Post on 22-Jan-2021
MODELLING COMMUTERS’
MODE CHOICE:
INTEGRATING TRAVEL BEHAVIOUR,
STATED PREFERENCES, PERCEPTION,
AND SOCIO-ECONOMIC PROFILE
Puteri Paramita
BSc.(Hons) Statistics and Mathematics, MSc. Transport Planning, AFHEA
Dr. MD Mazharul Haque, Professor Stephen Kajewski, Dr. Zuduo Zheng, Professor
Simon Washington, Professor Paul Hyland
Submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy
School of Civil Engineering and the Built Environment
Science and Engineering Faculty
Queensland University of Technology
2018
i
Keywords
Binomial logit, bus, car, commuter, comparison study, discrete choice experiment
(DCE), fixed parameter, heterogeneity, mixed logit model, mode choice behaviour,
mode shift behaviours, multinomial logit, nested logit, ordered logit, perception,
policy intervention, qualitative assessment, quantitative assessment, random
parameter, Revealed Preference, socio-economic profiles, Stated Preference,
satisfaction, shift behaviour, train, train fare, travel behaviour, traveller, trip
characteristics, urban traveller.
ii
Abstract
This study focuses on identifying the factors that influence travellers’ transport
mode choice, and on understanding the impact of these factors on their decision-
making process. The relationship between travellers’ socio-economic profiles and trip
characteristics and their chosen mode are quantified using the data collected from
urban travellers in five Australian capital cities: Sydney, Melbourne, Brisbane,
Adelaide, and Perth. Specifically, this study comprises three interconnected sub-
studies in the following order: Urban travellers’ satisfaction with train fares in five
Australian cities, Consistency between perceptions and stated preferences data in a
nationwide mode choice experiment, and Policy interventions study to encourage
behavioural shift from car to public transport. The second sub-study is based on the
findings of the first, while the third incorporates the findings of the first and second
studies.
In the public transport industry, travellers’ perceived satisfaction is a key
element in understanding their evaluation of, and loyalty to ridership. Despite its
notable importance, studies of customer satisfaction are under-represented in the
literature, and the most previous studies are based on survey data collected from a
single city only. This does not allow a comparison across different transport systems.
To address this underrepresentation, the first sub-study is a comparative analysis of
user satisfaction with train fares for their most recent home-based train trip in five
Australian capital cities. Two data sources are used: a nationwide survey, and objective
information on the train fare structure in each of the targeted cities. In particular,
satisfaction with train fares is modelled as a function of socio-economic factors and
train trip characteristics, using a random parameters ordered logit model that accounts
for unobserved heterogeneity in the population.
Results of this first sub-study indicate that gender, city of origin, transport mode
from home to the train station, eligibility for a student or senior concession fare, one-
way cost, waiting time, and five diverse interaction variables between city of origin
and socio-economic factors are the key determinants of passenger satisfaction with
train fares. In particular, this sub-study reveals that female respondents tend to be less
satisfied with their train fare than their male counterparts. Interestingly, respondents
who take the bus to the train station tend to feel more satisfied with their fare compared
with the rest of the respondents. In addition, notable heterogeneity is detected across
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respondents’ perceived satisfaction with train fare, specifically with regard to the one-
way cost and the waiting time incurred. An intercity comparison reveals that a city’s
train fare structure also significantly affects a traveller’s perceived satisfaction with
their train fare. The findings of this study are significant for both policy makers and
transport operators, allowing them to understand traveller behaviours, and to
subsequently formulate effective transit policies.
The second study assesses the consistency between respondents’ perceptions and
their stated preferences in a stated preference (SP) experiment. A nationwide survey
of urban travellers in five Australian capital cities yielded their perceptions of various
service factors—representing respondent revealed preferences (RPs). These same
respondents also completed SP experiment. Two random parameter logit models were
developed to understand respondents’ mode choice behaviour: one using their
perceptions, and one using their responses to the SP experiment. The consistency
between these two models in explaining respondents’ mode choice behaviours are
assessed both qualitatively and quantitatively. At the qualitative level, the factor
mapping of the two models shows that respondents’ perceptions of four service factors
– such as train waiting time, on-board crowding, the availability of a laptop station,
and increased road congestion, and their corresponding attributes in SP experiment,
are well aligned. At the quantitative level, the marginal utilities of choosing the train
mode in these two models are tested through rigorous numerical simulations for the
same four service factors, and the corresponding estimated probabilities of choosing
the train mode from the SP model are found to be similar to those estimated from the
RP model.
The third and final sub-study focuses on the impact of transit policy interventions
on mode shift. By utilising datasets from urban travellers in Sydney, Melbourne, and
Brisbane, this third study determines the socio-economic factors and travel attributes
that significantly influence travellers’ mode choice in each city. A nested logit model
is estimated to identify targets for policy interventions that could influence travellers
to shift from car to public transport. The nested structure used consists of a public
transport branch, which includes bus and train alternatives, and a degenerative private
transport branch. Each best-fitted nested logit model consists of four utility functions.
Each of these functions is characterized by key travel attributes and socio-economic
factors. The travel attribute elements are useful in identifying policy intervention
targets, while the socio-economic factor elements are important to an understanding
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of the underlying profile of the travellers who are most affected by changes in transport
policies.
This final study also simulates and analyses over one hundred policy intervention
scenarios to optimise the mode change from car to public transport in each city. These
interventions are divided into two categories: those that encourage public transport
ridership by improving passenger service quality; and those that discourage regular car
usage by increasing the cost of that usage. Based on a comprehensive understanding
of target traveller profiles and travel behaviour, the simulation studies demonstrate that
the most efficient transport policy interventions are a combination of both intervention
categories. The successful replication of the study in three different cities provides
sufficient evidence that the overall framework could be applied to many other urban
traveller datasets.
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Table of Contents
Keywords .................................................................................................................................. i
Abstract .................................................................................................................................... ii
Table of Contents ......................................................................................................................v
List of Figures ........................................................................................................................ vii
List of Tables .......................................................................................................................... ix
List of Abbreviations ................................................................................................................x
Statement of Original Authorship ........................................................................................... xi
Acknowledgements ................................................................................................................ xii
Chapter 1: Introduction ...................................................................................... 1
1.1 Background and context .................................................................................................1
1.2 Research question formulation .......................................................................................5
1.3 Study objectives, innovations, and contributions ...........................................................7
1.4 Research scopes ..............................................................................................................9
1.5 Thesis outline ................................................................................................................10
1.6 Publications from this study .........................................................................................11
Chapter 2: Literature Review ........................................................................... 13
2.1 Urban travellers’ satisfaction with train fares in five Australian cities.........................14
2.2 Consistency between perceptions and stated preferences data in a nationwide mode
choice experiment ...................................................................................................................17
2.3 Policy interventions study to encourage behavioural shift from car to public
transport ..................................................................................................................................20
Chapter 3: Dataset ............................................................................................. 31
3.1 Data collection ..............................................................................................................31
3.2 Mode choice experiment structure ................................................................................33
3.3 Data description ............................................................................................................35
3.4 Data utilisation ..............................................................................................................39
3.5 Train riders dataset .......................................................................................................41
3.6 Factor mapping of perceptions and attributes of mode choice experiment ..................46
3.7 Dataset for transit policy interventions study ...............................................................47
Chapter 4: Methodology .................................................................................... 49
4.1 Urban travellers’ satisfaction with train fares in five Australian cities.........................49
4.2 Consistency between perceptions and stated preferences data in a nationwide mode
choice experiment ...................................................................................................................51
4.3 Policy interventions study to encourage behavioural shift from car to public
transport ..................................................................................................................................56
vi
Chapter 5: Urban travellers’ satisfaction with train fares in five Australian
cities 59
5.1 Train fare structures in the five Australian cities ......................................................... 59
5.2 Modelling results .......................................................................................................... 62
5.3 Key socio-economic factors ......................................................................................... 64
5.4 Key Trip Characteristics .............................................................................................. 65
5.5 Interaction variables ..................................................................................................... 68
5.6 Heterogeneity ............................................................................................................... 71
5.7 Intercity comparison .................................................................................................... 77
5.8 Conclusions .................................................................................................................. 78
Chapter 6: Consistency between perceptions and stated preferences data in
a nationwide mode choice experiment .................................................................... 81
6.1 The random-parameters binomial logit model for perceptions ................................... 81
6.2 The mixed logit model for the SP data ......................................................................... 83
6.3 Qualitative assessment ................................................................................................. 85
6.4 Quantitative assessment ............................................................................................... 87
6.5 Conclusions .................................................................................................................. 98
Chapter 7: Policy interventions study to encourage behavioural shift from
car to public transport ........................................................................................... 101
7.1 Modelling results ........................................................................................................ 101
7.2 The goodness-of-fit test of the nested structure ......................................................... 103
7.3 The lower nest of the nested structure ........................................................................ 104
7.4 The upper nest of the nested structure ........................................................................ 107
7.5 The probability functions ........................................................................................... 109
7.6 The average profile of travellers for the baseline scenario ........................................ 110
7.7 The policy intervention scenario analysis .................................................................. 113
7.8 The discussions of combined policy intervention scenarios ...................................... 127
7.9 Conclusions ................................................................................................................ 128
Chapter 8: Conclusions.................................................................................... 131
8.1 Overarching conclusions ............................................................................................ 131
8.2 Contributions and policy implications ....................................................................... 134
8.3 Limitations of this study ............................................................................................ 135
8.4 Recommendations for future study ............................................................................ 136
Appendices .............................................................................................................. 137
Appendix A Urban Rail Travel Behaviour, Web-based Survey .......................................... 137
Bibliography ........................................................................................................... 139
vii
List of Figures
Figure 1.1 : The three interconnected sub-studies ....................................................... 7
Figure 2.1 : Body of literature of Travel Behaviour field. ......................................... 13
Figure 2.2 : Three guides underlying the theory of human behaviour (Ajzen,
1985) ............................................................................................................ 21
Figure 2.3 : The micro and macro process of the intervention method (Bamberg
& Schmidt, 1998) ......................................................................................... 25
Figure 3.1 : The diagram of the collected dataset ...................................................... 32
Figure 3.2 : The diagram of data utilisation ............................................................... 40
Figure 3.3 : Satisfaction with train fare in each city .................................................. 43
Figure 3.4 : The diagram of data utilisation in the transit policy interventions
study ............................................................................................................. 47
Figure 4.1 : The nested structure of the mode choice experiment ............................. 57
Figure 5.1 : The simulated marginal utility of one-way cost for 100 randomly
selected individuals ...................................................................................... 73
Figure 5.2 : The simulated marginal utility of waiting time for 100 randomly
selected individuals ...................................................................................... 75
Figure 6.1 : The simulated probability of individuals’ perception of train
running on schedule from the RP model ..................................................... 88
Figure 6.2 : The simulated probability of train waiting time from the SP model ...... 88
Figure 6.3 : The difference in probability value of the impact of a one unit
increase in individuals’ perception level of train running on schedule ....... 90
Figure 6.4 : The difference in probability value of the impact of one unit
increase in train waiting time in the SP experiment .................................... 90
Figure 6.5 : The simulated probability of individuals’ perception of the
probability of getting a seat from the RP model.......................................... 91
Figure 6.6 : The difference in probability value of the impact of a one unit
increase in individuals’ perception level of the probability of getting a
seat ............................................................................................................... 92
Figure 6.7 : The simulated probability of using a car for individuals
experiencing two different periods of car on-board time from the SP
model............................................................................................................ 95
Figure 6.8 : The corresponding simulated probability of taking the train for
individuals who experience two different periods of car on-board time from the SP model ....................................................................................... 95
Figure 6.9 : The difference in the probability value of using a car as the impact
of a one unit increase in car on-board time in the SP experiment ............... 96
Figure 6.10 : The difference in the probability value of taking the train as the
impact of a one unit increase in car on-board time in the SP
experiment.................................................................................................... 97
viii
Figure 7.1 : The probability of driving a car in Sydney as the result of policy
intervention related to bus and train waiting times and parking and toll
costs, while holding all other factors constant ........................................... 118
Figure 7.2 : The probability of taking public transport in Sydney as the result of
policy interventions in both bus and train waiting times and parking
and toll costs, holding all other factors constant ........................................ 118
Figure 7.3 : The probability of driving a car in Melbourne as the result of
policy interventions in bus and train waiting time and parking and toll
costs, holding all other factors constant ..................................................... 122
Figure 7.4 : The probability of taking public transport in Melbourne as the
result of policy interventions in bus and train waiting time and parking
and toll costs, holding all other factors constant ........................................ 122
Figure 7.5 : The probability of driving a car in Brisbane as the result of policy
interventions in bus and train waiting time and parking and toll cost,
holding all other factors constant ............................................................... 126
Figure 7.6 : The probability of taking public transport in Brisbane as the result
of policy interventions in bus and train waiting times and parking and
toll costs, holding all other factors constant ............................................... 126
ix
List of Tables
Table 3.1 The breakdown of train riders and non-riders across five cities .............. 33
Table 3.2 Attributes and their levels employed in the mode choice experiments ..... 34
Table 3.3 Socio-economic profiles of respondents from each city............................ 35
Table 3.4 Respondents’ perceptions of various service factors in each city ............ 36
Table 3.5 Trip characteristics of train riders from each city (Non-riders) .............. 37
Table 3.6 Socio-economic profiles of respondents from each city (Train riders) .... 43
Table 3.7 Trip characteristics of train riders from each city (Train riders) ............ 44
Table 3.8 Factor mapping of perceptions and attributes of mode choice
experiment .................................................................................................... 46
Table 5.1 Summary of the best fixed-parameter and random-parameter logit
models .......................................................................................................... 62
Table 5.2 Distribution of the random parameters .................................................... 63
Table 5.3 Heterogeneities in the random parameters ............................................... 64
Table 6.1 Summary of the best-fitted random-parameter binomial logit model ....... 81
Table 6.2 Summary of the best-fitted mixed logit model ........................................... 83
Table 6.3 Heterogeneities in car on-board time of car utility function within
best-fitted mixed logit model ........................................................................ 84
Table 6.4 Mapping of significant variables of the random-parameter binomial
logit (RP) model against the mixed logit (SP) model .................................. 85
Table 7.1 Summary of the best-fitted FIML of nested logit model .......................... 101
Table 7.2 The average profile of Sydney travellers for a baseline scenario........... 110
Table 7.3 The average profile of Melbourne travellers for a baseline scenario .... 111
Table 7.4 The average profile of Brisbane travellers for a baseline scenario ....... 112
Table 7.5 The probability values of driving a car and taking public transport
in Sydney: 176 different policy intervention scenarios .............................. 114
Table 7.6 The probability values of driving a car and taking public transport
in Melbourne: 176 different policy intervention scenarios........................ 119
Table 7.7 The probability values of driving a car and taking public transport
in Brisbane: 176 different policy intervention scenarios ........................... 123
x
List of Abbreviations
CEBE Civil Engineering and the Built Environment
CRC Cooperative Research Centres
CWA Cognitive Work Analysis
DCE Discrete Choice Experiment
FIML Full Information Maximum Likelihood
HDR Higher Degree Research
IV Inclusive Value
LOS Level of Service
MMNL Mixed Multinomial Logit
MNL Multinomial Logit
QUT Queensland University of Technology
RP Revealed Preference
SEF Science and Engineering Faculty
SP Stated Preference
TPB Theory of Planned Behaviour
QUT Verified Signature
xii
Acknowledgements
This journey would not be possible without the support of a number of people.
My deep appreciation to my supervisory team: Dr. Zuduo Zheng, Dr. MD Mazharul
Haque, Professor Stephen Kajewski, Professor Simon Washington, Professor Paul
Hyland. Zuduo and Shimul, I thank for believing in me, for your encouragement,
patience and understanding. You always provided me guidance and practical solutions
throughout the whole process. It is because of you I learned how to think critically and
analytically; I believe this has helped me tremendously to become a better researcher.
Professor Simon Washington and Professor Paul Hyland, I appreciate the valuable
advices and clear directions that you provided at the beginning of my study. Dr. Jake
Whitehead and Dr. Ashish Bhaskar, thank you for your supports and valuable
constructive feedbacks shared in my final seminar. Zuduo, Professor Simon
Washington and Professor Paul Hyland, my sincere gratitude for giving me the
permission to utilize the data gathered as part of Project R1.130 Understanding Urban
Rail Travel for Improved Patronage Forecasting funded by the CRC for Rail
Innovation (established and supported under the Australian Government's Cooperative
Research Centres program). A warm thank you to Professor Stephen Kajewski who
provided me with great supports from the School of Civil Engineering and the Built
Environment (CEBE). I also acknowledge and appreciate the assistance of the
professional editor (Ms. Denise Scott) for her timely supports in proofreading, editing,
and formatting of this thesis.
Thank you to supportive team from the Research Student Centre and Ms. Tiziana
La Mendola, a Higher Degree Research Support Officer for School of CEBE, it was
always pleasant to deal with all of you. This study is funded by Science and Engineering
Faculty (SEF). I gratefully acknowledge for the financial assistance. I wish to make a
special note of appreciation for the university counsellor (Mr. Samuel Zimmer),
university General Practitioner (Dr. Rhian Kenrick), my family and my long distance
best friends. Sam and Dr. Kenrick, I genuinely thank you, without your patience to
listening to me and continuous encouragements during my difficult time, the completion
of this study would not be possible. A warm thanks to my parents, sister and close
relatives, Andrés and Marie, who have been assisting a great deal in thriving my PhD
journey. I would also like to acknowledge all those who have provided feedback for this
xiii
work. I much appreciate your time, supports and interests in this study. Last but not the
least, I gracefully thank Jesus Christ and Mother Mary for their continuous supports every
single day.
Chapter 1: Introduction 1
Chapter 1: Introduction
This chapter provides a comprehensive introduction to the study of urban travel
behaviours in five Australian capital cities: Sydney, Melbourne, Brisbane, Adelaide,
and Perth. It begins with a detailed presentation of the background and context of the
study in Section 1.1. Section 1.2 formulates the study’s main research question, while
Section 1.3 presents its objectives, innovations, and contributions. Section 1.4 covers
the detailed inclusions and exclusions of the overall PhD study. Section 1.5 includes
an outline of the remaining chapters of the thesis. Finally, Section 1.6 summarizes the
publications extracted from this study.
1.1 BACKGROUND AND CONTEXT
Urban travellers’ transport mode choice decision has been widely discussed in
Australia over recent decades (Buys & Miller, 2011; Kamruzzaman, Baker,
Washington, & Turrell, 2014; McIntosh, Newman, & Glazebrook, 2013; McMillan,
2007; Soltani & Allan, 2006; Taplin & Qiu, 1997; Zheng, Washington, Hyland, Sloan,
& Liu, 2016; Zheng et al., 2013). In particular, it has been debated whether travellers’
travel experience and/or their socio-economic profile influence their daily mode choice
decision, especially for commuting purposes. In addition, other pressing questions
concern urban travellers’ level of satisfaction with their current mode choice, and
whether they would consider a mode shift in the future – especially from private to
public transport – if there were improved transit policies in place (Corpus, 2008;
Passenger Demand Forecasting Council (PDFC), 2013; Zheng et al., 2016; Zheng et
al., 2013).
A comprehensive understanding and knowledge of the factors that influence
urban travellers’ mode shift behaviour are critical for transport authorities to formulate
the most suitable policies to encourage mode shift. It is also important that transport
authorities provide sufficient public transport services to cater for increased public
demand as a result of shifting behaviours. Concurrently, the encouragement to shift
transport mode from private to public would ensure the sustainability of Australia’s
public transport systems across (Corpus, 2008; Zheng et al., 2016; Zheng et al., 2013).
2 Chapter 1: Introduction
This study aims to thoroughly investigate and address these public transport
issues. To this end, it is comprised of three interconnected sub-studies that address, in
turn: 1) urban traveller satisfaction; 2) consistency between urban travellers’ current
travel behaviour and their future preferences; and 3) policy interventions that could
influence mode shift. To facilitate the formulation of precise research questions, each
sub-study starts with an investigation of the background and context of its topic issue.
1.1.1 Urban traveller satisfaction
Satisfaction is an essential concept in the service industry and market research,
and an important factor in understanding customer behaviour (J. de Oña & de Oña,
2014; J. de Oña, de Oña, Eboli, & Mazzulla, 2013; R. de Oña, Machado, & de Oña,
2015; Eboli & Mazzulla, 2012; Fornell, 1992; Johnson & Gustafsson, 2006; Oliver,
2014). In public transport, user satisfaction is recognized as a key link between public
transport offerings and traveller reactions to these offerings (Fellesson & Friman,
2012). In general, knowledge of the level of user satisfaction with the existing public
transport system provides valuable information for both policy makers and public
transport operators, providing the basis for the development of effective strategies for
improving traveller experience and, in turn, increasing ridership.
Only a few studies in the literature focus on user satisfaction in the context of
public transport (Efthymiou, Antoniou, Tyrinopoulos, & Skaltsogianni, 2017;
Fellesson & Friman, 2012; Hensher, 2007; Thompson & Schofield, 2007). Available
studies identify that frequency, reliability, driver behaviour, information, cleanliness,
and comfort are typical factors that influence public transport users’ satisfaction with
services (J. Bates, Polak, Jones, & Cook, 2001; Beirão & Cabral, 2007; Eboli &
Mazzulla, 2012; Friman & Gärling, 2001). Despite its enormous importance, user
satisfaction studies are generally under-represented in the literature. In addition, the
fact that most previous studies are based on survey data collected from a single city
makes a comparative analysis across different public transport systems almost
impossible. Thus, the data used in many previous studies only partially captures the
potential factors that could be significantly linked to user satisfaction. An inter-city
comparison, supplemented by information on the characteristics of the current
transport system in each city, can be valuable in revealing underlying (or even causal)
factors that contribute to user satisfaction with public transport services.
Chapter 1: Introduction 3
1.1.2 Consistency between urban travellers’ current travel behaviours and their
future preferences
In the literature on travel behaviour, two types of data are commonly used:
revealed preferences (RP), and stated preferences (SP). Briefly speaking, RP are
respondents’ previously made mode-choice decisions, while SP are the hypothetical
mode-choice decisions that are made in a series of SP experiment. Each data type has
its pros and cons.
RP data emphasise the actual travel behaviours and recent trip characteristics of
travellers (Hensher, 1994). Thus, RP data are generally regarded as more reliable.
However, they have been criticized for having insufficient variation in explanatory
variables, high collinearity, and an inability to integrate new scenarios that differ
substantially from the current ones (Swait, Louviere, & Williams, 1994). On the other
hand, collecting SP data is usually more convenient and less expensive. SP data are
particularly useful for investigating respondents’ sensitivity to new transport modes or
to new attributes of an existing model (because relevant RP data would not yet exist).
However, SP are less reliable than RP data because information contained in the
former pertains to hypothetical scenarios (J. J. Louviere, Hensher, & Swait, 2000). For
example, personal constraints are not considered as constraints at the time of ‘choice’,
particularly when respondents do not take the SP task seriously. The task of the analyst
is, therefore, to make the hypothetical scenarios as realistic as possible (Hensher, Rose,
& Greene, 2005).
Hensher (1994) describes two broad categories of SP responses. In the first
category, a respondent indicates his or her preferences from a set of combinations of
attributes via a rating scale. The second category is a rating scale category that is
included when a respondent chooses only one of the combinations of attributes, and is
known as “first preference choice task”. To obtain meaningful interpretations, it is
crucial to ensure that respondents answer in a “rational” (i.e., internally consistent)
way (Miguel, Ryan, & Amaya‐Amaya, 2005). SP data is richer than RP data, and has
the ability to view the experiment as supplementary to RP data (Hensher, 1994;
Wardman, 1988). However, some SP responses might not reflect respondents’ current
preferences due to systematic bias, or to complications with the SP experiment
(Wardman, 1988). Despite the increasing usage of SP data in travel behaviour
research, there is only a small amount of empirical evidence to support the predictive
4 Chapter 1: Introduction
value of the hypothetical travel scenarios in similar real life situations (Lambooij et
al., 2015).
The persistent fundamental question is whether a carefully designed SP
experiment is able to elicit individuals’ true preferences, or whether the hypothetical
nature of the questions renders them irrelevant, regardless of the use of a truth-
revealing mechanism (Azevedo, Herriges, & Kling, 2003; Hensher, 1994; Wardman,
1988). Ideally, individuals’ SP responses can be compared to their observed choices
when the hypothetical scenarios are presented to them (Wardman, 1988). In practice,
however, such opportunities rarely occur, and are strictly constrained. Thus, travel
behaviour research is largely restricted to the comparison of SP related to a few unique
scenarios and the actual post-scenario behaviours (Chatterjee, Wegmann, &
McAdams, 1983; Couture & Dooley, 1981). As an alternative to these ‘before’ and
‘after’ studies, a validation test based on revealed travel behaviours and future travel
preferences can be carried out (Wardman, 1988).
Numerous validation studies comparing stated preferences and observed choices
have been undertaken in the past; these range from marketing studies (Green &
Srinivasan, 1978; Montgomery & Wittink, 1979; Parker & Srinivasan, 1976); to
healthcare studies (Lambooij et al., 2015) to transport studies. The comparison of RP
and SP models of travel behaviour research include implied values of time (Bates,
1984; Hensher, Li, & Ho, 2015; Hensher & Truong, 1985; Louviere et al., 1981), and
the comparison of the predicted (or individual choice) and real time market shares
(Benjamin & Sen, 1982; Kocur, Hyman, & Aunet, 1982; Lerman & Louviere, 1978;
J. J. Louviere & Hensher, 1982; J. J. Louviere & Kocur, 1983). Generally, findings
from these studies have encouraged on-going studies in a bid to establish a reliable
validation method (J. J. Bates, 1983; J. J. Bates & Roberts, 1986; Horowitz, 1985;
Leigh, MacKay, & Summers, 1984).
In addition, a group of researchers conducted comparison studies of SP and RP
data utilizing the willingness to pay (WTP) test (Blumenschein, Johannesson,
Yokoyama, & Freeman, 2001; Clarke, 2002; Ding, 2007; Lambooij et al., 2015; Ryan,
2004; Tselentis, Theofilatos, Yannis, & Konstantinopoulos, 2018). In summary, these
studies report that they overestimated the WTP factor in the SP experiment. Hence,
there are inconsistencies in the results of their comparative analysis.
Chapter 1: Introduction 5
1.1.3 Policy interventions to influence mode shift
In recent decades, trends in travel behaviour have been characterised by
increasing trip distances, and a mode shift from public transport services to private car
usage (Scheiner, 2010). A continuous increase in urban trips as the result of
urbanisation will ultimately lead to an immense increase in private car usage, and result
in both congestion and environmental concerns. In particular, an increasing level of air
pollution shows that transport is the fastest growing sector, and is responsible for
almost a quarter of all greenhouse gas emissions (Stanton et al., 2013). A shift to public
transport and other sustainable transport modes is crucial to slowing down, or even
reversing, this trend. Thus, a broad understanding of the factors that are influential in
transport mode choice and mode shift behaviour will support the ongoing promotion
of sustainable travel behaviour (Corpus, 2008). Concurrently, it will sustain public
transport services across Australia.
Numerous past studies of the travel behaviour context have investigated the
mode choice and mode shift behaviour of travellers from the perspective of various
psychological theories and intervention methods. Nevertheless, no study has yet used
a statistical model – estimated by using urban traveller datasets in an Australian capital
city – to identify key areas for targeted policy interventions, and to determine transit
policies that encourage mode shift. This study addresses this deficit by utilising urban
travellers’ mode choice responses in the three largest Australian capital cities: Sydney,
Melbourne, and Brisbane. Specifically, a nested multinomial logit (nested logit) model
is employed to estimate the utility functions of mode choice responses, and to identify
their related key travel attributes.
1.2 RESEARCH QUESTION FORMULATION
Having thoroughly identified the background and context of the three mode-
choice related issues, this study formulated the following main research question:
How do travellers’ socio-economic profiles, perceptions, and revealed travel
behaviour influence their choice of transport mode?
This question, in turn, determined the thesis title: Modelling Commuters’ Mode
Choice: Integrating Travel Behaviour, Stated Preferences, Perception, and Socio-
economic Profile. Throughout this thesis, the term ‘revealed travel behaviour’ has the
6 Chapter 1: Introduction
same definition as, and is used interchangeably with, the term ‘trip characteristics’.
Additionally, the term ‘mode choice experiment’ is used interchangeably with the term
‘discrete choice experiment (DCE)’ and with the term ‘SP experiment’, as well as the
term ‘commuters’ is used interchangeably with the term ‘travellers’.
To ensure the comprehensive nature of the research, an in-depth evaluation
process, the provision of significant theoretical and practical contributions, and to
ensure that all aspects of the research question were adequately addressed, the main
research question was further divided into three interconnected research sub-questions.
Each of the research sub-questions was addressed in a separate sub-study, as below:
1. What factors influence train riders’ satisfaction with the fare for their most
recent home-based train trip in five Australian capital cities: Sydney,
Melbourne, Brisbane, Adelaide, and Perth?
2. Is the carefully designed mode choice experiment able to present travellers’
true preferences? Are travellers’ perceptions of various service factors
constructively aligned, and consistent with their views of similar attributes
presented in the mode choice experiment? and
3. To what extent do the socio-economic factors and travel attributes in the mode
choice experiment influence the utility and probability values of travellers
taking the bus, train, and car, and encourage mode shift from car to public
transport service?
Based on the formulation of these sub-research questions, the three
interconnected sub-studies are:
1. Urban travellers’ satisfaction with train fares in five Australian cities
2. Consistency between perceptions and stated preferences data in a nationwide
mode choice experiment
3. Policy interventions study to encourage behavioural shift from car to public
transport
Figure 1.1 illustrates how these three sub-studies are interconnected and
cohesively work to address the main research question.
Chapter 1: Introduction 7
Figure 1.1 : The three interconnected sub-studies
1.3 STUDY OBJECTIVES, INNOVATIONS, AND CONTRIBUTIONS
This study focuses on identifying influential factors on mode choice and
understanding their impacts on travellers’ mode choice decision-making process
currently and in the future. The relationships between travellers’ socio-economic
profile and their trip characteristics versus their chosen mode are then quantified using
the data collected from urban travellers in five Australian capital cities: Sydney,
Melbourne, Brisbane, Perth and Adelaide. The consistency of travellers ‘revealed
travel behaviours is also assessed against their stated preferences. The detailed
objectives and uniqueness of each of the sub-study are discussed as below.
8 Chapter 1: Introduction
1.3.1 Urban travellers’ satisfaction with train fares in five Australian cities
To control confounding factors, this first sub-study, Urban travellers’
satisfaction with train fares in five Australian cities, specifically focuses on train
riders’ satisfaction with the paid train fare for their most recent home-based train trip
in five Australian capital cities: Sydney, Melbourne, Brisbane, Adelaide, and Perth. It
uses two main data sources: a nationwide survey; and objective information on the
characteristics of each city’s existing train system. A random-parameters ordered logit
model is developed to identify significant factors associated with train user satisfaction
levels. To gain more insights, underlying reasons behind differences in user
satisfaction across the five cities are traced back to relevant characteristics of the
existing train systems (and to fare structure, in particular) in these cities.
The practical contribution of this first sub-study is the provision of useful
knowledge for policy makers and authorities in formulating future transit policies to
increase travellers’ satisfaction level.
1.3.2 Consistency between perceptions and stated preferences data in a
nationwide mode choice experiment
This second sub-study, Consistency between perceptions and stated preferences
data in a nationwide mode choice experiment, aims to shed light on this ongoing
debate is whether a carefully designed SP experiment is able to provide individuals’
true preferences from a different perspective. Unlike previous studies, where RP data
were often used to assess the reliability of SP responses, this study employs another
valuable data source: travellers’ perceptions of the influence of various service factors.
In addition, sample sizes in previous studies were usually small, and respondents were
often from a particular user group that had been surveyed in previous studies. In
contrast, this study uses nationwide survey data relating to the most recent home-based
trip of urban travellers in five Australian capital cities (Sydney, Melbourne, Brisbane,
Adelaide, and Perth). Therefore, the sample size is large, and respondents come from
diverse backgrounds.
A distinctive model is estimated for each data type. More specifically, a random-
parameters binomial logit model that accounts for heterogeneity in the population is
estimated for the perceptions, while a mixed logit model is employed to model the
stated choice responses. To gain further understanding of the underlying reasons for
Chapter 1: Introduction 9
choosing a particular transport mode, the significant explanatory random parameters
are traced back to respondents’ diverse socio-economic backgrounds. Furthermore, the
consistency between these two models in terms of explaining respondents’ mode
choice behaviours are assessed both qualitatively and quantitatively.
The information consistency between the SP and RP datasets is assessed with a
comprehensive consistency assessment method. This method is a rich and significant
theoretical contribution to the SP literature.
1.3.3 Policy interventions study to encourage behavioural shift from car to
public transport
Having explored the theory of planned behaviour, cognitive work analysis, and
the intervention method, and having identified the research gaps, this third sub-study,
Policy interventions study to encourage behavioural shift from car to public transport,
aims to investigate the range of socio-economic factors and travel attributes that
influence the utility and probability values of travellers’ mode choice, and encourages
mode shift in each city. In addition, the magnitude of the model’s factor coefficients
is simulated to determine realistic policy interventions for mode shift behaviour.
Ultimately, this third sub-study contributes to the formulation of future transit
policies related to mode shift from car to public transport. Specifically: 1) It provides
useful knowledge for policy makers and authorities in their formulation of future
transit policies to encourage mode shift; 2) It provides an efficient and novel
framework for the analysis of mode shift, particularly, from car to public transport;
and 3) Its novel framework for mode shift analysis and policy intervention is a
theoretical contribution to the field of SP experiment.
1.4 RESEARCH SCOPES
This PhD study only investigates the research questions mentioned in Section
1.2 It is divided into the three interconnected sub-studies, namely Urban travellers’
satisfaction with train fares in five Australian cities, Consistency between perceptions
and stated preferences data in a nationwide mode choice experiment, and Policy
interventions study to encourage behavioural shift from car to public transport. The
overall study aims to achieve the stated objectives, innovations and contributions
(Section 1.3) within the duration of study (39 months). This study utilize the online
10 Chapter 1: Introduction
survey data collected by the project team of the “CRC for Rail Innovation Project
1.130: Urban Rail Travel Behaviour” from urban travellers in five Australian capital
cities: Sydney, Melbourne, Brisbane, Adelaide, and Perth (Zheng et al., 2013). The
data consists service demands and travellers’ experience from using private cars and
land-based public transport services, such as bus and train (excluding light rail and
tram) services, and private cars.
In overall, the study does not include the following details. Service demands and
travellers’ experience from water-based public transport, such as ferry services, and
active travel modes, such as walking and cycling, are not included in the study. The
transport card data are not available to serve as a comparison dataset. The original
project team did not conduct one-to-one interviews to the targeted urban travellers.
The train fare airport surcharge was not included in the study due to its irrelevancy
1.5 THESIS OUTLINE
The research questions are further investigated in Chapter 2: which focuses on a
review of the literature relevant to each sub-study, and identifies the main research
gaps. The last paragraph of each section of Chapter 2: summarises the research gaps
for each sub-study.
Chapter 3: explains the data collection process and the structure of the
questionnaire; provides a descriptive analysis of the socio-economic profiles,
perceptions and trip characteristics, as well as describes the utilisation of the collected
datasets (Section 3.1 to 3.4); and details the data preparation process for the first sub-
study (Section 3.5). Section 3.6 presents the factor mapping of perceptions and their
corresponding attributes of mode choice experiment for the second sub-study. Section
3.7 specifically illustrates the utilisation of each city dataset for the third sub-study.
Subsequently, Chapter 4: outlines and describes the modelling methodology of each
sub-study.
Chapter 5: begins with a summary of the characteristics of each city’s existing
train system in order to provide the study context, and information on the train fare
structure in each of the five capital cities. It then reports and analyses the modelling
results of the first sub-study (Urban travellers’ satisfaction with train fares in five
Australian cities). It also includes a discussion of heterogeneity, and the results of a
detailed comparison of train fare structures in the five Australian cities.
Chapter 1: Introduction 11
The beginning of Error! Reference source not found. reports and analyses the
modelling results of the second sub-study (Consistency between perceptions and
stated preferences data in a nationwide mode choice experiment). This is then
followed by a detailed qualitative and quantitative consistency assessment between
travellers’ perceptions and stated preferences in a nationwide mode choice survey.
The findings of the third sub-study (Policy interventions study to encourage
behavioural shift from car to public transport) are reported and analysed in Chapter
7:. In addition, Chapter 7: presents the simulation study to provide realistic illustrations
of how policy interventions are able to encourage mode shift behaviours.
Chapter 8: concludes the overall thesis, and discusses its contributions to wider
theoretical and practical contexts along with its policy implications. It also
acknowledges the limitations of the overall study, and provides potential directions for
future study.
1.6 PUBLICATIONS FROM THIS STUDY
This study has yielded three journal article manuscripts. One manuscript has
been published, one manuscript is under review, and another manuscript is being
prepared. These manuscripts are:
1. Paramita P, Zheng Z, Haque MM, Washington S, Hyland P. (2018). User
satisfaction with train fares: A comparative analysis in five Australian cities.
PLoS ONE 13(6): e0199449. https://doi.org/10.1371/journal.pone.0199449.
2. Paramita, P., Zheng, Z., Haque, M. M., & Washington, S. (2018). Consistency
between perceptions and stated preferences in a mode choice experiment:
Evidence from a national survey in Australia.
Manuscript under preparation to be submitted to Journal Transportation
Research Part A: Policy and Practice – Q1.
3. Paramita, P., Zheng, Z., Haque, M. M., Whitehead, J., & Washington, S.
(2018). Policy interventions to encourage behavioural shift from car to public
transport: A statistical approach.
Manuscript under preparation to be submitted to Journal Transportation
Research Part A: Policy and Practice – Q1.
Chapter 2: Literature Review 13
Chapter 2: Literature Review
The literature review chapter identifies and critically evaluates the relevant
previous literatures around each of the sub-study coherently. This study starts by
systematically reviewing wide range of aspects of travel behaviours literatures as
illustrated in Figure 2.1 below.
Figure 2.1 : Body of literature of Travel Behaviour field.
Following the formulation of the precise main research question and the three
interconnected sub research questions as elaborated in Section 1.2, this chapter focuses
on a review of the literature relevant to each sub-study, and identifies the main research
gaps. It consists of three sections. Each section demonstrates the synthesis and
integration of those reviews and arguments into one common theme of research gaps
for the respective sub-study. The last part of each section summarises the research gaps
for each sub-study.
14 Chapter 2: Literature Review
2.1 URBAN TRAVELLERS’ SATISFACTION WITH TRAIN FARES IN
FIVE AUSTRALIAN CITIES
2.1.1 Literature review
This section reviews notable studies on user satisfaction with public transport.
User satisfaction is a multi-dimensional concept (Oliver, 2014). Parasuraman et
al.(1985; 1994) and Zeithaml et al. (1988) identified five general dimensions of user
satisfaction, including assurance, reliability, empathy, tangibles, and responsiveness.
