (2009). Investigation into Travel Modes of TOD
Users: Impacts of Personal and Transit Characteristics. International Journal of ITS
Research. Special Issue: Sustainable Transportation Systems, 7(1), 3–13.
Refereed conference papers
Muley, D. S., Bunker, J. M. and Ferreira, L. (2009). Transit Oriented Developments: Results
from a Travel Survey. The Second Infrastructure Theme Postgraduate Conference. Brisbane,
Australia: Queensland University of Technology, 26th March 2009, 273–284.
Muley, D. S., Bunker, J. M. and Ferreira, L. (2008). Conducting visitor travel survey for a
TOD – case study from South East Queensland. 31st Australasian Transport Research
Forum. Gold Coast, Australia, 2 – 3 October 2008, 223–238.
Muley, D. S., Bunker, J. M. and Ferreira, L. (2007). Evaluating transit Quality of Service for
Transit Oriented Development (TOD). 30th Australasian Transport Research Forum.
Melbourne, Australia, 25–27 September 2007.
Book chapter
Muley, D. S., Bunker, J. M. and Ferreira, L. (2009). User characteristics of transit oriented
developments: the case of Kelvin Grove Urban Village. In: Yigitcanlar T., Sustainable Urban
and Infrastructure Development: Management, Engineering and Design.
Conference presentation
Muley, D. S., Bunker, J. M. and Ferreira, L. (2009). Assessing the Impact of Transit and
Personal Characteristics on Mode Choice of TOD Users. 12th TRB National Transportation
Planning Applications Conference, Houston, Texas, 17–21 May 2009.
Evaluating the transport impacts of TODs
Deepti Muley 234
Deepti Muley Page 235
Appendix B
B.1 Classified traffic count data sheet
Date:
Time period: In 15 minutes interval
Name of recorder:
Approach
Vehicles coming in study area Vehicles going out of study area
1
occupant
cars
2
occupant
cars
3
occupant
cars
Pedestrians Bicycles Motor
cycles
1
occupant
cars
2
occupant
cars
3
occupant
cars
Pedestrians Bicycles Motor
cycles
Deepti Muley Page 236
B.2 Shoppers survey sheet
Date:
Time period:
Name of recorder:
Classification of age groups:
1 0 to 18 years
2 18 to 30 years
3 30 to 45 years
4 45 to 65 years
5 65 years and above
Mode of
transport
Car occupancy /
Bus route
Home
postcode
Frequency
(per week)
Age
group Occupation
Deepti Muley Page 237
Appendix C
Table C.1 AM peak ITE comparison for Kelvin Grove Urban Village
Land use ITE
code
Number
of studies ITE equation
Traffic based on ITE
guidelines Actual
counts
% difference with respect to
ITE trip rates
Average
rate
Regression
equation Average rate
Regression
equation
Residential
220
231
252
78
7
8
𝑇 = 0.49 𝑋 + 3.73
𝐿𝑛 𝑇 = 0.90𝐿𝑛 𝑋 − 0.07
𝑇 = 0.19 𝑋 − 13.86
253 278
Retail 850 5 NA 146 146*
Education
760
550
550
530
28
6
4
68
𝐿𝑛 𝑇 = 0.82𝐿𝑛 𝑋 + 0.33
𝑇 = 0.21 𝑋 − 69.14
𝐿𝑛 𝑇 = 0.64𝐿𝑛 𝑋 + 2.08 NA
895 875
Office 710 163 𝐿𝑛 𝑇 = 0.86𝐿𝑛 𝑋 + 0.24 168 214
Total 1462 1513 1078 26 29
Note: *indicates regression equation not available hence value obtained using average rate is used.
