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MODELLING PASSENGER MODE CHOICE BEHAVIOUR USING COMPUTER AIDED STATED
PREFERENCE DATA
Omer Khan BE (Mathematical Modelling)
School of Urban Development Queensland University of Technology
Doctors of Philosophy (IF49)
July, 2007
Abstract
Redland Shire Council (RSC) has recently completed the preparation of Integrated
Local Transport Plan (ILTP) and started its implementation and monitoring program.
One of the major thrusts of the ILTP is to reduce the car dependency in the Shire and
increase the shares of sustainable environmental-friendly travelling modes, such as
walking, cycling and public transport.
To achieve these objectives, a mathematical model is needed that is capable of
modelling and forecasting the travelling mode choice behaviour in the multi modal
environment of Redland Shire. Further, the model can be employed in testing the
elasticity of various level-of-service attributes, under a virtual travel environment, as
proposed in the ILTP, and estimating the demand for the new travelling alternatives
to private car, namely the bus on busway, walking on walkway and cycling on
cycleway.
The research estimated various nested logit models for different trip lengths and trip
purposes, using the data from a stated preference (SP) survey conducted in the Shire.
A unique computer assisted personal interviewing (CAPI) instrument was designed,
using both the motorised (bus on busway) and non-motorised travelling modes
(walking on walkway and cycling on cycleway) in the SP choice set. Additionally, a
unique set of access modes for bus on busway was also generated, containing
hypothetical modes, such as secure park and ride facilities and kiss and ride drop-off
zones at the busway stations, walkway and cycleway facilities to access the busway
stations and a frequent and integrated feeder bus network within the Shire. Hence,
this study created a totally new virtual travel environment for the population of
Redland Shire, in order to record their perceived observations under these scenarios
and develop the mode choice models.
From the final model estimation results, it was found that the travel behaviour
forecasted for regional trip-makers is considerably different from that of local trip-
makers. The regional travellers for work, for instance, were found not to perceive the
i
non-motorised modes as valid alternatives to car, possibly due to longer trip lengths.
The value of time (VoT) determined for local work trip-makers (16.50 A$/hr) was
also found to be higher than that of regional work trip-makers (11.70 A$/hr).
From the survey analysis, a big part of the targeted population was found to be car
captives, who are not likely to switch from cars to public transport; even if a more
efficient transit infrastructure is implemented. In the past, the models have been
generally calibrated using the mode choice survey data only, while that of the captive
users were ignored. This yields a knowledge gap in capturing the complete travel
behaviour of a region, since the question of what particular biases can be involved
with each model estimation parameter by the captives remain unresolved. In this
research, various statistical analyses were performed on the car captive users' data by
categorising them into various trip characteristics and household parameters, in order
to infer the relative influence of the car captive population on the travel behaviour of
the study area.
The outcomes of the research can assist the policy makers in solving the strategic
issues of transit planning, including the future development of a busway corridor,
with an efficient transit access mode network. The research findings can also be
utilised in evaluating the feasibility of developing walkways and cycleways in the
Shire, along with appraising the relative influence of car captive users on the travel
behaviour forecasts for the study area.
------------------------------------------------------------------------------------------------------
Keywords: mode choice modelling; stated preference survey; CAPI; captive
analysis; busway; walkway; cycleway; access modes.
ii
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 other higher education institution. To the
best of my knowledge and belief, the thesis contains no material previously
published or written by another person except where due reference is made.
Omer Khan
Date: / /
iii
Acknowledgements
I wish to express profound gratitude to my principal supervisor Prof. Luis Ferreira
and my associate supervisor Dr. Jonathan Bunker for their thoughtful guidance and
constructive support in conducting this research. I also wish to thank Dr. Partha
Parajuli (Transport Advisor, Redland Shire Council) for his professional advice
during the preliminary and implementation phases of the study. Moreover, I would
like to acknowledge the Faculty of Built Environment and Engineering, QUT and
Redland Shire Council for providing financial support during my candidature.
I would like to express special appreciation to Mrs. Clara Tetther, Mr. Bradley
Jackson and Mr. Nasir Ahmad for conducting the travel surveys, as part of this study,
and for their assistance in the sample generation process. Finally, I would like to
thank my family, fellow researchers and friends for their consistent encouragement
during the research.
iv
Dedication
I wish to dedicate this PhD thesis to my grand-mother, Saeeda Ansari, my father,
Ishtiaq Ahmed Khan, and my mother, Shubnam Ishtiaq, for their unlimited prayers,
love and support, and to my brother, Osama, and two sisters, Nadia and Hinozia, for
their consistent appreciation and moral support, and to my two beautiful nieces,
Aliza and Eshal.
v
List of Abbreviations
• ILTP Integrated Local Transport Plan
• IRTP Integrated Regional Transport Plan
• RSTS Redland Shire Transportation Study
• SP Stated Preference
• RP Revealed Preference
• VoT Value of Time
• O-D Matrix Origin-Destination Matrix
• IIA Independence of Irrelevant Alternatives
• CAPI Computer Assisted Personal Interviewing
• PAPI Paper-and-Pencil based Interviewing
vi
Publications from this Research
• Khan, O., Ferreira, L., Bunker, J. and Parajuli, P. (2007). High speed bus-on-
busway market projections: stated preference survey design and mode choice
modelling, Transportation Research Record, (In Press).
• Khan, O., Ferreira, L., Bunker, J. and Parajuli, P. (2007). Modelling Multimodal
Passenger Demand using Computer-based Stated Preference Surveys,
Australasian Transport Research Forum (ATRF) 2007, (Paper submitted).
• Khan, O., Ferreira, L., Bunker, J. and Parajuli, P. (2005). Design of a computer
based survey instrument for modelling multimodal passenger demand. 27th
Conference of the Australian Institutes of Transport Research, Brisbane,
Australia.
• Khan, O., Ferreira, L. and Bunker, J. (2004). Modelling multimodal passenger
choices with stated preference data. 26th Conference of the Australian Institutes
of Transport Research, Melbourne, Australia.
• Conducted a session on "Mode Choice Modelling" in a 3-day short course on
“Modelling in Transport Planning”, held in March, 2007, in Brisbane, Australia.
vii
Table of Contents
PART I INTRODUCTION AND LITERATURE REVIEW
Chapter 1 Introduction 1
1.1. Background 1
1.2. Hypotheses 4
1.3. Research Questions 4
1.4. Research Aims and Objectives 5
1.5. Contribution to New Knowledge 5
1.6. Significance of the Research 7
1.7. Structure of the Thesis 7
Chapter 2 Mode Choice Modelling 11
2.1. Introduction 11
2.2. Four-Step Model 14
2.3. Modal Split Models 18
2.4. Model Estimation Techniques 31
2.5. Summary 33
Chapter 3 Stated Preference Travel Surveys 34
3.1. Introduction 34
3.2. Physical Forms of Survey Instruments 36
3.3. Pilot Survey 40
3.4. Sample Generation Methods 41
3.5. Sampling Errors and Biases 48
3.6. Summary 50
PART II STUDY AREA AND DATA COLLECTIONS
Chapter 4 Selection and Characteristics of the Study Area 51
4.1. Introduction 51
4.2. Study Area Profile 52
4.3. Socio-Demographic Characteristics 59
viii
4.4. Summary 70
Chapter 5 Stated Preference Survey Instrument Design 71
5.1. Introduction 71
5.2. Survey Instrument Design Methodology 72
5.3. Demonstration of CAPI Mode Choice Game 77
5.4. Features of WinMint 78
5.5. Pilot Survey Implementation 78
5.6. Summary 79
Chapter 6 Data Collection and Analysis 82
6.1. Introduction 82
6.2. Sample Generation 83
6.3. Survey Implementation Strategy 84
6.4. Sample Characteristics 86
6.5. Exploratory Data Analysis 90
6.6. Summary 96
PART III MODELLING RESULTS AND CONCLUSIONS
Chapter 7 Mode Choice Modelling for Regional Trips 99
7.1. Introduction 99
7.2. Attributes Used in the Models 101
7.3. Mode Choice Model for Work Trips 105
7.4. Mode Choice Model for Other Trips 127
7.5. Summary 135
Chapter 8 Mode Choice Modelling for Local Trips 138
8.1. Introduction 138
8.2. Mode Choice Model for Work Trips 139
8.3. Mode Choice Model for Shopping Trips 148
8.4. Mode Choice Model for Education Trips 155
8.5. Mode Choice Model for Other Trips 162
8.6. Summary 168
ix
Chapter 9 Statistical Analysis of Captive Data 170
9.1. Introduction 170
9.2. Data Analysis of Survey Sample 172
9.3. Classification of Car Captive Users for Work 181
9.4. Access Modes Distribution for PT Captive Users 182
9.5. Summary 183
Chapter 10 Conclusions 185
10.1. Research Summary 185
10.2. Research Findings 193
10.3. Industrial Application of Results 196
10.4. Future Research Directions 197
References 199
Appendix 1 WinMint 3.2F Programming Code of Stated Preference Survey Instrument
208
Appendix 2 Modal Splits for Survey Sample 245Appendix 3 Traveller Type Splits in the Survey Sample 248Appendix 4 Perceived Travel Choices of the Survey Sample 252Appendix 5 Absolute Frequencies of Level-of-Service Attributes 256Appendix 6 Correlation Tables 270Appendix 7 Forecasted Mode Shares 280Appendix 8 Modelling Results for Simple Binary Logit Model and Nested
Binary Logit Model for Regional Other Trips 291
Appendix 9 Elasticities of Level-of-Service Attributes of Various Mode Choice Models
293
Appendix 10 Modelling Results for Simple Multinomial Logit Model for Local Work Trips
315
Appendix 11 Modelling Results for Simple Multinomial Logit Model for Local Shopping Trips
316
Appendix 12 Modelling Results for Simple Multinomial Logit Model for Local Other Trips
317
Appendix 13 Statistical Data of Survey Sample 318Appendix 14 Work Destination Areas 320Appendix 15 Access Mode Distribution for PT captive users for all Trip
Purposes 321
x
List of Figures
Figure 1.1 Current Mode Shares for Journey to Work (2001 Census)
and Proposed Mode Shares (ILTP) for Redland Shire
3
Figure 1.2 Research Methodology 9
Figure 2.1 Role of Transport Modelling in Policymaking 12
Figure 2.2 Structure of Four-Step Model 13
Figure 2.3 Example of a Simple Binary Logit Model 22
Figure 2.4 Example of a Nested Binary Logit Model 24
Figure 2.5 Example of a Simple Multinomial Logit Model 25
Figure 2.6 Example of a Nested Multinomial Logit Model 26
Figure 2.7 Classifications of Mode Choice Models 29
Figure 3.1 CAPI Data Collection Process 37
Figure 3.2 Example of Multi-stage Sampling Process 44
Figure 4.1 Map of Redland Shire 53
Figure 4.2 Percentage Usage of Travelling Modes in the Study Area 56
Figure 4.3 Study Area Characteristics with respect to Household Size 60
Figure 4.4 Age Trends in Redland Shire from 1986 – 2001 62
Figure 4.5 Study Area Characteristics with respect to Age Group 63
Figure 4.6 Study Area Characteristics with respect to Modal Split for
Work Trips
64
Figure 4.7 Study Area Characteristics with respect to Modal Split for
Work Trips and Age Groups
66
Figure 4.8 Study Area Characteristics with respect to Education
Enrolment
67
Figure 4.9 Study Area Characteristics with respect to Car Ownership
Level
68
Figure 4.10 Study Area Characteristics with respect to Household Size
and Car Ownership Level
69
Figure 5.1 Block Diagram of the SP Survey Instrument Design
Methodology
73
Figure 5.2 RP Module presenting Hypothetical Travelling Modes to the
Respondents
76
xi
Figure 5.3 SP Mode Choice Game for Choice Users 77
Figure 6.1 The Survey Implementation Strategy 85
Figure 6.2 Population Split Comparisons between the Survey Sample
and 2001 Census Data
87
Figure 6.3 Modal Split Comparisons between the Survey Sample and
2001 Census Data for Journey to Work
88
Figure 6.4 Percentage Split of the Survey Sample with respect to
Traveller Type for Suburbs of the Study Area for All Trip
Purposes
89
Figure 6.5 Perceived Travel Choices of the Survey Sample for all Trip
Purposes
91
Figure 6.6 Frequency Chart of In-vehicle Travel Time of Car for
Regional Work Trips
93
Figure 6.7 Frequency Chart of Out-of-pocket Travel Cost of Car for
Regional Work Trips
93
Figure 6.8 Total Surveying Time for Choice Users 95
Figure 6.9 Total Surveying Time for Captive Users 95
Figure 7.1 Percentage Split of Mode Choice Users for Regional Work
Trips
105
Figure 7.2 Percentage Split of Mode Choice Users for Regional Work
Trips (with Access Modes to Bus on Busway)
107
Figure 7.3 Simple Binary Logit Model for Regional Work Trips 108
Figure 7.4 Simple Multinomial Logit Model for Regional Work Trips 109
Figure 7.5 Nested Binary Logit Model for Regional Work Trips 110
Figure 7.6 Forecasted Aggregated Mode Shares for Regional Work
Trips
120
Figure 7.7 Sensitivity of In-vehicle Travel Time of Bus on Busway
for Regional Work Trips
123
Figure 7.8 Sensitivity of Travel Fare of Bus on Busway for Regional
Work Trips
124
Figure 7.9 Sensitivity of Access Distance for Bus on Busway
for Regional Work Trips
125
xii
Figure 7.10 Percentage Split of Mode Choice Users for Regional Other
Trips (with Access Modes to Bus on Busway)
127
Figure 7.11 Nested Binary Logit Model for Regional Other Trips 129
Figure 7.12 Forecasted Aggregated Mode Shares for Regional Other
Trips
133
Figure 7.13 Sensitivity of In-vehicle Travel Time of Bus on Busway for
Regional Other Trips
134
Figure 8.1 Percentage Split of Mode Choice Users for Local Work
Trips
140
Figure 8.2 Nested Multinomial Logit Model for Local Work Trips 141
Figure 8.3 Sensitivity of Travel Distance for Local Work Trips 147
Figure 8.4 Percentage Split of Mode Choice Users for Local Shopping
Trips
148
Figure 8.5 Nested Multinomial Logit Model for Local Shopping Trips 149
Figure 8.6 Sensitivity of Travel Distance for Local Shopping Trips 154
Figure 8.7 Percentage Split of Mode Choice Users for Local Education
Trips
156
Figure 8.8 Simple Multinomial Logit Model for Local Education Trips 157
Figure 8.9 Sensitivity of Travel Fare of Bus on Busway for Local
Education Trips
160
Figure 8.10 Percentage Split of Mode Choice Users for Local Other
Trips
162
Figure 8.11 Nested Multinomial Logit Model for Local Other Trips 163
Figure 8.12 Sensitivity of Trip Length for Local Other Trips 167
Figure 9.1 Household Vehicle Ownership Level in Redlands and
Brisbane City
172
Figure 9.2 Sample Split according to Traveller Type 173
Figure 9.3 Sample Split according to Traveller Type and Trip Purpose 174
Figure 9.4 Sample Split according to Traveller Type with respect to
Trip Length and Trip Purpose
176
Figure 9.5 Sample Split according to Traveller Type with respect to
Household Size
177
xiii
Figure 9.6 Sample Split according to Traveller Type with respect to
Age Groups
178
Figure 9.7 Sample Split according to Traveller Type with respect to
Work Destinations
180
Figure 9.8 Types of Car Captive Users for Work Trips 182
Figure 9.9 Access Mode Distribution for PT Captive Users for all Trips 183
xiv
List of Tables
Table 2.1 Comparison of Common Mode Choice Models 30
Table 3.1 Comparison of Sample Generation Methods 47
Table 4.1 Population Trends of the Study Area 55
Table 4.2 Population Characteristics of the Study Area 55
Table 4.3 2011 Modal Split Targets for Redland Shire 57
Table 4.4 Average Household Size of the Study Area 59
Table 4.5 Dwelling Occupancy Composition of Redland Shire by
Household and Family Type
61
Table 4.6 Average Number of Vehicles per Household in Redlands and
Brisbane City
68
Table 5.1 Sample Split of Pilot Survey Respondents on the basis of
Traveller Type
79
Table 7.1 Number of SP Observations attained for each Regional Trip
Purpose
100
Table 7.2 Attributes associated to each Travelling Mode for Regional
Trips
102
Table 7.3 Model Estimation Results for Simple Binary Logit Model for
Regional Work Trips
112
Table 7.4 Model Estimation Results for Simple Multinomial Logit Model
for Regional Work Trips
114
Table 7.5 Model Estimation Results for Nested Binary Logit Model for
Regional Work Trips
116
Table 7.6 Comparison of Values of Times from BSTM and Modelling
Results for Regional Work Trips
117
Table 7.7 Fixed Values of Attributes for determining Sensitivity of In-
vehicle Travel Time for Bus on Busway for Regional Work
Trips
123
xv
Table 7.8 Model Estimation Results for Nested Binary Logit Model for
Regional Other Trips
131
Table 7.9 Fixed Values of Attributes for determining Sensitivity of In-
vehicle Travel Time for Bus on Busway for Regional Other
Trips
134
Table 8.1 Number of SP Observations attained for each Local Trip
Purpose
138
Table 8.2 Model Estimation Results for Nested Multinomial Logit Model
for Local Work Trips
143
Table 8.3 Comparison of Values of Times from BSTM and Modelling
Results for Regional Local Trips
145
Table 8.4 Model Estimation Results for Nested Multinomial Logit Model
for Local Shopping Trips
152
Table 8.5 Model Estimation Results for Simple Multinomial Logit Model
for Local Education Trips
158
Table 8.6 Fixed Values of Attributes for determining Sensitivity of Travel
Fare for Bus on Busway for Local Education Trips
161
Table 8.7 Model Estimation Results for Nested Multinomial Logit Model
for Local Other Trips
165
Table 8.8 Fixed Values of Attributes for determining Sensitivity of Trip
length for Local Other Trips
167
Table 8.9 Comparison of Values of Time (VoTs) for Different Trip
Purposes
169
xvi
1
1 Introduction
1.1. BACKGROUND
The choice of a transport mode is probably one of the most
important classic models in transport planning. This is
because of the key role played by public transport in policy
making.
(Ortuzar and Willumsen 1994)
Transport modelling is used as an effective tool to manage sustainable development
in most developed countries. Considerable investments have been made in transport
planning and policymaking in order to observe the travel behaviour and forecast the
future demand of travel. This forecasting needs to incorporate the designing of
transport systems, by making use of the global infrastructure and understanding the
travel behaviour of the residents of the study area, and develop a system that can
accommodate the travel demands for future.
The South East Queensland (SEQ) region of Australia covers around 1 % of
Queensland’s total area only, yet contains almost two-thirds of the state’s entire
population. It is one of Australia’s fastest growing regions with a population growth
predicted as 14 % between 2002 and 2007. Car use in the region is also high by
world standards, with approximately three quarters of all personal trips undertaken
by car (Socialdata Australia Ltd. 2005). The rising urban sprawl in the region inflates
the demand for better public transport infrastructure and services. Keeping this in
mind, many local councils of the region have started implementing the Integrated
Local Transport Plan (ILTP) that primarily focuses on the creation of an ecologically
sustainable transportation system.
Redlands is a Shire of South East Queensland, with an estimated population of
130,229 (Australian Bureau of Statistics 2007d) and a high annual population growth
rate of around 3 %, compared to 2.4 % for the city of Brisbane. One of the major
2
thrusts of ILTP is to reduce the car dependency and increase the share of sustainable
travel modes such as walking, cycling and public transport (Queensland Government
2000), as shown in Figure 1.1. However, in order to bring other forms of transport in
the level capable of competing with car, it is necessary to substantially improve the
transport infrastructure and facilities related to these modes.
Before starting the implementation to achieve these objectives, one would certainly
like to be sure under what conditions (level of infrastructure, facilities, cost, level of
comfort, etc), an individual would like to switch from car to an alternative travelling
mode. Therefore, certain potential measures need to be identified that can be put into
practice in order to attract a substantial number of car users to adopt public transport
to fulfil their travelling needs.
The main purpose of this research was to develop mode choice models which can
reflect the current travel behaviour of the residents of Redland Shire and forecast the
mode shares under different travel scenarios. These travel scenarios could be real or
virtual, depending on the data provided by the respondent. For this purpose, a unique
computer based travel survey instrument was designed to assess the respondents’
current and future travel behaviours, and further categorised them on the basis of
traveller type, i.e. captive (those who perceive to keep using their current mode) or
choice users (those who perceive to have a choice).
The model specifications developed for the study, for various trip lengths and trip
purposes, considered all the commonly used travelling modes in the study area
(including access modes for line haul public transport). Several level-of-service
attributes of the modes and household parameters, that were surmised to influence
the travel behaviour of the targeted population, were tested in order to generate
appropriate model specifications for each trip purpose. Various logit models were
estimated on the mode choice data, in order to forecast the travel behaviour of the
population of the study area, if the hypothetical travel environment, presented in the
surveys, can be implemented in practice.
3
78%
6%10%
6%
69%
8%
15%
8%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Car PT Walking Cycling
Current Mode Shares ILTP Target - 2011
Figure 1.1 Current Mode Shares for Journey to Work (2001 Census)
and Proposed Mode Shares (ILTP) for Redland Shire
4
1.2. HYPOTHESES
• Disaggregate passenger mode choice models can be developed for various trip
lengths and trip purposes, in a multi-modal environment to forecast the travel
behaviour using the data obtained through stated preference (SP) surveys.
• The computer aided survey instrument provides a valid way of understanding
residents’ current and future travel behaviour.
• The modelling process, used in the study, enables the policymakers to test
various real and hypothetical travel scenarios.
1.3. RESEARCH QUESTIONS
The research questions and sub-questions set up for this study are listed as follows,
1. How the values of estimated model parameters vary with the change in the
following trip characteristics,
trip purpose
i. work;
ii. shopping;
iii. education; and
iv. other.
trip length
i. regional (trips near the Brisbane CBD corridor); and
ii. local (trips within the Shire).
2. How can a Computer Assisted Personal Interviewing (CAPI) instrument improve
the efficiency of the survey design and result in a better response rate from the
sample?
5
3. How can the data of the car captive respondents be utilised in analysing the study
area’s travel behaviour?
1.4. RESEARCH AIMS AND OBJECTIVES
• To test the feasibility of developing disaggregate passenger mode choice models
in a multi-modal environment of the study area, for different trip lengths and trip
purposes;
• To design a computer based stated preference (SP) survey instrument presenting
the respondents with real and hypothetical travel scenarios in order to determine
the importance they associate with each attribute of the travelling mode used in
the model specification;
• To generate a survey sample, with an apposite size, that can be representative of
the whole population of the study area;
• To determine the sensitivity of various modal parameters, in order to identify
their relative influence on the travel behaviour;
• To forecast the travel behaviour of population of the Shire for unique trip lengths
and trip purposes; and
• To statistically analyse the data obtained from captive users and determine their
relative influence on the future travel behaviour.
1.5. CONTRIBUTION TO NEW KNOWLEDGE
Modelling a Virtual Multimodal Travel Environment
Previous stated preference (SP) mode choice studies have generally forecasted the
travel behaviour of the targeted population in the presence of a hypothetical
motorised alternative for car, such as a high-speed train or a bus on busway (Gunn et
al. 1992, Yao et al. 2002). This study focuses on using both motorised (bus on
busway) and non-motorised travelling modes (walking on walkway and cycling on
cycleway) as alternatives to car. Additionally, a unique choice set of access modes
for bus on busway was also generated, containing five hypothetical modes such as
6
secure park and ride facilities and kiss and ride drop-off zones at the busway
stations, walkway and cycleway facilities to access the busway stations and a
frequent and integrated feeder bus network within the Shire. Therefore, this research
modelled a totally new virtual multimodal travel environment for the population of
Redland Shire, in order to record their perceived observations under these scenarios
and develop the mode choice models.
Statistical Analysis of Mode Captive Data
Generally, the travel behaviour of members of an affluent society is highly
influenced by car (Australian Bureau of Statistics 2002). A big part of the targeted
population is generally car captive users, who are not likely to switch from cars to
public transport; even if a more efficient transit infrastructure is implemented. In the
past, the models have been generally calibrated using the mode choice survey data,
while that of the captive users were ignored. This yields a knowledge gap in
capturing the complete travel behaviour of a region, since the question of what
particular biases can be involved with each model estimation parameter by the
captives remain unresolved. Therefore, in this study, various statistical analyses were
carried out on the mode captive users’ data by categorising the survey sample, on the
basis of different trip characteristics (trip purposes and trip lengths), household
characteristics (household size, car ownership level, age-groups, etc) and work trip
destinations, in order to determine their relative influence on the travel behaviour
forecasts. Additionally, the mode captive users for work trips were further classified
according to two types of trip-makers; one of who strictly have to use car as part of
their work requirement, and those who currently do not perceive to have choice when
presented with mode choice scenarios in the SP survey. It is probable that the latter
set of respondents may shift from car to an attractive alternative mode, if the travel
environment can be practically implemented.
Variation in Travel Behaviour Forecasts for Different Trip Types
Despite the development of various passenger mode choice models to forecast the
travel behaviour in the past, little has been done to jointly analyse the sensitivity of
the travel behaviour of the population with characteristics of the trips undertaken. In
order to forecast the modal splits of a study area with a higher degree of accuracy,
mode choice modelling needs to be done using these characteristics, by categorising
7
the model specification into different trip lengths and trip purposes. In this study,
unique logit models were developed for four trip purposes (work, shopping,
education and other trips), and with two trip lengths (trips destined on the Brisbane
CBD corridor, known as regional trips, and those undertaken within the Shire,
known as local trips).
The modelling results for work trips, for instance, showed that the travel behaviour
forecasted for regional trip-makers is considerably different from that of local trip-
makers. The regional work trip-makers were found not to perceive the non-motorised
modes as valid alternatives to car, possibly due to longer trip lengths. The value of
time (VoT) determined for local work trip-makers (16.50 A$/hr) was also found to be
higher than that of regional work trip-makers (11.70 A$/hr), establishing that mode
choice modelling should not only be categorised according to the trip purposes, like
in previous studies, but also according to the trip lengths.
1.6. SIGNIFICANCE OF THE RESEARCH
• The research assists in developing a comprehensive understanding of the travel
behaviour of the residents of the study area;
• The research analyses the travel profile of the population in detail, by splitting it
into the two traveller types of captive and choice users and statistically
examining the influence of various level-of-service attributes and household
parameters in the mode choice for different trip purposes; and
• The research tests the feasibility of developing separate busways, with an
integrated network of access modes, and a network of walkways and cycleways.
1.7. STRUCTURE OF THE THESIS
The methodology for this research was developed using the state-of-the-art travel
demand modelling approach, as graphically shown in Figure 1.2. The thesis is also
structured following the same order, as shown in the figure.
8
Chapter 1 starts with defining the background knowledge of the research, along with
establishing the hypotheses and the research problem. Further, the aims and
objectives of the research, and the questions the research aims to answer are also
mentioned. The research questions further gave rise to the need for conducting a
state-of-the-art literature review on mode choice modelling and stated preference
surveys. The main findings from the literature review are respectively shown in
Chapters 2 and 3.
In Chapter 2, it was established that the logit models are the most commonly used
travel demand models, due to their simple formulation and estimation techniques.
Therefore, various logit models were developed to estimate the mode choice data and
forecast the travel behaviour, for various trip purposes and trip lengths, as shown in
Chapters 7 and 8. In Chapter 3, computer assisted personal interviewing (CAPI) was
found to be the most commonly used surveying technique, among the transport
planners, due to its attractive graphical design and high response rate. Moreover,
WinMint 3.2F, a standard CAPI instrument designing software, was selected for
designing the survey for this study. Various sample generation methods were also
studied in order to find the most appropriate survey sample for the study area,
resulting in the selection of the method of stratified random sampling due to its
simple theoretical framework and the capability to accurately generate a
representative sample for a study area, as compared to other sampling techniques.
The southern region of Redland Shire was selected as the study area for this research.
Chapter 4 presents various demographics and statistical profiles of the study area in
detail, along with demonstrating the key reasons for choosing this region for the
research.
The design of the stated preference (SP) survey instrument developed for this study
is presented in Chapter 5, along with a simple demonstration of how a CAPI mode
choice game is presented to the respondents. The findings from the pilot survey,
conducted in the study area with a small sample size, are also presented indicating
towards the possible editions in the survey instrument design. Chapter 6 further
illustrates the implementation strategy adopted for conducting the main surveys in
the region and the statistical analyses performed on the survey sample and the data.
9
Figure 1.2 Research Methodology
Research Problem
Research Aims & Objectives
Mode Choice Modelling
Stated Preference Surveys
- Profile - Demographics
- Model Development - Model Specification
Mode Choice
Modelling
Captive Analyses
SP Survey Instrument Design
Pilot Surveys
Main Survey Implementation
Thesis Writing
Literature Review
Study Area Selection
10
The travel characteristics of the survey sample were compared with the current travel
properties of the residents of the study area, taken from the 2001 Census results,
shown in Australian Bureau of Statistics (2007b) in order to ensure that the sample is
representative of the entire study area.
After conducting the SP surveys, the data obtained was categorised according to the
traveller type, i.e. the respondents perceiving to have a choice for car, known as
choice users, and those who do not, commonly referred to as car captive users. The
mode choice data was then, used to estimate various logit models, presented in
Chapters 7 and 8, for regional and local trips respectively.
The model specifications developed for all the models, i.e. work, shopping,
education and other trips, are presented in Chapters 7 and 8 for regional and local
trips respectively, along with the estimated coefficients and their sensitivities
influencing the travel behaviour forecasts for the study area. Chapter 9 shows various
statistical analyses carried out on the survey data by splitting it into the three traveller
types of choice, car captive and PT captive users, and categorising them according to
several travel characteristics and household parameters.
The main findings of the whole research are summarised in Chapter 10, evaluating
the results in contrast with the research aims and objectives, as set out in Chapter 1.
A direction for future research is also presented, identifying the implementation of
the results of this study in a four-step modelling framework. Finally, the references
cited through out the thesis are listed.
11
2 Mode Choice Modelling
2.1. INTRODUCTION
Modelling is an important part of most decision-making
processes … It is concerned with the methods, be they
quantitative or qualitative, which allows us to study the
relationships that underlie decision-making.
(Hensher and Button 2000)
Transportation is vital for sustaining economic development. Considerable
investments have been made in transportation planning and policymaking in order to
forecast the future demand of travel. This forecasting needs to incorporate the
designing of transportation systems, by making use of the existing infrastructure and
the travel behaviour of the residents of the study area. These designing and
forecasting techniques for strategic transport planning can be mathematically
enumerated and grouped together as transport modelling.
Transport modelling plays a key role in the complex system of transport planning
and policymaking that can be examined from Figure 2.1.
12
Figure 2.1 Role of Transport Modelling in Policymaking (Modified from Richardson (2003) )
The fundamentals of transport modelling were developed in the United States during
the 1950s, and were then imported into the UK in the early 1960s. Thereafter, the
following 20 years saw important theoretical developments in the field of transport
modelling leading to further work in specific sub-areas. A contemporary dimension
was the development of transport mode choice models representing the behaviour of
travellers of the study area. Since then, the interest in this field, as well as the
growing complexity has led to further development of various travel demand models.
However, most of these models trace their origin back to the classical transport
demand model, the four-step model (FSM), because of its overarching framework
and logical appeal. The basic structure of the model is illustrated in Figure 2.2.
PROBLEM DEFINITION
System Resources
Objectives
Criteria
TRANSPORT MODELS
Consequences
Evaluation
Selection
Implementation Constraints Monitoring
Alternatives
Data Collection
13
Figure 2.2 Structure of Four-Step Model (Modified from McNally (2000) )
This chapter presents a state-of-the-art literature review on passenger mode choice
modelling, with particular focus on logit modelling specifications and estimation
techniques. The literature review was carried out keeping in mind the development of
various mode choice models to forecast the travel behaviour of Redlands, the study
area selected for the research, in the travel environment of the Integrated Local
Transport Plan (ILTP), as proposed in Redland Shire Council (2002). The models
developed contained various modal parameters and household attributes, which were
perceived to influence the travel behaviour of the study area, based on previous mode
choice modelling studies and the travel scenarios proposed in the ILTP.
The literature reviewed in Section 2.2 includes work related to the broader topics of
public transport demand modelling, particularly in context of the four-step model
with each stage discussed in detail. Sections 2.3 and 2.4 illustrate the theoretical
framework and estimation techniques of various modal split models, along with
selecting a particular discrete choice model in order to forecast the travel behaviour
for this study. Finally, Section 2.5 summarises the main findings from the literature
review revealing the research framework, designed to forecast the travel behaviour of
the study area.
Trip Generation
Trip Distribution
Modal Split
Trip Assignment
Evaluation
14
2.2. FOUR-STEP MODEL
The four-step model has been extensively used in transport demand modelling
because of its indispensable rationale as being an overarching design framework. The
approach starts by considering the study area as a network of various zones
partitioned in order to attain an unbiased data sample from the population.
The data is used to estimate a model of the total trips generated and attracted by each
zone (trip generation), allocation of these trips to different destinations (trip
distribution), modelling the choice of mode (modal split) and allocating the trips by
each mode to their corresponding networks (trip assignment). Hence, the model
depicted in Figure 2.2 consists of four elementary stages, where each stage addresses
an intuitively reasonable query: how many travel movements will be made, where
will they go, by what mode will the travel be carried out, and what route will be
taken?
2.2.1. Trip Generation
The trip generation stage of the classical transport model aims at predicting the total
number of trips generated by and attracted to each zone of the study area. Since, it
essentially defines the total travel in the study area, it is after trip generation analysis
that the transportation planner comes up with the vital figures about the total number
of trips generated and attracted by each zone, purposes of these trips, and the
travelling modes generally used for these trips.
Ortuzar and Willumsen (2001) have demonstrated common trip generation patterns
on the basis of following standard trip purposes,
• Work trips;
• Educational trips;
• Shopping trips; and
• Other trips (social, recreational, medical, bureaucratic trips etc.).
15
The most commonly used analytical technique to develop the trip generation patterns
of a study area is multiple linear regression. In this technique, the dependent output
variable is assumed to have a linear dependence on the independent input variables,
which may or may not influence the trip generation, as shown in Equation 2.1.
Y = β0 + β1X1 + β2X2 + …. + BkXk + E (2.1)
where,
β0,1,…,k are coefficients of regression;
X1,2,…k are independent input variables;
Y is the dependent output variable; and
E is the error in estimating the output variable.
The definitions of the input and output variables vary with the type of linear
regression approach used in the research. Generally, two types of regression
techniques are applied in multi-modal transportation planning namely,
• Zonal-based Multiple Linear Regression; and
• Household-based Multiple Linear Regression.
The main difference between the two techniques is that the former is used to generate
the travel patterns on zonal basis, while the latter does it at an household level.
Therefore, for zonal-based regression, Y is generally taken as the number of trips
generated for and attracted by each zone in the study area, while various independent
variables can be considered and tested for estimation purposes such as,
• employment density of a zone1 (for work trips);
• school / university enrolment of a zone (for education trips); and
• shopping areas in a zone (for shopping, work, other trips).
1 The employment density of a zone can be further on the basis of the number of white-collar and blue-collar workers, if desired.
16
Similarly, household-based regression tends to utilise various parameters associated
with a household, in order to estimate the regression coefficients, such as,
• household size;
• number of vehicles in a household;
• number of adults in a household; and
• number of workers and students in a household.
Standard literature on statistical techniques and analysis of multiple linear regression
can be found in Cohen et al. (2003).
2.2.2. Trip Distribution
The trip distribution stage of the four-step tends to provide a standard pattern of trip
making by recombining the trip ends with the origins. The trip distribution model is
essentially a destination choice model and generates a trip table, for each trip
purpose utilised in the model as a function of activity-system attributes and network
attributes. This trip table, also commonly known as Origin-Destination Matrix (O-D
Matrix), provides a comprehensive illustration of the number of trips generated
between different zones of the study area.
A number of efforts have been made by transport researchers for developing efficient
and adaptive algorithms in order to optimise the O-D Matrix for achieving realistic
results. Nielsen (1994) presented two new methods for trip matrix estimation;
namely Single Path Matrix Estimation (SPME) and Multiple Path Matrix Estimation
(MPME), and demonstrated that the traffic models can be easily and cheaply
estimated using them. Three different approaches to O-D Matrix estimation were
reviewed and compared, in the context of transport planning, by Abrahamsson
(1996) who attempted to use the trip assignment parameters to calibrate the O-D
matrix of the study area. Later, Abrahamsson (1998) illustrated an O-D matrix for
Stockholm, Sweden that can reproduce the traffic counts, in terms of the number of
trips generated and attracted, using the previous distribution approaches improving
the accuracy of forecasting of O-D Matrices. Various computationally efficient
algorithms for estimating the trip distribution matrices were developed by Safwat and
17
Magnanti (2003) by using a simultaneous approach to develop a four-step model
rather than the conventional sequential method. Further, Ber-Gera and Boyce (2003)
developed a trip origin based algorithm for transportation forecasting models that
combine travel demand and network assignment variables in order to improve the
existing O-D flow models. Sherali et al. (2003) developed a non-linear approach to
estimate the O-D trip matrices by implicitly determining the path decomposition of a
network flow using a sequential linear programming approach. The challenge for
researchers in this area, in the immediate future, continues to be the development of a
standard optimised algorithm for forecasting accurate and realistic trip distributions.
2.2.3. Modal Split
The choice of transport mode is probably one of the most
important classic models in transport planning. This is
because of the key role played by public transport in policy
making.
(Ortuzar and Willumsen 2001)
The issue of selecting the most appropriate travelling mode has always been a critical
issue in travel behavioural modelling, since it tells an individual about the most
efficient travelling mode available. Therefore, it is vital to develop and use models
that are receptive to those attributes of travel that influence a certain individual’s
choice of mode. The quantification of this interaction in terms of mathematical
relationships is known as modal split and the travel demand models are referred to as
modal split or mode choice models. Hence, the modal split assists a transport planner
to assess the impact of each urban element on mode choice and permits testing and
evaluation of various transportation schemes.
For the model to be representative of the behaviour of the population of the study
area, it is essential that survey implementation should be carried out in the study area
to record travel data to be used for model calibration, rather than using the data from
previous case studies (Richardson 2003). It raises a critical issue of appropriately
designing a survey instrument that can record the required travel information of each
respondent in the study area, as discussed in Chapter 3.
18
Various discrete mode choice models, generally used for travel behaviour
forecasting, are presented in Section 2.3 discussing and comparing their specific
features in detail.
2.2.4. Trip Assignment
Trip assignment is the last stage of the four-step model, dealing with the allocation of
a given set of trip interchanges to a specific transport network. Its main objective is
to estimate the traffic volumes and the corresponding travel times or costs on each
link of the transportation system by the help of inter-zonal or intra-zonal trip
movements (determined by trip generation and distribution) and the travel behaviour
of the individuals (determined by modal split).
Patriksson (1994) has presented a list of useful purposes of trip assignment in context
with transport planning namely,
• assessing the deficiencies in the existing transportation system of the study area;
• evaluating the effects of limited improvements and extensions to the existing
transportation systems;
• developing construction priorities for the existing transportation system of the
study area; and
• testing alternative transportation system proposals.
2.3. MODAL SPLIT MODELS
2.3.1. Theoretical Framework
A behavioural model is defined as one which represents the decisions that consumers
make when confronted with alternative choices. These decisions are made on the
basis of the terms upon which the different travel modes are offered, i.e. the travel
times, costs, and other level-of-service attributes of the competing alternative
travelling modes. The models that tend to represent the travel behaviour of the
individuals when provided with a discrete set of travelling alternatives are commonly
known as discrete choice models.
19
An individual is visualised as selecting a mode which maximises his or her utility
(Ben-Akiva and Lerman 1985). The utility of a travelling mode is defined as an
attraction associated to by an individual for a specific trip. Therefore, the individual
is visualised to select the mode having the maximum attraction, due to various
attributes such as in-vehicle travel time, access time to the transit point, waiting time
for the mode to arrive at the access point, interchange time, travelling fares, parking
fees etc. This hypothesis is known as utility maximisation and all the travel demand
models, presented in this section, are based on this theory.
As a matter of computational convenience, the utility is generally represented as a
linear function of the attributes of the journey weighted by the coefficients which
attempt to represent their relative importance as perceived by the traveller. A
possible mathematical representation of a utility function of a mode m is shown in
Equation 2.2 as,
Umi = θ1xmi1 + θ2xmi2 + …… + θkxmik (2.2)
where,
Umi is the net utility function for mode m for individual i;
xmi1, …, xmik are k number of attributes of mode m for individual i; and
θ1, …, θk are k number of coefficients (or weights attached to each attribute)
which need to be inferred from the survey data.
The choice behaviour can be modelled using the random utility model which treats
the utility as a random variable, i.e. comprising of two distinctly separable
components: a measurable conditioning component and an error component.
Therefore,
Umi = Vmi + Emi (2.3)
where,
Vmi is the systematic component (observed) of utility of mode m for individual i;
and
Emi is the error component (unobserved) of utility of mode m for individual i.
20
For equation 2.3 to be correct, certain homogeneity is needed within the population
under study. In principle, it is required that all the individuals share a universal set of
alternatives and face the same constraints. Furthermore, in practical modelling work,
the difference between the socioeconomic characteristics of similar groups of
individuals is usually ignored (Ortuzar and Willumsen 2001). Although this
approach makes the whole process simple overall, there is still a possibility of
occurrence of severe differences among various groups of people. This can be
handled by segmenting the entire set of individuals into separate utility functions for
each group of more similar individuals so that individual characteristics could be
omitted from the utility function.
By ignoring the attributes of the decision maker, the systematic component of the
utility can be treated as a function of attributes of available modes only. Therefore, a
single utility function can be visualised to exist for all individuals. Similarly, the
error component of the utility can also be considered independent of socioeconomic
characteristics for the same reason. Assuming that the error component has zero
mean and an extreme value distribution (Kilburn and Klerman 1999), the net utility
function can be given as:
Um = Vm + Em (2.4)
Thus, if there are M number of total travelling modes available, the probability of an
individual selecting mode m, such that m Є M, is based on its associated utility
function Um, such that,
Um ≥ Ui (2.5)
where,
Um represents utility of travelling alternative m; and
Ui represents utility of any travelling alternative in the set of available travelling
modes.
Summarising the theory of utility maximisation presented in Equation 2.5, every
alternative associates a certain utility with itself determined by its various attributes
and an individual is supposed to select the alternative possessing the highest utility.
21
However, it is impractical to assume that the effects of all the variables in an
individual’s decision regarding the selection of a travel mode are perfectly
understood. The beauty of a random utility model is that it possesses the power to
estimate the effects of the observed variables without fully concerning that of the
unobserved ones incorporating all of them into the error component of the model, as
shown in Equation 2.4.
2.3.2. Logit Models
Logit models are the most commonly used modal split models in the area of
transportation planning, since they possess the ability to model complex travel
behaviours of any population with simple mathematical techniques. The
mathematical framework of logit models is based on the theory of utility
maximisation and is discussed in detail in Ben-Akiva and Lerman (Ben-Akiva and
Lerman 1985). Briefly presenting the framework, the probability of an individual i
selecting a mode n, out of M number of total available modes, is given as,
Pin = ∑Mmε
)Vexp()Vexp(
im
in
(2.6)
where,
Vin is the utility function of mode n for individual i;
Vim is the utility function of any mode m in the choice set for an individual i;
Pin is the probability of individual i selecting mode n; and
M is the total number of available travelling modes in the choice set for
individual i.
All logit models are specified on the basis of Equation 2.2 and are applied according
to Equation 2.6. The theoretical framework of logit models is based on three main
assumptions regarding the error term Em, as shown in Equation 2.4. The assumptions
are listed as follows,
• Em is Gumbel distributed;
• Em is independently distributed; and
• Em is identically distributed.
22
All these three assumptions serve as the main postulates of the structure of logit
models. The first assumption of the random component being Gumbel distributed
indicates that all the utilities associated with the travelling modes should be
considered as a linear sum of attributes and have the same scale parameter (Ben-
Akiva and Lerman 1985). The last two assumptions are normally grouped together to
be referred to as a property of Independence of Irrelevant Alternatives (IIA property),
simply meaning that all the travel modes used in modelling the travel behaviour are
independent of each other.
Logit models are generally classified into two main categories namely binary and
multinomial logit models. Binary choice models are capable of modelling with two
discrete choices only, i.e. the individual having only two possible alternatives for
selection, where as the multinomial logit models imply a larger set of alternatives.
2.3.2.1. Binary Logit Models
The mathematical framework of a binary logit model is a simplified representation of
Equation 2.6 with the total number of available alternatives limited to two, i.e. M =
2. An example of a binary logit model is shown in Figure 2.3 where the choice set
contains car and public transport as two competing alternatives.
Figure 2.3 Example of a Simple Binary Logit Model
Choice
Car
Public
Transport
23
Simplifying Equation 2.6, the probability of individual i selecting the mode m out of
two available travelling modes m and n is given as,
Pim = )exp()exp(
)exp(inim
im
VVV+
(2.7)
or,
Pim = )exp(1
1imin VV −+
(2.8)
and,
Pin = 1 – Pim (2.9)
where,
Vim is the utility function associated to alternative m for individual i;
Vin is the utility function associated to alternative n for individual i;
Pim is the probability that alternative m will be selected by individual i; and
Pin is the probability that alternative n will be selected by individual i.
The main limitation of the binary logit model, shown above, is that it is supposed to
be only applied if the travelling alternatives in the choice set are independent of each
other. However, when there are groups of more similar or correlated modes, the
assumption of having an independent and identical error term across all the modes
does not always remain valid.