Friman et al.(2001) proposed a model to assess user satisfaction based on simplicity
of design and information, treatment by staff, and service reliability. As a slightly
different concept, a service quality model of user satisfaction consisting of functional
and technical service attributes was proposed by Grönroos (1984, 1990). Technical
service attributes are related to what services the customer receives, while functional
service attributes are related to how the customer receives those services. Later,
Fellesson and Friman (2012) labelled these two attributes ‘Factor A’ and ‘Factor B’,
with Factor A being a safety factor related to feeling secure at stations and on-board
vehicles, and Factor B being a system factor related to frequency, and travel and
waiting times. In addition, Fellesson and Friman (2012) defined three other factors:
Factor C, related to public transport comfort (e.g., in-vehicle cleanliness and level of
crowding); Factor D related to the behaviour, knowledge, and attitude of the staff; and
Factor E related to service delivery.
Satisfaction is also considered to be the foundation of consumer loyalty and
behaviour (McDougall & Levesque, 2000; Olsen, 2007), and has a strong connection
with perceived value and service quality (Chen, 2008; J. de Oña & de Oña, 2014; J. de
Oña et al., 2013; R. de Oña et al., 2015; Eboli & Mazzulla, 2012; Hitayezu, Wale, &
Ortmann, 2016; Jen & Hu, 2003). Travellers who have experienced a good quality
public transport services are inclining to have a higher level of perceived satisfaction,
and intending to continue using the same services. Satisfaction is also related to a
traveller’s evaluation of the quality of their entire trip experience (Chen, 2008;
Efthymiou & Antoniou, 2017; Fornell, 1992; Solvoll & Hanssen, 2017).
Beirão and Cabral (2007), Gadziński and Radzimski (2016), Tyrinopoulos and
Antoniou (2008), and Efthymiou, Antoniou, and Tyrinopoulos (2017) analysed the
behaviour of public transport travellers and their perceived satisfaction with services
in order to understand the underlying reasons for their preferred transport mode. They
Chapter 2: Literature Review 15
found that travellers had a strong preference for a reliable and well-coordinated
transportation system. The cleanliness of vehicles, the condition of the waiting area,
service frequency, network coverage, and transfer distance were perceived as the most
important satisfaction attributes. This knowledge served as a foundation for policy
makers and transport operators to improve services by making operational adjustments
to frequency of services, transfer points, and network coverage (Brons, Givoni, &
Rietveld, 2009; Tyrinopoulos & Antoniou, 2008).
Travellers who have experienced unreliable services and long waiting time have
a low public transport user satisfaction level (Cantwell, Caulfield, & O’Mahony, 2009;
Dell’Olio, Ibeas, & Cecín, 2010). The importance of travel time reliability was
influenced by two factors: negative consequences for travellers arriving late at their
destinations, and the actual value that individual travellers placed on reliability of
transportation system (Bhat & Sardesai, 2006; Efthymiou & Antoniou, 2017). Lucas
and Heady (2002) discussed the concept of time urgency and assessed the difference
between travellers’ with a flexitime schedule and those without such flexibility. Time
urgency was found to be a personal concept relating to the individual’s perception of
time. Since flexitime scheduling greatly reduced commuting pressure, they argued that
flexitime travellers experienced less time urgency and more trip satisfaction(Ettema,
Friman, Gärling, Olsson, & Fujii, 2012).
Another essential factor in enhancing public transport service quality is the
reduction of on-board crowding (Cantwell et al., 2009). On-board crowding positively
contributes to the perceived walking and waiting time (Li & Hensher, 2011). In reality,
travel time is actually longer when vehicles are crowded because it takes longer to
board and alight when there are passengers standing in aisles. Li and Hensher (2011)
argued that a successful public transport system requires less crowding and increased
reliability at all phases of the service chain. Reliability is defined as the quality of
performing consistently well.
The availability of pre-departure travel information is vital for travellers, and
eventually affects their travel habits and satisfaction levels (Lyons, 2006). Pre-
departure travel information has a number of roles, such as providing an awareness of
the travel options for a particular trip; empowering travellers to make fully informed
travel choices; assisting travellers to successfully commence and complete a trip; and
reducing waiting travellers’ feelings of frustration stress (Cantwell et al., 2009;
Caulfield & O'Mahony, 2007; Dziekan & Kottenhoff, 2007; Hine & Scott, 2000;
16 Chapter 2: Literature Review
Lyons, 2006). The success of such information, however, depends on its reliability and
its accessibility by all travellers (Jou, 2001). Regular travellers formally obtain such
information when they first use a particular service, while occasional travellers rely on
informal information from others (Lyons, 2006).
With particular reference to train services, Brons et al. (2009) mentioned a
number of important factors that affect travellers’ perceived satisfaction: station
organization, real-time information, level of comfort, service punctuality, train
frequency, and accessibility. Accessibility includes bicycle parking, connections to the
wider public transport network, and car parking facilities at stations. Furthermore, in
order to increase ridership, Ellaway et al. (2003) and Brons et al. (2009) suggested the
provision of benefits similar to those enjoyed travelling by private transport. Examples
include the integration of cycling and train travel by permitting bicycles on board; the
provision of bike hooks on certain trains during off-peak hours; and the provision of
park-and-ride facilities at major stations to entice motorists to use the train as a part of
their trips (Andersson & Nässén, 2016; Pucher & Buehler, 2009).
In addition, individual characteristics such as age, income, education level,
household composition, travel budget, a drivers’ license, and access to a motor vehicle
affect mobility behaviour and the level of access to train services (Dekker, Hess,
Arentze, & Chorus, 2014; Dieleman, Dijst, & Burghouwt, 2002; Efthymiou et al.,
2017; Geurs & Van Wee, 2004). Specifically, in order to attract potential train riders
and increase ridership, train services should be designed according to the level of
service preferred by current riders (Beirão & Cabral, 2007).
2.1.2 Research gaps
Overall, the majority of the previous studies focussed on users’ overall
satisfaction, and only a few investigated user satisfaction with train fare (Efthymiou &
Antoniou, 2017). Despite the fact that train fares, as the only monetary cost incurred
by train riders, is often used as a powerful tool to influence ridership. Thus, our
understanding of the relationship between train fare and user experience remains
elusive. In addition, most of the previous studies were based on survey data collected
from a single city, thus precluding the possibility of intercity comparison. Hence, this
comparative analysis study, which is complemented by knowledge of the
characteristics of the current fare system in each city, is valuable in revealing the
underlying and causal factors that contribute to travellers’ perceived satisfaction. The
Chapter 2: Literature Review 17
data used in many previous studies only partially capture the potentially significant
factors linked to traveller satisfaction. In particular, characteristics of travellers’ most
recent trips are often not considered (Paramita, Zheng, Haque, Washington, & Hyland,
2018).
2.2 CONSISTENCY BETWEEN PERCEPTIONS AND STATED
PREFERENCES DATA IN A NATIONWIDE MODE CHOICE
EXPERIMENT
2.2.1 Literature review
SP responses are generally obtained from a SP experiment. SP experiment is a
quantitative method for simulating preferences that can be used in the absence of RP
data (Mangham, Hanson, & McPake, 2009). The technique involves asking
respondents to state their preferences in a number of hypothetical scenarios. Each
hypothetical scenario is described by various attributes. With the availability of SP
experiment and data, the demand for new products or services can be estimated. The
SP experiment is also able to stimulate responses about individuals’ behaviour so as
to ultimately identify their inclinations (Cascajo, Garcia-Martinez, & Monzon, 2017).
Earlier studies show that SP data can enrich RP data. The former have the ability
to view the experiment as supplementary to RP data (Hensher, 1994; Wardman, 1988).
SP data incorporates diverse attributes, which are not available in the market, and have
no corresponding RP history (Hensher, Louviere, & Swait, 1998; Mark & Swait,
2004). The explanatory variables of SP are able to introduce variability, and to remove
or reduce collinearity among variables. This enables a more accurate estimate of
contributions to the utility of the goods or services (Mark & Swait, 2004). In general,
in the near future, the analysis of a combination of SP and RP data will be able to
quantify the usage of new and more favourable products and services (Mark & Swait,
2004; Tselentis et al., 2018). The different characteristics of SP and RP data suggest
that the joint utilisation of both data types are able to enhance the modelling and
understanding of choice behaviours (Hensher, 1994). The combination of the two data
sources also provides considerable benefits for those who can focus on each source's
strengths, and has the potential to provide policy makers with more accurate
knowledge and information.
18 Chapter 2: Literature Review
The number of attributes of each choice within SP experiment reflects concerns
about task complexity and non-obligatory decision rules. However, the increasing
number of attributes and choices per respondent could have increased the cognitive
burden of the SP experiment respondents (Bech, Kjaer, & Lauridsen, 2011; de Bekker‐
Grob, Ryan, & Gerard, 2012; DeShazo & Fermo, 2002; J. J. Louviere, Islam, Wasi,
Street, & Burgess, 2008). When respondents are fatigued, bored, and less engaged;
and when they have the opt-out availability for all alternatives, they are more likely to
choose the latter as they proceed through the questionnaires (Miguel et al., 2005).
Some respondents, who were not familiar with SP surveys, simply perceived the
completion of the SP experiment as a complex task, and adopted various strategies to
simplify the process and reduce their effort (Brazil, Caulfield, & Bhat, 2017; Hoang-
Tung & Kubota, 2017). For instance, they failed to consider some information, or only
partially attended to the SP experiment attributes. This strategy is known as ‘non-
attendance’ (Brazil et al., 2017; Hensher, Rose, & Greene, 2012; Hess & Hensher,
2010; Hoang-Tung & Kubota, 2017). Attribute non-attendance can be influenced by
existing knowledge, or lack thereof, of a particular topic of interest (Brazil et al., 2017).
The predictive value of SP experiment can be limited since it measures stated
preferences that can differ from actual travel behaviours. That is, in line with Azevedo
et al.’s (2003) findings, respondents’ stated choices might be inconsistent with their
actual decisions in similar real-life situations (Lambooij et al., 2015; Severin, 2001).
Azevedo et al. highlight a diverse range of potential biases, where respondents might
overlook and underestimate some of the attributes and constraints when answering
hypothetical travel scenarios (Arrow et al., 1993; Kemp & Maxwell, 1993; Loomis,
Gonzalez-Caban, & Gregory, 1994; Wardman, 1988). Additional critique includes the
possibility of SP-based WTP estimates also failing to diverge according to the scope
of the resource being valued; this is known as the “embedding effect” (Azevedo et al.,
2003; Desvousges et al., 1992; Kahneman & Knetsch, 1992).
Despite these criticisms, there is substantial evidence that responses to carefully
designed SP experiment can produce valuable information. Mitchell and Carson
(1989) mention that the valuation estimates of SP methods are typically correlated in
the expected direction of the predicted independent variables. To further examine the
validity of SP information, a group of researchers have compared RP data-based
valuation estimates to estimates based on actual market prices or simulated market
Chapter 2: Literature Review 19
transactions (Adamowicz, Louviere, & Williams, 1994; Azevedo et al., 2003;
Cameron, 1992; Cummings, Brookshire, Bishop, & Arrow, 1986; Cummings, Elliott,
Harrison, & Murphy, 1997; Cummings & Taylor, 1999; J. Louviere, 1996). Carson et
al. (1996; 2007) found that, generally, the ratio of SP to RP valuations lies close to
one, and that the RP and SP estimates are highly correlated. These results suggest that
reliable information can be collected from carefully designed SP experiment (Azevedo
et al., 2003).
Over the past four decades, travel behaviour researchers have conducted
numerous external validation studies based on travellers’ revealed behaviours and
future preferences; however, these studies are limited to values of time (Bates, 1984;
Hensher & Truong, 1985; Louviere et al., 1981); predicted market shares (Benjamin
& Sen, 1982; Kocur et al., 1982; Lerman & Louviere, 1978; J. J. Louviere & Hensher,
1982; J. J. Louviere & Kocur, 1983); and the WTP test (Blumenschein et al., 2001;
Clarke, 2002; Ding, 2007; Hensher, 2010; Lambooij et al., 2015; Ryan, 2004).
Interestingly, almost all studies consistently suggest the need to establish a reliable
validation method to cope with the ever-growing SP literature.
With specific reference to the travel behaviour context, Wardman (1988) tested
the validity of SP data by relating RP and SP values of travel time to respondents’
socio-economic status. This method is consequently described as “segmentation
analysis” (Wardman, 1988). Wardman (1988) provides evidence that respondents’ SP,
as responses to hypothetical scenarios, are a reasonably accurate guide to their true
preferences. This segmentation analysis was able to avoid the unnecessary increases
in the standard errors, and provide detailed assessment of the SP data.
In the agricultural context , Azevedo et al. (2003) suggested that the validity of
SP and RP data can be identified by testing for their consistency. They developed a
combined model containing both SP and RP responses, and constructed hypotheses
concerning potential biases. On completion of the study, after analysing the survey
data of actual wetland usage patterns against the anticipated changes to those
hypothetical patterns with increasing trip costs, they reported conflicting conclusions;
that is, the hypothetical patterns were found to be inconsistent with actual patterns.
Lambooji et al. (2015) performed the consistency test for actual “before” and the
“after” scenarios in the healthcare context. The actual scenarios related to parents’
attitudes to vaccinating their new born child against hepatitis B. Parents were first
20 Chapter 2: Literature Review
asked to respond to a SP experiment related to their inclination to vaccinate their child;
then, after a specific timeframe, their actual vaccination behaviours were monitored.
Lambooji et al. (2015) concluded that the predictive value of SP experiment was
satisfactory for predicting the option to vaccinate, but unsatisfactory for predicting the
option of not vaccinating.
2.2.2 Research gaps
Overall, the fundamental question – of whether individuals’ responses to a
cautiously designed SP experiment are able to characterize their actual preference –
persists. In the on-going efforts to develop a reliable approach to testing the
consistency of SP data in relation to RP data, it is also essential to note that no
published study compares the perception of a particular mode choice with the SP
responses. These two research gaps motivate this study. It qualitatively and
quantitatively assesses the perception of various service factors that influence more
frequent train travel, and the mode choices indicated in response to hypothetical travel
scenarios. Both types of responses are obtained from the same set of respondents at
the same time.
2.3 POLICY INTERVENTIONS STUDY TO ENCOURAGE
BEHAVIOURAL SHIFT FROM CAR TO PUBLIC TRANSPORT
The following literature review starts by identifying and reviewing a number of
psychological theories that underlie mode choice and mode shift behaviours. It is
followed by the discussion of policy intervention methods that can influence the mode
choice process. A number of ways to overcome mode shift barriers and constraints and
to encourage car users to take public transport are then presented. This section ends by
recognizing the research gaps in the travel behaviour context.
2.3.1 Psychological theories that underlie change in travel behaviour
According to the theory, human behaviour is guided by behavioural beliefs,
normative beliefs, and control beliefs (as shown in Figure 2.2). Behavioural beliefs
result in a favourable or unfavourable attitude to a behaviour; normative beliefs relate
to perceived social pressure or subjective norms; and control beliefs result in perceived
behavioural control (Ajzen, 1985). The more helpful the attitude and the subjective
norm, and the larger the perceived control, the stronger should be the person’s
Chapter 2: Literature Review 21
intention to perform the behaviour in question. A few years after stating the theory of
human behaviour, Ajzen (1991) posits the theory of planned behaviour (TPB), which
links a person’s beliefs and their behaviour.
Figure 2.2 : Three guides underlying the theory of human behaviour (Ajzen, 1985)
TPB identifies intention as the most immediate cognitive antecedent of
behaviour (Ajzen, 1991; Mann & Abraham, 2006). It is the most used of all
psychological theories, and was developed to account for goal-directed behaviour that
is beyond complete volitional control (Ajzen, 1985, 1991; Thøgersen, 2006). The TPB
has been applied to studies of the relationships among beliefs, attitudes, perceptions,
intentions, and behaviours in various fields, such as transport choice, marketing,
advertising, public relations, advertising, and healthcare (Ajzen, 1985, 1991). Within
the framework of TPB, the intention to perform certain behaviours – such as the use
of public transport – can be accurately predicted by attitudes toward the behaviour,
subjective norms, and perceived behavioural control (Ajzen, 1991; Bamberg &
Schmidt, 2001). The intentions and perceptions of behavioural control account for
considerable variance in performed behaviour. Behavioural interventions are expected
to change the principal beliefs that ultimately guide the performed behaviour (Ajzen,
1985).
An unresolved issue within the TPB framework is whether the theory is
sufficient to accommodate the prediction of later behaviours based on past behaviours
(Ajzen, 1991). Bamberg, Ajzen, and Schmidt (2003) closely examined this issue in a
transport case study. They concluded that past behaviour is not always a good predictor
of future behaviour, and that prior behaviour contributes significantly to the prediction
of later behaviour only when circumstances remain unchanged (Bamberg et al., 2003;
Thøgersen, 2006). Interventions, such as new information and persuasive acts, were
found to affect and change attitudes toward subject matter, subjective norms, and
22 Chapter 2: Literature Review
perceptions of behavioural control. They also influenced intentions and behaviour in
the desired direction (Bamberg et al., 2003). The consequence of the intervention on
behaviour is mediated by the causal chain hypothesized by the TPB (Bamberg &
Schmidt, 2001). Human social behaviour is controlled by a certain level of cognitive
effort (Bamberg et al., 2003). Even a relatively minor event can disrupt the automatic
execution of behaviour and initiate reasoned action. Indeed, human social behaviours
consist of both automatic and reasoned elements. Consistent with TPB, travellers who
are faced with an unfamiliar choice will deliberate, and then choose the most attractive
goal-directed option (Gardner, 2009; Verplanken, Aarts, Knippenberg, & Moonen,
1998).
Thøgersen (2006), however, claims that travel mode choices are repetitive
behaviours, and are made in a stable situation (Ouellette & Wood, 1998; Ronis, Yates,
& Kirscht, 1989; Wood, Quinn, & Kashy, 2002). It is argued that travel mode choices
are usually performed in a habitual, rather than a reasoned or planned way (Thøgersen,
2006; Verplanken & Aarts, 1999; Verplanken, Aarts, Knippenberg, & Knippenberg,
1994; Verplanken et al., 1998). This perspective of habitual action suggests that when
travellers have strong travel choice habits, motivation has no effect on their behaviour.
This is especially the case in the unusual situation where habits are in conflict with
intentions (Gardner, 2009; Mann & Abraham, 2006). By contrast, the motivational
models neglect the often repetitive travel choice decisions because frequently repeated
behaviours can become habituated and, thus, automated (Gardner, 2009; Verplanken
et al., 1994; Verplanken et al., 1998). In the case where habit is weak, intention predicts
behaviour; however, where it is strong, intention neglects the effect on behaviour
(Gardner, 2009). Strong habit also reduces the likelihood that travellers will consider
alternatives to their regular modes (Aarts, Verplanken, & Knippenberg, 1998; Gärling
& Axhausen, 2003; Thøgersen, 2006; Wood et al., 2002).
Using TPB as a foundation, Thøgersen (2006) traced back the use of public
transport issue to attitudes to it and its reliability, and to attitudes to car ownership
(Hoyer & MacInnis, 1997; Ȫlander & ThØgersen, 1995; van Raaij, Bartels, &
Nelissen, 2002). He assumes that travel mode choices are partly individual (e.g.,
transport habits, car ownership), partly contextual (e.g., the availability of public
transportation), and partly volitional (i.e., influenced by the traveller’s evaluations and
motives) (Thøgersen, 2006). The influence of these variables is weakened when past
behaviour is considered (Fishbein, 1967; Van Raaij & Verhallen, 1983). The
Chapter 2: Literature Review 23
behavioural changes of those who do not own a car are more in line with current
attitudes and perceptions; these attitudes and perceptions, on the other hand, are
inconsequential to car owners. For example, when car owners have a more positive
attitude to driving than to public transport, their attitude to the latter is inconsequential
and rather irrelevant. Thøgersen (2006) also found that the temporal stability of
transport behaviour is higher for car owners than non-owners.
When habitual and intentional tendencies diverge, habits are revealed (Gardner,
2009). Other studies on the formulations of the motivation–behaviour relationship
explore habit-attenuating relations between personal norms for non-car and car travel
(Eriksson, Garvill, & Nordlund, 2008) (Eriksson et al., 2008). In a stable choice setting
such as commuting, behaviour is governed by habit, and is in line with motivation.
Commuting mode choice could be modelled as a planned behaviour (Gardner, 2009).
Socially acceptable economic incentives can restructure the decision making context
to disrupt habits and motivate a change in behaviour (Fujii & Kitamura, 2003; Gardner,
2009; Thøgersen & Møller, 2008). For instance, Thøgersen and Møller (2008) show
that a free one-month travel card is sufficient to break driving habits and to enhance
public transport ridership. Eriksson et al. (2008) claim that personal resources (i.e., the
time and knowledge to change travel behaviour) and contextual factors (i.e., the
availability of alternative travel modes and supportive social norms and policy
strategies) are crucial to reducing car usage.
The hypothesis of habit discontinuity states that a context change disrupts an
individual’s habit: a window opens to encourage an individual to deliberately consider
their behaviour (Verplanken, Walker, Davis, & Jurasek, 2008). The hypothesis of self-
activation states that when values incorporated in the self-concept are activated, they
are more likely to guide behaviour. The combination of these two hypotheses predicts
the context change in order to emphasize the important values that will guide
subsequent sustainable behaviours. For example, travellers who had recently moved
and were environmentally concerned, used the car less frequently for commuting to
work (Hamer, 2010; Verplanken et al., 2008), and the provision of park and ride
facilities near residential areas generated mode shift from private cars to more
sustainable transport modes, especially for commuting to workplaces (Hamer, 2010).
Cognitive work analysis (CWA) is an ever-growing technic in the Human
Factors and Ergonomics field, and is suited to the analysis of complex socio-technical
systems (Rasmussen, Pejtersen, & Goodstein, 1994). It has been implemented in
24 Chapter 2: Literature Review
various fields, such as in military activity allocation (Jenkins, Stanton, Salmon,
Walker, & Young, 2008); cognitive artefact design (Jenkins, Salmon, Stanton, &
Walker, 2010a); accident analysis (Jenkins, Salmon, Stanton, & Walker, 2010b);
large-scale system design process assessment (Bisantz et al., 2003); design concept
evaluation (Naikar & Sanderson, 2001); and train driver constraint comprehension
(Jansson, Olsson, & Erlandsson, 2006). Specifically, Stanton et al. (2013) utilize the
CWA perspective to explore the constraints in shifting to rail transport mode, and to
determine which groups are most impacted by each constraint. Constraints could be
linked to the relationships among travel purposes and functions, journey contexts and
types, and groups in the analysis. The CWA offers a contextual interpretation of these
constraints, rather than simply identifying them. Thus, it offers a new perspective
about mode shift issues, and generates insights into potential solutions.
2.3.2 The policy intervention methods
The habitual nature of mode choice behaviour has been recognized as an
important factor, and is included in models of mode choice (Bamberg et al., 2003;
Gärling & Axhausen, 2003; Verplanken et al., 1994; Verplanken et al., 1998;
Verplanken et al., 2008). Habituation limits the collection of information. (Verplanken
& Holland, 2002) suggest that values influence choices and behaviours only when
two conditions are met. These conditions are: a value should be part of the traveller’s
self-concept, and should be cognitively activated.
Klöckner, Matthies, & Hunecke (2003) propose normative decision making for
mode choice to include a dual-process account that consists of a norm-based route and
an habitual route (Klöckner et al., 2003). The norm-based route follows the principles
of normative decision making (Nordlund & Garvill, 2002; Schwartz & Howard, 1981),
while the habitual route consists of direct responses to situational signals and, thus,
bypasses normative considerations (Aarts et al., 1998; Ouellette & Wood, 1998).
Utilization of the intervention method requires habit measurement because
motivation shift would be sufficient to modify established behaviour when habit is not
considered. Mode choice could be modelled as the reasoned action of travellers with
weak or no habit (Gardner, 2009). Among habitual travellers, behaviour is dominated
by habitual tendencies rather than intentions. Initiatives that target attitude and belief
change, only partially influence these habituated behaviours. Therefore, intervention
Chapter 2: Literature Review 25
methods need to be employed to acknowledge the limited cognitive engagement in
habitual decision-making (Gardner, 2009).
Bamberg et al. (2003) investigate the effects of an intervention, and the logic of
the proposition that past behaviour is the best predictor of later behaviour. The
introduction of a free semester ticket for students caused a drastic decrease in students’
car usage and an increase in their bus usage (Bamberg & Schmidt, 2001). Both
interventions influenced attitudes to bus use, subjective norms, perceptions of
behavioural control, and intentions and behaviours in the desired direction(Bamberg
et al., 2003). The TPB is demonstrated as a conceptual framework for predicting mode
choice, and for understanding the effects of an intervention on behaviours (Bamberg
et al., 2003).
The micro and macro processes of the intervention method are shown in Figure
2.3. While the rationale for the policy intervention method is rather theoretical
(Chaminade & Edquist, 2010), it is important to acknowledge that policy formulation
is not always a rational process: The rationale can emerge as an ex-post analysis, and
not as an a priori exercise.
Figure 2.3 : The micro and macro process of the intervention method (Bamberg & Schmidt,
1998)
Habitual car use was interrupted by prompting the deliberate consideration to
reduce personal car use, and to form the intention to plan changes to travel behaviour
26 Chapter 2: Literature Review
(Eriksson et al., 2008). This intervention resulted in a weakened association between
car use and car habit, and strengthened the relationship between car use and personal
norm. Specifically, car users with a strong car use habit and a strong personal norm
were more likely to reduce car use than those with a weak car use habit and a weak
personal norm (Eriksson et al., 2008).
Frank et al. (2008) and Hu & Schneider (2017) found that urban housing and
workplace forms, and travel time and cost, were significant predictors of travel choice.
Travel time was the strongest predictor of mode choice, while urban form was the
strongest predictor of the location of public transport stops. The dominance of travel
time shows that a reduction in traffic congestion and motorized modes travel times
would lead to lower travel among the non-motorized modes. Better street connectivity,
greater retail density, and mixed land use were associated with increased walking,
public transport services, and cycling (Frank et al., 2008). The overall findings
demonstrate that transportation investment and more effective land use could
collectively and uniquely impact mode choice for both work and non-work purposes.
Thus, comprehensive land-use planning should be considered when formulating
transport and housing policies (Frank et al., 2008; Hamer, 2010; Hu & Schneider,
2017; Limtanakool, Dijst, & Schwanen, 2006).
2.3.3 Overcoming barriers to mode shift
Scheiner (2010) found that regardless of distance travelled, car usage had
increased enormously. This increase was mainly at the expense of walking and public
transport travel, and was more common in small towns than large cities. Due to cities’
mixed land-use structure and the close proximity of various facilities, car owners were
more inclined to walk in central urban areas than they were in small towns. In other
words, spatial and built environments had a strong impact on car usage, especially for
car owners (Limtanakool et al., 2006; Scheiner, 2010; Scheiner & Holz-Rau, 2007;
Simma & Axhausen, 2001). Other important factors affecting the propensity to walk
were personal motivation; available transport modes; financial resources; health; the
attractiveness of the route; and social roles and needs (Scheiner, 2010). Specifically,
Batty et al. (2015) identified 'Pull' and 'Push' mechanisms to encourage the mode shift
in urban areas. The 'Pull' mechanism includes the provision of an attractive, accessible,
and affordable public transport system that meets travellers' needs, while the 'Push'
Chapter 2: Literature Review 27
mechanism focuses on breaking the habit of private car usage (Batty et al., 2015;
Stanton et al., 2013).
Constraints to modal shift can be defined as the temporal, financial, physical,
cognitive, and affective efforts required to change to another mode (Accent, 2009;
Blainey, Hickford, & Preston, 2009; Stanton et al., 2013). In order to change their
travel behaviour, a traveller would need the motivation to change, the means to
facilitate a change, and the ability to overcome the existing constraints (Stanton et al.,
2013). The theory of human factor explains the human role in the transport system,
and offers perspectives on mode shift constraints and the disadvantages of certain
modes. The comprehensive functioning of rail transport depends on connectivity with
other modes that offer transport between origin or destination and station, and on
travellers’ ability to access the connecting modes (Napper, Coxon, & Allen, 2007;
Stanton et al., 2013). Stanton et al. (2013) identified ten key constraints for mode shift
to train. They are: cost; punctuality and reliability; frequency; comfort and cleanliness;
travel time; interchange and station facilities; safety and security; station access;
journey planning and information provision; and ticketing. These constraints highlight
the links between the purposes, functions, and processes within the rail system from a
traveller’s perspective.
The reliability of train journeys is known to be far from perfect (J. Bates et al.,
2001; Stanton et al., 2013). Reliability is described as the degree of difference between
the advertised and the actual departure and arrival times (Derek Halden Consultancy,
2003; Stanton et al., 2013). Reliability also affects the individual travel decision. As
opposed to rail journey, car travel tends to be associated with flexibility and control
over departure and arrival times (J. Bates et al., 2001; Stanton et al., 2013). Given the
positive elasticity in travel time for commuting trips, the promotion of mode shift from
private cars to trains is challenging (Limtanakool et al., 2006). Thus, to outweigh the
unpredictability and unreliability of rail travel, complementary measures are needed
to promote mode shift. Public transport systems need to be restructured to suit both
visitors and occasional users, and back-up transport options (such as taxis) need to be
provided in the event of breakdowns and interrupted networks (Stanton et al., 2013).
Taking public transport is known to be less convenient than driving, especially
for car owners. Mann and Abraham (2006) recognize that taking public transport is
physically demanding because it includes effort, and additional time to travel to/from
the station, and also requires specific cognitive effort in planning and remembering.
28 Chapter 2: Literature Review
Affective demand is also involved because of the reduced comfort and enjoyment, and
increased stress. Mann and Abraham (2006) suggest that psychological and affective
considerations are an important motivation; however, they are not adequately
represented in rational transport choice models.
Thøgersen (2006) argues that our habitual nature, a dependence on both
motivational and personal factors, and external constraints, should all be considered in
an effort to fully understand mode choice. The structure of the public transport system
should be changed to increase its attractiveness, and to reduce the attractiveness of car
usage. Public transport could be marketed in the same way that various consumer
products are. One possibility is to adopt a ‘hierarchy of effects’ approach that informs
travellers that public transport is a sufficiently attractive alternative to private cars
(Paul, Olson, & Gruner, 1999). First, car drivers need to be made aware of the available
public transport services; second, short-term incentives should be offered to encourage
them to give public transport a trial run; and, finally, their choice of public transport
should be reinforced and supported so that they continue to use it, and it eventually
becomes their new and habitual mode choice. The specific elements in such a targeted
approach include entertainment system on-board, sales promotion, and feedback
(Thøgersen, 2006). A number of past studies relating to the promotion of a mode shift
to sustainable transport are explored below.
2.3.4 Encouragements to shift to public transport
‘Travel Plan’ documents in the United Kingdom were found to be effective ways
of increasing awareness that contributed to significant mode shift among employees
(Rye, 2002). Key policies that could be introduced are: setting up a car sharing scheme;
providing cycle facilities; improving bus services, introducing flexi-work practices;
restricting car parking; and telecommuting (Kingham, Dickinson, & Copsey, 2001;
Limtanakool et al., 2006). Employees could also be encouraged to live closer to their
workplace by providing financial incentives for them to do so. ‘Travel plan’
documents are successful because they are site-specific plans that address issues such
as road congestion, limited parking space, and on-board crowding of public transport
services within the vicinity of the workplace. The collaborative work of companies
and organizations in this regard could build morale among employees and lobby
transport authorities and providers. The barrier of poor perceptions of alternative
Chapter 2: Literature Review 29
modes could be weakened by comprehensive marketing and the full support of
managements (Kingham et al., 2001; Rye, 2002).
Nurdden et al. (2007) aimed to study policies that discouraged private cars usage,
and to identify factors that prevented the use of public transport in Malaysia. Their
study found that travel time, cost, and the distance from home/to public transport/to
workplace are the key reasons why cars are chosen over public transport. Therefore,
an efficient public transport system, including higher capacity transit systems, bus
lanes, and intelligent transport systems should be available to promote less reliance on
cars. Finally, they contend that real commuter incentives should be provided to
encourage mode switch to sustainable modes (Nurdden et al., 2007).
To promote walking and cycling to rail access, Rastogi (2010) analysed the
results of a study of willingness to switch mode within suburban rail stations in
Mumbai, India. Given the study’s context, the analysis served as a method to identify
the population segments that could increase the rate of switch to non-motorized modes.
The study concludes that respondent characteristics, an area’s distinctiveness,
available information about alternative transport modes, and the distance travelled, all
contributed to a switch in mode. Specifically, employed respondents, those with a high
income, and those residing at shorter distances from their workplace, were less likely
to shift transport mode (Rastogi, 2010). Following the change in their travel scenarios,
the respondents' behaviour was dynamic. This knowledge is important to the success
of the implementation of improved travel plans.
2.3.5 Research gaps
Having reviewed numerous relevant studies, research gaps were identified to
determine the direction of further study in relation to urban travellers’ mode shift from
car to public transport. It is clear that no study has yet used the nested logit model on
Australian capital city traveller datasets to identify target policy interventions, and to
determine travellers who are most affected by changes in transport policies. When
determining methods to understand revealed travel behaviours and to encourage a shift
in these behaviours, it is also essential to consider travellers’ habitual tendencies,
utility, their affective concerns, and the spatial and built environment. To date, no
mode shift and travel behaviour study has been found to address all of these
considerations in one study. Thus, these research gaps motivate this current study.
30 Chapter 2: Literature Review
Chapter 3: Dataset 31
Chapter 3: Dataset
All the three interconnected sub-studies utilised the same dataset gathered as part
of Project R1.130 Understanding Urban Rail Travel for Improved Patronage
Forecasting funded by the CRC for Rail Innovation (established and supported under
the Australian Government's Cooperative Research Centres program). This chapter
begins with the explanation of data collection process (Section 3.1) and the structure
of mode choice experiment (Section 3.2). It is the followed by the description of the
notable responses of the survey questionnaire (Section 3.3). The data utilisation of the
collected datasets is elaborated in Section 3.4. Section 3.5 describes the data
preparation process for the first sub-study. Section 3.6 presents the factor mapping of
perceptions and their corresponding attributes of mode choice experiment for the
second sub-study. The last section (3.7) specifically illustrates the utilisation of each
city dataset for the third sub-study.
3.1 DATA COLLECTION
The data used in this study are the online survey data collected by the project
team of the “CRC for Rail Innovation Project 1.130: Urban Rail Travel Behaviour”
(Zheng et al., 2013). The team’s survey questionnaire was designed with four primary
objectives: to identify what factors drive the patronage of urban trains; to quantify the
relative importance of these patronage drivers; to allow national comparisons of
satisfaction with train usage in five Australian cities; and to develop a national
database as a resource for further research. In order to efficiently achieve these
objectives, the project team synthesized the earlier published best practice surveys
from Australia and overseas, identified the gaps in these works, and then addressed
these gaps (Zheng et al., 2013).
The final survey questionnaire contained five different sections, i.e. Section A
to E. Each section had its own objectives. Section A to D of the questionnaire
represented the Revealed Preference (RP) type of questions and Section E represented
Stated Preference (SP) type of questions. Correspondingly, the responses obtained
from Section A to D signified the respondents’ revealed travel behaviours, perceptions,
and socio-economic profiles. While the responses obtained from Section E signified
32 Chapter 3: Dataset
the respondents’ stated preferences in mode choice experiments. The five sections of
survey questionnaire were
1. Section A aimed to exclude respondents who were 16 years and younger and
the ones had a conflict of interests to avoid bias responses, as well as to identify
whether they were belong to train rider or non-rider category. The train rider
category in this study is defined as respondents who made more than one trip
by train (excluding light rail and tram) in the last month prior to the survey,
and the others belong to the non-rider category (Zheng et al., 2016; Zheng et
al., 2013);
2. Section B aimed to gather data about the trip characteristics experienced by the
train riders;
3. Section C aimed to acquire information about the trip characteristics
experienced by the non-riders;
4. Section D aimed to collect all respondents’ perceptions of service factors and
their socio-economic profiles; and
5. Section E, the mode choice experiment, aimed to gain all respondents’ stated
mode preferences for six hypothetical travel scenarios (Zheng et al., 2013). The
structure of mode choice experiment is detailed in Section 0.
The detailed responses collected from the survey questionnaire are demonstrated
in Figure 3.1 below. A copy of the survey questionnaire is available on Appendix A.
Figure 3.1 : The diagram of the collected dataset
Chapter 3: Dataset 33
The survey was conducted via a web-based survey platform in five Australian
cities from 9 April 2013 to 16 May 2013. A consent form was provided to each
respondent at the first page of the questionnaire. The survey was hosted by ORU (The
Online Research Unit: http://www.theoru.com/) utilising their ‘research only’ online
consumer panels. The resources provided access to more than 300 000 profiled
persons. This also allowed structured data collection method that provided
representative sampling from each city (Zheng et al., 2013).
Seven thousand respondents were targeted, 2000 in both Sydney and Melbourne,
and 1000 in each of the remaining cities (i.e., Brisbane, Adelaide, and Perth). The
target number for each city was proportional to its population. Half of the respondents
from each city were expected to be train riders, and the other, non-riders. Eventually,
a total of 6731 respondents participated in the survey. With the exception of the
Adelaide train rider group, all targeted quotas were achieved (Zheng et al., 2013). Age
and gender groups obtained were close to those in the ABS data, which confirms the
representativeness of the sample (Zheng et al., 2013). Out of 6731 respondents, 3231
are train riders and 3500 are non-riders as shown in Table 3.1.
Table 3.1 The breakdown of train riders and non-riders across five cities
Rider
Category
Sydney Melbourne Brisbane Adelaide Perth Total
N=2000 N=2000 N=989 N=742 N=1000 N=6731
Train rider 1000(50%) 1000(50%) 489(49%) 242(33%) 500(50%) 3231(48%)
Non-rider 1000(50%) 1000(50%) 500(51%) 500(67%) 500(50%) 3500(52%)
3.2 MODE CHOICE EXPERIMENT STRUCTURE
The DCE section (Section E) of the survey is useful in gaining insights into the
significance and relative significance of various factors in the mode decision making
process. Ngene (ChoiceMetrics, 2012), a specialised software for designing DCE, was
employed to generate the choice scenarios. An orthogonal design was adopted for the
pilot surveys to achieve balance and independence among the attribute levels.
However, the main survey was upgraded via an efficient design by using parameters
of the factors that were estimated based on the data collected from the pilot surveys.
The efficient design generated parameter estimates with the smallest standard errors
(Bliemer & Rose, 2006; Rose, Bliemer, Hensher, & Collins, 2008). In order to generate
the most realistic combinations of different attribute levels, the mode choice sets
presented to each individual were personalised according to the available modes
during their most recent trip (Hensher, Rose, & Collins, 2011). The realistic attribute
34 Chapter 3: Dataset
levels for access time to/from station/bus stop, waiting time, on-board time, and bus/
train ticket were determined based on the data collected from the pilot surveys. (Details
of how the travel attribute levels were defined and calibrated can be found in Zheng et
al. (2016; 2013). Table 3.2 summarises the final attributes and their levels considered
in the mode choice scenarios in the main survey.