Deepti Muley Page 238
Table C.2 PM peak ITE comparison for Kelvin Grove Urban Village
Land use ITE
code
Number
of studies ITE equation
Traffic based on ITE
guidelines Actual
counts
% difference with respect to
ITE trip rates
Average
rate
Regression
equation
Average
rate
Regression
equation
Residential
220
231
252
90
6
8
𝑇 = 0.55 𝑋 + 17.65
𝐿𝑛 𝑇 = 0.89𝐿𝑛 𝑋 − 0.07
𝑇 = 0.24 𝑋 − 16.45
306 390
Retail 850 40 𝐿𝑛 𝑇 = 0.61𝐿𝑛 𝑋 + 3.95 426 498
Education
760
550
550
530
29
8
4
40
𝐿𝑛 𝑇 = 0.81𝐿𝑛 𝑋 + 0.40
𝑇 = 0.19 𝑋 + 118.58
𝐿𝑛 𝑇 = 0.652𝐿𝑛 𝑋 + 3.12 NA
920 1158
Office 710 173 𝐿𝑛 𝑇 = 0.37𝐿𝑛 𝑋 + 60.08 161 250
Total 1813 2296 1051 42 54
Table C.3 Peak period RTA comparison for Kelvin Grove Urban Village
Land use Max rate Traffic based on RTA guidelines Actual counts % difference with respect to RTA trip rates
Residential 0.5
0.2
280
Retail 12.3 464
Education* 980
Office 33.33 133
Total 1857 1078 42
Note: * indicates use of ITE (2008) values
Deepti Muley Page 239
Appendix D
Perceptions of Kelvin Grove Urban Village’s users
D.1 Introduction
Chapter 7 outlined the demographic and travel characteristics of Kelvin Grove Urban Village
(KGUV) users. To further explore the opinions of the KGUV users, this appendix documents
the perceptions of the various groups at KGUV about the public transport at KGUV and
KGUV as a TOD. The first section explains the perception of KGUV users about the existing
public transport service at KGUV and lists the suggestions and improvements. Later the
ratings of KGUV users on various aspects of KGUV are explained with the comments from
the respondents. In the following section, other development issues are mentioned with a
brief summary.
D.2 Determination of KGUV users’ perceptions
All the user groups surveyed were asked to specify their opinion or perception about the
public transport service and KGUV. The responses obtained from these questions were
analysed separately to gain the overall perception of KGUV from each user group. Same
parameters were provided and asked to rate on a five point rating scale. The lowest ranking
was assigned a value 1 and the highest tanking was assigned a value of 5 for the purpose of
analysis. Two outcomes were determined; the average of the ratings for each user group and
the average score for each parameter. The details of the outcomes are presented in the
following sections.
D.3 Perception about existing public transport
All the respondents were asked to rate their perception about the current public transport
service on a rating scale of one to five with one being very poor and five being excellent. An
option of not applicable was also provided if a respondent thought that he or she should not
answer the question or the parameter was not seen as appropriate. The current public
transport service at KGUV was rated based on the frequency of service, on time performance,
overall quality of service, waiting time, accessibility to stop, safety for passengers at stops,
public transport signage and cost of public transport. These indicators provided the perceived
Evaluating the transport impacts of TODs
Deepti Muley Page 240
quality of public transport service as opposed to the physical measures determined by the set
of guidelines.
To get an idea about the expected improvements in the public transport service, few KGUV
users were asked to specify the importance of the improvements required for the public
transport service. A rating scale was designated for this purpose with one indicating not
important and five representing extremely important or essential. The improvements were
ranked based on the minimum waiting time, more amenities at the station, good quality of
service, cost, walking time to and from the stop and frequency of service. The following
subsections report the responses of KGUV users for the perception about existing service and
the importance of the improvements.
D.3.1 Ratings of employees on existing public transport service
The professional employees were asked to rate their perception about current public transport
service as well as the importance of the improvements to it. These questions were not asked
to the retail shop employees due to the circumstantial constraints. Table D.1 presents the
results for the professional employee analysis. The individual average rating for the public
transport system ranged from 1 to 4.75 with an average of 3.3 out of 5. The highest rating
was obtained for safety and lowest rating was waiting time and on time performance.
