A Study of Car and House Ownership in the face of ...€¦ · Ownership in the face of Increasing...
136
A Study of Car and House Ownership in the face of Increasing Commuting Expenses (CHOICE) by Elli Maria Papaioannou A thesis submitted in conformity with the requirements for the degree of Masters of Applied Science Graduate Department of Civil Engineering University of Toronto © Copyright by Elli Maria Papaioannou, 2014
Transcript of A Study of Car and House Ownership in the face of ...€¦ · Ownership in the face of Increasing...
A Study of Car and House Ownership in the face of Increasing Commuting Expenses
(CHOICE)
by
Elli Maria Papaioannou
A thesis submitted in conformity with the requirements for the degree of Masters of Applied Science
Graduate Department of Civil Engineering University of Toronto
© Copyright by Elli Maria Papaioannou, 2014
ii
A Study of Car and House Ownership in the face of Increasing
Commuting Expenses (CHOICE)
Elli Maria Papaioannou
Masters of Applied Science
Department of Civil Engineering University of Toronto
2014
Abstract
This research presents the design, implementation and results of a survey of Car and House
Ownership in the face of Increasing Commuting Expenses (CHOICE). The CHOICE survey is a
web-based survey that combines RP and SP components, and was designed to collect
information of commuting mode choices, housing and neighbourhood preferences along with
vehicle ownership choices of households with cross-regional commuters in the Greater Toronto
Area. Investigations of the survey data revealed that for small increases in commuting costs
people are willing to change to more efficient cars. As commuting costs reached higher levels,
participants chose to relocate their home in order to commute shorter distances. This study
provides evidence that vehicle ownership and especially residential location decisions are a
complex process and are interrelated. The findings of this study show some of the possible
reactions of households in the GTA in the face of extreme increases in transportation costs.
iii
Acknowledgments
This two-year-long journey flew by and I am standing at the end of it today trying to think of all
the challenges I faced, all the wonderful and bright people I met, all the things I learned, and all
the friends I earned.
First, I would like to sincerely thank my supervisor, Khandker Nurul Habib, for his valuable
guidance and genuine care. He is an enthusiastic teacher and a great person who treats his
students as his children. I want to thank him for his help, advice and kindness. Many thanks to all
my other professors for their important input: Amer Shalaby, Lloyd McCoomb, and especially
Matthew Roorda for spending the time and energy as a second reviewer of my thesis.
Second, I want to thank Dimitris Panagiotakopoulos, the programmer who not only helped in
coding the survey, but with his patience and experience also played a definitive role in the
outcome of the study.
I also want to thank my friends and colleagues within the transportation group at the University
of Toronto. Mohamed Salah Mahmoud offered his valuable help and support countless times
throughout these two years, and I consider myself lucky for having the opportunity to learn from
him. Many thanks go to Adam Weiss for offering his help during the development of the survey.
Of course, these two years would not have been the same without my good friends and office-
mates: Adam Wenneman and Nico Malfara; you two made my days in that office “brighter”.
Finally, I want to thank my family: my mother and my brother for their support and love, and
especially my father who throughout my life has not only been a parent but my mentor and my
biggest supporter. Many thanks to my “sisters”: Lindsey, Lida and Theofili for their love and
countless sessions of skype-laughter. And last but not least, I want to thank my Toronto-people:
Lee, Holly, Jane, Kaisa and Aitor who together with the rest of the crew made these two years
the most memorable and adventurous journey of my life.
Elli Papaioannou
September 2014
iv
Table of Contents
Acknowledgments ........................................................................................................................... ii
Table of Contents ........................................................................................................................... iv
List of Tables ................................................................................................................................ vii
List of Figures ................................................................................................................................ ix
GLOSSARY .................................................................................................................................. xi
1 Introduction ................................................................................................................................ 1
1.1 Research Goals and Objectives ........................................................................................... 3
1.2 Thesis Layout ...................................................................................................................... 4
2 Literature Review ....................................................................................................................... 6
2.1 Vehicle Ownership .............................................................................................................. 6
2.2 Transit-pass Ownership ...................................................................................................... 8
2.3 Residential Location Choice and Travel Costs ................................................................... 9
2.4 Web surveys: Advantages and Disadvantages .................................................................. 12
2.4.1 Errors in Surveys ................................................................................................... 14
2.5 Revealed Preference (RP) and Stated Preference (SP) Data............................................. 16
3 A Survey of Car and House Ownership in the face of Increasing Commuting Expenses (CHOICE) and the Background of the Study Area .................................................................. 18
3.1 Study Area ........................................................................................................................ 18
3.1.1 The Greater Toronto Area ..................................................................................... 20
3.1.2 The Transportation Tomorrow Survey ................................................................. 23
3.2 Survey Sample Design and Target Population ................................................................. 23
3.2.1 Sample Size Determination ................................................................................... 24
4 CHOICE Survey Design .......................................................................................................... 26
v
4.1 RP ...................................................................................................................................... 27
4.1.1 Housing Information ............................................................................................. 27
4.1.2 Socio-demographic and Commuting Trip Information ........................................ 27
4.1.3 Vehicle Information .............................................................................................. 29
4.2 SP ...................................................................................................................................... 31
4.2.1 Experimental Design ............................................................................................. 31
4.2.2 SP1: Effects of Price Changes on Mobility Tool Ownership ............................... 31
4.2.3 SP2: Residential Relocation as a Reaction to Increased Transportation Costs .... 37
4.2.4 Comments on the SP Component ......................................................................... 42
4.3 Software ............................................................................................................................ 44
4.4 Recruiting .......................................................................................................................... 44
4.5 Sample Distribution and Representativeness .................................................................... 44
5 CHOICE Survey Results .......................................................................................................... 48
5.1 RP Descriptive Analysis ................................................................................................... 48
5.1.1 Household Descriptive Analysis ........................................................................... 48
5.1.2 Descriptive Statistics of Individuals’ Characteristics ........................................... 51
5.2 SP1 Descriptive Analysis: Mobility Tool Ownership ...................................................... 53
5.2.1 Fuel Consumption and Engine Type ..................................................................... 54
5.2.2 Vehicle Type ......................................................................................................... 55
5.2.3 Influence of Different Factors on Operation Costs ............................................... 56
5.2.4 Transit Pass Ownership ......................................................................................... 57
5.2.5 Relocating Decision .............................................................................................. 57
5.3 SP2 Descriptive Analysis: Residential Location Choice .................................................. 59
5.3.1 GTA Overview of SP2 .......................................................................................... 60
5.3.2 Conclusion of Residential Location Choice Descriptive Analysis ....................... 66
5.4 Dwelling Type Choice ...................................................................................................... 66
vi
6 Modelling Mobility Tool Ownership and Residential Relocation ........................................... 68
6.1 Mobility Tool Ownership Model ...................................................................................... 68
6.1.1 Data Preparation .................................................................................................... 68
6.1.2 General Model Specification ................................................................................ 69
6.1.3 Empirical Analysis ................................................................................................ 70
6.2 Trade-offs Between Mobility Costs and Residential Location ......................................... 72
6.2.1 Data Preparation .................................................................................................... 72
6.2.2 General Model Specification ................................................................................ 72
6.2.3 Empirical Analysis ................................................................................................ 73
6.2.4 Elasticities ............................................................................................................. 74
7 Modelling Dwelling Type Choice ............................................................................................ 78
7.1 Dwelling Type Model ....................................................................................................... 78
7.1.1 Non Linear Interactions ........................................................................................ 78
7.1.2 Utility Functions ................................................................................................... 79
7.1.3 Results ................................................................................................................... 80
8 Conclusions and Future Work .................................................................................................. 83
8.1 Summary ........................................................................................................................... 83
8.2 Recommendations for Improving the CHOICE Survey ................................................... 85
8.3 Future Work ...................................................................................................................... 86
References ..................................................................................................................................... 87
Appendix A: Survey Software ...................................................................................................... 93
Appendix B: Statistics of Current Home Location and Location Chosen in SP2 by Region ..... 112
Appendix C: Fixed and Variable Costs of all Vehicle Combinations in SP1 ............................. 116
Appendix E: Transit Fare O-D Matrix for Scenario 1 ................................................................ 123
vii
List of Tables
Table 1: GTA Population Change 2006 to 20011 (TTS Data) ..................................................... 20
Table 2: Attribute Characteristics of SP1 ..................................................................................... 33
Table 3: Car Choice Characteristics ............................................................................................. 34
Table 4: Number of Total Responses at each Part of the Survey ................................................. 45
Table 5: CHOICE Sample Representation of Household Attributes Based on TTS 2006 Data .. 45
Table 6: Sample Representation of Individual Attributes Based on TTS 2006 Data ................... 46
Table 7: Recruitment and Response Rate ..................................................................................... 48
Table 8: Descriptive Analysis of Household Attributes ............................................................... 49
Table 9: Descriptive Analysis of Individuals’ Characteristics ..................................................... 52
Table 10: Home and Work Location Distribution of Sample ....................................................... 53
Table 11: Estimation Results of Linear Regression Model for Operation Costs .......................... 56
Table 12: Statistics of Housing Price in SP2 ................................................................................ 67
Table 13: Estimation Results of MNL for Mobility Choice in SP1 ............................................. 70
Table 14: Estimation Results of Binary Model for Leaving Current Home Location in SP1 ...... 74
Table 15: Residence Relocation Choice Elasticity with respect to Increasing Transportation Costs
(derived from the results of the binary model in Section 6.2.3) ................................................... 76
Table 16: Estimation Results of MNL for Dwelling Type Choice in SP2 ................................... 81
Table 17: Spatial Location Statistics of York Region Households ............................................. 112
Table 18: Spatial Location Statistics of City of Toronto Households ........................................ 113
Table 19: Spatial Location Statistics of Halton Region Households .......................................... 114
viii
Table 20: Spatial Location Statistics of Durham Region Households ........................................ 114
Table 21: Spatial Location Statistics of Peel Region Households .............................................. 115
Table 22: Variable and Fixed Costs of Vehicles in SP1 ............................................................. 116
Table 23: Dwelling Type Price for One-bedroom Units by Neighborhood ............................... 117
Table 24: Dwelling Type Price for Two-bedroom Units by Neighborhood ............................... 118
Table 25: Dwelling Type Price for Three-bedroom Units by Neighborhood ............................. 119
Table 26: Dwelling Type Price for Four-bedroom Units by Neighborhood .............................. 120
Table 27: Dwelling Type Price for Five-bedroom Units by Neighborhood ............................... 121
Table 28: Qualitative Characteristics of Neighbourhood ........................................................... 122
Table 29a: Transit Fares for SP1 ................................................................................................ 123
ix
List of Figures
Figure 1: Errors in surveys (figure reproduced from Groves (2005)) .......................................... 15
Figure 2: Greater Toronto and Hamilton Area ............................................................................. 19
Figure 3: GO Transit system map (source: GO Transit website) ................................................. 22
Figure 4: Survey design overview ................................................................................................ 26
Figure 5: RP data model of CHOICE survey ................................................................................ 30
Figure 6: SP2 map overview ......................................................................................................... 38
Figure 7: Neighbourhood characteristics and housing prices in SP2 ........................................... 39
Figure 8: RP and SP data model connections ............................................................................... 41
Figure 9: Number of possible choice scenarios ............................................................................ 43
Figure 10: Reasons for moving from previous to current house .................................................. 50
Figure 11: Reasons for choosing current neighbourhood ............................................................. 51
Figure 12: Fuel consumption, car ownership and engine types depending on fuel price in SP1 . 54
Figure 13: Vehicle type choice in SP1 .......................................................................................... 55
Figure 14: Relocating percentage of GTA regions as a result of SP1 cost increases ................... 57
Figure 15: Home and work location concentrations of CHOICE respondents ............................. 59
Figure 16: Home location, work location and SP2 home location choice of Durham residents .. 61
Figure 17: Home location, work location and SP2 home location choice of Peel residents ........ 62
Figure 18: Home location, work location and SP2 home location choice of York residents ....... 63
Figure 19: Home location, work location and SP2 home location choice of Halton residents ... 64
x
Figure 20: Home location, work location and SP2 home location choice of City of Toronto
residents ........................................................................................................................................ 65
Figure 21: Residence relocation choice elasticity and weighted elasticity with respect to
increasing transportation costs ...................................................................................................... 76
Figure 22: Welcome screen .......................................................................................................... 94
Figure 23: Help map for home and work location ........................................................................ 95
Figure 24: Part 1- Retrospective house information ..................................................................... 96
Figure 25: Part 2- Household sociodemographic information and typical commuting trip ....... 100
Figure 26: Part 3- Retrospective vehicle ownership ................................................................... 103
Figure 27: Help tab for choosing vehicle category ..................................................................... 104
Figure 28: SP1- Mobility tool ownership ................................................................................... 105
Figure 29: SP2- Home location choice- Screenshot of main map .............................................. 107
Figure 30: SP2- Screenshot of the divisions in the selected region ............................................ 108
Figure 31: SP2- Screenshot of the districts in the selected division ........................................... 109
Figure 32: SP2- Screenshot of the neighborhoods in the selected district .................................. 110
xi
GLOSSARY
AFV Alternative Fuel Vehicles
CHOICE Car and House Ownership in the face of Increasing Commuting Expenses
CMA Census Metropolitan Area
DEFF Design Effect
GTA Greater Toronto Area
GTHA Greater Toronto and Hamilton Area
MLS Multiple Listing Service
MNL Multinomial Logit
NRCAN Natural Resources Canada
PD Planning District
RP Revealed Preference
RUM Random Utility Maximization
SEM Structural Equation Model
SP Stated Preference
SP1 Mobility Tool Ownership Stated Preference Experiment
SP2 Residential Location Choice Stated Preference Experiment
TAZ Traffic Analysis Zone
TTC Toronto Transit Commission
TTS Transportation Tomorrow Survey
1
1 Introduction
Transportation cost changes affect travel behaviour in many ways. These changes can affect
short-term decisions and actions of people (e.g. departure time, trip frequency, mode choice), but
they can also change people’s commitment to places and activities (e.g. change home location,
change work location). According to transportation specialists (Meurs, 1990); (Bhat, 1998),
vehicle ownership and residential location choice are two of the most important determinants of
travel behaviour and affect among others the mode choice, the trip frequency, the trip distance,
and the general travel patterns of people.
Identifying how travel cost changes can affect these decisions is of extreme importance to
different parties for different reasons. In the short run and given a small increase in transportation
costs, trip makers react by reducing the frequency or the distance of their trips, by altering
departure time to avoid congested periods, and by switching to less expensive modes of travel. In
situations of higher and longer-lasting increases in transportation costs people will decide to
modify their mobility tools (vehicles and transit passes). However, humans are creatures of habit
and many of their decisions are defined by it. In particular, when we are examining mode choice
decisions, we observe that habit plays a significant role and people insist on choosing their usual
mode instead of rationally comparing the alternatives (Idris, 2013). It is, therefore, difficult to
capture behavioural changes for short term or small changes in cost.
The first time that researchers noticed a change in this apathy was after the crude oil shock in
2008 (Erath & Axhausen, 2010). The unusual and consistent increase in travel costs led many
trip makers to abandon their private vehicles. At the same time, the automobile industry in
Canada (which is closely linked to the automobile industry in the US due to the Automotive
Products Trade Agreement1) noticed a significant drop in the sales of large vehicles. In 2008 the
sales of pickup trucks dropped down by 17%, and large sport vehicles were down 29%.
Meantime the sales of subcompact cars increased by 33%, and the Toyota Prius hybrid was
among the most popular options with 23% increase in sales (NBC News, 2008).
1 The Canada—United States Automotive Products Agreement, commonly known as the Auto Pact or APTA, was an
important trade agreement between Canada and the United States. It removed tariffs on cars, trucks, buses, tires, and automotive parts between the two countries, greatly benefiting the large American car makers.
2
This change reveals the demand for smaller and more efficient mode options in the face of the
new cost circumstances. Taking this relationship a step further, one may argue that in case of
permanent changes in transportation costs people will reconsider their long-term decisions and
possibly change residential location.
These potential reactions could change the mobility and housing reality as we know it today.
Altering, what we consider, medium and long-term decisions (vehicle ownership and residential
location) would cause inconvenience and commotion to governments, individuals, agencies and
private companies, and is, therefore, important to find answers to some of these questions.
Governments would be interested to know how people would travel given the new cost
circumstances; with such information they would prepare alternative plans to adjust the urban
system and provide solutions for different scenarios. At the same time public transit agencies
would have forecasts to adjust their service and their network according to the future demand.
Companies from the automobile industry would get a preview of the consumers’ preferences and
would target their research and resources towards products that are probably going to be more
popular among the public. Real estate agencies and construction companies might get
information about the type of residence and the areas of the city that will be in high demand.
Finally, individuals will have more accurate information about all these issues and they will be
able to make educated decisions about their mobility options and their housing choices.
When examining these issues for the North American reality it is even more important to identify
how major cost changes could affect individuals’ travel patterns and mobility-related decisions.
The Greater Toronto Area (GTA) is the largest metropolitan area in Canada, and the fifth largest
in North America with a population of about 6 million residents based on the 2011 Census.
According to the population growth projections of the Ontario Ministry of Finance, the
population of the GTA will reach 8.9 million people by 2036 (Ontario Ministry of Finance,
2013). The City of Toronto will grow an approximate 23% by 2036, and the other census
divisions of the GTA (Halton, Peel, York and Durham) will experience a significantly faster
growth (approximately 1.9 million people by 2036).
The residents of the GTA use primarily their private car for commuting trips and add traffic to
the already congested street network. According to the Big Move (Metrolinx, 2008) – the
3
regions’ transportation master plan – commuters spend approximately 82 minutes in their cars
and travel on average 26 km daily. The only metropolitan-wide transit service – GO Transit – is
expensive, not easily accessible, and integrates poorly with the local transit agencies, resulting
into a very low share of transit to the modal split (only 16.5% of the morning rush hour trips are
taken in transit).
Taking all these issues into consideration and combining the possibility of another oil shock
event in the future, it becomes apparent what questions this study is attempting to answer. It is of
vital importance to understand how people will react, and what aspects of their every-day
decisions they will change in order to face a new reality.