In these cases, a nested logit model can be used that relaxes the constraints of the
simple logit models by allowing correlation between the utilities of the alternatives
in common groups. The structure of a nested logit model is characterised by
grouping all the subsets of correlated alternatives in hierarchies or nests. Each nest,
in turn, is represented by a composite alternative which competes with the others
available to the individual. An example of a nested logit model, an extension of
Figure 2.3, is presented in Figure 2.4 by nesting the two elementary and identical
modes of bus and train into the composite mode of public transport.
24
Figure 2.4 Example of a Nested Binary Logit Model
The theoretical framework of the nested logit model is based on the same
assumptions as the multinomial logit model, except that the correlation of error terms
is assumed to exist among various modes. Due to the tree structure of these models,
Equation 2.6 is reassessed and is mentioned in Daly (1987), for trees having two
levels, as,
Pij = Pi . Pj|i (2.10)
Pj|i = ∑∈ )i(Ck
k|i
j|i
)exp()exp(
VV (2.11)
Pi = ∑∈Rt
t
i
)exp()exp(
VV (2.12)
Choice
Car
Public
Transport
Bus
Train
25
Vj|i = Xj|i (2.13)
Vi = Xi + hi ln ∑∈ )i(Ck
k|i)exp(V (2.14)
where,
C(i) is a set of lower-level alternatives that each form part of the higher-level
alternative i;
R is the set of higher-level alternatives;
Xj|i is the measured attractiveness of alternative j conditional on i;
Xi is the measured attractiveness of alternative i; and
hi is the scale parameter.
2.3.2.2. Multinomial Logit Models
Similar to binary logit models, the multinomial logit models are also categorised into
simple and nested multinomial logit models, based on the characteristics of the
available travelling alternatives in the choice set. The examples of simple and nested
multinomial logit models are presented in Figures 2.5 and 2.6 respectively.
Figure 2.5 Example of a Simple Multinomial Logit Model
Choice
Car
Cycle
Walk
Bus
26
Figure 2.6 Example of a Nested Multinomial Logit Model
The multinomial logit models use the same mathematical framework as shown in
Equations 2.2 to 2.14 and are generally estimated using maximum likelihood method,
discussed in Section 2.4.1.
2.3.3. Probit Models
Certain situations can occur where the utilities of some alternatives are correlated in
a complex way or possess different variances. In these cases, the multinomial logit
models can make erroneous forecasts regarding the probabilities of mode shares
when the attributes associated to one or more travelling alternatives are varied. The
probit model has been proposed as one of the possible methods to overcome this
problem. The model follows normal distribution for error terms and does not work
under the strict assumptions as that of logit models.
Similar to logit models, the probit model is also based on random utility theory,
representing the utility function as the sum of the systematic component and an error
component. The standard equation for the utility of an alternative i has the form
(Horowitz 1991) as shown in Equation 2.15,
Choice
Car
Cycle
Walk
Bus
Car as
Driver
Car as
Passenger
Walk
Car
27
Ui = V(xi,s) + εi (2.15)
where,
Ui is the utility of alternative i;
V is the systematic (observed) component of the utility function;
ε is the error (unobserved) component of the utility function;
xi is the vector of observed attributes of alternative i; and
s is the vector of observed characteristics of the individuals of the study area.
Due to the complex estimation algorithms of probit models, the transport planners
generally prefer using logit models as they possess simple mathematical framework
and can accurately model the travel behaviour of a study area. Ghareib (1996)
compared logit and probit models by using them to estimate the travel behaviour for
different cities of Saudi Arabia and concluded that the logit models are superior to
their probit counterparts in terms of their goodness-of-fit measures and tractable
calibration. Dow and Endersby (2004) later supported his findings by concluding that
the logit models should always be preferred over probit models and the latter should
only be utilised if the travel behaviour of the targeted population to be determined is
observed to be complexly correlated.
2.3.4. General Extreme Value Models
In an important simplification of multinomial logit models, generalised extreme
value (GEV) models were developed based on the stochastic utility maximisation.
Although there exist a limitless number of possible models within this class, only a
few have been truly explored.
This model is based on a function G(y1, y2, …, yJn), for y1, y2, …, yJn ≥ 0, that has to
satisfy certain conditions discussed in detail in Ben-Akiva and Lerman (1985). The
basic equation of the model is given as,
Pn(i) = ))Vexp(),...,Vexp(),V(exp(G
))Vexp(),...,Vexp(),V(exp(G).Vexp(nJn2n1
nJn2n1iin
n
n
μ (2.16)
28
where,
V is the systematic (observed) component of the utility function;
μ is the degree of homogeneity; and
Pn(i) is the probability of individual n selecting alternative i.
In addition to the three modal split models discussed above, there also exist a few
discrete choice models which can be referred as the generalisations of logit models,
namely Random Coefficient Logit, Tobit and Ordered Logistic models. Due to the
occurrence of high limitations in the specifications and estimation complexities of
these models, they are rarely put into practice by transport planners. A detailed
mathematical framework of these models is presented in Ben-Akiva and Lerman
(1985) and Amemiya (1994).
2.3.5. Comparison of Modal Split Models
The first step in modal split modelling is to generate a travel profile of the study area
and determine a representative choice set, based on the travel characteristics of the
targeted population. The size of the choice set determined assists in the selection of
an appropriate mode choice model in order to forecast the travel behaviour of the
study region. If the choice set consists of two travelling modes, or two sets of
travelling modes, a binary modal split model can be applied. Contrarily, multinomial
modal split models can be selected for bigger choice sets. This classification of the
discrete mode choice models on the basis of the choice set is illustrated in Figure 2.7.
29
Figure 2.7 Classifications of Mode Choice Models
Various disparities among the three most common mode choice models, namely the
logit, probit and general extreme value models, are tabulated in Table 2.1, identifying
the main distinguishing factors among the specifications and applications of these
models.
Mode Choice Models
Binary Choice Models
Multinomial Choice Models
Binary Logit Model
Binary Probit Model
Multinomial Logit Model
Multinomial Probit Model
General Extreme Value Model
Simple Multinomial Logit Model
Nested Multinomial Logit Model
Simple Binary
Logit Model
Nested Binary
Logit Model
30
Table 2.1 Comparison of Common Mode Choice Models
Logit
Models
Probit
Models
General
Extreme Value
Models
Basic
Hypothesis
Extreme Value
Distribution
Normal
Distribution
Multivariate
Extreme Value
Distribution
Major
Constraints
Error terms should
necessarily be
identically and
independently
distributed
Error terms need not
necessarily be
identically and
independently
distributed
Error terms need
not necessarily be
identically and
independently
distributed
Model
Formulation
Simple Complex Complex
Model
Estimation
Simple Complex Complex
Introduction
of
Access
Modes
Model formulation
and calibration
becomes complex to a
small degree
Model formulation
and calibration
becomes highly
complex
Model
formulation and
calibration
becomes highly
complex
Application High Limited Limited
Accuracy High Low Low
Table 2.1 shows the general reasons of why the logit models are most commonly
used among the transportation planners for estimating and forecasting the travel
behaviour of a study area. The specifications developed for logit models associate
certain limitations due to the IIA property, discussed in Section 2.3.2; however, the
main reasons for choosing them are their simple model formulation and estimation
techniques. Other mode choice models such as probit and general extreme value
models have relaxed the IIA restriction at the cost of possessing highly complex
mathematical structure and computational estimation. Therefore, the logit models
continue to remain dominant in the transport modelling arena.
31
2.4. MODEL ESTIMATION TECHNIQUES
Generally, two model estimation techniques are used for estimating the discrete
mode choice models, in order to infer the values of the unknown coefficients θ1, θ2,
… , θk shown in Equation 2.2, namely the maximum likelihood and least squares
method. Brief model formulations of these models are presented in Sections 2.4.1
and 2.4.2 respectively. A detailed literature of the theoretical framework,
applications and limitations of these models is presented in Greene (2003).
2.4.1. Maximum Likelihood Method
The method of maximum likelihood is the most common procedure used for
determining the estimators in simple and nested logit models. Stated simply as,
The maximum likelihood estimators are the values of the
parameters for which the observed sample is most likely to
have occurred.
(Ben-Akiva and Lerman 1985)
The method requires a sample of individual mode choice decision-makers along with
the data regarding the travelling mode chosen and the attributes of that particular
mode. The basic formulation of the method, that involves the maximisation of the
likelihood function, is shown in Equation 2.17 as,
L = ∏=
M
mm mtP
1)( , (2.17)
where,
L is the likelihood the model assigns to the vector of available alternatives;
M is the total number of available alternatives;
m is any alternative present in the set of available alternatives;
tm is the mode observed to be chosen in alternative m; and
P(tm,m) is the probability for choosing alternative m.
32
The most widely used approach is to maximise the logarithm of L rather than L itself.
It does not change the values of the parameter estimates since the logarithmic
function is strictly monotonically increasing. Thus, the likelihood function is
transformed to a log-likelihood function and is given as,
L1 = ∑=
M
m 1log [P(tm,m)] (2.18)
Given the mode choice data, most existing estimation computer programs estimate
the coefficients that best explain the observed choices in the sense of making them
most likely to have occurred. Standard commercial packages such as ALOGIT
(Hague Consulting Group 1992) are generally implied for estimating logit models,
mostly due to their capability of handling complex nested logit structures, both linear
and non-linear.
2.4.2. Least Squares Method
The method of least squares is generally stated as,
The least square estimators are the values that minimise
the sum of squared differences between the observed and
expected values of the observations.
(Ben-Akiva and Lerman 1985)
The coefficients of regression are estimated by the basic objective function F which
is given by (see Equation 2.1),
F = min ∑ E2 = min ∑ (β0 + β1X1 + β2X2 + …. + BkXk – Y)2 (2.19)
The desired coefficients are estimated by taking (k+1) derivatives of equation 2.19
and solving for (k+1) unknowns. This method is usually called the Ordinary Least-
Squares (OLS). Generally, the least-squares estimators are unbiased under general
assumptions. However, it should be noted that the least-square method works
consistently and efficiently for linear models only, and can surmise erroneous
33
coefficients’ values in case of complex model specifications. Therefore, due to its
higher applications, the maximum likelihood method is generally preferred over the
least square method by the transport statisticians and planners.
2.5. SUMMARY
This chapter presented the main findings of the state-of-the-art literature review
conducted on passenger mode choice modelling in a travel behavioural framework.
The main aim of appraising the literature was to determine a modal split model that
can be implied to forecast the travel behaviour of the population of Redland Shire,
study area selected for the research, under the ILTP travel environment.
Firstly, the four-step model was reviewed since it is regarded as the basic
overarching framework for travel demand modelling. Each step of the model was
briefly discussed, with major focus on mode choice where the theoretical framework
and main properties of various discrete choice models were examined. It was
concluded that logit models associate the most practical modelling framework, out of
all modal split models, although they are based on the IIA property which assumes
that all the travelling modes used in the choice set are independent of each other.
This condition is, however, relaxed with the use of a tree structure that combines the
correlated modes into one nest.
Logit models are generally classified into two main categories, namely the binary
and multinomial logit models, depending on the size of the choice set generated for
the study area. For choice set presenting two travelling alternatives to the targeted
population, a binary logit model was preferred. Contrarily, multinomial logit models
were implied for bigger choice sets. Maximum likelihood method was found to be
the most commonly used estimation technique for logit models, due its ability to
handle complex structures. Computer estimation packages such as ALOGIT are
generally used for model calibration purposes, mainly due to their capability to
perform numerous mathematical iterations using various statistical techniques.
34
3 Stated Preference Travel Surveys
3.1. INTRODUCTION
Economists typically display a healthy scepticism about relying
on what consumers say they will do compared with observing
what they actually do; however, there are many situations in
which one has little alternative but to take consumers at their
words.
(Louviere et al. 2000)
The standard framework of travel demand modelling requires data which can
precisely reflect the travel characteristics of the targeted population. This data can be
gathered by conducting surveys in the study area, asking the respondents regarding
the attributes associated to their current or future travelling modes. This data may
also involve elicitation of various travel preferences and choices, identifying the
respondents in the survey sample having choice over a certain mode. This elicitation
needs to be realistic and practical in order to forecast the travel behaviour with a
higher degree of accuracy. Therefore, the surveys conducted should not only involve
questions regarding essential current travelling attributes but also be capable of
observing the behaviour of the respondents when faced with hypothetical attributes
and conditions (Stopher and Jones 2003). These surveys are generally referred as
stated preference (SP) travel surveys and are generally used in forecasting the travel
behaviour of a study area in a hypothetical travel environment. Contrarily, the
surveys involving questions regarding the current travelling attributes in a real
environment are classified as revealed preference (RP) surveys and thus, can be used
to estimate the current travel behaviour of a study area.
During the last few years, stated preference methods have become established as one
of the key tools of demand analysis as they are frequently adopted by transportation
planners for the analysis of the impact of transport policies on travel demand (Fujii
35
and Garling 2003). Some of the main reasons behind this popularity of SP surveys
are summarised as follows,
• they can predict travel behaviour of a study area under various hypothetical travel
scenarios proposed in the transport policies for that area;
• they can ensure that the current transport planning reflects all the essential
attributes of the travelling modes used in the study area; and
• they can detect the relative importance of qualitative or latent variables such as
comfort, convenience, safety etc, which may be inaccurately estimated by RP
data (Ortuzar 1996b).
As stated in Chapter 1, the main aim of this research was to develop mode choice
models in order to forecast the travel behaviour of the residents of Redland Shire
under hypothetical ILTP scenarios for various trip purposes. Therefore, stated
preference (SP) surveys were conducted in the study area in order to observe the
perception that the respondents associate to various travelling alternatives to the car.
Further, the SP data, obtained from the respondents with mode choice, was entailed
in calibrating various logit models for different trip lengths and purposes. The
theoretical framework and estimation techniques of logit modelling were discussed
in Chapter 2 in detail.
This chapter presents the findings of the state-of-the-art literature review conducted
on stated preference survey instrument designing, use of pilot survey in finalising the
instrument, and sampling techniques to generate a representative set of respondents
for the study area. The chapter starts by presenting various physical forms of the
survey instrument designs, generally used by the transportation planners. Various
instrument forms such as computer-based interviewing, mail-back questionnaires and
face-to-face surveying are discussed. After comparing the properties of the physical
forms of each survey instrument, Computer Assisted Personal Interviewing (CAPI)
were selected for conducting SP surveys in the study area due to their specific design
and high response rates. Various advantages of conducting a pilot survey on a small
sample size within the study area, before the actual survey implementation, are also
discussed. The main benefit of the pilot survey was found to be the editing and
finalising of the survey instrument, for the actual survey, based on the reactions of
36
the respondents on the graphical interface of the instrument design. Several
techniques for generating the survey sample are also presented and compared,
resulting in the selection of the method of stratified random sampling due to its
simple theoretical framework and the capability to accurately generate a
representative sample for a study area. Finally, a brief discussion on sampling errors
and biases is presented, discussing the possible influences of the two on the travel
behaviour forecasts for a study area.
3.2. PHYSICAL FORMS OF SURVEY INSTRUMENTS
The development of a stated preference survey instrument has always been a
challenging task for the designers since the travel data needed to be collected by the
instrument is entirely dependent on the study area and the behaviour of the residents.
It is also essential that the survey instrument should be appropriately designed as to
record only the travel data that is vital for model estimation rather than over-
burdening the respondent with excessive questioning (Sanchez 1992). Further, the
selection of appropriate, simple and clear wording for the questions also result in a
high response rate for the survey. However, the most vital aspect of the survey
instrument is the physical nature of the form on which the data is to be recorded.
The survey instruments can be designed by various physical forms depending on the
nature of the travel data being collected. Currently, the two common forms in
practise are computer assisted and paper-and-pencil survey designing. Other forms
such as mail-back and telephone surveys have become dormant since the current
ones effectively reduce the survey non-response rates (Murakami et al. 2003) and
reflect more genuine travel behaviour (Wermuth et al. 2003). However, the paper-
and-pencil interviewing is also gradually becoming extinct because of the
flexibilities and easiness computer assisted interviewing provides to the interviewers
and the respondents.
37
3.2.1. Computer Assisted Personal Interviewing (CAPI)
The movement to computer based survey methods is not an
option. It seems as inexorable as the transition to computers
in most other organised human activities in modern society.
(Couper and Nichols 1998)
Computer Assisted Personal Interviewing (CAPI) is a computer assisted data
collection method used for surveying and collecting data in person. It is usually
conducted at the home, workplace or business of the respondent using a portable
personal computer, such as a notebook. CAPI can also include Computer Assisted
Self-Interview (CASI) session where the interviewer hands over the computer to the
respondent for a short period, but remains available for any instructions or assistance
for the respondent. After finishing the interview, the data is generally sent to a
central computer, where all the survey databases are managed. A block diagram of
CAPI survey data recording process is shown in Figure 3.1.
Figure 3.1 CAPI Data Collection Process
Survey Sample
Previous Travel
Behaviour Information
Survey Instrument
(CAPI)
CAPI Management
System
Remote Devices
Survey Implementation
Survey Database
Results
Pilot Survey
38
The role of the interviewer is a significant factor in conducting successful CAPI
interviews. Wojcik and Hunt (1998) suggested various training techniques for the
CAPI interviewers; some of them being maintaining the focus on the administration
of the survey instrument, designing the instruments on latest available technologies
and developing objective measures for assessing the success of the interviewers in
achieving the survey objectives. Sperry et al. (1998) further added to factors of the
successful completion and higher response rates of CAPI by stressing the importance
of sound communication skills and harmony between the interviewers and the
respondents. Additionally, the use of computers in data collection can considerably
reduce the amount of work and provide automatic data coding techniques that
improve the data quality and thus, estimate the model with a higher level of
accuracy. Various standard CAPI instrument designing packages, such as WinMint
(HCG 2000), are commonly used by the survey designers, mainly due to their
capability of generating random hypothetical SP games based on current travel
characteristics of the respondents.
3.2.2. Paper-and-Pencil Interviewing (PAPI)
Paper-and-Pencil Interviewing (PAPI) is an orthodox manual method of data
collection implemented with the help of the interviewers involved in face-to-face
interviews with the respondents. PAPI can also have a mail-back self-interviewing
part which is generally filled by the respondents themselves.
Contrarily to CAPI, this method involves manual data coding and recording by the
designers and interviewers respectively. Therefore, the probability of having errors
and biases in the survey instrument design is higher than that of CAPI (Kalfs 1995,
Wermuth et al. 2003). Further, examination and comparison of various aspects of
PAPI and computer-based surveying using telephones by Bonnel and Nir (1998)
suggested that the former is a very expensive method in terms of survey instrument
designing, data coding and data recording. Due to these and many other reasons,
PAPI are becoming extinct, particularly for surveying in the developed countries.
3.2.3. Other Forms of Survey Instruments
Apart from computer assisted and paper-and-pencil interviewing, there also exist
various other survey methods for data collection. However, these methods have
39
already become non-existent due to the fact that the current two methods provide
higher flexibility for the instrument designers in terms of coding and designing and
to the interviewers and respondents in terms of data recording. Some of these
methods are briefly described in this section.
3.2.3.1. Postal Survey
A postal survey, by definition, is another method of self-administered interviewing.
Generally, it involves mailing a questionnaire to the respondent’s home by post so
that they can mail it back to the survey administration after completing the surveys.
As a result, the presence of an interviewer is not required in specific.
Although the absence of the interviewer causes the survey to be less expensive, in
terms of cost and time, as compared to the current survey methods, it does raise the
issue of having no interviewer helping the respondent in answering the questions
(Jenkinson and Richards 2004). An excellent detailed comparison of different aspects
of postal, face-to-face and telephone interviewing such as survey implementation
cost, data sampling, quality control and flexibility along with examples can be found
in Bonnel (2001).
3.2.3.2. Internet Survey
An internet survey is comparatively a contemporary self-interviewing method for
data collection in which the respondent is generally supposed to fill the questionnaire
over the internet. The identity of the respondent filling the questionnaire is generally
unknown and thus, the validity of the data provided by the respondent is usually
difficult to determine (Lazar and Preece 1999).
Timmermans et al. (2003) and Adler et al. (2002) presented results from an internet
based travel survey concluding that although this method offers potential in
administering relatively complex tasks such as stated preference experiments, it can
be highly unreliable. Therefore, the model estimated from the internet survey cannot
be totally judged to generate accurate results. Secondly, the sampling frame for
internet surveys is often not available as it cannot be known that the respondents may
behave totally differently to the population of interest.
40
3.2.4. Advantages of Computer-based Survey Instrument
Sarasua and Meyer (1996) identified the following major advantages that computer
assisted interviewing has over other surveying methods,
• interesting and flexible presentation format;
• consistent format across the interviewers and the respondents;
• automatic question branching and prompting;
• automatic data coding and storage; and
• ability to incorporate checks to avoid inconsistent or wrongly entered answers.
Based on the advantages of implementing CAPI listed above, it is concluded that
computer-based surveying is superior to other forms of survey instruments.
3.3. PILOT SURVEY
A pilot survey is a complete run through of the actual survey, done over a small set
of population in order to determine the level of credibility of the instrument, data
coding and data recording. Further, analysis of the results is also done along with the
calibration of the model so that the data validity could also be properly known. The
actual aim of conducting the whole exercise is to identify the potential flaws in the
survey instrument design and data recording, observe the response of the respondents
and determine the discrete discrepancies in the survey administration before the
interviewers begin conducting the actual survey.
Although, pilot testing forms one of the most important components of the survey
procedure, it is also one of the most neglected because of the lack of time and money
on the side of the survey administration. However, Ampt (1993) fully supported the
use of pilot surveys by stating that the pilot testing should be done even on those
survey techniques and questionnaires which have been used successfully in similar
circumstances on anyone other than the target population. Pratt (2003) added that this
testing should not be confined to the designer’s work associates but should
41
substantially include people from the same population that are to be surveyed in the
main survey.
Richardson et al. (1995) described various uses of conducting a pilot survey in detail.
Some of them are listed here as follows,
• determine the adequacy of the sampling frame;
• observe the variability of the parameters within the survey population;
• examine the causes of the non-response rates;
• scrutinize the method used for data collection;
• check the question wording and layout of the questionnaire;
• study the procedures of data entry, editing and analysis; and
• swot the cost of the survey.
The size of the pilot survey is a trade-off between cost and efficiency. It cannot be as
extensive as the main survey but nevertheless it should be large enough to yield
significant results. Richardson et al. (1995) further pointed out a rule of thumb for
the survey cost that the survey administration should allocate at most ten percent of
the actual survey budget for the pilot survey.
3.4. SAMPLE GENERATION METHODS
Sample generation is regarded as a vital step in travel demand modelling since the
modal split models are generally estimated using the data collected by surveying a
sample of respondents from the targeted population. Therefore, it is essential that the
sample generated for the research is representative of the characteristics of the
population of the study area. Inappropriate sample generation can lead to erroneous
modelling results involving biased estimated coefficients and non-representative
travel behaviour forecasts.
42
This section discusses and compares various commonly used sample generation
techniques, with focus on selecting a suitable method to generate an apposite sample
for this study in particular.
3.4.1. Simple Random Sampling
Simple random sampling is the simplest approach out of all sample generation
techniques and is the basis of all other random sampling methods. In this method, a
totally random sample is chosen from the target population, using a sampling frame
with the units numbered. Since the sampling is totally random, every member of the
target population set has an equal probability of being selected. Therefore, if the set
of target population contains N number of members, and the sample is supposed to
have n members, provided that n ε N, the probability to generate the sample in n
number of draws, using simple random sampling, is presented in Equation 3.1 as,
NPn = N!
n)!(Nn! − (3.1)
where,
NPn is the probability to select n number of members from a set of N members,
such that n ε N.
This method is also known as random sampling without replacement. Further
mathematical details of the method are given in Govindarajulu (1999).
Although this method is simple, it becomes highly impractical for larger sample
sizes. Ampt and Ortuzar (2004) proved that the method often produces highly
variable results from repeated applications for high sample sizes. Therefore, the
method is only applicable for generating small sample sizes and is limited to simple
sampling approaches.
3.4.2. Stratified Random Sampling
In stratified random sampling, the targeted population is split into distinct sub-
populations, known as strata. These strata are classified on the basis of various
factors of relevant interest to the survey and are obtained by simple random sampling
43
within each stratum. For example, for a mode choice survey, the strata can be
categorized on the basis of the users of various travelling modes, i.e. the individuals
using private cars and public transport (Tsamboulas et al. 1992, Steg 2003).
Similarly, the classification can also be done on the basis of various socioeconomic
conditions of the households such as structure, age groups and income-levels.
Chang and Wen (1994) explain that if the entire population contains N units, then
stratified random sampling can be done by dividing it into L number of non-
overlapping strata such that,
N1 + N2 + ….. + NL = N (3.2)
where,
N1,2, … , L are the number of units in each strata L.
Whilst stratified sampling is useful, in general, to ensure that the correct proportions
of each stratum are obtained in the sample, it becomes highly significant in
identifying relatively small sub-groups within the population. Therefore, it
enormously increases the precision of the estimates of attributes of the targeted
population of a study area. However, considerable prior information regarding the
attributes of the population should be known before generating the sample.
3.4.3. Multi-stage Sampling
Multi-stage sampling is a random sampling technique for study areas with large
populations. It is based on the process of selecting a sample in two or more
successive contingent stages. It proceeds by defining aggregates of the units that are
subjects of the survey, where a list of the aggregates is easily available or can be
readily created.
Richardson et al. (1995) explained the process of multi-stage sampling within
Australian context by splitting it into five distinct stages as shown in Figure 3.2.
44
Figure 3.2 Example of Multi-stage Sampling Process
The major disadvantage of multi-stage sampling is its low level of accuracy of the
parameter estimates for a given sample size as compared to that estimated using a
simple random sample for the same study area. However, the reduction in accuracy is
often traded off against the reduction in costs and efficiency in administration of the
sampling process that the multi-stage sampling associate. Hossain et al. (2003)
proved this argument by presenting various population models based on different
sampling techniques, out of which the most efficient method, in terms of application
and economy, was found to be multi-stage sampling.
3.4.4. Cluster Sampling
Cluster sampling is a slight variation of multi-stage sampling where the targeted
population is first divided into clusters of sampling units, and then sampled
Country (Australia)
States
Local Government
Areas
Census Collectors’
Districts
Households
Individuals
Total Population
1st – Stage Sample
2nd – Stage Sample
3rd – Stage Sample
4th – Stage Sample
5th – Stage Sample
FINAL SAMPLE
45
randomly. The units within the cluster are either selected in total or else sampled at a
very high rate. Detailed literature on the theoretical framework of the method, along
with some useful examples, is presented in Stehman (1997).
Similar to multi-stage sampling, cluster sampling can also be highly economical and
administratively efficient as compared to simple random sampling, especially for
study areas with large populations. Additionally, if the study areas are well-defined, a
transport modeller can easily manage to have a high degree of quality control on the
conduct of the interviews. However, the main disadvantage, like multi-stage
sampling, continues to be the less accuracy in estimating the coefficients for any
given sample size as compared to that estimated using simple random sampling.
3.4.5. Systematic Sampling
Systematic sampling is perhaps the most widely known non-random sampling
technique among the transport modellers. The method involves selecting each kth
member of the targeted population. The first member is chosen randomly and then,
after every kth interval, another member is selected to be part of the sample. For
example, if the targeted population contains N members and the desired sample size
is n, then after selecting the first member randomly, the other members are selected
every N/nth interval. However, this constraint does not need to be strictly enforced
and can be modified by the modeller according to the level of model complexity. In
study areas where the size of the targeted population is very large or almost infinite,
Stopher (2000) suggested that every twentieth member of the set should be selected
as part of the sample.
Although systematic sampling is the easiest and simplest sampling method known, it
possesses various limitations. First, and most importantly, the sample set generated
using systematic sampling generally contains various biases because the targeted
population sometimes exhibit a periodicity with respect to the parameter being
measured. This causes the resulting sampling set to be significantly biased towards
that certain parameter. The second limitation is the scenario in which the resulting
sample set may not effectively represent the users of a certain travelling mode. This
situation generally occurs in enormously populous study areas where there is
46
assorted practise of travelling modes and the transport modellers unconsciously
ignore these users, causing bias in the sample set.
3.4.6. Comparison of Sample Generation Methods
The sections above presented various sample generation techniques that are
commonly implied by the transport modellers in order to generate an apposite sample
for the study area. The benefits and limitations of each sampling method are
presented in Table 3.1 in order to select the most appropriate sampling technique for
this study.
Table 3.1 Comparison of Sample Generation Methods
Sampling
Methods
Benefits Limitations
Infeasible for study areas
with large populations. Simple
Random
Sampling
Highly simple and does
not involve complex
computer algorithms. Inconsistent most of the
times by giving highly
variable results.
Useful when data of
known precision are
wanted for certain
subdivisions.
Significant administrative
convenience, particularly
for transport surveys.
Stratified
Random
Sampling
Precise estimates of the
characteristics of the
targeted population.
Considerable prior
information regarding the
attributes of the
population is needed
before the actual
sampling can take place.
47
Feasible for study areas
having large populations.
Multi-stage
Sampling
At each stage of the
process, different sampling
methods can be applied
giving more flexibility to
the transport modeller.
Level of accuracy of
parameter estimates for a
given sample size tends
to be less than if a simple
random sample had been
collected.
Highly economical and
administratively efficient
as compared to simple
random sampling,
especially for study areas
with large populations.
Cluster
Sampling If the study areas are well-
defined, a transport
modeller can easily
manage to have a high
degree of quality control
on the conduct of the
interviews.
Less accuracy in
estimating the
coefficients for any given
sample size as compared
to simple random
sampling.
Simplest of all other
methods and is often easier
to execute.
The set of target
population can exhibit a
periodicity with respect
to the parameter being
measured causing bias in
the results.
Systematic
Sampling
Generally more precise
than simple and stratified
random sampling, since it
is spread more evenly over
the population.
For mode choice study,
unique travelling mode
users may get ignored.
48
Previous SP mode choice studies have suggested that a random sample should be
generated in order to minimise the bias that may be attached to a certain mode by the
targeted population (Louviere and Street 2000, Parajuli and Wirasinghe 2001).
Therefore, the method of systematic sampling was ruled out as it directs at generating
a non-random sample from the population of the study area.
The major issue with using the methods of multi-stage and cluster sampling was that
their main applications are generally limited to study areas with huge populations
only. The study area selected for this research, the southern suburbs of Redland
Shire, contain a total population of around 55,760 only, according to the 2004
estimate presented in Australian Bureau of Statistics (2007d). Therefore, given the
small population of the study area, implying the methods of multi-stage and cluster
sampling were not considered for this research.
Comparing the remaining two methods of simple and stratified random sampling, it
was concluded that the latter generates representative samples with higher level of
accuracy. Thus, the sampling technique selected in order to generate an appropriate
sample for this study was stratified random sampling with its stratum being the
population of each suburb in the study area, as presented in Chapter 4.
3.5. SAMPLING ERRORS AND BIASES
From the stages of data collection to that of final model estimation, the data are
generally subject to various sorts of errors and biases.
A sampling error arises simply because of the fact that a modeller deals with a
sample rather than with the whole population of a study area. Thus, the sampling
error cannot be totally eliminated even if the sample is very carefully selected and
the instrument well designed. Richardson et al. (1995) defined sampling error as
primarily a function of the sample size and the inherent variability of the parameter
under consideration. However, the sampling error generally does not affect the
estimated parameter values and merely influence the variability around these
averages (Brownstone et al. 2002).
49
Sampling bias is a total different concept from sampling error and arises mainly
because of the mistakes made by the modeller in choosing an appropriate sampling
method. Having bias in the sample survey results is a more severe problem than
sampling error itself since it directly affects the estimated values. The results can get
highly unrealistic due to the induction of sampling bias and therefore, forecasting
travel behaviour becomes impractical. However, sampling bias can be virtually
eliminated by careful attention to the various aspects of sample survey design and by
adopting the most appropriate sampling method.
In an attempt to improve the accuracy of sample surveys, a modeller needs to be
aware of the likely sources of sampling bias and the possible measures to be taken in
order to eradicate them. The most significant of these common sources and possible
measures are mentioned in detail by Richardson et al. (1995). Some of the significant
safeguards against the introduction of sampling bias in travel surveys are listed as
follows,
• using a random sampling selection process and fully adopting the sample
generated by it;
• designing the survey instrument in such a manner that there is no need for doing
further sampling;
• performing random call-backs on some respondents in order to check the validity
of the travel data obtained by surveying them;
• performing cross-checks with other secondary sources of data to check on the
validity of the responses;
• increasing the response rates; and
• having significant information regarding the travel characteristics of the entire
sample.
Sampling bias generally varies with the type of survey method used by the modeller
and the parameters which the survey seeks to estimate. Therefore, conducting a pilot
survey with a small but significant sample in order to determine sampling bias is
highly recommended, before the actual survey implementation.
50
3.6. SUMMARY
This chapter presented the findings of the state-of-the-art literature review conducted
on stated preference (SP) survey instrument designing and sample generation
techniques. The main aim of reviewing the literature was to determine the most
suitable form of the survey design and an appropriate method to generate a
representative sample for the study area chosen for this research.
Various physical forms of the survey instruments were considered, including the two
most common designs of computer assisted personal interviewing (CAPI) and paper-
and-pencil interviewing (PAPI). CAPI was found to be most famous SP surveying
technique among the survey designers due to its graphically attractive presentation
format and higher response rates as compared to other surveying methods. WinMint,
a software programming tool, was found to be one of the most commonly used CAPI
designing packages being used by the survey designers. Moreover, the uses of
conducting pilot surveys in the study area were elaborated with the main benefit
being the editing and finalising the design of the survey instrument for the actual
survey implementation.
Similarly, five sample generation techniques were presented in Section 3.4
comparing the benefits and limitations of each method in Table 3.1. For this
research, the method of stratified random sampling was deemed as the most suitable
sampling technique considering the small population size of the study area. Finally, a
brief discussion on sampling errors and biases associated to the various steps of data
collection and model estimation was presented. It was found that the sampling errors
cannot be totally eliminated from the modelling results, however, they generally have
an insignificant influence on the values of the estimated coefficients and the
variability around them. Contrarily, sampling bias was found to be a bigger problem
than the sampling error since it can substantially affect the travel behaviour forecasts
for a study area. However, sampling bias can be virtually eliminated by careful
attention to the various aspects of sample survey design and by adopting the most
appropriate sampling method.
51
4 Selection and Characteristics of the Study Area
4.1. INTRODUCTION
For transportation research purposes, a study area is generally regarded as a
geographical region in which transport planning needs to be done, for reasons such
as estimating and forecasting the travel behaviour of the population. It is essential for
a transport modeller to have accurate information and statistics on the boundaries,
land features, population growth, and transport infrastructure of the study area. It
helps in determining the travelling modes commonly used by the residents, along
with their significant attributes. A comprehensive description of defining the study
area boundaries for travel surveying purposes is given in Ortuzar and Willumsen
(2001).
The southern region of Redland Shire (in South-East Queensland) was selected as the
study area for this research. This chapter presents various demographics and
statistical profiles of the study area in detail. The main reasons for choosing this
study area are also discussed. Sections 4.2.1 and 4.2.2 present a thorough description
of the study area, with boundaries, transport infrastructure and population statistics.
The current travel behaviour of the study area is discussed in detail in section 4.3
with the help of various graphical illustrations on the population and travel profile of
the residents of each suburb included in the study area. A brief discussion is provided
on how these socio-demographic characteristics and trends are influencing (or may
influence) the travel behavioural framework of the population. In the end, Section 4.4
concludes the findings of the chapter by focussing on the main factors that impinge
(or are supposed to impinge) on the mode choice decision-making of the travellers
for various trip purposes. However, a complete travel profile can only be presented
with the help of mode choice modelling results that can forecast the travel behaviour
in the ILTP environment as discussed in Chapters 7, 8 and 9.
52
4.2. STUDY AREA PROFILE
This section shows the map and profile of the study area along with a brief
background on the travel behaviour characteristics of the residents.
4.2.1. Selection of the Study Area
The study area targeted for the research covers the five southern suburbs of Redland
Shire namely,
• Victoria Point;
• Thornlands;
• Redland Bay;
• Mount Cotton; and
• Sheldon.
This study area was defined and finalised under the supervision of Redland Shire
Council. Figure 4.1 shows the map of the suburbs of the Shire that were selected as
part of the study area for the research. These suburbs account for around 31 % area
of the whole of Shire. The southern part of the suburb of Capalaba, outside the study
area, was selected as the control area2 for surveying purposes. Therefore, in addition
to the residents of the above five suburbs, the survey sample also contained a
significant number of the residents of Capalaba.
2 Control area is defined as a region surveyed for model validation purposes or for conducting additional surveys.
53
Figure 4.1 Map of Redland Shire
54
There were two main reasons for specifically selecting the southern suburbs of the
Shire. First pragmatically, the northern suburbs were covered under the TravelSmart
study (Queensland Government 2004) and therefore, the Council was not interested
in re-conducting a travel survey in these suburbs. Secondly, the study area selected
does not have a proposed railway corridor; therefore, the model specification
requirements became different as the SP set of hypothetical travelling modes could
not contain train as a valid alternative to car. The model specification developed for
this research, as discussed in section 4.4, had to be limited to four all-the-way modes
(car, bus, walking, cycling), along with five modes to access the public transport
(walking, cycling, feeder bus, park & ride, and kiss & ride). However, the number of
travelling modes varies among different model specifications for different trip
lengths as discussed in chapters 7 and 8.
4.2.2 Study Area Characteristics
Redland Shire is a Local Government Area (LGA)3 of South East Queensland, with
an area of 537 square kilometers. It is geographically positioned with Brisbane to the
north, Logan to the west and Gold Coast to the south. Redlands is part of the fastest
growing area in Queensland and one of the fastest growing in Australia (Australian
Bureau of Statistics 2007e).
The Shire has an estimated population of 130,229 (Australian Bureau of Statistics
2007d) with a high annual population growth rate of around 3 %, compared to 2.4 %
for the city of Brisbane. The population trends of the five suburbs of the study area
and their growth rates are presented in Tables 4.1 and 4.2 in order to indicate the
high increase in the number of residents in these suburbs in the last few years and
show the population projections for the year 2016 for these areas.
3 A Local Government Area refers to an administrative division of Australia
55
Table 4.1 Population Trends of the Study Area4
Suburb Area
(sq.
km.)
Population
(1991)
Population
(1996)
Population
(2001)
Population
(2004)
Projected
Population
(2016)
Capalaba 19 14,143 16,206 17,238 17,827 20,700
Redland
Bay
48 4,501 5,554 6,876 9,550 18,800
Sheldon –
Mt. Cotton
65 2,632 3,208 4,283 4,943 6,900
Thornlands 22 5,954 7,131 7,360 9,711 17,400
Victoria
Point
14 6,040 9,760 11,903 13,729 17,300
TOTAL 168 33,270 41,859 47,660 55,760 81,100
Table 4.2 Population Characteristics of the Study Area
Suburb Population Density
( persons / km.2 )
Population
Growth Rate (%)
( 1996 - 2001 )
Capalaba 938.2 1.2
Redland Bay 203.4 4.4
Sheldon – Mt. Cotton 75.6 6.0
Thornlands 445.9 0.8
Victoria Point 1022.2 3.9
Due to the high population growth rate in the Shire, it is estimated that the total
population of Redlands can reach almost 167,500 by 2016 (Local Govt. & Planning
2005) meaning a possible population growth of around 37,000 from 2005 to 2016.
This rising urban sprawl in the region inflates the demand for an improved and
frequent public transport network, with an enhanced facilities for non-motorised
modes, in order to cope with the day-to-day travel needs of people. Australian
Bureau of Statistics (2007c) analysed the usage of the main travelling modes in the
4 All the population statistics is taken from the website of Australian Bureau of Statistics (www.abs.gov.au) and is based on census data, except for that of 2004 which is an estimate
56
study area for work trips on the basis of Census (2001). As expected, the private car
usage has come out to be extremely high (around 91%) as compared to other
travelling modes in the study area as presented in Figure 4.2. The main reason for
such a high car usage can be attributed to the fact that the current public transport
network in Redlands associate a deficient infrastructure and the facilities for
walkways and cycleways to the residents are scarce.
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
50.00%
55.00%
60.00%
65.00%
70.00%
75.00%
80.00%
85.00%
90.00%
95.00%
100.00%
Car Public Transport Walk Cycle
CapalabaRedland BaySheldon - Mt CottonThornlandsVictoria Point
Figure 4.2 Percentage Usage of Travelling Modes in the Study Area
With the high car usage and increase in population growth of the study area in mind,
Redland Shire Council prepared an Integrated Local Transport Plan (ILTP),
57
focussing on the creation of an ecologically sustainable transport system (Redland
Shire Council 2003). One of the major thrusts of the ILTP is to reduce the car
dependency and increase the share of other, more sustainable, modes of travel such
as walking, cycling and public transport. Additionally, the ILTP also aims to reduce
the total daily trips, current fuel consumption trends and the average daily vehicle
kilometres travelled per person and increase the overall vehicle occupancy.
The ILTP target for the modal split for the Shire in the year 2011 is presented in
Table 4.3 as,
Table 4.3 2011 Modal Split Targets for Redland Shire
Travelling Mode 2011 Target
Private Car 69 %
Public Transport 8 %
Walking 15 %
Cycling 8 %
Vehicle Occupancy 1.4
The analysis of travel demand undertaken as part of the Redland Shire
Transportation Study (RSTS) in (2000) suggested the overall characteristics of travel
demand in the Shire in 2011 will be,
• the total number of vehicle (including commercial vehicles) trips generated in the
Shire will increase from 214,000 to 357,000 trips per day;
• the average vehicle speed on the road network will fall by approximately 10%;
and
• the total number of trips attracted to public transport will increase, but its share of
the total travel market will probably fall slightly.
Further, RSTS also established that the targets set above by ILTP were not realistic
and practically unachievable, given the current travel behaviour of the population,
level of public transport infrastructure prevailing in and around the Shire, state of
58
pricing and other policy level issues influencing the urban travel decisions.
Contrarily, the Shire community is not in favour of building more roads to cater for
the increased number of private motor vehicle trips as a result of increased mobility
needs of the growing number of population in the Shire. However, the community is
also not prepared to switch to the use of other forms of transport at its current
available state that can possibly reduce the need of building more roads (Redland
Shire Council 2000).
In order to meet the ILTP objectives and address the concerns raised by the
community, this research has been conducted in order to develop a comprehensive
understanding of the travel behaviour of the population of the study area and to
forecast the usage of different travelling modes under various scenarios (real or
hypothetical).
These scenarios were presented as part of the SP surveys conducted in the study area
in which the respondents were asked to compare between the level-of-service
attributes of car and the sustainable travelling modes of bus on busway, walking on
walkway and cycling on cycleway, as proposed in ILTP. For each SP mode choice
game, the alternative to the car was chosen by the respondent depending on various
factors such as the purpose of the trip undertaken (work, shopping, education or other
trip), perception of the attributes of the alternative and length of the journey (local or
regional trips). The attributes, associated to the travelling modes, shown to the
respondent in each SP scenario were also based on the current values of the mode
parameters in order to determine the realistic mode choice at an aggregate level.
After finishing the survey implementation, various mode choice models were
calibrated from the survey data in order to forecast the travel behaviour of the
targeted population under the ILTP scenarios and to check the operational feasibility
of all the proposed alternatives. Additionally, direct and cross elasticities of various
level-of-service attributes were also determined in order to observe the modal
parameters that can significantly influence the travel behaviour under the ILTP
environment.
59
4.3. SOCIO-DEMOGRAPHIC CHARACTERISTICS
This section presents a detailed graphical profile of the socio-demographics
characteristics of the population of the study area that overall stimulate the travel
behavioural framework of the region5. These characteristics are selected according to
the findings of the literature review done on the population parameters that may
influence the travel behaviour of any study area.
4.3.1. Household Size Profile
At a disaggregate level, the decision to choose a particular travelling mode for a
certain trip by an individual depends on the number of people living in that
household and the number of vehicles owned by them. Table 4.4 shows the average
household size for each suburb of the study area. Figure 4.3 further elaborates the
percentage split in the different household sizes starting from one-person households
to those having more than three residents. The car ownership profile of the Shire’s
population is separately discussed in Section 4.3.5.
Table 4.4 Average Household Size of the Study Area
Suburb of
the Study Area
Average Household Size
(Persons / Household)
Total Number of
Households
Capalaba 2.80 6,367
Redland Bay 2.73 3,498
Sheldon – Mt. Cotton 3.09 1,600
Thornlands 2.93 3,314
Victoria Point 2.77 4,956
2.86 19,735
5 The data used for developing all the graphs and tables in this section is taken from the website of Australian Bureau of Statistics (www.abs.gov.au)
60
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Capalaba RedlandBay
Sheldon -M t Cotton
Thornlands VictoriaPoint
3+ Person Households3 Person Households2 Person Households1 Person Households
Figure 4.3 Study Area Characteristics with respect to Household Size
Considering the average household size in the study area (2.86), one may expect a
theoretical dwelling occupancy ratio to be approximately 3 persons per household.
However, there is a significant number of 3+ person households in the study area
along with a substantial number of 2 person households as presented in Figure 4.3
while the 3 person households are actually in minority. To illustrate this point at a
more detailed level, Table 4.5 shows the dwelling occupancy composition of the
whole Shire by household and family type.