Table D.1 Rating of current public transport by professional employees at KGUV
Description Very
poor Poor Average Good Excellent
Average
rating
Frequency of service 5.7 16.0 41.5 30.2 6.6 3.2
On time performance 9.5 20.0 42.9 22.9 4.8 2.9
Overall quality of service 2.8 16.0 43.4 35.8 1.9 3.2
Waiting time 7.6 22.9 40.0 27.6 1.9 2.9
Accessibility to stop 5.7 8.5 26.4 44.3 15.1 3.5
Safety for passengers at stop 2.8 5.7 26.4 51.9 13.2 3.7
Public transport signage 2.8 9.3 28.0 47.7 12.1 3.6
Cost of public transport 6.7 9.6 38.5 38.5 6.7 3.3
Note: The numbers represent the percentages for each ranking
D.3.1.1 Ratings of employees for improvements in public transport service
To improve the existing public transport the professional employees were asked to rate the
importance of various parameter. An overview of the responses is given in Table D.2. The
professional employees rated frequency of service as the most important factor which can
improve the public transport system. The improvements in the amenities at the station were
marked as the least priority parameter.
Perceptions of KGUV’s users
Deepti Muley Page 241
Table D.2 Improvements rating by professional employees
Description Not
important
Less
important
Medium
importance
Highly
important
Extremely
important
Average
rating
Minimum
waiting time
0.0 2.7 13.5 49.5 34.2 4.2
More
amenities at
station
17.3 40.4 26.0 13.5 2.9 2.4
Good quality
service
0.9 4.6 20.4 54.6 19.4 3.9
Should be
cheaper
2.9 19.0 29.5 29.5 19.0 3.4
Less walking
time to &
from the stop
7.5 28.0 27.1 21.5 15.9 3.1
Frequent
service
0.9 2.7 14.3 42.0 40.2 4.2
Note: The numbers represent the percentages for each ranking
D.3.2 Ratings of students on existing public transport service
Table D.3 presents the proportion of the ratings for each rank for various variables. The
results were determined by combining the responses for each question from school students
as well as university students. The average rating for the existing public transport was 3.5 out
of five. The individual minimum average rating was two and the maximum rating was 4.9 out
of five. Similar to the professional employees, the safety was rated highest and waiting time
and on time performance was rated lowest. This was because of the excessive delays
observed while waiting for the bus during the peak periods, as the buses did not turn up on
the scheduled time and no information was provided to the users about the scheduled arrival.
Table D.3 Rating of current public transport by students at KGUV
Description Very poor Poor Average Good Excellent Average
rating
Frequency of service 0.9 10.7 29.5 48.2 10.7 3.6
On time performance 5.4 25.9 29.5 34.8 4.5 3.1
Overall quality of service 0.0 5.4 37.5 51.8 5.4 3.6
Waiting time 3.6 23.2 35.7 35.7 1.8 3.1
Accessibility to stop 0.9 9.8 17.9 45.5 25.9 3.8
Safety for passengers at stop 0.0 6.3 17.0 45.5 32.1 4.0
Public transport signage 1.8 5.4 24.1 42.9 25.9 3.9
Cost of public transport 2.7 8.9 45.5 31.3 11.6 3.4
Note: The numbers represent the percentages for each ranking
Evaluating the transport impacts of TODs
Deepti Muley Page 242
D.3.2.1 Ratings of students for improvements in public transport service
The university student students were asked to rate the improvements based on their views
while the school students were not asked this question because the questionnaire was
simplified for them and this question was removed from the survey forms. Table D.4 shows
the variation in the university students’ perception. The improvements ratings observed
similar trend in results as the professional employees. This indicated that although different
age and profile for these two user groups the priority parameters remain same.