1.1 Research Goals and Objectives
With few exceptions most studies found in literature use aggregate or disaggregate revealed
preference data for mobility tool ownership and residential location choices, and are, therefore,
incapable of capturing reactions to cost ranges higher than those that were observed at the time.
This study is trying to cover this gap by investigating the effects of long-term, extreme increases
in transportation costs in the context of North American cities and regions.
In order to collect the data needed for this study a survey of Car and House Ownership for
Increasing Commuting Expenses (CHOICE) was designed and conducted among a randomly
selected sample in the Greater Toronto Area (GTA). The survey combined revealed and stated
preference components, and aimed to collect information about the retrospective residential
location, housing and vehicle ownership choices of the households. The stated preference
component of the survey collected data of the effects of increasing transportation costs on
mobility tool ownership levels and residential location choices. Such data could support
investigations about the effects of economic recession, increasing traffic congestion and the
possible implementation of pricing policies on the change in urban form (the relocation of
households in and out of the GTA).
These investigations are very important for regional planning agencies to make informed
decisions and/or evidence-based planning. Analyses and outputs of the CHOICE dataset could
allow us to test alternative land use and travel demand policies. These could be used by local
4
agencies to examine if their short-term goals are met. In addition, the GTA’s regional transit
authority – Metrolinx – could use such information to validate if their transportation demand
management policies, and their numerous projects are effective and complying with the long-
term objectives of the regions’ Master Plan (e.g: smart growth, mobility hubs, smart commuter
program Metrolinx’s Big Move).
The collected data were used to develop a series of models in order to understand and
interpret the decisions of GTA households in the past, and to form a better understanding
about their intentions and willingness to accept higher costs before changing their current
vehicle fleet, and/or residential location. Overall, the thesis aims to improve our knowledge
on the link between transportation and land use. The data collected through the CHOICE
survey will provide a step towards filling the apparent gap between travel demand modelling
and land use modelling in practice.
1.2 Thesis Layout
The thesis consists of eight chapters. Chapter 1 presented a brief introduction of the problem, and
the purpose of this study.
Chapter 2 attempts an overview of the research efforts and studies that dealt with the
development of models for residential location and vehicle ownership. Main focus is given to
studies that investigated these choices in combination with stated preference data and varying
cost ranges. Additional literature is provided for advantages of combining revealed preference
and stated preference data, and finally a review of the different survey collection methods is
presented, highlighting the advantages and weaknesses of web-based surveys.
Chapter 3 introduces the CHOICE survey, the study area and the data collection process.
Chapter 4 includes a detailed description of the different parts of the survey and the experimental
design, mainly focusing on the stated preference components of the survey and the design
process.
Chapter 5 covers the descriptive analysis of all parts of the survey and is meant to be the first
approach to understand the study area and the sample.
5
After the first analysis, a more thorough and analytical approach is taken in Chapters 6 and 7
with the construction of a series of models. These models cover the two SP parts and answer to
the main questions of this study: how will people modify their mobility tools; when will people
move; and where will they move.
Finally, Chapter 8 discusses the findings of this study and the interpretation of the analysis, and
concludes with a detailed subsection for future work and recommendations to improve the
survey for the second phase of the data collection.
6
2 Literature Review
This chapter starts with an overview of studies that investigated vehicle and transit pass
ownership. Emphasis is given to studies that examined these choices using stated preference data
for increasing travel costs. Subsequently, residential location studies are cited to review what has
been found about the relationship between travel costs, and in particular commuting expenses,
and house location. Finally, a brief overview of the advantages and shortcomings of web-based
surveys is presented, followed by the strengths of combining different data sources for
modelling.
2.1 Vehicle Ownership
In developing vehicle ownership models the main challenge that researchers faced, was to
identify which mechanisms represent more accurately the true underlying choice process, and
how these choices change in response to changing attributes. The first vehicle ownership models
focused on different aspects of vehicle purchase, ownership and use. Most of these first models
were based on aggregate data and explained vehicle ownership choices at a more generic level,
such that of a traffic analysis zone, a region, a state or a country (Stanovnik, 1990). The biggest
limitation of aggregate models lies in their incapability to be used in policy-sensitive analyses
and their incompatibility with agent-based approaches in transportation modelling (Potoglou &
Susilo, 2008).
These limitations led to a more microscopic-level approach and to the estimation of vehicle
ownership models based on disaggregate data. The first disaggregate models were based on
individual or household level data, and addressed the issues of the number and type of vehicles
owned and the explanatory variables that influenced these choices; mainly socioeconomic
variables, income, purchase price, and travel opportunities by other modes (Tardiff, 1984). Some
examples of the first disaggregate vehicle ownership models can be found in Lave and Train
(1979) who used multinomial logit methodology to model choice of car type; and in Hensher et
al. (1989), who used a nested structure to model car type, size and quantity choices.
Nolan (2010) found that car ownership started becoming a necessity rather than a luxury over the
years, and that the effect of income in explaining the variation of car ownership was declining. In
7
her study Nolan used six-year longitudinal data, and estimated the dynamic decisions of
households regarding vehicle ownership. The findings indicated, that while higher income still
appeared to affect car ownership in a positive way, the size and composition of the household,
and the characteristics of the head of the household (age, gender, whether worker or not) were
the most significant determinants in the model estimation.
While these early studies analysed vehicle choices in terms of number and type of vehicles,
increasing concern about the environment and the energy consumption led to the inclusion of
different engine technologies and fuel consumption in the research questions. Brownstone et al.
(2000); Hensher and Greene (2001); Potoglou and Kanaroglou (2007) are some of the
researchers that combined RP and SP data in their studies to evaluate the willingness for
alternative fuel vehicles (for more information about RP and SP data refer to section 2.5).
According to Brownstone et al. (2000), RP data are useful to acquire realistic vehicle size and
type choices of respondents and SP data are useful for examining attributes that are not available
in the market. Iten et al. (2005) developed an SP experiment to account for the make, type,
engine size, fuel consumption and fuel efficiency class of the vehicle.
Similar to Iten et al. (2005), Potoglou and Kanaroglou (2007) collected SP data in Hamilton,
Canada to examine the factors and incentives that were more likely to influence a household’s
decision towards alternative fuel vehicles (AFV). Potoglou and Kanaroglou (2007) used a nested
multinomial logit structure to investigate preferences for body type and engine technology. They
found that age, gender, and household characteristics are factors affecting individuals’
willingness to pay for AFV. Interestingly, long-distance commuters were found to be more
hesitant to adopt AFV. The main reason for this seems to be the shorter travel range of AFV as
compared to gasoline/diesel fuelled vehicles. Other studies that used SP data to investigate
willingness to switch to AFV technology can be found at Louviere et al. (2000); Alder et al.
(2003); Ewing and Sarigollu (2000); and Dagsvik et al. (2002)
All of these models are able to forecast the demand for different fuel economy policies, and to
model the future fuel consumption, but they have their limitations. First, they are incapable of
capturing the shifts in car purchase caused by changes in fuel price, but most importantly they
examine the choice of each vehicle as independent, even in the context of the same household.
8
Bhat and Pulugurta (1998) developed a multiple discrete continuous model, which accounts for
multiple vehicles and use levels within the same decision making unit. They used SP data from a
Swiss survey about household choices of fleet size and characteristics under different cost
schemes. The results showed that households with members that commute farther tend to own
more cars; however, they do not favour efficient cars. The authors concluded that inertia effects,
in the context of downgrading the size and composition of fleet, cease in the face of drastic
changes in cost. In contrast, minor cost changes lead to shifts from gasoline to diesel alternatives.
These findings suggest that increasing costs will lead to more efficient alternatives and less
energy consumption, but they will not affect the mobility of households (Jäggi et al., 2011).
More recently, Erath and Axhausen (2010) developed a structural equation model (SEM) to
estimate the household’s consumption for different fuel types (gasoline, diesel, natural gas) in
response to increasing costs. In order to develop the model they collected stated preference data
from Swiss households in 2009 by using a novel survey approach. A three-stage questionnaire
collected information about the mobility tools (vehicle and transit passes), the housing location
and the housing costs of the households. Then the respondent was asked to add the desired
mobility bundle for each commuting member based on different residential locations and for
increasing gas price scenarios. Finally, in the last stage of the experiment the respondent had to
indicate which combination from stage two (residential location and mobility tools) they would
ultimately choose. Their investigation showed that older people, frequent public transit users,
and people living in less accessible areas react more to higher transportation costs. In contrast,
households with more adult members and households with big cars (luxury, sport, SUV) react
less to higher costs.
2.2 Transit-pass Ownership
There are many studies that investigate vehicle ownership, but little can be found about public
transit pass ownership and use. Even though it should be self-evident that transit pass ownership
replaces, to a certain extent, vehicle ownership, there are very few studies that account for this
interaction. Relevant studies found that the commitment to a mode affects significantly the use of
the respective mode and that consequently strong substitution effects exist between vehicle and
transit pass ownership (Simma & Axhausen, 2003).
9
This substitutive relationship becomes more evident when fuel prices increase significantly.
König and Axhausen (2003) developed a web-based stated preference survey in the framework
of the project “Mobiplan” that aimed to understand the travel behaviour of German and Dutch
households. The participants had to state the mode choice and mode use based on real-time
information about their commuting trip, and housing costs. The collected data were analysed by
Darren et al. (2006) and confirmed that the substitution effects between car and transit ownership
are significant, and ignoring such relationship may lead to bias in results.
Erath and Axhausen (2010) developed a more advanced version of the same experiment to
account for varying price schemes. In one of the parts of the experiments the respondent had to
choose the desired mobility bundle (cars and transit passes) for the household, given different
travel costs. The results showed that increasing travel costs have a more distinct impact on the
type of vehicle choice rather than the mode selected. Respondents were found more likely to
downgrade the size and/or the engine technology of the vehicle rather than switch to public
transit.
Triggered by the economic recession and the boosted oil prices of 2008, researchers and
governments expressed increasing interest in studies that investigate travel behaviour in such
occasions, so that they could prepare the system for similar events in the future. The topics in
question include short-term reactions of the kind that König and Axhausen (2003) and Simma
and Axhausen (2003) analysed in their studies, but also longer-term reactions that could lead to
complete changes in travel patterns and in the structure of the urban form.
2.3 Residential Location Choice and Travel Costs
Increasing transportation costs could lead to complete changes in urban form. Unlike the large
number of studies investigating the mode shifts and mobility tool ownership changes under
increasing transportation costs, investigations about the residential location choice under the
same circumstances are rarely encountered. However, residential location choice on its own has
been extensively studied. Most residential location studies use longitudinal, revealed preference
or stated preference data, whereas a large portion of the residential location studies focuses on
the location choice as a result of the decision maker’s lifestyle (Beige, 2008); (Morrow-Jones,
2007).
10
Studies that are based on longitudinal data are incapable of capturing how travel cost changes
influence the location choice. On the other hand, studies based on revealed preference (RP) data
cannot directly capture information about the mobility costs, since the alternative/non-chosen
locations are not sampled. Therefore, the mobility costs associated with the location of a
household are subject to many uncertainties. Instead of using the mobility costs themselves,
most studies using RP data include accessibility measures as an indirect indication of the
mobility opportunities (locations with high access are related to lower travel costs). Bürgle
(2006) concluded that only few travel-related variables influence the residential location choice;
in particular those related to an individual’s commuting trip.
Employing SP data to analyse residential location choice is considered by many researchers
(Hunt, 2001); (Earnhart, 2002); (Kim et al., 2003) to be the most powerful approach, because
such formulations overcome the main constraints of RP (multi-collinearity issues and high data
requirements). SP data overcome an additional constraint that RP data fail to address; they
measure the impact of new policies and they capture behaviour and choices in hypothetical
situations, such as changes in transportation costs, toll implementation, congestions pricing,
introduction of new technologies etc. Studies from Hunt (2001) and Molin and Timmermans
(2003) that used SP data found that in order for a household to downgrade residential unit, a
drastic decrease in commute time to work would be required.
However, most studies are in agreement that residential location choice is mostly defined by
neighbourhood attributes, housing characteristics, and household composition and demographics,
rather than accessibility attributes (Weisbord et al., 1980); (Bhat & Guo, 2007); (Waddell, 2006).
Building on that, many studies focused on the different neighbourhood characteristics and
housing needs that households have with respect to their lifestyle. For example, households
highlighting the accessibility to highways by car, or valuing the parking availability when
choosing a residential location implies people heavily dependent on their motorized vehicles. On
the other hand, people who value pedestrian and cycling infrastructure, or the availability of
transit service close to their home, indicates more active, environment and health conscious
individuals. (Walker & Li, 2007); (Mikiki & Papaioannou, 2014). Similarly, Mikiki and
Papaioannou (2012) found that people, who are active and environmentally friendly, change
more easily from car to other less polluting and energy consuming modes.
11
Finally, residential location choice has been looked at as a decision that is strongly defined by
the life cycle stage of the household. Evidently, over a person’s lifetime different factors will
have greater influence on the chosen residential location (Morrow-Jones, 2007). Households
with children will most likely value the safety of a neighbourhood, the quality of schools, the
distance to school, or spacious house units with yard. Older people will probably prefer
neighbourhoods with easy access on foot or by transit to facilities, services and shopping areas
(Cao et al., 2006).
Although various researchers have covered the area of modelling residential location choices, the
influence of cost changes on the choice of residence is yet to be investigated. The crude oil price
shock of 2008 revealed the first signs of willingness of households to reconsider their mobility
tools –vehicles and public transit passes. In 2009 Erath and Axhausen (2009) collected
information based on six scenarios, in order to evaluate the respondent’s propensity to move due
to increasing travel costs. These scenarios encouraged the respondent to choose between various
residential situations under different housing and transportation cost schemes. A multinomial
logit model was used to question the willingness to accept higher mobility costs before changing
residential location. The actual model examined the way people perceive transportation and
housing costs and how income influences their cost perception. It further explored the
relationship between travel time characteristics and residential location choice. Their results
indicated that housing costs are perceived to be the most onerous; nevertheless, strong inertia
was present before changing spatial location.
**********
Undoubtedly, the interactions and trade-offs among residential location choice, mobility tool
ownership, and travel patterns are of multidimensional and complex nature. Most studies that can
be found in the literature focus on one side of this relationship, leaving most of the interactions
out of the equation. In particular, while previous research covers extensively the area of car
ownership at the individual level, the household level is left unexamined. SP experiments that
have been conducted in the past, collected data for a small range of fuel prices, leaving
unanswered scenarios of extreme fuel price increases.
12
Most residential location studies conclude that neighbourhood attributes are more defining
compared to travel attributes when choosing a location, but most of these studies cover a
restricted range of travel costs. The example that can be found in the literature ((Erath &
Axhausen, 2010) in sections 2.1-2.3) that investigated the interactions between mobility and
housing costs under different cost schemes used a novel stated adaptation approach that proved
to be successful. However, this study was based on Swiss data and applies to the European
reality, where transit attracts a significantly higher percentage of trip makers, and where the land
use and urban design differs notably from the North American situation.
This thesis aims to investigate the relationship that underlies between housing and commuting
expenses. In particular, the following research questions are addressed:
What are the preferences of each commuter regarding vehicle type and engine technology
in each case?
What is the tipping point (trade off) between transportation costs and housing costs?
What factors play the most important role when choosing a new residence?
2.4 Web surveys: Advantages and Disadvantages
The proposed CHOICE survey designed for this study was conducted via the Internet and it
would be beneficial to underline some of the advantages of using web-based techniques as well
as the limitations that come with new approaches.
Evolving technology allowed people to respond via Internet and this method led to cost savings
for the researcher and time saving for both the researcher and the respondent. According to
researchers, (Berge et al., 1996); (Schmidt, 1997); (Zhang, 1999), the greatest time saving of
web-surveys comes from the turnaround time, especially when compared to the delay and
potential data loss of mail-surveys. Web-based surveys also reduce the input errors from coding
the answers into a database (Zhang, 1999), since the collected information is directly stored in
electronic format. The greatest advantage, however, is the design flexibility that web-surveys
offer (Dillman, 2000); (Zhang, 1999). Web-surveys combined with a dynamic process that
includes pop-up instructions, skip logic, and incorporation of attractive features, such as maps
and graphics, may increase the respondent’s willingness and motivation to fill the survey
13
(Schmidt, 1997); (Zhang, 1999). From a research perspective web-surveys allow the embodiment
of specialized software, algorithms and datasets that can improve the validity of the results and
the accurate representation of the subject in question.
While the most commonly cited disadvantage of all surveys is the bias due to errors (refer to
Section 2.4.1) that occur at the different life-cycle stages of the survey (Couper, 2000); (Dillman,
2001); (Dillman, 1998), there are some disadvantages that are solely associated with web-
surveys. Among the survey errors (coverage error, sampling error, measurement error and
nonresponse error), nonresponse errors are surprisingly common in web-surveys. Exogenous
factors might interrupt the survey process (e.g. internet connection loss), or specific socio-
demographic groups may be under-represented or excluded due to limited access to the Internet
(people of low education or low income, older people, etc. (Zhang, 1999)). Typical response
rates of travel surveys approximate 20%, but some studies suggest that response rates are lower
in web-surveys compared to the traditional paper- and-pencil surveys (Crawford et al., 2001);
(Sax et al., 2003); (Underwood et al., 2000). One common problem of web-surveys is the
variability of the interface appearance. This problem arises due to the use of different web
browsers, screen configurations and operating systems, and in practice may result in a
problematic appearance or malfunction of the survey (Dillman, 2000; 2001). These problems
may result in higher drop-outs (Smith, 1997); (Zhang, 1999), and, therefore, explain the lower
response rates of web-surveys.
Web-surveys also deal with various ethical considerations. Data confidentiality and security
constitute a general concern of surveys, but it becomes an even bigger challenge in web-surveys.
In web-surveys it is more difficult to persuade the respondent about the integrity of the process,
and to assert that the data will be safely and anonymously stored and handled. Finally, some
people feel that web-surveys are crossing their personal boundaries. Some consider that
receiving surveys via mailing lists is an invasion of privacy; and others are concerned about the
degree of access that the researcher may have in their private information (e.g. researchers often
collect supplementary information without the permission of the respondent: what browser was
used to complete the survey, when they started and finished the survey etc.).