Table 3.2 Attributes and their levels employed in the mode choice experiments
Mode Access time
to train
station/ bus
stop
Waiting
time
On-board
time
(minutes)
Ticket
($)
On-board
crowdedn
ess level
Access time
from train
station/ bus
stop/ car park
Bus Short (2.5),
Medium (5),
Long period
(7.5 minutes)
Short (3),
Medium (6),
Long period
(9 minutes)
16, 32, 48 1.2,
2.4, 3.6
Not
crowdeda,
Crowdedb
Short (2.5),
Medium (5),
Long period
(7.5 minutes)
Train Short (5),
Medium (10),
Long period
(15 minutes)
Short (4),
Medium (8),
Long period
(12 minutes)
15, 30, 45 1.5, 3,
4.5
Not
crowdeda,
Crowdedb
Short (5),
Medium (10),
Long period
(15 minutes)
Car NAc NAc 10, 20, 30 NAc NAc Short (1),
Medium (5),
Long period
(10 minutes)
Mode Availability
of wireless
connection
Availability
of laptop
station
Fuel Cost
($)
Parking
Cost ($)
Toll Cost ($)
Bus Yes, No Yes, No NAc NAc NAc
Train Yes, No Yes, No NAc NAc NAc
Car NAc NAc 0.98,1.31,
1.63,1.96,
2.62,2.95,
3.27,
3.93,
4.91
0, 10, 20,
30
0, 5, 10, 15
a Not crowded: a traveller found a seat for the entire journey; b Crowded experience: a traveller has to stand up prior to finding a vacant seat on-board and a
traveller has to stand up for the entire journey and no vacant seat available (Cantwell et al.,
2009) c NA : This attribute is not applicable to this mode
In total, Ngene generated 216 mode-choice scenarios (ChoiceMetrics, 2012): 72
for train and bus; 72 for train, bus, and car; 36 for train and car; and 36 for bus and car.
Specifically, to minimize respondents’ workload and to optimize the accuracy of their
responses, only six scenarios were randomly presented to each respondent. To avoid
any confusion, each hypothetical travel scenario was presented in the form of a
pictogram.
Chapter 3: Dataset 35
3.3 DATA DESCRIPTION
3.3.1 Socio-economic profiles of all respondents
Each train rider’s socio-economic profile of all respondents were extracted from
Section D of the questionnaire. Socio-economic background includes information
about their gender, age, employment status, pre-tax household weekly income level,
highest education level, whether they hold a driver’s license, whether they have access
to a vehicle, whether a motor vehicle is required for their work, whether train services
influenced the choice of their current home location, and the composition of their
household. The socio-economic profiles of all respondents from each city are shown
in the Table 3.3.
Table 3.3 Socio-economic profiles of respondents from each city
Socio-
economic
factor
Classification Sydney Melbourne Brisbane Adelaide Perth
N=2000 N=2000 N=989 N=742 N=1000
Gender Female 46% 45% 42% 48% 42%
Male 54% 55% 59% 52% 58%
Age group 16-17 years old 0% 0% 1% 1% 1%
18-30 years old 17% 17% 17% 18% 18%
31-40 years old 21% 22% 24% 18% 19%
41-50 years old 19% 22% 15% 23% 19%
51-60 years old 23% 22% 25% 20% 21%
60+ years old 19% 18% 20% 21% 23%
Employment
status Full time 42% 39% 36% 32% 34%
Part time 17% 18% 16% 18% 18%
Self-employed 6% 8% 7% 6% 6%
Outside
workforce 35% 35% 41% 43% 42%
Pre-tax
household
weekly
income
Low income 28% 27% 31% 38% 34%
Middle income 32% 33% 33% 31% 28%
High income 20% 18% 19% 13% 19%
Not reported 20% 23% 18% 19% 20%
Highest
educational
qualifications
attained
Pre-bachelor
degree
23% 23% 22% 16% 19%
Bachelor degree
level
23% 23% 19% 18% 23%
Graduate level
and above
54% 53% 59% 65% 59%
Driving
licence
ownership
Yes 90% 91% 91% 90% 92%
No 10% 9% 9% 10% 8%
Vehicle
access
No access and
solely
dependent on
public transport 14% 12% 11% 11% 11%
36 Chapter 3: Dataset
Socio-
economic
factor
Classification Sydney Melbourne Brisbane Adelaide Perth
N=2000 N=2000 N=989 N=742 N=1000
Access to either
privately
owned,
company owned
or shared motor
vehicles 87% 89% 89% 89% 89%
Whether a
motor vehicle
is required for
working
Yes 27% 30% 28% 29% 31%
No 74% 70% 73% 71% 69%
Train
services’
influence on
the current
home location
decision
Significant 47% 26% 13% 14% 11%
Moderate 23% 19% 18% 13% 12%
Insignificant 30% 56% 68% 73% 78%
Household
composition Adults and kids 36% 38% 34% 37% 33%
Adults only 51% 49% 52% 53% 56%
Multiple family
household 13% 13% 14% 10% 11%
Trip purpose Work/School 58% 56% 53% 51% 53%
Other trip
purposes 42% 44% 47% 49% 47%
Departure
time Off-peak 54% 55% 55% 58% 57%
AM peak 37% 32% 33% 31% 31%
PM peak 10% 13% 11% 11% 12% a ”Outside work force” employment status includes being student, retired, on maternity or on other
official working leaves, and being unemployed.
3.3.2 Perception rates towards seven service factors
Each respondent was asked to rate their perceptions of seven service factors in
influencing their decision to take a train more often (Section D). The service factors
included: better access to train station; trains running on schedule; the probability of
getting a seat; the ability to access up-to-date train information; availability of an on-
board entertainment system; increased road congestion; and congestion charge or toll
for private vehicles entering the city centre during peak hours. Their responses are
tabulated in Table 3.4 below.
Table 3.4 Respondents’ perceptions of various service factors in each city
Perception of … to
take a train more
often
Levels of
influence
Sydney
(%)
Melbourne
(%)
Brisbane
(%)
Adelaide
(%)
Perth
(%)
Better access to train
station
Strong/Not
strong influence 23/ 77 23/77 22/ 78 21/ 79 31/ 69
Train running on
schedule
Strong/Not
strong influence 29/ 71 29/ 71 30/ 70 28/ 72 33/ 67
Chapter 3: Dataset 37
Perception of … to
take a train more
often
Levels of
influence
Sydney
(%)
Melbourne
(%)
Brisbane
(%)
Adelaide
(%)
Perth
(%)
The probability of
getting a seat
Strong/Not
strong influence 30/ 70 29/ 71 27/ 73 26/ 74 28/ 72
The ability to access
up-to-date train
information
Strong/Not
strong influence 26/ 74 24/ 76 24/ 76 23/ 77 25/ 75
Availability of an on-
board entertainment
system
Strong/Not
strong influence 11/ 89 9/ 91 8/ 92 8/ 92 6/ 94
Increased road
congestion
Strong/Not
strong influence 28/ 72 27/ 73 27/ 73 24/ 76 31/ 69
Congestion charge or
toll for private vehicles
entering city centre
during peak hours
Strong/Not
strong influence 22/ 78 22/ 78 22/ 78 18/ 82 21/ 79
3.3.3 Trip characteristics of non-riders
The trip characteristics of non-riders’ most recent home-based trip by using bus
or any other motor vehicles were collected in the survey via Section C (the trip
characteristics experienced by the non-riders) of the questionnaire. The description
analysis of the trip characteristics are presented in the Table 3.5 below.
Table 3.5 Trip characteristics of train riders from each city (Non-riders)
Trip
characteristic
Classification Sydney
(n=1000)
Melbourne
(n=1000)
Brisbane
(n=500)
Adelaide
(n=500)
Perth
(n=500)
Main
transport
mode used
Motor vehicle 80% 86% 81% 76% 87%
Passenger in a
motor vehicle 5% 6% 6% 4% 5%
Company motor
vehicle 3% 2% 3% 2% 3%
Taxi 1% 1% 1% 0% 1%
Bus 11% 5% 10% 16% 5%
Departure
time Off Peak 57% 59% 59% 57% 56%
AM Peak 31% 30% 29% 31% 31%
PM Peak 12% 11% 11% 12% 13%
Trip purpose Work/ School 39% 41% 39% 39% 40%
Social/
Recreational 13% 10% 9% 13% 10%
Shopping/
Personal Business 48% 49% 51% 48% 50%
On-board
time
Short period
(<15 minutes) 39% 42% 43% 40% 39%
Medium period
(15-30 minutes) 29% 29% 31% 30% 32%
Long period
(>30 minutes) 32% 30% 26% 30% 29%
Total out-
vehicle timea
Short period
(<15 minutes) 86% 91% 87% 84% 90%
Medium period
(15-30 minutes) 8% 5% 7% 10% 6%
38 Chapter 3: Dataset
Trip
characteristic
Classification Sydney
(n=1000)
Melbourne
(n=1000)
Brisbane
(n=500)
Adelaide
(n=500)
Perth
(n=500)
Long period
(>30 minutes) 6% 5% 6% 7% 5%
Total travel
time
Short period
(<15 minutes) 23% 26% 26% 24% 25%
Medium period
(15-30 minutes) 29% 32% 31% 30% 30%
Long period
(>30 minutes) 48% 42% 43% 45% 44%
Total one-way
cost Low fare (<$2) 79% 86% 82% 85% 87%
Medium fare
($2-$6) 13% 6% 11% 8% 7%
High fare (>$6) 9% 8% 8% 7% 6%
Main reasons
for not taking
train
I like driving 10% 11% 6% 7% 11%
Driving was faster 16% 20% 17% 12% 16%
Work-related
vehicle 3% 2% 2% 3% 3%
No available train
service 41% 29% 38% 52% 35%
Personal mobility
constraints 2% 2% 3% 2% 2%
Needed to
transport bulky
items 2% 2% 2% 1% 2%
I was offered a
free ride 2% 2% 1% 1% 1%
I prefer bus/ ferry/
tram 1% 1% 0% 2% 0%
The nearest train
station was too far 9% 9% 15% 8% 12%
The trip was too
short for taking
train 6% 9% 5% 2% 7%
The train is not
sufficiently safe 1% 1% 0% 0% 1%
The train does not
run frequently
enough 1% 1% 1% 1% 0%
The train is not
convenient for
visiting multiple
destinations 2% 2% 2% 2% 3%
The train is
generally for
people who don’t
have access to
motor vehicles 1% 1% 1% 0% 1%
The train is too
crowded 1% 1% 0% 0% 1%
The train fare is
too expensive 1% 2% 2% 0% 1%
Others 5% 6% 4% 6% 4%
Reasons for
choosing
transport
mode used
No other mode
was available 23% 19% 24% 18% 22%
Cheapest mode
available 19% 14% 23% 22% 17%
Chapter 3: Dataset 39
Trip
characteristic
Classification Sydney
(n=1000)
Melbourne
(n=1000)
Brisbane
(n=500)
Adelaide
(n=500)
Perth
(n=500)
(multiple
selections
allowed)
Fastest mode
available 46% 47% 46% 41% 45%
Most convenience
mode available 64% 65% 63% 64% 65%
Safest mode
available 11% 12% 11% 9% 10%
Most comfortable
mode available 31% 30% 25% 26% 27%
Work-related
vehicle 4% 3% 4% 4% 4%
Personal mobility
constraints 6% 4% 4% 4% 4%
Needed to
transport bulky
items 5% 6% 8% 7% 7%
Weather condition 3% 5% 3% 2% 4%
Bus on-board
crowding
(only for Bus
users)b
Sydney
(n=114)
Melbourne
(n=48)
Brisbane
(n=50)
Adelaide
(n=82)
Perth
(n=27)
Not crowded 93% 94% 100% 95% 96%
Crowded 2% 2% 0% 1% 0%
Overcrowded 5% 4% 0% 4% 4% aTotal out-vehicle time is defined as the total of the time a respondent spent travelling between their
point of origin and the bus stop/ car park, the waiting time for bus services, when applicable, and the
time they spent travelling between the bus stop/ car park and their destination.
bThe on-board crowding is self-reported with three pre-defined categories: not crowded (a traveller
found a seat for the entire journey); crowded (a traveller had to stand up prior to finding a vacant seat
on-board); and overcrowded (a traveller had to stand up for the entire journey and no vacant seat
available) (Cantwell et al., 2009).
3.4 DATA UTILISATION
According to the structure of the survey questionnaire describe above, at least
four types of responses were obtained from each respondents, such as responses from
Section A, either Section B or C (depending on whether the respondent belongs to
Train rider or Non-rider category), Section D, and Section E. Nonetheless, not all of
those responses were analysed in each of the sub-study. Based on the objectives of
each study, a different set of responses were analysed. The detailed utilisation of the
responses collected from the survey questionnaire are demonstrated in Figure 3.2.
The first sub-study (Urban travellers’ satisfaction with train fares in five
Australian cities) utilized only the train riders dataset consisting of 3231 respondents
from all five Australian capital cities as well as specifically used the data from Section
B and Section D, only the socio-economic profiles, of the questionnaire.
40 Chapter 3: Dataset
The second sub-study (Consistency between perceptions and stated preferences
data in a nationwide mode choice experiment) utilized both the train rider and non-
rider dataset consisting of 6731 respondents from all five Australian capital cities.
Specifically, it analysed two the data types, the RP data type was obtained from Section
D, both the perception rates and socio-economic profiles, and the SP data type was
acquired from Section E, the mode choice experiment responses.
In order to achieve the objective of investigating the travel behavioural shifts in
Sydney, Melbourne, and Brisbane, the third sub-study (Policy interventions study to
encourage behavioural shift from car to public transport) only utilised both the train
rider and non-rider dataset collected from these three cities. This gave a total of 4989
respondents. Specifically, it analysed responses from Section B, Section C, Section D
(only the socio-economic profiles), and Section E of the questionnaire. The Section B,
C, and socio-economic profiles represented the respondents’ revealed travel
behaviours. On the other hand, the Section E represented the respondents’ future travel
behaviours.
Figure 3.2 : The diagram of data utilisation
Chapter 3: Dataset 41
3.5 TRAIN RIDERS DATASET
Initially, the sub-study of “User satisfaction with train fares: A comparative
analysis in five Australian cities” utilized the whole train riders dataset consisting of
3231 respondents from all five Australian capital cities. Prior to the analysis process,
the data were then prepared and its outliers were removed. Therefore, only 2927 train
riders data were included in the model estimation and further analysed (Paramita et al.,
2018).
3.5.1 Data preparation
To ensure the quality of the survey data, several factors were checked for
outliers, including one-way train fare, travel time, waiting time, and access time.
Respondents were asked to provide the amount of fare they paid for their most recent
train trip. As a commonly used outlier detection method, train fares outside the range
of Q1 – 3 ∗ (Q3 − Q1) ($ 0) to Q3 + 3 ∗ (Q3 − Q1) ($ 17.6) were regarded as
potential outliers, where Q3 and Q1 were the third and first quantile, respectively
(DiLalla & Dollinger, 2006; Hawkins, 1980). The same procedure was then applied to
on-board time, and any on-board time outside the range of 0 to 135 minutes was
regarded as a potential outlier. Similarly, waiting time outside the range of 0 to 25
minutes was regarded as a potential outlier, and any total access time outside the range
of 0 to 75 minutes was also regarded as a potential outlier (Paramita et al., 2018).
Prior to removing any outliers, the precaution of seeking additional information
to confirm their outlier status was taken. More specifically, by checking the actual train
fare structures in the five cities, this study found that the maximum train fare for a one-
way trip of around 135 minutes is $28. Thus, $28 was used as the upper fare limit
(Paramita et al., 2018). After this data cleansing, 2927 respondents (out of 3231) were
included in the detailed analysis.
Correlation analysis was performed prior to modelling in order to obtain valuable
knowledge about potential relationships among explanatory variables. More
specifically, Pearson correlation analysis was performed for the continuous variables,
while Spearman correlation analysis was performed for the categorical and ordinal
variables (Lund & Lund, 2013a, 2013b). It was found that i) total travel time has a
significant positive correlation with on-board time, waiting time, and total access time;
and that ii) age group has significant correlations with the type of concession fare.
Therefore, total travel time and age group were not included in the model. In addition,
42 Chapter 3: Dataset
paid fare status and departure time were strongly correlated because paid fare status
was derived from departure time. Thus, departure time was also not included in the
model (Paramita et al., 2018).
3.5.2 Data description
Each train rider’s socio-economic profile and the characteristics of their most
recent train trip were collected in the survey via Section D and B of the questionnaire,
respectively. Socio-economic background includes information about their gender,
age, employment status, pre-tax household weekly income level, highest education
level, whether they hold a driver’s license, whether they have access to a vehicle,
whether a motor vehicle is required for their work, whether train services influenced
the choice of their current home location, and the composition of their household.
Meanwhile, characteristics of their most recent train trip include departure time, the
purpose of their trip, pre-departure information check, home-to-station transport mode,
whether there was any on-board crowding or on-board activities, transport mode from
the station to their destination, the one-way trip cost, the time spent on-board, waiting
time, total access time, and total travel time.
In the five cities chosen for this study, at least two types of concessions fare are
available: A senior concession fare, and a student concession fare (Paramita et al.,
2018). However, respondents were not directly questioned about these fares in the
survey. Given the eligibility requirements for concession fares set by transport
authorities, the following assumptions were made in order to accurately understand
train riders’ satisfaction with their fare: (a) A respondent who is at least 60 years old
and is retired is eligible for a senior concession fare; and (b) A respondent who is
between 16 and 30 years old and is a student is eligible for a student concession fare
(Dell’Olio, Ibeas, & Cecin, 2011; Prideaux, Wei, & Ruys, 2001). The respondents who
did not belong to either of the concession groups were assumed to pay the full adult
fare (Paramita et al., 2018). Meanwhile, train fares can differ depending on the
travelling period. Respondents who departed from home between 7:00 PM and 7:00
AM and between 9:00 AM and 3:00PM were assumed to pay a discounted fare, and
those who departed between 7:00 and 9:00 AM and between 3:00 and 7:00 PM were
assumed to pay full fare (Paramita et al., 2018).
The respondents were also asked to rate their perceived satisfaction with the train
fare for their most recent home-based train trip, using a 5-point Likert scale; i.e.,
Chapter 3: Dataset 43
extremely satisfied, satisfied, neutral, dissatisfied, and extremely dissatisfied. Figure
3.3 shows the perceived satisfaction in each city. About 46% to 50% of respondents
from Sydney, Melbourne, and Brisbane were satisfied or extremely satisfied with the
fare paid for their most recent train trip, while less than 28% of respondents from each
of those cities were not satisfied or extremely dissatisfied. Over 59% of respondents
of Adelaide and Perth were satisfied or extremely satisfied with the fare paid for their
most recent train trip, and less than 12% of respondents from each of those two cities
were not satisfied or extremely dissatisfied (Paramita et al., 2018).
Figure 3.3 : Satisfaction with train fare in each city
The train riders’ socio-economic background and their most recent train trip
characteristics are summarised in Table 3.6 and Table 3.7, respectively.
Table 3.6 Socio-economic profiles of respondents from each city (Train riders)
Socio-
economic
factor
Classification Sydney
(n=889)
Melbourne
(n=912)
Brisbane
(n=425)
Adelaide
(n=222)
Perth
(n=479)
Gender Male 48% 49% 40% 51% 41%
Female 52% 51% 60% 49% 59%
Age group
Under 31
years old
22% 25% 20% 23% 21%
Between 31
and 60 years
old
62% 62% 63% 63% 55%
Above 60
years old
16% 14% 17% 14% 24%
Employment
status
Full time 50% 48% 44% 37% 35%
Part time 18% 17% 16% 17% 19%
Self-employed 6% 6% 6% 3% 5%
44 Chapter 3: Dataset
Socio-
economic
factor
Classification Sydney
(n=889)
Melbourne
(n=912)
Brisbane
(n=425)
Adelaide
(n=222)
Perth
(n=479)
Outside work
forcea
27% 29% 34% 43% 41%
Pre-tax
household
weekly
income level
Low income 23% 25% 29% 39% 35%
Middle
income
35% 35% 36% 34% 27%
High income 23% 21% 21% 8% 18%
Not reported 19% 20% 14% 19% 20%
Highest
education
level
Pre-bachelor
degree
46% 44% 52% 60% 56%
Bachelor
degree level
27% 29% 24% 24% 26%
Graduate level
and above
27% 27% 24% 16% 19%
Driver
license
Yes 88% 88% 89% 83% 86%
No 12% 12% 11% 17% 14%
Vehicle
access
No access and
solely
dependent on
public
transport
20% 17% 17% 19% 18%
Access to
either
privately
owned,
company
owned, or
shared motor
vehicles
80% 83% 83% 81% 82%
Whether a
motor
vehicle is
required for
work
Yes 21% 25% 25% 23% 25%
No 79% 75% 75% 77% 75%
Train
services’
influence on
the current
home
location
decision
Significant 49% 44% 39% 24% 36%
Moderate 21% 24% 24% 26% 18%
Insignificant 29% 32% 37% 50% 46%
Household
composition
Adults and
children
36% 37% 35% 40% 33%
Adults only 48% 46% 48% 47% 50%
Multiple-
family
16% 17% 17% 13% 17%
a ”Outside work force” employment status includes being student, retired, on maternity or on other
official working leaves, and being unemployed.
Table 3.7 Trip characteristics of train riders from each city (Train riders)
Chapter 3: Dataset 45
Trip
characteristic
Classification Sydney
(n=889)
Melbourne
(n=912)
Brisbane
(n=425)
Adelaide
(n=222)
Perth
(n=479)
Departure time
Off-peak 50% 52% 51% 58% 57%
AM peak 42% 34% 37% 33% 31%
PM peak 8% 14% 12% 9% 12%
Trip purpose
Work/School 69% 66% 62% 58% 57%
Social/
Recreational
7% 10% 7% 12% 14%
Shopping/
Personal
Business
25% 24% 31% 30% 29%
Pre-departure
information
check
Yes 47% 49% 51% 54% 37%
No 53% 51% 49% 46% 63%
Transport mode
from home to
the train station
Bus 19% 16% 15% 18% 31%
Walking 52% 43% 33% 45% 23%
Driving 20% 29% 40% 25% 32%
Cycling 0% 1% 0% 1% 2%
Dropped-off 8% 11% 13% 12% 12%
On-board
crowdinga
Not crowded 82% 82% 88% 92% 74%
Crowded 11% 8% 6% 4% 10%
Overcrowded 7% 11% 6% 4% 16%
On-board
activities
Work/study 8% 5% 3% 4% 2%
Leisure 92% 95% 97% 96% 98%
Transport mode
from the train
station to the
destination
Bus/tram 93% 83% 92% 87% 91%
Walking 2% 2% 5% 4% 4%
Picked up 1% 14% 1% 8% 2%
Cycling 3% 2% 3% 2% 4%
Concession fare
Student
concession
fare
5% 5% 4% 10% 6%
Senior
concession
fare
9% 8% 13% 11% 16%
No concession
(Adult) fare
86% 87% 84% 79% 78%
Paid fare status
Full Fare 50% 48% 49% 42% 43%
Discounted
Fare
50% 52% 51% 58% 57%
One-way cost
($)
Minimum 0 0 0 0 0
Median 2.5 1.9 2.95 1.5 0.97
Maximum 25.8 20 26 25 25.5
Mean 3.511 2.460 3.319 2.022 1.891
Standard
deviation
3.535 2.881 4.038 2.627 2.667
On-board time
(Minutes)
Minimum 1 1 3 1 1
Median 30 30 30 25.5 20
Maximum 135 130 135 120 90
Mean 32.285 31.912 33.904 31.059 24.282
46 Chapter 3: Dataset
Trip
characteristic
Classification Sydney
(n=889)
Melbourne
(n=912)
Brisbane
(n=425)
Adelaide
(n=222)
Perth
(n=479)
Standard
deviation
20.287 18.316 21.040 17.358 16.055
Waiting time
(Minutes)
Minimum 1 1 1 1 1
Median 8 7 10 10 7
Maximum 25 25 25 25 25
Mean 8.351 8.231 9.299 8.824 7.816
Standard
deviation
4.861 4.911 5.008 4.966 4.435
Total access
timeb (Minutes)
Minimum 1 3 1 1 2
Median 20 20 20 25 20
Maximum 75 75 75 75 67
Mean 23.172 23.113 22.758 26.752 22.132
Standard
deviation
13.570 13.945 13.452 15.399 13.171
Total travel time
(Minutes)
Minimum 8 8 8 3 14
Median 60 59 60 63.5 50
Maximum 180 194 185 165 160
Mean 63.808 63.257 65.960 66.635 54.230
Standard
deviation
25.962 24.876 28.284 26.053 23.189
a The on-board crowding is self-reported with three pre-defined categories: not crowded (a traveller
found a seat for the entire journey); crowded (a traveller had to stand up prior to finding a vacant seat
on-board); and overcrowded (a traveller had to stand up for the entire journey and no vacant seat
available) (Cantwell et al., 2009).
b Total access time is defined as the total of the time a respondent spent travelling between their point
of origin and the train station and the time they spent travelling between the train station and their
destination.
3.6 FACTOR MAPPING OF PERCEPTIONS AND ATTRIBUTES OF
MODE CHOICE EXPERIMENT
In order to examine the consistency in respondents’ perceptions relating to
service factors, and their choices in travel scenarios, the perception data were closely
mapped to the attributes of the mode choice experiments. For six perception data, this
study identified the most suitable attributes that closely represents the similar
information, as presented in Table 3.8.
Table 3.8 Factor mapping of perceptions and attributes of mode choice experiment
Strongly influenced perceptions of… to take a train
more often
Attributes in mode choice experiments
Better access to train station Access time to train station
Train running on schedule Train waiting time
The probability of getting a seat Train on-board crowding level
Chapter 3: Dataset 47
Strongly influenced perceptions of… to take a train
more often
Attributes in mode choice experiments
The ability to access up-to-date information on train
services (such as current train status)
Not Available
Availability of an on-board entertainment system Availability of wireless connection
Availability of laptop station
Increased road congestion Car on-board time
Congestion charge or toll for private vehicles entering
city centre during peak hours
Car toll cost
3.7 DATASET FOR TRANSIT POLICY INTERVENTIONS STUDY
The third sub-study only utilised the data collected from Sydney, Melbourne,
and Brisbane. This gave a total of 4989 respondents; 2000 respondents from Sydney,
2000 respondents from Melbourne, and 989 respondents from Brisbane. The
knowledge of travellers’ travel behaviours and documented information about each
city were utilised to identify an average traveller’s profile for each transport mode in
Sydney, Brisbane, and Melbourne (Queensland Government, 2016; Transport for
NSW, 2016; Victoria State Government, 2016). While, the responses of the mode
choice experiment section were utilised to estimate a nested logit model for each city.
The detailed utilisation of the dataset in this sub-study is illustrated in Figure 3.4.
Figure 3.4 : The diagram of data utilisation in the transit policy interventions study
48 Chapter 3: Dataset
To ensure the quality of the RP data for travellers’ profiling, the socio-economic
factors and the revealed travel behaviours were checked for outliers. For example, to
determined realistic one-way train fare for the train riders, the data were extracted from
the average of respondents’ responses on the amount of fare they paid for their most
recent train trip. As a commonly used outlier detection method, train fares outside the
range of Q1 – 3 ∗ (Q3 − Q1) to Q3 + 3 ∗ (Q3 − Q1) were regarded as potential
outliers, where Q3 and Q1 were the third and first quantile, respectively (DiLalla &
Dollinger, 2006; Hawkins, 1980). Prior to removing any outliers, the precaution of
seeking additional documented information to confirm their outlier status was also
taken.
The same procedure was then applied to other socio-economic factors and trip
characteristics. The additional documented information was obtained by checking the
actual train fare structures in the five cities as well as the reported access time to/from
bus stop or train station, waiting time, and on-board time for bus and train from various
related published articles, websites and journal papers (Queensland Government,
2016; Transport for NSW, 2016; Victoria State Government, 2016).
Chapter 4: Methodology 49
Chapter 4: Methodology
In correspond to the defined three interconnected sub-studies (Section 1.2), this
chapter contains three sections, each of them outlines and describes the modelling
methodology of each sub-study. A random parameter ordered logit model was
developed to identify significant factors associated with train riders’ satisfaction with
train fare as part of the first sub-study (Section 4.1). A random parameters binomial
logit model that accounts for heterogeneity in the population was estimated for the
perceptions, and a mixed logit model was employed to model the stated choice
responses as part of the second sub-study (Section Error! Reference source not
found.). Section 4.3 describes the utilisation of a nested logit model to estimate the
utility functions of mode choice responses.
4.1 URBAN TRAVELLERS’ SATISFACTION WITH TRAIN FARES IN
FIVE AUSTRALIAN CITIES
The satisfaction with train fare for the most recent train trip from home was
ordered and categorical. In this analysis, satisfaction was treated as ordinal rather than
nominal to provide simpler interpretations, greater flexibility, greater detection power,
and more similarity to ordinary regression analysis (Efthymiou et al., 2017; Zheng,
Liu, Liu, & Shiwakoti, 2014). In this study, travellers’ satisfaction with train fare was
estimated as a function of a number of socio-economic factors and trip characteristics,
using an ordered logit model. The logit model used the cumulative distribution
function of the logistic distribution (Albright, 2016). The logit model can be
generalized to account for non-constant error variances in more advanced econometric
settings, such as heteroskedastic or random-parameter logit model. Constraining all
parameters to be fixed while they are actually heterogeneous could lead to biased,
inefficient, and inconsistent parameter estimates (Bordagaray, dell'Olio, Ibeas, &
Cecín, 2014; Washington, Karlaftis, & Mannering, 2011).
In the context of this study, heterogeneity could arise in many factors such as
on-board crowding, one-way cost, on-board time, waiting time, total access time, and
total travel time (Beirão & Cabral, 2007; Bordagaray et al., 2014; Brons et al., 2009;
J. de Oña & de Oña, 2014; Hine & Scott, 2000; Zheng et al., 2016). To capture such
50 Chapter 4: Methodology
heterogeneity, these factors were considered as random parameters in the model
development because different respondents might perceive them differently. For
instance, a $2 one-way cost and 15 minutes waiting time can be perceived by one user
as a cheap fare, and a short waiting time; however, another user could consider the fare
to be expensive and the waiting time to be long. Such heterogeneity is influenced by
individuals’ various socio-economic factors and trip experience (Paramita et al., 2018).
The formulation of the ordinal data modelling problem, which is motivated by
the latent regression perspective, is defined as below.
𝑌 = 𝑗 𝑖𝑓 𝛼𝑗−1 < 𝑌∗ ≤ 𝛼𝑗 [4-1]
𝑌∗ is a continuous latent variable and is assumed to underlie the observed ordinal data.
Particularly, 𝑌∗ = 𝛽′𝑋 + 𝜀 and X is a vector of explanatory variables, 𝛽 is a vector of
coefficients, and 𝜀 is an error term. While, 𝑗 is an ordinal responses and 𝛼 is a set of
cut points of the continuous scale for 𝑌∗ (Agresti, 2010; Zheng et al., 2014). 𝑌 is
observed to be in category j when the latent variable falls in the 𝑗𝑡ℎ interval.
In order to maintain the order of ordinal dependent variables, the logit
transformation is applied to the cumulative probabilities, as below.
l𝑜𝑔𝑖𝑡[𝑃(𝑌𝑖 ≤ 𝑗)] = 𝑙𝑜𝑔 (𝑃(𝑌𝑖≤𝑗)
1−𝑃(𝑌𝑖≤𝑗) ) [4-2]
A general model for the cumulative logits is shown below
𝑙𝑜𝑔𝑖𝑡 [𝑃(𝑌𝑖 ≤ 𝑗)] = 𝛽1𝑋1 + 𝛽2𝑋2 + ⋯ + 𝛽𝑛𝑋𝑛 + 𝜀𝑖= 𝛽′𝑋 +𝜀𝑖 [4-3]
,where 𝑗 = 1, … , 𝑐 − 1; 𝑐 is the total number of categories. 𝑋1, 𝑋2, … , 𝑋𝑛 are the 𝑛
explanatory variables; 𝛽1, 𝛽2, … , 𝛽𝑛 are the corresponding coefficients (Zheng et al.,
2014). In this study, 𝑌𝑖 denotes the perceived satisfaction with the train fare of 𝑖𝑡ℎ
respondent.
In the fixed parameter ordered logit model above, the vector of parameters 𝛽 is
the same for all observations. On the other hand, a random-parameter ordered logit
model explicitly accounts for heterogeneities by allowing the regression coefficients
to vary across observations (Agresti, 2010; Long & Freese, 2006), as shown in below.
𝛽𝑖 = 𝛽 + 𝑢𝑖 [4-4]
,where 𝛽𝑖 is a vector of random regression coefficients and 𝑢𝑖 is a vector of randomly
distributed terms for each regression coefficient. The additional error term 𝑢𝑖 is
correlated with the error term 𝜀𝑖 of the perceived satisfaction function, and thus
translates individual heterogeneities into parameter heterogeneities. From Equations
4-3 and 4-4, the function for the perceived satisfaction level becomes
Chapter 4: Methodology 51
𝑙𝑜𝑔𝑖𝑡 [𝑃(𝑌𝑖 ≤ 𝑗)] = 𝛽𝑖′𝑋 +𝜀𝑖 = 𝛽′ 𝑋𝑖 + (𝑢𝑖′ 𝑋𝑖 + 𝜀𝑖). [4-5]
In this study, coefficients of on-board crowding, one-way cost, on-board time,
waiting time, total access time, and total travel time variables are considered as
candidate random parameters. More specifically, each of the random parameters is
assumed to follow a lognormal distribution restricted to the negative side because the
expected sign of the estimates is known to be negative (Paramita et al., 2018).
A simulation-based maximum likelihood method is employed to estimate the
random-parameter ordered logit model. Halton sequence (that provides more accurate
approximations for numerical integrations than purely random draws) is used to obtain
the simulation-based estimation (Bhat, 2003; Halton, 1960; Train, 2009). Following
the recommendation in the literature, 500 random Halton draws are used in estimating
the random parameters (Bhat, 2003; Hensher & Greene, 2002; Train, 1999). Following
the standard model development process, the best fixed- and the random-parameter
models are obtained based on the statistical significance independent variables and the
Akaike’s Information Criteria (AIC) (Akaike, 1992).
In the case of availability of two points from the explanatory variables, 𝑋𝑎 and
𝑋𝑏, then the cumulative logit is defined as
𝑙𝑜𝑔𝑖𝑡 [𝑃(𝑌𝑖 ≤ 𝑗|𝑋𝑎)] − 𝑙𝑜𝑔𝑖𝑡 [𝑃(𝑌𝑖 ≤ 𝑗|𝑋𝑏)] = 𝛽′( 𝑋𝑎 − 𝑋𝑏). [4-6]
The equation above specifies that the odds of making response 𝑌 ≤ 𝑗 at 𝑋𝑎 are
𝑒𝑥𝑝(𝛽′ ∗ ( 𝑋𝑎 − 𝑋𝑏)) times the odds of 𝑋𝑏. The log odds ratio is proportional to the
distance between these two points, and the proportionality remains constant across
different categories. Hence, Equation 4-3 is referred to as a “proportional odds” model.
Due to its easy interpretation, this model has been widely used in the literature
(Agresti, 2010; Agresti & Kateri, 2011; Greene & Hensher, 2010; Zheng et al., 2014).
4.2 CONSISTENCY BETWEEN PERCEPTIONS AND STATED
PREFERENCES DATA IN A NATIONWIDE MODE CHOICE
EXPERIMENT
As this study employed two data types (perceptions and mode choice experiment
responses) from the same survey, two different statistical models were estimated. A
statistical model for each data type. The probability of taking train service more than
once a month was estimated as a function of a number of perceptions of service factors
and socio-economic factors using random-parameter binomial logit model (Albright,
52 Chapter 4: Methodology
2016). While, the mixed logit model is employed to fit the mode choice experiment
responses (Hensher et al., 2005; Train, 2009).
4.2.1 Mixed logit model
Based on the mode choice experiment responses, this study develops three utility
functions and three probability functions for each of the three modes: bus, train, and
car. Each utility function has its own set of attributes and socio-economic factors.
Specifically, six mode choice experiment responses are collected from each
respondent. As the six responses come from the same decision maker, they are likely
to be correlated. Hence, the mixed multinomial logit (MMNL), or mixed logit model,
is employed to fit the mode choice experiment responses using the NLOGIT (Greene,
2000). This model is able to capture random taste variation, unrestricted substitution
patterns, and correlation in unobserved factors over repeated measures (Hensher et al.,
2005; Train, 2009).
A mixed logit model accounts for heterogeneities among respondents. A part, or
all, of the parameter estimates in this model are randomly distributed among
respondents (Hensher et al., 2005). The regression coefficients vary across
respondents.
𝛽𝑖𝑗𝑘 = 𝛽𝑗𝑘 + 𝑤𝑖𝑘 [4-7]
where 𝑤𝑖𝑘 is a random variable from some underlying distributions. If 𝐸[𝑤𝑖𝑘] = 0
and ŋ𝑘 is the standard deviation of 𝑤𝑖𝑘 , then
𝛽𝑖𝑗𝑘 = 𝛽𝑗𝑘 + (ŋ𝑘 ∗ 𝑧𝑖𝑘) [4-8]
where 𝛽𝑗𝑘 is the mean marginal utility in the sampled population, and ŋ is the standard
deviation of the marginal utilities held by respondents for attribute 𝑘 belonging to
alternative 𝑗 in choice set 𝑠. 𝑧𝑖𝑘 represents some underlying distributions, such as a
standard normal distribution [𝑧𝑖𝑘~ 𝑁(0,1)] (Hensher et al., 2005). Therefore, the
utility function of the mixed logit model is defined as follows:
𝑈𝑖𝑠𝑗 = ∑(𝛽𝑖𝑗𝑘 ∗
𝑘
𝐾=1
𝑥𝑖𝑠𝑗𝑘) + 𝜀𝑖𝑠𝑗 = ∑(( 𝛽𝑗𝑘 + (ŋ𝑘 ∗ 𝑧𝑖𝑘)) ∗
𝐾
𝑘=1
𝑥𝑖𝑠𝑗𝑘) + 𝜀𝑖𝑠𝑗
= 𝑉𝑖𝑠𝑗 + 𝜀𝑖𝑠𝑗 [4-9]
where 𝑖 signifies the respondent, 𝑠 is choice task, 𝑗 denotes alternative, and
𝑘 represents attribute. 𝑉𝑖𝑠𝑗 is a function of travel attributes, and 𝛽𝑗𝑘 is a function of
Chapter 4: Methodology 53
socio-economic factors influencing random parameter attributes and
𝜀𝑖𝑗~ 𝐼𝐼𝐷 𝐸𝑥𝑡𝑟𝑒𝑚𝑒 𝑉𝑎𝑙𝑢𝑒 𝑡𝑦𝑝𝑒 (𝐸𝑉)1(Caussade, de Dios Ortúzar, Rizzi, & Hensher,
2005). Congruently, the choice probability is defined as:
𝑃𝑖𝑠𝑗 =exp(∑ ( 𝛽𝑗𝑘+ŋ𝑘𝑧𝑖𝑘)∗𝑥𝑖𝑠𝑗𝑘
𝐾𝑘=1 )
∑ (exp(∑ ( 𝛽𝑗𝑘+ŋ𝑘𝑧𝑖𝑘)∗𝑥𝑖𝑠𝑗𝑘𝐾𝑘=1 ))
𝐽𝑗=1
[4-10]
Heterogeneity can arise in a number of attributes within mode choice
experiments. These attributes could be perceived differently across respondents, as
they are influenced by their socio-economic profiles. The candidates for random
parameters are: access time to/from train station/bus stop, bus/train waiting time, on-
board time, bus/train ticket, car toll cost, car fuel cost, availability of wireless
connection, and availability of laptop station. For instance, the $4.5 one-way train
ticket could be perceived as affordable by a respondent who is employed full time, but
might be perceived differently by an unemployed respondent. Specifically, the
availability of a wireless connection and a laptop station are assumed to follow a
standard normal distribution. The rest of the random parameter candidates are assumed
to follow a lognormal distribution because the sign of the estimates is expected to be
negative.