Table D.4 Improvements rating by university students
Description Not
important
Less
important
Medium
importance
Highly
important
Extremely
important
Average
rating
Minimum
waiting time
1.1 1.1 21.3 46.1 30.3 4.0
More
amenities at
station
8.0 33 38.6 12.5 8.0 2.8
Good quality
service
0.0 12.5 30.7 35.2 21.6 3.7
Should be
cheaper
3.5 7.1 31.8 30.6 27.1 3.7
Less walking
time to &
from the stop
8.3 23.8 33.3 22.6 11.9 3.1
Frequent
service
0.0 0.0 11.2 41.6 47.2 4.4
Note: The numbers represent the percentages for each ranking
D.3.3 Ratings of residents on existing public transport service
The results from the analysis for residents’ responses on current public transport system as
noted in Table D.5. The residents did not show much variation in the ratings of KGUV.
Accessibility to stop secured highest rating in addition to the safety and on time performance
and waiting time was biggest concern for the residents as well. These ratings indicate that the
visitors and residents at KGUV have similar perceptions about the public transport system.
Perceptions of KGUV’s users
Deepti Muley Page 243
Table D.5 Rating of current public transport by residents at KGUV
Description Very poor Poor Average Good Excellent Average
rating
Frequency of service 1.3 1.3 19.2 61.5 16.7 3.9
On time performance 2.6 7.7 33.3 47.4 9.0 3.5
Overall quality of service 1.3 5.1 21.8 56.4 15.4 3.8
Waiting time 3.8 10.3 30.8 43.6 11.5 3.5
Accessibility to stop 2.6 1.3 14.3 54.5 27.3 4.0
Safety for passengers at stop 1.3 1.3 17.9 53.8 25.6 4.0
Public transport signage 2.6 5.1 20.5 60.3 11.5 3.7
Cost of public transport 3.8 9.0 26.9 42.3 17.9 3.6
Note: The numbers represent the percentages for each ranking
D.3.4 Opinion about public transport system
These were the observations emphasized collectively by the respondents’.
Respondents placed a strong emphasis on the frequency and reliability of public
transport service to afford a mode shift from car to public transport. The travel time
difference and the absence of a direct public transport link1 were also pointed out as
the main reasons for using personalised modes of transport. This indicates that for a
TOD to be successful from a transport point of view, a good quality direct public
transport service is required from various destinations, not only from the CBD. Some
respondents preferred train over bus because of its on time performance or reliability.
As this area lies near the CBD, some respondents suggested having a “loop service”
(with minimum or no cost) running at a 15 minute interval from the CBD to KGUV
which also connects the nearby Roma street railway station to make KGUV more
attractive. (It is noted that, after this survey, TransLink implemented a high frequency
bus service, Route 66, along the busway system between Kelvin Grove busway
station to the east of KGUV, through the CBD via Roma Street railway station and on
to the inner southeast suburb of Woolloongabba. A peak hour service Route 933 was
also started to facilitate the KGUV users travel to and from city.)
A professional employee also suggested having strictly enforced parking restrictions
on the local streets in KGUV with increased parking cost at the work place and
incentives for employees who travel by sustainable modes of transport from the
organisations to promote the more sustainable modes of transport.
1 Brisbane has a hub and spoke public transport network with most services intersecting at the CBD. One often
needs to change service, particularly for buses, to access a destination on the other side of the CBD. The
imposition of a seat change has been reported to make public transport less attractive.
Evaluating the transport impacts of TODs
Deepti Muley Page 244
Shoppers suggested that the shopping centre should be an “all the time and one stop
shop” and have more retail outlets aimed at young customers.
The responses also indicated that on time performance and actual arrival of the bus at
scheduled time or some information which would provide them an idea about the bus
arrival were highlighted as important factors. This was linked to the over crowding
and passenger unhappiness as some passengers were unable to get on the bus. This
issue was prominent in peak times when visitors travelled to KGUV and also back
home after finishing their activity at KGUV. Access to the real time mobile
information was suggested as a solution for this.
The lack of public transport in outlying suburbs was also a parameter for concern and
reason for using personalised modes of transport. The employees analysed the cost
and benefits of a mode to determine the travel mode for them.