14
Finally, there is some technical difficulty in developing web-surveys. There are survey design
software available in the market (Qualtrics, Surveymonkey,etc) that offer a variety of question
types and applications, but they have limits in their flexibility and/or offer a very small portion of
their tools in a free trial version, but they are very costly if full access to the application is
required. Due to this restriction most web-surveys are developed by programmers and are
customized to the needs of a specific survey topic. Over the long run this approach is not cost-
effective since it requires the involvement and compensation of specialists, and the result is very
case-specific and cannot be re-used for other purposes. The last technical difficulty is the storage
location and administration requirement of web-surveys. Web-surveys require a database to store
the information on and a server to host the survey. Servers are costly to acquire, are generally
hard to configure, and the database setup is a very important part of the process, because it has to
be ensured that all the information is properly stored.
2.4.1 Errors in Surveys
The most commonly cited disadvantage in web-surveys, and in all surveys, is the bias due to
errors: coverage error, sampling error, measurement error and nonresponse errors. (Couper,
2000; (Dillman and Bowker, 2001); Dillman et al., 1998). Figure 1 illustrates the different
sources of errors and the stage of the life cycle of the survey at which they are encountered.
15
Figure 1: Errors in surveys (figure reproduced from Groves (2005))
Coverage error in surveys occurs when there is a mismatch between the target and the frame
population. Target population is the population that can be covered, in other words the
population of inference, while frame population or sampling frame describes the set of
population members that has the chance to be selected into the survey sample. Sampling errors
result from the non-observational gap between sampling frame and the sample. Sampling error is
dependent on the sample size; therefore, larger sample size reduces the sampling error and
provides higher quality of data. However, a larger sample size does not eliminate the other
sources of error. Measurement errors are the gap between the ideal measurement and the
response obtained. Some researchers identified that respondents who completed a paper and
pencil version of the same web-survey answered differently (Sax et al, 2003). Therefore,
16
suggesting that technology-related surveys experience larger bias due to inaccurate responses.
Finally, nonresponse error concerns the non-observed gap between the sample and the
respondent pool. In other words, nonresponse error arises when individuals in a sample are
unwilling or unable to complete the survey. This error is surprisingly common in web-surveys..
2.5 Revealed Preference (RP) and Stated Preference (SP) Data
The CHOICE survey combined revealed and stated preference (RP and SP) components to
collect information about housing and vehicle ownership of households in the GTA. This section
offers a brief overview of each data type, their advantages and weaknesses, and the way they are
used and/or combined in choice modelling.
RP methods describe the actual market behaviour of respondents based on measured variables;
they are, therefore, limited to analysing the effects of existing factors and cannot provide direct
information on new (non-existing) alternatives or examine hypothetical situations. SP data on the
other hand are based on the intention or choices of respondents in hypothetical scenarios (Ben-
Akiva et al., 1994). SP experiments have received increasing attention in the transportation field,
where they are used to determine the influence of independent variables on the observed
outcome (Louviere et al., 2000). In traditional SP experiments a number of choice scenarios are
presented to the respondent. Each scenario describes some alternative choices with a set of pre-
defined attributes, where the attributes can take a range of pre-selected levels, and the respondent
is typically asked to choose one alternative. In general, the higher the number of attributes and
levels involved the more complex it becomes for the respondent to comprehend and respond
accurately to the experimental situation presented.
RP and SP data are usually collected in the form of discrete choice observations and provide
choice models with data to investigate trade-offs among variables. The ultimate target is to
develop models that can forecast future demand, either for existing or for non-existing products
and situations. When trying to find relationships and explain non-existing
situations/products/choices, RP data are inadequate and only SP data can cover this deficiency.
SP data have numerous advantages over RP data: the attribute levels can be extended to cover a
wider range; the correlation between attributes can be minimized; the attributes are free from
measurements errors; and most importantly SP data can elicit people’s preferences for new
17
alternatives. However, SP data are subject to perception errors, and it still remains to be
concluded how consistent they are with actual behaviour. (Ben-Akiva et al., 1994).
Evidently, each method has its advantages and its backdrops and can be respectively applied.
However, when combining RP with SP data we create powerful models, where the utility
functions have common preference parameters and usually express the trade-offs that the
respondents carry out among the attributes they consider most important. When the RP and the
SP data come from the same source it is likely that there will be correlation between the random
component observations. Ben-Akiva and Morikawa (1990) developed a method that adjusts this
correlation and also controls the correlation between different SP responses from the same
individual. It is generally believed that stated preference surveys may yield biases and large
random errors (Ben-Akiva and Morikawa, 1990). Therefore, using RP data that have similar
characteristics as complementary to SP data can minimize or eliminate these deficiencies.
Ultimately, joint RP/SP data are considered to increase the statistical efficiency of models and
started giving new insight and explanatory power to the developed choice models.
18
3 A Survey of Car and House Ownership in the face of Increasing Commuting Expenses (CHOICE) and the Background of the Study Area
A survey of Car and House Ownership in the face of Increasing Commuting Expenses
(CHOICE) was designed and conducted in the Greater Toronto Area (GTA). The CHOICE
survey is a web-based survey designed to collect information about commuting mode choices,
housing and neighbourhood preferences, along with vehicle ownership choices of households
with cross-regional commuters in the GTA. The multidimensional nature of this topic requires an
unconventional approach. The CHOICE survey is designed as a combination of two stated
adaptation experiments that are pivoted on retrospective revealed preference information of
commuting trips, car ownership and house ownership of the participating households. The survey
is software-based and uses actual commuting costs and information of alternative mobility tool
ownership, neighbourhood information and home type/cost in the face of increasing
transportation costs.
This chapter starts by introducing the study area and other important information about the GTA.
Then, the sampling technique and the sample size calculations are presented.
3.1 Study Area
The study area of the CHOICE survey is the Greater Toronto Area (GTA). The GTA covers an
area of 7,124 km2 and consists of the City of Toronto (Etobicoke, York, Downtown, Toronto,
East York and Scarborough), and the regions of Peel, Durham, Halton and York. The GTA
experienced a population growth of more than 12% in 5 years, and as of 2011 has 6,057,400
residents. Figure 2 illustrates the boundaries of the GTA in addition to the City of Hamilton
(GTHA).
19
Figure 2: Greater Toronto and Hamilton Area
The GTHA is served by nine separately-governed local transit agencies and only one regional
transit provider, GO Transit (for more information about GO Transit, refer to Section 3.1.1.1).
The poorly integrated transit system makes the cross-boundary trips inconvenient, unattractive
and costly. This shortcoming becomes more evident if we take into consideration the large
number of cross-boundary trips (approximately 25%) that take place in the GTHA. According to
the Big Move2, a commuter spends on average 82 minutes travelling per day. More than 85% of
all commuters use their private vehicle and travel approximately 26 km per day (Metrolinx,
2008). These figures combined with the expected population growth in the near future emphasize
the importance of this study. The CHOICE survey targeted households located in the GTA with
at least one cross-regional commuter.
2 The Big Move is the regional transportation plan published in 2008 by Metrolinx for the Greater Toronto and
Hamilton Area. It makes specific recommendations for transit projects and investment strategies.
20
3.1.1 The Greater Toronto Area
The GTA consists of the City of Toronto and the regions of Peel, Durham, Halton and York. The
City of Toronto is according to Statistics Canada home to 2,503,281 people, and is the largest
City in Canada and one of the most populous cities in North America. After the amalgamation
the City of Toronto consists of six districts: Etobicoke, York, Scarborough, Downtown Toronto,
East York, and North York. The Region of Peel consists of three municipalities: Mississauga,
Caledon and Brampton and is one of the biggest employment centres, after the City of Toronto.
Durham Region is the largest region covering 2,523,63 km2 and consists of 8
municipalities/townships: Pickering, Ajax, Whitby, Oshawa, Clarington, Uxbridge, Scugog, and
Brock. Halton Region includes the municipalities of Oakville, Halton Hills, Milton, and
Burlington. Finally, York Region incorporates the cities of: Vaughan, Richmond Hill, King,
Aurora, Newmarket, Georgina, Markham, Whitchurch-Stouffville, and East Gwillimbury.
Table 1 shows the population of each region and respective municipalities based on
Transportation Tomorrow Survey (TTS) data for 2011 and 2006 (for more information about
TTS refer to Section 3.1.2).
Table 1: GTA Population Change 2006 to 20011 (TTS Data)
Region City/Township
TTS 2011 TTS 2006 Change %
City of Toronto 2,616,800 2,445,900 6.99%
Downtown 737,900 685,300 7.68%
York 140,000 142,300 -1.62%
East York 115,500 112,000 3.13%
North York 649,000 604,400 7.38%
Etobicoke 348,100 319,900 8.82%
Scarborough 626,300 582,000 7.61%
Halton Region 502,000 422,700 18.76%
Burlington 175,900 157,400 11.75%
Oakville 182,600 159,700 14.34%
Milton 84,400 52,900 59.55%
Halton Hills 59,100 52,700 12.14%
21
Peel Region 1,297,600 1,119,100 15.95%
Mississauga 713,900 648,400 10.10%
Brampton 524,200 416,400 25.89%
Caledon 59,500 54,300 9.58%
York Region 1,032,700 857,500 20.43%
Vaughan 288,400 231,200 24.74%
King 19,900 18,300 8.74%
Aurora 53,300 45,100 18.18%
Newmarket 80,000 72,300 10.65%
Richmond Hill 185,500 158,000 17.41%
Markham 301,900 249,000 21.24%
Whitchurch-Stouffville 37,600 22,700 65.64%
East Gwillimbury 22,500 20,500 9.76%
Georgina 43,600 40,400 7.92%
Durham Region 608,300 539,500 12.75%
Pickering 88,700 84,200 5.34%
Ajax 109,600 87,700 24.97%
Whitby 122,100 105,200 16.06%
Oshawa 149,700 137,500 8.87%
Clarington 84,600 74,800 13.10%
Scugog 21,600 20,300 6.40%
Uxbridge 20,600 18,200 13.19%
Brock 11,400 11,600 -1.72%
Greater Toronto Area 6,057,400 5,384,700 12.49%
3.1.1.1 Transit Authorities in the GTA
The GTA (without Hamilton) is served by seven local transit agencies: the Toronto Transit
Commission (TTC) in the City of Toronto; Miway and Brampton Transit in Peel Region; York
Region Transit/Viva; Durham Region Transit; Burlington Transit and Oakville Transit in Halton
Region. There is only one regional transit provider, GO Transit, which serves all regions by bus
and train. GO Transit fares depend on origin and destination zones, $5.20 being the minimum
possible fare that a traveller could pay. The GO Transit fare system is integrated with six out of
the seven local transit agencies, but not with the TTC. This creates inconvenience and additional
22
charges for most of the cross-regional commuters, as the majority of them travels to the City of
Toronto. Figure 3 shows the GO Transit network, which mainly operates in the Greater Toronto
and Hamilton area and extends into many communities in the Greater Golden Horseshoe. The
GO Transit system covers 450 km by train and 2,760 km by bus, and carried more than 65
million passengers in 2012 (Metrolinx, 2014).
Figure 3: GO Transit system map (source: GO Transit website)
3.1.1.2 PRESTO Card
In 2007 Metrolinx and the government of Ontario started a trial for a smart card fare payment to
integrate public transit systems in Southern Ontario. The full implementation began in 2009 and
ever since more provinces and agencies are gradually joining the program. The PRESTO Card is
an electronic fare payment system, which uses a smart-card technology and can be used with
23
most of the transit agencies that operate in the GTA. PRESTO is becoming popular among the
cross-regional commuters, since it typically offers a discount fare compared to using cash fares.
3.1.2 The Transportation Tomorrow Survey
The Transportation Tomorrow Survey (TTS) is a survey that is conducted every five years in the
GTHA and collects retrospective household trip diary for 5% of all households in the study area.
The first TTS survey was conducted in 1986 and the most recent in 2011. In every survey a
random sample of the study area is invited to complete the questionnaire. Until 2006 the survey
was completed exclusively over the phone, but starting 2011 the respondents had the option to
complete the survey on-line. The survey collects travel data for all household members (11 and
older) for the day previous to the phone (or web) contact, in addition to some socio-economic
and demographic information.
The CHOICE survey was designed to be consistent with TTS data so many of the questions and
distributions defined in the following sections are based on TTS 2006 data. TTS 2006 was the
most recent data at the time of the experimental design and implemenation of the survey.
3.2 Survey Sample Design and Target Population
The CHOICE survey intended to measure the trade-offs between mobility tool expenses and
housing costs that households with cross-regional commuters are willing to make in the face of
increasing transportation costs. At the time of the preparation of the CHOICE survey the TTS
2006 was the most recent survey, and consequently its data was used to determine the
distributions of the survey sample. In more detail, the origin-destination matrices from TTS 2006
for all commuting trips were used to determine the total number of cross-regional commuters
among the five regions. Households with at least one cross-regional commuter were invited to
complete the survey. The survey population of the study (cross-regional commuters) is estimated
28,660 commuters distributed in the GTA. Given the large population and the limited resources
it is evident that a sample survey is the only feasible alternative.
There are two types of sampling techniques: non-probability sampling and probability sampling.
The first provides a fast and inexpensive way to collect the desired sample by using a non-
random method; however, it does not ensure a representative sample of the population. The
24
second method is called probability sampling. When employing this method, we randomly select
units from the population and avoid selection bias. Therefore, when using probability sampling
it is possible to generalize the results to reflect the whole population (Franklin et al., 2003).
The sampling procedure followed for the CHOICE survey was the simple stratified random
sampling based on the geographic home location of the population. The five regions of the GTA
were the five strata that made up the sample size, as explained in the next section.
3.2.1 Sample Size Determination
The determination of the sample size is essential because it defines the precision of the estimates.
The larger the sample size used in a study, the smaller the nonresponse errors, and therefore, the
greater the precision of the estimates. There are many factors that should be accounted for when
determining the sample size: the appropriate level of precision in terms of the margin of error,
the sampling variance that is required, and finally the size of the survey population.
Calculating the initial sample size: For the CHOICE survey we assumed a maximum
population variability of P=0.5. Additionally, a margin of error e=0.05 at a 95%
confidence interval is used to determine the sample. This means that there is a 5%
chance that the randomly selected sample will not produce an estimate within the
range of P±e (z=1.96).
�� =���(���)
�� (1)
�� =1.96� ∗0.5(0.5 − 1)
0.5�= 384
Adjust for the population N:
�� = ���
����= 384 (2)
Adjust the sample size for the effect of the sample design. The design effect factor
(DEFF) depends on the sampling strategy and for a random sampling procedure is
usually DEFF=1, and for stratified simple design DEFF≤1:
25
�� = ���� ∗�� (3)
�� = 0.9 ∗384 = 345
Adjust for response to determine the final sample size:
� =��
�=
���
�.�= 1725 (4)
A typical response rate of surveys is about 20% (r=0.2). The calculations indicated that 1725
households had to be invited to participate in the CHOICE survey. Given a response rate of 0.2
approximately 345 valid complete records need to be collected. In order to make sure that this
number would be reached after removal of incomplete or erroneous entrees, this number was
augmented by 0.45 leading to a total of 500 complete responses.
26
4 CHOICE Survey Design
The CHOICE survey was conducted among a sample of households in the GTA who have at
least one cross-regional commuter. The survey was designed to capture the collective decision
making process of the household as a unit. However, the interviewee stated the choices for all
household members, as this approach is organizationally simpler and is proven to be effective
(Beckman et al., 2002); (Vrtic et al., 2007); (Erath and Axhausen, 2010).
The survey consists of two main parts; a revealed preference (RP) part with three sections, and a
stated preference (SP) part with two sections. In the RP part the respondent had to indicate
information about all household members, typical commuting trips, and retrospective
information about housing and vehicle ownership. The information from the RP part was pivoted
into the SP part with the two stated adaptation experiments; in SP1 the interviewee was asked to
choose the preferred bundle of mobility tools for each member under different cost schemes; and
in the SP2 the respondent had to indicate the desired alternative place of residence. Figure 4
illustrates the flow of the experiment.
Figure 4: Survey design overview
27
4.1 RP
This Section presents the RP parts of the survey, namely the housing information, vehicle
ownership, and information about the commuting trip of each adult member. The screenshot of
the questionnaire can be found in Figure 24, Figure 25, and Figure 26 in Appendix A.
4.1.1 Housing Information
In section 1 of the survey each respondent had to indicate the postal code of their residence and
complete the following information about the current and the previous housing unit:
Dwelling type (single detached, semi-detached, townhouse/row house, apartment/condo
in detached duplex, apartment/condo in building with less than 5 stories, apartment/
condo in building with more than 5 stories)
Tenure (own, rent)
Moving dates (year, month)
Unit information (number of bedrooms, number of bathrooms, number of stories, area)
Reasons for moving
Rent cost/utilities for rental units
Asking price, receiving price, down payment for purchased units
Parking availability, rent/purchase price of parking spaces
Questions about the house searching procedure
Qualitative characteristics to support the choice of the specific house and neighbourhood
Type and price of units considered at the time of the move
Minimum required number of bedrooms
4.1.2 Socio-demographic and Commuting Trip Information
In section 2 of the survey the respondent was asked to fill information about the household
members. The following information was filled for all members, aged 12 and above:
Relationship to respondent, name or nickname for facilitating the experiment
Age
Education
28
Employment status (full time worker, part time worker, student, working from home, full
time homemaker, not employed, retired)
For members 18 years and older who are full time or part time workers the following
information was asked:
Industry category (as defined by the North American Industry Classification System
Canada)
Postal code of job location
Years at work
Annual income
Driver’s license, years
Parking cost at work
Frequency of mode used for commuting trip per week (car as driver, car as passenger,
public transit, park and ride, walk or bike, work from home)
Transit pass ownership and cost
For students 18 years and older:
Student status (secondary, college, university)
Postal code of school location
Parking cost at school
Frequency of mode used for commuting trip per week (car as driver, car as passenger,
public transit, park and ride, walk or bike, work from home
Transit pass ownership and cost (TTC, GO Transit, other agency)
The information about the origin and destination of each member’s commuting trip and the
availability of driver’s license was used in the SP experiment. This information also determined
the alternative options offered (members without a driver’s license could not choose a car in
SP1) and defined the calculations of the commuting costs.