61
Table 4.5 Dwelling Occupancy Composition of Redland Shire by Household and Family Type
Family Households Household
Type Couple
Family with
Children
Couple
Family
without
Children
One Parent
Family
Other
Family
Group
Household
Lone Person
Household
Total
(% split by
Household Type)
Separate House 15,231 9,865 3,665 284 852 4,601 34,498
(86.15%)
Semi-detached
house
355 896 613 33 157 1,742 3,796
(9.48%)
Flat / Unit /
Apartment
63 243 95 5 29 659 1,094
(2.73%)
Other Dwelling 31 112 26 3 15 284 471
(1.18%)
Not Stated 73 48 20 0 5 39 185
(0.46%)
Total
(% split by
Family Type)
15,753
(39.34%)
11,164
(27.88%)
4,419
(11.04%)
325
(0.81%)
1,058
(2.64%)
7,325
(18.29%)
40,044
62
The dwelling occupancy composition shown in Table 4.5 illustrates that most of the
dwellings contain family households with more than one resident. The fact was also
demonstrated in Figure 4.3 with the majority of households containing two or more
residents. Therefore, the household size was assumed to play a considerable role in
the travel behaviour of the population and therefore, was included in the model
specification developed for each modal split model, as presented in Chapters 7 and 8.
4.3.2. Age Profile
A review of age profile shifts in the Shire between 1986 and 2001, as illustrated in
Figure 4.4, reveals that proportions of the children aged up to 14 years, and younger
adults (aged 20 to 39 years) have declined noticeably since 1986. Conversely, the
number of older working adults (aged 45 to 64 years) and retirees aged 65 years and
over has increased substantially in the past few years. These shifts in the higher age
categories have augmented the median age of the Shire to 36 years in 2001, up from
31 years in 1991 (Australian Bureau of Statistics 2007d).
Figure 4.4 Age Trends in Redland Shire from 1986 - 2001
63
Based on the age-group statistics shown in Figure 4.4 and from the findings of the
literature review on population characteristics that impact the transport mode choice,
the population of the study area was separated into four main age-groups for
following surveying purposes,
• 18 years or younger;
• 18 to 45 years;
• 46 to 59 years; and
• 60 years or older
The percentage split of these four age-groups in all the five suburbs of the study area
is shown in Figure 4.5.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Capalaba Redland Bay She ldon - M tCotton
Thornlands VictoriaPoint
60 or Older46 - 5918 - 45Less than 18
Figure 4.5 Study Area Characteristics with respect to Age Group
64
The percentage proportions of the young age category (less than 18) and that of 46 to
59 were observed to stay uniform in all the suburbs of the study area. Redland Bay
and Victoria Point noticeably associate a higher proportion of old-age people (60 or
older) while the other three suburbs have higher young adult population (18 to 45),
and therefore, a higher working population.
4.3.3. Journey to Work Profile
Figure 4.6 presents the percentage mode shares for journey to work in each suburb of
the study area, based on the statistics from Figure 4.2. As discussed in Section 4.2.2,
the private car dominates the travel behaviour of the Shire with more than 90% of the
trips being car-trips for work purposes.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Capalaba RedlandBay
Sheldon -Mt Cotton
Thornlands VictoriaPoint
CyclingWalkingPublic TransportCar
Figure 4.6 Study Area Characteristics with respect to Modal Split for Work Trips
65
This car usage, however, does not remain uniform among the various age-groups of
the travellers as shown in Figure 4.7 for work trips. The percentage share of car-trips
for young workers (less than 18 years old) depreciates to around 63%, simply due to
the fact that most of them do not possess a valid driving license and may not have the
car available as compared to those in the higher age-groups. A brief discussion on car
ownership levels in the study area is provided in Section 4.3.5.
The cycling shares are expectedly very low considering the fact that there are no
cycleways from any part of Redlands to the Brisbane CBD. Therefore, apart from
cycling on the road (which may not be regarded as a safe option), cycling cannot be a
part of the logical choice set of the available travelling modes for someone working
in the Brisbane city (or on the CBD corridor). A similar reason can be given for the
low percentage share of walking for local work trips (within the Shire) since there
are scarce walkway facilities within the study area for the residents.
For this research, two unique mode choice models were developed for home-based
work trips on the basis of trip lengths, i.e. for the population travelling locally (within
the Shire) or regionally (on the CBD corridor). As expected, the specification
developed for regional work model had to exclude walking and cycling all-the-way
as the number of survey respondents perceiving the two modes as feasible car
alternatives was observed to be very low. The low perception of the future network
parameters for the two non-motorised modes for regional work trips is directly
related to the current travel situation, shown in Figure 4.6, with a small percentage of
the population using them.
66
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Less than 18 18 - 45 46 - 59 60 or Older
Age-Group
CycleWalkPublic TransportCar
Figure 4.7 Study Area Characteristics with respect to Modal Split for Work Trips and Age Group
4.3.4. Education Enrolment Profile
Figure 4.8 presents the current enrolment of all students in the study area, based on
pre-school, primary, secondary and tertiary education across the different suburbs.
From the figure, it can be seen that most of the students are enrolled for primary and
secondary schooling. Thus, for this research, it is regarded a priori that most of the
education trip-makers do not have car as driver as an available travelling mode in
the choice set for educational purposes. A second priori is made that the mode shares
for public transport and the non-motorised modes are highest for education trips as
compared to those of other trip purposes as found in previous studies (Cain and
Sibley-Perone 2005).
67
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Capalaba Redland Bay Sheldon - M tCotton
Thornlands Victoria Point
TertiarySecondaryPrimaryPre -School
Figure 4.8 Study Area Characteristics with respect to Education Enrolment
4.3.5. Car Ownership Profile
Car ownership is regarded as one of the most vital household characteristics to
impact on the travel behaviour of a study area. Car ownership levels associated with
a region can be used as an indicator to estimate and forecast the number of mode
choice users and car captives, and to generate the overall travel behaviour profile of
the study area (Ortuzar et al. 1998).
Figure 4.9 presents the car ownership levels across different suburbs of the study
area. Table 4.6 further compares the average number of motor vehicles per
household in all these suburbs as compared to the adjacent Brisbane City, indicating
a high car ownership level for the residents of the study area. This behaviour is
68
discussed in further detail in Chapters 7, 8 and 9 where the mode choice modelling
results and car captive analysis are presented.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Capalaba RedlandBay
She ldon -M t Cotton
Thornlands VictoriaPoint
2+ Car Households2 Car Households1 Car Households0 Car Households
Figure 4.9 Study Area Characteristics with respect to Car Ownership Level
Table 4.6 Average Number of Vehicles per Household in
Redlands and Brisbane City
Suburbs Average Number of Motor Vehicles Per Household
Capalaba 1.72
Redland Bay 1.77
Sheldon – Mt Cotton 2.10
Thornlands 1.85
Brisbane City 1.40
69
Figure 4.10 presents an interesting household demographic by combining the
household size and car ownership level of the study area together. Therefore, one can
observe the variation in the car ownership levels as the household size increases from
1 to 3+ households. As expected, there are currently few zero-car households in the
Shire, mostly those having only one resident in the whole dwelling. As the household
size increases to two, there is a steep escalation in the number of two-car households
pointing towards the high values of the average number of motor vehicles as
mentioned in Table 4.6. The percentage of 2+ car households is always increasing
with the household size, leading to the conclusion that the number of cars owned by
the household tend to increase with the increase in the number of residents in a house
for the study area.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 3+Household Size
Perc
enta
ge o
f Hou
seho
lds
2+ Cars2 Cars1 Car0 Car
Figure 4.10 Study Area Characteristics with respect to Household Size and Car Ownership Level
70
4.4. SUMMARY
This chapter presented various socio-demographic determinants of the study area that
were known to impact on the current and potential travellers in their decision-making
towards which mode to choose for a particular trip purpose. It started by defining the
boundaries of the study area for the research containing the six southern suburbs of
Redland Shire, namely Capalaba, Redland Bay, Thornlands, Sheldon, Mt. Cotton and
Victoria Point. The population trends, along with the current percentage mode
shares, were presented for all these suburbs.
Various household characteristics, such as household size, car ownership levels and
age-groups, and population characteristics, namely journey to work attributes and
education enrolment, were briefly discussed in order to observe the influence of these
factors on the travel behaviour of the residents.
It was found that the population of the study area has a higher socio-demographic
profile as compared to that of Brisbane’s or other urban areas’ residents. Therefore, it
is concluded that the sample generated for the survey is regarded as a relatively
difficult group to “get out of their cars” (Redland Shire Council 2003). This fact is
more evident in Chapters 7, 8 and 9 where the survey data from the choice users and
car captives is modelled and analysed respectively to forecast the mode shares for the
study area in ILTP environments.
71
5 Stated Preference Survey Instrument Design
5.1. INTRODUCTION
This chapter presents the instrument design of the stated preference (SP) surveys
conducted in the study area, in order to model the travel behaviour of the population
under various ILTP scenarios, as discussed in Chapter 1. Computer Assisted
Personal Interviewing (CAPI) was chosen as the physical form for designing the
mode choice surveys since it was found to have a higher response rate as compared
to other survey forms, such as PAPI, mail-back questionnaires, etc., due to its
attractive graphical interface, as discussed in Chapter 3.
The beauty of a stated preference (SP) survey design is that it can present various
virtual scenarios to the respondents with hypothetical travelling modes for future, in
the form of mode choice games. However, these scenarios should be based on the
potential future travel settings in order to avoid extrapolation, when using the model
to make predictions (Sanko et al. 2002). Contrarily, it is also vital that the scenarios
should not be too realistic; otherwise the orthogonality6 of the SP design may be
compromised, leading to the same sorts of co-linearity problems that generally
plague the revealed preference (RP) data (McMillan et al. 1997).
Section 5.2 presents the methodology developed for designing the survey instrument
based on the SP choice set. Since distinct choice sets were determined for each trip
purpose, the design of the instrument varied slightly, according to the concerned trip
purpose and the trip length, however, based on the same methodological framework.
The framework was divided into three main modules of the survey instrument
namely personal information, revealed preference and stated preference modules.
The final instrument design, prepared using the CAPI software WinMint 3.2F, is
presented in Section 5.3 illustrating the SP games presented to the choice users. The
6 One of the most essential requirements of the SP survey is that it should be orthogonal, i.e. all the attributes shown in a SP mode choice comparison game should be randomly generated. This rule is also referred as principle of orthogonality.
72
specific features of WinMint are listed in Section 5.4 in order to present the reader
with the unique facilities that the software possesses over other CAPI designing
computer packages. After designing the survey instrument, a pilot survey was
conducted within the study area, on a small sample size, in order to observe the
reactions of the respondents on the graphical interface of the instrument design.
Statistical analysis of the travel behaviour of these respondents, along with editing
and finalising the design of the instrument for actual survey implementation, are
discussed in Section 5.5. Finally, a brief summary is presented in Section 5.6
concluding the methodology used in designing the CAPI survey instrument using
WinMint 3.2F.
5.2. SURVEY INSTRUMENT DESIGN METHODOLOGY
The design of the survey instrument was based on the set of level-of-service
attributes associated to all the travelling modes in the SP choice set, as shown in
Table 8.2. The methodological framework developed for the survey instrument was
split into the following three main modules,
• personal information module related to the data on household characteristics
(age-group, household size, etc.);
• revealed preference (RP) module related to the questions regarding the attributes
of the current travelling mode of the respondent; and
• stated preference (SP) module related to the mode choice games showing
orthogonal comparison scenarios between the attributes of the current travelling
mode and the hypothetical travelling alternative perceived by the respondent.
According to the findings of the literature review on SP survey instrument designing,
as presented in Chapter 3, the mode choice games were based on the attributes’
values of the current mode that the respondent is using for a certain trip. Therefore,
all the mode choice games presented a realistic comparison situation to the
respondent, whilst following the principle of orthogonality.
73
Shopping Other
Personal Information Module
RP Module
(continued on next page)
Shopping / Other Trips
Figure 5.1 shows the framework designed for the survey instrument in the form of a
block diagram, illustrating the three main sections of the survey presented to the
respondent in order.
Household Characteristics
MODE
Walk Cycle Bus Car Other
Waiting Time
ACCESS MODE
Car As
Driver
Car As
Passenger
Travelling Cost
Reliability
Interchanges
- Origin - Destination
Trip Purpose
Traveller Type
Travelling Time
Parking Feasibility
Trip Purpose
Work Trips
74
RP Module
SP Module
Captive Users
Figure 5.1 Block Diagram of the SP Survey Instrument Design Methodology
The survey started with the personal information module asking questions regarding
the household characteristics of the respondent, as shown in Figure 5.1. The data
obtained from this module was later tested in the utility functions, at the time of
ACCESS MODE
Walk Cycle Feeder Bus
Park &
Ride
Kiss &
Ride
Travelling Time
Travelling Cost
Waiting Time
Access Time
TravellerType
Choice Users
Mode Choice Games
End
75
model estimation in order to determine the influence that these household
characteristics might have on the travel behaviour of the population of the study area.
The second part of the survey was based on the revealed preference (RP) module
presenting a set of questions on the level-of-service attributes of the current mode of
the respondent for a certain trip. Various question formats, such as open, closed and
field-coded questions, were implied in presenting the RP queries as discussed in
detail in Richardson et al. (1995). At the end of the RP module in the survey, the set
of the hypothetical travelling modes, as proposed in the ILTP, was presented as
alternatives to the respondent’s current mode of travel, as shown in Figure 5.2
demonstrating an example of the work trip survey. The respondents were then
identified as choice or captive users depending on whether they perceive choice for
their current modes or not.
The most important part of the survey instrument design was the stated preference
(SP) module. In this module, all the respondents identified as choice users were
presented with a set of eight mode choice games illustrating the comparison between
the attributes of their current mode and their perceived alternative for the mode. All
the attributes shown in each game were randomly generated, following the principle
of orthogonality. The data obtained from these games was later used in estimating
the disaggregate logit models for each trip length and trip purpose. The calibration
results of all the regional and local trip models developed for this study are presented
in Chapters 7 and 8 respectively.
No stated preference games were designed for the respondents identified as captive
towards their current travelling modes. However, the RP data collected for these
users was later analysed for various statistical characteristics, as discussed in Chapter
9 in detail, showing the influence of the captive users on the travel behaviour of the
study area.
The full programming code, using WinMint 3.2F, of the survey instrument design is
presented in Appendix 1.
76
Figure 5.2 RP Module presenting Hypothetical Travelling Modes to the Respondents
77
5.3. DEMONSTRATION OF CAPI MODE CHOICE GAME
A demonstration of a stated preference (SP) mode choice game, presented to the
respondents, is shown in Figure 5.3. In this example, the respondent is shown as a car
user for regional work trips perceiving bus on busway as a feasible alternative to car.
Figure 5.3 SP Mode Choice Game for Choice Users
78
5.4. FEATURES OF WINMINT
The computer package used to design the survey instrument, shown in Figure 5.3,
was WinMint 3.2F. The level of functionality and coding details of the software can
be found in HCG (2000). WinMint has the following unique features specific to the
SP scenarios,
• customisation of experimental choice attributes and levels to correspond to each
respondent's actual situation;
• randomisation of the order in which the choice alternatives are presented, to
reduce response bias;
• semi-randomisation of experimental designs, to increase statistical efficiency and
analysis flexibility; and
• self-adjustment of experimental designs, using previous responses to optimise the
choices offered subsequently
5.5. PILOT SURVEY IMPLEMENTATION
After designing the survey instrument, a pilot survey was conducted in the study
area. The specific aims for conducting this pilot study were to,
• record the reactions of the respondents on the graphical interface of the
instrument design;
• obtain a sample split on the basis of traveller type, i.e. mode choice, and car and
PT captive users, in order to infer the sample size for the actual survey; and
• edit and finalise the survey instrument in order to use it in the actual survey
implementation.
A sample of 75 respondents was generated using simple random sampling for the
pilot study. The respondents were randomly contacted using various Redland Shire
community e-groups and were asked to participate in the study.
79
The sample split obtained from the pilot survey, on the basis of traveller type, is
shown in Table 5.1.
Table 5.1 Sample Split of Pilot Survey Respondents on the basis
of Traveller Type
Traveller Type Number of Respondents Percentage of Respondents
Mode Choice Users 20 26.7 %
Car Captive Users 53 70.7 %
PT Captive Users 2 2.6 %
TOTAL 75 100 %
From Table 5.1, it was observed that only around 27 % of the pilot survey
respondents were mode choice users, indicating towards the high captive to mode
choice users' ratio. Therefore, it was decided that a significantly larger sample
needed to be generated from the population of the study area, in order to obtain a
substantial number of mode choice responses to be used for model estimation. The
statistical properties of the sample, generated for the actual survey, are presented in
Chapter 6.
No major survey instrument design editions were made as most of the respondents
were found to easily understand the question wordings and the graphical interface of
the instrument. The presence of the interviewer, to assist the respondents in
perceiving the mode choice scenarios, helped in achieving a high rate of valid
responses. The average time taken to complete the whole survey, including the eight
mode choice scenarios, was found to be 7 minutes; a reasonable time to keep the
respondents interested in the survey (Richardson et al. 1995).
5.6. SUMMARY
This chapter presented the methodological framework developed for designing the
computer assisted personal interviewing (CAPI) instrument to conduct the SP
surveys in the study area. Since distinct choice sets were determined for each trip
80
length and trip purpose, the design of the instrument varied slightly, however,
followed the same framework in each case. The framework consisted of three main
modules of the survey instrument namely personal information, revealed preference
(RP) and stated preference (SP) modules.
The personal information module was responsible for collecting information on
various household characteristics, such as the age-group and household size of the
respondents. The RP module questioned the respondents regarding the level-of-
service attributes of their current travelling modes. The set of the hypothetical
travelling modes was also presented as alternatives to the respondent’s current mode
of travel. The respondents were then identified as choice or captive users depending
on whether they perceived choice for their current modes or not. The SP module
presented the choice users with a set of eight mode choice scenarios to compare
between their current modes and the perceived hypothetical alternatives. Although
the attributes in each SP game were randomly generated, they were based on the
values of the level-of-service parameters obtained from the RP module in order to
make the comparison scenarios realistic for the respondent.
WinMint 3.2F was chosen to program the CAPI survey instrument for this research.
The full programming code for the instrument design, in WinMint 3.2F, is presented
in Appendix 1. The main features of WinMint, specific to the SP scenarios, are
discussed in Section 5.4. The main reason for selecting this computer package is that
it provides the facility to the survey designer of increasing the number of varying
levels for each attribute without changing the base design of the instrument. It further
ensures that the sets of choice alternatives with exactly the same levels for all design
variables are not presented; thus maintaining orthogonality.
After designing the CAPI survey instrument, a pilot survey was conducted in the
study area, on a small sample, in order to test various features of the instrument
design and observing the reactions of the respondents on the CAPI graphical
interface. No major survey instrument design editions were made as the respondents
were found to react positively to the CAPI graphical interface. A high captive to
mode choice users ratio was expectedly observed among the respondents, indicating
81
that a significantly larger sample needed to be generated in order obtain a substantial
number of mode choice responses for model estimation purposes.
The actual survey, on the full sample, was then implemented using the finalised
instrument design. The whole survey implementation framework is presented in
Chapter 6, along with illustrating the exploratory data analysis performed on the
instrument characteristics such as the frequency of mode choice and captive
responses for each trip purpose, time to complete the whole SP survey, etc.
82
6 Data Collection and Analysis
6.1. INTRODUCTION
Chapter 4 discussed the study area selected for this research, along with household
and travel characteristics of the population. Chapter 5 presented the instrument
design of the SP survey to be conducted in the study area, with the aim of estimating
the passenger mode choice travel behaviour and analysing the car captive population.
This chapter illustrates the implementation strategy adopted for conducting the SP
surveys in the region and the statistical analyses performed on the survey data.
The sample was generated using the method of stratified random sampling, with the
strata categorised as the population of each suburb in the study area and their
respective modal splits for work trips, as discussed in Section 6.2. A team of four
interviewers was created in order to conduct the face-to-face CAPI surveys at various
venues, such as the households, workplaces, shopping areas, council office rooms,
etc., chosen by the respondents themselves. This survey implementation process is
discussed in detail in Section 6.3.
After conducting the surveys, various statistical analyses were performed on the
sample generated and the survey data obtained, in order to infer the pre-modelled
travel behaviour of the population of the study area, before switching to the logit
model estimation phase. These analyses, presented in Sections 6.4 and 6.5, mainly
deal with statistically analysing the travel characteristics of the survey sample and
data respectively. Section 6.4 begins with comparing the modal splits from the
survey sample with the current mode shares for the study area, provided by
Australian Bureau of Statistics (2007c) in order to prove that the sample generated
for the survey was representative of the population of the study area. Further, the
sample was distributed on the basis of traveller type, i.e. choice and captive users.
Section 6.5 presents numerous exploratory analyses performed on the survey data
associated to the sample demonstrated in Section 6.4. The survey data is categorised
according to the current and future travelling modes to observe the possible
83
combinations between the current and perceived options, for different trip purposes.
Moreover, absolute frequencies of various level-of-service attributes are presented in
order to surmise the influence of these modal parameters on the travel behaviour.
Finally, Section 6.6 concludes the findings of all the statistical analyses performed on
the sample and the survey data, to be used for model calibration and data analysis, as
illustrated in Chapters 7 and 8, and 9 respectively.
6.2. SAMPLE GENERATION
In chapter 3, it was concluded that the method of stratified random sampling was to
be adopted for this research, as it was deemed as the most suitable sample generation
technique, considering the small size of the study area. The stratification for this
method was done on the basis of the population of each suburb in the study area (see
Table 4.1) and the current modal splits of the population for work trips (see Figure
4.2); in order to attain a sample representative of the travel behaviour of the study
area, as discussed in Section 6.4.
In order to achieve the specific stratification of the sample, as discussed above, the
residents of the study area were randomly contacted using,
• calling by telephone;
• e-mailing community groups;
• marketing in the newspaper “The Redland Times” (The Redland Times 2005),
distributed freely all over the Shire; and
• promotion done by Redland Shire Council.
The total number of respondents surveyed for this study was 2007. In order to
generate this sample, 2574 residents of the study area were randomly contacted using
the above-mentioned methods. Therefore, the positive response rate achieved for the
study, taken as a percentage ratio of the number of respondents surveyed over those
contacted, came out to be around 78 %. This response rate is satisfactorily high and
was consistent with that attained for the TravelSmart marketing study in the northern
84
suburbs of the Shire (Socialdata Australia Ltd. 2005). The sample size for the survey
was deemed adequate on the basis of previous SP mode choice surveys (Hensher and
Rose 2007).
6.3. SURVEY IMPLEMENTATION STRATEGY
A team of four interviewers was formed in order to conduct the SP surveys in the
region using portable laptops. These surveys were conducted within a period of
around four months, in addition to one month of pilot surveying.
The interviewers were first trained in WinMint, the software used for designing the
CAPI instrument, in order to handle the various features offered by the software such
as automatic data coding, question branching and prompting, etc. The surveys were
then conducted at various venues, such as the households, workplaces, shopping
areas, council office rooms, etc., chosen by the respondents themselves.
The confidentiality of the respondents was maintained by removing the residential
addresses of the responding households at the time of data release, so that they could
no longer be uniquely identified with their respective travel and activity data.
Although the CAPI interviews does not necessarily require data screening, as they
are inputted by the interviewers rather than the respondents, all the survey data,
collected from the mode choice and the captive users, was checked and filtered for
invalid responses. Various statistical analyses were then performed on the sample
and the data, as discussed in Sections 6.4 and 6.5.
Figure 6.1 summarises the whole survey implementation strategy adopted for this
study.
85
Figure 6.1 The Survey Implementation Strategy
Initial Contact with the Residents of the Study Area
Willingness to participate in the Study
Setting of: • Date of the survey • Time of the survey (30 min slot) • Venue for the survey
Survey Implementation
Data Screening
Confirmation Call
Participate other time
Final Survey Data
End
End
Yes
No
Yes
Yes
No
No
86
6.4. SAMPLE CHARACTERISTICS
The SP data collected from the survey sample was used in forecasting the mode
choice travel behaviours of the targeted population in the ILTP scenarios, for two trip
lengths and four trip purposes, as discussed in Chapters 7 and 8. It is indispensable
that this modelled travel behaviour is reflective of the whole study area, rather than
just the survey sample (Monzon and Rodriguez-Dapena 2006).
In order to ensure that the sample generated for the SP study is representative of the
whole study area, the characteristics of the sample are compared with that of the
study area, determined from 2001 Census analysis (Australian Bureau of Statistics
2007b), as shown in Figures 6.2 and 6.3.
Figure 6.2 compares the percentage population splits of each suburb of the study
area, determined from the 2001 Census with that of the sample. This comparison
further specifies that the sample generated for the study does not contain bias
towards the population of any suburb of the study area, which is necessary to
minimise, as it can highly influence the modelling results (Richardson et al. 1995).
Figure 6.3 compares the modal splits in the region for journey to work trips,
determined from the 2001 Census with that observed in the sample7. Since similar
type of travel statistics were not available for other trip purposes, such as shopping,
education and other trips, the modal splits of other purposes could not be compared.
However, all these modal splits, determined from the survey sample, are presented in
Appendix 2.
From Figures 6.2 and 6.3, it can be observed that the travel characteristics of the
sample generated for the research match closely with that determined, from the 2001
Census, for the same region. The minor disparity between the population splits
shown in Figure 6.2 may be due to the fact that the census was conducted in the year
2001 while the survey, for this study, was implemented in 2005; thus, causing small
shifts in the percentage population splits in the suburbs of the study area. A similar
7 An interesting point to note is that the survey data shown in Figure 6.3 represents all home-based work trips. Therefore, it is a combination of regional and local work trips data.
87
explanation can be given for the variation in the modal splits, shown in Figure 6.3,
particularly that for public transport users, as the new bus services in the region
might have increased the PT usage (Queensland Government 2007).
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Thornlands Redland Bay Victoria Point Mt Cotton -Sheldon
Perc
enta
gePo
pula
tion
Split
ofSt
udy
Are
a
2001 CensusSurvey Sample
Figure 6.2 Population Split Comparisons between the Survey Sample and 2001 Census Data
88
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
PT Car Walking Cycling
Perc
enta
ge o
f Pop
ulat
ion
in th
e St
udy
Are
a
2001 CensusSurvey Sample
Figure 6.3 Modal Split Comparisons between the Survey Sample and 2001 Census Data for Journey to Work
After establishing that the characteristics associated to the sample generated for the
survey match closely to those determined in the 2001 Census, the sample was split
into the five suburbs of the study area and distributed according to the three traveller
types of mode choice, car captive and PT captive users, as shown in Figure 6.4 for all
trip purposes. Similar travel type distributions of the sample for individual trip
purposes are presented in Appendix 3.
89
0%
10%
20%
30%
40%
50%
60%
70%
Thornlands Redland Bay Victoria Point Mt Cotton -Sheldon
Perc
enta
ge o
f Res
pond
ents
w.r
.t. T
rave
l Typ
e
Choice Users PT Captive Users Car Captive Users
Figure 6.4 Percentage Split of the Survey Sample with respect to Traveller Type
for Suburbs of the Study Area for All Trip Purposes
From Figure 6.4, it was observed that the traveller type distribution is uniform among
all the suburbs of the study area, indicating that the travel behaviours of the residents
of each suburb are fairly similar. Therefore, the mode choice modelling and captive
analysis, as discussed in Chapters 7 and 8, and 9, were carried out on the whole
survey sample, rather than splitting them on the basis of different suburbs.
90
6.5. EXPLORATORY DATA ANALYSIS
After observing the sample characteristics, various statistical analyses were
performed on the survey data in order to surmise the pre-modelled travel behaviour
of the targeted population and analyse the survey properties.
Firstly, the survey data was categorised according to the current mode used by the
respondents, and their respective preferred perceived travelling alternative (if any)
for the certain trip. Figure 6.4 has shown the traveller type distribution of the survey
sample on suburban basis. Figure 6.5 takes it to a further detailed level by
characterising the survey data, for all trip purposes, according to the travelling
modes, being used and perceived, by each respondent. The mode of cycle to public
transport was initially included as part of the model specification; however, it was
later removed as no respondent was found to currently use or perceive it as a valid
travelling option for any trip purpose. Therefore, all the choice sets, generated for
various trip purposes, included eight travelling modes, at most, as shown in Figure
6.5. Hence, 64 (8*8) total possible combinations of current and perceived modes
were developed, as can be seen in Figure 6.5.
Similar characterisation of the data, on the basis of travelling modes, is shown in
Appendix 4 for each unique trip purpose.
91
0
100
200
300
400
500
600
700
800
900
1000
CAD CAP FBB WB PRB KRB W C
Perceived Choices of Travelling Modes
Abs
olut
e Fr
eque
ncy
Car as Driver Car as Passenger Feeder Bus to PTWalk to PT Park & Ride to PT Kiss & Ride to PTWalking all-the-way Cycling all-the-way
Figure 6.5 Perceived Travel Choices of the Survey Sample for all Trip Purposes
Figure 6.5 reiterates the observation from Figure 6.3 that the car mode dominates the
overall travel behaviour of the population of the study area, as around 980, out of
2007, respondents were identified as car captives (combination of car-car). The
second biggest combination was found to be that of the car-walk to busway, making
a substantially sizeable group of mode choice users. As also seen in Figure 6.4, all
the possible combinations of PT captives, as shown in Figure 6.5, were found to
ascribe low absolute frequencies, indicating that a small population from the study
area falls under this category.
After splitting the sample on the basis of traveller type, as shown in Figure 6.4, the
mode choice data was subjected to various statistical analyses based on the level-of-
92
service modal parameters, in order to envisage the influence of these attributes on the
travel behaviour of the study area. From the findings of the literature review on mode
choice modelling, presented in Chapter 2, it was observed that the attributes of in-
vehicle travel time and out-of-pocket travel cost mainly influence the travel
behaviour of a region (Lee et al. 2003). The notion was also substantiated in the
recent mode choice studies, by estimating logit models from SP surveys on
hypothetical travelling modes (Maunsell Australia 2006, Hensher and Rose 2007).
Hence, absolute frequencies of travel times and costs, dispensed to the travelling
modes in the SP choice sets for different trip purposes, were determined. Figure 6.6
shows the set of all the values incurred for in-vehicle travel time of car for regional
work trip-makers, i.e. travellers working in the CBD or by the CBD corridor.
A substantially large range of travel times were observed, indicating a varied mix of
work destination locations for regional trip-makers resulting in complex trip
distributions. Similar analysis was done for the out-of-pocket travel cost of car users
for regional work trips, as shown in Figure 6.7. Unlike Figure 6.6, it was difficult to
infer an appropriate cost distribution for regional work trips since the travel cost,
defined in the model specification, is a sum of vehicle operating cost and the parking
fee at the destination. However, it was observed that a small percentage of travellers
are currently paying high parking cost for CBD-based work trips.
Similar statistical analyses, performed on the attributes of travel times and costs of
car for different trip purposes, are illustrated in Appendix 5.
93
0
20
40
60
80
100
120
12 19 26 33 40 47 54 61 68 75
In-vehicle Travel T ime of Car (min)
Abs
olut
e Fr
eque
ncy
Figure 6.6 Frequency Chart of In-vehicle Travel Time of Car for Regional Work Trips
0
20
40
60
80
100
120
140
160
270 504 739 973 1207 1441 1675 1909 2143 2378 2612 2846
Out-of-pocket Travel Cost of Car (cents)
Abs
olut
e Fr
eque
ncy
Figure 6.7 Frequency Chart of Out-of-pocket Travel Cost of Car for Regional Work Trips
94
In Chapter 3, from the findings of the literature review on survey instrument
designing, it was noted that survey instrument, particularly for CAPI, should be
designed in such a way that the questionnaire is concise in order to consume the
minimal time of the respondents (Kuhfeld et al. 1994). Figures 6.8 and 6.9 illustrate
this idea by presenting the times taken to complete the surveys for captive and choice
users respectively.
As expected, the time taken, by the captive user, to finish the survey is significantly
less than that of the choice user since there were no SP games designed for the
captive users. On the other hand, the choice users were presented with eight
randomly generated hypothetical travel scenarios. Even then, the average time taken
to finish one full SP survey, with the respondent successfully making choices in all
the unique eight mode choice games, was found to be around six minutes only, for
any trip purpose. The average time to complete a survey for a captive user was
determined to be around three minutes only. Hence, overall, it can be stated that the
survey completion time was significantly low, a characteristic associated to a good
survey instrument design (Pratt 2003).
95
0
20
40
60
80
100
120
140
160
2 3 4 5 6 7 9 10 12 13 14 15 16 33
Survey Completion Time (min)
Abs
olut
e Fr
eque
ncy
Figure 6.8 Total Surveying Time for Choice Users
0
50
100
150
200
250
300
350
400
450
500
1 2 3 4 5 7 9 13 15
Survey Completion Time (min)
Abs
olut
e Fr
eque
ncy
Figure 6.9 Total Surveying Time for Captive Users
96
6.6. SUMMARY
This chapter presented the implementation strategy adopted for the SP surveys and
the statistical analysis performed on the data collected. Firstly, the sample for the
survey was generated using the method of stratified random sampling. The
stratification for this method was done on the basis of the population of each suburb
in the study area and the current modal splits of the population for work trips.
A total number of 2574 residents of the study area were contacted to participate in
the study, out of which 2007 responded positively, resulting in a positive response
rate of 78 %. The survey implementation strategy designed for the study is shown in
Figure 6.1.
After collecting the SP data from the surveys, it was ensured that the characteristics
of the sample match that of the study area; so that the results from model estimation,
presented in Chapters 7 and 8, and the captive analysis, shown in Chapter 9, are
representative of the targeted population. To achieve this, percentage population
splits were determined from the sample on the basis of each suburb of the study area
and were compared with those observed in the 2001 Census (Australian Bureau of
Statistics 2007b). Further, the current modal split of the respondents was compared
with that of the entire population of the study area for work trips. Both comparisons
showed that the sample characteristics closely match that of the targeted population
justifying that the sample, generated for the study, is representative. Various
statistical analyses were then performed on the survey sample and the data, in order
to infer a picture of the pre-modelled travel behaviour of the population of the study
area.
Figure 6.4 shows the survey sample distribution on the basis of traveller type, i.e.
choice and captive users, for all trip purposes. It was observed that the traveller type
distribution is uniform among all the suburbs of the study area; therefore, there is no
need to model the travel behaviour separately for each suburb.
After analysing the characteristics of the sample, the data was subjected to various
exploratory analyses. First, the data set was categorised on the basis of current and
97
perceived travelling modes of the respondents for different trip purposes, as shown in
Figure 6.5. As expected, the combination of car-car was observed to have the highest
volume (980 out of 2007 respondents) indicating a principal presence of car captive
users in the study area. Therefore, it is anticipated that the model estimation results,
in Chapters 7 and 8, shall forecast a high car usage, even under the ILTP scenarios
for all trip purposes. However, the analysis for education trips, shown in Appendix 4,
demonstrated a high use of public transport modes. It indicates that a considerable
number of students currently use public transport for educational purposes.
An interesting mode choice data analysis was carried out in Figure 6.6, where the
values obtained for the current in-vehicle travel time of car users for regional work
trips were plotted against their respective absolute frequencies. A substantially large
range of travel times were observed, indicating a varied mix of work destination
locations for regional trip-makers resulting in complex trip distribution. Similar
analysis was done for the out-of-pocket travel cost of car users for regional work
trips, as shown in Figure 6.7. Unlike Figure 6.6, it is difficult to infer an appropriate
cost distribution for regional work trips since the travel cost, defined in the model
specification, is a sum of vehicle operating cost and the parking fee at the
destination. However, it was observed that a small percentage of travellers are
currently paying high parking cost for CBD-based work trips.
In a different context to travel behaviour analysis, the time taken by the respondents
to complete the surveys was also analysed for both choice and captive users, as a
good quality survey instrument design is not supposed to over-burden the
respondents with numerous questions and scenarios (Pratt 2003). The average survey
completion time for captive users was found to be around three minutes, while that
for choice users came out to be around six minutes only, indicating that the survey
was completed swiftly and a nominal amount of time of the respondents was
consumed.
Based on the sample characteristics and the survey data analysis, presented in this
chapter, it can be deduced that the mode choice modelling results, presented in
Chapters 7 and 8, may forecast high car usages, particularly for shopping trips.
However, a considerable volume of various car-PT combinations, shown in
98
Appendix 4, for work, education and other trip purposes indicate the presence of a
sizeable group of mode choice users among the targeted population. The direct and
cross elasticities for various level-of-service modal attributes, presented in Chapters
7 and 8 from the model estimation results, will further show the influence of these
parameters on the travel behaviour of the population of the study area.
99
7 Mode Choice Modelling for Regional Trips
7.1. INTRODUCTION
The general modelling methodology used in this research was described in Section
2.3, and the stated preference (SP) survey data collection procedure in the study area
was discussed in Chapter 6. The aim of this chapter is to present the results of the
disaggregate logit model estimations done on the mode choice data, obtained from
the SP surveys conducted in the study area, for the trips destined to CBD or those
made on the CBD-based corridors. These trips are generally referred as regional trips
and have been classified according to four purposes for which the trips were mainly
taken such as work, shopping, education and for other purposes. Since the survey
conducted in the study area was of SP nature, all these trips represent hypothetical
travel scenarios but are based on current travel characteristics of the sample
respondents as explained in Chapter 5.
The trips taken by the respondents within the Shire are referred as local trips and are
modelled separately since a priori used for this research is that the population travel
behaviour is corridor-influenced and varies with trip lengths (Tsamboulas et al. 1992,
Ortuzar and Willumsen 2001). It means that the residents of the Shire doing regional
trips have different travel behaviour as compared to those travelling locally and
therefore, should be modelled independently. The modelling results along with the
discussion on the estimated coefficients of the local trip models are presented in
Chapter 8.
For regional trips, only two different sets of disaggregate logit models were
estimated for the two trip purposes namely home-based work and other trips. The
models for shopping and educational trips could not be calibrated for regional trips
because the number of mode choice responses attained for these purposes were not
significant enough to estimate the models (Santoso and Tsunokawa 2005). It was
further verified in Sinclair Knight Merz (2006) that the number of trip attractions for
100
shopping and education purposes for the Brisbane city frame are significantly low
from the study area.
The work trips, in this research, refer to all the trips starting at the home and ending
at the workplace of the trip-maker. However, the other trips refer to both home-based
and non-home-based trips with any purpose other than work, shopping or education.
Table 7.1 shows the number of mode choice responses obtained for regional trips for
each purpose. An important point to remember here is that all the responses refer to
the travellers currently destined to CBD or on the CBD corridor from the study area,
by car for the four above-mentioned purposes and perceiving to have mode choice
for the other three sustainable hypothetical modes in future if the ILTP scenarios,
proposed in the Redland Shire Council (2002), are implemented in practice. As
explained in Section 5.1, the three main hypothetical travelling alternatives to the car
were as follows,
• bus on busway;
• walking on walkway; and
• cycling on cycleway.
Table 7.1 Number of SP Observations attained for each Regional Trip Purpose
Trip Purpose Number of SP Observations
Work 680
Shopping 120
Education 96
Other 670
TOTAL 1566
It is understandable that the number of travellers making regional trips for shopping
purpose from the study area (see Section 6.5) are very low since there are no specific
needs to travel long distances for shopping at the CBD (or close to CBD). For
educational purposes, the sample generated contained most of those students who are
enrolled in primary and secondary schools, located within the Shire and therefore,
101
are referred as local education trip-makers. The education enrolment profile
developed for the residents of the study area from Australian Bureau of Statistics
(2006a) also validated that a big majority of the students going to primary and
secondary schools are enrolled locally and thus, do not travel outside the Shire for
their education trips. From the whole survey sample of shopping and education trip-
makers, very few respondents were found to have a mode choice as well as shown in
Table 7.1 and therefore, the two trip purposes could not be considered for modelling
reasons. Sections 7.3 and 7.4 present the two sets of logit models developed for work
and other trips respectively for regional trip-makers, along with discussing the
results. Section 7.2 lists all the attributes associated to each travelling mode in the SP
choice set, that were used for modelling the mode choice survey data.
7.2. ATTRIBUTES USED IN THE MODELS
The explanatory mode attributes used in the logit models developed for work and
other trips were selected according to the findings of the state-of-the-art literature
review done on mode choice driving variables as discussed in Chapter 2. These
variables were mode-specific and include both level-of-service characteristics (times,
costs, etc.) and socio-economic variables (household size, etc.). Table 7.2 presents a
list of all these attributes used for mode choice modelling for regional trips.
102
Table 7.2 Attributes associated to each Travelling Mode for Regional Trips
Travelling
Mode
Attributes Notation of the
Attribute
In-vehicle travel time (min) TTCAD
Out-of-pocket travel cost (includes vehicle
operating cost8 and the parking cost at the
destination (if any)) (cents)
TCCAD
Car as Driver
(CAD)
Mode-specific constant CCAD
In-vehicle travel time (min) TTCAP Car as Passenger
(CAP) Mode-specific constant CCAP
In-vehicle travel time (min) TTFBB
Trip fare (cents) TCFBB
Waiting time at the busway station (min) WTFBB
Access time to reach the busway station
(min)
ATFBB
Feeder Bus to
Busway
(FBB)
Mode-specific constant CFBB
In-vehicle travel time (min) TTWB
Trip fare (cents) TCWB
Waiting time at the busway station (min) WTWB
Access time to reach the busway station
(min)
ATWB
Walk to Busway
(WB)
Mode-specific constant CWB
In-vehicle travel time (min) TTCB
Trip fare (cents) TCCB
Waiting time at the busway station (min) WTCB
Access time to reach the busway station
(min)
ATCB
Cycling to
Busway
(CB)
Mode-specific constant CCB
In-vehicle travel time (min) TTPRB
Trip fare (cents) TCPRB
Park & Ride to
Busway
(PRB) Waiting time at the busway station (min) WTPRB
8 Vehicle operating cost includes average fuel cost and maintenance cost
103
Access time to reach the busway station
(min)
ATPRB
Mode-specific constant CPRB
In-vehicle travel time (min) TTKRB
Trip fare (cents) TCKRB
Waiting time at the busway station (min) WTKRB
Access time to reach the busway station
(min)
ATKRB
Kiss & Ride to
Busway
(KRB)
Mode-specific constant CKRB
Walking time (min) TTW Walk all-the-way
(W) Mode-specific constant CW
Cycling time (min) TTC Cycle all-the-way
(C) Mode-specific constant CC
Household
Variable
Household size HHSIZE
For logit modelling, it was preferred to use mode-specific attributes rather than
generic attributes, since the disaggregate models calibrated from the data based on
mode-specific attributes are more representative of the population’s travel behaviour
as compared to those estimated using generic variables (Garrido and Ortuzar 1994).
However, some models estimated for local trips contained generic attributes in their
utility functions since some of the coefficients estimated using specific attributes
were found to be statistically unreliable as discussed in Chapter 8. After finalising
the attributes associated to each mode, the utility functions were developed
characterising the travel mode choice decision-making framework as discussed in
Chapter 2.
Since a utility is commonly represented as a linear function of the attributes of the
journey weighted by coefficients which attempt to represent their relative importance
as perceived by the traveller, Equations 7.1 and 7.2 mathematically present the utility
function associated to a mode m as perceived by an individual i as,
Umi = Bm1xmi1 + Bm2xmi2 + …… + Bmkxmik (7.1)
104
where,
Umi is the net utility function for mode m for individual i;
xmi1, …, xmik are k number of attributes of mode m for individual i; and
Bm1, …, Bmk are k number of coefficients (or weights attached to each attribute) of
mode m which need to be estimated from the survey data.
or,
Umi = ∑k
mikmk xB (7.2)
All the sets of the utility functions developed for this study have followed the
specification shown in Equations 7.1 and 7.2. After determining the unknown
coefficients (Bm1, …, Bmk) and, the disaggregate utilities shown in Equation 7.2 from
logit model estimations, the probability of choosing mode m by an individual i for a
certain trip purpose is given by Equation 7.3 as,
Pmi = ∑Mn
UU
ε)exp(
)exp(ni
mi
(7.3)
where,
Umi is the utility of mode m for individual i;
Uni is the utility of a mode n in the choice set for individual i;
Pmi is the probability of selecting mode m by an individual i from the
choice set; and
M is the set of all available travelling modes.
A detailed discussion on standard logit modelling framework is presented in Chapter
2. After finalising the model specification for regional trips, various logit models
(with unique specifications) were estimated using the SP mode choice data for work
and other trips. The results of these model estimations are presented and discussed in
Sections 7.3 and 7.4.
105
7.3. MODE CHOICE MODEL FOR WORK TRIPS
The total number of stated preference (SP) mode choice responses attained for
regional work trips were 680. The percentage split of the mode choice users
perceiving to have a choice for any of the three above-mentioned main travelling
alternatives to the car is shown in Figure 7.1.
95.29%
1.18%
3.53%
Bus on BuswayWalking on WalkwayCycling on Cycleway
Figure 7.1 Percentage Split of Mode Choice Users for Regional Work Trips
It is understandable that the mode choice perceived by the current car travellers of
the study for non-motorised modes, i.e., walking on walkway and cycling on the
cycleway, was ascertained to be very low for regional trips considering the
convenience factor involved in these long-distance trips which makes a trip taken by
a motorised mode highly attractive to that of a non-motorised mode (Bureau of
106
Transportation Statistics 2006, Ortuzar et al. 2006). Therefore, the model
specification developed for regional work trips had to ignore the two non-motorised
modes from the logit modelling framework. The SP choice set developed for regional
work trips, thus, contained only two main travelling competing modes namely car
and bus on busway, as discussed in Section 7.3.1. Figure 7.2 further elaborates on
Figure 7.1 by splitting the main travelling mode of bus on busway into the five
access modes in order to determine the perception that the choice users had for using
each of these hypothetical access modes as an alternative to car when presented with
the virtual SP scenarios along with the non-motorised modes.