Brisbane has a public transport system with spoke pattern; all the buses, trains and
ferries are oriented to or from the CBD. This often requires a transfer from one
service to another. This adds the trip length by public transport and consequently the
travel time. This additional travel time was a prominent reason to distract users from
using public transport.
Although the campus shuttle service was very well received by the students and staff
members, there were some issues regarding the overcrowding and waiting time at KG.
An increase in the frequency was suggested for better service. A consistent service
throughout the year was desired rather than only during university times.
Some respondents perceived the cost of the public transport as expensive. Mostly, the
students were quite happy with the cost as they travelled on the concession ticket
which is half the price of a full fare.
Few respondents suggested having route maps at the bus stop and to show via sign on
the buses to guide users. There are route maps in the city area but these needs to be
extended to the outer suburbs or the places of public interest this will aid users if they
are unsure.
Some respondents also highlighted that they have infrequent / inadequate services
during off peak times and due to work commitments it was not always possible to
travel during peak times. The frequent service should be extended to off peak hours as
well in addition to the peak hours to cover everyone’s needs.
Perceptions of KGUV’s users
Deepti Muley Page 245
The accessibility to Roma Street and Normanby busway was a concern for all KGUV
users. The respondents were ready to walk for about 2km to access their places of
interest provided they had good infrastructure for walking as this will yield them
health benefits as well.
The respondents having young children were required to access various destinations
as they were involved in multiple activities which made use of existing public
transport unsuitable.
The respondents demanded better signage and better sun / rain protection at the bus
stop. So the bus stops should not only look aesthetically good but also need to
perform better in terms of its function. The strong sun was blinding the vision; it made
the passenger to miss the bus. So shelter is important. Decent shelter and accurate
timetables were amenities requested by a user. Additional amenities like more seating
and lighting at bus stops was requested.
KGUV has a bus stop located up the hill on Kevin Grove road when travelling
outbound from the city, so the pedestrians were required to walk back to access the
KGUV. As this stop did not have close proximity to KGUV the safety for respondents
in the evenings was a concern. This indicates that the public transport stops or staions
can be located at the centre of a TOD or on the edge of a TOD but not very away from
the TOD boundary.
One suggestion to improve public transport was, remove busses completely from the
equation and give the roads over to cars and private mini–van services such as in
developing countries and use trains for longer distances.
The public transport at KGUV was seen as much better than any other parts of
Brisbane. Having the extra 66 bus is extremely useful and has made it a lot easier to
get a seat on a bus. The connection from city was seen as very good.
The respondents demanded a better management of bus fleet as there were many 'Not
in service buses' that passed through KGUV Busway station. On a particular day one
respondent counted 11 “not in service" or "sorry bus full" busses in evening peak.
This wait time for the buses was the frustrating part for public transport users. There
were many students waiting for the buses during peak hour, the bus frequency should
be supplied to cater for the demand so as to reduce long waiting times and crowding
at the station platform.
Evaluating the transport impacts of TODs
Deepti Muley Page 246
D.4 Perception about KGUV
The users were asked to assess KGUV based on the various design parameters such as social
life, facilities or quality of facilities, environment or air quality, aesthetics of urban village,
quality of sidewalks, presence of sidewalks, transport infrastructure, parks and open spaces,
commercial activities, pedestrian friendly environment, parking facilities and overall rating of
KGUV. A rating scale of one to five was provided with one representing very poor and five
representing excellent. A not applicable option was provided similar to other rating scale
questions. The following subsections describe the perceptions of KGUV users.
D.4.1 Perception of employees
Table D.6 shows the perceptions of professional employees about KGUV. The KGUV
obtained an average individual rating of 3.4 with minimum of one and maximum of 4.8. The
lowest average rating was given to the parking facilities and highest was given to the footpath
availability. A professional employee described as KGUV has no soul.