29
4.1.3 Vehicle Information
Section 3 of the survey was used to collect information about the vehicles that the household
members currently own and/or use. Additionally, information about the vehicles that were
replaced/ exchanged or traded off was asked in order to form a better understanding of the
longitudinal retrospective choices of the same household. The respondents were asked to
identify:
Make, model and vintage (information retrieved from “Edmunds” website, which is a
database with all the vehicles available in Canada and the United States after year 1990
(Older vehicles are not available in the database)
Member who owns or makes payments for the vehicle
Privately owned or leased
Fuel technology (gasoline, diesel, hybrid, electric, other)
Actual mileage
Principal driver
Year and type of purchase (new or used)
Reasons and method of replacing the vehicle
Vehicle category (subcompact/compact, midsize, full size, SUV, pickup truck, minivan)
Indicating the vehicle category correctly was really important because the same classification
was used for the SP experiment. For this reason a help-button allowed respondents to visually
compare the different categories and see information about each one (Figure 27 in Appendix A).
30
Figure 5: RP data model of CHOICE survey
31
4.2 SP
The following sections present the experimental design of SP1 and SP2. The screenshot of SP1
can be found in Figure 28 and screenshots of SP2 in Figure 29 to Figure 32 of Appendix A.
4.2.1 Experimental Design
The second part of the survey consisted of two experiments.
SP1: A mobility tool experiment consisting of maximum four scenarios, and
SP2: A stated choice experiment for residential location.
In practice, during SP1 the respondent had to adjust the mobility tools of the household for
different transportation costs. Once this cost exceeded their acceptable cost level they would
move to SP2, where the respondent had to consider different residential locations.
4.2.2 SP1: Effects of Price Changes on Mobility Tool Ownership
4.2.2.1 Construction of the Experimental Plan
SP1 was a stated adaptation exercise where the respondent had to adjust the mobility tools for all
household members given today’s home location and individuals’ work locations, but under new
transportation cost schemes. The experimental plan involved four cost scenarios, but the
respondent had the option to abandon after any of the four scenarios if he/she perceived the
presented cost as extreme. After choosing the mobility tools and reviewing the total cost for the
household, the respondent could choose to either stay in the current home location and
consequently continue to the next cost scenario, or could choose to move home location in order
to reduce the distances that some of the household members travel. This structure addresses a
limitation that Erath and Axhausen (2010) identified in their study. They found that having an
experimental structure that forces the respondent to change home location and choose the
appropriate mobility bundle for the new home location was note easily perceived. The CHOICE
survey allows the respondents to change home location at any stage of the experiment.
The four scenarios were generated using higher gas price values. At the time of the design stage
of the experiment (September 2013) gas price in Toronto was approximately $1.25 per litre. The
32
objective of this experiment was to push the respondents to the cost they consider unacceptable
and to capture the trade-offs between mobility tool ownership and residential costs for
households with cross-regional commuters. Residential location choice is a choice that is
complicated and influenced by various factors. Researchers identify numerous reasons for
residential mobility; among them the distance to work has been singled out as an influential
determinant (Morrow-Jones, 2007); (Lowry, 1964); (Waddell, 2002).
Taking into account the long-term commitment and the capital investment that is involved when
someone chooses a specific house and a specific location, the proposed transportation cost
increases in SP1 had to be significant to have an impact. Therefore, in order to generate the
hypothetical scenarios, the current gas price was doubled, tripled and quadrupled. Similar cost
increases for SP scenarios can be found in (Krumdieck et al., 2010); (Erath & Axhausen., 2010).
Each value of gas price was used in the respective scenario of SP1, and it is the first component
that was used in the calculations of the monthly commuting costs.
In addition to the auto costs, the new transit fares had to be determined for the four scenarios.
The suggested increase for the transit fare, however, could not be of the same magnitude as the
one used for the auto costs. Cross-regional transit fares are already very costly and a double,
triple or quadruple ticket would bring them off-limits for most individuals. Therefore, a
relationship had to be established to define how gas price affects the transit fare hikes.
Historical data for the price per barrel of gasoline and the cost for the Toronto Transit
Commission monthly pass since 1990 were gathered, adjusted for the inflation rate of 2006 and
used in different formulations to find which one explains best the relationship between gasoline
price and transit fare. A third degree polynomial equation was the best fit, and determined the
percentage increases for the three hypothetical scenarios. Table 2 shows the attributes used in
SP1 and their levels.
33
Table 2: Attribute Characteristics of SP1
Variable Definition Levels
Car Cost Fuel price in CDN/l 1.25/2.5/3.75/5
Public Transit Costs
Price level compared to today's price Today,+19%/+31%/+39%
4.2.2.2 Construction of the Choice Alternatives
SP1 used real time calculations to give direct feedback on the respondent’s mode choice. The
software was programmed to customize the cost of the trip based on the distance of each
respondent and for different combinations of variables. Therefore, all the costs associated with
the mode alternatives had to be predefined and stored in the database in addition to information
about the characteristics of each trip (time and distance by car and transit).
4.2.2.2.1 The Origin Destination Pair
In order to customize the commuting cost for each individual’s trip the EMME software was
used. EMME is a complete travel demand modelling system used in urban, regional and national
transportation forecasting. Data from the Transportation Tomorrow Survey 2006 (TTS) were
used as inputs to calculate zone-to-zone travel times by car and public transit during the AM
peak3. The TTS collects data at the level of traffic analysis zones (TAZ), an area based on
population. The general population cannot identify the TAZ; therefore, the postal codes collected
in the RP part of the survey were matched in real time to the respective TAZ. The values that
corresponded to the time and distance of the specific origin-destination TAZ were retrieved from
the EMME matrix and used in the calculations of the experiment.
4.2.2.3 Car Choice
When choosing a car the respondent had to indicate the car category and the fuel technology.
The levels of each variable are shown in Table 3.
3 AM Peak is from 6:30- 9:00
34
Table 3: Car Choice Characteristics
Car Type Fuel Technology
(Sub)compact Gasoline
Midsize Hybrid
Large size Electric
SUV
Pickup Truck
The respondent was able to examine different combinations of car type and fuel technology,
where each combination led to a different transportation cost for each participating household
member. Different individuals have different preferences and different needs with respect to the
size and the performance of the vehicle they choose to drive (Choo & Mohktarian, 2004). Also,
different vehicles lead to very different costs depending on the size of the vehicle, the engine
capacity, the fuel technology, the consumption rate etc. It would, therefore, be wrong to only
offer one vehicle option in the experiment. The vehicle categories chosen for the experiment are
some of the categories suggested by the Canadian Automobile Association (CAA) which have a
visible difference in size and shape and would therefore be easily identifiable by the respondent,
but they also have significant differences in costs.
Fixed costs for every combination of size and fuel technology that is available in market were
determined using information from the CAA, and include:
Maintenance fees and depreciation over a 5 year period
License fees
Insurance fees, and
Variable cost for each car type based on the consumption rate given in the CAA.
CAA did not provide cost information about the alternative fuel vehicles. Therefore, the
characteristics of all the electric and hybrid models available in Canada were collected and the
35
average value of the respective characteristic was used instead (NRCAN, 2013). The choice set
also included options for battery electric vehicles. In order to calculate the transportation cost for
the electric car alternatives, the cost per kWh was used (OEB, 2013). The mid-peak charge rate
of 10.9 cents/kWh was used for the experiment, based on the assumption that an individual
would charge the vehicle overnight at home, when the off-peak charge is effective, and then will
likely recharge it for a while at work, where the demand is high and the on-peak rate applies.
The software calculated the fixed and variable costs of the selected vehicles based on the origin-
destination pair of each member and provided direct feedback with the cost for each individual
and for the household as a total. The variable and fixed costs of all feasible combinations can be
found in Table 22 in Appendix C.
4.2.2.4 Public Transit Pass
In the Greater Toronto Area there are seven different agencies that operate independently, and
there is only one regional transit service, GO Transit, that connects the five regions. GO Transit
has a distance based fare system, which means that different origin and destinations have fares
that differ substantially. In 2009 the Ontario Ministry of Transportation together with GO Transit
and 8 other municipal transit agencies implemented an integrated fare system called PRESTO
Card (Section 3.1.1.2). When a transit rider uses a PRESTO Card, trips 1 to 35 have a reduced
price (compared to a regular GO-Transit fare), rides 36-40 are reduced to approximately 13% of
the initial fare, and after the 40th trip each additional trip from the same origin to the same
destination is free of charge.
This fare calculation structure was used to determine the cost for 40 trips per month based on the
origin and destination of each commuter, where the respective increases from Table 2 were
applied in each scenario. In order to determine the cost for the monthly transit pass in SP1:
a) For the cross-regional trips, the GO Transit fare calculator was used
b) For the within-the city/township trips, the local transit agency monthly pass was used.
An origin-destination matrix with the monthly transit fares was generated for each scenario. For
the cross-regional trips, the calculations are based on the use of the PRESTO Card for 40 trips
per month (five days a week, two times a day), and for trips within the region, the local transit
36
pass for each city was used. The exact fare for each origin-destination in scenario 1 can be found
in Table 29a) and 29b) in Appendix E.
37
4.2.3 SP2: Residential Relocation as a Reaction to Increased Transportation Costs
After voluntarily abandoning the current home location, or after completing all four cost
scenarios in SP1, the respondent had to complete SP2: the alternative residential location choice
experiment. The respondent had to choose the region, the city or township, and last the
neighbourhood of interest. Screenshots can be found in Figure 29, Figure 30, Figure 31, Figure
32 of Appendix A.
4.2.3.1 Construction of the Experimental Plan
During the preparation of SP2, various details had to be determined. The GTA had to be
segregated into divisions that would geographically make sense, but that would also be easily
identifiable by the respondents, and that would have somewhat homogeneous characteristics.
Most of the official area boundaries, such as the traffic analysis zones, the census tracts, the
forward sortation areas or the dissemination areas, are either not easily perceived, or they are too
small and similar that would not justify showing them as separate alternatives.
Figure 6: SP2 map overviewFigure 6 shows a screenshot of the map that each respondent would
view in SP2. The individual could click on the different regions and view a zoomed area of the
cities that belong to each region. For the City of Toronto, a more detailed specification was
provided. The City of Toronto is very diverse in terms of housing prices and neighbourhood
characteristics, and was, therefore, treated at a more detailed level. As supported by other studies
(Morrow-Jones, 2007), residential location choice is influenced by numerous factors, and is
heavily dependent on the life cycle stage of the household. Besides housing price, there are other
characteristics of a neighbourhood that will attract specific individuals or households.
38
Figure 6: SP2 map overview
4.2.3.1.1 Qualitative Characteristics of Neighbourhoods
Taking all these factors into account, the experiment included qualitative characteristics of
neighbourhoods in addition to housing pricing (Figure 7). After choosing the specific area of
interest, each respondent was able to review the following characteristics and decide if it is
suitable for their household needs, or if they should continue searching:
39
-Walk score: A score from 0 to 10 that measured walkability based on walking routes to
destinations such as grocery stores, schools, parks, restaurants, and retail (Manaugh & El-
Geneidy, 2010); (Carr et al., 2010); (Duncan et al., 2011). A score of 0 means that most daily
errands require a car, and a score of 10 indicates that most errands can be accomplished on foot.
-Neighbourhood safety: A score from 0 to 10 was used to indicate how safe a neighbourhood is.
The data were retrieved from Wellbeing Toronto (2013), which uses a range of sources (City of
Toronto); (GIC); (Statcan) to make thematic maps. Crime statistics (firearm incidents, assaults,
sexual assaults, break and enters, robberies, vehicle thefts, murder, arson) per 10,000 residents
from Toronto Police data were used to determine the neighbourhood safety index.
-School score: The 0 to 10 score described the performance of schools in different
neighbourhoods of Toronto. The data were retrieved from Fraser Institute’s 2013 report card
scores for both primary and secondary schools. The values shown in the experiment corresponds
to the average of all schools across a neighbourhood (Fraser Institute).
The analytical scores for each neighbourhood can be found in Appendix D.
Figure 7: Neighbourhood characteristics and housing prices in SP2
40
4.2.3.1.2 Housing Pricing
Housing price is one of the most heavily weighted factors that confine the residential location
choice. It usually sets the first constraint that will reduce the pool of available housing or
neighbourhood options. For this reason, after choosing the desired geographical location and
reviewing the qualitative characteristics of the neighbourhood, the respondent was presented
with different housing units and had to make the decision that would conclude the survey (Figure
7).
-Housing Price: Average pricing of different dwelling types (detached, townhouse, apartment)
and different number of bedrooms (one, two, three, four or more) for rent and for sale, was
defined for each neighbourhood. All the available housing units for sale that were advertised by
the Realosophy4 website in August 2013 were downloaded, categorized by dwelling type and
number of bedrooms, and stored in a database. The same procedure was followed for the housing
units that were for rent during September 2013 in the Multiple Listing Service (MLS) website.
After clicking on the desired city or neighbourhood, the respondent could review the qualitative
characteristics and also the average housing price of dwelling types. Each respondent would
review six dwelling units: a) a house for sale, b) a house for rent, c) a townhouse for sale, d) a
townhouse for rent, e) an apartment for sale, and f) an apartment for rent. The prices that each
respondent saw were real market prices for units that met the bedroom requirements of each
household (refer to Section 4.1.1). If the respondent found a neighbourhood and a dwelling type
that fulfilled the household’s needs, the survey terminated. If the respondent was not satisfied
with the presented characteristics, he/she could return to the initial map and examine different
neighbourhoods. The dwelling type price in each neighbourhood can be found in Appendix D.
This process was tracked and stored in the database in order to understand how individuals rank
different neighbourhoods, and to allow us to create a dataset that could be used to model the
house searching procedure. Figure 8 shows schematically how the SP parts of the survey were
customized based on the RP answers and the external datasets.
4 Realosophy presents all listings that are available for sale in MLS and REALTOR
41
Figure 8: RP and SP data model connections
42
4.2.4 Comments on the SP Component
The SP experiment was an adaptation exercise, where the respondent was given the flexibility to
either continue to the next stage or not. In SP1 the measure that each respondent had to consider
was the monthly household commuting cost. Based on real time calculations the commuting cost
of each member was shown. The respondent had to determine if the given total cost was
acceptable, and continue to the next stage (scenario) with a higher gas price, or if this cost was
excessive, and would ultimately change residential location in an attempt to cut back on
commuting expenses.
The idea behind this is that in most North American cities the majority of offices and services is
located in the city centre. Real estate and parking are more expensive in the central business
district, while land is cheaper in the outskirts. This land-use structure encourages people to live
where land is cheaper and to commute long distances to reach their work. However, this
compromise is rational provided that the cost of living at a central location surpasses the cost of
commuting long distances on a daily basis. In case of extreme increases in transportation costs,
this balance would be interrupted, making households reconsider this trade-off, and could
possibly lead to residential movements.
The traditional way that stated preference experiments work, is that a respondent is given a
certain number of cards or scenarios, and each time is asked to choose one alternative based on
the levels of the given attributes. When preparing the experimental design, the researcher has to
define all the possible levels that the attributes may take. Based on the number of attributes and
the possible levels of each attribute the experiment consists of numerous combinations, the
scenarios.
The traditional stated preference methodology was not suitable for this experiment, as the desired
household trade-offs could not be captured. Additionally, all the possible combinations for each
member, when combined together at the household level, were leading to an extraordinary high
number of scenarios. Furthermore, this number varied across participants based on the number of
commuters that live in the same household. Figure 9 illustrates all the possible combinations in
SP1.
43
Figure 9: Number of possible choice scenarios
Figure 9 shows that depending on the number of commuters per household the number of
possible alternatives in the choice set increases exponentially. A household of two would have
256 alternative options among the different car types, fuel technologies and the option for a
transit pass. A similar experimental design approach was used by Erath and Axhausen (2010) to
investigate the mobility behaviour of households in Switzerland (for more information on their
study refer to Sections 2.1, 2.2, and 2.3).
The SP1 experiment consisted of four scenarios. After each scenario the respondent had to
review the new cost based on the selected mobility tools, or move residential location. This
meant in practice that different respondents would abandon SP1 at different scenarios. As Erath
and Axhausen (2010) concluded after their experiment, forcing the respondent to make drastic
life changes, such as moving housing, is not easily perceived. The flexibility of the CHOICE
survey allowed for a voluntary move instead of a forced choice, and also captured how far in the
experiment different respondents would go.
44
4.3 Software
The CHOICE survey is a self-administrative web-based tool that was developed exclusively for
the purpose of this study. The software employed the languages of php, Javascript and JQuery,
was programmed to run on all web browsers (Internet explorer, Chrome, Firefox, Opera), and
combined G.I.S. applications, EMME outputs and other interfaces. The software incorporated a
dynamic process that included pop-up instructions, skip logic features, and attractive tools such
as maps and graphics, which as cited by researchers may increase the respondents’ willingness
and motivation to fill the survey (Schmidt, 1997); (Zhang, 1999).
Each interviewee had a unique ID and had the option to save parts of the survey and finish it
later without losing any of the so far answered questions. Screenshots of the survey can be found
in Appendix A. During the fieldwork, no problems were encountered with the software.
4.4 Recruiting
The survey distribution was commissioned to Research Now, a market research company that
maintains a panel of 35,000 individuals in the GTA. Email invitations were sent to the
respondents, who were asked to complete the survey, given that there was at least one cross-
regional commuter in their household. As an incentive, Research Now awarded air miles (points
to one of the most popular loyalty programs in Canada) to those who completed all parts of the
survey.
All interviews were conducted between March and April 2014. During that time the respondents
were able to contact the research group and ask for further instructions or clarifications.
4.5 Sample Distribution and Representativeness
The market research company was hired to collect a total of 500 complete responses. Incomplete
responses were stored in the database as well, leading to a different number of complete
responses for the different sections of the survey. Table 4 shows the number of total responses
for each of the five sections of the survey.