The access modes chosen for this study are listed as follows,
• feeder bus network to busway station;
• walking on walkway to busway station;
• cycling on cycleway to busway station;
• park and ride in a proper parking facility at the busway station; and
• kiss and ride at a proper passenger drop-off zone at the busway station.
The selection of these access modes was based on the findings of the literature
review done on access mode choice for public transport network (Mukundan et al.
1991, Hubbell et al. 1992, Crisalli and Gangemi 1997). These access modes also
confer with the Integrated Local Transport Plan (ILTP) requirements of the proposed
access mode network for public transport in future (Redland Shire Council 2003).
107
7.47%
65.39%
16.82%
5.61%
1.18%
3.53%
0.00%
Feeder BusWalk to BuswayCycle to BuswayPark & RideKiss & RideWalking all-the-wayCycling all-the-way
Figure 7.2 Percentage Split of Mode Choice Users for Regional Work Trips (with Access Modes to Bus on Busway)
7.3.1. Model Specification
From the mode choice data obtained from the SP surveys for regional work trips,
three unique logit models were developed and calibrated namely,
• simple binary logit model;
• simple multinomial logit model; and
• nested binary logit model.
The reason for developing three different model specifications was to find out the
most appropriate and representative model for regional work trips based on the
stability and statistical reliability of the coefficients’ estimates and the goodness-of-
108
fit values. Therefore, three unique model specifications were prepared characterising
the utility functions of the travelling modes in the SP choice set as discussed below.
7.3.1.1. Simple Binary Logit Model Specification
As stated above, the two main travelling modes that were found to be
competitive for regional work trips were car and bus on busway. The
simple binary logit model developed, thus, contained only these two
modes as shown in Figure 7.3.
Figure 7.3 Simple Binary Logit Model for Regional Work Trips
7.3.1.2. Simple Multinomial Logit Model Specification
For the simple multinomial logit model, all the seven modes,
mentioned in Table 7.2, were considered to be equally competitive for
the respondents having mode choice. From the SP survey, however, it
was observed that no respondent perceived cycling to busway as a
feasible alternative to the car for regional work trips as shown in
Figure 7.2. Therefore, the final model specification prepared for the
simple multinomial logit model representing the work trips on the
CBD corridor, from the study area, contained the other six travelling
modes for the SP choice set and excluded cycling to busway as shown
in Figure 7.4.
Choice
Car
Bus on Busway
109
Figure 7.4 Simple Multinomial Logit Model for Regional Work Trips
7.3.1.3. Nested Binary Logit Model Specification
The nested binary logit model developed for regional work trips
contained the same six travelling modes as used in the simple
multinomial logit model shown in Figure 7.4 grouped together using
the framework of the simple binary logit model shown in Figure 7.3.
In other words, the nested logit model combined the two other logit
models, discussed above, in a tree structure by assigning parent and
child nests as shown in Figure 7.5.
Choice
Car As
Driver (CAD)
Feeder Bus to
Bus on Busway (FBB)
Walk to Bus on Busway (WB)
Park &
Ride to
Bus on Busway (PRB)
Car As
Passenger (CAP)
Kiss &
Ride to
Bus on Busway (KRB)
110
Figure 7.5 Nested Binary Logit Model for Regional Work Trips
7.3.2. Modelling Results
This section lists all the sets of the utility functions developed for the three unique
logit model specifications, as discussed above, along with tabulating the estimates of
the coefficients obtained for each model. A discussion on the estimated values and
the statistical reliability of these coefficients is presented in Section 7.3.3.
The preliminary analysis on the SP mode choice data was carried out using S.P.S.S.
which is a standard computational tool for statistical analysis (S.P.S.S. Inc. 2006).
The data analysis involved checking the survey data for input errors, filtering out the
incorrect choices made by the respondents and transforming the data associated to a
certain trip purpose into a data file (.DAT format) which can be read by ALOGIT
3.2F.
Choice
Car As
Driver (CAD)
Feeder Bus to
Bus on Busway (FBB)
Walk to Bus on Busway (WB)
Park &
Ride to
Bus on Busway (PRB)
Car As
Passenger (CAP)
Kiss &
Ride to
Bus on Busway (KRB)
Car
Bus on
Busway
111
ALOGIT 3.2F is a standard logit model estimation software that was used for
estimating the coefficients’ values of the attributes associated to each mode in the SP
choice set (HCG 1992).
7.3.2.1. Simple Binary Logit Model Estimation
The two utility functions developed for the simple binary logit model
were based on the specification shown in Figure 7.3 and thus,
contained the two main travelling modes namely car and bus on
busway. The utility function developed for car represented the two
types of car users, drivers and passengers, as one travelling mode. For
the mode of bus on busway, the utility function incorporated the
attribute of access time in the main function since there was no unique
specification for access modes.
Various model estimation runs were carried out to find the most
appropriate specification of the utility functions by removing the
attributes with insignificant T-values at 95 % confidence interval. The
final form of the utility functions developed for car and bus on
busway are shown in Equations 7.4 and 7.5.
UCAR = Β11 TTCAR + B12 TCCAR + CCAR (7.4)
UB = B21 TTB + B22 TCB + B23 WTB + B24 ATB (7.5)
where,
UCAR is utility function for the car;
UB is utility function for the bus on busway;
TTCAR is in-vehicle travel time for car;
TCCAR is out-of-pocket travel cost for car;
TTB is in-vehicle travel time for bus on busway;
TCB is trip fare for bus on busway;
WTB is waiting time for bus on busway;
ATB is time to access the busway station;
B1,2,3,4 are relative weights for their respective attributes; and
CCAR is mode-specific constant for car.
112
Although the mode-specific constant of the car is conventionally used
as the base modal constant for model estimation, the initial model
calibration runs showed that the mode-specific constant for the bus on
busway had to be used as the base modal constant. Model estimation
runs were also carried out by using generic attributes for in-vehicle
travel time (TT) and trip cost (TC) rather than the mode-specific
attributes as shown in Equations 7.4 and 7.5. As expected, the model
specifications containing the mode-specific attributes were found to
have better statistical reliabilities since the respondents perceived the
attributes of the two modes very differently as shown in Table 7.3.
Table 7.3 Model Estimation Results for Simple Binary Logit Model
for Regional Work Trips
MODE Variable Coefficient T-Ratio Std.
Error
TTCAR -0.06593 -6.0 0.01090
TCCAR -0.00430 -2.2 0.00019
Car
CCAR -1.5500 -2.8 0.56300
TTB -0.04870 -4.7 0.01050
TCB -0.00384 -8.3 0.00046
WTB -0.05287 -2.3 0.02280
Bus on
Busway
ATB -0.04686 -1.5 0.03210
ρ2 0.1554
Number of SP Observations 680
The correlations found among the attributes used in the above model
are tabulated in Appendix 6.
7.3.2.2. Simple Multinomial Logit Model Estimation
The final form of the utility functions developed for the six travelling
modes of the simple multinomial logit models are shown from
Equations 7.6 to 7.11.
113
UCAD = Β11 TTCAD + B12 TCCAD + CCAD (7.6)
UCAP = CCAP (7.7)
UFBB = B31 TTFBB + B32 TCFBB + CFBB (7.8)
UWB = B41 TTWB + B42 TCWB + B44 ATWB (7.9)
UPRB = B51 TTPRB + B52 TCPRB + B53 WTPRB + B54 TTPRB + CPRB(7.10)
UKRB = B62 TCKRB + B63 WTKRB + B64 TTKRB + CKRB (7.11)
where,
UCAD is utility function for the car as driver;
UCAP is utility function for the car as passenger;
UFBB is utility function for the feeder bus to bus on busway;
UWB is utility function for the walk to bus on busway;
UPRB is utility function for the park & ride to bus on busway;
and
UKRB is utility function for the kiss & ride to bus on busway.
The final estimation results of the simple multinomial logit model for
regional work trips are presented in Table 7.4. A table containing all
the correlation values found among the attributes is shown in
Appendix 6.
114
Table 7.4 Model Estimation Results for Simple Multinomial Logit Model for Regional Work Trips
MODE Variable Coefficient T-Ratio Std.
Error
TTCAD -0.07084 -6.9 0.01020
TCCAD -0.00390 -2.0 0.00020
Car
as
Driver
CCAD -2.16800 -4.3 0.50900
Car as
Passenger
CCAP -9.21900 -12.3 0.75000
TTFBB -0.04237 -2.4 0.01760
TCFBB -0.00270 -2.5 0.00110
Feeder Bus
to
Bus on
Busway CFBB -4.69300 -4.8 0.96900
TTWB -0.04859 -4.8 0.01010
TCWB -0.00400 -7.6 0.00053
Walk
to
Bus on
Busway ATWB -0.19580 -6.9 0.02830
TTPRB -0.06249 -4.2 0.01500
TCPRB -0.00512 -4.9 0.00104
WTPRB -0.15470 -3.3 0.04640
ATPRB 0.40820 6.9 0.05890
Park & Ride
to
Bus on
Busway
CPRB -2.47500 -2.7 0.92500
TCKRB -0.00744 -3.0 0.00248
WTKRB -0.25040 -2.2 0.11500
ATKRB
0.59670 4.6 0.12900
Kiss & Ride
to
Bus on
Busway
CKRB -5.87500 -3.4 1.74000
ρ2 0.4729
Number of SP Observations 680
115
7.3.2.3. Nested Binary Logit Model Estimation
The nested binary logit model developed for regional work trips used
the same utility function specifications as defined from Equations 7.6
to 7.11, since the main travelling modes in Figure 7.4 became child
nests for this model. The utility functions for the composite modes (in
the parent nests) are mentioned in Equations 7.12 and 7.13.
UCAR = θCAR ln ∑=
I
i 1 eU
j (7.12)
UB = θB ln ∑=
K
k 1
eUm (7.13)
where,
UCAR is composite utility function for the car;
UB is composite utility function for the bus on busway;
j is the utility function of the jth mode in the car nest;
I is the total number of elements in the car nest9;
m is the utility function of the mth mode in the bus on busway
nest;
K is the total number of elements in the bus on busway nest10;
θCAR is the scale parameter for the car nest; and
θB is the scale parameter for the bus on busway nest.
The final estimation results of the nested binary logit model for
regional work trips are presented in Table 7.5. A table containing all
the correlation values found among the attributes is shown in
Appendix 6.
9 I = 2 for regional work trips 10 K = 4 for regional work trips
116
Table 7.5 Model Estimation Results for Nested Binary Logit Model for Regional Work Trips
MODE Variable Coefficient T-Ratio Std.
Error
TTCAD -0.06222 -2.8 0.02220
TCCAD -0.00320 -1.6 0.00022
Car
as
Driver
CCAD -1.61900 -2.1 0.78800
Car as
Passenger
CCAP -8.20100 -4.5 1.83000
TTFBB -0.06286 -2.8 0.02280
TCFBB -0.00503 -2.6 0.00270
Feeder Bus
to
Bus on
Busway CFBB -5.08200 -5.0 1.01000
TTWB -0.06887 -3.9 0.01790
TCWB -0.00386 -3.3 0.00235
Walk
to
Bus on
Busway ATWB -0.26870 -5.4 0.04930
TTPRB -0.08684 -4.1 0.02090
TCPRB -0.00583 -3.4 0.00286
WTPRB -0.21650 -3.6 0.06000
ATPRB 0.52120 5.0 0.10400
Park & Ride
to
Bus on
Busway
CPRB -2.49800 -2.5 0.99700
TCKRB -0.01219 -3.3 0.00365
WTKRB -0.31500 -2.5 0.12800
ATKRB 0.76140 4.5 0.16800
Kiss & Ride
to
Bus on
Busway
CKRB -7.39300 -3.6 2.05000
Car θCAR 0.94980 2.6 0.36000
Bus on
Busway
θB 0.47710 3.4 0.14000
ρ2 0.4766
Number of SP Observations 680
117
7.3.3. Discussion on the Estimated Coefficients
The strongest priori knowledge a transport modeller has about the estimated
coefficients is with regard to their signs. With every attribute held equal, it is
expected that deterioration in the level of service offered by any mode will reduce
the probability of that mode being chosen. Therefore, an essential requirement is that
the utility of any one mode should decrease as the values of the most quantitative
level-of-service variables increase11. From Tables 7.3, 7.4 and 7.5, one can observe
that the signs of most of the estimated coefficients are negative.
Another priori in examining the results was the set of values of times for work trips
estimated in the Brisbane Strategic Transport Model (BSTM) in Sinclair Knight
Merz (2006) for South-East Queensland. These values of times involve the value of
travel time (VoT), defined as the ratio of the coefficients of travel time and travel
cost converted into $/hour12, and the other two ratios of waiting time and access time
to travel time. A comparison table containing all the values taken from BSTM and
determined from the modelling results is presented in Table 7.6.
Table 7.6 Comparison of Values of Times from BSTM and Modelling Results for Regional Work Trips
BSTM SBLM13
(ρ2=0.1554)
SMLM14
(ρ2=0.4729)
NBLM15
(ρ2=0.4766)
Car 9.20 10.90 11.67 Value of Travel Time
(VoT)
($ / hr)
12.00
(All
Road
Users)
Bus on
Busway
7.61 8.01 9.05
Waiting Time / Travel
Time
2.50 1.09 2.48 2.49
Access Time / Travel
Time
1.75 0.71 1.63 2.48
11 This assumption is not true in case of some qualitative attributes like comfort. If comfort is measured on a scale that rises with the increasing comfort, then the utility function will increase with the increase in comfort. 12 Since the research was based in Australia, a $ refers to one Australian Dollar (AUD) unless mentioned otherwise 13 SBLM represents Simple Binary Logit Model 14 SMLM represents Simple Multinomial Logit Model 15 NBLM represents Nested Binary Logit Model
118
The values of travel time (VoTs) determined using the simple multinomial and
nested binary logit models for regional work trips matched that of BSTM closely.
Further verification of VoTs was established after comparing with the preliminary
results of a recent SP mode choice done in Brisbane (Maunsell Australia 2006)
where the value of time obtained for work trips was found to be 12.60 $/hour for
people travelling within Brisbane. The possible reasons for the minor differences in
the values of times, from our research and the BSTM, are summarised as follows,
• BSTM epitomizes the whole of South-East Queensland, rather than just Redlands
as in our study;
• all the values of time estimated in BSTM represent work trips at peak-hours only.
In our study, the SP survey covered all work trips taken by the sample
irrespective of the time of the day;
• BSTM does not split trips on the basis of trip lengths while our study separately
defined and modelled regional and local trips; and
• BSTM represents RP values of time based on the current travel behaviour while
our study signifies future travel behaviour based on the SP mode choice data.
A total of 618 SP observations were used for calibrating all the three logit models for
regional work trips. To ensure that the models fulfil the travel behavioural
framework requirements (Badoe and Miller 1995), the exact same sample was used
for each model. The reliability and stability of the estimated coefficients can further
be observed in several ways, namely the ρ2 (rho-squared) values obtained for each
model, relative magnitudes of the standard errors and the variability of the estimates
across different model specifications.
The ρ2 values obtained (ρ2 = 0.4729 for SMLM and ρ2 = 0.4766 for NBLM) are
consistent with previous logit modelling studies done for work trips in other parts of
the world (Ortuzar 1996a, Dissanayake and Morikawa 2002, Jovicic and Hansen
2003) where the ρ2 values were found to lie between 0.4 and 0.6 for similar model
specifications and choice sets. Standard literature on interpreting goodness-of-fit
values for discrete choice models in a practical manner is presented in Daganzo
(1982).
119
The magnitude of the standard errors of the estimated coefficients (compared with
the magnitude of the estimated coefficients) is relatively small for all the level-of-
service attributes in SMLM and NBLM, but is comparatively higher for some mode-
specific constants, particularly the modal constant for kiss and ride.
From Table 7.5, it is evident that the coefficients of all the level-of-service times are
statistically stable, particularly the coefficients of in-vehicle travel times (TT) for
each mode in the SP choice set. On the other hand, the mode-specific constants
appears to be relatively less stable with high magnitude of standard errors but
proving statistically significant due to their high magnitude of T-ratios (magnitude >
1.96 for 95% confidence interval). The same pattern was observed in the comparison
of the results of the three different logit models, presented in Tables 7.3, 7.4 and 7.5.
However, for all the three models, the correlation values determined among the
attributes were found to be low indicating towards the appropriate model
specifications used in the modelling framework. Standard literature on interpreting
correlation values among the variables in a model is presented in Cohen et al. (2003).
The coefficients of waiting times were found to be significant for the two car access
modes (park and ride and kiss and ride to bus on busway) implying that the
respondents walking or riding a feeder bus to the busway station do not perceive
waiting time as an influential attribute for their mode choice. The signs of the
coefficients of the access times for park and ride and kiss and ride came out to be
unexpectedly positive indicating that if the time to access the busway station
increases for all travelling modes, the respondents using bus on busway are likely to
have a shift in the mode choice towards car access modes. Further discussion on the
sensitivity of various level-of-service attributes is presented in Section 7.3.5.
Among the four access modes for the bus on busway mode, the respondents
perceiving to use walking to the busway station as an alternative for car were
estimated to have the highest value of time (VoT) for regional work trips (VoTWB =
10.70 $/hr from NMLM).
120
7.3.4. Forecasted Mode Choice
Using Equation 7.3, disaggregate probabilities were estimated for each choice user
making regional work trips. Further, these probabilities were aggregated as an
average of all the values in order to forecast the mode shares on aggregate basis as
shown in Figure 7.6 for the nested multinomial logit model. The aggregate
probability distribution for regional work trips using simple binary and simple
multinomial logit models are presented in Appendix 7.
46.39%
0.74%
1.96%
11.99%0.27%
38.64%
PCADPCAPPFBBPWBPPRBPKRB
Figure 7.6 Forecasted Aggregated Mode Shares for Regional Work Trips
121
For forecasting the mode choice for the population of the study area, the basic notion
considered was that the ILTP scenarios can be implemented in practice. It implies
that the forecasted mode shares can be true if the hypothetical travel modes, with
virtual level-of-service attributes, as shown in the SP survey can actually come into
practice.
From transport planning perspective, Figure 7.6 appears to be highly adequate for
implementing ILTP scenarios as around 53% of the current car users with mode
choice seem to perceive switching to bus on busway for work trips on the CBD
corridor. However, the number of travellers perceiving to make this change is
relatively small as choice users comprised of only 27 % of the survey sample for all
trip purposes. Further percentage splits of various types of sub-samples including
choice users and the mode captives are presented in Chapter 6.
Previous studies have shown that the SP aggregate forecasts are generally biased
towards the new mode (bus on busway in this case) as the respondents may not fully
perceive the level-of-service and network variables of the hypothetical mode
(Richardson et al. 1995, Polydoropoulou and Ben-Akiva 2001). Therefore, in order
to observe the forecasted travel behaviour in a better way, it is essential to
individually examine the behavioural framework attributes and their sensitivities that
influence an individual’s decision to select a particular mode, as presented in Section
7.3.5.
7.3.5. Sensitivity of Level-of-Service Attributes
The variables in the mode choice model which are of primary interest to a
transportation planner are the level-of-service attributes. In addition to the use of a
model for conventional area-wide forecasts, it can also be used to give indications of
the likely effects of changes in the selected level-of-service variables, given that all
other attributes remain constant. Such sensitivity analyses are expressed in terms of
elasticities and provide useful information for both the development and general
appraisal of possible new policies in the study area.
For regional work trips, sensitivity of various attributes associated to the travelling
modes in the SP choice set were determined in order to see the variables’ influence
122
on mode choice decision-making at an aggregate level. The direct and cross
elasticities for in-vehicle travel time and trip fare for the bus on busway and the
access distance to reach the busway station are shown in Figures 7.7, 7.8 and 7.9
respectively, based on the nested binary logit model estimations.
The reason for combining the direct elasticity of a mode’s attribute and the cross
elasticities of the corresponding attributes of other modes into one figure, was so that
the percentages of all the mode shares can be observed for a certain change in one
attribute of the mode. For determining sensitivity of a certain attribute, all other
attributes in the utility functions were fixed, based on the current values of the level-
of-service attributes. For example, in Figure 7.7, the sensitivity of in-vehicle travel
time of a hypothetical mode, bus on busway, is presented by keeping the other level-
of-service attributes fixed to their current observed values, as shown in Table 7.7.
123
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
30 40 50 60 70 80 90 100 110 120
In-vehicle Travel Time of Bus on Busway (min)
Car as Driver Car as Passenger Feeder Bus to BuswayWalk to Busway Park & Ride to Busway Kiss & Ride to Busway
Figure 7.7 Sensitivity of In-vehicle Travel Time of Bus on Busway
for Regional Work Trips
Table 7.7 Fixed Values of Attributes for determining Sensitivity of In-vehicle Travel Time for Bus on Busway for Regional Work Trips
Attributes Fixed Values Attributes Fixed Values
TTCAR16 40 min TCB
17 500 cents
TCCAD 800 cents WTB 10 min
ATFBB 8 min ATPRB 4 min
ATWB 10 min ATKRB 4 min
16 Same value for car as driver and car as passenger modes 17 Same value for feeder bus to busway, walk to busway, park and ride and kiss and ride modes
124
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
200 300 400 500 600 700 800
Travel Fare of Bus on Busway (cents)
Car as Driver Car as Passenger Feeder Bus to BuswayWalk to Busway Park & Ride to Busway Kiss & Ride to Busway
Figure 7.8 Sensitivity of Travel Fare of Bus on Busway for Regional Work Trips
Simply stating, these elasticities are the estimated reflections of the survey
respondents’ perceived sensitivity towards the attributes associated to the
hypothetical travelling modes in the SP choice set for regional work trips, as defined
to them by the interviewer. For example, a bus on busway (a hypothetical mode in
the SP survey) was defined as a public transport mode operating on a dedicated bus
corridor, destined to CBD, with frequent service (headway time <= 15 min),
particularly at peak-hours, and high reliability. Therefore, Figures 7.7, 7.8 and 7.9
represent the sensitivities of the attributes under the virtual ILTP scenarios only and
may not reflect the elasticities of the current level-of-service attributes.
Figure 7.6 established that the modes dominating the travel behaviour for regional
work trips are car as driver and walk to busway. This finding is also evident in
125
Figures 7.7, 7.8 and 7.9 where the population is found to be more sensitive towards
the attributes of these modes.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000 2,200 2,400
Access Distance for Bus on Busway (metres)
Car as Driver Car as Passenger Feeder Bus to Busw ayWalk to Busw ay Park & Ride to Busw ay Kiss & Ride to Busw ay
Figure 7.9 Sensitivity of Access Distance for Bus on Busway for Regional Work Trips
In Figure 7.7, the average in-vehicle travel time of the current bus service from the
study area to the CBD is shown as a bold line and is set to be 74 minutes
(Queensland Government 2007). It indicates that if the ILTP environment is
implemented with reliable and frequent buses operating with the average in-vehicle
travel time of current bus, 22% of the current car users with mode choice are likely to
switch to the bus on busway for travelling to work on the CBD corridor. However,
this percentage can increase even up to 47% if the travelling time of the buses on the
126
busway can be reduced to 40 min, which subject to the route of the busway, seems a
practically satisfactory mode share from transport planning perspective.
These sensitivity analysis can be further employed in order to forecast a travel
patronage of the study area, under the influence of the hypothetical travel
environment. For instance, for regional work trips, around 33 % of the respondents
were found to have mode choice for car, while 17 % of the respondents were
categorised as captive users to public transport (i.e. 17 % of the respondents are
already using public transport or all-the-way non-motorised modes). Therefore, from
Figure 7.7, if the in-vehicle travel time of bus on busway can be set to 40 minutes,
around 50 % of the population of the study area (sum of percentage shares of public
transport captive users and mode choice users at the specified in-vehicle travel time
of bus on busway), travelling to work in Brisbane City, are forecasted to use non-car
modes. Similarly, travel patronages of various other trip purposes and trip lengths,
included in the specification, can be estimated using sensitivity analysis for each
level-of-service attribute of the travelling modes, included in the SP choice set.
The population of the study area was found to be less sensitive to the travel fare on
the bus on busway as compared to its in-vehicle travel time. The single adult one-
way fare from the study area to the CBD is 480 cents (Queensland Government
2007) and is shown as a bold line in Figure 7.8. If the trip fare and other level-of-
service attributes are kept fixed at the current level of the bus network, around 20 %
of the current car users with mode choice are probable to switch to bus on busway
for work trips on the CBD corridor. However, if the trip fare of the hypothetical
mode is increased by even 20 %, the percentage of the car users switching to bus on
busway seems to decrease by 4 % only.
Figure 7.9 has used the distance to access the busway station as a principal variable
rather the conventional access time due to the high variability in the nature of the
access modes in the SP choice set, containing both motorised and non-motorised
modes. It is evident from Figure 7.9 that the access distance does not seem to
significantly influence the mode choice, unless it is very low (less than 400 metres).
127
Therefore, out of the three level-of-service attributes, the in-vehicle travel time of the
new hypothetical mode mainly appears to drive the mode choice of the residents of
the study area for regional work trips. This finding further verifies the results of
previous mode choice studies done for Brisbane city (Douglas et al. 2003) and for
other semi-urban areas, with similar travel environments, using the same model
specifications (Bhat and Sardesai 2006, Elisabetta and Ortuzar 2006).
7.4. MODE CHOICE MODEL FOR OTHER TRIPS
The total number of stated preference (SP) mode choice responses obtained for
regional other trips were 670. The percentage splits of the mode choice users
perceiving to have a choice for any of the travelling alternatives to the car, shown in
the SP survey, are presented in Figure 7.10.
71.75%
0.00%
25.00%
0.67%
0.00%
1.91%
0.66%
Feeder Bus to BuswayWalk to BuswayCycle to BuswayPark and RideKiss & RideWalking all-the-wayCycling all-the-way
Figure 7.10 Percentage Split of Mode Choice Users for Regional Other Trips (with Access Modes to Bus on Busway)
128
From Figure 7.10, it is evident that no user was found to perceive cycle to busway
and walking all-the-way as a valid alternative to the car for regional other trips.
Similarly, a very small number of respondents perceived kiss and ride, feeder bus to
busway and cycling all-the-way as suitable alternatives to the car for the same
purpose. Therefore the model specification developed for regional other trips
contained only two public transport modes namely walk to busway and park and
ride.
This section discusses the model specification prepared for the nested binary logit
model for regional other trips only, since it was found to be the most appropriate and
representative model for the concerned trip purpose. It further presents the values of
the estimated coefficients, aggregate probability distribution determined from these
values and the elasticity of level-of-service variables associated to the travelling
modes in the SP choice for regional other trips. The model estimations done under
simple binary and simple multinomial logit modelling frameworks are presented in
Appendix 8.
7.4.1. Model Specification
Since the nested binary logit model was found to be the most appropriate and
representative model for other trips, as presented in Section 7.4.2 and Appendix 8,
the specification developed for the concerned model is only presented here.
The model specification contains four travelling modes in total, namely the car as
driver, car as passenger, walk to busway and park and ride as shown in Figure 7.11.
129
Figure 7.11 Nested Binary Logit Model for Regional Other Trips
7.4.2 Modelling Results
For the nested binary logit model for regional other trips, the utility functions
developed, and finalised after numerous model estimation runs, for the four
travelling modes in the SP choice set, are shown from Equations 7.14 to 7.17.
UCAD = Β11 TTCAD + B12 TCCAD + CCAD (7.14)
UCAP = Β21 TTCAP + CCAP (7.15)
UWB = B31 TTWB + B32 TCWB + B33 WTWB + B34 ATWB (7.16)
UPRB = B41 TCPRB + B42 ATPRB + CPRB (7.17)
where,
UCAD is utility function for the car as driver;
UCAP is utility function for the car as passenger;
UWB is utility function for the walk to bus on busway; and
UPRB is utility function for the park & ride to bus on busway.
Choice
Car As
Driver (CAD)
Walk to Bus on Busway (WB)
Park &
Ride to
Bus on Busway (PRB)
Car As
Passenger (CAP)
Car Bus on
Busway
130
As shown in Figure 7.11, two parent modes were used, each having two child
modes, for the nested binary logit model. The utility functions developed for the
parent modes initially contained mode specific scale parameters. However, the
preliminary model calibration runs illustrated that the model can be better
represented with a generic scale parameter (θ), as shown in the utility functions
developed for the parent modes in Equations 7.18 and 7.19.
UCAR = θ ln ∑=
I
i 1 eU
j (7.18)
UB = θ ln ∑=
K
k 1
eUm (7.19)
where,
UCAR is composite utility function for the car;
UB is composite utility function for the bus on busway;
j is the utility function of the jth mode in the car nest;
I is the total number of elements in the car nest18;
m is the utility function of the mth mode in the bus on busway nest;
K is the total number of elements in the bus on busway nest19; and
θ is the scale parameter for the both car and bus on busway nests.
The final estimation results of the nested binary logit model for regional other trips,
obtained using ALOGIT 3.2F, are presented in Table 7.8. A table containing all the
correlation values found among the attributes is shown in Appendix 6.
18 I = 2 for regional other trips 19 K = 2 for regional other trips
131
Table 7.8 Model Estimation Results for Nested Binary Logit Model for Regional Other Trips
7.4.3. Discussion on the Estimated Coefficients
From the final estimation results of the nested binary logit model for regional other
trips, the value of travel time (VoT) was determined to be 21.90 $/hour for car
drivers. Although the value is comparatively bigger than the one observed for
regional work trips for the same model, shown in Table 7.6, it is difficult to assess
the value of time for regional other trips since the definition of this trip purpose
involves a mix of different sorts of trips such as entertainment, sports, health trips,
and so on. Therefore, it is hard to judge the importance of these trips together, and
determine the influence of level-of-service attributes on mode choice decision-
making at a disaggregate level. This notion is further established by determining the
MODE Variable Coefficient T-Ratio Std.
Error
TTCAD -0.03358 -4.1 0.00850
TCCAD -0.00092 -5.5 0.00017
Car
as
Driver CCAD -2.51200 -4.9 0.52000
TTCAP -0.07954 -4.5 0.01820 Car as
Passenger CCAP -3.79800 -4.9 0.78400
TTWB -0.01634 -2.0 0.00822
TCWB -0.00428 -8.5 0.00052
WTWB -0.04216 -1.8 0.02350
Walk
to
Bus on
Busway ATWB -0.16520 -6.5 0.02590
TCPRB -0.00344 -4.9 0.00072
ATPRB 0.40140 7.8 0.05200
Park & Ride
to
Bus on
Busway CPRB -5.96500 -8.8 0.68400
Scale
Parameter θ 1.03100 4.9 0.21100
ρ2 0.3726
Number of SP Observations 670
132
value of travel time (VoT) for walk to bus on busway which was found to be 2.30
$/hour, an unexpectedly small value and substantially different from that of car as
driver.
An affirmative finding from Table 7.8 is that the signs of all the estimated
coefficients associated to the level-of-service attributes are negative, with the
exception of access time for park and ride to bus on busway. It means that
deterioration in any level-of-service attribute (except for access time for park and
ride) will decrease the usage of that mode by a certain value. The positive sign of the
coefficient of access time to bus on busway showed that with the increase in access
time, the travellers are likely to switch to park and ride rather than walking to the
busway for regional other trips, as the latter may become inconvenient with the
additional walking time. Sensitivity analyses on various level-of-service attributes,
associated to the travelling modes in the SP choice set for regional work trips, were
performed and documented in Section 7.4.5 for in-vehicle travel time of bus on
busway while sensitivities of other variables are shown in Appendix 9.
Comparing with the results of regional work trips presented in Section 7.3, the values
of the coefficients of in-vehicle travel time and out-of-pocket travel cost for regional
other trips for car as driver were found to be half of that of their regional work
counterparts. It shows that, on aggregate level, the work trip-makers in the study area
are much more sensitive to times and costs as compared to those travelling on the
CBD corridor for other trips.
The magnitude of the standard errors of the estimated coefficients (compared with
the magnitude of the estimated coefficients) is relatively small for all the level-of-
service attributes, but is comparatively higher for some mode-specific constants,
particularly the modal constant for park and ride. However, having a big negative
modal constant value illustrates that the positive coefficient of access time will not
have a significant influence on the decision-making framework for park and ride.
These findings are further verified in Section 7.4.5 and Appendix 9.
133
7.4.4. Forecasted Mode Choice
The forecasted mode shares for regional other trips are shown in Figure 7.12 using
the nested binary logit model estimation. It shows an almost equal distribution
between the future car and bus on busway trip-makers. However, the forecasts are
made in ideally attractive public transport conditions, which may not always reflect
the practical travel scenario in the study area, and thus bias the travel behaviour of
the residents towards bus on busway for regional other trips. A better examination on
the forecasted mode shares is done in Section 7.4.5 by observing the sensitivity of
level-of-service attributes on the future mode shares.
44.49%
5.05%
37.70%
12.76%
PCADPCAPPWBPPRB
Figure 7.12 Forecasted Aggregated Mode Shares for Regional Other Trips
7.4.5. Sensitivity of In-vehicle Travel Time of Bus on Busway
Similar to regional work trips, the direct and cross elasticities for in-vehicle travel
time of bus on busway are shown together in Figure 7.13, for all the four modes in
the SP choice set for regional other trips. For determining this sensitivity, all other
level-of-service attributes were kept fixed based on the current network parameters
(Queensland Government 2007) as shown in Table 7.9.
134
0%
10%
20%
30%
40%
50%
60%
70%
80%
30 40 50 60 70 80 90 100 110 120Travel Time of Bus on Busway
(min)
Car as Driver Car as PassengerWalk to Busway Park & Ride to Busway
Figure 7.13 Sensitivity of In-vehicle Travel Time of Bus on Busway for Regional Other Trips
Table 7.9 Fixed Values of Attributes for determining Sensitivity of In-vehicle
Travel Time for Bus on Busway for Regional Other Trips
Attributes Fixed Values Attributes Fixed Values
TTCAR 38 min TCB 500 cents
TCCAD 1000 cents WTB 10 min
ATWB 9 min ATPRB 4 min
135
From Figure 7.13, car as driver and walk to bus on busway modes were found to be
highly sensitive to the in-vehicle travel time of the bus on busway, as compared to
the other two modes in the SP choice set which were drawn almost as a horizontal
straight line to the varying values of travel time. Another interesting observation, in
Figure 7.13, is the vertical dotted line showing the value of travel time of the bus on
busway for which an equal distribution of car as driver and walk to bus on busway
users can be reached for the aggregated mode forecast. It means that if a bus on
busway network can be implemented in practice, having an in-vehicle travel time to
the CBD as low as 38 minutes; the mode shares of car as driver and walk to busway
are expected to be same.
The average in-vehicle travel time of the current bus service from the study area to
the CBD is shown as a vertical solid line in Figure 7.13, set to 74 minutes. Therefore,
if a bus on busway, as defined in the SP survey, can start operating with no reduction
in travel time and other parameters, mentioned in Table 7.9, of the current bus
service, the percentage of car users with mode choice switching to bus on busway
will still be around 41 % for regional other trips. Subject to the route of the busway
and other land-use parameters, the mode share seems satisfactory from transport
planning perspectives.
7.5. SUMMARY
The mode choice logit models presented in this chapter is an attempt to verify one of
the hypotheses used for this study, stated in Chapter 1, that the travel behaviour of
the population of a study area (Southern Redland Shire, for this research) varies not
only with trip purpose (as assumed in previous mode choice modelling studies) but
also with the trip length. For this purpose, the survey sample was split into two main
categories of respondents based on their travel distances, defined as regional and
local trips. A regional trip was referred as a trip destined to CBD or one made on the
CBD-based corridor, while the local trip was undertaken within the study area. Each
trip was then divided into four trip purposes namely work, shopping, education and
other trips. Separate logit models were developed for each trip purpose, unlike the
136
previous mode choice studies that mainly attempt to model the work trips only, in
order to capture the most representative model for the study area.
This chapter presented the mode choice models developed for regional trips, along
with illustrating their specifications, logit structures, modelling results, and
discussions on the estimated coefficients, forecasted mode choice and sensitivities of
various level-of-service attributes to the travelling modes in the SP choice set for
each trip purpose. The samples generated for regional shopping and education trips
were not found to be substantial enough to calibrate the logit models, and thus, the
regional trip models involved two purposes only, work and other trips. Since the
mode choice models developed were based on the standard logit modelling
framework, using the stated preference (SP) data, it is recommended that they can
serve usefully in various transportation planning studies.
Whilst a number of conclusions can be drawn from the models estimated, it must be
remembered that they reflect the Integrated Local Transport Plan (ILTP) proposed
scenarios containing various hypothetical modes such as bus on busway with efficient
access mode network, walking on walkway and cycle on cycleway. These models, in
no way, aimed to observe the current travel behaviour of the population of the Shire
and therefore, the modelling results should not be categorically compared with any
revealed preference (RP) study, if undertaken in near future.
For all the regional trips, the survey sample was found to have a low perception for
using hypothetical non-motorised modes, walking on walkway and cycling on
cycleway, as alternatives to car for any trip purpose. Therefore, the SP choice sets
developed for both trip purposes contained only the motorised modes. From the logit
model estimations, the level-of-service attributes of car as driver and walk to busway
were found to be relatively less negative, illustrating that the two modes will
compete highly with each other, for both trip purposes, if an efficient and reliable
busway network from the study area to Brisbane city can be implemented in practice.
Contrarily, all other modes in the SP choice set were not found to influence the mode
choice significantly, except for park and ride to busway which seemed to compete
with car as driver, if the in-vehicle travel time of bus on busway can be less than or
equal to 40 minutes.
137
Three logit models, with each having a unique specification, were developed for both
work and other trips on the CBD corridor, namely simple binary, simple multinomial
and nested binary logit models. For both trip purposes, the nested binary logit model
was found to be the most appropriate and representative model. Most of the
estimated coefficients, in the preliminary utility function specifications, were found
to be statistically significant and stable, with negative signs. This was, however, not
the case for all the mode-specific constants, since some of them had high standard
error values, although always having negative signs. As expected, the in-vehicle
travel time, associated to any mode (with bus on busway in particular), was found to
be the most influential attribute in mode choice, at both aggregate and disaggregate
levels. The waiting time for the bus on busway was found to apparently not influence
the mode choice for both trip purposes, along with other socio-economic attributes
other than the household size for work trips. Moreover, the values of travel time
(VoT) for work trips (11.67 $/hour for car trips and 9.05 $/hour for bus on busway
trips) closely matched the ones determined previously in mode choice studies done
for the Brisbane statistical division.
The transformation of disaggregate models to aggregate models, for use as predictive
models, presented a considerable bias towards the hypothetical mode of bus on
busway. However, for practical purposes, the bias can be minimised by the use of
market segmentation, provided that the model specification can be separately
developed on the basis of sample characteristics. Further, the elasticities developed
for various level-of-service attributes represented the future travel behaviour more
appropriately and can be practically applied in implementing the ILTP travel
environment.
138
8 Mode Choice Modelling for Local Trips
8.1. INTRODUCTION
As discussed earlier, the travel behaviour of the residents of the study area, for this
study, was modelled on the basis of trip lengths and trip purposes. This chapter
presents the structures and specifications of various mode choice models, developed
for local trips, i.e. trips taken within Redland Shire by the residents of the study area,
along with discussing the estimated coefficients and their elasticities. The travel
behaviour modelled for local trips was found to be significantly different from
regional trips, since the respondents were found to perceive walking all-the-way and
cycling all-the-way as a valid feasible alternative to car, in addition to bus on busway
which was the only logical alternative to car in regional trips. Therefore, the SP
choice set developed for local trips was relatively complex, as seven hypothetical
modes had to compete with car for various trip purposes.
All the local trips were categorised into four purposes namely work, shopping,
education and other trips. Unlike regional trips, a significant number of mode choice
responses were attained for each trip purpose, as shown in Table 8.1. Therefore, four
unique sets of mode choice models were developed for local trips. The theoretical
framework used was the same as that of regional trips, with all trip purposes having
their origins at home, except for other trips which can be non-home-based as well.
The attributes associated to the travelling modes, used for modelling, were also the
same as that defined in Table 7.2 for regional trips.
Table 8.1 Number of SP Observations attained for each Local Trip Purpose
Trip Purpose Number of SP Observations
Work Trips 680
Shopping Trips 920
Education Trips 448
Other Trips 544
Total 2592
139
Although various logit models were developed and estimated for each trip purpose,
the structure and modelling results of the most appropriate and representative model
are presented here only. Section 8.2 presents the specification of the final mode
choice model developed for local work trips, along with discussing the results and
determining the sensitivities of various attributes associated to the travelling modes
in the SP choice set for local trips. Sections 8.3, 8.4 and 8.5 follow the same format
for shopping, education and other trips. The modelling results of all other mode
choice models, not illustrated in this chapter, are presented in Appendix 10 for work,
Appendix 11 for shopping and Appendix 12 for other trips.
8.2. MODE CHOICE MODEL FOR WORK TRIPS
The total number of stated preference (SP) mode choice responses attained for local
work trips were 68020. The percentage split of the mode choice users perceiving to
have a choice for the travelling alternatives to the car, presented to them in the SP
survey, is shown in Figure 8.1.
It is evident from Figure 8.1 that no respondent perceived to use cycling to busway as
a feasible alternative to car. Similarly, a few current car users selected feeder bus and
kiss and ride to busway for travelling to work within the Shire. For cycling to
busway, it seems a logical choice decision not to use it since it involves significant
inconvenience considering that all the concerned trips are of shorter lengths;
therefore, it may be easier to cycle all-the-way to the destination. However, the same
perception was also observed for regional work trips, as discussed in Chapter 7,
where no respondent perceived to use the mode to travel on the CBD corridor,
making it an unfeasible mode for all work trips. For feeder bus to busway, the main
reason for not perceiving to use it seems to be the transfer penalty involved for
changing the buses which can substantially influence the decision for short trips.
Although kiss and ride to busway mode does not involve any penalty, it does require
a supplementary driver to drop the traveller at the transit station, thus making it
inopportune for the respondents.
20 coincidentally, the same number of SP mode choice responses were obtained for regional work trips
140
0.86%
64.77%0.00%
3.97%
0.35%
8.64%
21.42%
Feeder Bus to BuswayWalk to BuswayCycle to BuswayPark & Ride to BuswayKiss & Ride to BuswayWalking all-the-wayCycling all-the-way
Figure 8.1 Percentage Split of Mode Choice Users for Local Work Trips
8.2.1. Model Specification
The SP choice set prepared for local work trips did not contain feeder bus, kiss and
ride and cycle to busway as only a small number of respondents perceived to use
these modes as an alternative to car (none for cycling). Based on this SP choice set,
two mode choice models were prepared to estimate the travel behaviour for local
work trips, namely simple multinomial and nested multinomial logit models. The
model structure developed for the nested multinomial logit model is shown in Figure
8.2.
141
Figure 8.2 Nested Multinomial Logit Model for Local Work Trips
In the preliminary model estimation runs, a parent mode was associated to walk and
cycle all-the-way namely non-motorised modes, with the similar pedigree as car and
bus on busway. However, the estimation results, particularly the scale parameters,
improved considerably with the removal of non-motorised modes parent mode. The
final model estimation results are presented in Section 8.2.2.
8.2.2. Modelling Results
Similar to regional trip models, the preliminary analysis on the SP mode choice data
was carried out using S.P.S.S. The data analysis involved checking the survey data
for input errors, filtering out the incorrect choices made by the respondents and
transforming the data associated to a certain trip purpose into a data file (.DAT
format) which can be read by ALOGIT 3.2F.
After performing numerous model estimation runs, the finalised form of the utility
functions associated to all the modes are presented from Equations 8.1 to 8.8.
Choice
Car As
Driver (CAD)
Walk to Bus on Busway (WB)
Park &
Ride to
Bus on Busway (PRB)
Car As
Passenger (CAP)
Car Bus on
Busway
Walk (W)
Cycle (C)
142
UCAD = Β1 TTCAD + B2 TCCAD (8.1)
UCAP = Β1 TTCAP + CCAP (8.2)
UWB = B1 TTWB + B2 TCWB + B31 ATWB (8.3)
UPRB = B1 TTPRB + B2 TCWB + B41 ATPRB + CPRB (8.4)
UW = B1 TTW + CW (8.5)
UC = B1 TTC + CC (8.6)
where,
UCAD is utility function for the car as driver;
UCAP is utility function for the car as passenger;
UWB is utility function for the walk to bus on busway;
UPRB is utility function for the park & ride to bus on busway;
UW is utility function for walking all-the-way to the destination; and
UC is utility function for cycling all-the-way to the destination.
UCAR = θCAR ln ∑=
I
i 1 eU
j (8.7)
UB = θB ln ∑=
K
k 1
eUm (8.8)
where,
UCAR is composite utility function for the car;
UB is composite utility function for the bus on busway;
j is the utility function of the jth mode in the car nest;
I is the total number of elements in the car nest21;
m is the utility function of the mth mode in the bus on busway nest;
K is the total number of elements in the bus on busway nest22;
θCAR is the scale parameter for the car nest; and
θB is the scale parameter for the bus on busway nest.
An interesting point to note from the above equations is that the coefficients used for
in-vehicle travel time (TT) and out-of-pocket travel cost (TC) are generic. The initial
model specification developed for local work trips followed the same modelling
21 I = 2 for local work trips 22 K = 2 for local work trips
143
framework as that for regional work trip models, by containing specific attributes for
each mode. However, the initial model estimation runs showed that most of the
coefficients associated to these attributes were determined to be insignificant and
statistically unstable. Therefore, the final model specification designed for local work
trips contained specific attributes for access time only, while others were treated as
generic. Another interesting result from the preliminary model estimation runs was
that waiting time for bus on busway was found to be insignificant for local work
trips, unlike for its regional counterpart, where the waiting time was found to be
around 2.5 times of the in-vehicle travel time for bus on busway. All these findings
lead to our hypothesis stating that the travel behaviour should be separately modelled
not only for various trip purposes but also for different travel distances, as the
perception of the population of the study area changes with the length of the trips.