The marginal utility for a random parameter with an underlying normal
distribution is defined as:
𝛽𝑖𝑗𝑘 = 𝛽𝑗𝑘 + ∑ (𝛽𝑙 ∗ 𝑦𝑙)𝐿𝑙=1 + (𝜎𝑗𝑘 ∗ 𝑛𝑖) , 𝑛𝑖~ 𝑁 (0,1)
𝑎𝑛𝑑 𝑢𝑛𝑖𝑞𝑢𝑒 𝑓𝑜𝑟 𝑒𝑎𝑐ℎ 𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑒𝑛𝑡. [4-11]
while the marginal utility for a random parameter with lognormal as the underlying
distribution is defined as
𝛽𝑖𝑗𝑘 = 𝑒𝑥𝑝 (𝛽𝑗𝑘 + ∑ (𝛽𝑙 ∗ 𝑦𝑙)𝐿𝑙=1 + (𝜎𝑗𝑘 ∗ 𝑛𝑖)) , 𝑛𝑖~ 𝑁 (0,1)
𝑎𝑛𝑑 𝑢𝑛𝑖𝑞𝑢𝑒 𝑓𝑜𝑟 𝑒𝑎𝑐ℎ 𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑒𝑛𝑡 [4-12]
where 𝛽𝑗𝑘 and 𝜎𝑗𝑘 are the coefficient and the standard deviation of the corresponding
random parameter, respectively. 𝛽1 𝑡𝑜 𝛽𝐿 are the coefficients of influential socio-
economic factors, and 𝑦1 𝑡𝑜 𝑦𝐿 signify the corresponding socio-economic factors
(Greene, 2012; Hensher et al., 2005).
4.2.2 Random-parameters binomial logit model
Meanwhile, for the train rider category, the probability of taking a train more
than once a month is estimated as a function of a number of perceptions of service
54 Chapter 4: Methodology
factors and socio-economic factors by using the binomial logit model. The response
variable, 𝜋𝑖, can take the values between one and zero. 𝜋𝑖 is the respondents’
probability of making more than one train trip in the past month prior to the survey,
while (1 − 𝜋𝑖) is the respondents’ probability of making one trip or less. The various
perceptions of service factors are the candidates for the explanatory variables. In
particular, the choice probability of binomial logit is defined as:
𝜋𝑖 =exp( ∑ (𝛽𝑘∗𝑥𝑖𝑘)𝐾
𝑘=1 )
1+exp( ∑ (𝛽𝑘∗𝑥𝑖𝑘)𝐾𝑘=1 )
[4-13]
where 𝑖 signifies respondent, 𝑘 denotes the number of explanatory variables, and
𝑥𝑖1 to 𝑥𝑖𝐾 represent a series of explanatory variables (Rodrıguez, 2007).
Heterogeneities might arise within a number of perceptions of service factors
(Borins, 1988; Brons et al., 2009; Cox, Houdmont, & Griffiths, 2006; Evans & Wener,
2007; Fan, Guthrie, & Levinson, 2016; Farag & Lyons, 2012; Givoni & Rietveld,
2007; Santos & Rojey, 2004; Schwieterman, Fischer, Field, Pizzano, & Urbanczyk,
2009; Stanton et al., 2013; Stopher, 2004; Van Exel & Rietveld, 2009). Therefore, a
random-parameter binomial logit model is considered. This binomial logit model is a
special case of a mixed logit model for dichotomous response variables. It can be
generalized to account for non-constant error variances in more advanced econometric
settings, such as heteroskedastic or random-parameter binomial logit model (Albright,
2016).
Different respondents might have a different understanding of each of the
perceptions of service factors. For instance, better access to train station could strongly
influence one respondent’s decision to take a train more often, but might not influence
other respondents. Such heterogeneity can be influenced by respondents’ socio-
economic profiles. At the start of the model estimation, all perceptions are considered
as prospective random-parameters. Specifically, the perception of the availability of
an on-board entertainment system is assumed to follow a standard normal distribution
because of the uncertainty of which sign to expect. The rest of the prospective random
parameters are assumed to follow a gamma distribution because the sign of the
estimates is expected to be positive.
Chapter 4: Methodology 55
The marginal utility for a random parameter with gamma as the underlying
distribution is defined as:
𝛽𝑖𝑚 = (𝛽𝑚 + ∑ (𝛽𝑛 ∗ 𝑦𝑛))𝑁𝑛=1 ∗ 𝑣𝑖 ,
𝑣𝑖~ 𝑔𝑎𝑚𝑚𝑎 (1,4) 𝑎𝑛𝑑 𝑢𝑛𝑖𝑞𝑢𝑒 𝑓𝑜𝑟 𝑒𝑎𝑐ℎ 𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑒𝑛𝑡 [4-14]
𝛽𝑚 and 𝜎𝑚 are the coefficient and the standard deviation of the corresponding
random parameter, respectively. 𝛽1 𝑡𝑜 𝛽𝑁 are the coefficients of influential socio-
economic factors, and 𝑦1 𝑡𝑜 𝑦𝑁 signify the corresponding socio-economic factors
(Greene, 2012; Hensher et al., 2005).
A simulation-based maximum likelihood method is employed to separately
estimate the mixed logit and random-parameters binomial logit model. Five hundred
draws of Halton sequence each is used to obtain the simulation-based estimation (Bhat,
2003; Halton, 1960; Train, 2009). Various mixed logit and random-parameters logit
models are separately developed, and their performances are assessed using the
significance of the independent variables, the Akaike’s Information Criteria (AIC),
and logical soundness (Akaike, 1998).
4.2.3 Consistency assessment of the perceptions and mode choice experiment
responses
By reference to Table 3.8, the qualitative and quantitative assessments are then
performed to the best-fitted binomial logit model and mixed logit model results. Both
assessments are important approaches to determine whether respondents’ perceptions
are consistently aligned to their responses on mode choice experiments, and also to
estimate the extent of their consistencies.
Qualitative assessment is performed by identifying and mapping the significant
independent variables in the random-parameter binomial logit model, and their
comparable significant attributes in each of three utility functions estimated within the
mixed logit model. If both a perception response and its corresponding attribute are
found to be significant, the former is considered to be consistently aligned to the latter
when respondents decide their mode preferences in the mode choice experiment. If
this is not the case, their consistency cannot be properly assessed.
Subsequently, the quantitative assessment – via probability functions – is
performed to further understand the magnitude of respondents’ preference changes in
the shift of one particular explanatory factor, while having all other factors constant.
56 Chapter 4: Methodology
The probability is one way of estimating the rate of change in one variable relative to
the rate of change in a second variable, and is expressed as a unit change. More
specifically, the change in probability value as the result of a unit change in one
variable, when other conditions remain the same, or that other things are equal
(Boumans & Morgan, 2001; Hensher et al., 2005).
4.3 POLICY INTERVENTIONS STUDY TO ENCOURAGE
BEHAVIOURAL SHIFT FROM CAR TO PUBLIC TRANSPORT
A restrictive property of a multinomial logit (MNL) model is its independence
from irrelevant alternatives (IIA). IIA assumes independence of disturbance
terms (𝜀𝑖𝑛), among alternative outcomes. In order to overcome the IIA limitation,
McFadden (1981) developed a class of models known as ‘generalized extreme value
(GEV) models’, which addressed the IIA problem (Washington et al., 2011). The
nested logit model is one of the commonly used models in the GEV category.
The nested logit model aims to group into the same nest alternative outcomes
that are suspected of sharing unobserved effects (Washington et al., 2011). Providing
that all alternatives in the nest share unobserved effects, these effects cancel out in
each nest. This cancelling out does not occur unless all alternatives in the same nest
share the unobserved effects. This is an IIA violation in the nest (Washington et al.,
2011). The nested structure is purely an empirical method for eliminating IIA
violations, and does not convey information about a hierarchical decision-making
process. It often contains more than two levels, depending on the number of
alternatives and the hypothesized disturbance correlations (Washington et al., 2011).
Within this study, the hypothesized nested structure accounts for the differences
in the availability, rules of usage, fare, and route status of the transport mode. The
mode choice is divided into two: public transport and private transport. ‘Public
transport’ is defined as the mode of transport that is available for public use; is based
on certain rules of usage; charges set fares; and runs on fixed routes with a certain
schedule (Stevenson, 2010). ‘Private transport’, on the other hand, consists of transport
modes that are not available for the general public; it does not have specific rules of
usage, set fares, or fixed routes and schedules. Bus and train are classified as public
transport, and cars are classified as private transport. Private transport is a degenerate
branch, with only one alternative within the nested logit structure (Hensher et al.,
2005). This mode choice framework is represented as a nested structure in Figure 4.1,
Chapter 4: Methodology 57
and the feasibility of this hypothesized structure is statistically tested in Section 7.2.
Accordingly, the estimated nested logit model contains a number of utility functions,
each with its own set of key travel attributes and socio-economic factors.
Figure 4.1 : The nested structure of the mode choice experiment
The proposed nested structure Figure 4.1 underlie the nested logit model utilised
in the third sub-study. If only one level would be utilised, then the modelling
methodology would have transformed to mixed logit model instead of nested logit
model. The mixed logit model would not be the preferred statistical model for the third
sub-study because it would not allow the identification of the shift behaviours between
public transport riders and car drivers.
McFadden (1981) shows that the GEV disturbances assumption leads to the
following model structure for observation 𝑛 choosing outcome 𝑖 :
𝑃𝑛(𝑖) = 𝐸𝑋𝑃 [ 𝛽𝑖𝑋𝑖𝑛+∅𝑖𝐿𝑖𝑛]
∑ 𝐸𝑋𝑃 [ 𝛽𝐼𝑋𝐼𝑛+∅𝐼𝐿𝑆𝐼𝑛]∀𝐼 [4-15]
𝑃𝑛(𝑗|𝑖) = 𝐸𝑋𝑃 [ 𝛽𝑗|𝑖𝑋𝑛]
∑ 𝐸𝑋𝑃 [ 𝛽𝐽|𝑖𝑋𝐽𝑛]∀𝐼 [4-16]
𝐿𝑆𝑖𝑛 = 𝐿𝑁 [∑ 𝐸𝑋𝑃 ( 𝛽𝐽|𝑖𝑋𝐽𝑛)]∀𝐼 [4-17]
where 𝑃𝑛(𝑖) is the unconditional probability of observation 𝑛 having discrete outcome
𝑖, 𝑋 are vectors of measurable characteristics that determine the probability of discrete
outcomes, and 𝛽 are vectors of estimable parameters. 𝑃𝑛(𝑗|𝑖) is the probability of
observation 𝑛 having discrete outcome 𝑗, conditioned on the outcome being in outcome
category 𝑖. 𝐿𝑆𝑖𝑛 is the inclusive value (IV), also known as IV, and ∅𝑖 is an estimable
parameter. Meanwhile, the unconditional probability of observation 𝑛 of having
outcome 𝑗 is defined as (McFadden, 1981),
𝑃𝑛(𝑗) = 𝑃𝑛(𝑖)𝑥𝑃𝑛(𝑗|𝑖) [4-18]
58 Chapter 4: Methodology
The estimation of a nested model was typically done in a sequential fashion
(McFadden, 1981). This method first estimated the conditional model (Equation 2),
using only the observations in the sample that were observed to have discrete
outcomes. Once these estimation results were obtained, the IV was calculated for all
observations, both those selecting 𝑗, and those not. Subsequently, these computed IVs
were used as independent variables in the functions, as shown in Equation 1. It was
not necessary for all unconditional outcomes to have an IV in their respective
functions. The downside of the sequential estimation method was that the variance-
covariance matrices were too small. Thus, t-statistics were inflated by about 10%-15%.
In order to address the disadvantage, the entire model was estimated at once, using
full-information maximum likelihood (FIML) and Nlogit, a modern software package
that provides for a simultaneous estimation of all nests (Greene, 2000). Details of this
simultaneous approach are available at Hensher, Rose, & Greene (2008)
The IV has two roles: to provide a basis for identifying behavioural relationships
among choices at each level of the nest, and to indicate the validity of the nested
structure (Hansen, 1987; Washington et al., 2011). The interpretation of the estimated
parameter associated with the IV (∅𝑖) has the following important elements
(Washington et al., 2011): ∅𝑖 must be between 0 to 1 in magnitude to be consistent
with the nested logit derivation (McFadden, 1981); if ∅𝑖 is equal to 1, the assumed
shared unobserved effects in the nest are not significant, and the nested model reduces
to a simple MNL; if ∅𝑖 is less than zero, then the factors that increase the likelihood of
an outcome being chosen in the lower nest will decrease the likelihood of the nest
being chosen; if ∅𝑖 is equal to zero, then changes in nest outcome probability will not
affect the probability of nest selection, and the correct model is separated (McFadden,
1981; Washington et al., 2011). The t-test is employed to test whether the parameter
estimate is significantly different from 1. The t-test is chosen over other tests because
of the non-normality in the logit model (Washington et al., 2011). It is defined as,
𝑡∗ =𝛽−1
𝑆.𝐸.(𝛽) [4-19]
Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities 59
Chapter 5: Urban Travellers’ Satisfaction
with Train Fares in Five
Australian Cities
This chapter discusses the findings of the first sub-study (Urban travellers’
satisfaction with train fares in five Australian cities) (Paramita et al., 2018). This study
uses two main data sources: objective information on the characteristics of each city’s
existing train system, and a nationwide survey. Hence, this chapter begins with the
summary of the former dataset in order to provide the study context and information
on the train fare structure in each of the targeted cities, which is the base knowledge
for analysing the nationwide survey data (Section 5.1). Subsequently, this chapter
reports and analyses the modelling results of the first sub-study (Section 5.2 to 5.5). It
also includes the heterogeneity discussions (Section 5.6) and detailed intercity
comparison results of train fare structures in the five Australian cities (Section 5.7).
Section 5.8 summarises the main conclusions of the study.
5.1 TRAIN FARE STRUCTURES IN THE FIVE AUSTRALIAN CITIES
A city’s geographical spread, land-use planning, and overall public transport
network also influence travellers’ perceptions of the overall transportation system, and
especially their perceived satisfaction with public transport services (Glaeser, Kahn,
& Rappaport, 2008). In the data analysis of this study, objective information on the
characteristics of the existing train fare structure in each of five Australian capital cities
was used to explain the diverse perceived satisfaction with train fare. Intuitively,
information on the current train fare structure in each of the targeted cities is the base
knowledge for analysing the nationwide survey data. Particularly, such information
can assist readers in better understanding underlying reasons behind the differences in
user satisfaction across the five cities revealed by our modelling analysis. Thus, the
actual train fare structure in each city based on the practice in 2016 is explained in this
sub section. Although train fare airport surcharge exists in some of the capital cities,
it is not included in the study due to its irrelevancy. The train riders, who have to access
60 Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities
the airports within their regular commutes, own the airport staff pass and hence, they
do not have to pay any airport surcharge.
5.1.1 Sydney
“Opal” is a smartcard system that has been implemented by Transport for New
South Wales for commuters using various public transport modes across Sydney, the
Blue Mountains, Central Coast, Hunter or the Illawarra region (Transport for NSW,
2016). The correct amount of fare is automatically deducted, and is based on the
distance travelled. Four different fares are available: Adult, Child/Youth, Concession
and Senior/Pensioner. With respect to train services, it costs $3.38 for an adult, $ 1.69
for a child, $ 1.69 for concession-eligible travellers, and $ 1.69 for seniors/pensioners
to travel between 0 to 10 km; and up to $8.30 for an adult, $ 4.15 for a child, $ 4.15
for concession-eligible travellers, and $ 2.50 for seniors/pensioners to travel above 65
km one-way during peak hours.
The off-peak fare is 70% of the peak fare. The off-peak periods are outside the
peak periods of 7:00 to 9:00 AM and 4:00 to 6:30 PM for Sydney train services, and
outside the peak periods of 6:00 to 8:00 AM and 4:00 to 6:30 PM for intercity train
services. There is a $ 2.50 cap for all Opal trips taken on Sundays, and a weekly travel
reward for travellers who make eight paid trips in a week (Transport for NSW, 2016).
Travellers are entitled to save around 20% by using their Opal card rather than
purchasing single trip tickets.
5.1.2 Melbourne
Public Transport Victoria (Victoria State Government, 2016) issues “Myki”, a
smart card, as its method of payment across public transport in Melbourne. This card
automatically deducts the lowest fare possible, based on the travellers’ departing and
alighting locations. The card is divided into two categories, Myki money and Myki
pass. Full and concession fare are available for each category. There is a cap of $6 for
a day fare during the weekend and public holiday when using Myki across Zones 1
and 2 (Victoria State Government, 2016). Travellers who have touched off before 7:15
AM are eligible for a free early bird train travel. Two fare bands, Zones 1+2 and Zone
2 are available for Myki money for both 2-hour usage and daily fare. Zone 1 of
Melbourne’s train, tram, and bus networks is the CBD and inner city suburbs, an
approximately 12 km radius. Zone 2 covers the middle and outer suburbs.
Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities 61
5.1.3 Brisbane
A smartcard system, “Go Card”, has been implemented by Translink in the South
East Queensland Region, including Brisbane. Go Card allows travellers to travel
seamlessly on all public transport; i.e., on Translink’s bus, ferry, train, and tram
services (Queensland Government, 2016). Travellers are entitled to savings when they
use a Go Card rather than paper tickets. The Go Card automatically calculates and
deducts the overall fare at either an adult or concession rate, based on the number of
zones travelled through the trip (Queensland Government, 2016).
The off-peak Go Card fares are 80% of the peak fares. The off-peak periods are
between 8:30 AM and 3:30 PM and 7:00 PM and 6:00 AM on weekdays, and all
weekend. Paper tickets cost 130% of the Go Card fare, while the concession fares are
50% of adult fares (Queensland Government, 2016).
5.1.4 Perth
Perth public transport has two types of ticket: A SmartRider card and cash
tickets. The SmartRider card is a refillable smart card with seven categories: standard,
concession, senior, pensioner, veteran, student, and tertiary (Government of Western
Australia, 2016).
With the exception of two-section fares (valid for a single one-way trip), cash
tickets have an expiry time; however, within the allowable time travellers are free to
ride on any number of buses, trains, and ferry services to complete their trip
(Government of Western Australia, 2016). Travellers who use SmartRider should
remember to touch on and off upon boarding and alighting from public transport
services. The expiry times are two hours for a trip of one to four zones, and three hours
for a trip of five or more zones. Overall, Transperth (public transport system serving
the city and suburban areas of Perth) covers services across nine different zones. Each
zone has an approximately 8 km radius. Within the city centre, there is a free zone
area, where all public transport is completely free.
62 Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities
5.1.5 Adelaide
There are two public transport tickets available in Adelaide, “Metrotickets” and
“Metrocard” (Government of South Australia, 2016). Metrotickets are paper tickets
for both single and day trips across Adelaide Metro trains, trams, and buses, and are
the best option for infrequent public transport users. Metrocard, on the other hand, is
an electronic smart card designed for multiple public transport trips on Adelaide Metro
trains, trams, and buses.
There is no public transport zone division in Adelaide; therefore, fares are not
calculated according to the distance travelled. Overall, there are four different fares
offered to Adelaide public transport users: regular fares, concession and tertiary
student fares, primary and secondary student fares, and senior Metrocard (Government
of South Australia, 2016). The interpeak, regular Metrocard fares are 55% - 75% of
the regular peak fares. The peak periods are before 9:01 AM and after 3:00 PM on
weekdays, and all day Saturday. Inter peak periods are Monday to Friday 9:01 AM to
3:00 PM, and all day Sundays and public holidays. Concession and tertiary student as
well as senior Metrocard fares are 50% of regular Metrocard fares, while primary and
secondary student Metrocard fares are 30% of regular Metroard fares.
5.2 MODELLING RESULTS
Table 5.1 shows the summary of the best fixed-parameter and random-parameter
ordered logit models of perceived satisfaction with train fare. A likelihood ratio test
was used to compare the performance of these models; the result shows that in terms
of explaining travellers’ perceived satisfaction with their train fare, the random-
parameter ordered logit model performs statistically better than the fixed-parameter
model, at a 95% significance level.
Table 5.1 Summary of the best fixed-parameter and random-parameter logit models
Explanatory
Variables
Fixed-parameter model Random-parameter model
Coefficient z-statistics p-value Coefficient z-statistics p-value
Constant 3.684 25.18 <0.05 4.349 22.14 <0.05
Female -0.255 -3.69 <0.05 -0.293 -4.15 <0.05
Sydney -0.328 -2.29 <0.05 -0.357 -2.14 <0.05
Melbourne -0.371 -2.75 <0.05 -0.419 -2.63 <0.05
Brisbane -0.478 -3.12 <0.05 -0.552 -3.16 <0.05
Perth 0.406 2.55 <0.05 0.398 2.22 <0.05
Employment
status: Outside
work force
0.205
1.91 0.057 0.118 1.07 0.284
Transport mode
from home to
<0.05 0.404 3.83 <0.05
Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities 63
Explanatory
Variables
Fixed-parameter model Random-parameter model
Coefficient z-statistics p-value Coefficient z-statistics p-value
the train station:
Bus
0.233
2.32
Student
concession fare
-0.488
-2.56 <0.05 -0.435 -2.16 <0.05
Senior
concession fare
2.349
11.4 <0.05 2.448 11.70 <0.05
Sydney *
Employment
status: Outside
work force
0.460
2.35
<0.05 0.608 3.11 <0.05
Perth *
Transport mode
from home to
the train station:
Bus
-0.438
-2.06
<0.05 -0.491 -2.23 <0.05
Sydney *
Student
concession fare
-0.856
-2.35
<0.05 -1.024 -2.74 <0.05
Melbourne *
Senior
concession fare
-1.288
-4.36
<0.05 -1.350 -4.31 <0.05
Brisbane *
Senior
concession fare
-1.155
-3.48
<0.05 -1.077 -2.95 <0.05
One-way cost
($)a 0.082 7.37 <0.05 -2.482 -16.49 <0.05
Waiting time
(Minutes)a 0.038 5.29 <0.05 -2.988 -16.51 <0.05
µ1b 1.514 35.80 <0.05 1.848 20.51 <0.05
µ2b 2.917 72.84 <0.05 3.404 33.90 <0.05
µ3b 4.781 84.09 <0.05 5.354 47.48 <0.05
Log-likelihood
at convergence
-4024.145
-3976.970
AIC 2.763 2.739 a random parameters b µ1 , µ2 and µ3 are the thresholds of perceived satisfaction with train fare estimated by the model.
The random-parameter ordered logit model includes two random parameters in
the final model: one-way cost and waiting time. For these two parameters, the standard
deviations of the lognormal distribution are significantly different from zero, as shown
in Table 5.2
Table 5.2 Distribution of the random parameters
Random parameter Underlying distribution
and constrained
Standard
deviation
z-statistics p-value
One-way cost ($) Lognormal - negative 1.268 14.56 <0.05
Waiting time
(Minutes)
Lognormal - negative 0.549 7.65 <0.05
As shown in Table 5.3, the random-parameter ordered logit model identifies a
number of socio-economic factors and trip characteristics that contribute to the
64 Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities
heterogeneities in the influence of one-way cost and waiting time on perceived
satisfaction with train fare.
Table 5.3 Heterogeneities in the random parameters
Random
Parameter
Attribute Coefficient
Estimate
z-statistics p-value
One-way cost ($) Household composition:
Adults and children -0.464 -4.13 <0.05
Transport mode from home
to the train station: Dropped-
off -0.727 -3.69 <0.05
Transport mode from home
to the train station: Driving -0.485 -3.63 <0.05
Waiting time
(Minutes)
Employment status: Self-
employed
0.619 3.59
<0.05
Transport mode from home
to the train station: Driving
-0.345
-2.17 <0.05
Trip purpose:
Shopping/Personal/ Business -0.364 -2.35 <0.05
Paid fare status: Full fare 0.615 4.66 <0.05
Train services’ influence on
the current home location
decision: Significant -0.604 -4.27 <0.05
Train services’ influence on
the current home location
decision: Moderate -0.747 -3.53 <0.05
5.3 KEY SOCIO-ECONOMIC FACTORS
The best fitted random-parameter ordered logit model is estimated by the socio-
economic factors discussed below.
5.3.1 Gender
Female respondents are significantly (p-value < 0.05) less satisfied with their
train fare compared with their male counterparts. When other factors are controlled, a
female respondent’s estimated odds of responding “extremely satisfied”, rather than
“satisfied”, “extremely dissatisfied”, “dissatisfied”, or “neutral”, decrease by 25% in
comparison to a male respondent.
This finding is in line with gender differences in general. Women tend to be more
sensitive to monetary costs, more likely to shop for higher quality products or services
than men, and more effective in distributing their income (Dwyer, 1983; Furnham,
2016) . Dwyer (1983) also found that women allocate most of their income to buying
goods rather than procuring services, and allocate a smaller amount to their travel
budgets. Consequently, women are more likely to have a higher expectation of train
services for the fare paid compared with their male counterparts (Paramita et al., 2018).
Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities 65
In addition, Ellaway et al. (2003) found that male travellers prefer to use private
vehicles, and are most likely to have alternatives to public transport (Andersson &
Nässén, 2016; Paulley et al., 2006). This implies that they tend to ride trains less often
and have less familiarity with train services when compared with female travellers.
Thus, male travellers tend to be more accepting of the asking fare, and do not have the
same high expectation of train services as female travellers. (Paramita et al., 2018)
5.3.2 City of origin
Table 5.1 shows that respondents’ perceived satisfaction with train fare differs
significantly across cities. When other factors are controlled, Sydney, Melbourne, and
Brisbane respondents feel less satisfied with train fare compared with respondents in
Adelaide. Specifically, by holding other factors constant, when compared with a
respondent from Adelaide, the estimated odds of a Sydney respondent responding that
they were “extremely satisfied”—rather than “satisfied”, “extremely dissatisfied”,
“dissatisfied”, or “neutral”—decrease by 30% (i.e., (1 − exp(−0.357)) ∗ 100%)
(Agresti & Kateri, 2011). Similarly, the estimated odds of a Melbourne respondent
responding that they were “extremely satisfied”—rather than “satisfied”, “extremely
dissatisfied”, “dissatisfied”, or “neutral”—decrease by 34%. Also, for a respondent
from Brisbane, these estimated odds decrease by 42%.
However, when other factors are controlled, compared with respondents from
Adelaide, Perth respondents feel more satisfied with their train fare. Specifically, when
controlling for other factors, when compared with a respondent from Adelaide, a Perth
respondent’s estimated odds of responding that they are “extremely satisfied”, rather
than “satisfied”, “extremely dissatisfied”, “dissatisfied”, or “neutral”, increase by 49%
(i.e., (exp(0.398) − 1) ∗ 100%) (Agresti & Kateri, 2011).
These findings are not surprising, given the different characteristics of the public
transport network and the diverse train fare structures across the five cities as
elaborated in the Context: train fare structures in the five Australian cities section. This
issue is further discussed in the Intercity comparison section (Paramita et al., 2018).
5.4 KEY TRIP CHARACTERISTICS
Our modelling results also clearly show that characteristics of their most recent
train trip significantly influence respondents’ satisfaction with the fare for that trip, as
elaborated below.
66 Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities
5.4.1 Transport mode from home to the train station
Respondents who take the bus to the train station are significantly (p-value <
0.05) more satisfied with their paid train fare than other respondents. Specifically,
when controlling for other factors, the estimated odds of an “extremely satisfied”
response for someone who takes the bus rather than other transport modes to the
station—rather than a “satisfied”, “extremely dissatisfied”, “dissatisfied”, or “neutral”
response—increase by 50%.
Again, this finding is not surprising, as the importance of train station access is
widely acknowledged in the literature (Brons et al., 2009; Caulfield & O'Mahony,
2007; Daniels & Mulley, 2013; Rissel, Curac, Greenaway, & Bauman, 2012). First,
the transport mode from home to the station influences how much effort travellers need
to exert and, also, the travel cost. This latter factor is especially vital in this study
because of the availability of fare transfer between public transport trips within a
certain time window in Sydney, Melbourne, Brisbane, Adelaide, and Perth. This
transferability often leads to a reduced train fare for respondents who take the bus to
the station (Government of South Australia, 2016; Government of Western Australia,
2016; Queensland Government, 2016; Transport for NSW, 2016; Victoria State
Government, 2016). A respondent’s experience with their mode of station access is
able to influence their satisfaction with the train trip in general, and the paid train fare
in particular (Brons et al., 2009). The existence of advance transit systems in major
train stations in Sydney, Melbourne, Brisbane, Adelaide, and Perth enables an easy
transfer between bus and train. Therefore, as revealed in our analysis, bus access to the
train station appears to positively contribute to a respondent’s satisfaction with their
train fare (Paramita et al., 2018).
5.4.2 Concession fare
Eligibility for a concession fare significantly (p-value < 0.05) affects a
respondent’s satisfaction with their train fare. Respondents who are eligible for a
student concession fare feel less satisfied with train fare than respondents who are
ineligible. When other factors are controlled, the estimated odds of an “extremely
satisfied” response for a respondent who is eligible for student concession fare —rather
than a “satisfied”, “extremely dissatisfied”, “dissatisfied”, or “neutral” response—
decrease by 35% in comparison to a respondent who is ineligible. However, compared
Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities 67
with respondents that are not eligible for any concession fare, respondents who are
eligible for a senior concession fare feel more satisfied with their fare. Specifically,
when controlling for other factors, compared with a respondent who is not eligible for
any concessional fare, the estimated odds of an “extremely satisfied” response from a
respondent who is eligible for a senior concession fare —rather than a “satisfied”,
“extremely dissatisfied”, “dissatisfied”, or “neutral” response—increase by 10.6
times.
Student and senior concession fare are designed to provide subsidies for students
and senior citizens respectively. The availability of subsidies influences seniors’ and
student travellers’ perceived satisfaction with train fare. Respondents who are eligible
for a senior concession fare pay less than respondents who are ineligible for any
concession fare. Having received a subsidy through a reduced train fare, senior
travellers feel more satisfied with their paid train fare. Subsidies are also directed to
the disadvantaged transport group (Starrs & Perrins, 1989) who have decreased
mobility, earn a low income, or both.
Interestingly, this study found that respondents who are eligible for student
concession fares feel less satisfied with their train fare, despite having received a
subsidy and only paying part of the adult non- concessional fare. This might simply
imply that the student concession fare in the five targeted Australian cities is still
perceived as too expensive by most students, who have no regular income and a limited
travel budget (Paramita et al., 2018).
5.4.3 One-way cost
Not surprisingly, the one-way cost paid by each respondent significantly (p-
value < 0.05) influenced his/her perceived satisfaction with train fare. Specifically, this
study identified heterogeneity in the influence of one-way cost on the perceived
satisfaction with train fare over the sampled population. The parameter of one-way
cost varies significantly across respondents, as indicated by the significant standard
deviation (p-value < 0.05) of its parameter. This finding is in line with earlier studies
(Hine & Scott, 2000; Wardman, 2004; Weisbrod & Reno, 2009). The marginal utility
of one-way cost is linked with both socio-economic factors (household composition)
and trip characteristics (i.e., transport mode from home to the train station) (Paramita
et al., 2018). Further discussion on the heterogeneity of one-way cost is provided in
the Heterogeneity sub-section.
68 Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities
5.4.4 Waiting time
This study found that the impact of waiting time varies across respondents, as
indicated by the significant (p-value < 0.05) standard deviation of its parameter. Table
5.3 shows how the perception of waiting time differs according to employment status,
transport mode from home to the train station, trip purpose, paid fare status, and the
influence of train services on the current home location. This finding is not surprising
because the waiting time is perceived as an unproductive period, and travellers (who
often experience long waiting times) feel less satisfied with public transport service
and, consequently, more stressed than their counterparts (Beirão & Cabral, 2007;
Cantwell et al., 2009; Dell’Olio et al., 2010; Dziekan & Kottenhoff, 2007). Long
waiting times are caused by services not running to schedule. This lack of reliability
of public transport results in travellers feeling a diminished sense of control (Cantwell
et al., 2009). Over time, long waiting times also lead to intense and prolonged feelings
of stress. On the other hand, an increase on-board accessibility leads to both an increase
in the train ridership increment, and a perceived satisfaction with the paid train fare
(Brons et al., 2009; Delmelle, Haslauer, & Prinz, 2013).
The heterogeneity of waiting time is also not surprising because the perception
of waiting time can be influenced by numerous factors (Paramita et al., 2018). For
example, the availability of at-stop, real-time information and a comfortable waiting
area can improve the perceived quality of public transport services by reducing the
perceived waiting time (Dziekan & Kottenhoff, 2007; Litman, 2008). The availability
of real-time information also reduces the unit costs of waiting time because travellers
experience a more organized trip and reduced stress (Litman, 2008). The marginal
utility of one-way cost and further discussion on heterogeneity of waiting time is
provided in the Heterogeneity sub-section.
5.5 INTERACTION VARIABLES
The significant (p-value < 0.05) key interaction variables identified in the best-
fitted ordered logit model and how they affect the respondents’ level of satisfaction
with train fare are discussed below.
Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities 69
5.5.1 Sydney and Employment status: Outside work force
There is a positive relationship between interaction of Sydney and outside work
force against the level of satisfaction with train fare. By controlling for other factors,
the estimated odds of an outside work force Sydney respondent responding that they
were “extremely satisfied”—rather than “satisfied”, “extremely dissatisfied”,
“dissatisfied”, or “neutral”—increase by 1.07 times (i.e., (exp(0.608+0.118)-1)
*100%), compared with a respondent who is from Sydney but is not outside work
force. In addition, regardless of the city, the outside work force employment status is
positively related to a respondent’s satisfaction level with the paid train fare.
The aforementioned finding might be related to the impact of employment status
on trip frequency and travel budget allocation (Taylor & Morris, 2015). Travellers’
perceptions of their trip is an important determinant of their satisfaction level (Beirão
& Cabral, 2007), and the frequent and regular train rides of employed commuters
enable them to establish a sense of familiarity with, and expectations of services. Not
surprisingly, regular riders tend to have high expectations of train services for the fare
they paid. However, outside workforce respondents’ train travel is generally less
frequent and more irregular (Daniels & Mulley, 2013). Thus, they are more likely to
be less familiar with, and have lower expectations of train services. In addition, they
are more likely to be satisfied with the fare paid for their most recent trip (Paramita et
al., 2018).
5.5.2 Perth and Transport mode from home to the train station: Bus
Interaction of Perth and taking bus to train station is negatively related with the
level of satisfaction with train fare. This is quite interesting because such interaction
makes taking bus from home to train station impact differently on the level of
satisfaction with the paid train fare for a respondent from Perth, compared with that
for a respondent from the other cities. More specifically, for a Perth respondent who
takes bus from home to the train station, by controlling for other factors, the estimated
odds of being “extremely satisfied”—rather than “satisfied”, “extremely dissatisfied”,
“dissatisfied”, or “neutral”—decrease approximately by 8%, compared with a
respondent who is from the same city but uses other modes for travelling from home
to the train station. In contrast, for a respondent from any other city who takes bus
from home to the train station, by controlling for other factors, the estimated odds of
being “extremely satisfied”—rather than “satisfied”, “extremely dissatisfied”,
70 Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities
“dissatisfied”, or “neutral”—increase by about 50%, compared with a respondent who
is from the same city but uses other modes for travelling from home to the train station.
The finding above highlights the importance of the supporting role of bus
service. The poor quality of the bus service that is part of a train trip can significantly
and negatively impact a train user’s satisfaction with the train fare paid. It appears that
Perth respondents are less pleased with the reliability of bus services to connect their
homes to the nearest train stations than other cities’ respondents. It is possible that the
bus stops are not evenly located across the surrounding neighbourhood or the
schedules’ are not properly aligned with train services to cater the train riders’
demands (Paramita et al., 2018).
5.5.3 Sydney and Student concession fare
A negative relationship is found between the interaction of Sydney and Student
concession fare and the level of satisfaction with train fare. This demonstrates that
students in Sydney have less appreciation towards student concession fare offered by
Transport for New South Wales (NSW). The Transport for NSW official website
mentions that student concession fare is only provided for primary and secondary
students in NSW, full time Australian tertiary students and limited full time
international students who are fully funded by specified Australian Government
scholarships (Transport for NSW, 2016). The best-fitted model perhaps reflects the
scenario that many Sydney respondents who are 16-30 years old and are student do
not actually meet the eligibility requirements for receiving the student concession fare
(Paramita et al., 2018).
5.5.4 City of origin and Senior concession fare
Our modelling analysis also reveals some complex interactions between city of
origin and senior concession fare in terms of respondents’ satisfaction level with the
paid train fare. Although receiving senior concession fare generally increases a
respondent’s level of satisfaction with the paid train fare, this trend can be significantly
influenced by respondents’ city of origin. For example, for a Melbourne respondent
who receives the senior concession fare, by controlling for other factors, the estimated
odds of being “extremely satisfied”—rather than “satisfied”, “extremely dissatisfied”,
“dissatisfied”, or “neutral”— increase approximately by 2 times, compared with a
Melbourne respondent who is not eligible for receiving the senior concession fare. A
Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities 71
similar impact of receiving the senior concession fare is found for respondents from
Brisbane as the estimated odds for a Brisbane respondent of being “extremely
satisfied”—rather than “satisfied”, “extremely dissatisfied”, “dissatisfied”, or
“neutral”— increase approximately by 2.9 times , by controlling for other factors. The
positive impact of receiving the senior concession fare in the other cities are even
stronger.
These findings establish that Melbourne and Brisbane respondents who are 60
or more years old and are retired appear to have less appreciation towards the senior
concession fare offered by Victoria State Government (Victoria State Government,
2016) and by Queensland Government (Queensland Government, 2016), respectively.