45
Table 4: Number of Total Responses at each Part of the Survey
Number of complete
responses
RP Part 1 1090
Part 2 1013
Part 3 927
SP1 Part 4 646
SP2 Part 5 501
The sample was allocated to the five regions based on the percentage of cross-regional
commuters that originate from each region, according to TTS 2006 data. The desired number of
complete questionnaires was: 126 for the City of Toronto, 72 for Halton Region, 138 for York
Region, 126 for Region of Peel, and 61 for Durham Region. Table 5 shows the sample
representation of different households attributes based on the 901 households that completed (at
least) part one, two and three of the survey. .
Table 5: CHOICE Sample Representation of Household Attributes Based on TTS 2006
Data
Region
Count Percentage TTS 2006
Durham 116 12.89% 7.00%
Halton 87 9.67% 6.00%
Peel 201 22.33% 16.00%
Toronto 297 33.00% 32.00%
York 199 22.11% 12.00%
Household Size
Count Percentage TTS 2006
1 191 21.22% 21.00%
2 283 31.44% 33.00%
3 202 22.44% 18.00%
4 158 17.56% 18.00%
5 54 6.00% 10.00%
6 10 1.11%
7 2 0.22%
Household vehicles
Count Percentage TTS 2006
0 98 10.89% 14.00%
1 313 34.78% 40.00%
2 369 41.00% 36.00%
3 120 13.33% 8.00%
4 2.00%
46
Table 6 shows the sample representation of individuals’ characteristics compared to TTS 2006
data (gender, age, employment status, driver’s license, and transit pass ownership). This analysis
is based on the 901 households that completed all three parts of the RP sections and which
amounts to 1394 individuals.
Table 6: Sample Representation of Individual Attributes Based on TTS 2006 Data
Gender
Count Percentage TTS 2006
Female 898 51.49%
Male 846 48.51%
1744
Age
Count Percentage TTS 2006
12-17 114 6.42% 0-10 13.00%
18-24 176 9.90% 11-15 7.00%
25-34 227 12.77% 16-25 11.00%
35-44 317 17.84% 26-45 28.00%
45-54 380 21.38% 46-64 24.00%
55-64 386 21.72% 65 or older 15.00%
65 or older 177 9.96%
1777
Employment Status
Count Percentage TTS 2006
Full time 1032 58.08% 37.00%
Part time 136 7.65% 8.50%
Student 226 12.72% 22.50%
Working from home 55 3.10% 3.00%
Homemaker 44 2.48%
Not employed 70 3.94%
Retired 206 11.59%
None 8 0.45%
House Type (current)
Count Percentage TTS 2006
Single Detached 555 61.67% 67.00%
Semi Detached 61 6.78%
Townhouse 112 12.44% 7.00%
Apartment in duplex 16 1.78% 25.00%
Apartment in less than 5 19 2.11%
Apartment in more than 5 131 14.56%
N/A 6
47
Driver’s License
Count Percentage TTS 2006
No 201 14.42% 34.50%
Yes 1193 85.58% 65.50%
1394
Transit Pass
Count Percentage TTS 2006
No 1214 87.09% 93.00%
Yes 180 12.91% 7.00%
1394
48
5 CHOICE Survey Results
This chapter features the descriptive analysis of the CHOICE survey data. A total of 501
households completed all five parts of the survey. However, the survey software allowed
respondents to save their answers, and continue the survey at a different time. This resulted in
different completion rates for the different parts of the survey. The analysis of this part is based
on the respondents that completed the four first parts of the survey including SP1.
5.1 RP Descriptive Analysis
The first part of this section covers the analysis of the RP part of the survey (parts one, two and
three). After the removal of erroneous or missing records 413 households and 757 individuals
were left and formed the data basis of the analysis. . As seen in Table 7, which summarizes the
recruitment and response rates, the total panel size of the research company reaches 35,000
individuals, which exceeds the sample size requirements calculated in Section 3.2.1. Also, the
complete responses surpass the respective requirements.
Table 7: Recruitment and Response Rate
Total Panel Size: 35,000 Total Responses: 1,090
Recruited: 5,682 Completes: 501
Recruitment rate (%): 16.23% Incompletes: 589
Qualified: 1,609 Completion rate (%): 45.96% Qualification rate (%): 28.32%
Final Sample: 413 households, 757 individuals
5.1.1 Household Descriptive Analysis
Table 8 summarizes the descriptive analysis of all household attributes. Most of the participating
households own their residence, and over 60% live in single detached units. More than 65% of
the households own more than two vehicles. Household size is somewhat uniformly distributed
with exception the category of five or more people, which participates with 8% in the total
sample. On average, the sample shows that GTA households with cross-regional commuters are
two or three person households, own more than one vehicle and have one to two commuters.
49
Regarding the housing unit, most participating households live in three bedroom houses, pay on
average $1,086 per month for housing expenses (rent or mortgage payment), and purchased their
unit before 1990 for more than $300,000.
Table 8: Descriptive Analysis of Household Attributes
Household Descriptive Statistics
Variable Value Frequency Percentage
House tenure Own 340 82.32%
Rent 59 14.29%
Other 14 3.39%
Dwelling type House 274 66.34%
Townhouse 61 14.77%
Apartment 75 18.16%
No. of vehicles 0 18 4.36%
1 152 36.80%
2 196 47.46%
3+ 47 11.38%
Household Size 1 93 22.52%
2 111 26.88%
3 86 20.82%
4 87 21.07%
5+ 36 8.72%
Household Averages Persons 2.69
Vehicles 1.66
Children 0.62
Commuters 1.87
Quantitative Household Statistics
Mean St.dev Min Max
House area (sqf) 1,761.38 931.27 200 9,000
Number of bedrooms 3.11 0.98 1.00 5.00
Monthly housing costs 1,086.33 806.82 0.00 10,000.00
Asking Price 337,879.41 181,751.57 5,000.00 1,595,000.00
Discount (Asking-Receiving Price) 9,680.00 31,894.35 0.00 360,000.00
5.1.1.1 Neighbourhood Preferences
Part 1 of the survey collected information about the respondent’s current and previous house.
Additionally, information about the reasons for moving to their current unit, the reasons for
choosing the specific unit, and the reasons for choosing the specific neighbourhood were asked
to form a better understanding of the household’s attachment to the current location.
Furthermore, this information would be important to understand what drives people to change
50
residential location, and how different housing attributes or neighbourhood characteristics affect
these decisions.
Among the reasons for choosing the current unit, most respondents stated that they wanted a
bigger or newer house, and that they wanted to switch from renting to owning. However, the
third most selected reason for moving to a new house was to be closer to work, which confirms
that people take into account the commuting distance when choosing their residential location.
Figure 10 shows the reasons and the respective percentage distribution.
Figure 10: Reasons for moving from previous to current house
Regarding the reasons for choosing the specific neighbourhood, most important ones were the
price of the house and the attractiveness of the neighbourhood. The distance to work is the fifth
most selected reason. Furthermore, we can see that the access to bus/transit is among the most
important reasons for choosing the current home location. Figure 11 shows the percentage of
respondents’ choices.
0%
10%
20%
30%
40%
50%
60%
70%
80%
Per
cen
tag
e
51
Figure 11: Reasons for choosing current neighbourhood
5.1.2 Descriptive Statistics of Individuals’ Characteristics
This section summarizes the statistics of the 757 individuals. The information presented in Table
9 was collected in the second part of the survey and concerns members 12 year or older. Most of
the household members belong to the age group 35-54, have higher education (bachelor’s degree
and above), and hold a driver’s license. More than 80% of the sample are full time workers and
are employed in professional, scientific and technical services. Finally, most individuals (30% of
the sample) belong to the income category that earns between $50,000 and $75,000 per year.
0%
10%
20%
30%
40%
50%
60%
70%
80%
Per
cen
tag
e
52
Table 9: Descriptive Analysis of Individuals’ Characteristics
Variable Value Frequency Percentage
Gender Male 379 50.07%
Female 369 48.75%
Missing 9 1.19%
Age 12-17 22 2.91%
18-24 70 9.25%
25-34 106 14.00%
35-44 194 25.63%
45-54 204 26.95%
55-64 140 18.49%
65 or older 21 2.77%
Education Elementary 2 0.26%
Junior High 8 1.06%
High School 133 17.57%
Diploma 193 25.50%
Bachelor 308 40.69%
Masters or above 112 14.80%
None 1 0.13%
Employment status
Full time 626 82.69%
Part time 67 8.85%
Students 64 8.45% Occupation Agriculture, forestry, fishing and hunting 1 0.13% (According to the North American Industry Classification System Canada (NAICS 2013)
Mining, quarrying, and oil and gas extraction
1 0.13%
Utilities 13 1.72%
Construction 25 3.30%
Manufacturing 60 7.93%
Wholesale trade 18 2.38%
Transportation and warehousing 25 3.30%
Information and cultural industries 14 1.85%
Finance and insurance 88 11.62%
Real estate, rental and leasing 8 1.06%
Professional, scientific and technical services
92 12.15%
Management of companies and enterprises
16 2.11%
Administrative and support, waste management and remediation services
35 4.62%
Educational services 53 7.00%
Health care and social assistance 51 6.74%
Arts, entertainment and recreation 21 2.77%
Accommodation and food services 7 0.92%
Other services 122 16.12%
Public administration 43 5.68%
Students 64 8.45%
Driver's license Yes 692 91.41%
No 65 8.59% Personal income in CDN$
0-24,999 56 7.40%
25,000-49,999 148 19.55%
50,000-74,999 243 32.10%
75,000-99,999 131 17.31%
100,000 or more 115 15.19%
53
5.1.2.1 Job Location
In the second part of the survey, information about the commuting trip of each member was
collected. Table 10 shows the number of trips’ origin and destinations across the GTA regions.
Most individuals (57%) work in different districts of Toronto (York, East York, North York,
Scarborough, Etobicoke, and downtown Toronto). The second greatest work trip attractor is the
Region of Peel with 16.5%, followed by York Region with 15.9%.
Even though the CHOICE survey targeted cross-regional (or between Toronto districts) trips, the
origin and destination of all commuting members were collected. Table 10 shows that the region
with the lowest intra-region trip rate is Halton with 22%. Halton Region is primarily a residential
area with only 26% employment rate. This fact justifies the low number of commuting trips
within the region’s boundaries. The region with the highest rate of cross-regional trips is Durham
Region, with 63.5% of all trips crossing the boundaries and destining primarily to the City of
Toronto and then to York Region.
Table 10: Home and Work Location Distribution of Sample
Work Home Toronto Durham York Peel Halton Total
Toronto 156 5 32 22 5 220
Durham 61 22 10 3 0 96
York 117 2 61 5 1 186
Peel 82 0 12 71 18 183
Halton 23 1 5 23 16 68
Total 439 30 120 124 40 753
5.2 SP1 Descriptive Analysis: Mobility Tool Ownership
This section discusses the reaction of respondents to increasing transportation costs as captured
in SP1. As presented in previous sections (Section 4.2), the respondent had the option to adjust
the household mobility tools by choosing among different car size and fuel technology
combinations or by selecting a transit pass. After the removal of erroneous entries (entries with
critical information missing, such as trip distance, or no mobility tool chosen) the analysis was
done on 413 households, which resulted into a total of 1758 choice situations.
54
5.2.1 Fuel Consumption and Engine Type
Figure 12 illustrates the changes in fuel consumption and engine technology preferences of
households throughout the experiment. Interestingly, the average number of vehicles per
household declines at a very slow rate and only after scenario two. This indicates that people are
unwilling to change to a different mode despite the cost increases. However, after scenario two
respondents start switching to more efficient vehicles, either smaller body size or alternative fuel
technology.
As seen in the figure, the share of hybrid vehicles becomes bigger as costs triple and quadruple.
This leads to a 13% reduction in the monthly consumption of the household. Generally, electric
vehicles are not chosen; this indicates that despite the technological advancements, the drop in
purchase price over the years, and their efficient performance electric vehicles are still
considered unreliable and are not widely preferred.
Figure 12: Fuel consumption, car ownership and engine types depending on fuel price in
SP1
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1 2 3 4
hybrid
electric
gasoline
90.8 91.7
78.9 78.8
Averange household consumption [l/housedhold-month]
Nu
mb
er o
f ca
rs p
er h
ou
seh
old
Scenario of cost increase
55
The analysis showed that the households that mostly reduced their total monthly consumption
belong to the regions of Durham and Peel. These regions commute the longest distances
according to our data collection and, therefore, faced the highest cost increases during the SP
scenarios.
5.2.2 Vehicle Type
Looking at the preferences for specific vehicle types, we were not able to identify any major
changes throughout the cost scenarios. As shown in Figure 13 subcompact vehicles dominate the
respondents’ preferences throughout all cost scenarios. There is a minor increase of subcompact
cars as the cost scenarios progress and their selection percentage reaches its peak at scenario
three. Large size, SUV, and pick-up truck percentages show a less clear pattern. This is an
interesting result, since these cars are the most inefficient and were the ones mostly affected by
the fuel price increases imposed in SP1. A similar pattern was found in the study of Erath and
Axhausen (2010), and could be justified by the fact that people who own larger vehicles (large
size and SUV) have typically higher income and are consequently less affected by additional
cost. On the other hand, pick-up trucks could presumably fulfil work requirements, and are
therefore chosen despite the cost increases. Overall, it appears that there is strong inertia for
downgrading the size of the vehicles. Respondents would rather change the engine technology,
as shown in Section 5.2.1, than change the body type.
Figure 13: Vehicle type choice in SP1
0%5%
10%15%20%25%30%35%40%45%50%
1 2 3 4
Per
cen
tag
e
SP Scenario
Subcompact
Mid-size
Large
SUV
Pick-up
56
5.2.3 Influence of Different Factors on Operation Costs
The influence of different trip attributes and cost factors on the commuting costs was tested by
estimating a linear regression model. The explanatory variables used in the model include the
monthly distance travelled from home to work, the fuel price, the consumption, the fixed cost per
km of the chosen vehicle, and the average speed for the commuting trip of each individual. The
dependent variable is the operational cost per km. The standardized regression coefficients are
multiplied by the correlation coefficients of the independent variable with the dependent
variable, and the summation of these values gives the total explained variance (Aigner, 1971).
Table 11 lists the coefficients of the linear regression model.
Table 11: Estimation Results of Linear Regression Model for Operation Costs
Operational cost per km Stand. Coeff.
t-test Correlation Variance Explained
Fixed cost per km 0.931458 133.02 0.864 80.48%
Total distance travelled per month -0.00574 -0.82 -0.5306 0.30%
Fuel price 0.171428 30.96 0.1936 3.32%
Average commuting speed -0.05969 -10.9 -0.1887 1.13%
Consumption of vehicle 0.043694 7.88 0.028 0.12%
Constant 5.35
R2 =0.92
As observed in the regression results, the greatest part of the operational commuting cost is
explained by the fixed cost of the vehicle used. The fixed cost of a vehicle includes the monthly
fees for registration and license, the insurance fee, and the cost of maintenance and depreciation
(over a 5 year period). Next, but in a much smaller scale, the fuel price explains 3% of the total
variance in the model. The influence of the consumption of the vehicle and total distance
travelled per month are the least important. Finally, the model coefficients show that higher fuel
price and higher fixed costs lead to higher operational cost per km, whereas longer commuting
distances and faster commuting speeds lead to lower operational costs.
57
5.2.4 Transit Pass Ownership
Most of the respondents did not consider switching from private vehicle to transit for their
commuting trip. This finding is in agreement with Erath and Axhausen’s study (2010), which
found the same unwillingness of respondents to switch mode in the face of the new cost
circumstances.
This finding is justified at a greater extent for the GTA considering the limited options of travel
for cross-regional commuters. Cross-regional trips by transit are in many cases more expensive
than using a private vehicle for the same trip, and add inconvenience and time for the trip maker.
Therefore, the fact that only 17% of the respondents chose to commute by transit throughout the
four SP1 scenarios, was a finding somewhat expected.
5.2.5 Relocating Decision
SP1 concluded each scenario by asking the respondent to choose between staying at the current
home location or leaving. There were in total four scenarios, and the majority of the respondents
(84.67%) decided to stay for all four scenarios, which means that they accepted the new higher
cost in exchange of staying at their current house location. Figure 14 shows the percentage of
respondents that chose to leave based on their current region of residence.
Figure 14: Relocating percentage of GTA regions as a result of SP1 cost increases
The majority of all respondents, regardless where they live today, decided to leave after scenario
two, when the gas price doubled. On the contrary, residents of York Region decided to leave at
0%
2%
4%
6%
8%
10%
1 2 3 4
Per
cen
tag
e
SP Scenario
Durham
Halton
Peel
Toronto
York
58
scenario four. Figure 14 shows that there is not a consistent behaviour across the regions. In each
scenario residents from different parts of the GTA decide to change homes. Durham residents
abandoned their current home location during the first three scenarios. On the other hand, York
residents kept a constant rate during the first three scenarios, but their majority changed homes in
scenario four. Toronto, Halton and Peel residents had a more uniform distribution of voluntary
residential relocation.
59
5.3 SP2 Descriptive Analysis: Residential Location Choice
In SP2 the participants identified the area where they would most likely move. All respondents
had to fill this part, whether they voluntarily chose to move or not. Figure 15 illustrates the
concentration of current home and work locations of cross-regional commuters in the GTA based
on the data collected from the CHOICE survey. Home locations are uniformly distributed in the
study area, with the highest concentrations at the borders of the City of Toronto with North York,
and in Mississauga. Work locations, however, are concentrated closer to the city centre, with the
highest density in the downtown core of Toronto.
Figure 15: Home and work location concentrations of CHOICE respondents
60
5.3.1 GTA Overview of SP2
In SP2 the respondents had to identify in what part of the GTA they would most likely move.
The experimental design of SP2 included the fragmentation of the GTA in smaller geographical
units; for the City of Toronto into 81 neighbourhoods; and for the surrounding regions into the
24 corresponding cities. This led to a total of 105 residential location alternatives. The exact
screenshot of the experiment can be found in Appendix A.