The final model estimation results obtained for local work trips, using ALOGIT 3.2F,
are presented in Table 8.2.
Table 8.2 Model Estimation Results for Nested Multinomial Logit Model for Local Work Trips
MODE Variable Value T-Ratio Std. Error
TT -0.06986 -4.4 0.01600 Generic
Variables TC -0.00254 -3.1 0.00041
Car as Driver
Car as Passenger CCAP -2.17000 -12.7 0.17000
Walk to Bus on Busway ATWB -0.23080 -2.8 0.08190
ATPRB 0.45330 2.8 0.15900 Park & Ride to
Bus on Busway CPRB -6.05400 -5.9 1.02000
Walk CW -2.71700 -2.7 0.99300
Cycle CC -1.35900 -3.9 0.34500
Car θCAR 1.05800 6.2 0.17200
Bus on Busway θB 0.73510 4.8 0.15500
ρ2 0.4154
Number of SP Observations 680
144
8.2.3. Discussion on the Estimated Coefficients
The perception of the level-of-service attributes of the travelling modes for local
work trips was found to be significantly different from that of regional trips, with the
exception of a few similarities. Firstly, the SP choice set developed for local work
trips did not contain feeder bus and kiss and ride to busway, unlike its regional
counterpart. Moreover, walking and cycling all-the-way were included as competing
alternatives to the car, since the respondents perceived to use these non-motorised
modes considering the short travel distances involved.
From the final model estimation results for local work trips, using the nested
multinomial logit model, it can be seen that all the estimated coefficients were found
to have negative signs, other than access time for park and ride to busway. This
finding is consistent with regional work trips where the two car-access modes were
found to have positive signs for access times as well. For local work trips, it indicates
that mode choice users tend to switch to park and ride rather than walking to the
busway station as the access time increases. For the estimated scale parameters, the
signs of both car and bus on busway were found to be expectedly positive and
significant. However, the value of θCAR came out to be slightly greater than one
which is contradictory to findings of the literature review done on nested logit
modelling (Abdel-Aty and Abdel Wahab 2001), but acceptable (Hensher et al. 2005).
Similar to regional work trips, a priori in examining the modelling results of local
work trips was the set of values of times for work trips estimated in the Brisbane
Strategic Transport Model (BSTM) in Sinclair Knight Merz (2006) for South-East
Queensland, irrespective of trip lengths. A comparison table, similar to Table 7.6, is
reproduced in Table 8.3 comparing the values of time and ratios of waiting and
access times over in-vehicle travel times of bus on busway.
145
Table 8.3 Comparison of Values of Times from BSTM and Modelling Results for Regional Local Trips
BSTM SMLM
(ρ2=0.4122)
NMLM
(ρ2=0.4154)
Value of Travel Time
(VoT)
($ / hr)
12.00
(All Road
Users)
22.40 16.50
Waiting Time / Travel
Time
2.50 NA NA
Access Time / Travel
Time
1.75 3.29 3.94
The value of time (VoT) observed for the nested multinomial logit model is slightly
higher than that of BSTM. The main reason for the difference in the two VoTs is that
the BSTM represents the current travel behaviour of Brisbane Statistical District
while the models developed in this study tend to model the future ILTP travel
behaviour in Redland Shire. For the ratio of waiting time over travel time for bus on
busway, no comparison can be made since the model estimation for local work trips
did not found waiting time to be statistically significant. Contrarily, the ratio of
access time over travel time of the bus on busway was determined to be quite high as
compared to BSTM and the regional work trip model, indicating that the local trip-
makers perceive the access time as the most vital parameter in mode choice decision-
making. These results are further established in Section 8.2.4 and Appendix 9 where
sensitivities of different level-of-service attributes were determined for local work
trips.
It can be certainly seen that the nested binary logit model developed for regional
work trips matched the findings of BSTM much closely than the nested multinomial
logit model estimated for local work trips. However, since the travel behaviour
modelled in this study tends to represent a hypothetical ILTP environment, the
comparison with BSTM cannot be used to fully examine the modelling results.
The coefficient values for all the level-of-service attributes, involved in the model
specification, were found to be statistically stable. However, the modal constants for
146
all travelling modes in the SP choice set were estimated to have higher values of
standard error. This finding is consistent with regional work trips, where the
coefficients of level-of-service attributes were relatively stable as compared to the
mode-specific constants. However, the modal constants estimated for local work
trips have comparatively lower values of standard error than that observed for
regional work trips.
The aggregated forecasted mode shares determined for the two logit models, for local
work trips, are shown in Appendix 7.
8.2.4. Sensitivity of Travel Distance
Similar to the regional trip models, the sensitivities of various level-of-service
attributes were determined. Figure 8.3 presents the direct and cross elasticities of trip
length determined for the nested multinomial logit model for local work trips.
Sensitivities of various other level-of-service attributes are presented in Appendix 9.
The reason for selecting the trip length rather than the conventional way of using the
travel time was simply because of the fact that distance remains same for each mode.
On the other hand, the travel times for motorised and non-motorised modes are
different for the same trip lengths due to the difference in speed. Therefore, using
travel distance appeared to be the logical choice as it was merely determined as a
linear factor of the travel time.
Figure 8.3 shows that all the travelling modes were found to have significant mode
shares for small travel distances. An interesting finding was that the car as driver
mode did not dominantly drive the travel behaviour as compared to the regional work
trips where its aggregated mode shares were forecasted to be around 80 % for higher
values of travel times. For local work trips, the percentage mode split of car as driver
did not even reached 50 % for all trip lengths. For travel distances less than 2
kilometres, cycling all-the-way and walking to the busway were found to
significantly compete with car as driver, an essential finding for the transportation
planners.
147
0%
10%
20%
30%
40%
50%
60%
500 1000 1500 2000 2500 3000 3500 4000
Trip Length(metres)
Car as Driver Car as Passenger Walk to BuswayPark & Ride to Busway Walk Cycle
Figure 8.3 Sensitivity of Travel Distance for Local Work Trips
The percentage mode split of car as passenger remained unchanged for different trip
lengths indicating that the mode usage is unaffected, although remains low, with
varying distances. Expectedly, the mode share curve for walk all-the-way mode was
found to have a higher negative slope as compared with those of other travelling
modes showing that its aggregated mode share becomes rapidly inconsequential with
increasing distances. This result is consistent with the findings of a pilot TravelSmart
study conducted in Townsville, Queensland showing that people do not prefer
148
walking all-the-way for distances of more than 2 kilometres (Socialdata Australia
Ltd. 2004). The percentage mode split curve for park and ride to busway was
determined to be an almost straight horizontal line with a low value of intercept
(around 2 %).
8.3. MODE CHOICE MODEL FOR SHOPPING TRIPS
The total number of stated preference (SP) mode choice responses attained for local
shopping trips were 920. The percentage split of the mode choice users perceiving to
have a choice for the travelling alternatives to the car, presented to them in the SP
survey, is shown in Figure 8.4.
5.76%
80.22%
0.00%
0.87%
0.65%
5.87%6.63%
Feeder Bus to BuswayWalk to BuswayCycle to BuswayPark & Ride to BuswayKiss & Ride to BuswayWalk all-the-wayCycle all-the-way
Figure 8.4 Percentage Split of Mode Choice Users for Local Shopping Trips
Similar to all the trip purposes discussed earlier, no respondent was found to perceive
cycling to busway as a valid alternative to car. Furthermore, park and ride and kiss
149
and ride to busway were found to have a very low percentage split of mode choice
users as well. Therefore, the model specification developed for the local shopping
trips had to exclude all these modes from the SP choice set.
8.3.1. Model Specification
A nested multinomial logit model was estimated and found to be most appropriate to
represent the travel behaviour of the population of the study for local shopping trips.
The SP choice set determined for the model specification involved six main
travelling modes, split into parent and child nests, as shown in Figure 8.5.
Figure 8.5 Nested Multinomial Logit Model for Local Shopping Trips
The structure of the logit model shown in Figure 8.5 is similar to that developed for
local work trips, except that it contained the mode of feeder bus to busway rather
than park and ride to busway.
Choice
Car As
Driver (CAD)
Feeder Bus to Bus on Busway (FBB)
Walk to Bus on Busway (WB)
Car As
Passenger (CAP)
Car Bus on
Busway
Walk (W)
Cycle (C)
150
8.3.2. Modelling Results
After finalising the structure of the nested multinomial logit model for local shopping
trips, various estimation runs were carried out using ALOGIT 3.2F in order to
determine the unknown coefficients’ values associated to all the utility functions.
The final form of the utility functions of the travelling modes associated to both
parent and child nodes are presented from Equations 8.9 to 8.16.
UCAD = Β1 TTCAD + B2 TCCAD (8.9)
UCAP = Β1 TTCAP + CCAP (8.10)
UFBB = B1 TTFBB + B2 TCFBB + CFBB (8.11)
UWB = B1 TTWB + B2 TCWB + B41 ATWB (8.12)
UW = B1 TTW (8.13)
UC = B1 TTC + CC (8.14)
where,
UCAD is utility function for the car as driver;
UCAP is utility function for the car as passenger;
UFBB is utility function for the feeder bus to bus on busway;
UWB is utility function for the walk to bus on busway;
UW is utility function for walking all-the-way to the destination; and
UC is utility function for cycling all-the-way to the destination.
UCAR = θCAR ln ∑=
I
i 1 eU
j (8.15)
UB = θB ln ∑=
K
k 1
eUm (8.16)
151
where,
UCAR is composite utility function for the car;
UB is composite utility function for the bus on busway;
j is the utility function of the jth mode in the car nest;
I is the total number of elements in the car nest23;
m is the utility function of the mth mode in the bus on busway nest;
K is the total number of elements in the bus on busway nest24;
θCAR is the scale parameter for the car nest; and
θB is the scale parameter for the bus on busway nest.
A few interesting similarities was found in the final specifications determined of
work and shopping trips, within the Shire, listed as follows,
• the utility functions developed for both trip purposes contained generic attributes
for in-vehicle travel time (TT) and out-of-pocket travel cost (TC);
• the model estimation statistics improved considerably, with small convergence
values, with the removal of the parent node for walking and cycling all-the-way;
and
• the attribute of waiting time for bus on busway was found to be statistically
insignificant for the utility functions of all access modes for bus on busway.
Table 8.4 presents the final model calibration results for local shopping trips, using
the nested multinomial logit model. The correlation values determined among the
attributes, for simple multinomial and nested multinomial logit model estimations are
tabulated in Appendix 6.
23 I = 2 for local shopping trips 24 K = 2 for local shopping trips
152
Table 8.4 Model Estimation Results for Nested Multinomial Logit Model
for Local Shopping Trips
8.3.3. Discussion on the Estimated Coefficients
A first observation that can be made by looking at Table 8.4 is that the signs of all
the coefficients shown are negative, other than the scale parameters which have
expectedly positive signs. This observation satisfies the fundamental priori of mode
choice modelling that the signs of the estimated coefficients associated to the
quantitative level-of-service attributes of all travelling modes to be negative and the
scale parameters associated to the composite modes to be positive.
Similar to local work trips, generic attributes were used for travel time and trip cost
for local shopping trips, indicating that the respondents, travelling locally, perceive
the two parameters similarly for each mode in the choice set. Regarding the other
specific attributes of waiting and access times, the former was found to be
MODE Variable Value T-Ratio Std. Error
TT -0.10870 -13.3 0.00818 Generic
Variables TC -0.00739 -5.7 0.00131
Car as Driver
Car as Passenger CCAP -4.27600 -13.3 0.32200
Feeder Bus to Bus on
Busway
CFBB -5.00400 -7.7 0.65400
Walk to Bus on Busway ATWB -0.20510 -3.3 0.06160
Walk
Cycle CC -1.61900 -7.6 0.21200
Car θCAR 0.71050 6.4 0.11100
Bus on Busway θB 0.48970 5.9 0.08340
ρ2 0.5307
Number of SP Observations 920
153
statistically insignificant for both the bus on busway modes used in the specification.
The local shopping mode choice users were only found to perceive the access time
for walk to busway as an influencing attribute on travel behaviour.
The value of time (VoT) for all modes, for local shopping trips, was observed to be
8.85 $/hour. This value is almost half of that determined for local work trips (16.50
$/hour) but however rational because it indicates that the respondents perceive their
journey to work as imperative to shopping trips. Similarly, the ratio of access time
over travel time for walk to busway mode, observed to be 1.9, was considerably
smaller than that determined for local work trips (3.3) showing that the perception of
the respondents is consistent for access times as well. An unadorned conclusion here
can be made that on aggregate basis, the respondents travelling locally weigh their
travel to work more important as compared to their respective shopping trips.
All the estimated coefficients along with mode-specific constants, shown in Table
8.4, were found to be statistically stable, with small values of standard errors. The ρ2
value obtained was also statistically satisfactory and the best of all the models,
developed for different trip purposes, discussed so far. The forecasted mode choice,
on aggregate level, was determined for the two logit models for local shopping trips
and is presented in Appendix 7.
8.3.4. Sensitivity of Travel Distance
Similar to local work trips, an sensitivity distribution was determined for travel
distance, rather than the attribute of travel time, for local shopping trips in order to
rationally observe the variation in mode choice at an aggregate level, as shown in
Figure 8.6.
154
0%
10%
20%
30%
40%
50%
60%
500 1000 1500 2000 2500 3000 3500 4000
Trip Length(metres)
Car as Driver Car as Passenger Feeder Bus to BuswayWalk to Busway Walk Cycle
Figure 8.6 Sensitivity of Travel Distance for Local Shopping Trips
Figure 8.6 presents a distinct set of sensitivity distribution curves, observed for all
the travelling modes present in the SP choice set developed for local shopping trips.
The shares of the two non-motorised modes were found to be considerably high for
small travel distances indicating that the respondents perceive them as highly feasible
alternatives to car for shopping for distances of 1500 metres or less. However, the
sensitivity curve for walking all-the-way expectedly associated a high negative slope
illustrating that the mode share decreases swiftly with the increasing trip length.
For distances greater than 3000 metres, the motorised travelling modes were found to
be dominantly driving the travel behaviour. The sensitivity curves obtained for
155
feeder bus to busway and car as passenger were almost horizontal with low
intercepts. The low percentage split value obtained for car as passenger points
towards the low vehicle occupancy in the study area for non-work trips, determined
by Queensland Transport (2007). A satisfactory finding from the transportation
planning perspective is that the elasticity distribution for walk to busway was found
to be an increasing curve, unlike the trip purposes discussed previously. It shows that
for any trip length, a competing travelling mode to car as driver was always present
for shopping trips within the Shire, and thus the ILTP environment seems practically
operatable, at least for local shopping trips.
For other level-of-service attributes, sensitivities were determined using the results of
nested multinomial logit model for local shopping trips and can be found in
Appendix 9.
8.4. MODE CHOICE MODEL FOR EDUCATION TRIPS
The education enrolment profile generated for the study area, presented in Section
4.3.4, showed that the majority of the students residing in the region are primary or
secondary school students. The trip distribution matrices developed for the study
area by Sinclair Knight Merz (2006) showed that the number of trip attractions in the
schooling zones match that of the school enrolment closely establishing that most of
the primary and secondary school students travel locally for educational purposes.
Additionally, since the Shire contains no tertiary institution, other than one TAFE (in
the suburb of Alexandra Hills), it was observed that most of the local education trip-
makers in the survey sample were primary or secondary school students. This lead to
an assumption for local education trips that car as driver and park and ride to
busway modes are not dominant in mode choice, as only a few primary or secondary
students possess valid driving licences.
The total number of stated preference (SP) mode choice responses attained for local
education trips were 448. Although, the sample generated associate a smaller size as
compared to that of other trip purposes in this research, it was found that the sample
was still representative of journey to education trips in the study area as total number
156
of education trip-makers is very low, as compared to other parts of South-East
Queensland (Australian Bureau of Statistics 2006b). The percentage split of the
mode choice users perceiving to have a choice for the travelling alternatives to the
car, presented to them in the SP survey, is shown in Figure 8.7.
81.63%
0.00%
0.00%
8.14%
0.79%
9.45%
0.00%
Feeder Bus to BuswayWalk to BuswayCycle to BuswayPark & RideKiss & RideWalk all-the-wayCycle all-the-way
Figure 8.7 Percentage Split of Mode Choice Users for Local Education Trips
No respondent was found to perceive the modes of cycle to busway, park and ride
and feeder bus to busway as feasible travelling alternatives to car, as shown in Figure
8.7. Therefore, the SP choice set generated for local education trips excluded these
three modes.
157
8.4.1. Model Specification
The model structure designed for local education trips contained six travelling modes
in the SP choice set. Simple and nested multinomial logit models were developed
based on these modes and were estimated using ALOGIT 3.2F. The estimated
coefficients determined from all the nested multinomial logit model estimation runs
showed instability and statistical unreliability, possibly due to the small sample size
and less variations in the coefficients of the nesting structure of the model. As a
result, the simple multinomial logit model was used for travel behaviour modelling
for local education trips, as shown in Figure 8.8. All the results discussed in Sections
8.4.2, 8.4.3 and 8.4.4 refer to the results of the simple multinomial logit model.
Figure 8.8 Simple Multinomial Logit Model for Local Education Trips
8.4.2. Modelling Results
The final form of the utility functions associated to the six travelling modes, shown
in Figure 8.8, is presented from Equations 8.17 to 8.22 for the simple multinomial
logit model for local education trips. The values of the estimated coefficients
obtained from the final model calibration are presented in Table 8.5.
Choice
Car As
Driver (CAD)
Walk to Bus on Busway (WB)
Kiss & Ride to Bus on Busway (WB)
Car As
Passenger (CAP)
Walk (W)
Cycle (C)
158
UCAD = Β11 TTCAD + B1 TCCAD + CCAD (8.17)
UCAP = Β21 TTCAD + Β22 HHSIZE (8.18)
UWB = B1 TCWB + B31 ATWB + CWB (8.19)
UKRB = B1 TCKRB + B41 ATKRB + CKRB (8.20)
UW = B51 TTW (8.21)
UC = B61 TTC (8.22)
where,
UCAD is utility function for the car as driver;
UCAP is utility function for the car as passenger;
UWB is utility function for the walk to bus on busway;
UKRB is utility function for the kiss & ride to bus on busway;
UW is utility function for walking all-the-way to the destination; and
UC is utility function for cycling all-the-way to the destination.
Table 8.5 Model Estimation Results for Simple Multinomial Logit Model
for Local Education Trips
MODE Variable Value T-Ratio Std. Error
Generic Attribute TC -0.00215 -3.1 0.00070
TTCAD -0.09002 -3.4 0.02630 Car as Driver
CCAD -1.66200 -4.1 0.41000
TTCAP -0.11290 -5.5 0.02060 Car as Passenger
HHSIZE -0.25950 -3.5 0.07390
ATWB -0.14720 -2.8 0.05260 Walk to
Bus on Busway CWB -1.55500 -2.6 0.60500
ATKRB 0.58060 4.8 0.12100 Kiss & Ride to
Bus on Busway CKRB -7.14000 -7.7 0.92600
Walk TTW -0.11150 -6.0 0.01850
Cycle TTC -0.20410 -7.5 0.02730
ρ2 0.3097
Number of SP Observations 448
159
8.4.3. Discussion on the Estimated Coefficients
Although the preliminary model estimation runs were carried out with all mode-
specific attributes, it was observed that the modelling results were not satisfactory as
convergence was achieved with low ρ2 values. After numerous runs, the model was
found to converge satisfactorily with the eccentric specification, as shown in
Equations 8.17 to 8.22.
The attributes of in-vehicle travel time and waiting time for bus on busway were
found to be insignificant and were excluded from the model specification. The
exclusion of the attribute of waiting time from the specification is consistent with the
previous modelling results on local trip purposes, where the variable was found to be
insignificant and not influence the travel behaviour of the residents of the study area.
However, the parameter of in-vehicle travel time for bus on busway has always been
significant and driving the mode choice decision-making framework for all the trip
purposes modelled previously. It shows that the out-of-pocket travel cost may be the
most vital attribute influencing the decision-making of the students for which mode
to select. The notion is further corroborated by the forecasted mode choice, shown in
Appendix 7, and the sensitivity distribution curves for travel costs shown in Figure
8.9.
The final ρ2 value determined for the model (ρ2 = 0.3097) was the smallest achieved
for any trip purpose modelled so far, indicating that the estimated coefficients may
not be fully representative of the journey to education travel behaviour of the study
area. Previous mode choice models, developed specifically for education trips, have
also shown low ρ2 values, using similar modelling framework (Jovicic and Hansen
2003). The main reasons for not satisfactorily calibrating a mode choice model for
education trips presented in Cain (2006) listing certain restrictions on the students’
mobility such as driving age regulations, travel costs and parental safety concerns
that play a considerable role in travel behaviour in addition to the level-of-service
attributes, used in the modelling framework.
The coefficients estimated from the final model estimation were found to be
statistically stable with low values of standard errors. On the contrary, the values of
160
the estimated mode-specific constants were found to associate high standard error
values and were found insignificant for the non-motorised modes.
8.4.4. Sensitivity of Travel Fare of Bus on Busway
Figure 8.9 presents the direct and cross sensitivity curves for out-of-pocket travel
cost of bus on busway for local education trips. For determining the elasticities, the
cost is taken as single concession fare for a one-way trip within the Shire while other
level-of-service attributes are kept constant as presented in Table 8.6. Since the
concerned trip is local, the upper limit of the fare is set to 4.00 dollars, since it is
irrational to increase the cost of the hypothetical bus on busway mode beyond the
current limit for small travel distances. Queensland Government (2007) presents a
detailed table of current public transport concession tickets in South-East
Queensland.
0%
10%
20%
30%
40%
50%
60%
70%
80 120 160 200 240 280 320 360 400
Trip Fare of Bus on Busway(cents)
Car as Driver Car as Passenger Walk to BuswayKiss & Ride to Busway Walk all-the-way Cycle all-the-way
Figure 8.9 Sensitivity of Travel Fare of Bus on Busway for Local Education Trips
161
Table 8.6 Fixed Values of Attributes for determining Sensitivity of Travel Fare for Bus on Busway for Local Education Trips
Attributes Fixed Values Attributes Fixed Values
TTCAR25 12 min TTB
26 12 min
TCCAD 180 cents WTB9 10 min
ATWB 8 min ATKRB 3 min
For the attribute of out-of-pocket travel cost for bus on busway, all linear curves
were estimated for direct and cross elasticities as opposed to other level-of-service
attributes where non-linear sensitivity distributions were observed as shown in
Appendix 9.
The walk to busway mode was found to be sensitive to the trip fare of the bus on
busway, while the other public transport mode, kiss and ride, was not influenced with
the variation in the attribute values. The non-motorised modes of walking and cycling
all-the-way were also found to be insensitive to the trip fare of the bus on busway
and were determined to have low percentage mode-share intercept values. On the
contrary, the percentage mode split of both the car modes expectedly increased with
the rising fares. It indicates that only three modes, the two car modes and walk to
busway, are found to be elastic for trip fare of the bus on busway for education trips
within the Shire, as shown in Figure 8.9.
The current two zone TransLink single concession ticket ($ 1.30) is shown in Figure
8.9 as the solid vertical line. It shows that if a reliable and efficient busway network
can become functional in future, as proposed in the ILTP, and all the level-of service
are kept constant at the current level, the percentage mode share for bus on busway
will reach 22 %, with most of the travellers walking to the busway. Therefore, in
order to attract a big number of current car users with mode choice to practically
switch to the bus on busway, walk on walkway or cycle on cycleway for local
education trips, the level-of-service attributes need to be substantially improved,
along with setting up of an efficient public transport network.
25 Same value for car as driver and car as passenger modes 26 Same value for walk to busway and kiss and ride modes
162
8.5. MODE CHOICE MODEL FOR OTHER TRIPS
As defined in Section 7.4, the other trips include all non-home based trips, in
addition to those home-based trips which are not undertaken for work, shopping or
an educational purpose. Therefore, the other trips categorise such a variety of
different types of trips that any pre-conceived notion on the mode choice of the
respondents is difficult to visualise.
After conducting the SP surveys in the study area, the number of mode choice
observations attained for local other trips was 544. The percentage split of the mode
choice users perceiving to have a choice for the travelling alternatives to the car,
presented to them in the SP survey, is shown in Figure 8.10.
0.00%
92.48%
0.00%
0.00%
0.00%
3.97%
3.55% Feeder Bus to BuswayWalk to BuswayCycle to BuswayPark & Ride to BuswayKiss & Ride to BuswayWalk all-the-wayCycle all-the-way
Figure 8.10 Percentage Split of Mode Choice Users for Local Other Trips
163
Figure 8.10 depicts an eccentric distribution for local other trips, with no respondent
perceiving mode choice for four elementary bus on busway modes, presented to them
in the SP survey, namely feeder bus, park and ride, kiss and ride and cycle to
busway. Since local other trips include a mix of various trip purposes for short
distances, it is difficult to state possible reasons for such an unorthodox mode choice.
However, the model specification developed for local other trips had to eliminate
these four modes from the SP choice set.
8.5.1. Model Specification
As discussed above, the model structure designed for local other trips comprised of
five elementary travelling modes, including the two car and non-motorised modes
and only one bus on busway mode. Simple and nested multinomial logit models were
tested on the SP mode choice data, using ALOGIT 3.2F. Both the models were found
to converge satisfactorily, with acceptable ρ2 values. The model structure developed
for the nested multinomial logit model for local other trips is shown in Figure 8.11.
Figure 8.11 Nested Multinomial Logit Model for Local Other Trips
Choice
Car As
Driver (CAD)
Walk to Bus on Busway (WB)
Car As
Passenger (CAP)
Car Bus on
Busway
Walk (W)
Cycle (C)
164
8.5.2. Modelling Results
The final form of the utility functions associated to the five elementary and two
composite travelling modes are presented from Equations 8.23 to 8.29 for the nested
multinomial logit model for local other trips. The values of the estimated coefficients
obtained from the final model calibration are presented in Table 8.7.
UCAD = Β1 TTCAD + B2 TCCAD (8.23)
UCAP = Β1 TTCAP + CCAP (8.24)
UWB = Β1 TTWB + B2 TCWB + B31 ATWB (8.25)
UW = B1 TTW (8.26)
UC = B1 TTC + CC (8.27)
where,
UCAD is utility function for the car as driver;
UCAP is utility function for the car as passenger;
UWB is utility function for the walk to bus on busway;
UW is utility function for walking all-the-way to the destination; and
UC is utility function for cycling all-the-way to the destination.
UCAR = θCAR ln ∑=
I
i 1 eU
j (8.28)
UB = θB ln ∑=
K
k 1
eUm (8.29)
where,
UCAR is composite utility function for the car;
UB is composite utility function for the bus on busway;
j is the utility function of the jth mode in the car nest;
I is the total number of elements in the car nest27;
m is the utility function of the mth mode in the bus on busway nest;
K is the total number of elements in the bus on busway nest28;
θCAR is the scale parameter for the car nest; and
θB is the scale parameter for the bus on busway nest.
27 I = 2 for local other trips 28 K = 1 for local other trips
165
Table 8.7 Model Estimation Results for Nested Multinomial Logit Model
for Local Other Trips
MODE Variable Value T-Ratio Std. Error
TT -0.06989 -10.3 0.00675 Generic Attribute
TC -0.00631 -2.9 0.00217
Car as Driver
Car as Passenger CCAP -4.07900 -8.3 0.49100
Walk to Bus on Busway ATWB -0.18360 -1.96 0.11500
Walk
Cycle CC -1.93100 -6.8 0.28300
Car θCAR 0.53620 3.1 0.17400
Bus on Busway θB 0.34130 2.9 0.11900
ρ2 0.3977
Number of SP Observations 544
8.5.3. Discussion on the Estimated Coefficients
The model estimation results attained for local other trips were slightly distinct from
those of models calibrated previously for different trip purposes. Firstly, generic
attributes were used for both in-vehicle travel time and out-of-pocket travel cost since
the preliminary modelling results, done using specific attributes, were found to be
statistically unsatisfactory. It indicates that the respondents perceived both the
attributes uniformly, irrespective of the travelling mode. Secondly, similar values of
estimated coefficients were devised from simple and nested multinomial logit model
estimations, showing that any of the two can be used as a representative model for
the travel behaviour of the population of the study area for local other trips, without
any priority. The model calibration results for the simple multinomial logit model for
local other trips are tabulated in Appendix 12.
The value of time (VoT) was calculated to be 6.65 $/hour for local other trips. Since
the attributes of travel time and cost, shown in Table 8.7, were found to associate
166
generic attributes, only one VoT was determined for the whole population,
irrespective of the travelling mode used.
The VoT determined for local other trips is smaller than those observed for different
trip purposes modelled previously. For local other trips, although the VoT may
associate a significant sampling bias, it still indicates that the population prioritise
their trips according to different purposes, with other trips having the least
importance. A comparison of VoTs observed for different modelled trip purposes is
presented in Table 8.9.
The generic coefficients estimated from the nested multinomial logit model were
found to associate statistically significant and reliable values, along with the mode-
specific constants. Contrarily, the attribute of the access time for walk to the busway
mode was found to not considerably affect the travel behaviour of the population for
local other trips, as shown in the sensitivity distributions curves in Appendix 9.
8.5.4. Sensitivity of Travel Distance
Figure 8.12 presents the direct and cross elasticity curves for travel distance for all
the travelling modes in the SP choice set generated for local education trips. For
determining the elasticities, all the level-of-service attributes, other than the in-
vehicle travel times, were kept constant as shown in Table 8.8.
167
0%
10%
20%
30%
40%
50%
60%
500 1000 1500 2000 2500 3000 3500 4000
Trip Length(metres)
Car as Driver Car as Passenger Walk to Busway Walk Cycle
Figure 8.12 Sensitivity of Trip Length for Local Other Trips
Table 8.8 Fixed Values of Attributes for determining Sensitivity of Trip length for
Local Other Trips
Attributes Fixed Values Attributes Fixed Values
TCCAD 150 cents TCWB 220 cents
ATWB 7 min WTWB 10 min
168
From Figure 8.12, the most important finding, from transportation planning
perspectives, was that the percentage mode share of car as driver never exceeded 50
% of all the mode shares. It indicates that the mode choice users, identified in the SP
study for local other trips, showed a considerable potential to switch to the
sustainable travelling alternatives to car for the concerned trip purpose. Another
satisfactory finding was the high aggregated forecast for the usage of non-motorised
travelling modes, particularly for travel distances less than 2000 metres. The car as
passenger mode was found to be inelastic to travel distance, since its curve was
determined to be almost horizontally straight with a low intercept value.
8.6. SUMMARY
This chapter has presented various mode choice logit models developed for four
unique trip purposes for local trips, along with discussing the modelling results of the
most representative model for each purpose. The modelling results of the logit
models not discussed here and the forecasted mode choice, at an aggregate level, for
all the trip purposes are presented in Appendix 6 and 7 respectively.
Unlike the regional trips, a significant number of SP mode choice responses were
observed for each purpose in local trips as shown in Table 8.1. Therefore, four sets of
logit models were developed in order to forecast a unique travel behaviour for each
trip purpose. However, a few significant similarities were found for each trip
purpose.
Firstly, the waiting time for bus on busway was found to not influence the travel
behaviour of the study substantially for all for all trip purposes. A possible reason for
such a consistent pattern across each trip purpose may be associated to the definition
of the busway network, described to the survey respondents, based on the ILTP
proposal which defines it to be frequent, efficient and reliable. Thus, for short
distances, the respondents might have perceived the headway time of bus on busway
to be not very long and thus, did not weighed it highly in the mode choice. Secondly,
for each trip purpose, the level-of-service attribute which was found to considerably
drive the mode choice in the study area, associated a generic coefficient. It indicates
169
that the respondents practically had a uniform perception for the attribute,
irrespective of the travelling mode in the SP choice set generated for that specific
purpose. Additionally, although the access time for bus on busway was found to be
statistically significant for most bus on busway modes in each trip purpose, the
travelling modes were generally observed to be insensitive to the variation in the
attribute values.
Similar to regional trips, all the specifications developed for the local trip models
were based on the travel environment, proposed in the ILTP. The mode choice
perception of the respondents, however, was found to be significantly different from
each other. For regional trips, a few respondents were found to perceive the non-
motorised modes as valid travelling alternatives to the car and thus, the model
specification involved only car and bus on busway modes competing with each
other. On contrary, complex nested logit model specifications were designed for
local trips since the respondents perceived to use walking and cycling all-the-way in
addition to the travelling modes generated in the regional model specifications.
The values of time (VoTs) observed for each model varied not only by trip purpose,
but also by trip lengths. A comparison table for all the VoTs of the most
representative model for each trip purpose discussed previous is presented in Table
8.9.
Table 8.9 Comparison of Values of Time (VoTs) for Different Trip Purposes
Trip Length
Trip Purpose
Value of Time (VoT)
($/hour)
ρ2 Value
Car 11.67 Work Bus on Busway 9.05
0.4766
Car 21.90
Regional
Other
Bus on Busway 2.30
0.3726
Work 16.50 0.4154 Shopping 8.85 0.5307 Education 12.5529 0.3097
Local
Other
6.65 0.3977
29 Since the travel time for bus on busway modes for local education trips was found insignificant, the VoT observed for car drivers was halved to represent all types of respondents in the sample
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9 Statistical Analysis of Captive Data
9.1. INTRODUCTION
Travellers are generally classified into two main categories namely choice and
captive users. The choice users are those who select transit services or automobiles
or other non-motorised modes when they view one option as superior30, whereas the
captive users are those having only one travel option. In other words, the choice
users can be regarded as the travellers having mode choice for a certain trip purpose
while the mode captive users are the ones without any opportunity to switch to
another mode. The inability of the captive users to switch to an alternative mode can
be due to certain factors such as unavailability of the alternative modes for a certain
trip, or the unattractive level-of-service or network parameters that the alternative
modes offer. However a traveller who is captive to a mode for a certain trip does not
necessarily behave in the same manner for other types of trips. For example, an
individual doing a shopping trip on a week-end may be a car captive due to the high
waiting times of public transport but may have a choice for work trips.
This chapter presents various statistical analyses performed on the survey sample by
categorising it into the two traveller types (captives and choice users) on the basis of
different trip purposes, trip lengths, household sizes and age-groups. The main aim is
to ascertain the percentage shares of captive and choice users in the study area for
each characteristic in order to surmise the influence on travel behaviour by the two
categories of traveller types. From a transport modelling perspective, little is known
about the captivity effect on the travel mode choice decision-making since there are
no standard techniques for modelling captive users’ data. Thus, this chapter focuses
only on the statistical analyses of these captive users and presents the findings in the
form of graphs so that the reader can infer the travel behaviour of the mode captive
population of Redlands. The statistical data used in sketching all these graphs is
presented in Appendix 13.
30 Based on the theory of utility maximisation
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For this research, the captive users are further split into two main groups namely,
• car captives; and
• public transport (PT) captives.
Since the study area selected for the research, southern suburbs of Redland Shire, has
a higher car ownership levels as compared to some other areas of South-East
Queensland, as discussed in Section 4.3.5, the majority of the sample were found to
be car captives. Further analyses were performed on the data obtained by surveying
the car captive population in order to determine the main reasons for high captivity
among the targeted population.
For this study, a person captive towards car was defined as someone who is currently
using car and:
• perceives to keep using it when presented with the hypothetical SP scenario, as
discussed in Chapter 5;
• selects an alternative mode from the SP choice set other than car but when
presented with SP mode choice games, selects car as the preferred mode for all
the eight scenarios31;
• has to use car for a certain trip purpose as part of his/her travel requirements32;
and
• has got fuel or parking paid by someone else (employer, etc)33.
The main assumption used in the analysis is that the captivity can vary among
different trip purposes and trip lengths. For example, a person captive to public
transport for local work trips may perceive a choice for work trip destined on the
CBD corridor. Therefore, similar to the choice users data, the captive data was also 31 This type of person is regarded as captive since he/she never selected the alternative mode in all 8 SP mode choice games; although he/she claims to have a choice which means that either the person is not stating the truth or will only choose the alternative mode if provided with highly attractive values for the attributes of the alternative mode. Since the SP survey instrument is designed to present hypothetical but realistic scenarios, it means that this person, in actual practice, will not select the alternative mode; thus making himself/herself captive towards car 32 For example, someone who works in the lawn-mowing industry may have to use an automobile in order to carry various machines and hardware tools that cannot be carried using public transport or someone with a company car etc. 33 This person does not necessarily have to be a captive but in most of the cases, he/she will be highly attracted towards using the car
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divided into two main categories of regional and local trips and then further sub-
divided into four categories of work, shopping, education and other trips in order to
perform statistical analyses on all these categories.
9.2. DATA ANALYSIS OF SURVEY SAMPLE
In Chapter 4, it was observed that the population of the study area selected for this
research associate significantly high car ownership levels as compared to that of
Brisbane City, as presented in Table 4.6. An illustration of the car ownership at
household level of the study area, along with comparing with that of Brisbane City,
is presented in Figure 9.1, based on the results of the 2001 census (Australian Bureau
of Statistics 2007a).
Household Vehicle Ownership
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
0 1 2 2+
Number of Vehicles
Perc
enta
ge o
f Hou
seho
lds
in S
ubur
bs
CapalabaRedland BaySheldon - Mt CottonThornlandsVictoria PointBrisbane City
Figure 9.1 Household Vehicle Ownership Level in Redlands and Brisbane City
173
Figure 9.1 justifies the assumption made prior to the survey implementation, as
discussed in Chapter 4, that most of the sample consists of car captive population due
to the high car ownership level in the region. This notion is graphically illustrated in
Figure 9.2, by categorising the total survey sample into the three traveller types of
car captive, PT captive and choice users and forecasting their percentage splits.
Majority of the survey sample were observed to be car captives (around 60 %),
indicating that the travel behaviour of the study area is significantly influenced by
these users.
Since the surveys conducted in the study area involved SP scenarios with
hypothetical travelling alternatives to the car, as discussed in Chapter 5, Figure 9.2 is
a forecast of the percentage sample splits, based on the three user types, provided the
proposed travel environment in the ILTP can be established in practice. However, the
percentage split of the car captive users may considerably increase if the scenarios,
shown in the form of SP mode choice games to the respondents, do not get
implemented as the respondents were found to perceive highly of the level-of-service
attributes associated to the car, as discussed in Chapters 7 and 8.
60.54%
12.31%
27.15%
Car Captive UsersPT Captive UsersChoice Users
Figure 9.2 Sample Split according to Traveller Type
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9.2.1. Trip Purpose
Figure 9.3 presents an enhanced illustration of Figure 9.2, by splitting the sample
according to the three travellers types and four unique trip purposes of work,
shopping, education and other trips, indicating the variation among the captive and
choice users for each trip purpose.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Work Shopping Education Other
Choice UsersPT Captive UsersCar Captive Users
Figure 9.3 Sample Split according to Traveller Type and Trip Purpose
From Figure 9.3, it can be seen that the car captive users formed the majority in the
sample generated for each trip purpose other than for education trips. The possible
reason for observing low number of car captive users from the SP surveys for
education trips could be that the full-time students receive 50 % concession on the
public transport fares. This fact was also depicted in the SP mode choice games
where the bus on busway trip fare for full-time students was presented as half to that
of an adult’s ticket. This may have resulted in making the car as an unattractive mode
when compared to the bus on busway for education trips. Moreover, the highest
number of travellers currently captive to the public transport was also found for
education trips indicating that a considerable percentage of education trip-makers are
175
even currently using public transport. Secondly, most of the primary and secondary
school students do not possess valid driving licences. Therefore, the car as driver
mode was found to be almost non-existent in the travel behaviour determined for
local education trips, as discussed in Chapter 8.
The highest percentage split of car captive users was obtained for home-based
shopping trips. The possible reason for such behaviour can be the convenience and
comfort associated with the car, as compared to the public transport modes, for
shopping trips. Additionally, the lowest percentage split for public transport captive
users was also observed for the shopping trips (around 3 %). It indicates that most of
the trip-makers perceive highly of the attributes associated to the car for shopping
purposes and thus, do not find the attributes of other travelling alternatives attractive
enough to compete with the car for the concerned purpose.
For work trips, around 35 % of the respondents perceived to have a choice for
travelling with other modes than the car while around 10 % are currently captive to
public transport. This is a vital finding from transportation planning perspectives
since it indicates that with a maximum shift34, a significant percentage of the current
car users in the working population of the study area perceive switching to public
transport and non-motorised modes for work trips while the other 55 % will keep
using their cars.
The statistics obtained for other trips is similar to that of work with around 58 % of
the travellers resulting to be car captives for this trip purpose. However, it is difficult
to make any travel behaviour forecast at the aggregate level for other trips since they
involve a mix of various trip types and thus, may not lead to an imperative
conclusion.
9.2.2. Trip Length
One of the main hypothesis for this research, mentioned in Chapter 1, was that the
travel behaviour does not only vary among different trip purposes, as stated in
34 A maximum shift refers to the travel behaviour change that can occur if the hypothetical scenarios are actually implemented and all the choice users start using alternative modes to car, exactly as they perceive
176
previous mode choice modelling studies, but also among various trip lengths. Figure
9.4 justifies this notion by illustrating the percentage sample splits for the three
traveller types, further split into the two trip lengths and four trip purposes.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Regional-Work
Regional-Shopping
Regional-Education
Regional-Other
Local-Work Local-Shopping
Local-Education
Local-Other
ChoiceUsers
PTCaptiveUsers
CarCaptiveUsers
Figure 9.4 Sample Split according to Traveller Type with respect to Trip Length and Trip Purpose
Higher car captive and lower public transport captive shares were found for all the
four local trips as compared to their corresponding regional trips. It can be deemed as
a vital finding from the transportation planning perspective as a significant
percentage of the trip-makers, travelling on the CBD corridor, were found to
perceive mode choice for car as compared to those travelling within the Shire. It
indicates that for long travel distances, particularly for work and education purposes,
a considerable percentage of the population of the study area perceive switching to
bus on busway, as compared to short distance travellers who perceive using cars for
177
most of their trips. Moreover, since most of the regional trips were found to be
destined to the CBD as shown in Appendix 14, there may be various other reasons,
such as high in-vehicle travel time of car due to congestion, parking cost in the CBD,
parking search time, etc that may influence the mode choice decision-making of the
travellers. The direct and cross elasticities determined for some of these modal
parameters for regional work trips are discussed in Chapter 7.
9.2.3. Household Characteristics
Figures 9.5 and 9.6 present percentage sample splits, similar to those shown in
Figures 9.3 and 9.4, on the basis of the characteristics of the household, rather than
that of the trip. These household characteristics are the size of the household and the
age-group of the respondents respectively.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 3+
Choice UsersPT Captive UsersCar Captive Users
Figure 9.5 Sample Split according to Traveller Type with respect
to Household Size
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Less than18
18 - 45 46 - 59 60 or Older
Choice UsersPT Captive UsersCar Captive Users
Figure 9.6 Sample Split according to Traveller Type with respect to Age Groups
In Figure 9.5, the percentage share of car captive users for all households with more
than one person is substantially higher than that of 1 person households. Similarly, 1
person households seem to have a higher percentage split for choice users as
compared to those with more than 1 person. However, the percentage split of public
transport captive users seems to remain same among all the four types of household
sizes. Overall, it indicates that the percentage share of the car captive users is not
directly proportional to the household size since it remains almost constant for
households with more than 1 person.
Figure 9.6 shows that the percentage of car captive users almost becomes double for
people in the combined age brackets of 18-59 as compared to those who are under 18
years of age. A possible reason for the drastic change in the travel behaviour can be
179
associated to the fact that almost all the travellers, under 18 years of age, do not
possess a valid driving license, and therefore, do not have car as driver mode as an
available travelling option in their specific SP choice sets. Moreover, the highest
percentage share of public transport captive users was also found for the travellers,
under 18 years of age. There is a small decrease in the percentage share of the car
captive users for the travellers falling in the age bracket of 60 and above mainly due
to the fact that some of them may not be able to drive and therefore become captive
to public transport as shown in Figure 9.6.
9.2.4. Work Trip Destinations
Figure 9.7 shows an interesting statistics on the work destinations that were found to
be popular among the residents of the study area. These destinations were designed
according to the number of travellers that currently travel to these areas for work
purposes, taken from the working population profile developed in Australian Bureau
of Statistics (2007c) and the Origin-Destination matrices generated for the four-step
Brisbane Strategic Transport Model (BSTM) in Sinclair Knight Merz (2006).
Appendix 14 presents a list of these work trip destinations of the suburbs of South-
East Queensland which are represented in Figure 9.7.
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
BrisbaneCBD
Cleveland/
Capalaba
RedlandsOther
Suburbs
BrisbaneSouthernSuburbs
BrisbaneNorthernSuburbs
Logan
Choice UsersPT Captive UsersCar Captive Users
Figure 9.7 Sample Split according to Traveller Type with respect to Work Destinations
As expected, the highest percentage share of the public transport captive users is
those currently travelling to Brisbane CBD for work purposes. The possible reasons
for selecting the public transport for these particular trips seem to be the high in-
vehicle travel time of the car due to congestion on the main roads to CBD35 at the
peak-hours, significant parking costs and the easy accessibility of the current public
transport services destined to CBD as compared to those for other destinations. For
choice users, there is almost a uniform percentage share among the travellers to all
these areas for work. However, the percentage shares for public transport captive
users vary substantially among all these areas with there currently being no traveller
using public transport for going to Logan area for work.