This could be due to different eligibility criteria, such as minimum age limit, minimum
paid working hours, and residency status set by transport authorities in each state
(Government of South Australia, 2016; Government of Western Australia, 2016;
Queensland Government, 2016; Transport for NSW, 2016; Victoria State
Government, 2016). Our model may have reflected the scenario that many Melbourne
and Brisbane respondents, who are at least 60 years old and are retired, are not actually
eligible for the senior concession fare in their respective city of origin (Paramita et al.,
2018).
5.6 HETEROGENEITY
As discussed in the previous sub-sections, notable heterogeneity is detected
across respondents in their perceived satisfaction with train fare. Specifically,
significant heterogeneity is observed for one-way cost and waiting time. The analysis
shows that this heterogeneity can be explained, to a certain extent, by socio-economic
factors and trip characteristics, as elaborated below.
5.6.1 Household composition
Respondents who belong to the household of adults and children have a
significant influence on the heterogeneity in respondents’ sensitivity to one-way costs.
The marginal utility of one-way cost is: − 𝑒𝑥𝑝 [−2.482 −
0.464 𝑥 (ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑜𝑓 𝑎𝑑𝑢𝑙𝑡𝑠 𝑎𝑛𝑑 𝑐ℎ𝑖𝑙𝑑𝑟𝑒𝑛) −
0.727 𝑥 (𝑏𝑒𝑖𝑛𝑔 𝑑𝑟𝑜𝑝𝑝𝑒𝑑 𝑜𝑓𝑓 𝑎𝑡 𝑡ℎ𝑒 𝑡𝑟𝑎𝑖𝑛 𝑠𝑡𝑎𝑡𝑖𝑜𝑛) − 0.485 𝑥 (𝑑𝑟𝑖𝑣𝑒 𝑡𝑜 𝑡ℎ𝑒 𝑡𝑟𝑎𝑖𝑛 𝑠𝑡𝑎𝑡𝑖𝑜𝑛) +
1.268 𝑥 𝑛], where 𝑛 is a random number generated from a standard normal distribution.
To gain insights into the influence of household composition on respondents’
72 Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities
sensitivity to one-way cost, this marginal utility has been simulated for 100 randomly
selected individuals by holding both the transport modes from home to the train station
factors constant, as shown in the top-left subfigure of Figure 5.1. Specifically, 50% of
the randomly selected individuals belong to a household of adults and children. This
figure clearly shows strong heterogeneity across individuals, regardless of their
household composition.
By controlling randomness and other factors, individuals who belong to a
household of adults and children are less sensitive to one-way cost than individuals
from other type of households. Although individuals who belong to household of
adults and children are likely to pay a large amount of train fare when travelling as a
family, a heavily discounted fare is available for the children. On weekdays, the
children fare is a small portion of the adult fare and, under certain circumstances, is
free during weekends (Government of South Australia, 2016; Government of Western
Australia, 2016; Queensland Government, 2016; Transport for NSW, 2016; Victoria
State Government, 2016). As a result, this group of individuals is less sensitive to the
variation in one-way cost than their counterparts (Paramita et al., 2018).
Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities 73
Figure 5.1 : The simulated marginal utility of one-way cost for 100 randomly selected
individuals
5.6.2 Transport mode from home to the train station
Similarly, two other simulation exercises of marginal utility of one-way cost are
performed for the mode to the train station variables, as shown in the top right and the
bottom subfigures of Figure 5.1. Each simulation run aims to attain a deeper
understanding on the influence of a particular key variable on the marginal utility of
one-way cost, by controlling for the rest of variables. Each simulation specifies that
half of the randomly selected individuals belong to a key variable in focus and the
other half do not. In both simulations, clear heterogeneities across individuals were
74 Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities
detected, regardless of their mode of transport from home to the station. Specifically,
travellers who were dropped-off at, and who drove to the train station appear to be less
sensitive to one-way cost than other travellers. This is probably because they are able
to save money on fuel, parking, and toll costs by taking the train for the rest of their
trip. They are also able to reduce their access time by driving to or being dropped off
at the station instead of taking other modes of transport. (Paramita et al., 2018)
The marginal utility for waiting time is: − 𝑒𝑥𝑝 [−2.988 + 0.619 𝑥 (𝑠𝑒𝑙𝑓 −
𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑑) – 0.345 𝑥 (𝑑𝑟𝑖𝑣𝑒 𝑡𝑜 𝑡ℎ𝑒 𝑡𝑟𝑎𝑖𝑛 𝑠𝑡𝑎𝑡𝑖𝑜𝑛) − 0.364 𝑥 (𝑠ℎ𝑜𝑝𝑝𝑖𝑛𝑔/
𝑝𝑒𝑟𝑠𝑜𝑛𝑎𝑙 𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠 𝑡𝑟𝑖𝑝) +
0.615 𝑥 (𝑓𝑢𝑙𝑙 𝑓𝑎𝑟𝑒) – 0.604 𝑥 (𝑠𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡 𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑐𝑒 𝑜𝑓 𝑡𝑟𝑎𝑖𝑛 𝑠𝑒𝑟𝑣𝑖𝑐𝑒𝑠 𝑜𝑛 𝑡ℎ𝑒 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 ℎ𝑜𝑚𝑒
𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 𝑑𝑒𝑐𝑖𝑠𝑖𝑜𝑛) – 0.747 𝑥 (𝑚𝑜𝑑𝑒𝑟𝑎𝑡𝑒 𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑐𝑒 𝑜𝑓 𝑡𝑟𝑎𝑖𝑛 𝑠𝑒𝑟𝑣𝑖𝑐𝑒𝑠 𝑜𝑛 𝑡ℎ𝑒 𝑐𝑢𝑟𝑟𝑒𝑛𝑡
ℎ𝑜𝑚𝑒 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 𝑑𝑒𝑐𝑖𝑠𝑖𝑜𝑛) + 0.549 𝑥 𝑛], where 𝑛 is a random number generated from
a standard normal distribution. Like the previous one, this marginal utility has been
simulated for 100 randomly selected individuals as shown in the top-left subfigure of
Figure 5.2. Specifically, half of the randomly selected individuals are driving to the
station. This study finds travellers who drove to the station are less sensitive to waiting
time compared to other travellers. This is consistent with our everyday experience
because driving to the station often gives a traveller more control over their departure
time and they are more likely to arrive at the station as scheduled to endure less waiting
time (Paramita et al., 2018).
Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities 75
Figure 5.2 : The simulated marginal utility of waiting time for 100 randomly selected
individuals
Similarly, another five simulations of the marginal utility of waiting time are
performed for the rest key variables (three of them are presented in Figure 5.2 for
illustration purpose), and notable heterogeneities of marginal utility values are also
found in the simulation results, as elaborated below.
5.6.3 Employment status
By controlling randomness and other factors, the marginal disutility values of
waiting time of self-employed individuals are much larger than respondents with other
employment status. This finding is not surprising because it is in self-employed
76 Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities
respondents’ best interest to be time-conscious so as not to disadvantage business;
hence, they tend to be very sensitive to waiting time. In addition, as shown in the top-
right subfigure of Figure 5.2, regardless of the employment status, significant
heterogeneities exist across individuals (Paramita et al., 2018).
5.6.4 Trip purpose
When randomness and other factors are controlled, the marginal disutility values
of waiting time of individuals whose trips are for shopping or personal business
purposes are smaller than those of individuals whose trips are for other purposes.
Generally, the group of individuals who are travelling for shopping or personal
business purposes have irregular travel patterns. Hence, they tend not to be sensitive
to fluctuations in waiting time (Paramita et al., 2018).
5.6.5 Paid fare status
The marginal utility function for waiting time also indicates that by controlling
randomness and other factors, individuals who pay full fare and travel during peak
hours are generally more sensitive to waiting time than individuals who pay a
discounted fare and travel during off-peak hours. This finding is also consistent with
our daily experience. As a trade-off for paying full fare and experiencing on-board
crowding, peak hour travellers are more likely to expect the advantage of higher
frequency train services compared with travelling off-peak. Consequently, they tend
to be more sensitive to waiting for their targeted train services. In addition, as shown
in the bottom-left subfigure of Figure 5.2, significant heterogeneities exist across
individuals, regardless of the amount of fare they pay (Paramita et al., 2018).
5.6.6 Influence of train services on the current home location decision
As shown in the bottom-right subfigure of Figure 5.2 the moderate and
significant influences of train services on the current home location decision also
contribute to the explanation of preference heterogeneity detected in the marginal
utility for waiting time. This marginal utility function reveals that when randomness
and other factors are controlled, the marginal disutility values of individuals who are
moderately influenced by train services on the current home location decision are
smaller than those of their counterparts. Similar trend has been found on the marginal
disutility values of individuals who are significantly influenced by train services on
Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities 77
the current home location decision are smaller in comparison to those of their
counterparts.
These findings are both interesting and somewhat surprising. Respondents who
are moderately and significantly influenced by train services in making their current
home location decision would be expected to be sensitive to waiting time (Noland,
Weiner, DiPetrillo, & Kay, 2017). Nevertheless, this study finds quite the opposite. It
seems that once this group of respondents have considered train services in making
their current home location decision, they tend to accept variations in waiting time
(Paramita et al., 2018).
5.7 INTERCITY COMPARISON
“Context: train fare structures in the five Australian cities” section has described
the actual fare structure in each city based on the practice in 2016, which reveals
notable differences in how train fares are structured across these five cities. More
specifically, train fares in Sydney, Melbourne, and Brisbane rise as the number of
zones or distance travelled increase. Our data show that a number of respondents from
Sydney, Melbourne, and Brisbane travelled about 15 km during their most recent
home-based train trip. In Sydney, Melbourne, and Brisbane, public transport travellers
respectively pay $4.20, $3.90, and $5.96 for a one-way, 15 km trip during peak hours
on any weekday (Queensland Government, 2016; Transport for NSW, 2016; Victoria
State Government, 2016). The regular public transport fare in Adelaide for a single
trip using MetroCard is $ 3.54, regardless of the distance travelled (Government of
South Australia, 2016). According to our data, the structure of Adelaide’s public
transport fares benefits most Adelaide respondents whose trip origins are within 15 km
of their destinations. For the same travelled distance, Adelaide respondents pay the
least one-way fare. Consequently, Adelaide respondents feel more satisfied with their
train fares than Sydney, Melbourne, and Brisbane respondents (Paramita et al., 2018).
Meanwhile, when comparing Perth and Adelaide, the fact that many Adelaide
respondents are required to pay a fixed fare regardless of the distance travelled can
negatively impact their satisfaction with their train fare. In Perth, on the other hand,
public transport fares are determined by the number of zones travelled. According to
the survey responses, Perth respondents’ origins for their most recent train trips were
often located around 16 km from their destinations. Since one public transport zone in
Perth is approximately 8 km in radius, the respondents commute daily for at least two
78 Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities
zones one-way, and pay at least $ 3.91 for a regular SmartRider fare during peak
periods (Government of Western Australia, 2016). In contrast, Adelaide respondents
who travel a short distance from home on a train can pay the same fare as those who
travel a long distance. Therefore, Adelaide respondents tend to be less contented with
their train fares than Perth respondents (Paramita et al., 2018).
5.8 CONCLUSIONS
Based on a nationwide survey, this study focuses on train riders’ satisfaction
with the fare they paid for their most recent trip. The influence of their socio-economic
profiles and their specific trip characteristics on these perceived satisfactions are
assessed and quantified using an ordered logit model.
This study identifies that train riders’ socio-economic profiles can significantly
influence their satisfaction with their train fare(Paramita et al., 2018). Specifically,
female respondents tend to be less satisfied with the fare than their male counterparts.
In addition, respondents’ perceived satisfaction with train fare significantly differs
across cities. When other factors are controlled, Sydney, Melbourne and Brisbane
respondents feel less satisfied with train fare than Adelaide respondents . To attain a
deeper knowledge, modelling results are discussed in the context of the different train
fare structures in the five cities. Specifically, for the same travelled distance, Adelaide
respondents pay the least for a one-way trip compared with Sydney, Melbourne, and
Brisbane respondents. On the contrary, Perth respondents are likely to be more
contented with their train fares than Adelaide respondents (Paramita et al., 2018).
Around one fifth of respondents in Sydney, Melbourne, Adelaide, and Perth did
not report their income level, while about 15% of respondents in Brisbane chose not
to report their income level (Paramita et al., 2018). Respondents’ reluctance of
reporting their income is a widely acknowledged challenge in the survey literature
(Moore, 1988; Tourangeau & Yan, 2007). Such reluctance’s impact on the data
analysis can be very complex. The literature suggests that people who are not willing
to disclose their income tend to feel more insecure, and are consequently more
sensitive to monetary costs (Alm, Bahl, & Murray, 1993; Hurst, Li, & Pugsley, 2014;
Johansson, 2005). It would be interesting to test and differentiate high and low non-
reported income groups’ effect. Unfortunately, by the very nature of being not
reported, it would be very difficult to categorize a non-reported income into a high or
low income group, which makes it almost impossible to detect and scrutinize any
Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities 79
phenomenon caused by different non-reported incomes (Paramita et al., 2018). In this
study, the ‘Not reported income’ variable has been found not to be statistically
significant in the best-fitted ordered logit model.
Meanwhile, characteristics of the train trip also significantly influence a rider’s
satisfaction with their train fare (Paramita et al., 2018). Respondents’ perceived
satisfaction is significantly impacted by station access and their eligibility for a
concession fare. Respondents who take the bus to the train station appeared to be more
contented with their paid train fares in comparison to other respondents. Nonetheless,
it is not the case for Perth respondents. A Perth respondent who takes bus from home
to the train station tend to be less satisfied compared with a respondent who is from
the same city but uses other modes to reach the train station. It is possible that the bus
services in Perth are not seamlessly connected with the train stations.
Respondents who are eligible for a student concession fare feel less satisfied with
train fares than ineligible respondents. Specifically, a negative relationship is found
between the interaction of Sydney and Student concession fare and the level of
satisfaction with train fare. Sydney respondents who are 16-30 years old and are
students tend to have a low appreciation towards the concession fare offered by
Transport for New South Wales (NSW) (Transport for NSW, 2016). It may be due to
strict eligibility rules imposed by Transport for New South Wales (NSW). Conversely,
respondents who are eligible for a senior concession fare feel more pleased with their
train fares compared with respondents that are not eligible for any concession fare
(Paramita et al., 2018).
Having taken into account findings in previous literature (Hensher et al., 2011;
Zheng et al., 2016), on-board crowding is considered as a random-parameter at the
start of model estimation. However, this variable turns out to be not statistically
significant in the best-fitted ordered logit model. This result may be caused by the
potential confounding effect of the self-reported crowdedness because the self-
reported crowdedness can be inconsistent with the actual crowdedness (Paramita et al.,
2018). Future studies could use some objective measure crowdedness (e.g., number of
passengers) into the train fare satisfaction model developed in this study.
Moreover, notable heterogeneity in their perceived satisfaction with train fare is
detected across respondents. Specifically, one-way cost and waiting time are found to
be the significant random parameters (Paramita et al., 2018). The marginal utility of
one-way cost is linked with both socio-economic factors and trip characteristics.
80 Chapter 5: Urban Travellers’ Satisfaction with Train Fares in Five Australian Cities
Household composition and station access are found to have a significant influence on
heterogeneity in respondents’ sensitivity to one-way cost. Meanwhile, the disutility
value of waiting time varies significantly across respondents, and is influenced by their
employment status, transport mode from home to the station, trip purpose, paid fare
status, and the influence of train services on their current home location.
A number of earlier studies mentioned the important role of pre-departure
information. This information allows travellers to plan their trip in advance, and
provides a basis for their loyalty to a particular transport mode (Cantwell et al., 2009;
Caulfield & O'Mahony, 2007; Dziekan & Kottenhoff, 2007; Hine & Scott, 2000;
Lyons, 2006). Having obtained their trip information, travellers can anticipate a
straightforward trip, as well as the ability to make route changes should the unexpected
occur (Lyons, 2006). However, this study finds that once they had chosen the train as
their transport mode, respondents’ perceived satisfaction with their train fare is not
significantly influenced by whether they had checked pre-departure information or not
(Paramita et al., 2018). Despite the apparent demand for the provision of complete trip
information at train stations, there is still a need to scientifically examine the impact
of the availability, reliability, and usefulness of such information on train ridership and
traveller satisfaction (Jou, 2001). This is especially the case, given that the provision
of unreliable trip information can be a major source of decline in train ridership
(Cantwell et al., 2009).
The importance and urgency of determining factors that influence travellers’
perceived satisfaction with train fares is frequently recognized in the literature (Beirão
& Cabral, 2007; Brons et al., 2009; Dieleman et al., 2002; Ellaway et al., 2003; Geurs
& Van Wee, 2004), as such knowledge is critical for policy makers and transport
operators in developing effective future transit policies. In this regard, the findings of
this study are significant and useful. On-going efforts in this study area need to
determine the influence of specific socio-economic factors and trip characteristics on
travellers’ choice of transport mode (Paramita et al., 2018). It will be useful, for
example, to determine whether the key variables found in this study affect
respondents’ preference for a particular transport mode, and their loyalty to that mode.
The stated-preference data collected in this survey can assist in this further
investigation.
Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment 81
Chapter 6: Consistency between Perceptions
and Stated Preferences Data in
A Nationwide Mode Choice
Experiment
The first two sections of this chapter reports and analyses the modelling results
from each model separately, the random-parameters binomial logit model (Section 6.1)
and the mixed logit mode (Section 6.2). It is then followed by the elaboration of the
qualitative consistency assessment (Section 6.3) and quantitative consistency
assessment (Section 6.4) between the perceptions and the corresponding attributes of
SP experiment from a nationwide survey. Section 6.5 synthesises the study’s findings,
and discusses its limitations.
6.1 THE RANDOM-PARAMETERS BINOMIAL LOGIT MODEL FOR
PERCEPTIONS
Table 6.1 shows the five significant (p-values < 0.05) service factors that
respondents claimed to be strong influences on the frequency of their train usage in the
best-fitted random-parameter binomial logit model. These factors are: whether a train
is running on schedule; the probability of getting a seat; the ability to access up-to-
date information on train services; the availability of an on-board entertainment
system; and increased road congestion. Notable heterogeneity was detected across
respondents, who stated that trains running on schedule and the probability of getting
a seat significantly influence the frequency of their train usage.
Table 6.1 Summary of the best-fitted random-parameter binomial logit model
Strongly influenced perception of… to take
a train more often
Coefficient Estimate Z-statistics
Train running on schedulea 0.418 8.54
The corresponding standard deviation 0.672 14.48
The probability of getting a seata 0.234 4.64
The corresponding standard deviation 0.157 3.7
The ability to access up-to-date information
on train services (such as current train status) 0.120 2.33
82 Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment
Strongly influenced perception of… to take
a train more often
Coefficient Estimate Z-statistics
Availability of an on-board entertainment
system 0.582 7.32
Increased road congestion 0.167 3.61
Socio-economic factors Coefficient Estimate Z-statistics
Age group 0.092 7.23
Influence of train services on the current home
location decision -0.284 -21.56
Vehicle access: Access to either privately
owned, company owned, or shared motor
vehicles -0.568 -7.37
Gender: Female 0.078 2.0
Own driving licence 0.244 3.06
Employment status: Full time 0.494 9.54
Employment status: Part time 0.286 4.76
Employment status: Self-employed 0.352 4.06
Trip purpose: Work/School 0.522 12.36
Require a motor vehicle for working -0.335 -6.93
Number of observations 6,731
Log-likelihood -3,888.453
AIC 1.16 a random parameters with gamma as the underlying distribution
A respondent’s probability of taking more than one train trip in a month is
mathematically given below:
𝜋𝑖 =exp( ∑ 𝛽𝑘𝑥𝑖𝑘
15𝑘=1 )
1+exp( ∑ 𝛽𝑘𝑥𝑖𝑘15𝑘=1 )
, where [6-1]
∑ (𝛽𝑘 ∗ 𝑥𝑖𝑘
15
𝑘=1) =
[ (0.419 ∗ 𝑣_𝑖) ∗ (𝑡𝑟𝑎𝑖𝑛 𝑟𝑢𝑛𝑛𝑖𝑛𝑔 𝑜𝑛 𝑠𝑐ℎ𝑒𝑑𝑢𝑙𝑒) + (0.234 ∗ 𝑣_𝑖) ∗
(𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑔𝑒𝑡𝑡𝑖𝑛𝑔 𝑎 𝑠𝑒𝑎𝑡) + 0.120 ∗ (𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑡𝑜 𝑎𝑐𝑐𝑒𝑠𝑠 𝑢𝑝 − 𝑡𝑜 −
𝑑𝑎𝑡𝑒 𝑖𝑛𝑓𝑜 𝑜𝑛 𝑡𝑟𝑎𝑖𝑛 𝑠𝑒𝑟𝑣𝑖𝑐𝑒𝑠) + 0.582 ∗ (𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑎𝑛 𝑜𝑛 −
𝑏𝑜𝑎𝑟𝑑 𝑒𝑛𝑡𝑒𝑟𝑡𝑎𝑖𝑛𝑚𝑒𝑛𝑡 𝑠𝑦𝑠𝑡𝑒𝑚) + 0.167 ∗ (𝑖𝑛𝑐𝑟𝑒𝑎𝑠𝑒𝑑 𝑟𝑜𝑎𝑑 𝑐𝑜𝑛𝑔𝑒𝑠𝑡𝑖𝑜𝑛) + 0.092 ∗
(𝑎𝑔𝑒 𝑔𝑟𝑜𝑢𝑝) + (−0.284) ∗
(𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑐𝑒 𝑜𝑓 𝑡𝑟𝑎𝑖𝑛 𝑠𝑒𝑟𝑣𝑖𝑐𝑒𝑠 𝑜𝑛 𝑡ℎ𝑒 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 ℎ𝑜𝑚𝑒 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 𝑑𝑒𝑐𝑖𝑠𝑖𝑜𝑛) + (−0.568) ∗
(𝑣𝑒ℎ𝑖𝑐𝑙𝑒 𝑎𝑐𝑐𝑒𝑠𝑠) + 0.078 ∗ (𝑏𝑒𝑖𝑛𝑔 𝑓𝑒𝑚𝑎𝑙𝑒) + 0.244 ∗ (𝑑𝑟𝑖𝑣𝑖𝑛𝑔 𝑙𝑖𝑐𝑒𝑛𝑐𝑒 𝑜𝑤𝑛𝑒𝑟𝑠ℎ𝑖𝑝) + 0.494 ∗
(𝑓𝑢𝑙𝑙 − 𝑡𝑖𝑚𝑒 𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡) + 0.286 ∗ (𝑝𝑎𝑟𝑡 − 𝑡𝑖𝑚𝑒 𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡) + 0.352 ∗ (𝑠𝑒𝑙𝑓 −
𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑑 𝑠𝑡𝑎𝑡𝑢𝑠) + 0.522 ∗ (𝑤𝑜𝑟𝑘 𝑜𝑟 𝑠𝑐ℎ𝑜𝑜𝑙 𝑡𝑟𝑖𝑝 𝑝𝑢𝑟𝑝𝑜𝑠𝑒) + (−0.336) ∗
(𝑟𝑒𝑞𝑢𝑖𝑟𝑒 𝑎 𝑚𝑜𝑡𝑜𝑟 𝑣𝑒ℎ𝑖𝑐𝑙𝑒 𝑓𝑜𝑟 𝑤𝑜𝑟𝑘𝑖𝑛𝑔)
where 𝑣𝑖 is from a gamma distribution where the shape parameter is equal to 1, and
the scale parameter is equal to 4 (Greene, 2012).
Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment 83
6.2 THE MIXED LOGIT MODEL FOR THE SP DATA
Table 6.2 shows the result of the best-fitted mixed logit model for the SP data.
Waiting time, on-board time, ticket, and on-board crowding level have been identified
as significant attributes within the bus utility function (p-value < 0.05). The significant
attributes of the train utility function are: waiting time, on-board time, ticket, on-board
crowding level, access time from the train station, and availability of a laptop station.
Meanwhile, the utility function of car consists of on-board time, fuel cost, parking
cost, and toll cost.
Table 6.2 Summary of the best-fitted mixed logit model
Mode choice Attributes Coefficient Estimate Z-statistics
Bus Bus waiting time -0.147 -9.91 Bus on-board time -0.040 -40.92 Bus ticket -0.275 -22.77
Bus on-board crowding level -0.373 -12.72
Train Train waiting timea -1.820 -14.46
The corresponding standard
deviation 2.120 16.34 Train on-board time -0.035 -34.17 Train ticket -0.213 -21.78 Train on-board crowding level -0.301 -9.85 Access time from train station -0.167 -10.97
Availability of laptop station -0.101 -3.39
Car Car on-board timea -0.615 -6.62
The corresponding standard
deviation 0.932 29.41 Car fuel cost 0.956 35.01
Car parking cost -0.094 -58.63
Car toll cost -0.110 -39.34
Log likelihood -30519.187 AIC 1.513
Replication
for simulated
probabilities
500 Halton sequences used for
simulations
Number of group for
RPL model with
panel
6,731
Fixed number
of group 6
Number of
observations 40,386
a random parameters with lognormal as the underlying distribution
Two significant (p-value < 0.05) random parameters are found in the model:
train waiting time and car on-board time. Their standard deviations of the assumed
lognormal distribution are significantly different from zero, as shown in Table 6.2. The
heterogeneity of train waiting time cannot be explained by the observed socio-
economic factors. On the other hand, the heterogeneity of car on-board time is
explained by a series of observed socio-economic factors, as shown in Table 6.3.
84 Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment
Table 6.3 Heterogeneities in car on-board time of car utility function within best-fitted
mixed logit model
Random parameter Socio-economic factors Coefficient Estimate Z-statistics
Car on-board time Age group 0.0402 3.49
Train service influence on the
current home location decision -0.257 -12.86
Vehicle access: Access to either
privately owned, company
owned, or shared motor vehicles -0.514 -8.07
Require a motor vehicle for
working -0.215 -6.51
Full time employeda 0.158 4.51
Part time employeda 0.147 3.45
Weekly income level -0.051 -3.95
Being female -0.121 -4.03 a Both full time and part time variables are two significant dummy variables derived from the final
mixed logit model. It is important to note that an individual can only have one employment status and
cannot be both part time and full time.
The three utility functions are mathematically given below,
𝑉_𝐵𝑢𝑠 = (−0.147) ∗ (𝑤𝑎𝑖𝑡𝑖𝑛𝑔 𝑡𝑖𝑚𝑒) + (−0.040) ∗ (𝑜𝑛 − 𝑏𝑜𝑎𝑟𝑑 𝑡𝑖𝑚𝑒) + (−0.275) ∗
(𝑏𝑢𝑠 𝑡𝑖𝑐𝑘𝑒𝑡) + (−0.373) ∗ (𝑏𝑢𝑠 𝑜𝑛 − 𝑏𝑜𝑎𝑟𝑑 𝑐𝑟𝑜𝑤𝑑𝑖𝑛𝑔 𝑙𝑒𝑣𝑒𝑙) [6-2]
𝑉_𝑇𝑟𝑎𝑖𝑛 = [(−1) ∗ 𝑒𝑥𝑝 (−1.820 + (2.120 ∗ 𝑛_𝑖 ))] ∗ (𝑤𝑎𝑖𝑡𝑖𝑛𝑔 𝑡𝑖𝑚𝑒) + (−1.820) ∗ (𝑜𝑛 −
𝑏𝑜𝑎𝑟𝑑 𝑡𝑖𝑚𝑒) + (−0.035) ∗ (𝑡𝑟𝑎𝑖𝑛 𝑡𝑖𝑐𝑘𝑒𝑡) + (−0.301) ∗ (𝑡𝑟𝑎𝑖𝑛 𝑜𝑛 − 𝑏𝑜𝑎𝑟𝑑 𝑐𝑟𝑜𝑤𝑑𝑖𝑛𝑔 𝑙𝑒𝑣𝑒𝑙) +
(−0.167) ∗ (𝑎𝑐𝑐𝑒𝑠𝑠 𝑡𝑖𝑚𝑒 𝑓𝑟𝑜𝑚 𝑡𝑟𝑎𝑖𝑛 𝑠𝑡𝑎𝑡𝑖𝑜𝑛) + (−0.101) ∗ (𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑙𝑎𝑝𝑡𝑜𝑝 𝑠𝑡𝑎𝑡𝑖𝑜𝑛)
where 𝑛𝑖 is a random number generated from a standard normal distribution.
[6-3]
𝑉𝐶𝑎𝑟 = [(−1) ∗ exp〖(−0.615 + (0.0402) ∗ (𝑎𝑔𝑒 𝑔𝑟𝑜𝑢𝑝) + (−0.257)
∗ (𝑡𝑟𝑎𝑖𝑛 𝑠𝑒𝑟𝑣𝑖𝑐𝑒𝑠 𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑐𝑒 𝑜𝑛 𝑡ℎ𝑒 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 ℎ𝑜𝑚𝑒 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 𝑑𝑒𝑐𝑖𝑠𝑖𝑜𝑛)
+ (−0.514) ∗ (𝑣𝑒ℎ𝑖𝑐𝑙𝑒 𝑎𝑐𝑐𝑒𝑠𝑠) + (−0.215)
∗ (𝑟𝑒𝑞𝑢𝑖𝑟𝑒 𝑎 𝑚𝑜𝑡𝑜𝑟 𝑣𝑒ℎ𝑖𝑐𝑙𝑒 𝑓𝑜𝑟 𝑤𝑜𝑟𝑘𝑖𝑛𝑔) + (0.158) ∗ (𝑓𝑢𝑙𝑙 − 𝑡𝑖𝑚𝑒 𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑑)
+ (0.147) ∗ (𝑝𝑎𝑟𝑡 − 𝑡𝑖𝑚𝑒 𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑑) + (−0.0512) ∗ (𝑤𝑒𝑒𝑘𝑙𝑦 𝑖𝑛𝑐𝑜𝑚𝑒 𝑙𝑒𝑣𝑒𝑙)
+ (−0.121) ∗ (𝑏𝑒𝑖𝑛𝑔 𝑓𝑒𝑚𝑎𝑙𝑒) + (0.9318 ∗ 𝑛𝑖))]〗 ∗ (𝑜𝑛 − 𝑏𝑜𝑎𝑟𝑑 𝑡𝑖𝑚𝑒)
+ (0.956) ∗ (𝑓𝑢𝑒𝑙 𝑐𝑜𝑠𝑡) + (−0.094) ∗ (𝑝𝑎𝑟𝑘𝑖𝑛𝑔 𝑐𝑜𝑠𝑡) + (−0.110) ∗ (𝑡𝑜𝑙𝑙 𝑐𝑜𝑠𝑡)
where 𝑛𝑖 is a random number generated from a standard normal distribution.
[6-4]
To illustrate how the equation [6-4] works, the following details are offered. The
“weekly income level” is chosen as an example independent variable. The income
variable is part of the equation of coefficient of random parameter “car on-board time”.
This random parameter follows log-normal distribution. The equation [6-4] is
Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment 85
essential to explain the heterogeneity of “car on-board time”. When all other factors
are constant, the higher the “weekly income level”, the overall coefficient of “car on-
board time” is also increasing. Eventually, it improves the utility of car.
Correspondingly, the probability function is mathematically given below:
𝑃𝑖 =exp(𝑉𝑖)
[exp(𝑉𝑖)+exp(𝑉𝑗)+exp (𝑉𝑘)] , where 𝑖, 𝑗, 𝑘 represents bus, train, and car,
respectively. [6-5]
6.3 QUALITATIVE ASSESSMENT
The best-fitted random-parameter binomial logit (RP) model and mixed logit
(SP) model are assessed qualitatively and quantitatively against one-another. With
reference to Table 3.8, the qualitative assessment starts by the mapping of the
significant explanatory variables from the RP model against the SP model, as shown
in Table 6.4.
Table 6.4 Mapping of significant variables of the random-parameter binomial logit (RP)
model against the mixed logit (SP) model
Significant variables of best-fitted RP model:
Strongly influenced perception of … to take a
train more often
Significant variables of best-fitted SP
model
Trains running on schedulea Train waiting timea
The probability of getting a seata Train on-board crowding
Availability of an on-board entertainment system Availability of a laptop station
Increased road congestion
Car on-board timea (part of utility Car)
Its heterogeneity is explained by the
following socio-economic factors: Age
group; Train service influence on current
home location decision; Vehicle access;
Whether car is required for work; Employed
full time; Employed part time; Weekly
income level; and Being female.
The ability to access up-to-date information on train
services (such as current train status) Not Applicableb
Not Applicableb Car toll cost (part of utility Car) a random parameters
b Not Applicable: the particular variable is not found to be significant
The qualitative analysis reveals that four out of the seven perceptions of service
factors that strongly influenced respondents’ frequency of train usage, are significant
variables in the RP model. The attributes that are either identical or similar to these,
also appear to be significant variables in the model for the mode choice experiment
data. More specifically, the perception of trains running on schedule is a significant
variable in the RP model. A similar attribute, train waiting time, appears to be
86 Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment
significant in the SP model. In addition, significant heterogeneities have been detected
in each of these two variables. Both the RP model and the SP model reveal that
crowding level is a significant factor that influences respondents’ train usage: It is
reflected in the significance of perception of the probability of getting a seat in the RP
model, and in the significance of on-board crowding in the SP model. The perception
of the probability of getting a seat in the RP model shows significant heterogeneity,
while on-board crowding in the SP model is treated as a fixed parameter. Although
the perception of the availability of on-board entertainment system, which is a
significant variable in the RP model, does not have an exact counterpart in the SP
model, the availability of a laptop station, which has a close association with this
variable, appears to be also significant in the SP model. Similarly, even though the
perception of increased road congestion – a significant variable in the RP model –
does not have an exact counterpart in the SP model, car on-board time, which is
assumed to capture similar information, also appears to be significant in the SP model.
The car on-board time is treated as a random parameter, and its heterogeneity is
explained by a series of socio-economic factors, as shown in Table 6.3. In addition,
neither the perception of better access to train station nor similar attributes turn out to
be significant in either the RP model or the SP model.
Meanwhile, some inconsistences in the results of these two models are also
observed. The perception of the availability of up-to-date information on train services
is a significant variable in the RP model; however, no corresponding or similar
attribute is included in the mode choice experiments. Therefore, their consistency
cannot be assessed in this study. On the other hand, toll cost is a significant variable in
the SP model, yet the corresponding variable in the RP model, congestion charge and
toll cost, appears to be insignificant.
In summary, the result of the qualitative assessment shows that a number of
variables, which are gathered as respondents’ perceptions or as responses to the mode
choice experiment, consistently appear in both the RP model and the SP model.
Particularly, the respondents’ views on train waiting time, train on-board crowding
level, availability of laptop station, and increased road congestion within the SP
experiment are regarded as being aligned to respondents’ perceptions of the same or
similar service factors.
Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment 87
6.4 QUANTITATIVE ASSESSMENT
To further appraise the consistency in respondents’ perceptions of service factors
on the frequency of train usage and their responses to the same or similar attributes in
the SP experiment, a quantitative assessment was subsequently performed. This was
based on the comparison of probability values estimated from both best-fitted models,
as elaborated below.
6.4.1 ‘Perception of train running on schedule’ versus ‘Train waiting time’
Perception of train running on schedule in the best-fitted RP model is found to
be a random parameter that follows the gamma distribution restricted to the positive
side. However, its heterogeneity cannot be explained by the observed socio-economic
factors of the respondents. Other factors, for example, weather condition and security
level on-board and on the station – factors that are not contained in the dataset used by
this study – could have influenced the respondents’ perceptions for train running on-
schedule (Fan et al., 2016). The marginal utility of this variable is[0.419 ∗ 𝑣𝑖], where
𝑣𝑖 is a random number generated from a gamma distribution where the shape
parameter equals 1, and the scale parameter equals 4 (Greene, 2012).
Train waiting time in the best-fitted SP model is found to be a random parameter,
which follows the lognormal distribution restricted to the negative side. Similarly, its
heterogeneity cannot be explained by the observed socio-economic factors. The
marginal utility of this variable is [(−1) ∗ exp(−1.820 + (2.120 ∗ 𝑛𝑖))], where 𝑛𝑖is a
random number generated from a standard normal distribution (Greene, 2012).
To better understand the taste variation across individuals captured through these
two random parameters, their impacts on the probability of respondents taking the
train have been separately simulated for 200 randomly selected individuals by
controlling for all other factors, as shown in Figure 6.1 and Figure 6.2. Specifically, in
the first simulation, half of the randomly selected individuals have a strongly
influenced perception of train running on schedule, and in the second simulation, half
of the randomly selected individuals experience a medium waiting time (that is, 8
minutes), while the other half experience a short wait time (that is, 4 minutes).
88 Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment
Figure 6.1 : The simulated probability of individuals’ perception of train running on schedule
from the RP model
Figure 6.2 : The simulated probability of train waiting time from the SP model
Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment 89
Figure 6.1 and Figure 6.2 reveal that by holding all other factors constant, there
are variations across different individuals in the impact of perception of train running
on schedule and train waiting time. To gain more insight, the difference in the
probability of taking the train more than once a month between each pair of selected
individuals in the first simulation is depicted in Figure 6.3. This figure shows that for
the randomly selected 200 individuals, their different perception of train running on
schedule can cause the difference in the probability of taking the train more than once
per month, up to approximately 28.9%. More specifically, for 30% of the cases, the
difference in the probability of taking the train more than once per month is in the
range of [0.1%, 4.9%]; for almost 25% of the cases, the difference in the probability
of taking the train more than once per month is in the range of (4.9%, 9.7%]; for about
15% of the cases, the difference in the probability of taking the train more than once
per month is in the range of (9.7%, 14.5%]; for 20% of the cases, the difference in the
probability of taking the train more than once per month is in the range of (14.5%,
19.3%]; and for about 10% of the cases, the difference in the probability of taking train
more than once per month is more than 19.3%.
90 Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment
Figure 6.3 : The difference in probability value of the impact of a one unit increase in
individuals’ perception level of train running on schedule
Figure 6.4 : The difference in probability value of the impact of one unit increase in train
waiting time in the SP experiment
Similarly, the difference in the probability of choosing the train because of the
impact of train waiting time between each pair of the selected individuals in the second
simulation is depicted in Figure 6.4. This figure shows that, for the randomly selected
200 individuals, the increment of train waiting time can cause a difference of up to
27.1% in the probability of their taking the train or not . Particularly, for almost 43%
of cases, the difference in the probability of taking the train due to the impact of train
waiting time is less than 4.6%; for about 23% of the cases, the difference in the
probability of the taking train is in the range of (4.6%, 9.1%]; for about 18% of the
cases, the difference in the probability of taking the train is in the range of (9.1%,
13.6%]; and for about 16% of the cases, the difference in the probability is more than
13.6%.
Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment 91
6.4.2 Perception of the ‘probability of getting a seat’ compared to ‘Train on-board
crowding’
The perception of the probability of getting a seat is found to be a random
parameter that follows the gamma distribution restricted to the positive side.
Nonetheless, its heterogeneity cannot be explained by the observed socio-economic
factors. Other factors that are not contained in the dataset used in this study (for
example, mental and physical health conditions) could have influenced the
respondents’ perception of the probability of getting a seat (Cox et al., 2006; Evans &
Wener, 2007; Singleton, 2018). The marginal utility of this variable is: 0.234 ∗ 𝑣𝑖 ,
where 𝑣𝑖 is a random number generated from a gamma distribution, where the shape
parameter equals 1, and the scale parameter equals 4 (Greene, 2012).