Table D.6 Perception of employees about KGUV
Description Very
poor Poor Average Good Excellent
Average
rating
Social life 6.8 13.6 44.7 31.1 3.9 3.1
Facilities/quality of
facilities
1.7 5.8 27.5 49.2 15.8 3.7
Environment/Air quality 1.7 9.4 29.1 47.9 12.0 3.6
Aesthetics of urban village 5.1 6.8 33.1 39.8 15.3 3.5
Quality of sidewalks 6.7 3.4 22.7 45.4 21.8 3.7
Presence of sidewalks 5.0 4.2 22.5 46.7 21.7 3.8
Transport infrastructure 8.1 17.1 34.2 34.2 6.3 3.1
Parks and open spaces 2.5 12.7 29.7 42.4 12.7 3.5
Commercial activities 0.9 10.4 37.4 44.3 7.0 3.5
Pedestrian friendly
environment
7.5 13.3 22.5 44.2 12.5 3.4
Parking facilities 35.8 30.2 23.6 9.4 0.9 2.1
Overall rating of KGUV 3.4 7.7 29.9 47.9 11.1 3.6
Note: The numbers represent the percentages for each ranking
D.4.2 Perception of students
Similar to the previous analysis, the perception analysis was also performed for a combined
dataset for school students and university students. Table D.7 outlines the details of the
rankings for students at KGUV. The individual average rating of 3.7 was obtained for the
design of KGUV. The KGUV was rated a minimum of 2.6 and a maximum of 4.8 by
students. Except for parking very few responses rated other parameters as very poor or poor.
Perceptions of KGUV’s users
Deepti Muley Page 247
Table D.7 Perception of students about KGUV
Description Very
poor Poor Average Good Excellent
Average
rating
Social life 0.0 8.0 39.3 42.0 10.7 3.6
Facilities/quality of facilities 0.0 0.0 23.9 54.9 21.2 4.0
Environment/Air quality 0.0 4.4 22.8 48.2 24.6 3.9
Aesthetics of urban village 0.0 2.6 13.2 56.1 28.1 4.1
Quality of sidewalks 0.0 0.9 13.2 57.0 28.9 4.1
Presence of sidewalks 0.0 0.9 17.5 51.8 29.8 4.1
Transport infrastructure 0.0 8.0 39.3 44.6 8.0 3.5
Parks and open spaces 0.9 8.0 26.5 40.7 23.5 3.8
Commercial activities 0.9 5.6 49.5 39.3 4.7 3.4
Pedestrian friendly
environment 0.9 1.8 25.4 49.1 22.8 3.9
Parking facilities 21.8 29.7 24.8 19.8 4.0 2.5
Overall rating of KGUV 0.0 0.9 24.6 62.3 12.3 3.9
Note: The numbers represent the percentages for each ranking
D.4.3 Perception of residents
All the residents’ responses were combined and analysed together to obtain their perspective
about KGUV. Table D.8 displays the percentage of responses for each ranking on the rating
scale. Similar to other user groups, the residents also gave lowest rating for the parking
facilities. All other aspects scored good scores for their design and provision. Overall, the
perception did not vary much by the user groups as usually seen in the travel characteristics.
One resident quoted, “Quality of life at Kelvin Grove Urban village is very excellent, shop,
dental clinic, gym, and parks are available, there is easy access to public transport. Now we
have market once a week”. The residents appreciated clustering of activities.
Evaluating the transport impacts of TODs
Deepti Muley Page 248
Table D.8 Perception of residents about KGUV
Description Very
poor Poor Average Good Excellent
Average
rating
Social life 6.3 10 27.5 46.3 10.0 3.5
Facilities/quality of facilities 1.2 4.9 21.0 54.3 18.5 3.9
Environment/Air quality 0.0 4.9 14.8 59.3 21.0 4.0
Aesthetics of urban village 1.2 1.2 14.8 61.7 21.0 4.0
Quality of sidewalks 0.0 1.2 21.0 56.8 21.0 4.0
Presence of sidewalks 1.2 0.0 18.3 57.3 23.2 4.0
Transport infrastructure 1.2 1.2 23.5 49.4 24.7 4.0
Parks and open spaces 2.5 2.5 28.4 48.1 18.5 3.8
Commercial activities 3.8 10.0 23.8 43.8 18.8 3.7
Pedestrian friendly
environment
0.0 6.2 28.4 50.6 14.8 3.8
Parking facilities 45.7 17.3 21.0 11.1 4.9 2.1
Overall rating of KGUV 0.0 1.2 14.8 67.9 16.0 4.0
Note: The numbers represent the percentages for each ranking
D.4.4 Comments about KGUV
The main concern in the development of this area was parking. More parking was
required & parking attendants need to enforce no parking more stringently.