35 Generally, the two main roads, other than the South-East Motorway, used by the residents of Redland Shire to travel to CBD are Old Cleveland Road and Mount Gravatt-Capalaba Road
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9.3. CLASSIFICATION OF CAR CAPTIVE USERS FOR WORK
For this study, the travel data obtained from the SP surveys regarding the car captive
users for work trips was further broken down into the following four different types
of car captive users,
I. those who did not perceive to have a choice other than car for a specific trip
purpose in the SP part of the survey (also referred as non-traders);
II. those who have to use car as part of their work requirement (tradesman, self-
employed etc);
III. those who are given automobiles by the company; and
IV. those whose trip destinations are geographically located as such that the
public transport modes or the non-motorised modes are inaccessible or highly
unattractive to those areas.
The main aim of determining the above split was to distinguish between those car
captive users who truly cannot switch to the sustainable travelling alternatives to the
car (Types II, III and IV) in the ILTP environment and those who currently do not
perceive to use the non-car modes (Type I). Figure 9.8 shows the percentage split of
these car captive users based on the four categories for work purpose.
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68.57%
12.35%
14.50%
4.58%
IIIIIIIV
Figure 9.8 Types of Car Captive Users for Work Trips
Figure 9.8 shows that around 68 % of the car captive travellers were found to belong
to Type I, i.e. currently not perceiving to switch to any mode other than car for their
work trips. It means that if the ILTP environment can be implemented in practice, it
is somewhat probable that a few Type I car captive users may switch to the non-car
modes for work purposes since their perception for the busways or walkways or
cycleways may change with the operational ILTP environment. However, the
remaining 32 % of the users belonging to Types II, III and IV are highly unlikely to
switch to any other mode than car due to the reasons stated above.
9.4. ACCESS MODES DISTRIBUTION FOR PT CAPTIVE USERS
The survey data obtained for PT captives was distributed on the basis of their current
transit access modes. The main aim of determining this split was to observe the most
commonly used access mode among the users. Figure 9.9 presents the access mode
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distribution for PT captive users for all trips. Similar distributions, for individual trip
purposes, are presented in Appendix 15.
4.45%
57.09%
0.00%
33.20%
5.26%
Feeder Bus to PTWalk to PTCycle to PTPark & Ride to PTKiss & Ride to PT
Figure 9.9 Access Mode Distribution for PT Captive Users for all Trips
9.5. SUMMARY
The main aim of this chapter was to analyse the percentage of users captive towards
car and public transport and those perceiving to have choice for various trip types.
Various statistical analysis were performed on the survey data of the sample, obtained
from conducting the SP surveys in the study area, on the basis of traveller type by
splitting it into the trip characteristics, such as trip purpose and trip length, and the
household parameters, namely household size and age-group. As expected, the
number of car captives was found to be in the dominant majority of the survey sample
for each split case. It means that overall travel behaviour of the population of the study
area is highly influenced by the travellers who are currently captive towards car and
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do not perceive to use any other mode in the future, even with the practical
implementation of the proposed ILTP travel environments.
However, when the car captive users were further classified into four unique
categories of work purpose, it was found that more than two-thirds of the users were
non-traders who simply did not perceive to have a choice other than car for work
trips but may change their minds under the impact of operational ILTP environment.
Additionally, the sample split revealed a significant percentage of potential choice
users, particularly for work and education purposes and public transport captive users
for education purposes.
From a transport modelling perspective, there are no standard techniques for
modelling the captive user data, unlike the choice user information. Nonetheless, the
extensive statistical analyses performed in this chapter on the survey sample does
explain the car captivity effect on mode choice decision-making to some extent and
can assist the urban planners in assessing the potential feasibility of busways,
walkways and cycleways in the study area, as proposed in the ILTP. Further, this
captivity effect can also be observed on the three different traveller types, split into
various trip types as discussed in Section 9.2.
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10 Conclusions
10.1. RESEARCH SUMMARY
This PhD research was conducted with the aim of estimating mode choice models to
forecast the travel behaviour of the population of Redland Shire under hypothetical
travel scenarios. For this purpose, a computer-based stated preference (SP) survey
was designed and conducted in the study area, recording perceived mode choice
observations of the respondents to the hypothetical travelling alternatives to the car.
The SP choice set of the hypothetical travelling alternatives to the private car was
generated, based on the modes proposed in the Integrated Local Transport Plan
(ILTP), developed by the Redland Shire Council (2002). One of the major thrusts of
ILTP is to reduce the car dependency and increase the share of sustainable travel
modes such as walking, cycling and public transport, as shown in Figure 1.1.
However, in order to bring other forms of transport in the level capable of competing
with car, it is necessary to substantially improve the transport infrastructure and
facilities related to these modes.
Despite the development of various passenger mode choice models to forecast the
travel behaviour in the past, little has been done to jointly analyse the sensitivity of
the travel behaviour of the population with characteristics of the trips undertaken. In
order to forecast the modal splits of a study area with a higher degree of accuracy,
mode choice modelling needs to be done using these characteristics, by categorising
the model specification into different trip lengths and trip purposes. In this study,
unique logit models were developed for four trip purposes (work, shopping,
education and other trips), and with two trip lengths (trips destined on the Brisbane
CBD corridor, known as regional trips, and those undertaken within the Shire,
known as local trips).
Previous stated preference (SP) mode choice studies have generally forecasted the
travel behaviour of the targeted population in the presence of a hypothetical
186
motorised alternative for car, such as a high-speed train or a bus on busway (Gunn et
al. 1992, Yao et al. 2002). This study focused on using both motorised (bus on
busway) and non-motorised travelling modes (walking on walkway and cycling on
cycleway) as virtual alternatives to the private car. Additionally, a unique choice set
of access modes for bus on busway was also generated, containing hypothetical
modes such as secure park and ride facilities and kiss and ride drop-off zones at the
busway stations, walkway and cycleway facilities to access the busway stations and a
frequent and integrated feeder bus network within the Shire. Therefore, this study
created a totally new virtual travel environment for the targeted population, in order
to record their perceived observations under these scenarios and develop the mode
choice models.
A transitory review on travel demand modelling, done before the actual research
implementation, showed that the travel behaviour of the population of economically
developed countries is highly influenced by car (Fontaine 2003), with most of the
trips being made in a single-occupant vehicle (SOV). From the 2001 Census, similar
travel characteristics were observed for Redland Shire, the study area selected for
this research (Australian Bureau of Statistics 2007b).
A big part of the targeted population is generally car captives who are not likely to
switch from cars to public transport; even if a more efficient transit infrastructure is
implemented. In the past, the mode choice models have been generally calibrated
using the mode choice survey data, while that of the captive users were ignored. This
yields a knowledge gap in capturing the complete travel behaviour of a region, since
the question of what particular biases can be involved with each model estimation
parameter by the captives remain unresolved. Therefore, in this study, the captive
user data, obtained from the surveys, was statistically analysed in order to estimate
their relative influence on mode choice, in specific, and travel behaviour, in general.
10.1.1. Findings from the Literature Review
A state-of-the-art literature review was conducted on travel demand modelling and
stated preference (SP) surveys. The main aim of appraising the literature was to
determine a modal split model that can be implied to forecast the travel behaviour of
187
the population of Redland Shire under the ILTP travel environment, for different trip
lengths and trip purposes, using a futuristic SP survey instrument design.
It was found that the logit models associate the most practical modelling framework,
out of all modal split models, although they are based on the IIA property; which
assumes that all the travelling modes used in the choice set are independent of each
other. This condition can, however, be relaxed with the use of a tree structure that
combines the correlated modes into one nest. Logit models are generally classified
into two main categories, namely the binary and multinomial logit models,
depending on the size of the choice set generated for the study area. For choice set
presenting two travelling alternatives to the targeted population, a binary logit model
was preferred. Contrarily, multinomial logit models were implied for bigger choice
sets. Maximum likelihood method was found to be the most commonly used
estimation technique for logit models, due its ability to handle complex structures.
Computer estimation packages such as ALOGIT were found to be generally implied
by the transport modellers for model estimation purposes, mainly due to their
capability to perform numerous mathematical iterations using various statistical
techniques.
In order to estimate the mode choice models for forecasting purposes, a stated
preference (SP) survey need to be conducted in order to present the respondents with
the hypothetical travel scenarios, discussed above. Therefore, the literature based on
various physical forms of the survey instruments was reviewed. The two most
common survey instrument designs were found to be the computer assisted personal
interviewing (CAPI) and paper-and-pencil interviewing (PAPI).
CAPI was found to be most famous SP surveying technique, among the survey
designers, due to its graphically attractive presentation format and higher response
rates as compared to other surveying methods. WinMint, a software programming
tool, was found to be one of the most commonly used CAPI designing packages
being used by the transport surveyors.
For generating an apposite sample from the study area, five sample generation
techniques were reviewed and compared, as shown in Table 3.1. The method of
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stratified random sampling was deemed as the most suitable sampling technique
considering the small population size of the study area for the research.
10.1.2. SP Survey Instrument Designing and Implementation
Prior to implementing the data collection phase of the research, several socio-
demographic determinants of the study area were studied that were known to impact
on the current and potential travellers in their mode choice decision-making for
different trip purposes. It was found that the population of the study area has a higher
socio-demographic profile as compared to that of Brisbane’s or other urban areas’
residents. Therefore, it was concluded that the sample generated for the survey is to
be regarded as a relatively difficult group to “get out of their cars” (Redland Shire
Council 2003).
The methodological framework for designing the computer assisted personal
interviewing (CAPI) instrument, in order to conduct the SP surveys in the study area,
was then developed, as shown in Figure 5.1. Since distinct choice sets were
determined for each trip length and trip purpose, the design of the instrument varied
slightly, however, followed the same framework in each case. The framework
consisted of three main modules of the survey instrument namely personal
information, revealed preference (RP) and stated preference (SP) modules.
WinMint 3.2F was chosen to program the CAPI survey instrument for this research.
The main reason for selecting this computer package is due to the facility it provides
to the survey designer of increasing the number of varying levels for each attribute,
without varying the base design of the instrument. It further ensures that the sets of
choice alternatives with exactly the same levels for all design variables are not
presented; thus maintaining orthogonality.
After designing the CAPI survey instrument, a pilot survey was conducted in the
study area, on a small sample, in order to test various features of the instrument
design and observing the reactions of the respondents on the CAPI graphical
interface. No major survey instrument design editions were made as the respondents
were found to react positively to the CAPI graphical interface. A high captive to
mode choice users ratio was expectedly observed among the respondents, indicating
189
that a significantly larger sample needed to be generated in order obtain a substantial
number of mode choice responses for model estimation purposes.
The actual survey, on the full sample, was then implemented using the finalised
instrument design. The sample for the survey was generated using the method of
stratified random sampling, with stratification done on the basis of the population of
each suburb, in the study area, and the current modal splits of the population for
work trips.
A total number of 2574 residents of the study area were contacted to participate in
the study, out of which 2007 responded positively, resulting in a positive response
rate of 78 %.
After collecting the SP data from the surveys, it was ensured that the characteristics
of the sample match that of the study area; so that the forecasts of the mode shares,
as done in Chapters 7 and 8, are representative of the targeted population. To achieve
this, percentage population splits were determined from the sample on the basis of
each suburb of the study area and were compared with those observed in the 2001
Census. Further, the current modal split of the respondents was compared with that
of the entire population of the study area for work trips. Both comparisons showed
that the sample characteristics closely match that of the targeted population justifying
that the sample, generated for the study, is representative. Various statistical analyses
were then performed on the survey sample and the data, in order to infer a picture of
the pre-modelled travel behaviour of the population of the study area.
The survey sample was distributed on the basis of traveller type, i.e. choice and
captive users, for all trip purposes. It was observed that the traveller type distribution
was uniform among all the suburbs of the study area; therefore, there is no need to
model the travel behaviour separately for each suburb.
The survey data set was also categorised on the basis of current and perceived
travelling modes of the respondents for different trip purposes, as shown in Figure
6.5. As expected, the combination of car-car was observed to have the highest
volume (980 out of 2007 respondents) indicating a principal presence of car captive
190
users in the study area. Hence, it was anticipated that the model estimation results, in
Chapters 7 and 8, shall forecast a high car usage, even under the hypothetical travel
scenarios, for all trip purposes. However, the analysis for education trips
demonstrated a high use of public transport modes, indicating that a considerable
number of students currently use public transport for educational purposes.
10.1.3. Model Estimation and Data Analysis
After completing the research phase of survey instrument designing and data
collection, numerous logit models were estimated for different trip lengths and trip
purposes, in order to forecast the travel behaviour of the study area under the
influence of the hypothetical travel environment. The model specifications, logit
structures, modelling results, forecasted mode shares and the elasticities of various
level-of-service attributes associated the travelling modes in the SP choice set for
each trip purpose, are presented in Chapters 7 and 8 for regional and local trips
respectively.
For regional trips, the number of respondents surveyed for shopping and education
purposes were not statistically large enough to calibrate the logit models. Thus, the
regional trip models involved two purposes only, namely work and other trips. The
respondents were also found to have a low perception for using hypothetical non-
motorised modes, namely walking on walkway and cycling on cycleway, as
alternatives to car for any trip purpose, as shown in Figures 7.2 and 7.10.
Consequently, the SP choice sets developed for both regional trips contained only the
motorised mode of bus on busway, along with the servicing access modes.
From the logit model estimations of regional trips, the level-of-service attributes of
car as driver and walk to busway were found to be relatively less negative,
illustrating that the two modes can compete highly with each other, for both trip
purposes, if an efficient and reliable busway network from the study area to Brisbane
city can be implemented in practice. Contrarily, all other modes in the SP choice set
were not found to influence the mode choice significantly, apart from park and ride
to busway which seemed to compete with car as driver, if the in-vehicle travel time
of bus on busway can be less than or equal to 40 minutes.
191
Three logit models, with each having a unique specification, were developed for both
work and other trips on the CBD corridor, namely simple binary, simple multinomial
and nested binary logit models. For both trip purposes, the nested binary logit model
was found to be the most appropriate and representative model. Most of the
estimated coefficients, in the preliminary utility function specifications, were found
to be statistically significant and stable, with negative signs. This was, however, not
the case for all the mode-specific constants, since some of them had high standard
error values, although always having negative signs. As expected, the in-vehicle
travel time, associated to any mode (with bus on busway in particular), was found to
be the most influential attribute in mode choice, at both aggregate and disaggregate
levels. The waiting time for the bus on busway was found to apparently not influence
the mode choice for both trip purposes, along with other socio-economic attributes
other than the household size for work trips.
Moreover, the values of travel time (VoT) for regional work trips (11.67 $/hour for
car trips and 9.05 $/hour for bus on busway trips) closely matched the ones
determined previously in mode choice studies done for the Brisbane statistical
division, as shown in Table 7.6.
For local trips, a significant number of SP mode choice responses were observed for
all four trip purposes, unlike its regional counterpart, as shown in Table 8.1.
Therefore, four unique sets of logit models were developed in order to forecast the
travel behaviour for each trip purpose.
Contrarily to the regional trip models, nested multinomial logit structures were
employed for local trips, since the respondents were found to perceive walking and
cycling all-the-way as valid travelling alternatives to car, in addition to the bus on
busway.
From the final model estimation run for each purpose, the waiting time for bus on
busway was found to not influence the travel behaviour of the study area
substantially. A possible reason for such a consistent pattern across each trip purpose
may be associated to the definition of the busway network, described to the survey
respondents, based on the ILTP proposal which defines it to be frequent, efficient
192
and reliable. Thus, for short distances, the respondents might have perceived the
headway time of bus on busway to be not very long and thus, did not weighed it
highly in the mode choice. Additionally, for all local trip purposes, the level-of-
service attribute which was found to considerably drive the mode choice in the study
area, associated a generic coefficient. It indicates that the respondents practically had
a uniform perception for the attribute, irrespective of the travelling mode in the SP
choice set generated for that specific purpose. Moreover, the access time for bus on
busway, although estimated to be statistically significant, was found to be inelastic to
the forecasted mode shares, indicating that the attribute may not considerably
influence the travel mode choice of the targeted population.
After estimating mode choice models for different trip lengths and trip purposes,
various statistical analyses were performed on the survey sample, by categorising it
into the two traveller types of captive and choice users and splitting them on the basis
of different trip purposes, trip lengths, household sizes and age-groups. The main aim
for the analysis was to ascertain the percentage shares of captive and choice users in
the study area for each characteristic, in order to surmise the influence on the travel
behaviour by the two categories of traveller types.
As expected, the number of car captives was found in the dominant majority of the
survey sample for each split case. It indicates that the overall travel behaviour of the
population of the study area is highly influenced by the travellers who are currently
captive towards car and do not perceive to use any other mode in the future, even
with the practical implementation of the proposed ILTP travel environments.
However, when the car captive users were further classified into four unique
categories of work purpose, it was found that more than two-thirds of the users were
non-traders who simply did not perceive to have a choice other than car for work
trips but can change their minds under the impact of operational ILTP environment.
Additionally, the sample split revealed a significant percentage of potential choice
users, particularly for work and education purposes, and public transport captive
users for education purposes.
193
From a transport modelling perspective, there are no standard techniques for
modelling the captive user data unlike the choice user information. Nonetheless, the
extensive statistical analyses performed on the survey sample did explain the car
captivity effect on mode choice decision-making to some extent. It can also assist the
urban planners in evaluating the potential feasibility of developing busways with an
access modes network, walkways and cycleways in the study area, as proposed in the
ILTP.
10.2. RESEARCH FINDINGS
The main findings of this research, in context with hypothesis and research aims
(stated in Chapter 1), are shown as follows,
• Significantly distinct travel behaviours were obtained for each type of trip,
categorised according to trip lengths and trip purposes. The values of time
(VoTs) observed for each model also varied with the type of the trip, as shown in
Table 8.9, justifying the research hypothesis that unique mode choice models
should be created not only on the basis of different trip purposes, but also on trip
lengths.
• For work trips, destined on the Brisbane CBD corridor, the in-vehicle travel time
was found to be the most influencing parameter on travel behaviour forecast for
the targeted population. From Table 7.7, it was observed that around 45 % of the
mode choice users perceived to switch to bus on busway if the in-vehicle travel
time can be reduced to 40 minutes. The attributes of out-of-pocket travel cost and
access distance to the busway station were also found to considerably influence
the travel mode choice of the respondents.
• For regional trips for other purposes, the modes of car as driver and walk to bus
on busway were only found to be highly sensitive to the in-vehicle travel time of
the bus on busway, as compared to the other two modes in the SP choice set
which were drawn almost as a horizontal straight line to the varying values of
travel time, as shown in Figure 7.13. All other attributes were found to be
194
relatively inelastic in varying the forecasted percentage modal splits for regional
other trips.
• For work trips within the Shire, all the travelling modes were found to associate
significant mode shares for small travel distances. Unlike regional work trips, the
mode of car as driver was found to not dominantly drive the mode choice (the
percentage mode share of car as driver did not even reach 50 % for all trip
lengths), as shown in Figure 8.3. For short travel distances (less than 2
kilometres), cycling all-the-way and walking to the busway were found to
significantly compete with car, indicating that a considerable number of mode
choice users perceive the two hypothetical modes as valid alternatives to private
car. This is an essential finding, from transportation planning perspective, in
evaluating the feasibility of developing cycleways within the Shire.
• For local shopping trips, low mode shares were forecasted for bus on busway and
the non-motorised alternatives to the private car. The attribute of in-vehicle travel
time was found to be the only significantly influencing parameter on travel mode
choice.
• For local education trips, substantially high percentage modal splits were
forecasted for the modes of walk to bus on busway and the non-motorised modes
of walking and cycling all-the-way, indicating that most of the students perceive
to switch to other travelling alternatives to car, if put into practice. Unlike other
trip purposes, out-of-pocket travel cost was found to be the most elastic
parameter for the passenger mode choice for local trips for education purposes.
• The percentage modal split forecasts for local other trips was similar to that of
work trips, within the Shire, with the mode of car as driver not dominating the
travel behaviour under the hypothetical travel environment. The mode shares for
the non-motorised alternatives to car were forecasted to be significantly high,
particularly for short travel distances (less than 2 kilometres).
195
• Statistical analyses were performed on the survey data by splitting it on the basis
of traveller type, i.e. car captive, PT captive and mode choice users; and further
categorising them according to several trip characteristics, such as the trip
purpose and trip length, and household parameters, namely the household size
and age-group. The number of car captives was found to be in the dominant
majority of the survey sample for each split case indicating that the overall travel
behaviour of the population of the study area is highly influenced by the
travellers who are currently captive towards car and do not perceive to use any
other mode in the future, even with the practical implementation of the proposed
ILTP travel environments. However, further classification of car captive users
into four unique categories of work purpose revealed that more than two-thirds of
the users were non-traders, who did not perceive to have a choice for work trips
but may change their minds under the impact of an operational ILTP
environment. The sample split also revealed a significant percentage of overall
potential choice users, particularly for work and education purposes and public
transport captive users for education purposes.
• A uniquely designed CAPI stated preference (SP) survey was conducted in the
study area in order to comprehensively record the current travel behaviour and
future mode choice of the respondents, without over-burdening with excessive
questions. The average time to complete the survey for choice users (playing
eight unique mode choice games) was observed to be around six minutes, with
that of captive users to be three minutes only.
• The modelling process, used in this research, has considered both motorised (bus
on busway with five access modes) and non-motorised modes (walking on
walkway and cycling on cycleway) as travelling alternatives to the car for
different types of trips. The modelling results, obtained from the logit
estimations, can be effectively used by the transport planners in evaluating the
potential feasibility of developing busways with an access modes network,
walkways and cycleways in the study area.
196
• Elasticities of various level-of-service attributes associated to the travelling
modes in the SP choice set were determined for each trip length and trip purpose,
at an aggregated level. These values can be employed in appraising and setting
up the modal parameters for the proposed ILTP environment, considering their
sensitivity in mind for each hypothetical mode.
10.3. INDUSTRIAL APPLICATION OF RESULTS
The findings of this study can be practically implemented for various applications in
the industry. A few of the main applications are listed as follows,
• The travel behaviour forecasts, under the hypothetical travel environment, can be
utilised in assessing the feasibility of developing busways with access modes
network, walkways and cycleways in the Shire.
• The values of the estimated coefficients can be used to test the relative
importance of each attribute, included in the model specification, associated to
the hypothetical travelling modes in the SP choice set for different trip lengths
and trip purposes.
• The statistical analyses performed on the survey sample shows the percentage
splits of car captive, PT captive and mode choice users for different travel
characteristics and household parameters. These analyses are essential from the
transport planning perspective since it shows the high percentage of car captive
users that are likely to be present, even with the practical implementation of the
travel environment, as proposed in the ILTP.
• The unique CAPI design, developed in this study, can be used to conduct mode
choice surveys in other semi-urban areas of South East Queensland to identify
the feasibility of developing busways, and walkways and cycleways in those
areas.
197
• The mode choice models, developed in this study, are not directly transferable to
other regions of Australia, since a representative survey sample for this research
was generated based on the travel characteristics of the population of this study
area only. However, the model specification developed for this study can be
utilised in other areas, particularly the semi-urban areas of South East
Queensland, with similar socio-demographic characteristics, since similar travel
behaviours were observed in the 2001 Census for all non-urban statistical local
areas (SLAs) of the region (Australian Bureau of Statistics 2007e).
• A four-step model (FSM), as discussed in Section 2.2, can be developed for the
study area using the mode choice modelling results, from this study, to determine
the trip assignment under the hypothetical travel environment. Various strategic
transport modelling computer packages, such as EMME (INRO 2007), can be
used in appraising the impact of developing busways, walkways and cycleways
on the auto, public transport and non-motorised modes assignment of the study
area.
10.4. FUTURE RESEARCH DIRECTIONS
From the findings of this research, several areas were identified that require further
investigations. These areas are briefly discussed as follows,
• The model specification developed for this research did not consider qualitative
attributes associated to the travelling modes, such as comfort, safety, reliability,
etc. for modelling purposes. These attributes can be introduced in the logistic
regression equations of the utility functions of the SP modes, as ordinal or
nominal variables, and checked for validity.
• The CAPI survey instrument, designed for this research, presented the
respondents with stated preference mode choice games between car and the
perceived alternative of the traveller. Further research can be done on redesigning
the survey instrument, to include more than two modes in the SP choice scenario,
198
in order to forecast the mode choice with a higher degree of accuracy. For
instance, the respondent can be asked to compare between car, the most preferred
perceived alternative and the second-most preferred perceived alternative for a
specific trip purpose. However, this may lead to a complex format of the SP
mode choice scenario, resulting in the respondents making invalid choice
observations.
• The work trips, as defined in the model specification, can be further classified on
the basis of white-collar and blue-collar workers, depending on the employment
industry of the traveller. Unique logit structures can be developed on the basis of
the two classifications, and categorised according to trip lengths, regional and
local trips.
• The education trips, as defined in the model specification, can also be further
categorised on the basis of primary, secondary and tertiary students, depending
on the type of student trip-maker. Separate logit modelling framework can be
developed for all these categories, and tested for observing possible differences
in the travel behaviour for each classification.
• Further statistical analyses can be conducted on the captive user data, obtained
from the SP surveys in the study area, by analysing their absolute (or relative)
significance of the level-of-service attributes and household parameters that may
be influencing their travel behaviour.
199
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Appendix 1 WinMint 3.2F Programming Code of Stated
Preference Survey Instrument
Appendix 1 presents full programming code, written in WinMint 3.2F, of the survey
instrument design prepared as part of the study. In order to develop sound
understanding of this programming code, the software manual (HCG 2000) needs to
be consulted.
* Mode choice survey for all respondents * Demonstration Questionnaire for WinMINT 2.1 * Created by Omer Khan * Queensland University of Technology * Developed by Omer Khan on 4th April, 05 * Edited by Omer Khan on 18th May, 05 * Major Editions on 6th July, 05 * * P S RP Questions * * Introductory Screen * Q 0 RP-INTRODUCTION R 11 T T T Hello T T We are conducting a transport survey in order to find out & _that how the residents of Redland Shire fulfil their & _day-to-day travel demands. T W R 14 T T In this regard, we will like to ask you some questions & _about your preferred travelling modes (^I+means of transport^I-) & _for different types of trips. T W T The results of this survey will help Redlands Shire Council & _to improve the current transport system. > * S Trip Purpose * * What is the purpose of the trip of the respondent? * Q 1 TRIPPURPOSE T What trip purpose is of our interest? T T (^B+To be entered by the interviewer^B-) A work
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A shopping A education O Other > * * * Where does the respondent starts the trip? * Q 2 ORIGIN T Generally, where do you begin your #TRIPPURPOSE# trip? T T (Write the ^B+ street name/suburb/postcode ^B-) T T T (^C10 Do not necessarily have to be complete address^C14) T T (^I+ Provide as much details as you wish^I-) > * * * What is the destination of the respondent's trip? * Q 2 DEST T Where is your #TRIPPURPOSE# place located? T T (Write the ^B+ street name/suburb/postcode ^B-) T T (^C10Do not necessarily have to be complete address^C14) T T (^I+ Provide as much details as you wish^I-) > * * * What mode the respondent uses for the trip? * Q 1 MODE T What is your ^I+PRIMARY^I- travelling mode for #TRIPPURPOSE#? T T T (^B+please note that your PRIMARY travelling mode is the one in which you spend most of trip time^B-) A walking A cycling A car A bus O other > * * * Does the respondent has car available for the trip? * J #MODE# EQ 3 1 Q 1 CARAVAIL T Do you have a car generally available for #TRIPPURPOSE# trip? A Yes A No > * * Specific RP Questions about the selected travelling mode *
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* For Walking as All-The-Way Mode J #MODE# EQ 2 OR #MODE# EQ 3 OR #MODE# EQ 4 OR #MODE# EQ 5 1 * Q 6 WTIME T How long does it take to reach #TRIPPURPOSE# place by #MODE#? T T (^B+in hours and min^B-) > * * For Cycling as All-The-Way Mode J #MODE# EQ 1 OR #MODE# EQ 3 OR #MODE# EQ 4 OR #MODE# EQ 5 1 * Q 6 CTIME T How long does it take to reach #TRIPPURPOSE# place by #MODE#? T T (^B+in hours and min^B-) > * * For Private Car as All-The-Way Mode J #MODE# EQ 1 OR #MODE# EQ 2 OR #MODE# EQ 4 OR #MODE# EQ 5 10 * Q 1 CARMODE T Do you generally drive the car as a ^B+^I+driver^I-^B- or as a ^B+^I+passenger^I-^B-? A driver A passenger O Other > * Q 6 CARTIME T How long does it usually take to reach #TRIPPURPOSE# place by #MODE#? T T (Give an average estimate in ^B+^I+hours^I-^B- and ^B+^I+min^I-^B-) > * Q 3 CARDIST * For determining fuel cost to be added to total travelling cost by car T How long is the estimated distance from ^B+^I+#ORIGIN#^I-^B- to #TRIPPURPOSE#? T T (in kilometres) > * Q 3 CARNUM T How many people usually travel with you in the car including yourself? T T (^B+if you travel alone, enter 1^B-) > I #CARNUM# LE 0 CARNUM * Q 4 FUELCOST V 4 TEMPCOST V 3 TEMPDIST M TEMPDIST = #CARDIST# * Assuming that 1 litre fuel costs 1 dollar = 100 cents M TEMPDIST * 100 * Assuming in 1 litre fuel, the car can travel 10 kms M TEMPDIST / 10 M TEMPDIST / #CARNUM# M TEMPCOST = #TEMPDIST# F 1 #TEMPCOST# *
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Q 4 CARPFEE T What parking cost do you pay at the destination? T T (Remember, in case of more than one passenger, you may & _like to divide the parking cost by total number of passengers & _, if apply) T T (in dollars and cents) > * J #CARPFEE# EQ 0 2 Q 1 PFEEBASIS T How often do you pay this parking cost? A Daily A Weekly A Fortnightly A Monthly A 3 months A 6 months A Yearly 0 Other (specify) > * V 3 TEMPFEE M TEMPFEE = #CARPFEE# X #PFEEBASIS# EQ 2 TEMPFEE [ = #CARPFEE# / 5 ] X #PFEEBASIS# EQ 3 TEMPFEE [ = #CARPFEE# / 10 ] X #PFEEBASIS# EQ 4 TEMPFEE [ = #CARPFEE# / 20 ] X #PFEEBASIS# EQ 5 TEMPFEE [ = #CARPFEE# / 60 ] X #PFEEBASIS# EQ 6 TEMPFEE [ = #CARPFEE# / 120 ] X #PFEEBASIS# EQ 7 TEMPFEE [ = #CARPFEE# / 240 ] * J #PFEEBASIS# EQ 1 OR #PFEEBASIS# EQ 8 1 Q 0 CARACTPFEE T Your daily parking cost comes out to be #TEMPFEE# cents. > * Q 6 CARPSTIME T How long does it usually take to search for parking at #TRIPPURPOSE# place? T T (Give an average estimate in ^B+^I+min^I-^B-) > * J #TRIPPURPOSE# NE 1 1 Q 1 CARFREQWORK T Generally, whats your frequency of reaching late at work? T T (where ^B+^I+late^I-^B- means ^B+^I+5 min^I- longer than expected^B-) A Never A Almost every day A Once a week A 1 to 3 times a month A Once a month O Other > * J #TRIPPURPOSE# EQ 1 1 Q 1 CARFREQSHOP T Generally, how difficult is it to find proper parking place near the & _#TRIPPURPOSE# area?
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A easy A normal A difficult > * * For Public Bus as All-The-Way Mode * J #MODE# EQ 1 OR #MODE# EQ 2 OR #MODE# EQ 3 OR #MODE# EQ 5 15 * Q 6 BTIME T How long does it take to reach #TRIPPURPOSE# place by #MODE#? T T (in hours and min) > * Q 4 BCOST T How much is your total travelling fare by #MODE#? T T [include TOTAL cost for both ways] T T T (in dollars and cents) > * Q 1 BCOSTBASIS T How do you pay this travelling fare? A Daily A Off-peak A Weekly A Monthly ticket A Ten-trip saver O Other (specify) > * V 4 TEMPBCOST M TEMPBCOST = #BCOST# X #BCOSTBASIS# EQ 3 OR #BCOSTBASIS# EQ 5 TEMPBCOST [ = #BCOST# / 5 ] X #BCOSTBASIS# EQ 4 TEMPBCOST [ = #BCOST# / 20 ] M TEMPBCOST N 5 * J #BCOSTBASIS# EQ 1 OR #BCOSTBASIS# EQ 2 OR #BCOSTBASIS# EQ 6 1 Q O BACTCOST T Your daily bus fare comes out to be #TEMPBCOST# cents. > * Q 6 BWAIT T How long do you have to generally wait at the bus-stop before the #MODE# arrives? T T (in hours and min) > * Q 1 BINT T How many interchanges you have to make for the primary bus? T T (^B+^I+^C11primary^C15^I- means that transport mode on which you spend & _most of the travelling time to #TRIPPURPOSE#^B-) A zero A one A two A three or more
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> * Q 1 BACCMODE T How do you reach the bus-stop from #ORIGIN#? T T (^B+In Option No. 5, ^C11feeder^C15 bus basically means any other bus taken & _in order to reach bus-stop for the primary bus^B-) A walking A cycling A driving & parking A getting dropped by car A feeder bus O Other (specify) > * * Information about Access Modes * * For Walking as Access Mode * J #BACCMODE# EQ 2 OR #BACCMODE# EQ 3 OR #BACCMODE# EQ 4 OR #BACCMODE# EQ 5 OR #BACCMODE# EQ 6 1 * Q 6 AWTIME T How long does it take to reach the bus-stop by #BACCMODE#? T T (in hours and min) > * * For Cycling as Access Mode * J #BACCMODE# EQ 1 OR #BACCMODE# EQ 3 OR #BACCMODE# EQ 4 OR #BACCMODE# EQ 5 OR #BACCMODE# EQ 6 1 * Q 6 ACTIME T How long does it take to reach the bus-stop by #BACCMODE#? T T (in hours and min) > * * For Park `n Ride as Access Mode * J #BACCMODE# EQ 1 OR #BACCMODE# EQ 2 OR #BACCMODE# EQ 4 OR #BACCMODE# EQ 5 OR #BACCMODE# EQ 6 1 * Q 6 APRTIME T How long does it take to reach the bus-stop by #BACCMODE#? T T (in hours and min) > * * For Kiss `n Ride as Access Mode * J #BACCMODE# EQ 1 OR #BACCMODE# EQ 2 OR #BACCMODE# EQ 3 OR #BACCMODE# EQ 5 OR #BACCMODE# EQ 6 1 * Q 6 AKRTIME T How long does it take to reach the bus-stop by #BACCMODE#? T T (in hours and min) >
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* * For Feeder Bus as Access Mode * J #BACCMODE# EQ 1 OR #BACCMODE# EQ 2 OR #BACCMODE# EQ 3 OR #BACCMODE# EQ 4 OR #BACCMODE# EQ 6 4 * Q 6 ABTIME T How long does it take to reach the bus-stop for the main bus by travelling in a #BACCMODE#? T T (in hours and min) > * Q 4 ABCOST T How much is your travelling fare by #BACCMODE#? T T [^I+Enter ZERO if fare is integrated with the next mode^I-] T T T (in dollars and cents) > * Q 6 ABWAIT T How long you have to generally wait at the bus-stop for #BACCMODE# before it arrives? T T (in hours and min) > * Q 6 ABACCTIME T How long does it usually take from #ORIGIN# to reach the bus-stop for #BACCMODE#? T T (in hours and min) > * * To know the 2nd most preferred mode of the respondent * Q 1 ALTMODE T For the #TRIPPURPOSE# trip, you currently prefer to use #MODE# T as your travelling mode. T T Which alternative travelling mode will you like to select for the T same #TRIPPURPOSE# trip, if available? T T Keep the ^I+^B+#TRIPPURPOSE#^B-^I- trip in mind, with the same & _origin (^I+^B+#ORIGIN#^B-^I-) and destination (^I+^B+#DEST#^B-^I-). T T T You can select NONE if you do not like to travel by any other mode T A walking A cycling A car A bus on busway A none O other > * * To know the 2nd most preferred access mode of the respondent * J #ALTMODE# NE 4 1 Q 1 ALTAMODE