To gain more knowledge of the taste variation across individuals captured
through perception of the probability of getting a seat, its impact on the probability of
taking the train more than once per month has been simulated for 200 randomly
selected individuals by controlling for all other factors, as shown in Figure 6.5. In this
simulation, half of the randomly selected individuals have a strongly influenced
perception of the probability of getting a seat.
Figure 6.5 : The simulated probability of individuals’ perception of the probability of getting
a seat from the RP model
92 Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment
Observation of Figure 6.5 exposes that by holding all other factors constant,
there are variations across different individuals due to the diversely perception of the
probability of getting a seat. Figure 6.6 presents the difference in the probability of
taking the train more than once a month for each pair of selected individuals in the
simulation. For the randomly selected 200 individuals, their different perception of
train running on schedule can cause an approximately 25% difference in the
probability of taking the train more than once per month. In particular, for the majority
(43%) of cases, the difference in the probability of taking the train more than once per
month is in the range of (0%, 5%]; for 38% of the cases, the difference is in the range
of (5%, 15%]; and for almost 13% and 6% of the cases, the difference is in the range
of (15%, 20%] and (20%, 25%], respectively.
Figure 6.6 : The difference in probability value of the impact of a one unit increase in
individuals’ perception level of the probability of getting a seat
Train on-board crowding in the best-fitted SP model is found to be a fixed
parameter. When controlling for all factors, respondents who feel a one unit increase
in the train on-board crowding experience are less likely to take the train than their
counterparts. In particular, the estimated odds of their taking the train decrease by 26%
(i.e. (1-exp(-0.301))*100%) compared with other respondents (Agresti & Kateri,
2011). As they experience increased on-board crowding, they feel less comfortable,
and their personal space is diminished (Cantwell et al., 2009; Dziekan & Kottenhoff,
2007). These travellers would take train services less frequently in the future.
Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment 93
6.4.3 Perception of ‘availability of an on-board entertainment system’ versus
‘Availability of laptop station’
The perception of the availability of an on-board entertainment system in the
best-fitted RP model is found to be a fixed parameter. All other things being equal,
respondents who have a strongly influenced perception of the availability of on-board
entertainment system are more likely to take a train more than once a month compared
with the other respondents. Particularly, the estimated odds of their doing so increase
by 79% (i.e. (exp(0.582)-1)*100%) for respondents who have a strongly influenced
perception of the availability of on-board entertainment system compared with their
counterparts (Agresti & Kateri, 2011).
An increasing number of travellers have consciously selected bus, train, and
flight services that provide on-board support for portable technology, such as wireless
connection, LCD screens, digital music players, and electrical plugs (Schwieterman et
al., 2009). Travellers who are influenced by the availability of on-board entertainment
systems would enjoy videos, music, or wireless connection as well as have the chance
to work or study rather than being idle. Despite the fact that public transport services
that are equipped with advanced technology cost more than conventional services,
travellers are not deterred from choosing such services (Delclòs-Alió, Marquet, &
Miralles-Guasch, 2017; Naudts et al., 2013). The provision of advanced technology on
train services appears to be positively correlated with travellers’ perceptions of a
greater level of comfort.
Availability of laptop station in the best-fitted SP model is found to be a fixed
parameter. The negative sign of its coefficient belies the findings of previous
researches, where it was found that the availability of broadband internet and other
portable on-board technologies, including laptop stations, strongly motivated
travellers to take trains and buses more often (Delclòs-Alió et al., 2017; Naudts et al.,
2013; Schwieterman et al., 2009; Stanton et al., 2013). In contrast, in this study, the
estimated odds of taking the train decrease by 10% for respondents who are on trains
with laptop stations. It appears that trains with laptop stations correspond with the
increased dissatisfaction of train travellers. Having examined the details of mode
choice experiments, there are two possible explanations: first, the installation of laptop
stations requires additional space and, consequently, can reduce passengers’ personal
space and create cramped conditions; and, second, the longest train on-board time
94 Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment
shown in mode choice experiments was 45 minutes, and it is unlikely that train riders
would need charging stations during this time.
6.4.4 Perception of ‘increased road congestion’ versus ‘car on-board time’
The perception of increased road congestion in the best-fitted RP model was
found to be a fixed parameter. While controlling all other factors, respondents who
have a strongly influenced perception of increased road congestion are more likely to
take a train more than once a month than the other respondents. In particular, the
estimated odds of taking a train more than once a month increase by 18% for
respondents who have a strongly influenced perception of increased road congestion.
Travellers who are sensitive to road congestion are more likely to care more about the
reliability of all road-based transport modes. As an alternative, these travellers would
opt for train services, as trains have their own ‘right of way’, and contribute to the
reduction of carbon emissions (Stopher, 2004). In the long run, these travellers would
take the train more frequently to avoid road congestion (Nguyen, Soltani, & Allan,
2018; Van Exel & Rietveld, 2009).
Car on-board time in the best-fitted SP model is found to be a random parameter
that follows the lognormal distribution restricted to the negative side. Its heterogeneity
is explained by a series of socio-economic factors to a certain extent, and its marginal
utility is: [(−1) ∗ exp(−0.615 + (0.0402) ∗ (𝑎𝑔𝑒 𝑔𝑟𝑜𝑢𝑝) + (−0.257) ∗
(𝑡𝑟𝑎𝑖𝑛 𝑠𝑒𝑟𝑣𝑖𝑐𝑒𝑠 𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑐𝑒 𝑜𝑛 𝑡ℎ𝑒 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 ℎ𝑜𝑚𝑒 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 𝑑𝑒𝑐𝑖𝑠𝑖𝑜𝑛) + (−0.514) ∗
(𝑣𝑒ℎ𝑖𝑐𝑙𝑒 𝑎𝑐𝑐𝑒𝑠𝑠) + (−0.215) ∗ (𝑟𝑒𝑞𝑢𝑖𝑟𝑒 𝑎 𝑚𝑜𝑡𝑜𝑟 𝑣𝑒ℎ𝑖𝑐𝑙𝑒 𝑓𝑜𝑟 𝑤𝑜𝑟𝑘𝑖𝑛𝑔) + (0.158) ∗
(𝑓𝑢𝑙𝑙 − 𝑡𝑖𝑚𝑒 𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑑) + (0.147) ∗ (𝑝𝑎𝑟𝑡 − 𝑡𝑖𝑚𝑒 𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑑) + (−0.0512) ∗
(𝑤𝑒𝑒𝑘𝑙𝑦 𝑖𝑛𝑐𝑜𝑚𝑒 𝑙𝑒𝑣𝑒𝑙) + (−0.121) + (−0.121) ∗ (𝑏𝑒𝑖𝑛𝑔 𝑓𝑒𝑚𝑎𝑙𝑒) + (0.9318 ∗ 𝑛𝑖))],
where 𝑛𝑖 is a random number generated from a standard normal distribution (Greene,
2012).
To better understand the taste variation across individuals captured through car
on-board time, this parameter’s impact on the probability of respondents using their
car was simulated for 200 randomly selected individuals by controlling for all other
factors, as shown in Figure 6.7. The corresponding probabilities of taking the train for
the same 200 randomly selected individuals are illustrated in Figure 6.8. Specifically,
half of the randomly selected individuals experience a 20-minutes car on-board time,
while the other half experience a 10-minute car on-board time.
Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment 95
Figure 6.7 : The simulated probability of using a car for individuals experiencing two different
periods of car on-board time from the SP model
Figure 6.8 : The corresponding simulated probability of taking the train for individuals who
experience two different periods of car on-board time from the SP model
96 Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment
A detailed examination of Figure 6.7 and Figure 6.8 reveal that by holding all
other factors constant, there are variations in the probability of different individuals
using a car, and in the corresponding probability of their taking the train. To further
observe the implication of the probability variations of the two groups, the difference
in the probability of each pair of the selected individuals using a car is illustrated in
Figure 6.9. The change in car on-board time can affect the difference in the probability
of using a car up to approximately 60.2% for the randomly selected 200 individuals.
For the majority (50%) of cases, the difference in the probability of using car is in the
range of [0%, 8.6%]; for approximately 25% of the cases, it is in the range of (8.6%,
17.2%]; for 10% of the cases, it is in the range of (17.2%, 25.8%]; and for 15% of the
cases, it is in the range of (25.8%, 60.2%].
Figure 6.9 : The difference in the probability value of using a car as the impact of a one unit
increase in car on-board time in the SP experiment
Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment 97
Figure 6.10 : The difference in the probability value of taking the train as the impact of a one
unit increase in car on-board time in the SP experiment
Similarly, the difference in the corresponding probability of taking the train due
to the change of car on-board time between each pair of the selected individuals is
shown in Figure 6.10. For the same randomly selected 200 individuals, the figure
reveals that the difference in the probability of their decision to take the train can be
up to 39.2%. Specifically, for almost 45% of the cases, the difference in the probability
of taking the train is in the range of [0%, 5.6%]; for about 28% of the cases, it is in the
range of (5.6%, 11.2%]; for about 15% of the cases, it is in the range of (11.2%,
16.8%]; and for about 12% of the cases, the difference is more than 16.8%.
6.4.5 The summary of quantitative assessment
The quantitative assessment evaluates and compares the magnitude of the
changes in probability values. Specifically, when holding all other factors constant,
our simulation analysis clearly shows that there are heterogeneities in the probability
changes across randomly selected individuals as the impact of a one unit increase in a
random parameter – either the perception of a service factor, or the corresponding
attribute in SP experiment. Despite the range of variations, the magnitude of
differences as the result of a one unit increase in the perception of a service factor is
comparable to those differences that are the result of a one unit increase in the
corresponding attribute in SP experiment.
98 Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment
6.5 CONCLUSIONS
By utilizing a nationwide survey that targeted urban travellers from five
Australian capital cities, this study investigated the consistency of travellers’
perceptions of service factors and their stated travel mode choices. More specifically,
this study utilized nationwide survey data related to the most recent home-based trip
of urban travellers in Sydney, Melbourne, Brisbane, Adelaide, and Perth. In so doing,
it addressed two limitations of many previous studies: their small sample size, and the
use of data from respondents from a particular user group in a single city.
This study offers a notable theoretical contribution to the ongoing validation
issue with regard to RP and SP data. Previous studies often used RP data to assess the
reliability of SP responses. In this study, however, another valuable data source is used;
that is, respondents’ perceptions of the influence of various service factors. A random-
parameter binomial logit (RP) model and a mixed logit (SP) model are estimated for
the perceptions of service factors, and the responses to mode choice experiments,
respectively. In terms of explaining respondents’ mode choice behaviours, the
consistency between the model based on their perceptions, and the model based on the
SP responses has been assessed from both a qualitative and a quantitative perspective.
At the qualitative level, the significant factor mapping from the two models
reveals that perception of the four service factors and their corresponding attributes
including in SP experiment are well aligned; more specifically, the respondents’ views
on train waiting time, on-board crowding level, availability of laptop station, and
increased road congestion within the SP experiment are regarded as being aligned to
their perceptions of the same or similar service factors.
At the quantitative level, the marginal utilities of choosing the train mode in
these two models are tested through rigorous numerical simulations for the same four
service factors, and the estimated probabilities of choosing the train mode from the SP
model are found to be similar to the probabilities of choosing the train mode estimated
from the RP model.
The RP model identifies two significant random parameters: the perception of
train running on schedule and of the probability of getting a seat. Correspondingly, the
SP model also identifies two random parameters: train waiting time and car on-board
time. To gain a further understanding of the underlying reasons for the heterogeneities,
the significant random parameters are traced back to respondents’ diverse socio-
Chapter 6: Consistency between Perceptions and Stated Preferences Data in A Nationwide Mode Choice
Experiment 99
economic backgrounds, and a series of numerical simulations was implemented to
better detect and interpret the heterogeneities in these random parameters. While the
heterogeneity of car on-board time can be explained by the observed socio-economic
factors, this is not the case for the heterogeneities of perception of train running on
schedule, of the probability of getting a seat, and of train waiting time.
Our simulation analysis clearly shows significant heterogeneities in the
probability changes across randomly selected individuals as the impact of a one unit
increase in a random parameter – either the perception of a service factor, or the
corresponding attribute in SP experiment. Nevertheless, the magnitudes of differences
as a result of a one unit increase in the perception of a service factor are comparable
to the results for a one unit increase in the corresponding attribute in SP experiment.
Thus, the quantitative assessment confirms that while choosing their mode preference
in SP experiment, respondents’ perception of the four service factors are consistent
with their reasoning with regard to their corresponding attributes.
However, the overall consistency between the respondents’ perceptions and their
responses to the corresponding attribute in SP experiment, does not indicate that one
dataset can replace the other. Rather, our analysis confirms that these two types of data
sources are complementary in helping us to better understand travellers’ complex
mode choice behaviour. Our qualitative and quantitative assessments demonstrate that
respondents’ perception of four service factors are constructively consistent with their
reasoning surrounding their corresponding attributes in SP experiment.
Finally, this study has at least two limitations. First, access to real-time train
service information is not considered in the SP experiment, and this makes it
impossible to assess the consistency of respondents’ perceptions and their
corresponding responses in the SP experiment on this important attribute. In addition,
previous studies mention the importance of the weather, security, safety, and the health
and psychological condition of the respondents in understanding the underlying
heterogeneity of perception of train running on schedule, train waiting time, and the
probability of getting a seat (Cox et al., 2006; Evans & Wener, 2007; Fan et al., 2016;
Singleton, 2018). However, such information is not collected in the survey used in this
study.
Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport 101
Chapter 7: Policy Interventions Study to
Encourage Behavioural Shift
from Car to Public Transport
The findings of the third sub-study (Policy interventions study to encourage
behavioural shift from car to public transport) are reported and analysed in Section 7.1
to 7.4. Section 7.5 presents the corresponding probability function of bus, train, car,
and public transport. Section 7.6 identifies the average traveller’s profile for each mode
in each city. Section 7.7 simulates and presents the impacts of over a hundred scenarios
of policy intervention. Section 7.8 discusses the most efficient combined policy
interventions based on the simulations. The last section (7.9) concludes the study,
highlights its limitations, and recommends further related studies.
7.1 MODELLING RESULTS
With reference to Figure 4.1, three nested logit models are estimated for each of
the three cities (Sydney, Melbourne, and Brisbane). Each nested logit estimation (using
Nlogit) produces a simple MNL model, and a FIML of nested logit (Greene, 2000).
The three best-fitted FIML of nested logit models produced by Nlogit are summarized
in Table 7.1.
Table 7.1 Summary of the best-fitted FIML of nested logit model
Mode Choice Explanatory Variables
Sydney
dataset:
Estimated
Coefficient
Melbourne
dataset:
Estimated
Coefficient
Brisbane
dataset:
Estimated
Coefficient
Lower Nestb
Bus ASC_Busa 1.573 2.161 2.062 Bus waiting time -0.046 -0.035 -0.028 Bus on-board time -0.033 -0.034 -0.032 Bus ticket -0.168 -0.233 -0.263 Bus on-board crowding -0.220 -0.191 -0.269 Employment status -0.129 -0.171 -0.219
Age group -0.111
Train ASC_Traina 2.221 2.250 2.761
Access time to train
station -0.032 -0.104 -0.250
Train waiting time -0.017 -0.005 -0.058
Train on-board time -0.031 -0.033 -0.033
Train ticket -0.200 -0.174 -0.260
102 Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport
Mode Choice Explanatory Variables
Sydney
dataset:
Estimated
Coefficient
Melbourne
dataset:
Estimated
Coefficient
Brisbane
dataset:
Estimated
Coefficient
Train on-board crowding -0.241 -0.328
Access time from train
station -0.027 -0.118 -0.209
Train services’ influence
on the current home
location decision
-0.346 -0.227 -0.195
Age group 0.037 -0.111 0.070
Employment status -0.129 -0.171 -0.219
Car Car on-board time 0.017 0.012 0.018 Car parking cost -0.055 -0.055 -0.062 Car toll cost -0.054 -0.058 -0.073
Car fuel cost -0.060 Employment status -0.129 -0.171 -0.219
Whether car is required
for working 0.339 0.242 0.446
Driving licence 0.452 0.618 Income level 0.122 0.135 Age group -0.111 0.070
Gender 0.559
Upper Nest
Public
Transport ASC_Publica 0.503 0.424 0.273
Highest educational
qualification -0.253 -0.119 -0.307
Employment status -0.129 -0.171 -0.219
Gender -0.180 -0.123 0.559
Age group -0.111 0.070
IV Parameters Public Transport 0.424 0.536 0.410
Public Transport :
Standard Error 0.052 0.051 0.069
Private Transportc 1 1 1
Log likelihood function -10,178.073 -10,223.316 -4,939.586
P-value <0.01 <0.01 <0.01
Degrees of freedom 24 25 24
McFadden Pseudo R-
squared
0.287
0.278 0.306
Number of group for
RPL model with panel
2,000
2,000 989
Number of observations 12,000 12,000 5,934
Fixed number of
observations/
group
6
Replication
for simulated
probability
500 Halton
sequences
used for
simulations a
ASC: alternative specific constant b All three nested logit models are being normalized at the lower level (i.e. RU1 command in Nlogit). c Due to its degenerative nature, the IV parameter of the private transport branch is being normalized
at 1 for all three nested logit models.
Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport 103
7.2 THE GOODNESS-OF-FIT TEST OF THE NESTED STRUCTURE
The p-value from each city dataset (presented in Table 7.1) is calculated based
on the likelihood ratio test between the nested logit model and the simple MNL model
from each city dataset. It shows that the nested logit model is statistically better than
the simple MNL model in explaining the mode choice responses in the Sydney,
Melbourne, and Brisbane dataset at a 99% significance level. The “McFadden Pseudo
R-squared” (𝜌2) value represents the second indicator for the goodness-of-fit of the
nested logit model (Hensher & Johnson, 1981; McFadden, 1973). McFadden (1973)
constructed the goodness-of-fit measures for logit models from the values of log of the
model’s likelihood function when the coefficients assume various values (Sobel,
1980). The likelihood ratio index is equivalent to “McFadden Pseudo R-squared” (𝜌2)
(Hensher & Johnson, 1981; McFadden, 1973). The ideal value of 𝜌2 is between 0.2
and 0.4. As the nested logit model from Sydney, Melbourne, and Brisbane has 𝜌2 of
0.287, 0.278, and 0.306, respectively, the three nested logit models are considered
extremely-good-fit models.
An additional method to validate the nested structure is to assess whether the
estimated coefficient of the IV parameter for public transport from each best-fitted
model is significantly above zero, and significantly less than one (Hansen, 1987;
Washington et al., 2011). The significant (p-value < 0.05) estimated coefficients of the
IV parameter for public transport for Sydney, Melbourne, and Brisbane are 0.424,
0.536, and 0.410, respectively. The associated standard errors for Sydney, Melbourne,
and Brisbane datasets are 0.052, 0.051, and 0.069, respectively. In order to test if each
of the estimated coefficients of the IV is significantly less than one, Equation 4-19 is
applied for each dataset (Washington et al., 2011).
𝑡 − 𝑆𝑦𝑑𝑛𝑒𝑦∗ =𝛽 − 1
𝑆. 𝐸. (𝛽)=
0.424 − 1
0.052= −11.108
𝑡 − 𝑀𝑒𝑙𝑏𝑜𝑢𝑟𝑛𝑒∗ =𝛽 − 1
𝑆. 𝐸. (𝛽)=
0.536 − 1
0.051= −9.098
𝑡 − 𝐵𝑟𝑖𝑠𝑏𝑎𝑛𝑒∗ =𝛽 − 1
𝑆. 𝐸. (𝛽)=
0.410 − 1
0.069= −8.489
A one-tailed t test gives a confidence level of over 99% for each city dataset.
This provides convincing evidence that the estimated coefficient of the IV parameter
for public transport is significantly less than 1.0 for each dataset. Thus, the nested
logit model structure (as shown in Figure 4.1) is preferred to the simple MNL structure
104 Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport
for all three datasets. There is a correlation between the bus and train riders’
disturbance terms in each dataset.
7.3 THE LOWER NEST OF THE NESTED STRUCTURE
7.3.1 The lower nest of the Sydney dataset
The lower nest of each best-fitted nested model consists of bus and train
alternatives under the public transport branch, and the car alternative under the private
transport branch. Based on the best-fitted model presented in Table 7.1, the utility
functions of bus, train, and car of the Sydney dataset are defined as below.
7.3.1.1 The bus branch
The bus utility function consists of six independent variables; an alternative
specific constant, ‘employment status’, and four travel attributes. The four travel
attributes that characterize the probability of taking the bus are waiting time, on-board
time, ticket, and on-board crowding. As expected, the increments of waiting time, on-
board time, fare, and on-board crowding correspond to a reduction in the probability
of taking the bus. The bus utility function is mathematically given below as,
𝑉𝐵𝑢𝑠 = 1.573 + (−0.046) ∗ (𝑏𝑢𝑠 − 𝑤𝑎𝑖𝑡𝑖𝑛𝑔 − 𝑡𝑖𝑚𝑒) + (−0.033) ∗ (𝑏𝑢𝑠 − 𝑜𝑛𝑏𝑜𝑎𝑟𝑑 −
𝑡𝑖𝑚𝑒) + (−0.168) ∗ (𝑏𝑢𝑠 − 𝑡𝑖𝑐𝑘𝑒𝑡) + (−0.220) ∗ (𝑏𝑢𝑠 − 𝑐𝑟𝑜𝑤𝑑𝑒𝑑𝑛𝑒𝑠𝑠 − 𝑜𝑛𝑏𝑜𝑎𝑟𝑑) +
(−0.129) ∗ (𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 − 𝑠𝑡𝑎𝑡𝑢𝑠) [7-1]
7.3.1.2 The train branch
The train utility function is estimated by an alternative specific constant, access
time to the train station, waiting time, on-board time, ticket, on-board crowding, access
time to train station, ‘level of influence of train service on the choice of current home
location’, ‘employment status’, and ‘age group’. By having the last nine variables at
zero, the utility value of taking the train is positive at 2.221, as shown by the sign and
magnitude of its estimated alternative specific constant. The sign of each estimated
coefficient of key factors is as expected. The train utility function is mathematically
given below as,
Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport 105
𝑉𝑇𝑟𝑎𝑖𝑛 = 2.221 + (−0.032) ∗ (𝑎𝑐𝑐𝑒𝑠𝑠 − 𝑡𝑖𝑚𝑒 − 𝑡𝑜 − 𝑠𝑡𝑎𝑡𝑖𝑜𝑛) + (−0.017) ∗ (𝑡𝑟𝑎𝑖𝑛 −
𝑤𝑎𝑖𝑡𝑖𝑛𝑔 − 𝑡𝑖𝑚𝑒) + (−0.031) ∗ (𝑡𝑟𝑎𝑖𝑛 − 𝑜𝑛𝑏𝑜𝑎𝑟𝑑 − 𝑡𝑖𝑚𝑒) + (−0.200) ∗ (𝑡𝑟𝑎𝑖𝑛 −
𝑡𝑖𝑐𝑘𝑒𝑡) + (−0.241) ∗ (𝑡𝑟𝑎𝑖𝑛 − 𝑐𝑟𝑜𝑤𝑑𝑒𝑑𝑛𝑒𝑠𝑠 − 𝑜𝑛𝑏𝑜𝑎𝑟𝑑) + (−0.027) ∗ (𝑎𝑐𝑐𝑒𝑠𝑠 −
𝑡𝑖𝑚𝑒 − 𝑓𝑟𝑜𝑚 − 𝑠𝑡𝑎𝑡𝑖𝑜𝑛) + (−0.346) ∗ (𝑡𝑟𝑎𝑖𝑛 − 𝑠𝑒𝑟𝑣𝑖𝑐𝑒𝑠 − 𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑐𝑒 − 𝑜𝑛 − ℎ𝑜𝑚𝑒 −
𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛) + ∗ 0.037 (𝑎𝑔𝑒 − 𝑔𝑟𝑜𝑢𝑝) + (−0.129) ∗ (𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 − 𝑠𝑡𝑎𝑡𝑢𝑠) [7-2]
7.3.1.3 The car branch
The car utility function is estimated by six independent variables; three travel
attributes, and three socio-economic factors. The key travel attributes are on-board
time, parking cost, and toll cost. The vital socio-economic factors are ‘employment
status’, ‘whether a car is required for work’, and ‘whether a driving licence is held’.
The car utility function is mathematically given below as,
𝑉𝐶𝑎𝑟 = (0.017) ∗ (𝑐𝑎𝑟 − 𝑜𝑛𝑏𝑜𝑎𝑟𝑑 − 𝑡𝑖𝑚𝑒) + (−0.055) ∗ (𝑐𝑎𝑟 − 𝑝𝑎𝑟𝑘𝑖𝑛𝑔 − 𝑐𝑜𝑠𝑡) +
(−0.054) ∗ (𝑐𝑎𝑟 − 𝑡𝑜𝑙𝑙 − 𝑐𝑜𝑠𝑡) + (−0.129) ∗ (𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 − 𝑠𝑡𝑎𝑡𝑢𝑠) + 0.339 ∗ (𝑐𝑎𝑟 −
𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑑 − 𝑓𝑜𝑟 − 𝑤𝑜𝑟𝑘𝑖𝑛𝑔) + 0.452 ∗ (𝑑𝑟𝑖𝑣𝑖𝑛𝑔 − 𝑙𝑖𝑐𝑒𝑛𝑐𝑒 ) [7-3]
7.3.2 The lower nest of the Melbourne dataset
Based on the best-fitted model presented in Table 7.1, the utility functions of
bus, train, and car of the Melbourne dataset are defined as below.
7.3.2.1 The bus branch
The bus utility function consists of seven independent variables; an alternative
specific constant, ‘employment status’, ‘age group’, and four travel attributes. The four
travel attributes that characterize the probability of taking the bus are waiting time, on-
board time, ticket, and on-board crowding. As expected, the increments of waiting
time, on-board time, fare, and on-board crowding correspond to a reduction in the
probability of taking the bus. The bus utility function is mathematically given below
as,
𝑉𝐵𝑢𝑠 = 2.161 + (−0.035) ∗ (𝑏𝑢𝑠 − 𝑤𝑎𝑖𝑡𝑖𝑛𝑔 − 𝑡𝑖𝑚𝑒) + (−0.034) ∗ (𝑏𝑢𝑠 − 𝑜𝑛𝑏𝑜𝑎𝑟𝑑 −
𝑡𝑖𝑚𝑒) + (−0.233) ∗ (𝑏𝑢𝑠 − 𝑡𝑖𝑐𝑘𝑒𝑡) + (−0.191) ∗ (𝑏𝑢𝑠 − 𝑐𝑟𝑜𝑤𝑑𝑒𝑑𝑛𝑒𝑠𝑠 − 𝑜𝑛𝑏𝑜𝑎𝑟𝑑) +
(−0.171) ∗ (𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 − 𝑠𝑡𝑎𝑡𝑢𝑠) + (−0.111) ∗ (𝑎𝑔𝑒 − 𝑔𝑟𝑜𝑢𝑝) [7-4]
7.3.2.2 The train branch
The train utility function is estimated by an alternative specific constant, access
time to train station, waiting time, on-board time, ticket, access time to train station,
106 Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport
‘level of influence of train services on the current choice of home location’,
‘employment status’, and ‘age group’. By having the last eight variables at zero, the
utility value of taking the train is positive at 2.250, as shown by the sign and magnitude
of its estimated alternative specific constant. The sign of each estimated coefficient of
key factors is as expected. The train utility function is mathematically given below as,
𝑉𝑇𝑟𝑎𝑖𝑛 = 2.250 + (−0.104) ∗ (𝑎𝑐𝑐𝑒𝑠𝑠 − 𝑡𝑖𝑚𝑒 − 𝑡𝑜 − 𝑠𝑡𝑎𝑡𝑖𝑜𝑛) + (−0.005) ∗ (𝑡𝑟𝑎𝑖𝑛 −
𝑤𝑎𝑖𝑡𝑖𝑛𝑔 − 𝑡𝑖𝑚𝑒) + (−0.033) ∗ (𝑡𝑟𝑎𝑖𝑛 − 𝑜𝑛𝑏𝑜𝑎𝑟𝑑 − 𝑡𝑖𝑚𝑒) + (−0.174) ∗ (𝑡𝑟𝑎𝑖𝑛 −
𝑡𝑖𝑐𝑘𝑒𝑡) + (−0.118) ∗ (𝑎𝑐𝑐𝑒𝑠𝑠 − 𝑡𝑖𝑚𝑒 − 𝑓𝑟𝑜𝑚 − 𝑠𝑡𝑎𝑡𝑖𝑜𝑛) + (−0.227) ∗ (𝑡𝑟𝑎𝑖𝑛 −
𝑠𝑒𝑟𝑣𝑖𝑐𝑒𝑠 − 𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑐𝑒 − 𝑜𝑛 − ℎ𝑜𝑚𝑒 − 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛) + ∗ 0.111 (𝑎𝑔𝑒 − 𝑔𝑟𝑜𝑢𝑝) +
(−0.171) ∗ (𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 − 𝑠𝑡𝑎𝑡𝑢𝑠) [7-5]
7.3.2.3 The car branch
The car utility function is estimated by nine independent variables; four travel
attributes and five socio-economic factors. The key travel attributes are on-board time,
parking cost, toll cost, and fuel cost. The vital socio-economic factors are ‘employment
status’, ‘whether a car is required for work’, ‘whether a driving licence is held’,
‘income level’, and ‘age group’. The car utility function is mathematically given below
as,
𝑉𝐶𝑎𝑟 = (0.012) ∗ (𝑐𝑎𝑟 − 𝑜𝑛𝑏𝑜𝑎𝑟𝑑 − 𝑡𝑖𝑚𝑒) + (−0.055) ∗ (𝑐𝑎𝑟 − 𝑝𝑎𝑟𝑘𝑖𝑛𝑔 − 𝑐𝑜𝑠𝑡) +
(−0.058) ∗ (𝑐𝑎𝑟 − 𝑡𝑜𝑙𝑙 − 𝑐𝑜𝑠𝑡) + (−0.060) ∗ (𝑐𝑎𝑟 − 𝑓𝑢𝑒𝑙 − 𝑐𝑜𝑠𝑡) + (−0.171) ∗
(𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 − 𝑠𝑡𝑎𝑡𝑢𝑠) + 0.242 ∗ (𝑐𝑎𝑟 − 𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑑 − 𝑓𝑜𝑟 − 𝑤𝑜𝑟𝑘𝑖𝑛𝑔) + 0.618 ∗
(𝑑𝑟𝑖𝑣𝑖𝑛𝑔 − 𝑙𝑖𝑐𝑒𝑛𝑐𝑒 ) + 0.122 ∗ (𝑖𝑛𝑐𝑜𝑚𝑒 − 𝑙𝑒𝑣𝑒𝑙 ) + 0.111 ∗ (𝑎𝑔𝑒 − 𝑔𝑟𝑜𝑢𝑝 ) [7-6]
7.3.3 The lower nest of the Brisbane dataset
Based on the best-fitted model presented in Table 7.1, the utility functions of
bus, train, and car of the Brisbane dataset are defined as below.
7.3.3.1 The bus branch
The bus utility function consists of six independent variables; an alternative
specific constant, ‘employment status’, and four travel attributes. The four travel
attributes that characterize the probability of taking the bus are waiting time, on-board
time, ticket, and on-board crowding. As expected, the increments of waiting time, on-
board time, fare, and on-board crowding correspond to a reduction in the probability
of taking the bus. The bus utility function is mathematically given below as,
Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport 107
𝑉𝐵𝑢𝑠 = 2.062 + (−0.028) ∗ (𝑏𝑢𝑠 − 𝑤𝑎𝑖𝑡𝑖𝑛𝑔 − 𝑡𝑖𝑚𝑒) + (−0.032) ∗ (𝑏𝑢𝑠 − 𝑜𝑛𝑏𝑜𝑎𝑟𝑑 −
𝑡𝑖𝑚𝑒) + (−0.263) ∗ (𝑏𝑢𝑠 − 𝑡𝑖𝑐𝑘𝑒𝑡) + (−0.269) ∗ (𝑏𝑢𝑠 − 𝑐𝑟𝑜𝑤𝑑𝑒𝑑𝑛𝑒𝑠𝑠 − 𝑜𝑛𝑏𝑜𝑎𝑟𝑑) +
(−0.219) ∗ (𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 − 𝑠𝑡𝑎𝑡𝑢𝑠) [7-7]
7.3.3.2 The train branch
The train utility function is estimated by an alternative specific constant, access
time to the train station, waiting time, on-board time, ticket, on-board crowding, ‘level
of influence of train services on the current choice of home location’, ‘employment
status’, and ‘age group’. By having the last nine variables at zero, the utility value of
taking the train is positive at 2.761, as shown by the sign and magnitude of its estimated
alternative specific constant. The sign of each estimated coefficient of key factors is
as expected. The train utility function is mathematically given below as,
𝑉𝑇𝑟𝑎𝑖𝑛 = 2.761 + (−0.250) ∗ (𝑎𝑐𝑐𝑒𝑠𝑠 − 𝑡𝑖𝑚𝑒 − 𝑡𝑜 − 𝑠𝑡𝑎𝑡𝑖𝑜𝑛) + (−0.058) ∗ (𝑡𝑟𝑎𝑖𝑛 −
𝑤𝑎𝑖𝑡𝑖𝑛𝑔 − 𝑡𝑖𝑚𝑒) + (−0.033) ∗ (𝑡𝑟𝑎𝑖𝑛 − 𝑜𝑛𝑏𝑜𝑎𝑟𝑑 − 𝑡𝑖𝑚𝑒) + (−0.260) ∗ (𝑡𝑟𝑎𝑖𝑛 −
𝑡𝑖𝑐𝑘𝑒𝑡) + (−0.328) ∗ (𝑡𝑟𝑎𝑖𝑛 − 𝑐𝑟𝑜𝑤𝑑𝑒𝑑𝑛𝑒𝑠𝑠 − 𝑜𝑛𝑏𝑜𝑎𝑟𝑑) + (−0.209) ∗ (𝑎𝑐𝑐𝑒𝑠𝑠 −
𝑡𝑖𝑚𝑒 − 𝑓𝑟𝑜𝑚 − 𝑠𝑡𝑎𝑡𝑖𝑜𝑛) + (−0.195) ∗ (𝑡𝑟𝑎𝑖𝑛 − 𝑠𝑒𝑟𝑣𝑖𝑐𝑒𝑠 − 𝑖𝑛𝑓𝑙𝑢𝑒𝑛𝑐𝑒 − 𝑜𝑛 − ℎ𝑜𝑚𝑒 −
𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛) + ∗ 0.070 (𝑎𝑔𝑒 − 𝑔𝑟𝑜𝑢𝑝) + (−0.219) ∗ (𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 − 𝑠𝑡𝑎𝑡𝑢𝑠) [7-8]
7.3.3.3 The car branch
The car utility function is estimated by eight independent variables; three travel
attributes, and five socio-economic factors. The key travel attributes are on-board time,
parking cost, and toll cost. The vital socio-economic factors are ‘employment status’,
‘whether a car is required for work’, ‘income level’, ‘age group’, and ‘gender’. The
car utility function is mathematically given below as,
𝑉𝐶𝑎𝑟 = (0.018) ∗ (𝑐𝑎𝑟 − 𝑜𝑛𝑏𝑜𝑎𝑟𝑑 − 𝑡𝑖𝑚𝑒) + (−0.062) ∗ (𝑐𝑎𝑟 − 𝑝𝑎𝑟𝑘𝑖𝑛𝑔 − 𝑐𝑜𝑠𝑡) +
(−0.073) ∗ (𝑐𝑎𝑟 − 𝑡𝑜𝑙𝑙 − 𝑐𝑜𝑠𝑡) + (−0.219) ∗ (𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 − 𝑠𝑡𝑎𝑡𝑢𝑠) + 0.446 ∗ (𝑐𝑎𝑟 −
𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑑 − 𝑓𝑜𝑟 − 𝑤𝑜𝑟𝑘𝑖𝑛𝑔) + 0.135 ∗ (𝑖𝑛𝑐𝑜𝑚𝑒 − 𝑙𝑒𝑣𝑒𝑙 ) + 0.070 ∗ (𝑎𝑔𝑒 − 𝑔𝑟𝑜𝑢𝑝 ) +
0.559 ∗ (𝑔𝑒𝑛𝑑𝑒𝑟 ) [7-9]
7.4 THE UPPER NEST OF THE NESTED STRUCTURE
The upper nest of each best-fitted nested model consists of a public transport and
a private transport branch.
108 Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport
7.4.1 The private transport branch for all datasets
Because of the degenerative nature of the private transport branch (refer to
Figure 4.1), the utility function of private transport for each city is equal to the utility
function of car for each city. The IV parameters of private transport have been
normalized to 1 during model estimation (𝜆(𝐶𝑎𝑟|𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡) =
1; 𝜆𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡 = 1; 𝑎𝑛𝑑 𝐼𝑉𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡 = 1). There is no more variance at
the top (Private transport) of a degenerative branch than there is at the bottom of the
branch (Car) (Hensher et al., 2005; Hunt, 2000). Hence, the private transport utility
function is exactly the same as the car utility function: 𝑉𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡 = 𝑉𝐶𝑎𝑟.