The initial impression of the home relocation choice was not clear, because of the two-way
exchange of population between the city and the surrounding regions. The following section
presents region by region the home relocation choice of the participants.
5.3.1.1 Region of Durham
Figure 16 presents the current home location, the work location of all commuters/students, and
the chosen planning district of the Durham households that participated in the survey. Currently,
the majority of the participants (60.38%) are concentrated in the cities of Whitby and Ajax. The
work location of individuals is illustrated by the black points, and is somewhat uniformly
distributed across the regions of Durham, the southern part of North York, and the City of
Toronto. The right-hand side of Figure 16 shows the location chosen in SP2, where it becomes
obvious that the distribution disperses in a wider area. The cities of Whitby, Ajax and Clarington
each received approximately 15% of the households that relocated, followed by the East End of
Toronto (PD 15) that received 9%, and downtown Toronto with 7%.
61
Figure 16: Home location, work location and SP2 home location choice of Durham
residents
5.3.1.2 Region of Peel
Participating households from the Region of Peel are primarily located in Mississauga (60.37%).
Mississauga is a strong employment base with more than 417,000 jobs and is expected to grow
by more than 15% by 2031, (Mississauga, 2014). Most of the residents of the Region of Peel
work in Mississauga, Brampton, downtown Toronto or in the adjacent Halton Region.
The SP2 (right-hand side of Figure 17) showed that the relocation choice is evenly distributed
across different cities and planning districts. While the City of Mississauga is still the most
selected choice (28.57%), the City of Brampton, the City of Caledon and downtown Toronto are
the next most popular locations. Halton Region, North York and some parts of Etobicoke attract
62
the remaining households, except for a few households that decided to move to Ajax and
Oshawa.
Figure 17: Home location, work location and SP2 home location choice of Peel residents
5.3.1.3 York Region
Participating households from York Region are concentrated in the City of Markham (34.91%),
and in the City of Vaughan (25.47%). The work locations of York Region commuters are
gathered in the south part of York Region and along the corridor that leads to downtown Toronto
(Planning Districts 11, 4 and 1). In SP2, most households relocated to the cities of Vaughan,
Markham, and Richmond Hill, followed by the town of Newmarket and the three planning
districts in the City of Toronto with the highest rate of work trips.
63
Figure 18: Home location, work location and SP2 home location choice of York residents
5.3.1.4 Halton Region
Compared to the so far presented regions, Halton residents appear to have the greatest difference
between the before and after spatial distribution of home locations. The left-hand side of Figure
19 shows the home location density of the Halton households that were interviewed. 34% of the
participants live in the town of Oakville. However, looking at the black points of Figure 19 it
becomes apparent that Halton Region has a very large number of cross-regional commuters. The
majority of work trips are destined to Mississauga, Brampton, and downtown Toronto. It is
therefore easily justifiable that the SP home location choice (in the right-hand side of Figure 19)
covers a wider area and expands to cities and regions farther than those immediately surrounding
Halton Region. One quarter of all Halton households selected the town of Milton as an alternate
home location, and approximately 16% chose the town of Oakville. Next most popular areas
64
include the City of Mississauga, the City of Burlington, the Town of Halton Hills, and downtown
Toronto.
Figure 19: Home location, work location and SP2 home location choice of Halton residents
65
5.3.1.5 City of Toronto
The City of Toronto is divided into 6 districts: Scarborough, Etobicoke, North York, York, East
York and downtown Toronto. The participating households from the City of Toronto were
required to either have a cross- regional commuter or a member that would travel across districts
to reach the work location. Approximately 15% of the participants currently live in the Planning
Districts 11 and 8, which belong to the districts of North York and Etobicoke, respectively.
Since there are numerous business centres in the City of Toronto, work trips are directed to
various areas. As presented in Figure 20 most of the trips are concentrated in the downtown core,
but also in Scarborough, Etobicoke, and the central part of North York. In SP2 households
spread across the GTA moving closer to the black points, which represent their work locations.
Figure 20: Home location, work location and SP2 home location choice of City of Toronto
residents
66
5.3.2 Conclusion of Residential Location Choice Descriptive Analysis
Section 5.3.1.1 to 0 presented the home relocation choice of households from each GTA region.
Most results of the SP2 showed that the residential location concentrations were redistributed in
a more even pattern across regions, cities and planning districts. In general, households preferred
relocating within the boundaries of their current region. Additionally, a pattern of “suburban”
relocation choice seemed to dominate currently suburban residents. For example, in the case of
Halton Region (Figure 19), households chose cities located in the south part of York Region, or
in the Region of Peel, but avoided the City of Toronto itself, even though most work trips are
destined there. This pattern is an indication that households prioritize reasons other than the
commuting distance when selecting a specific location. The detailed distributions for the current
and the chosen in the SP residential location can be found in Appendix B.
5.4 Dwelling Type Choice
In the last part of SP2 the respondent had to select the desired housing unit among six options
based on the dwelling type, the preferred tenure and the price of the available options. The
dwelling units presented to each respondent corresponded to the number of bedrooms that each
household needed (as stated in part one of the survey) in the neighbourhood of their preference.
Most households preferred to buy two-bedroom detached houses, followed by three and four-
bedrooms units. Only 16.4% of respondents chose to rent a unit, and approximately half of the
respondents chose townhouses or apartments.
When comparing these results with their current tenure and dwelling type (from Section 5.1.1),
we identify a trend towards downgrading the type of the unit, but not a pattern of switching
tenure (owning/ renting). Based on the RP results, 66.34% of the participating households live in
detached houses. In SP2, 50.90% chose this type of unit, and the remaining respondents are
distributed evenly between townhouses and apartments. Respondents preferred owning their
residence; 83.60% chose to buy a unit in SP2, compared to 82.32% that actually owns the unit.
Based on the respondents’ choices, the average rent they are willing to pay is $1,702 per month
and $586,222 to purchase a unit. Table 12 shows the statistics of the housing price based on the
answers of the CHOICE survey.
67
Table 12: Statistics of Housing Price in SP2
Mean St.dev Min Max
Number of bedrooms 2.6 0.97 1.00 5.00
Monthly housing cost for rent 1,702.98 627.57 775 4,200
Purchase price 586,222.20 410,743.73 106.950 3,975,000.00
68
6 Modelling Mobility Tool Ownership and Residential Relocation
The first part of this chapter is intended to capture the mobility tool ownership choices of
individuals with respect to increasing commuting costs. For the analysis of this part only data
from SP1 were used and only the response of the interviewee was considered. A multinomial
logit model (MNL) was employed to identify the intention of respondents to downgrade vehicle,
change mode, or do nothing in each cost scenario. The second part of this chapter presents a
binary model based on household data for their decision to stay or leave during the stages of SP1.
The models of this chapter were constructed with the software STATA version 10.
6.1 Mobility Tool Ownership Model
6.1.1 Data Preparation
The answers of each respondent for the SP1 scenarios two, three and four were compared to the
choice selected in scenario one, and the cost difference was used in the model. Scenario one
reflects today’s cost situation. In subsequent scenarios respondents had to choose their mobility
tools again. Respondents who chose a smaller body type or changed from gasoline to alternative
fuel vehicles downgraded their mobility tool to cut back costs. Those who chose a public transit
pass instead of their private vehicle accepted the inconvenience and the additional travel time.
Finally, those who kept the same mobility tool as in scenario one belong to the “do-nothing”
category and are characterized by apathy and a bigger tolerance to increasing costs. Respondents
who chose a bigger body type or switched to a less efficient car compared to scenario one were
removed from the dataset. In total 774 observations were used for the model.
69
6.1.2 General Model Specification
Multinomial logit models have been broadly used to describe different choice situations in
transportation and are derived from the fundamental random utility maximization (RUM) theory:
��� = �(���; �) + ��� (5)
Where:
��� : is the utility that individual i obtains from alternative m
� : is the systematic component of the utility
���: is the vector of explanatory variables, including attributes of individual i and alternative m
β : is the vector of parameters and,
���: is the random component of the utility
The current model considers three alternatives: (1) Do nothing/keep same mobility tools as in
scenario one (reference), (2) downgrade mobility tool (choose smaller vehicle or more efficient
technology), (3) switch mode (change to public transit).
The distribution of the random component of the utility �� is assumed to follow the
independently and identically distributed extreme value type 1 property (IID). This assumption
leads to the choice probability
��� =����
∑ �����∈��
(6)
Where:
��� : is the probability that individual i selects alternative m
���: is the utility that individual i obtains from alternative m (i=1, …, I, m=1,…,N)
�� : is the choice set of alternatives N for individual i
70
6.1.3 Empirical Analysis
As discussed in the previous section an MNL was employed to investigate the reaction of
respondents to increasing commuting costs. Many alternative structures were tested leading to
the final formulation that is presented in Table 13. The model estimates the probability that an
individual will choose any of the three alternative reactions (do nothing, downgrade, switch
mode) in response to double, triple and quadruple commuting costs.
Table 13: Estimation Results of MNL for Mobility Choice in SP1
Variables Coef. Std. Error t-stats
Alternative specific constant
Downgrading vehicle -5.3078 0.7929 -6.69**
Changing mode -5.11966 0.9859 -5.19**
Total Cost Difference
Downgrading vehicle 0.0150 0.0017 8.76**
Changing mode 0.0177 0.0018 9.84**
Job in Region of Durham
Downgrading vehicle -3.872 1.4700 -2.63**
Changing mode -5.4506 1.7418 -3.13**
Household Income
Downgrading vehicle 5.76E-06 2.92E-06 1.97*
Changing mode -4.88E-06 5.82E-06 -0.84
Household without children
Downgrading vehicle -0.8247 0.2439 -3.38**
Changing mode -0.2672 0.3988 -0.67
More than one commuter
Downgrading vehicle -0.986 0.3266 -3.02**
Changing mode -0.92044 0.51936 -1.81
Consumption of vehicle chosen in scenario 1
Downgrading vehicle 45.1516 9.7087 4.65**
Changing mode 30.2836 11.93215 2.54*
Number of Observations 756
Log. Likelihood -343.84713
LR chi2(12) 313.59
Prob>chi2 0.00
Pseudo R2 0.3132
** Significant at the 0.01 level
* Significant at the 0.05 level
71
Most of the variables included in the model are significant at the 0.05 level (or 0.01). Most
importantly the cost difference, or cost savings are perceived to have a positive influence for the
choice of the participant. Higher commuting expenses lead the participants to either change
vehicle or change mode to achieve a more inexpensive way of travel. Downgrading their vehicle
means that they chose a smaller body type or a more efficient engine technology. Members who
belong to households with higher income are more likely to change their vehicle instead of
switching to public transit. Considering the inconvenience of cross-regional trips this is not a
surprising finding. High-income households, in particular, are expected to have limited tolerance
to inconvenience and delays.
Participants who belong to households with more than one commuter are less likely to modify
their current vehicle compared to households with only one commuter. This is reasonable due to
the more demanding schedule and needs of households with multiple commuters. Furthermore,
individuals who currently own a vehicle with a high consumption rate are more likely to
downgrade their vehicle in consequent scenarios compared to individuals who already drive
efficient cars. The probability to change to transit is also higher for those who drive cars with
high consumption.
Finally, the model includes a dummy variable to represent individuals who work in the Region of
Durham. It appears that these individuals are less likely to downgrade their vehicles or change to
transit compared to individuals from the other four regions. This may be explained in two ways:
either the individuals who work in Durham Region belong to the income brackets that have the
luxury to accept higher costs; or the low number of respondents who work in Durham Region
combined with the poor transit service of the region creates an unclear pattern and makes these
individuals appear more tolerant to cost increases.
Overall the model performed well with a McFadden R� of 0.32. Unlike linear regression models,
logistic regression does not have the exact equivalent ofR� , since it is based on a different
estimation methodology. The McFadden R� gives the relationship between the Log Likelihood
of the estimated model and the Log Likelihood of the model with no parameters. According to
Louviere et al. (2000) McFadden R�values of 0.2 to 0.4 are considered to be really good model
fits.
72
6.2 Trade-offs Between Mobility Costs and Residential Location
This section presents a binary logit model that estimates the probability of leaving the current
home location as a reaction to the increasing commuting costs. As described in previous sections,
in SP1 the head of the household had to determine the mobility tools for the household. Then, the
interviewee was asked to indicate whether they would stay at the current home location or not
given the total commuting cost of each scenario.
6.2.1 Data Preparation
For this model all household commutes were aggregated to generate household level costs based
on the choices they made in the different scenarios of SP1. All completed scenarios are
considered separate records in the analysis, but with the same household characteristics (socio-
demographic). In total 1382 records were used to estimate the binary model presented in the
following section.
6.2.2 General Model Specification
In binary logistic models the prediction is measured as the possibility of an event occurring
compared to the possibility that this event will never occur. The dependent variable refers to the
logarithm of the odds ratio, and the mathematical expression can be written as a generalized
linear model:
ln ���
����� = �� + ����,� + ����,� + ⋯+ ����,� (7)
In terms of odds the model can be written as:
��
����= �(�������,������,��⋯�����,�) , (8)
or in terms of the probability of the outcome:
�� =�(�������,������,��⋯�����,�)
���(�����,��,������,��⋯�����,�)
(9)
In the binary model of the following section we are calculating the probability of leaving the
current home location due to the increased transportation costs.
73
6.2.3 Empirical Analysis
The parameter estimates of the binary model are presented in Table 14. Many alternative model
formulations were tested and the one presented here provided the best fit and included variables
whose coefficients delivered the expected signs and magnitude. The estimates presented in the
table describe the choice to leave the current home location due to the imposed cost increases. It
can be noticed that increasing car costs lead to higher probability of leaving the current home
location. Public transit costs were not found to be significant. This is not surprising as public
transit was not widely chosen as an alternative, and there is therefore not enough sample size to
draw a conclusion.
As the scenarios proceed and the fuel prices rise, there is higher probability that households will
decide to relocate. Furthermore, it seems that single parents and households consisting of a
couple (two adults without children) perceive relocation the most negative among all other
household types, and are less likely to leave their current home location. Households with only
one commuter seem to be more flexible to relocate. This result indicates that for households with
only one commuter the decision is simpler. On the contrary, households with more than one
commuter have to consider more than one work destinations and a “middle ground” solution has
to be found.
Finally, the model included the area (square footage) of the current house as an independent
variable. This variable captures various other household characteristics, because the size of the
household can be used as an indicator for: the housing expenses, the needs of the household
members, the income of the family, and the location and type of the house (urban/suburban,
apartment/ detached). While other combinations of variables were tested during the model
formulation, the square footage of the house appeared to have the greatest significance in the
model estimation. Households that live in bigger houses are less likely to change home location
despite the increases of commuting expenses.
74
Table 14: Estimation Results of Binary Model for Leaving Current Home Location in SP1
Coef. Std. Error t-stats
Variables
Alternative specific constant
Leave -3.7363 0.3812 -9.80**
Car Costs 0.0008 0.0003 2.71**
Fuel Price 0.4684 0.0767 6.10**
Couple without children -1.6296 0.3984 -4.09**
Single Parent -1.1054 0.3621 -3.05**
Household with one commuter 1.1677 0.3417 3.42**
Area of current house -0.0004 0.0001 -2.69**
Number of Observations 1382
Log. Likelihood -343.4036
LR chi2(12) 85.86
Prob>chi2 0.00
Pseudo R2 0.1111
** Significant at the 0.01 level
*Significant at the 0.05 level
In general, the model performed adequately with a McFadden R� of 0.11. This value, while
significantly lower than the one for the mobility tool model presented in Section 6.1, is
satisfying, due to the multidimensional nature of this choice and the multilevel components that
were involved in this experiment. Making the assumption that commuting costs play the only
role in choosing house location would be a misguided simplification that does not comply with
the findings of many researchers (see Section 2.3). However, this experiment showed that
members’ commuting expenses when added up at the household level have a significant impact
on a household’s decision to change residential location. The relocation choice is examined in
the following chapter.
6.2.4 Elasticities
Based on the parameter estimates of the model presented in Section 6.2.3, the direct elasticity of
the household transportation cost can be calculated. Direct elasticity measures the percentage
75
change in the probability of choosing an alternative of the choice set with respect to the
percentage change in an attribute of this alternative.
Equation 10 calculates the point elasticity for the binary model:
�����
��� =����
�����∗����
��� (10)
Where:
�����
��� : is the elasticity of choosing alternative i with respect to a change in variable k
���: is the probability of choosing alternative i for person q
����: is the value of variable k of alternative i for person q
For linear formulations the utility term takes the form of Equation 11 for each individual:
�����
��� = �������(1 − ���) (11)
Usually, in order to acquire the aggregate elasticity Equation 11 is calculated for the sample
average ��� and ���(average estimated probability of choice of alternative). However, this is not
generally correct because logit models are non-linear, and, therefore, the estimated logit function
might not pass through the points defined by the sample averages. This mistake typically causes
errors in estimating the change in choice probabilities with respect to changes in a variable ����
(Louviere et al., 2000).
A better approach is to calculate Equation 11 for each individual q in the sample and then weigh
each individual elasticity by the individual’s probability of choice. This technique is shown in
Equation 12 and is called “the method of sample enumeration”. This method was used to
calculate the direct weighted elasticities (Table 15 and Figure 21).
�������� =
∑ ���������
�������
∑ ��������
(12)
76
����: is an estimated choice probability
��� : is the aggregated probability of choice of alternative i
Table 15 shows the average direct elasticity of the sample with respect to increasing
transportation costs. The point elasticity for each household record was calculated for each gas
price scenario of the SP and then aggregated.
Table 15: Residence Relocation Choice Elasticity with respect to Increasing Transportation
Costs (derived from the results of the binary model in Section 6.2.3)
Transport Cost Increase Factor
Elasticity Weighted Elasticity Probability
1 0.4082 0.4534 0.0315
2 0.5093 0.5605 0.0621
3 0.5420 0.6135 0.1012
4 0.5561 0.6082 0.1721
Figure 21: Residence relocation choice elasticity and weighted elasticity with respect to
increasing transportation costs
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1 2 3 4
Elas
tici
ty o
f re
sid
en
ce re
loca
tio
n
cho
ice
pro
bab
ilit
y
Transport cost increase factor
Elasticity
Weighted Elasticity
77
The elasticity of residential relocation choice was calculated for each of the cost increases
imposed by the four scenarios in SP1. Table 15 shows that the weighted elasticity ranges
between 0.45 and 0.61, while the average elasticity takes values 0.40 to 0.56. The elasticity sign
is positive (correctly) showing that as transport costs increase, more households will choose to
relocate to reduce the overall living costs. Similarly, the elasticity value increases with increasing
transport costs, indicating that people become more sensitive to higher increases.