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T For the #TRIPPURPOSE# trip, you said that you will like to use #ALTMODE# & _, if available. T T Which access mode will you like to select for this T trip, if available? T T Keep the ^I+^B+#TRIPPURPOSE#^B-^I- trip in mind, with the same & _origin (^I+^B+#ORIGIN#^B-^I-) and destination (^I+^B+#DEST#^B-^I-). T T A walking A cycling A driving & parking A getting dropped by car A feeder bus O Other (specify) > * * Possible between-mode SP games * * SP Game 1 = Walking vs Cycling * SP Game 2 = Walking vs Car * SP Game 3 = Walking vs Bus on Busway * SP Game 4 = Cycling vs Car * SP Game 5 = Cycling vs Bus on Busway * SP Game 6 = Car vs Bus on Busway * * Possible within-mode SP games * * SP Game 7 = Walking vs Walking * SP Game 8 = Cycling vs Cycling * SP Game 9 = Car vs Car * * I #MODE# EQ 1 AND #ALTMODE# EQ 1 Walk-Walk I #MODE# EQ 1 AND #ALTMODE# EQ 2 Walk-Cycle I #MODE# EQ 1 AND #ALTMODE# EQ 3 Walk-Car I #MODE# EQ 1 AND #ALTMODE# EQ 4 Walk-Bus I #MODE# EQ 1 AND #ALTMODE# EQ 5 Walk-Walk I #MODE# EQ 1 AND #ALTMODE# EQ 6 CONCLUSION * I #MODE# EQ 2 AND #ALTMODE# EQ 1 Walk-Cycle I #MODE# EQ 2 AND #ALTMODE# EQ 2 Cycle-Cycle I #MODE# EQ 2 AND #ALTMODE# EQ 3 Cycle-Car I #MODE# EQ 2 AND #ALTMODE# EQ 4 Cycle-Bus I #MODE# EQ 2 AND #ALTMODE# EQ 5 Cycle-Cycle I #MODE# EQ 2 AND #ALTMODE# EQ 6 CONCLUSION * I #MODE# EQ 3 AND #ALTMODE# EQ 1 Walk-Car I #MODE# EQ 3 AND #ALTMODE# EQ 2 Cycle-Car I #MODE# EQ 3 AND #ALTMODE# EQ 3 Car-Car I #MODE# EQ 3 AND #ALTMODE# EQ 4 Car-Bus I #MODE# EQ 3 AND #ALTMODE# EQ 5 Car-Car I #MODE# EQ 3 AND #ALTMODE# EQ 6 CONCLUSION * I #MODE# EQ 4 CONCLUSION * I #MODE# EQ 5 CONCLUSION * * ASSUMPTION :
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* Average Walking Speed = 5 km/hr * Average Cycling Speed = 20 km/hr * Average Car Speed = 50 km/hr * Average Bus on Busway Speed = 50 km/hr * P * S SP Questions For Trip Maker * * SP Game 1 Q 0 Walk-Cycle R 15 T Now we will like to see your perceived importance & _of ^I+^B+#MODE#^B-^I- (your current travelling mode) by & _comparing its variables with that of ^I+^B+#ALTMODE#^B-^I- & _(your 2nd preferred travelling mode) W T T T _Answer all the questions with the ^I+^B+#TRIPPURPOSE#^B-^I- trip & _in your mind, with the same & _origin (^I+^B+#ORIGIN#^B-^I-) and destination (^I+^B+#DEST#^B-^I-). W T T T On the following screens, we give you a & _number of possible changes for your journey. & _These changes can be real as well as hypothetical. & _Please compare these carefully and then tell us which mode & _of transport you would have preferred in & _this situation. R 14 > * G B 2 SPGAME-Walk-Cycle G V 3 G A 18 G L 1 6 G L 2 6 G L 3 6 * * SP Variable 1 = Total Travelling Time for Walking and Cycling V 6 TIMELEVEL * X #MODE# EQ 1 AND #ALTMODE# EQ 2 TIMELEVEL = #WTIME# X #MODE# EQ 2 AND #ALTMODE# EQ 1 TIMELEVEL = #CTIME# M TIMELEVEL N 1 M TIMELEVEL P 80 M TIMELEVEL N 1 G T 1 1 1 Travelling time by #MODE# becomes G T 1 1 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 1 3 G N 1 1 #TIMELEVEL# * X #MODE# EQ 1 AND #ALTMODE# EQ 2 TIMELEVEL = #WTIME# X #MODE# EQ 2 AND #ALTMODE# EQ 1 TIMELEVEL = #CTIME# M TIMELEVEL N 1 M TIMELEVEL P 120 M TIMELEVEL N 1 G T 1 2 1 Travelling time by #MODE# becomes
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G T 1 2 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 2 3 G N 1 2 #TIMELEVEL# * X #MODE# EQ 1 AND #ALTMODE# EQ 2 TIMELEVEL = #WTIME# X #MODE# EQ 2 AND #ALTMODE# EQ 1 TIMELEVEL = #CTIME# M TIMELEVEL N 1 G T 1 3 1 Travelling time by #MODE# becomes G T 1 3 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 3 3 G N 1 3 #TIMELEVEL# * * SP Variable 2 = Hypothetical Facility (Showering) for Walking and Cycling G T 2 1 1 G T 2 1 2 No shower facility at destination G T 2 1 3 G N 2 1 0 G T 2 2 1 G T 2 2 2 Shower facility at destination G T 2 2 3 G N 2 2 1 G T 2 3 1 G T 2 3 2 No Shower Facility at destination G T 2 3 3 G N 2 3 0 * SP Variable 3 = Hypothetical Facility (Ironing) for Walking and Cycling G T 3 1 1 G T 3 1 2 No ironing facility at destination G T 3 1 3 G N 3 1 0 G T 3 2 1 G T 3 2 2 Ironing facility at destination G T 3 2 3 G N 3 2 1 G T 3 3 1 G T 3 3 2 Ironing facility at destination G T 3 3 3 G N 3 3 1 * * SP Variable 1 = Total Travelling Time for Walking and Cycling V 6 ACTUALTIME X #MODE# EQ 1 AND #ALTMODE# EQ 2 ACTUALTIME = #WTIME# X #MODE# EQ 2 AND #ALTMODE# EQ 1 ACTUALTIME = #CTIME# X #MODE# EQ 1 AND #ALTMODE# EQ 2 TIMELEVEL [ = #ACTUALTIME# P 25 ] X #MODE# EQ 2 AND #ALTMODE# EQ 1 TIMELEVEL [ = #ACTUALTIME# P 400 ] M TIMELEVEL N 1 X #TIMELEVEL# GT 60 TIMELEVEL = 60 M TIMELEVEL P 80 M TIMELEVEL N 1 G T 1 4 1 Travelling time by #ALTMODE# becomes G T 1 4 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 4 3 G N 1 4 #TIMELEVEL# * X #MODE# EQ 1 AND #ALTMODE# EQ 2 ACTUALTIME = #WTIME# X #MODE# EQ 2 AND #ALTMODE# EQ 1 ACTUALTIME = #CTIME# X #MODE# EQ 1 AND #ALTMODE# EQ 2 TIMELEVEL [ = #ACTUALTIME# P 25 ] X #MODE# EQ 2 AND #ALTMODE# EQ 1 TIMELEVEL [ = #ACTUALTIME# P 400 ] M TIMELEVEL N 1 X #TIMELEVEL# GT 60 TIMELEVEL = 60
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M TIMELEVEL P 120 M TIMELEVEL N 1 G T 1 5 1 Travelling time by #ALTMODE# becomes G T 1 5 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 5 3 G N 1 5 #TIMELEVEL# * X #MODE# EQ 1 AND #ALTMODE# EQ 2 ACTUALTIME = #WTIME# X #MODE# EQ 2 AND #ALTMODE# EQ 1 ACTUALTIME = #CTIME# X #MODE# EQ 1 AND #ALTMODE# EQ 2 TIMELEVEL [ = #ACTUALTIME# P 25 ] X #MODE# EQ 2 AND #ALTMODE# EQ 1 TIMELEVEL [ = #ACTUALTIME# P 400 ] M TIMELEVEL N 1 X #TIMELEVEL# GT 60 TIMELEVEL = 60 G T 1 6 1 Travelling time by #ALTMODE# becomes G T 1 6 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 6 3 G N 1 6 #TIMELEVEL# * * SP Variable 2 = Hypothetical Facility (Showering) for Walking and Cycling G T 2 4 1 G T 2 4 2 No shower facility at destination G T 2 4 3 G N 2 4 0 G T 2 5 1 G T 2 5 2 Shower facility at destination G T 2 5 3 G N 2 5 1 G T 2 6 1 G T 2 6 2 Shower facility at destination G T 2 6 3 G N 2 6 1 * * SP Variable 3 = Hypothetical Facility (Ironing) for Walking and Cycling G T 3 4 1 G T 3 4 2 No ironing facility at destination G T 3 4 3 G N 3 4 0 G T 3 5 1 G T 3 5 2 Ironing facility at destination G T 3 5 3 G N 3 5 1 G T 3 6 1 G T 3 6 2 No ironing facility at destination G T 3 6 3 G N 3 6 0 * * * Set response scale G H 3 G C 8 G F 1 8 1 1 9 G F 1 8 2 10 18 G X 1 (A) #MODE# G X 2 (B) #ALTMODE# * G R 5 G Y 1 Definitely A G Y 2 Probably A G Y 3 Not sure G Y 4 Probably B
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G Y 5 Definitely B G Z 1 1 G Z 2 1 G Z 3 0 G Z 4 2 G Z 5 2 * Set mixing and highlighting G M 0 G H 9 U L R 14 G > I #MODE# EQ 1 OR #MODE# EQ 2 CONCLUSION P * * * SP Game 2 Q 0 Walk-Car R 15 T Now we will like to see your perceived importance & _of ^I+^B+#MODE#^B-^I- (your current travelling mode) by & _comparing its variables with that of ^I+^B+#ALTMODE#^B-^I- & _(your 2nd preferred travelling mode) W T T T _Answer all the questions with the ^I+^B+#TRIPPURPOSE#^B-^I- trip & _in your mind, with the same & _origin (^I+^B+#ORIGIN#^B-^I-) and destination (^I+^B+#DEST#^B-^I-). W T T T On the following screens, we give you a & _number of possible changes for your journey. & _These changes can be real as well as hypothetical. & _Please compare these carefully and then tell us which mode & _of transport you would have preferred in & _this situation. R 14 > * G B 2 SPGAME-Walk-Car G V 3 G A 18 G L 1 6 G L 2 6 G L 3 6 * * SP Variable 1 = Total Travelling Time for Walking and Car V 6 TIMELEVEL V 6 ACTUALTIME * X #MODE# EQ 1 AND #ALTMODE# EQ 3 ACTUALTIME = #WTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 1 ACTUALTIME = #CARTIME# X #MODE# EQ 1 AND #ALTMODE# EQ 3 TIMELEVEL = #ACTUALTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 1 TIMELEVEL [ = #ACTUALTIME# P 1000 ] M TIMELEVEL N 1 X #TIMELEVEL# GT 60 TIMELEVEL = 60 M TIMELEVEL P 80 M TIMELEVEL N 1
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G T 1 1 1 Travelling time by walking becomes G T 1 1 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 1 3 G N 1 1 #TIMELEVEL# * X #MODE# EQ 1 AND #ALTMODE# EQ 3 ACTUALTIME = #WTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 1 ACTUALTIME = #CARTIME# X #MODE# EQ 1 AND #ALTMODE# EQ 3 TIMELEVEL = #ACTUALTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 1 TIMELEVEL [ = #ACTUALTIME# P 1000 ] M TIMELEVEL N 1 X #TIMELEVEL# GT 60 TIMELEVEL = 60 M TIMELEVEL P 120 M TIMELEVEL N 1 G T 1 2 1 Travelling time by walking becomes G T 1 2 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 2 3 G N 1 2 #TIMELEVEL# * X #MODE# EQ 1 AND #ALTMODE# EQ 3 ACTUALTIME = #WTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 1 ACTUALTIME = #CARTIME# X #MODE# EQ 1 AND #ALTMODE# EQ 3 TIMELEVEL = #ACTUALTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 1 TIMELEVEL [ = #ACTUALTIME# P 1000 ] M TIMELEVEL N 1 X #TIMELEVEL# GT 60 TIMELEVEL = 60 G T 1 3 1 Travelling time by walking becomes G T 1 3 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 3 3 G N 1 3 #TIMELEVEL# * * SP Variable 2 = Hypothetical Facility (Showering) for Walking and parking cost for Car G T 2 1 1 G T 2 1 2 No shower facility at destination G T 2 1 3 G N 2 1 0 * G T 2 2 1 G T 2 2 2 Shower facility at destination G T 2 2 3 G N 2 2 1 * G T 2 3 1 G T 2 3 2 No Shower Facility at destination G T 2 3 3 G N 2 3 0 * SP Variable 3 = Hypothetical Facility (Ironing) for Walking and parking search time for Car G T 3 1 1 G T 3 1 2 No ironing facility at destination G T 3 1 3 G N 3 1 0 * G T 3 2 1 G T 3 2 2 Ironing facility at destination G T 3 2 3 G N 3 2 1 * G T 3 3 1 G T 3 3 2 Ironing facility at destination G T 3 3 3 G N 3 3 1 *
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X #MODE# EQ 1 AND #ALTMODE# EQ 3 ACTUALTIME = #WTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 1 ACTUALTIME = #CARTIME# X #MODE# EQ 1 AND #ALTMODE# EQ 3 TIMELEVEL [ = #ACTUALTIME# P 10 ] X #MODE# EQ 3 AND #ALTMODE# EQ 1 TIMELEVEL = #ACTUALTIME# M TIMELEVEL N 1 X #TIMELEVEL# LT 5 TIMELEVEL = 5 M TIMELEVEL P 80 M TIMELEVEL N 1 G T 1 4 1 Travelling time by car becomes G T 1 4 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 4 3 G N 1 4 #TIMELEVEL# * X #MODE# EQ 1 AND #ALTMODE# EQ 3 ACTUALTIME = #WTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 1 ACTUALTIME = #CARTIME# X #MODE# EQ 1 AND #ALTMODE# EQ 3 TIMELEVEL [ = #ACTUALTIME# P 10 ] X #MODE# EQ 3 AND #ALTMODE# EQ 1 TIMELEVEL = #ACTUALTIME# M TIMELEVEL N 1 X #TIMELEVEL# LT 5 TIMELEVEL = 5 M TIMELEVEL P 120 M TIMELEVEL N 1 G T 1 5 1 Travelling time by car becomes G T 1 5 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 5 3 G N 1 5 #TIMELEVEL# * X #MODE# EQ 1 AND #ALTMODE# EQ 3 ACTUALTIME = #WTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 1 ACTUALTIME = #CARTIME# X #MODE# EQ 1 AND #ALTMODE# EQ 3 TIMELEVEL [ = #ACTUALTIME# P 10 ] X #MODE# EQ 3 AND #ALTMODE# EQ 1 TIMELEVEL = #ACTUALTIME# M TIMELEVEL N 1 X #TIMELEVEL# LT 5 TIMELEVEL = 5 G T 1 6 1 Travelling time by car becomes G T 1 6 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 6 3 G N 1 6 #TIMELEVEL# * V 4 COSTLEVEL V 4 FUELCOSTLEVEL V 2 PFEEBASISLEVEL X #MODE# EQ 1 AND #ALTMODE# EQ 3 COSTLEVEL = 5 X #MODE# EQ 3 AND #ALTMODE# EQ 1 COSTLEVEL = #CARPFEE# X #MODE# EQ 1 AND #ALTMODE# EQ 3 FUELCOSTLEVEL = 1 X #MODE# EQ 3 AND #ALTMODE# EQ 1 FUELCOSTLEVEL = #FUELCOST# M COSTLEVEL P 80 M COSTLEVEL N 1 G T 2 4 1 Parking cost becomes G T 2 4 2 (costs ^B+$#COSTLEVEL#^B-) G T 2 4 3 Estimated fuel cost for the trip is ^B+$#FUELCOSTLEVEL#^B- G N 2 4 #COSTLEVEL# * X #MODE# EQ 1 AND #ALTMODE# EQ 3 COSTLEVEL = 5 X #MODE# EQ 3 AND #ALTMODE# EQ 1 COSTLEVEL = #CARPFEE# X #MODE# EQ 1 AND #ALTMODE# EQ 3 FUELCOSTLEVEL = 1 X #MODE# EQ 3 AND #ALTMODE# EQ 1 FUELCOSTLEVEL = #FUELCOST# M COSTLEVEL P 120 M COSTLEVEL N 1 G T 2 5 1 Parking cost becomes G T 2 5 2 (costs ^B+$#COSTLEVEL#^B-) G T 2 5 3 Estimated fuel cost for the trip is ^B+$#FUELCOSTLEVEL#^B-
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G N 2 5 #COSTLEVEL# * X #MODE# EQ 1 AND #ALTMODE# EQ 3 COSTLEVEL = 5 X #MODE# EQ 3 AND #ALTMODE# EQ 1 COSTLEVEL = #CARPFEE# X #MODE# EQ 1 AND #ALTMODE# EQ 3 FUELCOSTLEVEL = 1 X #MODE# EQ 3 AND #ALTMODE# EQ 1 FUELCOSTLEVEL = #FUELCOST# G T 2 6 1 Parking cost becomes G T 2 6 2 (costs ^B+$#COSTLEVEL#^B-) G T 2 6 3 Estimated fuel cost for the trip is ^B+$#FUELCOSTLEVEL#^B- G N 2 6 #COSTLEVEL# * V 6 PSTIMELEVEL X #MODE# EQ 1 AND #ALTMODE# EQ 3 PSTIMELEVEL = 5 X #MODE# EQ 3 AND #ALTMODE# EQ 1 PSTIMELEVEL = #CARPSTIME# M PSTIMELEVEL P 80 M PSTIMELEVEL N 1 G T 3 4 1 Search time for finding parking G T 3 4 2 (takes ^B+#PSTIMELEVEL#^B- min) G T 3 4 3 G N 3 4 #PSTIMELEVEL# * X #MODE# EQ 1 AND #ALTMODE# EQ 3 PSTIMELEVEL = 5 X #MODE# EQ 3 AND #ALTMODE# EQ 1 PSTIMELEVEL = #CARPSTIME# M PSTIMELEVEL P 120 M PSTIMELEVEL N 1 G T 3 5 1 Search time for finding parking G T 3 5 2 (takes ^B+#PSTIMELEVEL#^B- min) G T 3 5 3 G N 3 5 #PSTIMELEVEL# * X #MODE# EQ 1 AND #ALTMODE# EQ 3 PSTIMELEVEL = 5 X #MODE# EQ 3 AND #ALTMODE# EQ 1 PSTIMELEVEL = #CARPSTIME# G T 3 6 1 Search time for finding parking G T 3 6 2 (takes ^B+#PSTIMELEVEL#^B- min) G T 3 6 3 G N 3 6 #PSTIMELEVEL# * * * Set response scale G H 3 G C 8 G F 1 8 1 1 9 G F 1 8 2 10 18 G X 1 (A) Walking G X 2 (B) Car * G R 5 G Y 1 Definitely A G Y 2 Probably A G Y 3 Not sure G Y 4 Probably B G Y 5 Definitely B G Z 1 1 G Z 2 1 G Z 3 0 G Z 4 2 G Z 5 2 * Set mixing and highlighting G M 0 G H 9
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U L R 14 G > I #MODE# EQ 1 OR #MODE# EQ 3 CONCLUSION P * * * SP Game 3 Q 0 Walk-Bus R 15 T Now we will like to see your perceived importance & _of ^I+^B+#MODE#^B-^I- (your current travelling mode) by & _comparing its variables with that of ^I+^B+#ALTMODE#^B-^I- & _(your 2nd preferred travelling mode) W T T T _Answer all the questions with the ^I+^B+#TRIPPURPOSE#^B-^I- trip & _in your mind, with the same & _origin (^I+^B+#ORIGIN#^B-^I-) and destination (^I+^B+#DEST#^B-^I-). W T T T On the following screens, we give you a & _number of possible changes for your journey. & _These changes can be real as well as hypothetical. & _Please compare these carefully and then tell us which mode & _of transport you would have preferred in & _this situation. R 14 > * G B 2 SPGAME-Walk-Bus G A 18 G L 1 6 G L 2 6 G L 3 6 G L 4 6 * * SP Variable 1 = Total Travelling Time for Walking and Bus on Busway V 6 TIMELEVEL V 6 ACTUALTIME * M ACTUALTIME = #WTIME# M TIMELEVEL [ = #ACTUALTIME# P 10] M TIMELEVEL N 1 X #TIMELEVEL# LT 5 TIMELEVEL = 5 M TIMELEVEL P 80 M TIMELEVEL N 1 G T 1 1 1 Travelling time by bus on busway becomes G T 1 1 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 1 3 G N 1 1 #TIMELEVEL# * M ACTUALTIME = #WTIME# M TIMELEVEL [ = #ACTUALTIME# P 10] M TIMELEVEL N 1 X #TIMELEVEL# LT 5 TIMELEVEL = 5 M TIMELEVEL P 120 M TIMELEVEL N 1
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G T 1 2 1 Travelling time by bus on busway becomes G T 1 2 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 2 3 G N 1 2 #TIMELEVEL# * M ACTUALTIME = #WTIME# M TIMELEVEL [ = #ACTUALTIME# P 10] M TIMELEVEL N 1 X #TIMELEVEL# LT 5 TIMELEVEL = 5 M TIMELEVEL N 1 G T 1 3 1 Travelling time by bus on busway becomes G T 1 3 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 3 3 G N 1 3 #TIMELEVEL# * * SP Variable 2 = Ironing Facility for Walking and Total Travelling Cost for Bus on Busway * G T 2 1 1 Daily travelling fare becomes G T 2 1 2 (costs ^B+$ 2.0 ^B-) G T 2 1 3 G N 2 1 2 * G T 2 2 1 Daily travelling fare becomes G T 2 2 2 (costs ^B+$ 3.0 ^B-) G T 2 2 3 G N 2 2 3 * G T 2 3 1 Daily travelling fare becomes G T 2 3 2 (costs ^B+$ 4.0 ^B-) G T 2 3 3 G N 2 3 5 * * SP Variable 3 = Shower Facility for Walking and Waiting Time for Bus on Busway * G T 3 1 1 Waiting time for the bus to arrive G T 3 1 2 (becomes ^B+5^B- min) G T 3 1 3 G N 3 1 5 * G T 3 2 1 Waiting time for the bus to arrive G T 3 2 2 (becomes ^B+8^B- min) G T 3 2 3 G N 3 2 8 * G T 3 3 1 Waiting time for the bus to arrive G T 3 3 2 (becomes ^B+10^B- min) G T 3 3 3 G N 3 3 10 * * SP Variable 4 = Access Mode Time for Bus on Busway and Nothing for Walking * G T 4 1 1 Total access time by #ALTAMODE# G T 4 1 2 (becomes ^B+3^B- min) G T 4 1 3 G N 4 1 3 * G T 4 2 1 Total access time by #ALTAMODE# G T 4 2 2 (becomes ^B+7^B- min) G T 4 2 3 G N 4 2 7
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* G T 4 3 1 Total access time by #ALTAMODE# G T 4 3 2 (becomes ^B+10^B- min) G T 4 3 3 G N 4 3 10 * M TIMELEVEL = #WTIME# M TIMELEVEL P 80 M TIMELEVEL N 1 G T 1 4 1 Travelling time by walking becomes G T 1 4 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 4 3 G N 1 4 #TIMELEVEL# * M TIMELEVEL = #WTIME# M TIMELEVEL P 120 M TIMELEVEL N 1 G T 1 5 1 Travelling time by walking becomes G T 1 5 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 5 3 G N 1 5 #TIMELEVEL# * M TIMELEVEL = #WTIME# G T 1 6 1 Travelling time by walking becomes G T 1 6 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 6 3 G N 1 6 #TIMELEVEL# * G T 2 4 1 G T 2 4 2 No shower facility at destination G T 2 4 3 G N 2 4 0 * G T 2 5 1 G T 2 5 2 Shower facility at destination G T 2 5 3 G N 2 5 1 * G T 2 6 1 G T 2 6 2 No Shower Facility at destination G T 2 6 3 G N 2 6 0 * G T 3 4 1 G T 3 4 2 No ironing facility at destination G T 3 4 3 G N 3 4 0 * G T 3 5 1 G T 3 5 2 Ironing facility at destination G T 3 5 3 G N 3 5 1 * G T 3 6 1 G T 3 6 2 Ironing facility at destination G T 3 6 3 G N 3 6 1 * G T 4 4 1 G T 4 4 2
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G T 4 4 3 * G T 4 5 1 G T 4 5 2 G T 4 5 3 * G T 4 6 1 G T 4 6 2 G T 4 6 3 * * Set response scale G H 3 G C 8 G F 1 8 1 1 9 G F 1 8 2 10 18 G X 1 (A) Bus G X 2 (B) Walking * G R 5 G Y 1 Definitely A G Y 2 Probably A G Y 3 Not sure G Y 4 Probably B G Y 5 Definitely B G Z 1 1 G Z 2 1 G Z 3 0 G Z 4 2 G Z 5 2 * Set mixing and highlighting G M 0 G H 9 U L R 14 G > I #MODE# EQ 1 CONCLUSION P * * * SP Game 4 Q 0 Cycle-Car R 15 T Now we will like to see your perceived importance & _of ^I+^B+#MODE#^B-^I- (your current travelling mode) by & _comparing its variables with that of ^I+^B+#ALTMODE#^B-^I- & _(your 2nd preferred travelling mode) W T T T _Answer all the questions with the ^I+^B+#TRIPPURPOSE#^B-^I- trip & _in your mind, with the same & _origin (^I+^B+#ORIGIN#^B-^I-) and destination (^I+^B+#DEST#^B-^I-). W T T T On the following screens, we give you a & _number of possible changes for your journey. & _These changes can be real as well as hypothetical. & _Please compare these carefully and then tell us which mode
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& _of transport you would have preferred in & _this situation. R 14 > * G B 2 SPGAME-Cycle-Car G V 3 G A 18 G L 1 6 G L 2 6 G L 3 6 * * SP Variable 1 = Total Travelling Time for Cycling and Car V 6 TIMELEVEL V 6 ACTUALTIME * X #MODE# EQ 2 AND #ALTMODE# EQ 3 ACTUALTIME = #CTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 2 ACTUALTIME = #CARTIME# X #MODE# EQ 2 AND #ALTMODE# EQ 3 TIMELEVEL = #ACTUALTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 2 TIMELEVEL [ = #ACTUALTIME# P 250 ] M TIMELEVEL N 1 X #TIMELEVEL# GT 60 TIMELEVEL = 60 M TIMELEVEL P 80 M TIMELEVEL N 1 G T 1 1 1 Travelling time by cycling becomes G T 1 1 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 1 3 G N 1 1 #TIMELEVEL# * X #MODE# EQ 2 AND #ALTMODE# EQ 3 ACTUALTIME = #CTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 2 ACTUALTIME = #CARTIME# X #MODE# EQ 2 AND #ALTMODE# EQ 3 TIMELEVEL = #ACTUALTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 2 TIMELEVEL [ = #ACTUALTIME# P 250 ] M TIMELEVEL N 1 X #TIMELEVEL# GT 60 TIMELEVEL = 60 M TIMELEVEL P 120 M TIMELEVEL N 1 G T 1 2 1 Travelling time by cycling becomes G T 1 2 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 2 3 G N 1 2 #TIMELEVEL# * X #MODE# EQ 2 AND #ALTMODE# EQ 3 ACTUALTIME = #CTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 2 ACTUALTIME = #CARTIME# X #MODE# EQ 2 AND #ALTMODE# EQ 3 TIMELEVEL = #ACTUALTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 2 TIMELEVEL [ = #ACTUALTIME# P 250 ] M TIMELEVEL N 1 X #TIMELEVEL# GT 60 TIMELEVEL = 60 G T 1 3 1 Travelling time by cycling becomes G T 1 3 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 3 3 G N 1 3 #TIMELEVEL# * * SP Variable 2 = Hypothetical Facility (Showering) for Cycling and parking cost for Car G T 2 1 1 G T 2 1 2 No shower facility at destination G T 2 1 3 G N 2 1 0 * G T 2 2 1
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G T 2 2 2 Shower facility at destination G T 2 2 3 G N 2 2 1 * G T 2 3 1 G T 2 3 2 No Shower Facility at destination G T 2 3 3 G N 2 3 0 * SP Variable 3 = Hypothetical Facility (Ironing) for Cycling and parking search time for Car G T 3 1 1 G T 3 1 2 No ironing facility at destination G T 3 1 3 G N 3 1 0 * G T 3 2 1 G T 3 2 2 Ironing facility at destination G T 3 2 3 G N 3 2 1 * G T 3 3 1 G T 3 3 2 Ironing facility at destination G T 3 3 3 G N 3 3 1 * X #MODE# EQ 2 AND #ALTMODE# EQ 3 ACTUALTIME = #CTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 2 ACTUALTIME = #CARTIME# X #MODE# EQ 2 AND #ALTMODE# EQ 3 TIMELEVEL [ = #ACTUALTIME# P 40 ] X #MODE# EQ 3 AND #ALTMODE# EQ 2 TIMELEVEL = #ACTUALTIME# M TIMELEVEL N 1 X #TIMELEVEL# LT 5 TIMELEVEL = 5 M TIMELEVEL P 80 M TIMELEVEL N 1 G T 1 4 1 Travelling time by car becomes G T 1 4 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 4 3 G N 1 4 #TIMELEVEL# * X #MODE# EQ 2 AND #ALTMODE# EQ 3 ACTUALTIME = #CTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 2 ACTUALTIME = #CARTIME# X #MODE# EQ 2 AND #ALTMODE# EQ 3 TIMELEVEL [ = #ACTUALTIME# P 40 ] X #MODE# EQ 3 AND #ALTMODE# EQ 2 TIMELEVEL = #ACTUALTIME# M TIMELEVEL N 1 X #TIMELEVEL# LT 5 TIMELEVEL = 5 M TIMELEVEL P 120 M TIMELEVEL N 1 G T 1 5 1 Travelling time by car becomes G T 1 5 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 5 3 G N 1 5 #TIMELEVEL# * X #MODE# EQ 2 AND #ALTMODE# EQ 3 ACTUALTIME = #CTIME# X #MODE# EQ 3 AND #ALTMODE# EQ 2 ACTUALTIME = #CARTIME# X #MODE# EQ 2 AND #ALTMODE# EQ 3 TIMELEVEL [ = #ACTUALTIME# P 40 ] X #MODE# EQ 3 AND #ALTMODE# EQ 2 TIMELEVEL = #ACTUALTIME# M TIMELEVEL N 1 X #TIMELEVEL# LT 5 TIMELEVEL = 5 G T 1 6 1 Travelling time by car becomes G T 1 6 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 6 3 G N 1 6 #TIMELEVEL#
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* V 4 COSTLEVEL V 4 FUELCOSTLEVEL X #MODE# EQ 2 AND #ALTMODE# EQ 3 COSTLEVEL = 5 X #MODE# EQ 3 AND #ALTMODE# EQ 2 COSTLEVEL = #CARPFEE# X #MODE# EQ 2 AND #ALTMODE# EQ 3 FUELCOSTLEVEL = 1 X #MODE# EQ 3 AND #ALTMODE# EQ 2 FUELCOSTLEVEL = #FUELCOST# M COSTLEVEL P 80 M COSTLEVEL N 1 G T 2 4 1 Parking cost becomes G T 2 4 2 (costs ^B+$#COSTLEVEL#^B-) G T 2 4 3 Estimated fuel cost for the trip is ^B+$#FUELCOSTLEVEL#^B- G N 2 4 #COSTLEVEL# * X #MODE# EQ 2 AND #ALTMODE# EQ 3 COSTLEVEL = 5 X #MODE# EQ 3 AND #ALTMODE# EQ 2 COSTLEVEL = #CARPFEE# X #MODE# EQ 2 AND #ALTMODE# EQ 3 FUELCOSTLEVEL = 1 X #MODE# EQ 3 AND #ALTMODE# EQ 2 FUELCOSTLEVEL = #FUELCOST# M COSTLEVEL P 120 M COSTLEVEL N 1 G T 2 5 1 Parking cost becomes G T 2 5 2 (costs ^B+$#COSTLEVEL#^B-) G T 2 5 3 Estimated fuel cost for the trip is ^B+$#FUELCOSTLEVEL#^B- G N 2 5 #COSTLEVEL# * X #MODE# EQ 2 AND #ALTMODE# EQ 3 COSTLEVEL = 5 X #MODE# EQ 3 AND #ALTMODE# EQ 2 COSTLEVEL = #CARPFEE# X #MODE# EQ 2 AND #ALTMODE# EQ 3 FUELCOSTLEVEL = 1 X #MODE# EQ 3 AND #ALTMODE# EQ 2 FUELCOSTLEVEL = #FUELCOST# G T 2 6 1 Parking cost becomes G T 2 6 2 (costs ^B+$#COSTLEVEL#^B-) G T 2 6 3 Estimated fuel cost for the trip is ^B+$#FUELCOSTLEVEL#^B- G N 2 6 #COSTLEVEL# * V 6 PSTIMELEVEL X #MODE# EQ 2 AND #ALTMODE# EQ 3 PSTIMELEVEL = 5 X #MODE# EQ 3 AND #ALTMODE# EQ 2 PSTIMELEVEL = #CARPSTIME# M PSTIMELEVEL P 80 M PSTIMELEVEL N 1 G T 3 4 1 Search time for finding parking G T 3 4 2 (takes ^B+#PSTIMELEVEL#^B- min) G T 3 4 3 G N 3 4 #PSTIMELEVEL# * X #MODE# EQ 2 AND #ALTMODE# EQ 3 PSTIMELEVEL = 5 X #MODE# EQ 3 AND #ALTMODE# EQ 2 PSTIMELEVEL = #CARPSTIME# M PSTIMELEVEL P 120 M PSTIMELEVEL N 1 G T 3 5 1 Search time for finding parking G T 3 5 2 (takes ^B+#PSTIMELEVEL#^B- min) G T 3 5 3 G N 3 5 #PSTIMELEVEL# * X #MODE# EQ 2 AND #ALTMODE# EQ 3 PSTIMELEVEL = 5 X #MODE# EQ 3 AND #ALTMODE# EQ 2 PSTIMELEVEL = #CARPSTIME# G T 3 6 1 Search time for finding parking G T 3 6 2 (takes ^B+#PSTIMELEVEL#^B- min) G T 3 6 3 G N 3 6 #PSTIMELEVEL# *
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* * Set response scale G H 3 G C 8 G F 1 8 1 1 9 G F 1 8 2 10 18 G X 1 (A) Cycling G X 2 (B) Car * G R 5 G Y 1 Definitely A G Y 2 Probably A G Y 3 Not sure G Y 4 Probably B G Y 5 Definitely B G Z 1 1 G Z 2 1 G Z 3 0 G Z 4 2 G Z 5 2 * Set mixing and highlighting G M 0 G H 9 U L R 14 G > I #MODE# EQ 2 OR #MODE# EQ 3 CONCLUSION P * * * SP Game 5 Q 0 Cycle-Bus R 15 T Now we will like to see your perceived importance & _of ^I+^B+#MODE#^B-^I- (your current travelling mode) by & _comparing its variables with that of ^I+^B+#ALTMODE#^B-^I- & _(your 2nd preferred travelling mode) W T T T _Answer all the questions with the ^I+^B+#TRIPPURPOSE#^B-^I- trip & _in your mind, with the same & _origin (^I+^B+#ORIGIN#^B-^I-) and destination (^I+^B+#DEST#^B-^I-). W T T T On the following screens, we give you a & _number of possible changes for your journey. & _These changes can be real as well as hypothetical. & _Please compare these carefully and then tell us which mode & _of transport you would have preferred in & _this situation. R 14 > * G B 2 SPGAME-Cycle-Bus G A 18 G L 1 6 G L 2 6 G L 3 6
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G L 4 6 * * SP Variable 1 = Total Travelling Time for Cycling and Bus on Busway V 6 TIMELEVEL V 6 ACTUALTIME * M ACTUALTIME = #CTIME# M TIMELEVEL [ = #ACTUALTIME# P 40] M TIMELEVEL N 1 X #TIMELEVEL# LT 5 TIMELEVEL = 5 M TIMELEVEL P 80 M TIMELEVEL N 1 G T 1 1 1 Travelling time by bus on busway becomes G T 1 1 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 1 3 G N 1 1 #TIMELEVEL# * M ACTUALTIME = #CTIME# M TIMELEVEL [ = #ACTUALTIME# P 40] M TIMELEVEL N 1 X #TIMELEVEL# LT 5 TIMELEVEL = 5 M TIMELEVEL P 120 M TIMELEVEL N 1 G T 1 2 1 Travelling time by bus on busway becomes G T 1 2 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 2 3 G N 1 2 #TIMELEVEL# * M ACTUALTIME = #CTIME# M TIMELEVEL [ = #ACTUALTIME# P 40] M TIMELEVEL N 1 X #TIMELEVEL# LT 5 TIMELEVEL = 5 M TIMELEVEL N 1 G T 1 3 1 Travelling time by bus on busway becomes G T 1 3 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 3 3 G N 1 3 #TIMELEVEL# * * SP Variable 2 = Ironing Facility for Cycling and Total Travelling Cost for Bus on Busway * G T 2 1 1 Daily travelling fare becomes G T 2 1 2 (costs ^B+$ 2.0 ^B-) G T 2 1 3 G N 2 1 2 * G T 2 2 1 Daily travelling fare becomes G T 2 2 2 (costs ^B+$3.0 ^B-) G T 2 2 3 G N 2 2 3 * G T 2 3 1 Daily travelling fare becomes G T 2 3 2 (costs ^B+$ 4.0 ^B-) G T 2 3 3 G N 2 3 4 * * SP Variable 3 = Shower Facility for Cycling and Waiting Time for Bus on Busway * G T 3 1 1 Waiting time for the bus to arrive G T 3 1 2 (becomes ^B+5^B- min) G T 3 1 3
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G N 3 1 5 * G T 3 2 1 Waiting time for the bus to arrive G T 3 2 2 (becomes ^B+8^B- min) G T 3 2 3 G N 3 2 8 * G T 3 3 1 Waiting time for the bus to arrive G T 3 3 2 (becomes ^B+10^B- min) G T 3 3 3 G N 3 3 10 * * SP Variable 4 = Access Mode Time for Bus on Busway and Nothing for Walking * G T 4 1 1 Total access time by #ALTAMODE# G T 4 1 2 (becomes ^B+3^B- min) G T 4 1 3 G N 4 1 3 * G T 4 2 1 Total access time by #ALTAMODE# G T 4 2 2 (becomes ^B+7^B- min) G T 4 2 3 G N 4 2 7 * G T 4 3 1 Total access time by #ALTAMODE# G T 4 3 2 (becomes ^B+10^B- min) G T 4 3 3 G N 4 3 10 * M TIMELEVEL = #CTIME# M TIMELEVEL P 80 M TIMELEVEL N 1 G T 1 4 1 Travelling time by cycling becomes G T 1 4 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 4 3 G N 1 4 #TIMELEVEL# * M TIMELEVEL = #CTIME# M TIMELEVEL P 120 M TIMELEVEL N 1 G T 1 5 1 Travelling time by cycling becomes G T 1 5 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 5 3 G N 1 5 #TIMELEVEL# * M TIMELEVEL = #CTIME# G T 1 6 1 Travelling time by cycling becomes G T 1 6 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 6 3 G N 1 6 #TIMELEVEL# * G T 2 4 1 G T 2 4 2 No shower facility at destination G T 2 4 3 G N 2 4 0 * G T 2 5 1 G T 2 5 2 Shower facility at destination G T 2 5 3 G N 2 5 1
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* G T 2 6 1 G T 2 6 2 No Shower Facility at destination G T 2 6 3 G N 2 6 0 * G T 3 4 1 G T 3 4 2 No ironing facility at destination G T 3 4 3 G N 3 4 0 * G T 3 5 1 G T 3 5 2 Ironing facility at destination G T 3 5 3 G N 3 5 1 * G T 3 6 1 G T 3 6 2 Ironing facility at destination G T 3 6 3 G N 3 6 1 * G T 4 4 1 G T 4 4 2 G T 4 4 3 * G T 4 5 1 G T 4 5 2 G T 4 5 3 * G T 4 6 1 G T 4 6 2 G T 4 6 3 * * Set response scale G H 3 G C 8 G F 1 8 1 1 9 G F 1 8 2 10 18 G X 1 (A) Bus G X 2 (B) Cycling * G R 5 G Y 1 Definitely A G Y 2 Probably A G Y 3 Not sure G Y 4 Probably B G Y 5 Definitely B G Z 1 1 G Z 2 1 G Z 3 0 G Z 4 2 G Z 5 2 * Set mixing and highlighting G M 0 G H 9 U L R 14 G > I #MODE# EQ 2 CONCLUSION
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P * * * SP Game 6 Q 0 Car-Bus R 15 T Now we will like to see your perceived importance & _of ^I+^B+#MODE#^B-^I- (your current travelling mode) by & _comparing its variables with that of ^I+^B+#ALTMODE#^B-^I- & _(your 2nd preferred travelling mode) W T T T _Answer all the questions with the ^I+^B+#TRIPPURPOSE#^B-^I- trip & _in your mind, with the same & _origin (^I+^B+#ORIGIN#^B-^I-) and destination (^I+^B+#DEST#^B-^I-). W T T T On the following screens, we give you a & _number of possible changes for your journey. & _These changes can be real as well as hypothetical. & _Please compare these carefully and then tell us which mode & _of transport you would have preferred in & _this situation. R 14 > * G B 2 SPGAME-Car-Bus G A 18 G L 1 6 G L 2 6 G L 3 6 G L 4 6 * * SP Variable 1 = Total Travelling Time for Car and Bus on Busway V 6 TIMELEVEL V 6 ACTUALTIME * M ACTUALTIME = #CARTIME# M TIMELEVEL = #ACTUALTIME# M TIMELEVEL N 1 M TIMELEVEL P 80 M TIMELEVEL N 1 G T 1 1 1 Travelling time by bus on busway becomes G T 1 1 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 1 3 G N 1 1 #TIMELEVEL# * M ACTUALTIME = #CARTIME# M TIMELEVEL = #ACTUALTIME# M TIMELEVEL N 1 M TIMELEVEL P 120 M TIMELEVEL N 1 G T 1 2 1 Travelling time by bus on busway becomes G T 1 2 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 2 3 G N 1 2 #TIMELEVEL# * M ACTUALTIME = #CARTIME#
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M TIMELEVEL = #ACTUALTIME# M TIMELEVEL N 1 G T 1 3 1 Travelling time by bus on busway becomes G T 1 3 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 3 3 G N 1 3 #TIMELEVEL# * * SP Variable 2 = Parking Cost for Car and Total Travelling Cost for Bus on Busway * G T 2 1 1 Daily travelling fare becomes G T 2 1 2 (costs ^B+$ 3.0 ^B-) G T 2 1 3 G N 2 1 3 * G T 2 2 1 Daily travelling fare becomes G T 2 2 2 (costs ^B+$5.0 ^B-) G T 2 2 3 G N 2 2 5 * G T 2 3 1 Daily travelling fare becomes G T 2 3 2 (costs ^B+$ 8.0 ^B-) G T 2 3 3 G N 2 3 8 * * SP Variable 3 = Parking Search Time for Car and Waiting Time for Bus on Busway * G T 3 1 1 Waiting time for the bus to arrive G T 3 1 2 (becomes ^B+5^B- min) G T 3 1 3 G N 3 1 5 * G T 3 2 1 Waiting time for the bus to arrive G T 3 2 2 (becomes ^B+10^B- min) G T 3 2 3 G N 3 2 10 * G T 3 3 1 Waiting time for the bus to arrive G T 3 3 2 (becomes ^B+15^B- min) G T 3 3 3 G N 3 3 15 * * SP Variable 4 = Access Mode Time for Bus on Busway and Nothing for Car * G T 4 1 1 Total access time by #ALTAMODE# G T 4 1 2 (becomes ^B+3^B- min) G T 4 1 3 G N 4 1 3 * G T 4 2 1 Total access time by #ALTAMODE# G T 4 2 2 (becomes ^B+7^B- min) G T 4 2 3 G N 4 2 7 * G T 4 3 1 Total access time by #ALTAMODE# G T 4 3 2 (becomes ^B+10^B- min) G T 4 3 3 G N 4 3 10 * M TIMELEVEL = #CARTIME# M TIMELEVEL P 80
236
M TIMELEVEL N 1 G T 1 4 1 Travelling time by car becomes G T 1 4 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 4 3 G N 1 4 #TIMELEVEL# * M TIMELEVEL = #CARTIME# M TIMELEVEL P 120 M TIMELEVEL N 1 G T 1 5 1 Travelling time by car becomes G T 1 5 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 5 3 G N 1 5 #TIMELEVEL# * M TIMELEVEL = #CARTIME# G T 1 6 1 Travelling time by car becomes G T 1 6 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 6 3 G N 1 6 #TIMELEVEL# * V 4 COSTLEVEL M COSTLEVEL = #CARPFEE# M COSTLEVEL P 80 M COSTLEVEL N 1 G T 2 4 1 Parking cost becomes G T 2 4 2 (costs ^B+$#COSTLEVEL#^B-) G T 2 4 3 Estimated fuel cost for the trip is ^B+$#FUELCOST#^B- G N 2 4 #COSTLEVEL# * M COSTLEVEL = #CARPFEE# M COSTLEVEL P 120 M COSTLEVEL N 1 G T 2 5 1 Parking cost becomes G T 2 5 2 (costs ^B+$#COSTLEVEL#^B-) G T 2 5 3 Estimated fuel cost for the trip is ^B+$#FUELCOST#^B- G N 2 5 #COSTLEVEL# * M COSTLEVEL = #CARPFEE# G T 2 6 1 Parking cost becomes G T 2 6 2 (costs ^B+$#COSTLEVEL#^B-) G T 2 6 3 Estimated fuel cost for the trip is ^B+$#FUELCOST#^B- G N 2 6 #COSTLEVEL# * V 6 PSTIMELEVEL M PSTIMELEVEL = #CARPSTIME# M PSTIMELEVEL P 80 M PSTIMELEVEL N 1 G T 3 4 1 Search time for finding parking G T 3 4 2 (takes ^B+#PSTIMELEVEL#^B- min) G T 3 4 3 G N 3 4 #PSTIMELEVEL# * M PSTIMELEVEL = #CARPSTIME# M PSTIMELEVEL P 120 M PSTIMELEVEL N 1 G T 3 5 1 Search time for finding parking G T 3 5 2 (takes ^B+#PSTIMELEVEL#^B- min) G T 3 5 3 G N 3 5 #PSTIMELEVEL# *
237
M PSTIMELEVEL = #CARPSTIME# G T 3 6 1 Search time for finding parking G T 3 6 2 (takes ^B+#PSTIMELEVEL#^B- min) G T 3 6 3 G N 3 6 #PSTIMELEVEL# * G T 4 4 1 G T 4 4 2 G T 4 4 3 * G T 4 5 1 G T 4 5 2 G T 4 5 3 * G T 4 6 1 G T 4 6 2 G T 4 6 3 * * Set response scale G H 3 G C 8 G F 1 8 1 1 9 G F 1 8 2 10 18 G X 1 (A) Bus G X 2 (B) Car * G R 5 G Y 1 Definitely A G Y 2 Probably A G Y 3 Not sure G Y 4 Probably B G Y 5 Definitely B G Z 1 1 G Z 2 1 G Z 3 0 G Z 4 2 G Z 5 2 * Set mixing and highlighting G M 0 G H 9 U L R 14 G > I #MODE# EQ 3 CONCLUSION P * * * SP Game 7 Q 0 Walk-Walk R 15 T Now we will like to see your perceived importance & _for each variable of ^I+^B+#MODE#^B-^I- (your current travelling mode). W T T T _Answer all the questions with the ^I+^B+#TRIPPURPOSE#^B-^I- trip & _in your mind, with the same & _origin (^I+^B+#ORIGIN#^B-^I-) and destination (^I+^B+#DEST#^B-^I-). W
238
T T T On the following screens, we give you a & _number of possible changes for your journey. & _These changes can be real as well as hypothetical. & _Please compare these carefully and then tell us which mode & _of transport you would have preferred in & _this situation. R 14 > * G B 2 SPGAME-Walk-Walk G V 3 * * SP Variable 1 = Total Travelling Time by Walking V 6 TIMELEVEL * M TIMELEVEL = #WTIME# M TIMELEVEL P 80 M TIMELEVEL N 1 G T 1 1 1 Time by walking becomes 20% LESS G T 1 1 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 1 3 G N 1 1 #TIMELEVEL# * M TIMELEVEL = #WTIME# M TIMELEVEL P 120 M TIMELEVEL N 1 G T 1 2 1 Time by walking becomes 20% MORE G T 1 2 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 2 3 G N 1 2 #TIMELEVEL# * M TIMELEVEL = #WTIME# G T 1 3 1 Time by walking remains SAME G T 1 3 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 3 3 G N 1 3 #TIMELEVEL# * G O 1 5 G M 1 * SP Variable 2 = Hypothetical Facility (Showering) G L 2 2 G T 2 1 1 G T 2 1 2 No shower facility at destination G T 2 1 3 G N 2 1 0 G T 2 2 1 G T 2 2 2 Shower facility at destination G T 2 2 3 G N 2 2 1 * G O 2 4 G M 2 * SP Variable 3 = Hypothetical Facility (Ironing) G L 3 2 G T 3 1 1 G T 3 1 2 No ironing facility at destination G T 3 1 3 G N 3 1 0
239
G T 3 2 1 G T 3 2 2 Ironing facility at destination G T 3 2 3 G N 3 2 1 * G 0 3 4 G M 3 * Set response scale G C 6 G H 3 G X 1 (A) Walking G X 2 (B) Walking * G R 5 G Y 1 Definitely A G Y 2 Probably A G Y 3 Not sure G Y 4 Probably B G Y 5 Definitely B G Z 1 1 G Z 2 1 G Z 3 0 G Z 4 2 G Z 5 2 * Set mixing and highlighting G M 0 G H 9 U L R 14 G > I #MODE# EQ 1 CONCLUSION P * * * SP Game 8 Q 0 Cycle-Cycle R 15 T Now we will like to see your perceived importance & _for each variable of ^I+^B+#MODE#^B-^I- (your current travelling mode). W T T T _Answer all the questions with the ^I+^B+#TRIPPURPOSE#^B-^I- trip & _in your mind, with the same & _origin (^I+^B+#ORIGIN#^B-^I-) and destination (^I+^B+#DEST#^B-^I-). W T T T On the following screens, we give you a & _number of possible changes for your journey. & _These changes can be real as well as hypothetical. & _Please compare these carefully and then tell us which mode & _of transport you would have preferred in & _this situation. R 14 > * G B 2 SPGAME-Cycle-Cycle G V 3 *
240
* SP Variable 1 = Total Travelling Time by Cycling V 6 TIMELEVEL * M TIMELEVEL = #CTIME# M TIMELEVEL P 80 M TIMELEVEL N 1 G T 1 1 1 Time by cycling becomes 20% LESS G T 1 1 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 1 3 G N 1 1 #TIMELEVEL# * M TIMELEVEL = #CTIME# M TIMELEVEL P 120 M TIMELEVEL N 1 G T 1 2 1 Time by cycling becomes 20% MORE G T 1 2 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 2 3 G N 1 2 #TIMELEVEL# * M TIMELEVEL = #CTIME# G T 1 3 1 Time by cycling remains SAME G T 1 3 2 (entire journey takes ^B+#TIMELEVEL#^B- min) G T 1 3 3 G N 1 3 #TIMELEVEL# * G O 1 5 G M 1 * SP Variable 2 = Hypothetical Facility (Showering) G L 2 2 G T 2 1 1 G T 2 1 2 No Shower Facility at destination G T 2 1 3 G N 2 1 0 G T 2 2 1 G T 2 2 2 Shower Facility at destination G T 2 2 3 G N 2 2 1 G O 2 4 G M 2 * SP Variable 3 = Hypothetical Facility (Ironing) G L 3 2 G T 3 1 1 G T 3 1 2 No Ironing Facility at destination G T 3 1 3 G N 3 1 0 G T 3 2 1 G T 3 2 2 Ironing Facility at destination G T 3 2 3 G N 3 2 1 G O 3 4 G M 3 * Set response scale G C 6 G H 3 G X 1 (A) Cycling G X 2 (B) Cycling * G R 5 G Y 1 Definitely A G Y 2 Probably A
241
G Y 3 Not sure G Y 4 Probably B G Y 5 Definitely B G Z 1 1 G Z 2 1 G Z 3 0 G Z 4 2 G Z 5 2 * Set mixing and highlighting G M 0 G H 9 U L R 14 G > I #MODE# EQ 2 CONCLUSION P * * * SP Game 9 Q 0 Car-Car R 15 T Now we will like to see your perceived importance & _for each variable of ^I+^B+#MODE#^B-^I- (your current travelling mode). W T T T _Answer all the questions with the ^I+^B+#TRIPPURPOSE#^B-^I- trip & _in your mind, with the same & _origin (^I+^B+#ORIGIN#^B-^I-) and destination (^I+^B+#DEST#^B-^I-). W T T T On the following screens, we give you a & _number of possible changes for your journey. & _These changes can be real as well as hypothetical. & _Please compare these carefully and then tell us which mode & _of transport you would have preferred in & _this situation. R 14 > * G B 2 SPGAME-Car-Car G V 3 * * SP Variable 1 = Total Travelling Time by Car V 6 TIMELEVEL * M TIMELEVEL = #CARTIME# M TIMELEVEL P 80 M TIMELEVEL N 1 G T 1 1 1 Travelling time by car becomes 20% LESS G T 1 1 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 1 3 G N 1 1 #TIMELEVEL# * M TIMELEVEL = #CARTIME# M TIMELEVEL P 120 M TIMELEVEL N 1 G T 1 2 1 Travelling time by car becomes 20% MORE G T 1 2 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min)
242
G T 1 2 3 G N 1 2 #TIMELEVEL# * M TIMELEVEL = #CARTIME# G T 1 3 1 Travelling time by car remains SAME G T 1 3 2 (entire journey takes ^B+#TIMELEVEL#^B- hr/min) G T 1 3 3 G N 1 3 #TIMELEVEL# * G O 1 5 G M 1 * SP Variable 2 = Parking Fee by Car V 4 COSTLEVEL * M COSTLEVEL = #CARPFEE# M COSTLEVEL P 80 M COSTLEVEL N 1 G T 2 1 1 #PFEEBASIS# parking cost becomes 20% LESS G T 2 1 2 (costs ^B+$#COSTLEVEL#^B-) G T 2 1 3 G N 2 1 #COSTLEVEL# * M COSTLEVEL = #CARPFEE# M COSTLEVEL P 120 M COSTLEVEL N 1 G T 2 2 1 #PFEEBASIS# parking cost becomes 20% MORE G T 2 2 2 (costs ^B+$#COSTLEVEL#^B-) G T 2 2 3 G N 2 2 #COSTLEVEL# * M COSTLEVEL = #CARPFEE# * M COSTLEVEL / 100 G T 2 3 1 #PFEEBASIS# parking cost remains SAME G T 2 3 2 (costs ^B+$#COSTLEVEL#^B-) G T 2 3 3 G N 2 3 #COSTLEVEL# * G O 2 5 G M 2 * * SP Variable 3 = Parking Search Time V 6 PSTIMELEVEL * M PSTIMELEVEL = #CARPSTIME# M PSTIMELEVEL P 80 M PSTIMELEVEL N 1 G T 3 1 1 G T 3 1 2 Searching for parking place G T 3 1 3 (takes ^B+#PSTIMELEVEL#^B- min) G N 3 1 #PSTIMELEVEL# * M PSTIMELEVEL = #CARPSTIME# M PSTIMELEVEL P 120 M PSTIMELEVEL N 1 G T 3 2 1 G T 3 2 2 Searching for parking place G T 3 2 3 (takes ^B+#PSTIMELEVEL#^B- min) G N 3 2 #PSTIMELEVEL# * M PSTIMELEVEL = #CARPSTIME#
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G T 3 3 1 G T 3 3 2 Searching for parking place G T 3 3 3 (takes ^B+#CARPSTIME#^B- min) G N 3 3 #CARPSTIME# G O 3 5 G M 3 * * Set response scale G C 6 G H 3 G X 1 (A) Car G X 2 (B) Car * G R 5 G Y 1 Definitely A G Y 2 Probably A G Y 3 Not sure G Y 4 Probably B G Y 5 Definitely B G Z 1 1 G Z 2 1 G Z 3 0 G Z 4 2 G Z 5 2 * Set mixing and highlighting G M 0 G H 9 U L R 14 G > > P * * S Final Word Q 0 CONCLUSION T Finally, we would like to ask some questions about you? > * Q 1 AGE T What is your age group? A 18 or younger A 18 to 45 A 46 to 59 A 60 or older > * Q 3 SIZEOFHH T How many people reside in your & _household (INCLUDING YOURSELF)? L 1 H 20 > * P S THANKS Q 0 REMARKS T T T
244
T T T ^B+Thanks a lot for filling out the questionnaire^B- T >
245
Appendix 2 Modal Splits for Survey Sample
Appendix 2 presents the sample modal splits determined for each trip purpose. The
modal split for work trips is presented in Figure 6.3.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
PT Car Walking Cycling
Perc
enta
ge o
f Pop
ulat
ion
in S
tudy
Are
a
Figure A2.1 Modal Split for All Trips from the Survey Sample
246
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
PT Car Walking Cycling
Perc
enta
ge o
f Pop
ulat
ion
in S
tudy
Are
a
Figure A2.2 Modal Split for Shopping Trips from the Survey Sample
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
PT Car Walking Cycling
Perc
enta
ge o
f Pop
ulat
ion
in S
tudy
Are
a
Figure A2.3 Modal Split for Education Trips from the Survey Sample
247
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
PT Car Walking Cycling
Perc
enta
ge o
f Pop
ulat
ion
in S
tudy
Are
a
Figure A2.4 Modal Split for Other Trips from the Survey Sample
248
Appendix 3 Traveller Type Splits in the Survey Sample
0%
10%
20%
30%
40%
50%
60%
70%
Thornlands Redland Bay Victoria Point Mt Cotton - SheldonPerc
enta
ge o
f Res
pond
ents
w.r.
t. Tr
avel
Ty
pe
Car Captive Users PT Captive Users Choice Users
Figure A3.1 Percentage Split of the Survey Sample with respect to Traveller Type
for Suburbs of the Study Area for Work Trips
249
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Car Captive Users PT Captive Users Choice Users
Perc
enta
ge o
f Res
pond
ents
w.r.
t. Tr
avel
Ty
pe
Car Captive Users PT Captive Users Choice Users
Figure A3.2 Percentage Split of the Survey Sample with respect to Traveller Type
for Suburbs of the Study Area for Shopping Trips
250
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Thornlands Redland Bay Victoria Point Mt Cotton -SheldonPe
rcen
tage
of R
espo
nden
ts w
.r.t.