7.4.2 The public transport branch of the Sydney dataset
The best-fitted model presented in Table 7.1 shows that public transport users in
Sydney are characterized by an alternative specific constant, ‘highest educational level
attained’, ‘employment status’, and ‘gender’. The utility function of public transport
is also influenced by the estimated coefficient of IV parameter of public transport and
the utility function of bus and train at the lower nest. The public transport utility
function is mathematically given below as,
𝑉𝑃𝑢𝑏𝑙𝑖𝑐 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡 = 0.424 ∗ [0.503 + (−0.253) ∗ (ℎ𝑖𝑔ℎ𝑒𝑠𝑡 − 𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑎𝑙 −
𝑞𝑢𝑎𝑙𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑠) + (−0.129) ∗ (𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 − 𝑠𝑡𝑎𝑡𝑢𝑠) + (−0.180) ∗ (𝑔𝑒𝑛𝑑𝑒𝑟) + 1 ∗
ln (𝐸𝑥𝑝(𝑉𝐵𝑢𝑠) + 𝐸𝑥𝑝(𝑉𝑇𝑟𝑎𝑖𝑛))], where 𝑉𝐵𝑢𝑠 and 𝑉𝑇𝑟𝑎𝑖𝑛 are calculated using Equation 7-
1 and 7-2, respectively. [7-10]
7.4.3 The public transport branch of the Melbourne dataset
The best-fitted model presented in Table 7.1 shows that public transport users in
Melbourne are characterized by an alternative specific constant, ‘highest educational
level attained’, ‘employment status’, ‘gender’, and ‘age group’. The utility function of
public transport is also influenced by the estimated coefficient of IV parameter of
public transport and the utility function of bus and train at the lower nest. The public
transport utility function is mathematically given below as,
𝑉𝑃𝑢𝑏𝑙𝑖𝑐 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡 = 0.536 ∗ [0.424 + (−0.119) ∗ (ℎ𝑖𝑔ℎ𝑒𝑠𝑡 − 𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑎𝑙 −
𝑞𝑢𝑎𝑙𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑠) + (−0.171) ∗ (𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 − 𝑠𝑡𝑎𝑡𝑢𝑠) + (−0.123) ∗ (𝑔𝑒𝑛𝑑𝑒𝑟) +
(−0.111) ∗ (𝑎𝑔𝑒 − 𝑔𝑟𝑜𝑢𝑝) + 1 ∗ ln (𝐸𝑥𝑝(𝑉𝐵𝑢𝑠) + 𝐸𝑥𝑝(𝑉𝑇𝑟𝑎𝑖𝑛))], where 𝑉𝐵𝑢𝑠 and 𝑉𝑇𝑟𝑎𝑖𝑛
are calculated using Equation 7-4 and 7-5, respectively. [7-11]
Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport 109
7.4.4 The public transport branch of the Brisbane dataset
The best-fitted model presented in Table 7.1 shows that public transport users in
Brisbane are characterized by an alternative specific constant, ‘highest educational
level attained’, ‘employment status’, ‘gender’, and ‘age group’. The utility function of
public transport is also influenced by the estimated coefficient of IV parameter of
public transport, and the utility function of bus and train at the lower nest. The public
transport utility function is mathematically given below as,
𝑉𝑃𝑢𝑏𝑙𝑖𝑐 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡 = 0.410 ∗ [0.273 + (−0.307) ∗ (ℎ𝑖𝑔ℎ𝑒𝑠𝑡 − 𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑎𝑙 −
𝑞𝑢𝑎𝑙𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑠) + (−0.219) ∗ (𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 − 𝑠𝑡𝑎𝑡𝑢𝑠) + (−0.559) ∗ (𝑔𝑒𝑛𝑑𝑒𝑟) +
(−0.070) ∗ (𝑎𝑔𝑒 − 𝑔𝑟𝑜𝑢𝑝) + 1 ∗ ln (𝐸𝑥𝑝(𝑉𝐵𝑢𝑠) + 𝐸𝑥𝑝(𝑉𝑇𝑟𝑎𝑖𝑛))], where 𝑉𝐵𝑢𝑠 and 𝑉𝑇𝑟𝑎𝑖𝑛
are calculated using Equation 7-7 and 7-8, respectively. [7-12]
7.5 THE PROBABILITY FUNCTIONS
With reference to Figure 4.1, 𝑃(𝑃𝑢𝑏𝑙𝑖𝑐 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡) is the unconditional
probability of a respondent taking public transport, and 𝑃(𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡) is the
unconditional probability of a respondent taking private transport. As previously
discussed in Section 7.4.1, the utility of car is equal to the utility of private transport.
Consequently, the unconditional probability of a respondent driving a car – 𝑃(𝐶𝑎𝑟) –
is equal to the unconditional probability of a respondent taking private transport,
𝑃(𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡). Utilising the utility functions derived in the previous section,
the probability functions at the upper nest are mathematically defined below (Hensher
et al., 2005; Hunt, 2000).
𝑃(𝑃𝑢𝑏𝑙𝑖𝑐 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡) =𝐸𝑋𝑃[ 𝑉𝑃𝑢𝑏𝑙𝑖𝑐 T𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡]
𝐸𝑋𝑃[ 𝑉𝑃𝑢𝑏𝑙𝑖𝑐 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡]+𝐸𝑋𝑃[ 𝑉𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡] [7-13]
𝑃(𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡) =𝐸𝑋𝑃[ 𝑉𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡]
𝐸𝑋𝑃[ 𝑉𝑃𝑢𝑏𝑙𝑖𝑐 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡]+𝐸𝑋𝑃[ 𝑉𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡]= 𝑃(𝐶𝑎𝑟) [7-14]
,where 𝑃(𝐵𝑢𝑠|𝑃𝑢𝑏𝑙𝑖𝑐 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡) is the probability of a respondent taking the bus
(conditional on their choosing public transport), and 𝑃(𝑇𝑟𝑎𝑖𝑛|𝑃𝑢𝑏𝑙𝑖𝑐 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡) is
the probability of a respondent taking the train (conditional on their choosing public
transport). These probabilities are mathematically defined below (Hensher et al., 2005;
Hunt, 2000).
𝑃(𝐵𝑢𝑠|𝑃𝑢𝑏𝑙𝑖𝑐 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡) =𝐸𝑋𝑃[ 𝑉𝐵𝑢𝑠]
𝐸𝑋𝑃[ 𝑉𝐵𝑢𝑠]+𝐸𝑋𝑃[ 𝑉𝑇𝑟𝑎𝑖𝑛] [7-15]
110 Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport
𝑃(𝑇𝑟𝑎𝑖𝑛|𝑃𝑢𝑏𝑙𝑖𝑐 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡)𝐸𝑋𝑃[ 𝑉𝑇𝑟𝑎𝑖𝑛]
𝐸𝑋𝑃[ 𝑉𝐵𝑢𝑠]+𝐸𝑋𝑃[ 𝑉𝑇𝑟𝑎𝑖𝑛] [7-16]
Hence, at the lower nest, 𝑃(𝐵𝑢𝑠) is the unconditional probability of a
respondent taking the bus, and 𝑃(𝑇𝑟𝑎𝑖𝑛) is the unconditional probability of a
respondent taking the train. These probabilities are estimated by the following
equations. They are mathematically defined below (Hensher et al., 2005; Hunt, 2000).
𝑃(𝐵𝑢𝑠) = 𝑃(𝑃𝑢𝑏𝑙𝑖𝑐 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡) ∗ 𝑃(𝐵𝑢𝑠|𝑃𝑢𝑏𝑙𝑖𝑐 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡) [7-17]
𝑃(𝑇𝑟𝑎𝑖𝑛) = 𝑃(𝑃𝑢𝑏𝑙𝑖𝑐 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡) ∗ 𝑃(𝑇𝑟𝑎𝑖𝑛|𝑃𝑢𝑏𝑙𝑖𝑐 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡) [7-18]
7.6 THE AVERAGE PROFILE OF TRAVELLERS FOR THE BASELINE
SCENARIO
Responses related to train riders’ experience, non-riders’ experience, and socio-
economic factors, reveal the respondents’ travel behaviours (Section 3.4). Using this
knowledge of travel behaviours, documented information about each city, and the key
variables from the best-fitted utility functions (Section 7.3 and 7.4), this section
identifies an average traveller’s profile for each transport mode in Sydney, Brisbane,
and Melbourne (Queensland Government, 2016; Transport for NSW, 2016; Victoria
State Government, 2016). In the next section (7.7), the average traveller’s profile and
experience are used to estimate the utility and probability value of taking the bus, train,
car, and public transport for each city at the baseline scenario.
7.6.1 The average profile of Sydney travellers
Based on the Sydney respondents’ travel experience and socio-economic
profiles at the time of their most recent home-based journey, their profile and trip
characteristics have been identified as follows.
Table 7.2 The average profile of Sydney travellers for a baseline scenario
Type of
traveller
Key variables from the
corresponding utility
function
Gathered from revealed travel behaviours
dataset and has been checked against
documented information
A bus rider Bus waiting time 8 minutes
Bus on-board time 32 minutes
Bus ticket $ 2.37
Bus on-board crowding Experience on-board crowding
Employment Status Full time employed
A train rider Access time to train station 16 minutes
Train waiting time 8 minutes
Train on-board time 33 minutes
Train ticket $ 3.40
Train on-board crowding Experience on-board crowding
Access time from train station 9 minutes
Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport 111
Type of
traveller
Key variables from the
corresponding utility
function
Gathered from revealed travel behaviours
dataset and has been checked against
documented information
Train services’ influence on
the current home location
decision Significant
Age group 31-40 years old
Employment Status Full time employed
A public Highest educational
qualification Bachelor degree
transport Employment status Full time employed
user Gender Female
A car driver Car on-board time 28 minutes
Car parking costa $ 5.00
Car toll costa $ 2.50
Employment status Full time employed
Whether car is required for
working No
Driving licence Yes a The baseline car parking and toll costs may not be a true representation of current parking and toll
costs. Rather, both costs represent the average parking and toll cost paid by all Sydney car driver
respondents. Based on their responses to the ‘Most recent experience’ questions, there are two very
different groups of car drivers: those who manage to pay nothing for parking and tolls, and those who
pay enormous amounts for them.
7.6.2 The average profile of Melbourne travellers
Based on the Melbourne respondents’ travel experience and socio-economic
profiles at the time of their most recent home-based journey, their profile and trip
characteristics have been identified as follows.
Table 7.3 The average profile of Melbourne travellers for a baseline scenario
Type of
traveller
Key variables from the
corresponding utility
function
Gathered from revealed travel behaviours
dataset and has been checked against
documented information
A bus rider Bus waiting time 8 minutes
Bus on-board time 29 minutes
Bus ticket $ 2.18
Bus on-board crowding Experience on-board crowding
Employment Status Full time employed
Age group 31-40 years old
A train rider Access time to train station 13 minutes
Train waiting time 8 minutes
Train on-board time 33 minutes
Train ticket $ 2.62
Access time from train station 11 minutes
Train services’ influence on
the current home location
decision Significant
Age group 31-40 years old
Employment Status Full time employed
A public Highest educational
qualification Bachelor degree
transport Employment status Full time employed
user Gender Female
Age group 31-40 years old
112 Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport
Type of
traveller
Key variables from the
corresponding utility
function
Gathered from revealed travel behaviours
dataset and has been checked against
documented information
A car driver Car on-board time 26 minutes
Car parking costa $ 5.00
Car toll costa $ 2.50
Car fuel costb $ 2.62
Employment status Full time employed
Whether car is required for
working No
Driving licence Yes
Income level
Pre-tax household weekly income $1,600 and
above a The baseline car parking and toll costs may not be a true representation of current costs. Rather, both
costs represent the average parking and toll costs paid by all Melbourne car driver respondents. Based
on their responses on the ‘Most recent experience’ questions, there are two very different groups of
Melbourne car driver respondents: those who manage to pay nothing for parking and tolls, and those
who pay enormous amounts for them. b The baseline car fuel toll cost represents the average fuel cost spent by Melbourne car-driving
respondents on a one-way, home-based trip to a single destination.
7.6.3 The average profile of Brisbane travellers
Based on the Brisbane respondents’ travel experience and socio-economic
profiles at the time of their most recent home-based journey, their profile and trip
characteristics have been identified as follows.
Table 7.4 The average profile of Brisbane travellers for a baseline scenario
Type of
traveller
Key variables from the
corresponding utility
function
Gathered from revealed travel behaviours
dataset and has been checked against
documented information
A bus rider Bus waiting time 9 minutes
Bus on-board time 32 minutes
Bus ticket $ 3.23
Bus on-board crowding Experience on-board crowding
Employment Status Outside workforce
A train rider Access time to train station 15 minutes
Train waiting time 11 minutes
Train on-board time 36 minutes
Train ticket $ 2.92
Train on-board crowding Experience on-board crowding
Access time from train station 10 minutes
Train services’ influence on
the current home location
decision Insignificant
Age group 31-40 years old
Employment Status Full time employed
A public Highest educational
qualification Bachelor degree
transport Employment status Full time employed
user Gender Female
Age group 31-40 years old
A car driver Car on-board time 24 minutes
Car parking costa $ 5.00
Car toll costa $ 2.50
Employment status Full time employed
Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport 113
Type of
traveller
Key variables from the
corresponding utility
function
Gathered from revealed travel behaviours
dataset and has been checked against
documented information
Whether car is required for
working No
Income level Unreported income
Age group 51-60 years old
Gender Female a The baseline car parking and toll costs may not be a true representation of current parking and toll
costs. Rather, both costs represent the average parking and toll costs paid by all Brisbane car driving
respondents. Based on their responses to the ‘Most recent experience’ questions, there are two very
different groups of Brisbane car driving respondents: those who manage to pay nothing for parking and
tolls, and those who pay enormous amounts for them.
7.7 THE POLICY INTERVENTION SCENARIO ANALYSIS
With reference to the defined utility function of bus, train, car, and public
transport in Section 7.3 and 7.4, two policy interventions have been determined, each
with a different purpose. The two different intervention purposes are: 1) to encourage
public transport (i.e., both bus and train) ridership; and 2) to discourage regular car
usage. Increased public transport ridership can be achieved by improving passengers’
level of service (LOS); specifically, by reducing public transport waiting times. On the
other hand, a reduction in regular car usage can be achieved by increasing its cost,
particularly by increasing parking and toll costs.
By holding all other factors constant, a sequence of scenarios were simulated by
changing a particular area of policy intervention for each city dataset, and for each
policy intervention purpose. Take, for example, a simulation to encourage public
transport ridership in Sydney: By holding all other factors constant and employing the
average traveller’s profile and experience (as defined in Section 7.6.1), bus waiting
time was reduced from the baseline scenario (i.e., 100%) to a 50% scenario with a 5%
interval. The calculated increment of public transport mode shares (public transport
shares) and the corresponding decrement of car mode shares (car shares) in Sydney
were observed.
Meanwhile, consider an example of a simulation to discourage regular car usage
in Sydney: By holding all other factors constant and employing the average traveller’s
profile and experience (as defined in Section 7.6.1), the parking cost was increased
from the baseline scenario (i.e., 100%) to a 200% scenario with a 10% interval. The
calculated decrement of car shares and the corresponding increment of public transport
shares for Sydney were detected. The single policy intervention scenario analysis was
114 Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport
replicated for bus waiting time and toll cost factors for each city. The resulting rapid
change in car and public transport shares are acknowledged in each simulation.
The single policy intervention scenario analyses indicate that the change in mode
shares – that is, either the increment of public transport share or the decrement of car
share – is not optimized, and is not an accurate or realistic representation of the way
policy interventions operate. Hence, in order to replicate actual policy intervention,
and to optimise the change in mode share, subsequent analyses incorporated both
policy intervention purposes, and held all other factors constant. In particular, to gain
insights into the direct impact of policy interventions on car and public transport mode
shares, 176 different travel scenarios were simulated for each city. By controlling all
other factors, the bus and train waiting time were reduced from the baseline scenario
(i.e., 100%) to a 50% scenario with a 5% interval. Furthermore, the combined parking
and toll cost was increased from a baseline scenario (i.e., 100%) to a 250% scenario
with a 10% interval. The calculated decrement of car shares and the corresponding
increment of public transport shares for each city were tabulated, and illustrated with
surface graphs. The findings from each city are presented below.
7.7.1 The impact of policy interventions in Sydney
With reference to the utility functions of bus, train, car, and public transport for
the Sydney dataset (Equations 7-1, 7-2, 7-3, and 7-10, respectively), and to the profile
of Sydney travellers (Table 7.2), the following probability of driving a car and the
probability of taking public transport are calculated for each cell of travel scenarios,
by controlling for all other factors. The probability values of the results of each travel
scenario are tabulated in Table 7.5.
Table 7.5 The probability values of driving a car and taking public transport in Sydney: 176
different policy intervention scenarios
Parking and Toll cost
100% 110% 120% 130% 140% 150%
$7.5 $8.25 $9 $9.75 $10.5 $11.25
P(Car)
P(Public Transport)
Bus and
Train
waiting
time
(Minutes)
100% 8 63.83% 62.88% 61.93% 60.96% 59.99% 59.01%
36.17% 37.12% 38.07% 39.04% 40.01% 40.99%
95% 7.6 63.70% 62.75% 61.79% 60.82% 59.85% 58.87%
36.30% 37.25% 38.21% 39.18% 40.15% 41.13%
90% 7.2 63.56% 62.61% 61.65% 60.69% 59.71% 58.73%
36.44% 37.39% 38.35% 39.31% 40.29% 41.27%
Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport 115
85% 6.8 63.43% 62.48% 61.52% 60.55% 59.57% 58.59%
36.57% 37.52% 38.48% 39.45% 40.43% 41.41%
80% 6.4 63.29% 62.34% 61.38% 60.41% 59.43% 58.45%
36.71% 37.66% 38.62% 39.59% 40.57% 41.55%
75% 6 63.16% 62.21% 61.24% 60.27% 59.29% 58.31%
36.84% 37.79% 38.76% 39.73% 40.71% 41.69%
70% 5.6 63.02% 62.07% 61.10% 60.13% 59.15% 58.16%
36.98% 37.93% 38.90% 39.87% 40.85% 41.84%
65% 5.2 62.89% 61.93% 60.97% 59.99% 59.01% 58.02%
37.11% 38.07% 39.03% 40.01% 40.99% 41.98%
60% 4.8 62.75% 61.79% 60.83% 59.85% 58.87% 57.88%
37.25% 38.21% 39.17% 40.15% 41.13% 42.12%
55% 4.4 62.61% 61.65% 60.69% 59.71% 58.73% 57.74%
37.39% 38.35% 39.31% 40.29% 41.27% 42.26%
50% 4 62.48% 61.52% 60.55% 59.57% 58.58% 57.59%
37.52% 38.48% 39.45% 40.43% 41.42% 42.41%
Parking and Toll cost
160% 170% 180% 190% 200%
$12 $12.75 $13.5 $14.25 $15
P(Car)
P(Public Transport)
Bus and
Train
waiting
time
(Minutes)
100% 8 58.02% 57.02% 56.021% 55.015% 54.005%
41.98% 42.98% 43.979% 44.985% 45.995%
95% 7.6 57.88% 56.88% 55.879% 54.872% 53.862%
42.12% 43.12% 44.121% 45.128% 46.138%
90% 7.2 57.74% 56.74% 55.737% 54.730% 53.718%
42.26% 43.26% 44.263% 45.270% 46.282%
85% 6.8 57.60% 56.60% 55.594% 54.586% 53.575%
42.40% 43.40% 44.406% 45.414% 46.425%
80% 6.4 57.45% 56.46% 55.451% 54.443% 53.431%
42.55% 43.54% 44.549% 45.557% 46.569%
75% 6 57.31% 56.31% 55.308% 54.299% 53.286%
42.69% 43.69% 44.692% 45.701% 46.714%
70% 5.6 57.17% 56.17% 55.164% 54.154% 53.141%
42.83% 43.83% 44.836% 45.846% 46.859%
65% 5.2 57.03% 56.03% 55.020% 54.009% 52.996%
42.97% 43.97% 44.980% 45.991% 47.004%
60% 4.8 56.88% 55.88% 54.875% 53.864% 52.850%
43.12% 44.12% 45.125% 46.136% 47.150%
55% 4.4 56.74% 55.74% 54.730% 53.719% 52.704%
43.26% 44.26% 45.270% 46.281% 47.296%
50% 4 56.60% 55.59% 54.584% 53.573% 52.558%
43.40% 44.41% 45.416% 46.427% 47.442%
Parking and Toll cost 210% 220% 230% 240% 250%
116 Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport
$15.75 $16.5 $17.25 $18 $18.75
P(Car)
P(Public Transport)
Bus and
Train
waiting
time
(Minutes)
100% 8 52.991% 51.975% 50.958% 49.939% 48.921%
47.009% 48.025% 49.042% 50.061% 51.079%
95% 7.6 52.848% 51.832% 50.814% 49.796% 48.778%
47.152% 48.168% 49.186% 50.204% 51.222%
90% 7.2 52.704% 51.688% 50.670% 49.652% 48.633%
47.296% 48.312% 49.330% 50.348% 51.367%
85% 6.8 52.560% 51.544% 50.526% 49.507% 48.489%
47.440% 48.456% 49.474% 50.493% 51.511%
80% 6.4 52.416% 51.399% 50.381% 49.362% 48.344%
47.584% 48.601% 49.619% 50.638% 51.656%
75% 6 52.271% 51.254% 50.236% 49.217% 48.200%
47.729% 48.746% 49.764% 50.783% 51.800%
70% 5.6 52.126% 51.108% 50.090% 49.072% 48.054%
47.874% 48.892% 49.910% 50.928% 51.946%
65% 5.2 51.980% 50.963% 49.944% 48.926% 47.909%
48.020% 49.037% 50.056% 51.074% 52.091%
60% 4.8 51.834% 50.817% 49.798% 48.780% 47.763%
48.166% 49.183% 50.202% 51.220% 52.237%
55% 4.4 51.688% 50.670% 49.652% 48.634% 47.617%
48.312% 49.330% 50.348% 51.366% 52.383%
50% 4 51.541% 50.523% 49.505% 48.487% 47.470%
48.459% 49.477% 50.495% 51.513% 52.530%
In order to identify the best combination of policy interventions, a certain target
of probability of driving the car and taking public transport has to be set at the outset.
This target was set at a 10% decrease in the probability of driving car in Sydney. Table
7.5 demonstrates that there are two possible travel scenarios that can reduce the
probability of driving a car in Sydney by at least 10% (i.e., from 64% to 54%). The
first scenario is where the bus and train waiting times are reduced to 80% of the
baseline scenario (i.e., from 8 minutes to 6.4 minutes), and the total parking and toll
cost is increased to 1.9 times the baseline scenario (i.e., from $7.5 to $14.25). The
second scenario is where the bus and train waiting times are the same as for the
baseline scenario (i.e., 8 minutes), and the total parking and toll costs are increased to
twice the baseline scenario (i.e., from $7.5 to $15).
Of these two scenarios, the second seems to be more realistic than the first. This
is because the implementation of plans to reduce bus and train waiting times requires
Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport 117
a higher initial cost than the cost of imposing higher parking and toll costs (Boardman,
Greenberg, Vining, & Weimer, 2017; Guo & Wilson, 2011; Wardman, 2004). For
instance, the additional funds obtained from the increment of parking and toll costs
can be invested to improve other aspects of passenger LOS, such as the provision of
real time public transport service information at major bus stops and train stations.
While holding the bus and train waiting times at the baseline scenario, and all other
factors constant, a further increment of total parking and toll costs up to 2.5 times the
baseline scenario reduces the car mode share from 64% to 49%. When the most
extreme travel scenario is applied (i.e., a 50% reduction in bus and train waiting times,
and a 250% increase in total parking and toll costs), the probability of driving a car in
Sydney decreases from 64% to 47%.
The change in the probability of driving a car and taking public transport are
separately illustrated in Figure 7.1 and Figure 7.2, respectively. Figure 7.1 shows that
the probability of driving a car in Sydney decreases with both the increment of parking
and toll costs (i.e., from $ 7.50 to $18.75), and the decrement of bus and train waiting
times (i.e., from 8 minutes to 4 minutes). Meanwhile, Figure 7.2 demonstrates that the
probability of taking public transport in Sydney increases both with the increment of
parking and toll costs (i.e., from $ 7.50 to $18.75), and the decrement of bus and train
waiting times (i.e., from 8 minutes to 4 minutes).
118 Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport
Figure 7.1 : The probability of driving a car in Sydney as the result of policy intervention
related to bus and train waiting times and parking and toll costs, while holding all other factors
constant
Figure 7.2 : The probability of taking public transport in Sydney as the result of policy
interventions in both bus and train waiting times and parking and toll costs, holding all other
factors constant
8
7.2
6.4
5.6
4.84
40.00%
45.00%
50.00%
55.00%
60.00%
65.00%
Bus a
nd T
rain
waitin
g t
ime (
min
ute
s)
Pro
babili
ty o
f drivin
g c
ar
Combined parking and toll cost ($)
40.00%-45.00% 45.00%-50.00% 50.00%-55.00% 55.00%-60.00% 60.00%-65.00%
8
7.2
6.4
5.6
4.84
30.00%
35.00%
40.00%
45.00%
50.00%
55.00%
Bus a
nd T
rain
waitin
g t
ime (
min
ute
s)
Pro
babili
ty o
f ta
kin
g p
ublic
tra
nsport
Combined parking and toll cost ($)
30.00%-35.00% 35.00%-40.00% 40.00%-45.00% 45.00%-50.00% 50.00%-55.00%
Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport 119
7.7.2 The impact of policy interventions in Melbourne
With reference to the utility function of bus, train, car, and public transport in
the Melbourne dataset (Equations 7-4, 7-5, 7-6, and 7-11, respectively) and the
Melbourne traveller’s profile (Table 7.3), the probability of driving a car and the
probability of taking public transport are calculated for each cell of travel scenarios,
by controlling for all other factors. The probability values of the results of each travel
scenario are tabulated in Table 7.6.
Table 7.6 The probability values of driving a car and taking public transport in Melbourne:
176 different policy intervention scenarios
Parking and Toll cost
100% 110% 120% 130% 140% 150%
$7.5 $8.25 $9 $9.75 $10.5 $11.25
P(Car)
P(Public Transport)
Bus and
Train
waiting
time
(Minutes)
100% 8 58.16% 57.129% 56.097% 55.059% 54.017% 52.971%
41.84% 42.87% 43.90% 44.94% 45.98% 47.03%
95% 7.6 57.99% 56.96% 55.93% 54.89% 53.84% 52.80%
42.01% 43.04% 44.07% 45.11% 46.16% 47.20%
90% 7.2 57.82% 56.79% 55.75% 54.71% 53.67% 52.62%
42.18% 43.21% 44.25% 45.29% 46.33% 47.38%
85% 6.8 57.65% 56.62% 55.58% 54.54% 53.50% 52.45%
42.35% 43.38% 44.42% 45.46% 46.50% 47.55%
80% 6.4 57.48% 56.45% 55.41% 54.37% 53.32% 52.28%
42.52% 43.55% 44.59% 45.63% 46.68% 47.72%
75% 6 57.31% 56.27% 55.24% 54.20% 53.15% 52.10%
42.69% 43.73% 44.76% 45.80% 46.85% 47.90%
70% 5.6 57.14% 56.10% 55.06% 54.02% 52.98% 51.93%
42.86% 43.90% 44.94% 45.98% 47.02% 48.07%
65% 5.2 56.96% 55.93% 54.89% 53.85% 52.80% 51.75%
43.04% 44.07% 45.11% 46.15% 47.20% 48.25%
60% 4.8 56.79% 55.76% 54.72% 53.67% 52.63% 51.58%
43.21% 44.24% 45.28% 46.33% 47.37% 48.42%
55% 4.4 56.62% 55.59% 54.54% 53.50% 52.45% 51.40%
43.38% 44.41% 45.46% 46.50% 47.55% 48.60%
50% 4 56.45% 55.41% 54.37% 53.33% 52.28% 51.23%
43.55% 44.59% 45.63% 46.67% 47.72% 48.77%
Parking and Toll cost
160% 170% 180% 190% 200%
$12 $12.75 $13.5 $14.25 $15
P(Car)
P(Public Transport)
Bus and
Train
100% 8 51.922% 50.872% 49.821% 48.770% 47.720%
48.08% 49.13% 50.179% 51.230% 52.280%
120 Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport
waiting
time
(Minutes)
95% 7.6 51.75% 50.70% 49.647% 48.596% 47.547%
48.25% 49.30% 50.353% 51.404% 52.453%
90% 7.2 51.57% 50.52% 49.473% 48.422% 47.373%
48.43% 49.48% 50.527% 51.578% 52.627%
85% 6.8 51.40% 50.35% 49.299% 48.249% 47.200%
48.60% 49.65% 50.701% 51.751% 52.800%
80% 6.4 51.23% 50.18% 49.125% 48.074% 47.026%
48.77% 49.82% 50.875% 51.926% 52.974%
75% 6 51.05% 50.00% 48.950% 47.900% 46.852%
48.95% 50.00% 51.050% 52.100% 53.148%
70% 5.6 50.88% 49.83% 48.776% 47.726% 46.678%
49.12% 50.17% 51.224% 52.274% 53.322%
65% 5.2 50.70% 49.65% 48.601% 47.552% 46.504%
49.30% 50.35% 51.399% 52.448% 53.496%
60% 4.8 50.53% 49.48% 48.427% 47.377% 46.330%
49.47% 50.52% 51.573% 52.623% 53.670%
55% 4.4 50.35% 49.30% 48.252% 47.203% 46.157%
49.65% 50.70% 51.748% 52.797% 53.843%
50% 4 50.18% 49.13% 48.077% 47.028% 45.982%
49.82% 50.87% 51.923% 52.972% 54.018%
Parking and Toll cost
210% 220% 230% 240% 250%
$15.75 $16.5 $17.25 $18 $18.75
P(Car)
P(Public Transport)
Bus and
Train
waiting
time
(Minutes)
100% 8 46.673% 45.628% 44.587% 43.551% 42.520%
53.327% 54.372% 55.413% 56.449% 57.480%
95% 7.6 46.500% 45.455% 44.415% 43.380% 42.350%
53.500% 54.545% 55.585% 56.620% 57.650%
90% 7.2 46.326% 45.283% 44.243% 43.209% 42.180%
53.674% 54.717% 55.757% 56.791% 57.820%
85% 6.8 46.153% 45.110% 44.072% 43.038% 42.010%
53.847% 54.890% 55.928% 56.962% 57.990%
80% 6.4 45.980% 44.938% 43.900% 42.867% 41.841%
54.020% 55.062% 56.100% 57.133% 58.159%
75% 6 45.807% 44.765% 43.728% 42.696% 41.671%
54.193% 55.235% 56.272% 57.304% 58.329%
70% 5.6 45.634% 44.593% 43.556% 42.526% 41.501%
54.366% 55.407% 56.444% 57.474% 58.499%
65% 5.2 45.460% 44.420% 43.385% 42.355% 41.332%
54.540% 55.580% 56.615% 57.645% 58.668%
60% 4.8 45.287% 44.247% 43.213% 42.184% 41.162%
54.713% 55.753% 56.787% 57.816% 58.838%
55% 4.4 45.113% 44.075% 43.041% 42.014% 40.993%
54.887% 55.925% 56.959% 57.986% 59.007%
Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport 121
50% 4 44.940% 43.902% 42.870% 41.843% 40.824%
55.060% 56.098% 57.130% 58.157% 59.176%
In order to identify the best combination of policy interventions, a certain target
of probability of driving a car and taking public transport has to be set at the outset. In
this case, the target was a 10% decrease (i.e., from 58% to 48%) in the probability of
driving a car in Melbourne. Table 7.6 demonstrates that, to reach this target, there are
two possible travel scenarios. The first is the reduction of bus and train waiting time
to 60% of the baseline scenario (i.e., from 8 minutes to 4.8 minutes), and an increase
in the total parking and toll cost to 1.8 times the baseline scenario (i.e., from $7.5 to
$13.5). The second travel scenario is where the bus and train waiting time is the same
as the baseline scenario (i.e., 8 minutes), and the total parking and toll cost is increased
to twice the baseline scenario (i.e., from $7.5 to $15).
Of these two scenarios, the second seems more realistic. This is because the
implementation of plans to reduce bus and train waiting time requires a higher initial
cost than the imposition of a higher parking and toll cost (Boardman et al., 2017; Guo
& Wilson, 2011; Wardman, 2004). In this instance, the additional funds obtained from
the increment of parking and toll cost could be invested to improve other aspects of
passenger LOS; for example, the provision of charging stations on board public
transport services. While holding the bus and train waiting time at the baseline
scenario, and all other factors constant, a further increment in total parking and toll
cost of up to 2.5 times the baseline scenario decreases the car mode share from 58%
to 43%. When the most extreme scenario is applied (i.e., a 50% reduction in bus and
train waiting time and a 250% increase in total parking and toll costs), the probability
of driving a car in Melbourne decreases from 58% to 41%.
The change in the probability of driving a car and the probability of taking public
transport are separately illustrated in Figure 7.3 and Figure 7.4, respectively. Figure
7.3 shows that the probability of driving a car in Melbourne decreases with both the
increment of parking and toll costs (i.e., from $ 7.50 to $18.75) and the decrement of
bus and train waiting time (i.e., from 8 minutes to 4 minutes). Meanwhile, Figure 7.4
demonstrates that the probability of taking public transport in Melbourne increases
with both the increment of parking and toll cost (i.e., from $ 7.50 to $18.75) and the
decrement of bus and train waiting time (i.e., from 8 minutes to 4 minutes).
122 Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport
Figure 7.3 : The probability of driving a car in Melbourne as the result of policy interventions
in bus and train waiting time and parking and toll costs, holding all other factors constant
Figure 7.4 : The probability of taking public transport in Melbourne as the result of policy
interventions in bus and train waiting time and parking and toll costs, holding all other factors
constant
8
7.2
6.4
5.6
4.84
40.00%
45.00%
50.00%
55.00%
60.00%
7.5910.5
1213.5
15
16.5
18
Bus a
nd T
rain
waitin
g t
ime (
min
ute
s)
Pro
babili
ty o
f drivin
g c
ar
Combined parking and toll
cost ($)
40.00%-45.00% 45.00%-50.00% 50.00%-55.00% 55.00%-60.00%
8
7.2
6.4
5.6
4.8
4
40.00%
45.00%
50.00%
55.00%
60.00%B
us a
nd T
rain
waitin
g t
ime (
min
ute
s)
Pro
babili
ty o
f ta
kin
g p
ublic
tra
nsport
Combined parking and toll cost ($)
40.00%-45.00% 45.00%-50.00% 50.00%-55.00% 55.00%-60.00%
Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport 123
7.7.3 The impact of policy interventions in Brisbane
With reference to the utility function of bus, train, car, and public transport for
the Brisbane dataset (Equations 7-7, 7-8, 7-9, and 7-12, respectively) and the Brisbane
traveller’s profile (Table 7.4), the probability of driving a car and the probability of
taking public transport is calculated for each cell of travel scenarios, by controlling for
all other factors. The probability values of the result of each travel scenario are
tabulated in Table 7.7.
Table 7.7 The probability values of driving a car and taking public transport in Brisbane:
176 different policy intervention scenarios
Parking and Toll cost
100% 110% 120% 130% 140% 150%
$7.5 $8.25 $9 $9.75 $10.5 $11.25
P(Car)
P(Public Transport)
Waiting
time
(Minutes)
100% 9 for Bus 82.96% 82.25% 81.52% 80.77% 79.99% 79.19%
11 for Train 17.04% 17.75% 18.48% 19.23% 20.01% 20.81%
95% 8.55 for Bus 82.88% 82.18% 81.44% 80.69% 79.91% 79.11%
10.45 for Train 17.12% 17.82% 18.56% 19.31% 20.09% 20.89%
90% 8.1 for Bus 82.81% 82.10% 81.36% 80.61% 79.82% 79.02%
9.9 for Train 17.19% 17.90% 18.64% 19.39% 20.18% 20.98%
85% 7.65 for Bus 82.74% 82.02% 81.28% 80.52% 79.74% 78.93%
9.35 for Train 17.26% 17.98% 18.72% 19.48% 20.26% 21.07%
80% 7.2 for Bus 82.66% 81.94% 81.20% 80.44% 79.65% 78.85%
8.8 for Train 17.34% 18.06% 18.80% 19.56% 20.35% 21.15%
75% 6.75 for Bus 82.58% 81.87% 81.12% 80.36% 79.57% 78.76%
8.25 for Train 17.42% 18.13% 18.88% 19.64% 20.43% 21.24%
70% 6.3 for Bus 82.51% 81.79% 81.04% 80.27% 79.48% 78.67%
7.7 for Train 17.49% 18.21% 18.96% 19.73% 20.52% 21.33%
65% 5.85 for Bus 82.43% 81.71% 80.96% 80.19% 79.40% 78.58%
7.15 for Train 17.57% 18.29% 19.04% 19.81% 20.60% 21.42%
60% 5.4 for Bus 82.36% 81.63% 80.88% 80.11% 79.31% 78.49%
6.6 for Train 17.64% 18.37% 19.12% 19.89% 20.69% 21.51%
55% 4.95 for Bus 82.28% 81.55% 80.80% 80.02% 79.23% 78.40%
6.05 for Train 17.72% 18.45% 19.20% 19.98% 20.77% 21.60%
50% 4.5 for Bus 82.20% 81.47% 80.72% 79.94% 79.14% 78.31%
5.5 for Train 17.80% 18.53% 19.28% 20.06% 20.86% 21.69%
Parking and Toll cost
160% 170% 180% 190% 200%
$12 $12.75 $13.5 $14.25 $15
P(Car)
P(Public Transport) Waiting
time
(Minutes)
100% 9 for Bus 78.37% 77.53% 76.66% 75.76% 74.85%
11 for Train 21.63% 22.47% 23.34% 24.24% 25.15%
95% 8.55 for Bus 78.28% 77.43% 76.56% 75.67% 74.75%
124 Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport
10.45 for
Train 21.72% 22.57% 23.44% 24.33% 25.25%
90% 8.1 for Bus 78.19% 77.34% 76.47% 75.57% 74.65%
9.9 for Train 21.81% 22.66% 23.53% 24.43% 25.35%
85% 7.65 for Bus 78.10% 77.25% 76.37% 75.47% 74.55%
9.35 for Train 21.90% 22.75% 23.63% 24.53% 25.45%
80% 7.2 for Bus 78.01% 77.16% 76.28% 75.38% 74.45%
8.8 for Train 21.99% 22.84% 23.72% 24.62% 25.55%
75% 6.75 for Bus 77.92% 77.06% 76.18% 75.28% 74.35%
8.25 for Train 22.08% 22.94% 23.82% 24.72% 25.65%
70% 6.3 for Bus 77.83% 76.97% 76.09% 75.18% 74.25%
7.7 for Train 22.17% 23.03% 23.91% 24.82% 25.75%
65% 5.85 for Bus 77.74% 76.88% 75.99% 75.08% 74.15%
7.15 for Train 22.26% 23.12% 24.01% 24.92% 25.85%
60% 5.4 for Bus 77.65% 76.78% 75.90% 74.98% 74.05%
6.6 for Train 22.35% 23.22% 24.10% 25.02% 25.95%
55% 4.95 for Bus 77.56% 76.69% 75.80% 74.88% 73.95%
6.05 for Train 22.44% 23.31% 24.20% 25.12% 26.05%
50% 4.5 for Bus 77.47% 76.60% 75.70% 74.79% 73.85%
5.5 for Train 22.53% 23.40% 24.30% 25.21% 26.15%
Parking and Toll cost
210% 220% 230% 240% 250%
$15.75 $16.5 $17.25 $18 $18.75
P(Car)
P(Public Transport)
Waiting
time
(Minutes)
100% 9 for Bus 73.91% 72.95% 71.97% 70.97% 69.94%
11 for Train 26.09% 27.05% 28.03% 29.03% 30.06%
95% 8.55 for Bus 73.81% 72.85% 71.86% 70.86% 69.83%
10.45 for
Train 26.19% 27.15% 28.14% 29.14% 30.17%
90% 8.1 for Bus 73.71% 72.74% 71.76% 70.75% 69.72%
9.9 for Train 26.29% 27.26% 28.24% 29.25% 30.28%
85% 7.65 for Bus 73.61% 72.64% 71.65% 70.64% 69.61%
9.35 for Train 26.39% 27.36% 28.35% 29.36% 30.39%
80% 7.2 for Bus 73.50% 72.53% 71.54% 70.53% 69.50%
8.8 for Train 26.50% 27.47% 28.46% 29.47% 30.50%
75% 6.75 for Bus 73.40% 72.43% 71.44% 70.42% 69.39%
8.25 for Train 26.60% 27.57% 28.56% 29.58% 30.61%
70% 6.3 for Bus 73.30% 72.32% 71.33% 70.31% 69.27%
7.7 for Train 26.70% 27.68% 28.67% 29.69% 30.73%
65% 5.85 for Bus 73.20% 72.22% 71.22% 70.20% 69.16%
7.15 for Train 26.80% 27.78% 28.78% 29.80% 30.84%
60% 5.4 for Bus 73.09% 72.11% 71.11% 70.09% 69.05%
6.6 for Train 26.91% 27.89% 28.89% 29.91% 30.95%
55% 4.95 for Bus 72.99% 72.01% 71.00% 69.98% 68.94%
6.05 for Train 27.01% 27.99% 29.00% 30.02% 31.06%
Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport 125
50% 4.5 for Bus 72.88% 71.90% 70.90% 69.87% 68.82%
5.5 for Train 27.12% 28.10% 29.10% 30.13% 31.18%
In order to identify the best combination of policy interventions, a certain target
for the probability of driving a car and taking public transport has to be determined at
the outset; in this case, the target of achieving a 10% decrease (i.e., from 83% to 73%)
in the probability of driving a car in Brisbane was set. Table 7.7 demonstrates that
there are two possible travel scenarios that would achieve this target. The first is a
reduction in bus and train waiting time to 75% of the baseline scenario (i.e., from 9
minutes to 6.75 minutes for the bus waiting time, and 11 minutes to 8.25 minutes for
train waiting time), and an increase in the total parking and toll cost to 2.1 times the
baseline scenario (i.e., from $7.5 to $15.75). The second is where the bus and train
waiting time are the same as the baseline scenario (i.e., 9 minutes for bus waiting time,
and 11 minutes for train waiting time) and the total parking and toll cost is increased
to 2.2 times the baseline scenario (i.e., from $7.5 to $16.5).