Elasticity of 0.45 means that in the case of 1% increase in transport cost at the current level, the
probability of choosing to move to a new residence location will increase by 0.45%. For a
transport cost that is twice higher compared to the current level the elasticity figure is 0.56,
meaning that 1% increase in transport cost at that level will change the relocation choice
probability by 0.56%
78
7 Modelling Dwelling Type Choice
This chapter is indented to investigate the complex procedure of residential location choice after
completing SP1. This chapter presents a model for the housing unit choice of the participating
households.
7.1 Dwelling Type Model
The last step of SP2 was to select among different dwelling type units in the indicated
neighbourhood. This part captured the trade-offs that households would be willing to make in
case of deciding to relocate. A multinomial logit model (MNL) was employed to capture the
probability of choosing between different units and different tenures. A total of six alternatives
were provided to each participant based on the necessary number of bedrooms in the selected
neighbourhood:
House for sale
House for rent
Townhouse for sale
Townhouse for rent
Apartment for sale, and
Apartment for rent.
The model was estimated with the software Biogeme version 2.3 and for its estimation all
responses of SP2 were used. After the removal of erroneous entries 483 records were used.
7.1.1 Non Linear Interactions
According to the economic theory, people with lower income should be more sensitive in
changes of costs. For this reason, the model includes non-linear interactions that describe the
relationship between income and cost perception. The majority of modelling analyses allow for
some interactions between the estimated parameters and socio-demographic attributes, but they
usually achieve that by using separate parameter coefficients in the same model. However,
previous researchers (Hess et al., 2008); (Erath & Axhausen, 2010); (Mackie et al., 2003) have
used in their analysis a formulation to account for the continuous interactions between multiple
variables. This interaction term is used in this context because of the averaging errors that occur
79
when grouping different income classes or when using the mean value of an income-class. The
CHOICE survey collected income information for each individual at ranges of 25K. The mean of
each range was used as a unique value for each individual, and thereafter, all individual incomes
were summed to form the household income.
The Equation bellow was used to determine these effects:
f(x, y) = β� ∗��
�����,�
∗x (13)
Where:
β�: is the parameter estimate of variable x
x: is the independent variable, such as housing price
y: is the observed value for a socio-demographic variable, such as income
�� : is a reference value for this variable, most frequently the mean of the sample.
��,� : gives the elasticity of the utility based on the value of variable x with respect in changes in
y
This formulation describes the sensitivity of a respondent to an attribute change with y. As Erath
and Axhausen (2010) discuss, �� can take any value and has no effect on the model fit or the
estimate for ��,�. In our formulation we use the mean value��; in this way we ensure that the
parameter estimates �� explain the sensitivity of x to the average of y in the sample. A negative
value of λ indicates that individuals with higher income are less sensitive to cost. For households
with income 80% higher than the average income, the fraction ������
����������������� is 1.8. Assuming a λ
parameter of -1, the sensitivity term is 0.55, which means that households with income 80%
above the average are 45% times less cost sensitive than the average household.
7.1.2 Utility Functions
An MNL was used to identify what tenure and what dwelling type was more likely to be chosen
by the participating households. The model formulation of MNLs can be found in Section 0.
The final housing model considers the following elements:
Perception of housing costs (rent cost/purchase price)
80
Influence of income on cost perception
Preference of dwelling type/ tenure depending on today’s dwelling type/tenure
The utility function of the suggested model takes the following form:
��� = ���� + ���������� ∗�������
�����������������
������������
∗���������� +����.��� ∗����� +
����.���� ∗������ +����.����� ∗������� +����.��������� ∗����������� +
����.��������� ∗����������� (14)
Where
���: is the systematic utility of alternative i,
i: is the alternative (house for sale, townhouse for sale, apartment for sale, house for rent, townhouse for rent, apartment for rent)
j: is household j
Income j : is the annual income of household j
�����������������: is the average annual income in the sample
House cost i,j: is the monthly housing cost
�����: is 1, if the household’s actual housing unit is owned, else 0
������: is 1, if the household’s actual housing unit is rented, else 0
�������: is 1, if the household’s actual housing unit is a single detached, else 0
�����������: is 1, if the household’s actual housing unit is a townhouse, else 0
�����������: is 1, if the household’s actual housing unit is an apartment, else 0
7.1.3 Results
As noted above an MNL was developed to investigate the dwelling type choice of the
participants in the study area. The presented model estimates the probability that a GTA
household will choose any given combination of housing type and tenure from the set of feasible
81
alternatives. After testing for alternative model structures the final model was developed and the
model estimates are presented in Table 16 .
Table 16: Estimation Results of MNL for Dwelling Type Choice in SP2
Coef. Std. Error t-stats
Variables
Alternative specific constant
House for sale 0.00 --- ---
House for rent 0.595 0.182 3.26**
Townhouse for sale -0.281 0.249 -1.13
Townhouse for rent -5.79 0.149 -38.86**
Apartment for sale -0.904 0.265 -3.42**
Apartment for rent 0.895 0.203 4.41**
Housing cost (rent/purchase price) -3.09e-007 1.49e-007 -2.07*
Actual unit, house 0.639 0.257 2.48**
Actual unit, townhouse 0.543 0.307 1.77
Actual unit, apartment 1.12 0.285 3.95**
Actual tenure, own 3.14 0.188 16.73**
Actual tenure, rent -1.15 0.225 -5.14**
λ own -0.313 0.335 -0.93
λ rent -0.00328 0.445 -0.01
Number of Observations 483
Log. Likelihood -619.417
Adjusted rho-square 0.269
** Significant at the 0.01 level
* Significant at the 0.05 level
As shown in Table 16 the final model was determined based on the inclusion of variables with
the proper signs and statistical significance. Most of the variables in the model are significant at
the 0.01 or 0.05 level. Only the perception for cost failed to derive a significant value for the 0.05
level; it had however the right parameter sign. The negative λ value indicates that individuals
with higher income perceive costs as less onerous.
That factor aside, we observe that the cost parameter, either for renting or purchasing a unit, is
negative and statistically significant. This indicates that costs have a negative influence on the
utility of the decision maker and therefore decrease the probability of the respective alternative
being chosen.
82
In addition, the model included dummy variables for the actual tenure and dwelling type of the
respondents. A value of 1 indicated that respondents chose the type of house or tenure that they
have today, and in other words are unwilling to change. Among the three dwelling types, those
who live in apartments have the highest probability to re-select an apartment, followed by those
who live in houses and last by those who live in townhouses.
Similarly, people who own their current dwelling type are more likely to choose to buy a unit in
SP2. Surprisingly, people who are currently renting their units are less likely to rent again, which
indicates that switching from renting to owning might be in their future plans.
Overall the model performed well with an adjusted R�value of 0.27. As noted in previous
sections, an R� value of 0.2 to 0.4 is considered a very good fit for logistic regression models.
83
8 Conclusions and Future Work
8.1 Summary
This thesis presented some of the possible reactions of GTA households in response to extreme
increases in transportation costs. The investigations concerned the medium and long-term
decisions, which are reflected through the mobility tool ownership – vehicle fleet and transit
passes – and the housing location and housing choices of GTA households. Most importantly,
this study intended to identify the tipping point (trade off) between mobility costs for commuting
purposes and housing costs.
The first findings of the analysis showed that the operational commuting costs mainly depend on
the fixed costs of the vehicle chosen. This finding indicates that fuel price variations explain only
3% of the total costs for maintaining and operating household vehicles.
The analysis showed that for increasing commuting costs, people would adjust their mobility
tools, mainly by changing the engine type and switching to more efficient technologies, such as
hybrid and electric vehicles. However, it appeared that the majority of respondents were not
willing to downgrade the size/type of their vehicle. This finding demonstrates strong inertia
effects. Insisting on keeping the same vehicle might have to do with the performance for the
specific car, and with the habit or pleasure that the driver gets from the vehicle. These factors
seem to be more important compared to the costs that are associated with this vehicle.
The estimates of the developed MNL for the possible mobility tool reactions showed that
individuals who belong to households with more than one commuter or households with
children, are less likely to change their mode choice or type of vehicle despite the cost increases.
Furthermore, respondents seemed overall unwilling to switch to transit for their commuting trip.
Once again this finding is not surprising if we take into consideration the target population of the
survey. Cross-regional trips by transit are not considered an inexpensive alternative. In many
cases the transit fare is of the same magnitude as driving the same distance by car. The expensive
fare, when combined with the additional travel time and inconvenience, makes transit a very
unattractive solution.
84
The design of the survey allowed the respondents to voluntarily change home location, move
closer to their work and reduce the commuting expenses of the household. The majority of the
respondents insisted on staying at the current home location throughout the four cost scenarios.
The weighted point elasticities for choosing to relocate with respect to increasing commuting
costs range between 0.45 and 0.61. The positive sign of the result demonstrates that for increases
in transport costs more households will choose to relocate to reduce the overall living costs. For
a 1% increase of transport costs at the current gas price level ($1.25/litre), the probability of
choosing to move to a new residence will increase by approximately 0.40%. Similarly, for a 1%
increase in transport costs, given a hypothetical quadruple gas price value, the probability of
households to relocate will increase by 0.61%. This finding shows that transport costs play some
role in the home relocation decision; however this relationship has to be further investigated to
arrive to more robust conclusions.
The binary model estimation showed that the household composition (single parent, no children,
number of commuters) have some significance in the choice of a household to leave or stay.
Commuting costs by car were significant, unlike public transit costs that appear to have no input
in a household’s decision to relocate.
At the end of the experiment all participants had to indicate their preferred neighbourhood
location. The analysis showed that while most participants moved from their current city,
municipality or planning district, they preferred to stay within the boundaries of their region. In
other cases it became apparent that households from the surrounding regions would much rather
move to another region than move in the City of Toronto.
Finally, the dwelling type preferences of households showed that most of the participants insisted
on staying at a dwelling type similar to what they have today. On the contrary most participants
chose to purchase a house, regardless of being renters or owners today.
Overall, this study provided the first finding of GTA households with cross-regional commuters
regarding their mobility tool ownership and housing type and location trade-offs. In order to
have a more representative and in depth analysis some recommendations are provided in the
following section.
85
8.2 Recommendations for Improving the CHOICE Survey
The CHOICE survey was conducted during March/April 2014 among a randomly selected
sample in the GTA. The CHOICE survey provided rich information about GTA household
choices. However, if it were to be implemented again, some minor improvements could lead to
having a more complete picture of the households and their expenses. The points that follow in
this section were identified during the data analysis, and could prove to be valuable additions to
the already collected information.
In part one of the survey, (Section 4.1.1), there should be additional questions about housing
costs. The current version asks for rent or mortgage payment, but during the data collection it
became apparent that many households have paid off their loans, and seemingly have zero
housing costs. In reality there are still maintenance costs, condo fees, utilities or taxes that were
not asked.
In the second part of the survey (Section 4.1.2), we collected information about the transit pass
ownership of each individual. However, there were no questions asked about single ride tickets.
Apparently, many commuters use multiple modes during a week, and, therefore, choose to buy
single ride tickets instead of a transit pass. In a future data collection, an additional question
about the single ride fare of those who selected transit as one of their weekly commuting modes
should be asked.
Finally, it proved to be challenging to connect the current commuting choices and preferences of
individuals with the options selected in SP1. The problem was that the vehicle information was
collected at the household level (how many cars, what type etc.), but during SP1 the vehicle/
transit pass was added for each respondent. It was, therefore, difficult to identify if the individual
downgraded vehicle, changed mode etc. This is the reason why in the model presented in Section
6.1 the scenario one of the SP1 was used as the reference scenario and not the RP information.
This is a part of the CHOICE survey that should be addressed in a different way if similar
analysis is to be done.
86
8.3 Future Work
This thesis covered the first analysis of the data collected by the CHOICE survey. The survey
provided a rich data source with information about retrospective, current and future preferences
of GTA households for residential location, housing, and mobility tools in the face of higher
transportation costs. This section offers some suggestions of future work that could be done with
this dataset to build on top of the analysis presented in this thesis.
The topic that should be investigated in more detail is the residential location choice of
households. More behavioural factors should be taken into consideration, as proved by the
results of Section 6.2. Commuting expenses appear to play a small part of the residential
location/housing expenditure decisions. The CHOICE survey provided information about the
home location and dwelling type choice of participants in the past, and this data could be used to
estimate some behavioural factors that guided their decision in the SP.
Furthermore, data from SP2 could be used to estimate a spatial location model for the GTA. The
neighbourhoods or cities that each respondent viewed will create the alternative choice-set for
each respondent and the qualitative factors combined with the housing prices could be used in a
nested logit structure to account for all aspect of the residential location choice. Alternatively, a
random choice-set could be created to make up for respondents who did not review enough
locations before deciding the final one.
Finally, for a complete picture of this issue, short-term reactions should be investigated as well.
Additional transportation options need to be investigated, such as change of departure time, more
mode options (e.g. car-pooling, car sharing, park & ride, etc.). These should be included in the
SP to identify how trade-offs occur at each stage of the decision-making.
87
References
Aigner, D. (1971). Basic Econometrics. Prentice-Hall. Ahn, J., G. Jöng, Y. Kim (2008). A forecast of household ownership and use of alternative fuel
vehicles: A multiple discrete-continuous choice approach. Energy Economics , 2091-2104.
Alder, T. W., L. Wargelin, L. Kostyniuk, C. Kavalec, G. Occhiuzzo, (2003). A large scale stated preference experiment. In proceedings of: 10th International Conference of Travel Behaviour Research, Aug 10-15. Lucerne.
Beckmann, K.J., C. Jürgens, M. Kreitz, K. Axhausen, R. Schliech, S. Schoenfelder, M. Friedrich, T. Haupt, A. Zimmermann, H. Klein, M. Kehle, B. Krebs, B. (2002) Mobiplan, Endbericht, Final Report, Federal Ministry of Education and Research, Institut fürStadtbauswesen (ISB) and PTV and IVT and Institut für Soziologie.
Beige, S. (2008). Long-term and mid-term mobility decisions during the life course. Ph.D. Thesis ETH Zurich
Ben-Akiva, M. e. (1994). Combining Revealed and Stated Preferences. Marketing Letters 5:4 , 335-350.
Ben-Akiva M., T. Morikawa (1990). Estimation of switching models from Revealed Preference and Stated Intentions. Transport Research-A , Vol. 24A, No 6,pp 485-496.
Berge, Z., M. Collins (1996). IPCT:Journal Ridership Survey. Journal for American Society for Information Schience , 47(9), 701-710.
Bhat, C. J. Guo (2007). A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels. Transportation Research Part B: Methodological , 41 (5), 506-526.
Bhat, C., V. Pulugurta (1998). A comparison of two alternative behavioral choice mechanisms for household ownership decisions. Transporta Research B , 32, 61-75.
Brownstone, D., D. Bunch, K.E. Train (2000). Joint mixed logit models of stated and revealed preferences for alternative-fueled vehicles. Transportation Research Part B: Methodological , 34 (4), 315-338.
Bürgle, M. (2006). Residential location choice model for the Greater Zurich area. Paper presented at the 6th Swiss Transport Research Conference. Ascona.
88
Cao, X., P. Mokhtarian, S. Handy (2007). Residential and travel choices of elderly residents of Northern California. 48th Annual Transportation Research Forum , 417-438.
Carr, L., S. Dunsinger, B. Marcus (2010). Vailidation of walk score for estimating access to walkable amenities. British Journal of Sports Medicine .
Choo, S., P. Mokhtarian (2004). What type of vehicle do people drive? The role of attitude and lifestyle influencing vehilce type choice. Transportation Research Part A: Policy and Practice , Vol. 38, Issue 3, pp 201-222.
City of Toronto. (n.d.). From www.toronto.ca
Couper, M. (2000). Web Surveys: A review of issues and approaches. Public Opinion Quarterly , 64, 464-494.
Crawford, S. C., M. P. Couper,M.J,Lamias (2001). Web Surveys: Perception of Burden. Social Science Computer Review , 19(2),146-162.
Dagsvik, J., T. Wennemo (2002). Potential demand for alternative fuel vehicles. Transportation Research B , 36, 361-384.
Darren, M., D. Scott, K. Axhausen (2006). Household mobility tool ownership: Modeling interactions between cars and season tickets. Transportation , 33 (4), 311-328.
De Palma, A. Rochat (2000). Mode choice for trips to work in Geneva: an empirical analysis. Transport Geography , 8, 43-51.
Dillman, D. A. (2000). Mail and Internet Surveys: The tailored design method (2nd edition). New York: Wiley.
Dillman, D., D. Bowker. (2001). The web questionnaire challenge to survey methodologists. Dimensions of Interent Science, Langerich Germany: Pabst Science , 159-178.
Dillman, D. A., R. Tortora, D. Bowker (1998). Principles for constructing web surveys: An initial statement (Technical Report No. 98-50). Washington State University: Social and Economic Sciences Center: Pullman, Washington.
Dissanayake, D., T. Morikawa (2010). Investigating household vehicle ownership, mode choice and trip sharing decisions using a combined revealed preference/stated preference Nested Logit model: case study in Bangkok Metropolitan Region. Journal of Transport and Geography , 402-410.
Duncan, D., J. Aldstadt, J. Whalen, S.J. Melly,S. L. Gortmaker (2011). Validation of walk score for estimatin neighborhood walkability:An analysis of four US Metropolitan Areas. International Journal of Environmental Research and Public Health , 8(11),4160-4179.
89
Earnhart, D. (2002). Combining revealed and stated data to examine housing decisions using discrete choice analysis. Journal of Urban Economics , 51 (1), 143-169.