Trav
el
Type
Car Captive Users PT Captive Users Choice Users
Figure A3.3 Percentage Split of the Survey Sample with respect to Traveller Type
for Suburbs of the Study Area for Education Trips
251
0%
10%
20%
30%
40%
50%
60%
70%
Thornlands Redland Bay Victoria Point Mt Cotton - SheldonPerc
enta
ge o
f Res
pond
ents
w.r.
t. Tr
avel
Ty
pe
Car Captive Users PT Captive Users Choice Users
Figure A3.4 Percentage Split of the Survey Sample with respect to Traveller Type
for Suburbs of the Study Area for Other Trips
252
Appendix 4 Perceived Travel Choices of the
Survey Sample
0
50
100
150
200
250
CAD CAP FBB WB PRB KRB W C
Perceived Choices of Travelling Modes
Abs
olut
e Fr
eque
ncy
Car as Driver Car as Passenger Feeder Bus to PTWalk to PT Park & Ride to PT Kiss & Ride to PTWalking all-the-way Cycling all-the-way
Figure A4.1 Perceived Travel Choices of the Survey Sample for Work Trips
253
0
50
100
150
200
250
300
350
400
450
CAD CAP FBB WB PRB KRB W C
Perceived Choices of Travelling Modes
Abs
olut
e Fr
eque
ncy
Car as Driver Car as Passenger Feeder Bus to PTWalk to PT Park & Ride to PT Kiss & Ride to PTWalking all-the-way Cycling all-the-way
Figure A4.2 Perceived Travel Choices of the Survey Sample for Shopping Trips
254
0
5
10
15
20
25
30
35
40
45
50
CAD CAP FBB WB PRB KRB W C
Perceived Choices of Travelling Modes
Abs
olut
e Fr
eque
ncy
Car as Driver Car as Passenger Feeder Bus to PTWalk to PT Park & Ride to PT Kiss & Ride to PTWalking all-the-way Cycling all-the-way
Figure A4.3 Perceived Travel Choices of the Survey Sample for Education Trips
255
0
50
100
150
200
250
300
350
CAD CAP FBB WB PRB KRB W C
Perceived Choices of Travelling Modes
Abs
olut
e Fr
eque
ncy
Car as Driver Car as Passenger Feeder Bus to PTWalk to PT Park & Ride to PT Kiss & Ride to PTWalking all-the-way Cycling all-the-way
Figure A4.4 Perceived Travel Choices of the Survey Sample for Other Trips
256
Appendix 5 Absolute Frequencies of Level-of-
Service Attributes
Local Work Trips
0
20
40
60
80
100
120
140
4 7 10 13 15 18 21 24 27 30 32
In-vehicle Travel T ime of Car (min)
Abs
olut
e Fr
eque
ncy
Figure A5.1 Frequency Chart of In-vehicle Travel Time of Car for Local Work Trips
257
0
50
100
150
200
250
34 120 207 293 379 465 552
Out-of-pocket Travel Cost of Car (cents)
Abs
olut
e Fr
eque
ncy
Figure A5.2 Frequency Chart of Out-of-pocket Travel Cost of Car for Local Work Trips
258
Regional Shopping Trips
0
5
10
15
20
25
30
8 11 15 18 22 25 28 32 35 39
In-vehicle Travel T ime of Car (min)
Abs
olut
e Fr
eque
ncy
Figure A5.3 Frequency Chart of In-vehicle Travel Time of Car for Regional Shopping Trips
259
0
2
4
6
8
10
12
14
16
18
2550 2839 3128 3417 3706 3995 4284 4573 4862 5151
Out-of-pocket Travel Cost of Car (cents)
Abs
olut
e Fr
eque
ncy
Figure A5.4 Frequency Chart of Out-of-pocket Travel Cost of Car for Regional Shopping Trips
260
Local Shopping Trips
0
20
40
60
80
100
120
140
2 4 6 8 10 12 14 16 18 20 22 24
In-vehicle Travel T ime of Car (min)
Abs
olut
e Fr
eque
ncy
Figure A5.5 Frequency Chart of In-vehicle Travel Time of Car for Local Shopping Trips
261
0
10
20
30
40
50
60
70
80
90
100
17 40 63 87 110 133 156 179 203 226 249 272 295
Out-of-pocket Travel Cost of Car (cents)
Abs
olut
eFr
eque
ncy
Figure A5.6 Frequency Chart of Out-of-pocket Travel Cost of Car for Local Shopping Trips
262
Regional Education Trips
0
2
4
6
8
10
12
14
16
18
20
24 31 37 44 51 57 64 71
In-vehicle Travel T ime of Car (min)
Abs
olut
e Fr
eque
ncy
Figure A5.7 Frequency Chart of In-vehicle Travel Time of Car for Regional Education Trips
263
0
5
10
15
20
25
168 310 452 594 736 878 1020 1162 1304
Out-of-pocket Travel Cost of Car (cents)
Abs
olut
e Fr
eque
ncy
Figure A5.8 Frequency Chart of Out-of-pocket Travel Cost of Car for Regional Education Trips
264
Local Education Trips
0
10
20
30
40
50
60
70
80
90
2 5 8 11 14 17 20
In-vehicle Travel T ime of Car (min)
Abs
olut
e Fr
eque
ncy
Figure A5.9 Frequency Chart of In-vehicle Travel Time of Car for Local Education Trips
265
0
50
100
150
200
250
300
350
15 176 336 496
Out-of-pocket Travel Cost of Car (cents)
Abs
olut
e Fr
eque
ncy
Figure A5.10 Frequency Chart of Out-of-pocket Travel Cost of Car for Local Education Trips
266
Regional Other Trips
0
20
40
60
80
100
120
16 22 28 34 40 46 52 57 63 69
In-vehicle Travel T ime of Car (min)
Abs
olut
e Fr
eque
ncy
Figure A5.11 Frequency Chart of In-vehicle Travel Time of Car for Regional Other Trips
267
0
20
40
60
80
100
120
140
160
180
238 472 706 939 1173 1407 1641 1874 2108
Out-of-pocket Travel Cost of Car (cents)
Abs
olut
e Fr
eque
ncy
Figure A5.12 Frequency Chart of Out-of-pocket Travel Cost of Car for Regional Other Trips
268
Local Other Trips
0
10
20
30
40
50
60
70
80
90
100
2 5 8 11 15 18 21 25
In-vehicle Travel T ime of Car (min)
Abs
olut
e Fr
eque
ncy
Figure A5.13 Frequency Chart of In-vehicle Travel Time of Car for Local Other Trips
269
0
10
20
30
40
50
60
70
80
17 56 95 134 172 211 250 289
Out-of-pocket Travel Cost of Car (cents)
Abs
olut
e Fr
eque
ncy
Figure A5.14 Frequency Chart of Out-of-pocket Travel Cost of Car for Local Other Trips
270
Appendix 6 Correlation Tables
Appendix 6 presents the sets of correlation values determined among the attributes
associated to the travelling modes in the SP choice set for different trip purposes
using various logit models.
1. REGIONAL WORK TRIPS
1.1. Simple Binary Logit Model
Table A6.1 Correlation Table for Simple Binary Logit Model for Regional Work Trips
CCAR TTCAR TCCAR TTB TCB WTB
TTCAR -0.119
TCCAR 0.016 -0.097
TTB 0.253 0.687 0.243
TCB 0.445 0.154 0.048 0.115
WTB 0.453 0.076 0.001 0.068 0.067
ATB 0.470 0.063 -0.006 0.098 0.129 0.041
1.2. Simple Multinomial Logit Model
Table A6.2 Correlation Table for Simple Multinomial Logit Model
for Regional Work Trips
CCAD TTCAD TCCAD CCAP CFBB TTFBB TCFBB
TTCAD -0.178
TCCAD 0.014 -0.165
CCAP 0.592 0.482 0.147
CFBB 0.333 0.029 -0.002 0.245
271
TTFBB 0.066 0.322 0.112 0.301 -0.598
TCFBB 0.097 0.067 0.018 0.116 -0.539 0.054
ATWB 0.491 0.100 -0.022 0.395 0.266 0.040 0.008
CCAD TTCAD TCCAD CCAP CFBB TTFBB TCFBB
TTWB 0.382 0.572 0.206 0.716 0.163 0.392 0.046
TCWB 0.581 0.137 0.045 0.500 0.201 0.058 0.192
CPRB 0.316 0.014 0.033 0.236 0.154 0.022 0.012
ATPRB 0.105 -0.006 -0.062 0.050 0.054 -0.015 -0.002
TTPRB 0.047 0.380 0.105 0.322 0.016 0.240 0.020
TCPRB 0.090 0.046 0.005 0.094 -0.003 0.015 0.089
WTPRB 0.063 0.063 0.012 0.090 0.038 0.031 0.004
CKRB 0.211 0.161 0.063 0.272 0.096 0.114 0.018
ATKRB 0.021 -0.069 -0.063 -0.053 0.017 -0.059 -0.005
TCKRB 0.024 0.062 0.015 0.065 -0.005 0.030 0.038
WTKRB 0.024 0.041 0.017 0.051 0.015 0.026 0
ATWB TTWB TCWB CPRB ATPRB TTPRB TCPRB
TTWB 0.091
TCWB 0.164 0.118
CPRB 0.205 0.182 0.197
ATPRB 0.070 -0.007 0.064 -0.046
TTPRB 0.072 0.401 0.064 -0.439 -0.008
TCPRB 0.021 0.035 0.148 -0.511 -0.259 -0.023
WTPRB 0.074 0.070 0.055 -0.503 -0.328 0.059 0.378
CKRB 0.152 0.272 0.145 0.146 -0.012 0.136 -0.027
ATKRB 0.018 -0.105 0.021 -0.018 0.153 -0.036 -0.035
TCKRB 0.004 0.057 0.053 -0.032 -0.024 0.016 0.101
WTKRB 0.035 0.055 0.016 -0.033 -0.002 0.012 0.009
272
WTPRB CKRB ATKRB TCKRB
CKRB -0.040
ATKRB -0.025 0.158
TCKRB 0.027 -0.769 -0.559
WTKRB 0.113 -0.751 -0.586 0.747
1.3. Nested Binary Logit Model
Table A6.3 Correlation Table for Nested Binary Logit Model for Regional Work Trips
CCAD TTCAD TCCAD CCAP CFBB TTFBB TCFBB
TTCAD 0.637
TCCAD 0.336 0.293
CCAP 0.860 0.901 0.435
CFBB 0.135 0.005 -0.015 0.059
TTFBB -0.092 -0.006 0.032 -0.039 -0.426
TCFBB -0.066 -0.012 -0.008 -0.035 -0.117 0.156
ATWB 0.131 0.011 -0.036 0.058 0.379 0.079 0.393
CCAD TTCAD TCCAD CCAP CFBB TTFBB TCFBB
TTWB -0.068 -0.009 0.042 -0.030 0.120 0.683 0.200
TCWB -0.040 -0.013 -0.007 -0.025 0.128 0.163 0.903
CPRB 0.111 -0.006 0.010 0.046 0.185 0.055 -0.016
ATPRB 0.164 0.026 -0.039 0.080 0.036 -0.076 -0.434
TTPRB -0.115 0 0.025 -0.047 0.031 0.530 0.224
TCPRB -0.074 -0.013 -0.008 -0.039 0.083 0.149 0.858
WTPRB -0.034 -0.006 0.001 -0.018 0.104 0.062 0.336
CKRB -0.004 -0.005 0.020 -0.003 0.121 0.312 0.149
ATKRB 0.124 0.022 -0.041 0.061 0.007 -0.251 -0.339
TCKRB -0.071 -0.01 -0.003 -0.036 0.059 0.191 0.667
WTKRB -0.021 -0.005 0.007 -0.011 0.047 0.107 0.158
273
ATWB TTWB TCWB CPRB ATPRB TTPRB TCPRB
TTWB 0.120
TCWB 0.410 0.208
CPRB 0.175 0.155 0.035
ATPRB -0.151 -0.090 -0.434 -0.014
TTPRB 0.146 0.657 0.242 -0.322 -0.140
TCPRB 0.425 0.201 0.894 -0.193 -0.521 0.210
WTPRB 0.298 0.109 0.343 -0.410 -0.486 0.136 0.431
CKRB 0.190 0.440 0.185 0.191 -0.098 0.351 0.125
ATKRB -0.143 -0.319 -0.343 -0.045 0.498 -0.238 -0.365
TCKRB 0.330 0.246 0.695 -0.039 -0.365 0.215 0.705
WTKRB 0.140 0.145 0.164 -0.045 -0.140 0.084 0.161
WTPRB CKRB ATKRB TCKRB
CKRB -0.008
ATKRB -0.254 -0.101
TCKRB 0.287 -0.343 -0.556
WTKRB 0.288 -0.586 -0.552 0.613
2. REGIONAL OTHER TRIPS
2.1. Simple Binary Logit Model
Table A6.4 Correlation Table for Simple Binary Logit Model for Regional Other Trips
CCAR TTCAR TCCAR TTB TCB WTB
TTCAR -0.061
TCCAR 0.111 -0.039
TTB 0.315 0.676 0.220
TCB 0.436 0.217 0.211 0.138
WTB 0.475 0.084 0.050 0.069 0.080
ATB 0.498 0.092 0.046 0.126 0.089 0.128
274
2.2. Simple Multinomial Logit Model
Table A6.5 Correlation Table for Simple Multinomial Logit Model
for Regional Other Trips
CCAD TTCAD TCCAD CCAP TTCAP TTWB TCWB
TTCAD -0.184
TCCAD -0.030 -0.184
CCAP 0.559 -0.002 0.039
TTCAP 0.011 0.303 0.068 -0.686
TTWB 0.306 0.480 0.123 0.199 0.260
TCWB 0.476 0.177 0.102 0.314 0.111 0.064
WTWB 0.463 -0.008 0.003 0.314 -0.010 -0.069 0.012
CCAD TTCAD TCCAD CCAP TTCAP TTWB TCWB
ATWB 0.421 0.063 0.020 0.283 0.031 -0.007 0.127
CPRB 0.595 0.275 0.080 0.394 0.150 0.527 0.289
TCPRB 0.047 0.125 0.080 0.033 0.077 0.048 0.273
ATPRB 0.084 0.052 -0.023 0.057 0.018 -0.022 0.058
WTWB ATWB CPRB TCPRB
ATWB 0.074
CPRB 0.367 0.349
TCPRB -0.040 -0.050 -0.397
ATPRB 0.014 0.171 -0.267 -0.092
275
2.3. Nested Binary Logit Model
Table A6.6 Correlation Table for Nested Binary Logit Model for Regional Other Trips
CCAD TTCAD TCCAD CCAP TTCAP TTWB TCWB
TTCAD 0.230
TCCAD 0.163 0.122
CCAP 0.628 0.258 0.173
TTCAP 0.248 0.502 0.222 -0.396
TTWB 0.342 0.459 0.173 0.240 0.300
TCWB 0.650 0.639 0.360 0.471 0.440 0.168
WTWB 0.451 0.066 0.042 0.327 0.040 -0.052 0.096
CCAD TTCAD TCCAD CCAP TTCAP TTWB TCWB
ATWB 0.589 0.423 0.224 0.427 0.285 0.085 0.537
CPRB 0.688 0.488 0.233 0.492 0.326 0.535 0.522
TCPRB 0.390 0.549 0.315 0.288 0.377 0.147 0.701
ATPRB -0.088 -0.172 -0.133 -0.066 -0.127 -0.069 -0.227
WTWB ATWB CPRB TCPRB
ATWB 0.122
CPRB 0.369 0.513
TCPRB 0.052 0.381 0.076
ATPRB -0.018 -0.046 -0.367 -0.284
276
3. LOCAL WORK TRIPS
3.1. Simple Multinomial Logit Model
Table A6.7 Correlation Table for Simple Multinomial Logit Model
for Local Work Trips
TT TC CCAP ATWB CPRB ATPRB CW
TC 0.113
CCAP 0.052 0.393
ATWB -0.052 -0.666 -0.149
CPRB -0.006 -0.158 -0.046 0.131
ATPRB -0.011 0.018 0.023 -0.005 -0.863
CW -0.614 0.007 0.019 0.003 -0.007 0.014
CC -0.783 0.102 0.092 -0.025 -0.015 0.019 0.508
3.2. Nested Multinomial Logit Model
Table A6.8 Correlation Table for Nested Multinomial Logit Model
for Local Work Trips
TT TC CCAP ATWB CPRB ATPRB CW
TC 0.133
CCAP 0.075 0.469
ATWB 0.250 -0.398 -0.160
CPRB 0.152 -0.265 -0.112 0.607
ATPRB 0.042 0.001 0.011 0.127 -0.618
CW -0.642 -0.003 -0.005 -0.307 -0.202 -0.026
CC -0.764 0.060 0.042 -0.402 -0.266 -0.033 0.613
277
4. LOCAL SHOPPING TRIPS
4.1. Simple Multinomial Logit Model
Table A6.9 Correlation Table for Simple Multinomial Logit Model
for Local Shopping Trips
TT TC CCAD CCAP CFBB ATWB
TC 0.535
CCAD 0.697 0.494
CCAP 0.630 0.587 0.821
CFBB 0.252 0.033 0.409 0.304
ATWB 0.516 0.070 0.835 0.622 0.438
CC 0.358 0.445 0.747 0.641 0.296 0.606
4.2. Nested Multinomial Logit Model
Table A6.10 Correlation Table for Nested Multinomial Logit Model for Local Shopping Trips
TT TC CCAP CFBB ATWB
TC -0.041
CCAP -0.033 0.776
CFBB -0.042 0.240 0.186
ATWB -0.055 0.320 0.248 0.717
CC -0.220 -0.088 -0.070 -0.032 -0.042
278
5. LOCAL EDUCATION TRIPS
5.1. Simple Multinomial Logit Model
Table A6.11 Correlation Table for Simple Multinomial Logit Model for Local Education Trips
VARIABLE TC CCAD TTCAD TTCAP HHSIZE CWB ATWB CKRB ATKRB TTW
CCAD -0.012
TTCAD -0.124 -0.395
TTCAP 0.186 0.186 0.486
HHSIZE 0 0.747 -0.014 0.028
CWB -0.457 0.466 0.266 0.320 0.503
ATWB 0.096 -0.012 0.034 0.065 -0.007 -0.581
CKRB -0.219 0.309 0.177 0.239 0.337 0.382 0.031
ATKRB -0.085 -0.025 0.042 0.016 -0.028 0.027 -0.016 -0.757
TTW 0.018 0.397 0.031 0.178 0.425 0.281 0.006 0.193 -0.013
TTC 0.051 0.704 0.119 0.394 0.761 0.526 0.015 0.362 -0.022 0.424
6. LOCAL OTHER TRIPS
6.1. Simple Multinomial Logit Model
Table A6.12 Correlation Table for Simple Multinomial Logit Model
for Local Other Trips
TT TC CCAP ATWB
TC 0.310
CCAP 0.131 0.276
ATWB -0.139 -0.736 -0.130
CC -0.195 0.155 0.072 -0.064
279
6.2. Nested Multinomial Logit Model
Table A6.13 Correlation Table for Nested Multinomial Logit Model
for Local Other Trips
TT TC CCAP ATWB
TC -0.087
CCAP -0.071 0.828
ATWB -0.012 0.413 0.342
CC 0.014 -0.046 -0.038 -0.002
280
Appendix 7 Forecasted Mode Shares
1. REGIONAL WORK TRIPS
1.1. Simple Binary Logit Model
60.19%
39.81%
PCARPB
Figure A7.1 Aggregated Mode Share Forecast for Simple Binary Logit Model for Regional Work Trips
281
1.2. Simple Multinomial Logit Model
50.19%
0.82%
1.96%
10.52%0.15%
36.36%
PCADPCAPPFBBPWBPPRBPKRB
Figure A7.2 Aggregated Mode Share Forecast for Simple Multinomial Logit Model
for Regional Work Trips
282
2. REGIONAL OTHER TRIPS
2.1. Simple Binary Logit Model
56.48%
43.52%
PCARPB
Figure A7.3 Aggregated Mode Share Forecast for Simple Binary Logit Model for Regional Other Trips
283
2.2. Simple Multinomial Logit Model
41.77%
4.74%
39.45%
14.04%
PCADPCAPPWBPPRB
Figure A7.4 Aggregated Mode Share Forecast for Simple Multinomial Logit Model
for Regional Other Trips
284
3. LOCAL WORK TRIPS
3.1. Simple Multinomial Logit Model
56.33%
6.40%
13.80%
13.13%
5.25%5.09%
PCADPCAPPWBPPRBPWPC
Figure A7.5 Aggregated Mode Share Forecast for Simple Multinomial Logit Model
for Local Work Trips
285
3.2. Nested Multinomial Logit Model
62.03%
11.05%
16.73%
8.37%
1.37%
0.45%
Car as DriverCar as PassengerWalk to BuswayPark & Ride to BuswayWalkCycle
Figure A7.6 Aggregated Mode Share Forecast for Nested Multinomial Logit Model
for Local Work Trips
286
4. LOCAL SHOPPING TRIPS
4.1. Simple Multinomial Logit Model
69.98%
3.21%
0.62%
20.21%
1.38%4.61%
PCADPCAPPFBBPWBPWPC
Figure A7.7 Aggregated Mode Share Forecast for Simple Multinomial Logit Model
for Local Shopping Trips
287
4.2. Nested Multinomial Logit Model
69.10%
3.21%
0.62%
21.37%
1.10%4.60%
Car as DriverCar as PassengerFeeder Bus to BuswayWalk to BuswayWalkCycle
Figure A7.8 Aggregated Mode Share Forecast for Nested Multinomial Logit Model
for Local Shopping Trips
288
5. LOCAL EDUCATION TRIPS
5.1. Simple Multinomial Logit Model
24.48%
43.80%
17.65%
2.56%
1.49%
10.03%
Car as DriverCar as PassengerWalk to BuswayKiss & Ride to BuswayWalkCycle
Figure A7.9 Aggregated Mode Share Forecast for Simple Multinomial Logit Model
for Local Education Trips
289
6. LOCAL OTHER TRIPS
6.1. Simple Multinomial Logit Model
60.06%
2.92%
29.44%
4.01%3.56%
PCADPCAPPWBPWPC
Figure A7.10 Aggregated Mode Share Forecast for Simple Multinomial Logit Model
for Local Other Trips
290
6.2. Nested Multinomial Logit Model
59.62%
2.93%
30.29%
3.59% 3.56%
Car as DriverCar as PassengerWalk to BuswayWalkCycle
Figure A7.11 Aggregated Mode Share Forecast for Nested Multinomial Logit Model
for Local Other Trips
291
Appendix 8 Modelling Results for Simple Binary Logit
Model and Nested Binary Logit Model for
Regional Other Trips
1. SIMPLE BINARY LOGIT MODEL
MODE Variable Coefficient T-Ratio Std.
Error
TTCAR -0.04267 -4.3 0.00985
TCCAR -0.00122 -6.7 0.00018
Car
CCAR -2.01300 -3.5 0.57200
TTB -0.02338 -2.4 0.00954
TCB -0.00437 -9.1 0.00048
WTB -0.05064 -2.2 0.02250
Bus on
Busway
ATB -0.03376 -1.0 0.03240
ρ2 0.2131
Number of SP Observations 670
Table A8.1 Model Estimation Results for Simple Binary Logit Model
for Regional Other Trips
292
2. SIMPLE MULTINOMIAL LOGIT MODEL
Table A8.2 Model Estimation Results for Simple Multinomial Logit Model
for Regional Other Trips
MODE Variable Coefficient T-Ratio Std.
Error
TTCAD -0.03473 -4.1 0.00850
TCCAD -0.00094 -5.5 0.00017
Car as
Driver
CCAD -2.55700 -4.9 0.52000
TTCAP -0.08095 -4.5 0.01820 Car as
Passenger CCAP -3.84500 -4.9 0.78400
TTWB -0.01653 -2.0 0.00822
TCWB -0.00440 -8.5 0.00052
WTWB -0.04252 -1.8 0.02350
Walk to
Bus on
Busway
ATWB -0.16780 -6.5 0.02590
TCPRB -0.00355 -4.9 0.00072
TTPRB 0.40380 7.8 0.05200
Park & Ride
to Bus on
Busway CPRB -6.01500 -8.8 0.68400
ρ2 0.3727
Number of SP Observations 670
293
Appendix 9 Elasticities of Level-of-Service Attributes of
Various Mode Choice Models
1. REGIONAL WORK TRIPS
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
16 20 24 28 32 36 40 44 48 52 56 60 64
In-vehicle Travel Time of Car(min)
Car as Driver Car as Passenger Feeder Bus to BuswayWalk to Busway Park & Ride to Busway Kiss & Ride to Busway
Figure A9.1 Sensitivity of In-vehicle Travel Time of Car using Nested Binary Logit Model
294
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
2 4 6 8 10 12 14 16
Waiting Time for Bus on Busway (min)
Car as Driver Car as Passenger Feeder Bus to BuswayWalk to Busway Park & Ride to Busway Kiss & Ride to Busway
Figure A9.2 Sensitivity of Waiting Time of Bus on Busway using Nested Binary Logit Model
295
2. REGIONAL OTHER TRIPS
0%
10%
20%
30%
40%
50%
60%
70%
80%
250 300 350 400 450 500 550 600 650 700 750Travel Fare of Bus on Busw ay
(cents)
Car as Driver Car as PassengerWalk to Busw ay Park & Ride to Busw ay
Figure A9.3 Sensitivity of Travel Fare of Bus on Busway using Nested Binary Logit Model
296
0%
10%
20%
30%
40%
50%
60%
70%
3 6 9 12 15 18 21 24 27 30
Waiting Time for Bus on Busway (min)
Car as Driver Car as PassengerWalk to Busway Park & Ride to Busway
Figure A9.4 Sensitivity of Waiting Time of Bus on Busway using Nested Binary Logit Model
297
0%
10%
20%
30%
40%
50%
60%
70%
80%
200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400
Access Distance to Bus on Busw ay (metres)
Car as Driver Car as PassengerWalk to Busw ay Park & Ride to Busw ay
Figure A9.5 Sensitivity of Access Distance to Bus on Busway using Nested Binary Logit Model
298
0%
10%
20%
30%
40%
50%
60%
70%
20 25 30 35 40 45 50 55 60In-vehicle Travel Time of Car
(min)
Car as Driver Car as PassengerWalk to Busway Park & Ride to Busway
Figure A9.6 Sensitivity of In-vehicle Travel Time of Car using Nested Binary Logit Model
299
3. LOCAL WORK TRIPS
0%
10%
20%
30%
40%
50%
60%
70%
80%
2 6 10 14 18 22 26
In-vehicle Travel Time of Bus on Busway(min)
Car as Driver Car as Passenger Walk to BuswayPark & Ride to Busway Walk all-the-way Cycle all-the-way
Figure A9.7 Sensitivity of In-vehicle Travel Time of Bus on Busway using Nested Multinomial Logit Model
300
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
2 4 6 8 10 12 14 16 18 20Travel T ime of Walk all-the-way
(min)
Car as Driver Car as Passenger Walk to BuswayPark & Ride Walk all-the-way Cycle all-the-way
Figure A9.8 Sensitivity of Travel Time of Walk all-the-way using Nested Multinomial Logit Model
301
0%
10%
20%
30%
40%
50%
60%
70%
2 4 6 8 10 12 14 16 18 20
Travel Time of Cycle all-the-way(min)
Car as Driver Car as Passenger Walk to BuswayPark & Ride Walk all-the-way Cycle all-the-way
Figure A9.9 Sensitivity of Travel Time of Cycle all-the-way using Nested Multinomial Logit Model
302
4. LOCAL SHOPPING TRIPS
5. LOCAL EDUCATION TRIPS
6. LOCAL OTHER TRIPS
Figure A9.10 Sensitivity of Travel Fare of Bus on Busway using Nested Multinomial Logit Model
0%
10%
20%
30%
40%
50%
60%
70%
100 125 150 175 200 225 250 275 300 325
Travel Fare of Bus on Busway(cents)
Car as Driver Car as Passenger Walk to BuswayPark & Ride to Busway Walk Cycle
303
0%
10%
20%
30%
40%
50%
60%
70%
200 400 600 800 1000 1200 1400 1600 1800 2000
Access Distance for Bus on Busway(metres)
Car as Driver Car as Passenger Walk to BuswayPark & Ride to Busway Walk Cycle
Figure A9.11 Sensitivity of Access Distance for Bus on Busway using Nested Multinomial Logit Model
304
4. LOCAL SHOPPING TRIPS
0%
10%
20%
30%
40%
50%
60%
70%
80%
2 4 6 8 10 12 14 16 18 20
In-vehicle Travel Time of Bus on Busway(min)
Car as Driver Car as Passenger Feeder Bus to BuswayWalk to Busway Walk all-the-way Cycle all-the-way
Figure A9.12 Sensitivity of In-vehicle Travel Time for Bus on Busway using Nested Multinomial Logit Model
305
0%
10%
20%
30%
40%
50%
60%
2 4 6 8 10 12 14 16 18 20
Travel Time of Walk all-the-way (min)
Car as Driver Car as Passenger Feeder Bus to BuswayWalk to Busway Walk all-the-way Cycle all-the-way
Figure A9.13 Sensitivity of Travel Time of Walk all-the-way using Nested Multinomial Logit Model
306
0%
10%
20%
30%
40%
50%
60%
70%
2 4 6 8 10 12 14 16 18 20 22 24
Travel Time of Cycle all-the-way (min)
Car as Driver Car as Passenger Feeder Bus to BuswayWalk to Busway Walk all-the-way Cycle all-the-way
Figure A9.14 Sensitivity of Travel Time of Cycle all-the-way using Nested Multinomial Logit Model
307
Figure A9.15 Sensitivity of Travel Fare of Bus on Busway using Nested Multinomial Logit Model
Elasticity wrt Travel Cost
0.00
0.10
0.20
0.30
0.40
0.50
0.60
100 120 140 160 180 200 220 240 260 280 300
Travel Fare (cents)
Prob
abili
ty
CycleWalk
Car as DriverCar as Passenger
Feeder Bus to BuswayWalk to Busway
308
0%
10%
20%
30%
40%
50%
60%
70%
200 400 600 800 1000 1200 1400 1600 1800 2000
Access Distance to the Busway Station (metres)
Car as Driver Car as Passenger Feeder Bus to BuswayWalk to Busway Walk all-the-way Cycle all-the-way
Figure A9.16 Sensitivity of Access Distance for Bus on Busway using Nested Multinomial Logit Model
309
5. LOCAL EDUCATION TRIPS
0%
10%
20%
30%
40%
50%
60%
10 15 20 25 30 35 40 45 50 55 60
Travel Time of Walk all-the-way (min)
Car as Driver Car as Passenger Walk to BuswayKiss & Ride to Busway Walk all-the-day Cycle all-the-way
Figure A9.17 Sensitivity of Travel Time of Walk all-the-way using Simple Multinomial Logit Model
310
0%
10%
20%
30%
40%
50%
60%
4 8 12 16 20 24 28 32 36 40 44 48
Travel Time of Cycle all-the-way (min)
Car as Driver Car as Passenger Walk to BuswayKiss & Ride to Busway Walk all-the-day Cycle all-the-day
Figure A9.18 Sensitivity of Travel Time of Cycle all-the-way using Simple Multinomial Logit Model
311
0%
10%
20%
30%
40%
50%
60%
70%
80 120 160 200 240 280 320 360 400
Trip Fare of Bus on Busway(cents)
Car as Driver Car as Passenger Walk to BuswayKiss & Ride to Busway Walk all-the-way Cycle all-the-way
Figure A9.19 Sensitivity of Trip Fare of Bus on Busway using Simple Multinomial Logit Model
312
0%
10%
20%
30%
40%
50%
60%
70%
200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400
Access Distance to Busway Station(metres)
Car as Driver Car as Passenger Walk to BuswayKiss & Ride to Busway Walk all-the-way Cycle all-the-way
Figure A9.20 Sensitivity of Access Distance for Bus on Busway using Simple Multinomial Logit Model
313
6. LOCAL OTHER TRIPS
0%
10%
20%
30%
40%
50%
60%
70%
100 140 180 220 260 300 340 380 420
Travel Fare of Bus on Busway(cents)
Car as Driver Car as Passenger Walk to BuswayWalk all-the-way Cycle all-the-way
Figure A9.21 Sensitivity of Travel Fare of Bus on Busway using
Nested Multinomial Logit Model
314
0%
10%
20%
30%
40%
50%
60%
70%
80%
200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400
Access Distance to Busway Station(metres)
Car as Driver Car as Passenger Walk to Busway Walk all-the-way Cycle all-the-way
Figure A9.22 Sensitivity of Access Distance for Bus on Busway using Nested Multinomial Logit Model
315
Appendix 10 Modelling Results for Simple Multinomial
Logit Model for Local Work Trips
1. SIMPLE MULTINOMIAL LOGIT MODEL
Table A10.1 Model Estimation Results for Simple Multinomial Logit Model
for Local Work Trips
MODE Variable Value T-Ratio Std. Error
TT -0.05407 -3.5 0.01550 Generic
Variables TC -0.00145 -4.3 0.00034
Car as Driver
Car as Passenger CCAP -2.23300 -13.7 0.16300
Walk to Bus on Busway ATWB -0.1018 -5.0 0.02030
ATPRB 0.48440 4.0 0.12000 Park & Ride to
Bus on Busway CPRB -5.07900 -7.2 0.70500
Walk CW -3.28900 -3.6 0.90400
Cycle CC -1.57600 -5.6 0.28300
ρ2 0.4122
Number of SP Observations 680
316
Appendix 11 Modelling Results for Simple Multinomial
Logit Model for Local Shopping Trips
1. SIMPLE MULTINOMIAL LOGIT MODEL
Table A11.1 Model Estimation Results for Simple Multinomial Logit Model
for Local Shopping Trips
MODE Variable Value T-Ratio Std. Error
TT -0.10630 -14.3 0.00744 Generic
Variables TC -0.00352 -8.0 0.00044
Car as Driver
Car as Passenger CCAP -3.62400 -16.9 0.21400
Feeder Bus to Bus on Busway CFBB -3.75500 -8.1 0.46100
Walk to Bus on Busway ATWB -0.03902 -2.1 0.01830
Walk
Cycle CC -1.68400 -8.8 0.19100
ρ2 0.5203
Number of SP Observations 920
317
Appendix 12 Modelling Results for Simple Multinomial
Logit Model for Local Other Trips
1. SIMPLE MULTINOMIAL LOGIT MODEL
Table A12.1 Model Estimation Results for Simple Multinomial Logit Model
for Local Other Trips
MODE Variable Value T-Ratio Std. Error
TT -0.07132 -12.0 0.00595 Generic Attribute
TC -0.00200 -4.5 0.00045
Car as Driver
Car as Passenger CCAP -3.34200 -11.7 0.28500
Walk to Bus on Busway ATWB -0.01936 -0.9 0.02140
Walk
Cycle CC -2.00600 -7.7 0.26200
ρ2 0.3885
Number of SP Observations 544
318
Appendix 13 STATISTICAL DATA OF SURVEY
SAMPLE
Table A13.1 Statistical Survey Data used for Figures 4, 5 and 6
S. No. Trip Length Person Type Sample Percentage Total
1 Car Captive Users 143 50.71%
2 PT Captive Users 45 15.96%
3
Regional - Work
Choice Users 94 33.33% 282
4 Car Captive Users 66 66.00%
5 PT Captive Users 10 10.00%
6
Regional -
Shopping
Choice Users 24 24.00% 100
7 Car Captive Users 11 26.83%
8 PT Captive Users 16 39.02%
9
Regional -
Education
Choice Users 14 34.15% 41
10 Car Captive Users 211 51.46%
11 PT Captive Users 103 25.12%
12
Regional - Other
Choice Users 96 23.41% 410
13 Car Captive Users 134 59.56%
14 PT Captive Users 9 4.00%
15
Local - Work
Choice Users 82 36.44% 225
16 Car Captive Users 411 76.82%
17 PT Captive Users 12 2.24%
18
Local - Shopping
Choice Users 112 20.93% 535
19 Car Captive Users 43 34.40%
20 PT Captive Users 28 22.40%
21
Local -
Education
Choice Users 54 43.20% 125
22 Car Captive Users 197 68.17%
23 PT Captive Users 23 7.96%
24
Local - Other
Choice Users 69 23.88% 289
TOTAL 2007 2007
319
Table A13.2 Statistical Survey Data used for Figure 7
S. No. Household
Size
Person Type Sample Percentage Total
1 Car Captive Users 63 44.06%
2 PT Captive Users 23 16.08%
3
1
Choice Users 57 39.86% 143
4 Car Captive Users 428 60.03%
5 PT Captive Users 92 12.90%
6
2
Choice Users 193 27.07% 713
7 Car Captive Users 207 63.69%
8 PT Captive Users 40 12.31%
9
3
Choice Users 78 24.00% 325
10 Car Captive Users 517 62.59%
11 PT Captive Users 92 11.14%
12
3+
Choice Users 217 26.27% 826
TOTAL 2007 2007
Table A13.3 Statistical Survey Data used for Figure 8
S. No. Age
Group
Person Type Sample Percentage Total
1 Car Captive Users 42 31.58%
2 PT Captive Users 39 29.32%
3
Less than
18
Choice Users 52 39.10% 133
4 Car Captive Users 473 64.53%
5 PT Captive Users 67 9.14%
6
18 - 45
Choice Users 193 26.33% 733
7 Car Captive Users 421 64.18%
8 PT Captive Users 62 9.45%
9
46 - 59
Choice Users 173 26.37% 656
10 Car Captive Users 279 57.53%
11 PT Captive Users 79 16.29%
12
60 or Older
Choice Users 127 26.19% 485
TOTAL 2007 2007
320
Appendix 14 WORK DESTINATION AREAS
All the suburbs of South-East Queensland that are combined to form the work
destination areas are shown below along with the total number of travellers from the
sample going to these individual suburbs, mentioned alongside the suburb’s name, in
parenthesis.
1. Brisbane CBD -> Brisbane (81)
2. Cleveland / Capalaba -> 84, 29
3. Redlands Other Suburbs -> Alexandra Hills (13), Birkdale (4), Burbank (1),
Carbrook (1), Manly (2), Manly West (1), Mt Cotton (8), Ormiston (6), Redland
Bay (21), Sheldon (7), Thornlands (13), Victoria Point (22), Wellington Point (4)
4. Brisbane Southern Suburbs-> Acacia Ridge (2), Archerfield (4), Boronia
Heights (1), Buranda (2), Cannon Hills (1), Carina (2), Carindale (8), Carole Park
(1), Coopers Plains (6), Coorparoo (11), Eight Mile Plains (1), Garden City (1),
Greenslopes (3), Hemmant (3), Holland Park (1), Kangaroo Point (1), Lytton (2),
MacGregor (1), Mansfield (2), Morning Side (3), Moorooka (1), Mt Gravatt (13),
Murrarie (6), Nathan (2), Redbank (1), Rocklea (1), Runcorn (1), Salisbury (1),
Seventeen Mile Rocks (1), South Brisbane (8), Sunny Bank (3), Sunny Bank
Hills (1), Tarragindi (1), Tingalpa (1), Underwood (1), West End (1), Willawong
(2), Wishart (1), Woolloongabba (4), Wynnum (5), Wynnum West (1)
5. Brisbane Northern Suburbs -> Amberly (1), Ascot (1), Ashgrove (1), Clayfield
(1), Eagle Farm (4), Enogerra (1), Fisherman Islands (3), Fortitude Valley (2),
Geebung (2), Hamilton (1), Hawthorne (1), Herston (1), Indooropilly (1), Kedron
(2), Laceys Creek (1), Milton (3), Morayfield (1), New Farm (2), New Market
(1), Newstead (1), North Gate (1), Nundah (1), Spring Hill (2), St Lucia (1),
Stafford (1), Toowong (4), Tweedheads (1), Virginia (2)
6. Logan -> Beenleigh (3), Coomera (2), CrestMead (2), Gold Coast (3), Ipswich
(1), Kingston (1), Logan (2), Loganholme (2), Loganlea (2), Robina (1), Shailer
Park (1), Springwood (8), Stapylton (1), Tabragalba (1), Tanamerah (1),
Woodridge (3), Yatala (1)
321
Appendix 15 ACCESS MODE DISTRIBUTION FOR PT
CAPTIVE USERS FOR ALL TRIP
PURPOSES
Work
5.45%
0.00%
35.10%
7.90%
51.55%
Feeder Bus to PTWalk to PTCycle to PTPark & Ride to PTKiss & Ride to PT
Figure A15.1 Access Mode Distribution for PT Captive Users for Work Trips
322
Shopping
0.00%
37.50%
1.19% 1.15%
60.16%
Feeder Bus to PTWalk to PTCycle to PTPark & Ride to PTKiss & Ride to PT
Figure A15.2 Access Mode Distribution for PT Captive Users for Shopping Trips
323
Education
0.50%
0.58%
65.15%
6.45%
27.32%
Feeder Bus to PTWalk to PTCycle to PTPark & Ride to PTKiss & Ride to PT
Figure A15.3 Access Mode Distribution for PT Captive Users for Education Trips
324
Other
0.00%
44.15%
3.55% 6.50%
45.80%
Feeder Bus to PTWalk to PTCycle to PTPark & Ride to PTKiss & Ride to PT
Figure A15.4 Access Mode Distribution for PT Captive Users for Other Trips