Of these two travel scenarios, the second seems more realistic. This is because
implementation plans to reduce bus and train waiting times require a higher initial cost
than the cost of imposing higher parking and toll cost (Boardman et al., 2017; Guo &
Wilson, 2011; Wardman, 2004). In this case, the additional funds obtained from the
increment of parking and toll cost could be invested to improve other aspects of
passenger LOS, such as the provision of new buses or trains with better seats and
reduced carbon emissions. While holding the bus and train waiting times at the
baseline scenario and all other factors constant, a further increment of total parking
and toll cost up to 2.5 times the baseline scenario reduces the car mode share from
83% to 70%. When the most extreme travel scenarios are applied (i.e., a 50% reduction
in bus and train waiting time, and a 2.5 times increase in total parking and toll costs),
the probability of driving a car in Brisbane decreases from 83% to 69%.
The change in the probability of driving a car and the probability of taking public
transport are separately illustrated in Figure 7.5 and Figure 7.6, respectively. Figure
7.5 shows that the probability of driving a car decreases with an increase in parking
and toll cost (i.e., from $ 7.50 to $18.75) and a decrease in bus and train waiting times
(i.e., from 9 minutes to 4.5 minutes for bus waiting time, and from 11 minutes to 5.5
minutes for train waiting time). Meanwhile, Figure 7.6 demonstrates that the
probability of taking public transport increases with an increase in parking and toll cost
126 Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport
(i.e., from $ 7.50 to $18.75) and a decrease in bus and train waiting times (i.e., from 9
minutes to 4.5 minutes for bus waiting time, and from 11 minutes to 5.5 minutes for
train waiting time).
Figure 7.5 : The probability of driving a car in Brisbane as the result of policy interventions in
bus and train waiting time and parking and toll cost, holding all other factors constant
Figure 7.6 : The probability of taking public transport in Brisbane as the result of policy
interventions in bus and train waiting times and parking and toll costs, holding all other factors
constant
9 for bus and 11 for train
8.1 for bus and 9.9 for train
7.2 for bus and 8.8 for train
6.3 for bus and 7.7 for train
5.4 for bus and 6.6 for train4.5 for bus and 5.5 for train
60.00%
65.00%
70.00%
75.00%
80.00%
85.00%
7.59.75
12
14.25
16.5
18.75
Bus a
nd T
rain
waitin
g t
ime (
min
ute
s)
Pro
babili
ty o
f drivin
g c
ar
Combined parking and toll
cost ($)
60.00%-65.00% 65.00%-70.00% 70.00%-75.00% 75.00%-80.00% 80.00%-85.00%
9 for bus and 11 for train
8.1 for bus and 9.9 for train
7.2 for bus and 8.8 for train
6.3 for bus and 7.7 for train
5.4 for bus and 6.6 for train
4.5 for bus and 5.5 for train
15.00%
20.00%
25.00%
30.00%
35.00%
Bus a
nd T
rain
waitin
g t
ime (
min
ute
s)
Pro
babili
ty o
f ta
kin
g p
ublic
tra
nsport
Combined parking and toll cost ($)
15.00%-20.00% 20.00%-25.00% 25.00%-30.00% 30.00%-35.00%
Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport 127
7.8 THE DISCUSSIONS OF COMBINED POLICY INTERVENTION
SCENARIOS
Holding all other factors constant, the impacts of 176 different travel scenarios,
involving a wide range of bus and train waiting times and various parking and toll
costs in Sydney, Melbourne, and Brisbane, have been analysed. Results show that
policy interventions to improve public transport services can work effectively to
discourage regular car use and vice versa. More specifically, results of this analysis
show that the mode shares of car and public transport rapidly reverse as the result of
combined policy interventions; that is, car use rapidly decreases, while public transport
use rapidly increases.
In particular, as suggested by the literature (Axhausen & Polak, 1991; Feeney,
1989; Marsden, 2006; Young, Thompson, & Taylor, 1991), the increment in parking
cost is in the form of an increment in the hourly rate for on-street parking spots, and
the decrement in on-street parking spots in higher density areas. Another possibility
is to tighten the regulations around, and to increase the permit and licensing costs of
building multi-storey car park lots in high density traffic areas. As the total cost of
building is high, developers would be forced to charge exorbitant parking fees for their
car parks. The implementation of at least one of these possibilities would certainly
discourage car driving and foster the use of public transport services, especially if trips
are on a regular basis. If car drivers experience difficulties in finding parking spots,
while at the same time, paying an exorbitant parking cost for a single trip, they will
carefully reconsider their mode choices.
The issue of tolls, a form of a congestion tax for drivers, was raised in the survey
questionnaires. The charging of tolls for expressways connecting various suburbs with
the city centre or sites of major attraction can prevent drivers from using them. In turn,
this lack of expressway patronage could eventually reduce traffic pressure when
driving to, or within such areas. In the wider context, another form of congestion tax
for drivers is a congestion charge, which has been implemented successfully in London
and Singapore (Santos, 2005). The implementation of a congestion charge for entering
areas of high traffic density during peak hours would have an impact similar to the
impact of tolls; that is, it would discourage drivers from regularly using their cars in
such areas at such times. The roads and areas most suited to tolls and congestion taxes
can be determined through the analysis of past and forecasted traffics data in each city.
Such charges are structured based on operational costs and traffic demands (Santos,
128 Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport
2005). The increment of both costs has led car drivers to re-consider their mode choice
and driving habits (Beirão & Cabral, 2007; Feeney, 1989; Gardner & Abraham, 2010;
Santos & Rojey, 2004).
At the same time, policy interventions to encourage public transport ridership
should be introduced. The co-timing of these interventions is vital to maintaining and
improving the attractiveness of public transport services for both existing users and
mode shifting car users. The supply of public transport services needs to be adjusted
in anticipation of an increased demand. Ideally, the additional funds obtained from
increasing parking and toll costs or other congestion taxes can be allocated to
improving the passenger LOS across all public transport services.
Increasing the frequency of high demand bus and train services during AM or
PM peak hours is one way to improve LOS. For example, there is the need for more
frequent services from residential areas to working areas during the morning peak
period, and more frequent services in the opposite direction during PM peak hours.
Another approach is to identify and synchronise bus and train services which, based
on passenger data related to their past trips, are believed to be connected. Hence,
travellers would have a minimum waiting time between the two connecting services;
for example, between a feeder bus service from a residential area to the nearest train
station, and a feeder bus service from a train station to a working area during morning
peak hours.
From a different perspective, the improvement of perceived waiting time would
also improve passenger LOS. This perception can be achieved by improving the
condition of waiting areas in major train stations, and major bus stops and terminals.
Improvements could include the provision of entertainment systems, shelter, real-time
arrival/departure information, and printed service schedules.
7.9 CONCLUSIONS
Having undertaken a comprehensive literature review of policy intervention
theories that influence travel behaviours, this study found that no earlier study had
conducted a DCE or used a nested logit model to identify policy intervention targets
for influencing transport mode shift behaviour. To address these research gaps, this
study used DCE and travel behaviour data provided by urban travellers in the three
largest Australian capital cities (i.e., Sydney, Melbourne, and Brisbane). Specifically,
this study focused on determining the socio-economic factors and travel attributes that
Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport 129
significantly influence travellers’ mode choice in each city. Analyses of a sequence of
combined policy intervention scenarios then identified the most efficient combination
of policy interventions for urban travel.
The nested structure presented in Figure 4.1 provides a reasonable econometric
specification of the key factors influencing mode choice, and a means to avoid biased
estimates. It divides mode choice into two upper nest branches, public transport and
private transport. The public transport branch is further divided into two lower nest
branches, bus and train. Both bus and train are assumed to contain unobserved
attributes of public transport modes. Meanwhile, private transport is a degenerative
branch that contains only a single lower nest branch, car. This nested structure served
as a foundation for nested logit model estimation. Three best-fitted nested models were
estimated for the Sydney, Melbourne, and Brisbane dataset. Each of the utility
functions estimated within the nested logit model contains key travel attributes and
socio-economic factors. The travel attribute elements were useful in identifying the
specific area of policy intervention, while the socio-economic factor elements were
important to an understanding of the underlying profile of travellers who are affected
by a change in transport policies.
Based on the defined utility functions and the main objective of this study, two
policy interventions – each with a different purpose – were determined. These
intervention purposes are: to encourage public transport ridership; and to discourage
regular car usage. To obtain the optimal change in car mode share and public mode
share, both types of policy interventions were implemented at the same time. In order
to gain insights into the direct impacts of policy interventions on mode share (i.e., of
car and public transport), 176 different travel scenarios were simulated for Sydney,
and then replicated for the other two capital cities. By controlling all other factors, the
bus and train waiting time was reduced from the baseline scenario (i.e., 100%) to a
50% scenario, with a 5% interval; at the same time, the total parking and toll cost was
increased up to 2.5 times the baseline scenario, with a 10% interval.
While controlling all other factors, the overall scenario analysis suggests that the
total parking and toll cost needs to increase by 1.9, 1.8, and 2.1 times the baseline
scenario as well as the bus and train waiting time needs to decrease by 80%, 60%, and
75% of the baseline scenario to achieve a 10% reduction in car mode share in Sydney,
Melbourne, and Brisbane, respectively. When the most extreme travel scenarios are
applied (i.e., a 50% reduction in bus and train waiting time and a 2.5 times increase in
130 Chapter 7: Policy Interventions Study to Encourage Behavioural Shift from Car to Public Transport
total parking and toll cost), the probability of driving a car in Sydney, Melbourne, and
Brisbane decreases from 64% to 47%, from 58% to 41%, and from 83% to 69%,
respectively. (While it is acknowledged that there is mode share competition between
public transport services [i.e., between bus and train services] in each city, this issue
is not the focus of this study.)
Overall, this study has recognised the continuing practical value of nested logits.
It has also highlighted an appealing empirical strategy, at the centre of which is a
carefully crafted DCE that contributes to the mode choice modelling environment data
that is capable of satisfying many of the statistical properties. This study has also
attained its objective to successfully address the existing research gaps. The
establishment of a novel method for the analysis of travellers’ behavioural changes
and policy interventions is a theoretical contribution to the field of SP experiment. The
successfully replicated nested model estimation and policy intervention scenario
analysis for three different urban traveller datasets also provide evidence that there are
many opportunities to replicate the overall framework of the policy intervention study
to influence mode shift behaviours in other urban traveller datasets.
The findings of this study provide useful knowledge for policy makers and
transport authorities. This knowledge will contribute to the formulation of future
transit policies that focus on mode shift from car to public transport for urban travellers
in any Australian or international capital city. The analysis of the simulated travel
scenarios demonstrates that combined policy interventions dedicated to each mode
choice can go a long way in influencing long-term behavioural change.
Although the nested logit mode is widely recognised as a way of building a more
parsimonious predictive method, where the real focus of the analysis is on the policy
value of the explanatory variables, it is not the most advanced choice of modelling
methods, and this is a limitation of this study. Another limitation is that the nested logit
model employed does not account for unobserved heterogeneity in the population.
The random-parameter nested logit model is considered as a new topic in the
field of statistics and econometrics, and will be useful in identifying random
parameters and their heterogeneities within the utility functions estimated in a nested
logit model. Another meaningful extension of this study will be a comprehensive cost-
benefit analysis of each possible travel scenario to determine which combined policy
interventions would be the most beneficial and economical in optimising behavioural
change and mode share shift from car to public transport.
Chapter 8: Conclusions 131
Chapter 8: Conclusions
This chapter consists of three sections: the first (Section 8.1) synthesises the
overall thesis, elaborates the linkages among the three interconnected sub-studies, and
highlights the findings; the second (Section 8.2) discusses its contributions to wider
theoretical and practical contexts as well as its policy implications; the third (Section
8.3) acknowledges the study’s limitations; and the last section (Section 8.4) suggests
directions for future study.
8.1 OVERARCHING CONCLUSIONS
The combined findings of the three interconnected sub-studies have successfully
addressed all the defined research questions, and achieved the target objectives. These
major findings and innovations are elaborated below.
The comprehensive investigation of train riders’ satisfaction with train fares and
a comparative analysis of train fares in five Australian cities (Sydney, Melbourne,
Brisbane, Adelaide, and Perth) indicate that the train fare structure in each city
significantly influences the train riders’ satisfaction levels. In particular, when other
factors are controlled, this study finds that Sydney, Melbourne, and Brisbane train
riders feel less satisfied with train fare than those in Adelaide. This difference is most
likely caused by the different train fare structures imposed by the transport authorities
in each city. Specifically, Sydney, Melbourne, and Brisbane apply zone-based fares,
whereas Adelaide applies fixed fares regardless of the distance travelled. The majority
of train rider respondents from all cities travelled for 15 KM one-way. Therefore,
Sydney, Melbourne, and Brisbane train riders paid $4.20, $3.90, and $5.96
(respectively) for this journey, while Adelaide respondents only paid $3.54; that is, for
the same travelled distance, Adelaide train riders pay the least amount and, hence, have
the highest satisfactions level of the four cities.
Satisfaction levels with train fares are also influenced by gender; eligibility for
a concession fare; transport mode from home to train station; waiting time; one-way
cost; and a number of interaction variables between city of origin and socio-economic
factors. Waiting time and one-way cost are also found to be significant random
parameters in the best-fitted random parameters ordered logit model. Urban travellers’
132 Chapter 8: Conclusions
views on both variables are significantly affected by their socio-economic profiles,
such as their employment status, household composition, and trip purpose.
Having successfully completed the traveller satisfaction studies utilising the
revealed travel behaviour dataset, a subsequent consistency study developed a novel
test to scrutinize the consistency between perceptions of various quality of service
related factors and their SP responses. The latter data were collected concurrently from
the same group of travellers in the five Australian capital cities. This study was a
crucial prior step to utilising and analysing the stated travel behaviours dataset to
understand travellers’ future mode choice behaviours.
In this novel consistency test, a statistical model was estimated for each type of
dataset: the random parameters binomial logit model (RP model) for the perceptions
dataset; and the mixed logit model (SP model) for the responses to the SP experiment.
The factor mapping from these two models shows that the perception rates of four
service factors – such as train waiting time, on-board crowding, the availability of
laptop stations, and increased road congestion - and their corresponding attributes in
the SP experiment were constructively aligned. Furthermore, the estimated
probabilities of choosing the train mode from the SP model are found to be similar to
the probabilities of choosing the train mode estimated from the RP model.
Having confirmed the consistency between the collected mode choice responses,
and understanding the influence of socio-economic profiles and trip characteristics on
urban travellers’ mode choice behaviours, the mode shift study then analysed the
travellers’ SP responses, their past trip experiences, and their socio-economic profiles.
This study aimed to determine the significant socio-economic factors and travel
attributes that influence travellers’ future mode choice, and the interventions that could
be used to encourage mode shift behaviour, especially from car to public transport.
Unlike the previous two studies, this study separately analysed the datasets from
Sydney, Melbourne, and Brisbane.
To suit the behaviour-shift objective of this study, a nested logit model based on
a particular nested structure, was estimated for each city’s dataset. The nested structure
divided the mode choice into two upper nest branches, public transport and private
transport. The public transport branch was further divided into two lower nest
branches, bus and train. Both bus and train were assumed to contain unobserved
elements of the public transport mode. Private transport was a degenerative branch that
contained a single lower nest branch only, namely, car.
Chapter 8: Conclusions 133
According to the modelling results, two policy interventions – each with a
different purpose – were determined. The two purposes were: to encourage public
transport ridership; and to discourage regular car usage. To obtain the optimal change
from car mode share to public mode share, both types of policy intervention were
implemented at the same time.
In order to gain insights into the direct impacts of these interventions on car and
public transport mode shares, 176 different travel scenarios were simulated for
Sydney, and replicated for the other two capital cities. For instance, while controlling
all other factors, the bus and train waiting times were reduced from the baseline
scenario (i.e., 100%) to a 50% scenario with a 5% interval, and the total parking and
toll cost was increased up to 2.5 times the baseline scenario with a 10% interval.
The overall scenario analysis suggests that by increasing twice the total parking
and toll cost, controlling the public transport waiting time at a baseline scenario, and
keeping other factors constant, the car mode share decreases from 64% to 54% for the
Sydney dataset. A further increase to 250% in total parking and toll cost, while holding
all other factors constant, further decreases the car mode share to 49%.
Overall, it can be inferred that combined policy interventions to increase the cost
of travelling by car would certainly encourage car drivers to leave their cars at home
and take public transport services, especially if their trips are on a regular basis. The
increment in parking cost can be implemented together with a decrement in on-street
parking spots in higher density areas. Tolls for expressways connecting various
suburbs with the city centre or areas of major attractions, or a congestion tax for private
vehicles entering areas of high traffic density during peak hours can reduce traffic
pressure in such areas.
Ideally, the additional funding obtained from increasing car travel costs can be
concurrently utilised to encourage public transport ridership. The concurrent timing of
both interventions is vital to maintaining and improving the attractiveness of public
transport services for both existing, and mode-shift users. For example, in anticipation
of an increment in demand for public transport services, the supply of public transport
services needs to be adjusted accordingly (for example, increasing the frequency of
public transport services during peak hours). Hence, the passengers’ level of service
across all public transport services can be maintained and improved over time.
134 Chapter 8: Conclusions
8.2 CONTRIBUTIONS AND POLICY IMPLICATIONS
Each of the sub-studies has successfully achieved its target objectives and
provides significant contributions. These achievements and policy implications are
detailed below.
8.2.1 Urban travellers’ satisfaction with train fares in five Australian cities
In a wider context, the overall satisfaction modelling framework can be
replicated to understand and to quantify the diverse factors influencing satisfaction
with the whole journey experience. The findings of the satisfaction study constitute
significant knowledge for both policy makers and transport operators. Specifically,
this knowledge provides them with a comprehensive understanding of traveller
behaviours. This understanding, in turn, can guide their formulation of effective transit
policies to increase train rider satisfaction with the paid fare and, eventually, with the
overall journey experience.
8.2.2 Consistency between perceptions and stated preferences data in a
nationwide mode choice experiment
Specifically, the consistency assessment study demonstrated that the views of
diverse travellers on train waiting time, on-board crowding, the availability of laptop
stations, and increased road congestion were reasonably consistent across the
perceptions data and the SP experiment data. Our analysis confirms that these two
types of data source (i.e., the perceptions and the SP responses) are complementary in
helping us to better understand travellers’ complex mode choice behaviour.
In order to amplify the benefits of the consistency test, the overall modelling
framework could be implemented in other types of behavioural research, where both
revealed preference and stated preference data are concurrently collected from groups
of diverse respondents. For example, in the health field, research into people’s attitudes
to vaccination would be a prime candidate for such a framework.
Chapter 8: Conclusions 135
8.2.3 Policy interventions study to encourage behavioural shift from car to
public transport
This policy intervention mode shift study has recognised the continuing practical
value of nested logits, and has established a novel method for the analysis of travellers’
behavioural changes. The successfully replicated nested model estimation and policy
interventions scenario analysis in three different urban traveller datasets provide
evidence that the overall framework of the policy intervention study could be used to
influence mode shift behaviours in any other urban traveller dataset.
The findings of this study also provide useful knowledge for policy makers and
transport authorities to contribute to the formulation of future transit policies that focus
on encouraging mode shift from car to public transport services for urban travellers in
any urban setting. The analysis of the simulated travel scenarios demonstrates that the
combined policy interventions can significantly influence behavioural change. At the
same time, these interventions should comply with current social norms, regulations,
and laws, and the existing policies.
8.3 LIMITATIONS OF THIS STUDY
The following limitations of the overall study are acknowledged:
The complex impact on the data analysis caused by the fact that 15-20% of
respondents (from each city dataset) did not report on their income.
The reported on-board crowding could be inconsistent with the actual crowding,
and this could cause a potential confounding effect. Future studies could
incorporate some objective measure of crowding (e.g., the number of passengers on
a particular service) in the urban travellers’ behavioural study.
The access to real-time train service information was not considered in the SP
Experiment. This made it impossible to assess the consistency between
respondents’ perceptions and their corresponding SP responses on this important
attribute.
Previous studies noted the importance of weather, security, safety, and the health
and psychological condition of respondents to an understanding of the underlying
heterogeneity of key travel attributes in the SP Experiment (such as waiting time
and on-board crowding). Unfortunately, such information was not collected in the
survey dataset used in this study.
136 Chapter 8: Conclusions
8.4 RECOMMENDATIONS FOR FUTURE STUDY
The following recommendations are offered for future studies:
The random-parameter nested logit model is considered as a new topic in the field
of statistics and econometrics. It will be useful in identifying random parameters
and their heterogeneities within the utility functions estimated in a nested logit
model.
A comprehensive cost benefit analysis of each possible travel scenario to strongly
quantify which combined policy interventions would be the most beneficial and
economical to implement (that is, in order to optimise the behavioural and mode
share shift from car to public transport services).
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Appendices 137
Appendices
Appendix A
Urban Rail Travel Behaviour, Web-based Survey
This appendix is a soft-copy of the revised questionnaire, dated 19th November 2012,
that was distributed to the respondents. This questionnaire was developed by a project
team as part of Project R1.130 Understanding Urban Rail Travel for Improved
Patronage Forecasting funded by the CRC for Rail Innovation (established and
supported under the Australian Government's Cooperative Research Centres program).
This questionnaire has been approved by QUT Office of Research Ethics and Integrity
and I have been officially granted permission to utilise the dataset for my PhD study.
[Extra page inserted to ensure correct even-page footer for this section. Delete
this when bibliography is at least 2 pages long.]
Urban Rail Travel Behaviour Web-based Survey
Revised Questionnaire – November 19, 2012
Note:
• The survey can be customized for particular cities, with a core of comparable questions; • The estimated workload for train riders: 15 to 18 minutes; • The estimated workload for non train rider: 13 to 16 minutes;
SCREENING & QUOTA SELECTION (Estimated Workload: 1 minute)
A research team from Southern Cross University, Queensland University of Technology and The University of South Australia are conducting a study for the Commonwealth Cooperative Research Centre for Rail Innovation to more fully understand the travel patterns and travel choices of people like you. Your experiences, opinions, and travel behaviour are extremely important in order for us to deliver improved future train services. The survey will take you approximately 15 - 20 minutes to complete. The study is being conducted for research purposes only, and no attempt will be made to sell you anything at any time. Your participation is entirely voluntary. All comments and responses are anonymous and will be treated confidentially. Data from the survey will be saved on secure servers and de-identified by ORU before transmission to the research team. The data held by ORU will be destroyed at the conclusion of the research project. The de-identified data will be retained for use in further research. Research findings flowing from the survey will be incorporated in a report to the Commonwealth Cooperative Research Centre for Rail Innovation, which will normally make the report or core outcomes available on its publications website page http://www.railcrc.net.au/publications. If you have any questions or require any further information about this survey please contact Adjunct Associate Professor Keith Sloan Associate or Professor Michael Charles of Southern Cross University at 02 662 000 or by email via the link given here …………. This survey has Southern Cross University ethics approval number ECN-12-307. If you have concerns about the ethical conduct of this research or the researchers you may contact: The Ethics Complaints Officer Southern Cross University PO Box 157 Lismore NSW 2480 Email: ethics.lismore@scu.edu.au
SQ1 How old are you? (1) Younger than 16 years (Survey Close) (2) 16-17 years (3) 18-30 years (4) 31-40 years (5) 41-50 years (6) 51-60 years (7) 60+ years
SQ2 Do you or does anyone in your household work for a motor vehicle manufacturer, a public transport
provider, city rail company, or the city transport department? (1) Yes (Survey Close) (2) No
SQ3 How often did you travel by train LAST MONTH?
(1) Eight times or more; (Go to TR1) (2) Four times or more, but less than eight times; (Go to TR1) (3) Two times or more, but less than four times; (Go to TR1) (4) Once; (Go to NR1) (5) None; (Go to NR1)
Assign respondent to REGULAR RIDER if the answer to SQ3 is 1 or2, and assign respondent to INFREQUENT RIDER if the answer to SQ3 is 3; otherwise assign respondent to NONRIDER. For REGULAR RIDER and INFREQUENT RIDER, go to TR1; For NONRIDER, go to NR1. IF RELEVANT QUOTAS ARE FULL, CLOSE THE SURVEY.
TRAIN RIDER EXPERIENCE
(Estimated Workload: 6-7 minutes) The questions in this section are going to ask you to recall the details of your most recent TRAIN trip from home.
TR1 Besides train, which modes of transport were available to you when you made your most recent train trip
from home (even if you never use these methods)? (Please check all that apply) (1) Motor vehicle (financed, leased, or owned) (2) Company/work vehicle (3) Taxi (4) Motor Bike or Scooter (5) Bus (6) Tram (7) Cycling (8) Walking
TR2 Please state the nearest intersection to your home (please write down the postcode and two street names): Postcode: _______ Street 1: _______ Street 2: _______
TR3 Please state the nearest intersection to your destination of your most recent TRAIN trip from home (please write down postcode and two street names): Postcode: _______ Street 1: _______ Street 2: _______
TR4 What time of day did you commence your most recent TRAIN trip from home?
(1) Before 7am (2) Between 7:01 am and 9 am (3) Between 9:01 am and 3 pm (4) Between 3:01 pm and 7 pm (5) Between 7:01 pm and 2 am (6) Not sure
TR5 What was the main purpose of this most recent TRAIN trip from home?
(1) Employment (2) Business (3) Education (4) Leisure (holiday)/Recreation/Social/Volunteer (5) Shopping (6) Personal activity (e.g., picking up kids, medical appointment, errand, banking, etc.) (7) Other (Please indicate) __________________
TR6 Before you departed on your most recent TRAIN trip from home, did you check the most current information on the train services, such as arrival time of the next train or service updates on any delays or cancellations, etc.? (1) Yes (2) No
TR7 How did you get to the station on your most recent TRAIN trip from home?
(1) Bus (Go to TR7a) (2) Walked (Go to TR8) (3) Parked (free) at the nearest station (Go to TR7b) (4) Paid for parking at the nearest station (Go to TR7b) (5) Dropped off at the station, including taxi (Go to TR8) (6) Bicycle (Go to TR8) (7) Other (please indicate)_______________________ (Go to TR8)
TR7a: How long did you have to wait for the bus?
_________hours and _________ minutes Go to TR8
TR7b: If the parking facility was not available when you took this trip, you would have:
(1) driven to the destination (2) driven to another station (3) taken bus to the station (4) taken bus to the destination (5) walked to the station (6) cycled to the station (7) asked somebody for a ride to the station (8) parked elsewhere and then taken the train (9) parked elsewhere and then taken the bus (10) walked to the destination (11) cycled to the destination (12) asked somebody for a ride to the destination (13) cancelled the trip (14) Other (please indicate) _____________________
TR8 About how long does it take you to get from your home to the nearest train station?
_________hours and _________ minutes
TR9 On your most recent train trip about how long did you wait for the train? _________hours and _________ minutes
TR10 About how long were you on the train (time between getting on and off from the train including switching trains if applicable)? _________hours and _________ minutes
TR11 Which statement best describes the crowding condition during your most recent TRAIN trip from home? (1) It was easy to find a seat for the entire journey (2) I found a seat for the entire journey but most seats were occupied (3) I was standing up for 5 minutes prior to finding a vacant seat (4) I was standing up for 6 - 15 minutes prior to finding a vacant seat (5) I was standing up for 16 - 25 minutes or more prior to finding a vacant seat (6) I stood for the entire journey, as no seats were available (7) I stood for the entire journey, as I didn’t want to sit (8) Other (please indicate) ___________________
TR12 How did you spend your time on the train during your most recent TRAIN trip from home?
(1) Work or study related activities (e.g., writing) (2) Reading (e.g., news, book, magazine, etc) (3) Entertaining myself using personal digital devices (e.g., smart phone, tablet, iPod, iPad, etc) (4) Chatting with other travelling companion (e.g., chatting with friends, minding kids) (5) Relaxing or doing nothing in particular (e.g., sitting, looking out the window at local scenery) (6) Other (please indicate) ___________________
TR13 Once you got off the train on your most recent TRAIN trip from home, how did you get to your final
destination? (1) Bus (Go to TR13a) (2) Tram (Go to TR14) (3) Walked (Go to TR14) (4) Passenger pick up, including taxi (Go to TR14) (5) Bicycle (Go to TR14) (6) Other (please indicate) ____________________ (Go to TR14)
TR13a: How long did you wait for bus?
_________hours and _________ minutes
TR14 About how long did it take you to get to the destination? _________hours and _________ minutes
TR15 How much did you pay for tickets for this ONE-WAY trip?
(1) _________ dollars and ______cents (2) I don’t know.
TR16 Thinking about the services you received, on a 5-point rating scale how satisfied were you with the PRICE
you paid for your most recent TRAIN trip from home? 1 = “Extremely dissatisfied”; 5 = “Extremely satisfied”.
¨ ¨ ¨ ¨ ¨
Extremely dissatisfied Dissatisfied Neutral Satisfied Extremely satisfied
1 2 3 4 5
TR17 All things considered, on a 5-point rating scale how satisfied were you with the experience of your most recent TRAIN trip from home? 1 = “Extremely dissatisfied”; 5 = “Extremely satisfied”.
¨ ¨ ¨ ¨ ¨
Extremely dissatisfied Dissatisfied Neutral Satisfied Extremely satisfied
1 2 3 4 5
BUS/MOTOR VEHICLE EXPERIENCE (Estimated Workload: 4-5 minutes)
The questions in this section are going to ask you to recall the details of your most recent trip from home that was taken by motor vehicle or by bus.
NR1 For your most recent trip from home that was taken by motor vehicle or by bus, which methods of transport listed below were available to you when you were planning this trip (even if you never use these methods)? (Please check all that apply) (1) Motor vehicle (financed, leased, or owned) (2) Company/work vehicle (3) Taxi (4) Motor Bike or Scooter (5) Bus (6) Tram (7) Train (8) Cycling (9) Walking
NR2 Please state the nearest intersection to your home (please write down the postcode and two street names): Postcode: _______ Street 1: _______ Street 2: _______
NR3 For your most recent trip from home that was taken by motor vehicle or by bus , please state the nearest intersection to your destination (please write down the postcode and two street names): Postcode: _______ Street 1: _______ Street 2: _______
NR4 For your most recent trip from home that was taken by motor vehicle or by bus, what time of day did you depart? (1) Before 7am (2) Between 7:01 am and 9 am (3) Between 9:01 am and 3 pm (4) Between 3:01 pm and 7 pm (5) Between 7:01 pm and 2 am (6) Not sure
NR5 For your most recent trip from home that was taken by motor vehicle or by bus , what was its main purpose? (1) Employment (2) Business (3) Education (4) Leisure (holiday)/Recreation/Social/Volunteer (5) Shopping (6) Personal activity (e.g., picking up kids, medical appointment, errand, banking, etc.) (7) Other (Please indicate) __________________
NR6 Approximately what was your in-vehicle travel time for this trip? _________hours and _________ minutes
NR7 For your most recent trip from home that was taken by motor vehicle or by bus, what transport mode did you
use? (1) Motor vehicle (financed, leased, or owned) (Go to NR7a-b) (2) Passenger in a motor vehicle driven by someone else (Go to NR7a-b) (3) Company/work vehicle (Go to NR7a-b) (4) Taxi(Go to NR7c) (5) Bus (Go to NR7d-g)
NR7a Approximately how much did you spend on parking for this trip? If you didn’t pay for parking, enter ‘0’.
_________ dollars and ______cents
NR7b Approximately how much did you spend on tolls for this trip? If you didn’t pay for toll, enter ‘0’.
Go to NR8
NR7c How much did you pay for this ONE-WAY trip?
_________ dollars and ______cents
NR7d How much did you pay for tickets for this ONE-WAY trip? _________ dollars and ______cents
NR7e About how long did it take you to walk to the bus stop from your home? If you did NOT walk to the bus stop, please estimate the would-be walking time.
_________ hours and _________ minutes
NR7f About how long did you wait for the bus? _________ hours and _________ minutes
NR7g Which statement best describes the crowding condition on the bus during your most recent trip? (1) It was easy to find a seat for the entire journey (2) I found a seat for the entire journey but most seats were occupied (3) I was standing up for 5 minutes prior to finding a vacant seat (4) I was standing up for 6 - 15 minutes prior to finding a vacant seat (5) I was standing up for 16 - 25 minutes or more prior to finding a vacant seat (6) I stood for the entire journey, as no seats were available (7) I stood for the entire journey, as I didn’t want to sit (8) Other (please indicate) ___________________
NR8 Why did you choose this transport mode? (1) No other mode was available (2) Cheapest mode available (3) Fastest mode available (4) Most convenient mode available (5) Safest mode available (6) Most comfortable mode available (7) Work-related vehicle (8) Personal mobility constraints (e.g., disabled) (9) Needed to transport bulky items (10) Weather conditions (11) Other (please indicate)________________________
NR9 What are the main reasons that you did not take the train for this trip? (1) I like driving (2) Driving was faster (3) Work-related vehicle (4) No available train service (5) Personal mobility constraints (e.g., disabled) (6) Needed to transport bulky items (7) I was offered a free ride (e.g., sitting in a car driven by another person) (8) I prefer bus/ferry/tram (9) The nearest train station was too far (10) The trip was too short for taking train (11) The train is not sufficiently safe (12) The train does not run frequently enough (13) The train is not convenient for visiting multiple destinations (14) The train is generally for people who don’t have access to motor vehicles (15) The train is too crowded (16) The train fare is too expensive (17) Other (please indicate) ________________________
NR10 After getting out of the motor vehicle or getting off the bus, about how long did it take you to reach your final destination? _________ hours and _________ minutes
GENERAL QUESTIONS
(Estimated Workload: 4-5 minutes) The questions in this section are designed to collect some general information regarding your travel behaviour and your socio demographic background. These questions have NOTHING to do with your most recent trip.
GQ1 On a 5-point rating scale, to what extent would the following factors influence you to take a train more often? 1 = “no influence at all”; 5 = “very strong influence”. (1) Better access (e.g., more bus services, more comfortable walking environment) to the station by bus
(2) Train runs on schedule
(3) Higher likelihood of getting a seat / less crowding
(4) The ability to access up to date information on train services such as current train status
(5) Availability of an entertainment system (e.g., video, audio, TV etc. )
(6) Increased road congestion
(7) Private vehicle drivers are charged a congestion tax or toll every time they enter the city in peak hours
GQ2 To what extent did access to rail services influence your decision to live in your current location? (1) Very significant (2) Significant (3) Moderate (4) Low (5) Was not a consideration
GQ3 What is your motor vehicle access status? (1) I don’t have access to a motor vehicle (2) I have access to my own motor vehicle (3) I have access to company/work vehicles (4) I have access to a shared motor vehicle
GQ4 Do you have a driver’s license?
(1) Yes (2) No
GQ5 Do you need a motor vehicle for your work or business?
(1) Yes (2) No (3) I do not work
GQ6 Your current employment status
(1) Full-time (paid employment) (2) Part-time (paid employment) (3) Self-employed (4) Not in the work force (e.g., maternity leave, unemployed) (5) Retired (6) Student (1) Other (please indicate)__________________________
GQ7 Which of the following best describes your current household? (1) Couple family with dependent children (2) One parent family with dependent children (3) Couple only (4) Multiple family household (5) Lone person (6) Group household (7) Other one family household (8) Other (please indicate)__________________________
GQ8 In which country were you born? (1) Australia (2) England (3) New Zealand (4) Italy (5) Vietnam (6) Scotland (7) Greece (8) China (9) India (10) Philippines (11) South Africa (12) Germany (13) Malaysia (14) Other (please indicate) __________________________
GQ9 What is your gender?
(15) Male (16) Female
GQ10 Into which of these groups does your pre-tax household weekly income fall?
(1) Under $200 (2) $200 to less than $300 (3) $300 to less than $400 (4) $400 to less than $500 (5) $500 to less than $600 (6) $600 to less than $700 (7) $700 to less than $800 (8) $800 to less than $900 (9) $900 to less than $1,000 (10) $1000 to less than $1,100 (11) $1,100 to less than $1,200 (12) $1,200 to less than $1,300 (13) $1,300 to less than $1,400 (14) $1,400 to less than $1,500 (15) $1,500 to less than $1,600 (16) $1,600 to less than $1,700 (17) $1,700 to less than $1800 (18) $1800 or more (19) Did not draw a wage or salary
GQ11 What is your highest educational qualification?
(1) Postgraduate Degree Level (2) Graduate Diploma and Graduate Certificate Level (3) Bachelor Degree Level (4) Advanced Diploma and Diploma Level (5) Certificate Level (6) School Education Level
HOW WOULD YOU TRAVEL? (Estimated Workload: 4-5 minutes)
Based on the information you have provided, we understand that your most recent trip from home by [train/bus/motor vehicle] started from [TR2/NR2] and ended at [TR3/NR3]. For this trip, the available transport modes were [TR1/NR1]. As you are probably aware, changes that are out of your control can take place, including availabilities of different transport options. In this section, you will be asked to participate in 6 HYPOTHETICAL experiments. In each of these experiments, a number of transport choices with different features are described. If these options were available to you with the features listed when you were planning your most recent trip described above, please indicate which travel option you would choose. Due to the large amount, it is impossible to attach all the experiments here. Instead, for illustration purpose, 6 experiments from the same block for {train, bus} are provided below.
Mode choice experiment 1:
Mode choice experiment 2:
Mode choice experiment 3:
Mode choice experiment 4:
Mode choice experiment 5:
Mode choice experiment 6:
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