Diverting the buses on Kelvin Grove road to pass through KGUV was suggested an
option to expose the KGUV to visitors by a retail shop employee as this might attract
more business opportunities for the retails shops.
Most of the KGUV users were happy with the standards of the KGUV; some
professional employees specially appreciated it. The growing popularity of the area
was a plus point. Provision of more places for social activities was suggested by
students.
To promote sport activities, a field for students was suggested for a casual sport.
One suggestion was giving right of way to pedestrians within the village and
minimum waiting time at the traffic lights for pedestrians to encourage walking.
A taxi rank was requested by employees as it was necessary for work purposes.
A professional employee suggested very high frequency public transport service
(similar to Hong Kong) to support KGUV. This suggestion should be considered by
keeping in mind the size respective transport system.
Some respondents were concerned about mixing land uses specially with the
education environment. A careful planning should be made before clustering of land
uses to maintain the performance of each land use.
Perceptions of KGUV’s users
Deepti Muley Page 249
Some respondents were having difficulties with the continual construction as it was
making the pedestrian movement difficult. It should be noted that the survey was
undertaken when the KGUV was underdevelopment. This problem will be resolved
after complete development of KGUV. However, this indicated the need for proper
planning of construction activities to minimize the interruption to a general user.
The residents appreciated the community feeling, centrally located shopping centre
and the proximity of bus stops. However, some students were concerned about the
pricing of the KGUV.
D.5 Other issues
The other development and transport issues were as noted below:
A suggestion was made to give pedestrians priority at intersections in KGUV to
reduce the walking time and make walking more attractive. Strong emphasis was
placed on having more pedestrian crossings and marked crosswalks.
Demand for bike facilities indicated that a good bike path is needed not only in
KGUV but also from home to the work place, as riding a bike on Brisbane’s roads
was described as “scary”.
One suggestion was to shift the residential area further away from commercial and
retail area.
The shoppers suggested that the shopping centre should be a “one stop shop”.
Currently, the Village Centre has a supermarket, food outlets, a pharmacy, a liquor
shop, a bar and coffee shops. He suggested to include a hair dresser or beauty salon,
deli and dress shop in the shopping centre. One more shopper also suggested having a
bigger scale shopping centre to offer more choices indicating the scope for expansion.
Another shopper found the shopping centre as convenient and accessible within a
short walk for people in the nearby suburbs.
The respondents were interested in having more greenery around the village to make
the development more attractive and provide shade during summer.
To obtain a better connection from the Roma Street station, a more environmental
friendly solution such as a free bike hire system was proposed where bikes can be
made available for a gold coin deposit to ride from KG to Roma Street and vice verse.
Some student residents quoted KGUV having good facilities but bit overpriced. The
issue of affordability should be considered especially for residents of KGUV.
Evaluating the transport impacts of TODs
Deepti Muley Page 250
D.6 Summary
The outcomes from this analysis indicated that the frequency of service was the most
important factor in deciding the mode choice. Hence, the effect of LOS for transit availability
was considered for investigating the travel modes of TOD users. The perceptions and
opinions will further aid practitioners while designing further TODs. KGUV observed good
rating except for its parking facilities. The inadequate parking facility was issue for all
KGUV users. However, these parking restrictions have encouraged the use of sustainable
modes of transport supporting that the reduced parking facilities at TODs contributes to the
sustainable mode share.