Erath, A., K. Axhausen (2009). Mobility Costs and Residence Location Choice Mobility Costs and Residence Location Choice.
Erath, A., K. Auxhausen (2010). Long term fuel price elasticity: effects on mobility tool ownership and residential locatin choice. Zurich, Switzerland: Eidgenössische Technische Hochschule Zürich, IVT.
Ewing, G., E. Sarigollu (2000). Assessing consumer preferences for clean-fuel vehicles: A discrete choice experiment. Transportation Research D , 3, 429-444.
Franklin, S., C. Walker (2003). Survey methods and practices. Statistics Canada, Social Survey Methods Division.
Fraser Institute. (n.d.). From www.fraserinstitute.org
Gannon, B. (1990). A dynamic analysis of disability and labour force participation in Ireland 1995-200. Health Economics , 14, 925-938.
GIC. (n.d.). Government of Canada. From www.cic.gc.ca
Groves, R. (2005). Survey errors and survey costs. John Wiley and Sons.
Hensher, D., P. Bernard, F. Milthorpe (1989), (1989). An empirical model of household automobile holdings. Applied Economics , 21, 35-37.
Hensher, D. ,W. Greene (2001). Choosing between conventional, electric and lpg/cng vehicles in single-vehicle households. Sydney: Sydney N.S.W.
Hess, S., A. Erath, K. Axhausen (2008). Joint valuation of travel time savings estimation on four separate Swiss data sets. Transportation Research Record , 2082, 43-55.
Hunt, J. (2001). Stated preference analysis of sensitivities to elements of transportation and urban form. Transportation Research Record , 1780, 76-86.
Idris, A. (2013). Modal Shift Forecasting for Transit Planning Service. Modal Shift Forecasting for Transit Planning Service . Toronto, Ontario.
Iten R., S. Hammer, M. Keller, N. Schmidt, K. Sammer, R. Wuestenhagen (2005). Massnahmen zur Absenkung des Flottenverbrauchs Abschaetzung der Wirkung. Retrieved from http://www.bfe.admin.ch/themen/00526/00535/index.html?lang=de&dossier_id=00819
Jäggi, B., A. Erath, C. Dobler, K. Axhausen (2011). Modelling household fleet choice as a function of fuel price by using a multiple discrete-continuous choice model. Zurich, Switzerland: Eidgenössische Technische Hochschule Zürich, IVT .
90
König, A., K. Axhausen. (2001). Möbilitätswerkzeuge und Wohnstandorte: Mobiplan stated-choice Experimente . Stadt Region Land , 71, 185-193.
Kim, J. H. , F. Pagliara, J. Preston (2003). An analysis of residential location choice behaviour in Oxfordshire, UK: A combined stated preference approach. Internation Review of Public Administration , 8 (1).
Krumdieck, S. M., M. Watcharasukarn, S. Page,S. (2010). TACA Sim: A survey for adaptability assessment. New Zealand: University of Canterbury,Mechanical Engineering.
Lave, C., K. Train. (1979). A disaggregate model for auto-type choice. Transportation Research Part A , 13 (1), 1-9.
Lerman, S. M. Ben-Akiva (1976). Disaggregate behavioral model of automobile ownership. Transportation Research Rec. 569 , 34-51.
Lerman, S. (1976). Location, housing, automobile ownership and mode to work: A joint model. Transport Research Rec. 610 , 6-11.
Louviere, J., A. Hensher, J. Swait (2000), Stated Choice Methods- Analysis and Applications, Cambridge University Press, Cambridge.
Lowry, I. (1964). A Model of Metropolis. Canta Monica, California: Rand Corporation.
Mackie, P., M. Wardman, A.S. Fowkes, G. Whelan, J. Nellthorp, J.J. Bates (2003). Values of travel time savings in the UK. Research Report, Department for Transport, Institute for Transport Studies, University of Leeds and John Bates Services, an Abingdon.
Manaugh, K., A.M. El-Geneidy (2010). Validating walkability indices. How do different households respond to the walkability of their neighborhood. Transportation Research Par-D: Transport and Environment , 16(4), 309-315.
Metrolinx, 2014, June, Info to Go., Retrieved from http://www.gotransit.com/public/en/docs/publications/quickfacts/Quick_Facts_Info_to_GO_EN.pdf.
Metrolinx, 2008, November, The Big Move: Transforming Transportation in the Greater Toronto and Hamilton Area.
Meurs, H. (1990). Trip generation models with permanent unobserved effects. Transportation Research , 24B, 145-158.
Mikiki F., P. Papaioannou (14-17 April 2014). A proposal for more efficient travel behaviour change intervantions. Proceedings of TRA 2014 International Conference. Paris.
91
Mikiki, F., P. Papaioannou (4-8 September 2012). Attitudinal aprroaches in travel behaviour research combining quantitative and qualitative methods. 1st International Symposium of Quantitative Methods. Lausanne.
Miller, E. (2005). Propositions for modelling household decision-making. Elsevier , 21-60.
Mississauga Data. (n.d.). Retrieved 07 2014, from http://www.mississauga.ca/portal/residents/mississaugadata
Molin, E., H Timmermans (2003). Accessibility considerations in residential choice decisions: Accumulated evidence from the benelux. Paper presented at the 82nd Annual Meeting of the Transportation Research Board. Washington, D.C.
Morrow-Jones, H. (2007), Why do home owners move? Push and pull factors in the movement of repeat home buyers, Housing and Society, 34,(2), 161-185
Nolan, A. (2010). A dynamic analysis of household car ownership. Transport Research Part A: Policy and Practice , 44 (6), 446-455.
NBC News, 2008, Gas rises put Detroit Big Tree in crisis mode, 06/01/2008, Retrieved 08/31/2014 from hhtp://www.nbcnews.com/id/24896359/#.VAMXUsV_tsI
NRCAN. (2013). Fuel Consumption Guide 2013. Retrieved 2013 йил November from Natural Resources Canada: oee.nrcan.gc/tranportation/tools/fuelrating/FCG2013_e.pdf
OEB. (2013). Ontario Energy Board, Electricity Prices. Retrieved 2013 йил November from Ontario Energy Board: www.ontarioenergyboard.ca/OEB/Consumers/Electricity+Prices
Ontario Ministry of Finance. (2013). Ontario Population Projections Update 2012-2036. Retrieved 07 2014, from http://www.fin.gov.on.ca/en/economy/demographics/projections/
Potoglou, D., Y. Susilo (2008). Comparison of vehicle-ownership models. Transportation Research Record , 97-105.
Potoglou, D., P. Kanaroglou (2008). Modelling car ownership in urban areas: a case study of Hamilton, Canada. Journal of Transport Geography , 42-54.
Potoglou, D., P. Kanaroglou (2007). Household demand and willingness to pay for clean vehicles. Transportation Research Part D: , 12, 264-274.
Sax, L. G., S. K. Gilmartin, A. N. Bryant (2003). Assessing response and nonresponse bias in web and paper surveys. Research in Higher Education , 44(4),409-431
Simma, A., K. Axhausen (2003). Commitments and modal usage: Analysis of German and Dutch panels. Transportation Research Record , 1854, 22-31.
92
Schmidt, W. C. (1997). Word wide web survey research: Benefits, potential problems and solutions. Behavioral Research Methods, Instruments and Computers , 29,274-279.
Smith, C. (1997). Casting the Net: Surveying an Internet based population. Journal of Computer Mediated Communication , 3,77-84.
Stanovnik, T. (1990). Automobile ownership in Yogoslavia. Transportation Research Part A , 24 (2), 113-119.
Statcan. (n.d.). Statistics Canada. From www.statcan.gc.ca
Tardiff, J. (1984). Vehicle choice models: Review of previous studies and directions for future research. Transport Research A , 14A, 327-335.
Underwood, D. M, H. Kim, M. Mattier(2000). To mail or to web: Comparisons of survey response rates and respondent characteristics. 40th Annual Forum of the Association for Institutional Research. Cincinnati, Ohio.
Vrtic, M., N. Schüssler, A. Erath, K. W. Axhausen, E. Frejinger, J. Stojanovic, M. Bierlaire, R. Rudel, S. Scagnolari and R. Maggi (2007). Einbezug von Reisekosten bei der Model- lierung des Mobilitätsverhaltens . Research Report, Forschungsauftrag Nr. 2005/004, Swiss Association of Transportation Engineers and Experts (SVI), IVT, ETH Zurich, TRANSP- OR, EPF Lausanne and Institute for Economic Research (IRE), University of Lugano, Zurich .
Waddell, P. (2002). UrbanSim: Modeling urban development for land use, transportation and environmental planning. Journal of the American Planning Association , 68(3), 297-343.
Waddell, P. (2006). Reconciling household residential location choices and neighborhood dynamics.
Walker, J., J. Li (2007). Latent lifestyle preferences and household decisions. Journal of Geographical Systems , 9 (1), 77-101.
Weisbrod, G., M. Ben-Akiva, S. Lerman (1980). Tradeoffs in residential location decisions: Transportation versus other factors. Transportation Policy and Decision-Making , 1 (1).
Wellbeing Toronto. (n.d.). From map.toronto.ca/wellbeing/
Zhang, Y. (1999). Using the Internet for survey research: A case study. Journal of Amrican Society for Informaiton Science , 51 (1), 57-68.
93
Appendix A: Survey Software
This appendix contains screenshots of the all parts of the survey. Each respondent was given a
unique ID as seen on the top of Figure 22. With this ID the participant was able to save the
responses and continue the rest of the survey later without losing any of the thus far answered
questions.
94
Figure 22: Welcome screen
95
Figure 23 shows a screenshot of the help tool that was incorporated in the software and that
allowed the respondents to pinpoint or search an address. Based on the point that each
respondent indicated, the program matched the postal code to the respective TAZ and this
information was used in SP1 for the travel time and cost calculations. When a respondent placed
the marker on a location there was a chance that this specific point would have an incomplete
postal code (as shown in Figure 23). In that case the closest full postal code would be used for
the matching process.
Figure 23: Help map for home and work location
96
Figure 24: Part 1- Retrospective house information
97
98
99
Figure 24 shows an example of a questionnaire for someone who owns the current house and
rented the previous house. It can be seen that the questions change dynamically based on
someone’s answers.
Figure 25 shows the second section of the RP part which collected sociodemographic
information of the household and detailed information about the working/studying adult
members of the household. Respondents could give the name or the initial of the household
members in order to facilitate following questions. Google maps were also embedded as shown
in Figure 23.
100
Figure 25: Part 2- Household sociodemographic information and typical commuting trip
101
102
103
Figure 26: Part 3- Retrospective vehicle ownership
104
Figure 27: Help tab for choosing vehicle category
105
Figure 28: SP1- Mobility tool ownership
106
SP1, illustrated in Figure 28, started by showing a video with instruction on how to complete the
following two parts of the survey. For each adult commuter of the household, as indicated in Part
2, there was a “column” with mode alternatives. In this example there are only two commuters,
the interviewee and the husband. The program uses the home location from Part 1 and the work
locations from part 2 and calculates the time by car and transit as shown in the Data box of
Figure 28. For each member the respondent can add a car by combining the type (compact,
midsize, large, SUV, pickup, or no car) and the fuel technology (gas, hybrid, electric), or add a
transit pass. In the monthly cost box the breakdown of costs is shown, including the parking
costs at work from Part 2.
Based on calculation of fuel consumption and trip distance the software shows the individual
commuting cost per month as well as the total cost for the household. The respondents can
modify the combinations until they reach a desired amount, and then they have to choose to
either stay at the current home or change home location. If they decide to stay at the current
home location they proceed to the next scenario. Otherwise, they continue to SP2 (shown in
Figure 29).
Figure 29 shows the last part of the survey. The respondent starts by selecting the region of
preference (Figure 29). After selecting a region the map zooms in the respective divisions
(Figure 30), districts (Figure 31), and finally neighbourhoods (Figure 32). Then the respondent
has to choose a neighbourhood and can review the qualitative information of the neighbourhood
(walk score, crime, school rating) and the prices of six dwelling units. If the options are not
satisfying the respondent can return to the main map and continue searching.
107
Figure 29: SP2- Home location choice- Screenshot of main map
108
Figure 30: SP2- Screenshot of the divisions in the selected region
109
Figure 31: SP2- Screenshot of the districts in the selected division
110
Figure 32: SP2- Screenshot of the neighborhoods in the selected district
111
112
Appendix B: Statistics of Current Home Location and Location Chosen in SP2 by Region
Table 17: Spatial Location Statistics of York Region Households
York Region
Current Home Location SP2 Home Relocation Choice
Municipality Count Percentage Municipality Count Percentage
Markham 37 34.91% Vaughan 17 16.04%
Vaughan 27 25.47% Markham 15 14.15%
Richmond Hill 20 18.87% Richmond Hill 13 12.26%
Newmarket 7 6.60% PD 1 of Toronto 7 6.60%
Aurora 5 4.72% PD 4 of Toronto 7 6.60%
East Gwillimbury 3 2.83% Newmarket 7 6.60%
Whitchurch-Stouffville 3 2.83% PD 11 of Toronto 6 5.66%
King 3 2.83% Aurora 6 5.66%
Georgina 1 0.94% Whitchurch-Stouffville 5 4.72%
Total 106 East Gwillimbury 4 3.77%
Ajax 3 2.83%
Georgina 3 2.83%
King 3 2.83%
PD 5 of Toronto 2 1.89%
PD 6 of Toronto 2 1.89%
PD 8 of Toronto 2 1.89%
Uxbridge 2 1.89%
Whitby 2 1.89%
Total 106
113
Table 18: Spatial Location Statistics of City of Toronto Households
City of Toronto
Current Home Location SP2 Home Relocation Choice
Municipality Count Percentage Municipality Count Percentage
PD 8 of Toronto 22 15.17% PD 1 of Toronto 16 11.03%
PD 11 of Toronto 21 14.48% PD 8 of Toronto 12 8.28%
PD 13 of Toronto 16 11.03% PD 4 of Toronto 10 6.90%
PD 16 of Toronto 15 10.34% PD 11 of Toronto 9 6.21%
PD 1 of Toronto 11 7.59% Vaughan 9 6.21%
PD 7 of Toronto 10 6.90% PD 7 of Toronto 8 5.52%
PD 4 of Toronto 8 5.52% PD 3 of Toronto 6 4.14%
PD 3 of Toronto 7 4.83% PD 13 of Toronto 6 4.14%
PD 12 of Toronto 7 4.83% Pickering 6 4.14%
PD 14 of Toronto 7 4.83% Mississauga 6 4.14%
PD 5 of Toronto 6 4.14% Markham 5 3.45%
PD 10 of Toronto 6 4.14% PD 2 of Toronto 4 2.76%
PD 9 of Toronto 4 2.76% PD 5 of Toronto 4 2.76%
PD 15 of Toronto 4 2.76% PD 16 of Toronto 4 2.76%
PD 2 of Toronto 1 0.69% Richmond Hill 4 2.76%
Total 145 Oakville 4 2.76%
PD 14 of Toronto 3 2.07%
Whitby 3 2.07%
Oshawa 3 2.07%
Whitchurch-Stouffville 3 2.07%
Milton 3 2.07%
Brampton 3 2.07%
PD 9 of Toronto 2 1.38%
King City 2 1.38%
Caledon 2 1.38%
Burlington 2 1.38%
PD 6 of Toronto 1 0.69%
PD 12 of Toronto 1 0.69%
PD 15 of Toronto 1 0.69%
Ajax 1 0.69%
Aurora 1 0.69%
Halton Hills 1 0.69%
Total 145
114
Table 19: Spatial Location Statistics of Halton Region Households
Halton Region
Current Home Location SP2 Home Relocation Choice
Municipality Count Percentage Municipality Count Percentage
Oakville 15 34.09% Milton 11 26.83%
Burlington 12 27.27% Oakville 7 17.07%
Milton 11 25.00% Halton Hills 5 12.20%
Halton Hills 6 13.64% Burlington 5 12.20%
Total 44 Mississauga 5 12.20%
PD 1 of Toronto 3 7.32%
PD 2 of Toronto 1 2.44%
Pickering 1 2.44%
Whitby 1 2.44%
Vaughan 1 2.44%
Brampton 1 2.44%
Total 41
Table 20: Spatial Location Statistics of Durham Region Households
Region of Durham
Current Home Location SP2 Home Relocation Choice
Municipality Count Percentage Municipality Count Percentage
Ajax 18 33.33% Whitby 9 16.67%
Whitby 14 25.93% Ajax 8 14.81%
Pickering 7 12.96% Pickering 6 11.11%
Oshawa 7 12.96% Clarington 6 11.11%
Clarington 6 11.11% PD 15 of Toronto 5 9.26%
Uxbridge 2 3.70% PD 1 of Toronto 4 7.41%
Total 54 Oshawa 4 7.41%
Uxbridge 3 5.56%
PD 13 of Toronto 2 3.70%
Scugog 2 3.70%
PD 6 of Toronto 1 1.85%
PD 12 of Toronto 1 1.85%
PD 14 of Toronto 1 1.85%
Whitchurch-Stouffville 1 1.85%
Markham 1 1.85%
Total 54
115
Table 21: Spatial Location Statistics of Peel Region Households
Region of Peel
Current Home Location SP2 Home Relocation Choice
Municipality Count Percentage Municipality Count Percentage
Mississauga 63 62.38% Mississauga 30 29.70%
Brampton 31 30.69% Brampton 12 11.88%
Caledon 7 6.93% Caledon 8 7.92%
Total 101 PD 1 of Toronto 7 6.93%
PD 8 of Toronto 6 5.94%
Oakville 6 5.94%
Milton 6 5.94%
Halton Hills 6 5.94%
Burlington 5 4.95%
PD 7 of Toronto 3 2.97%
Richmond Hill 2 1.98%
Markham 2 1.98%
Vaughan 2 1.98%
PD 2 of Toronto 2 1.98%
Ajax 2 1.98%
Oshawa 2 1.98%
Total 101
116
Appendix C: Fixed and Variable Costs of all Vehicle Combinations in SP1
Table 22: Variable and Fixed Costs of Vehicles in SP1
Variable Costs Fixed Costs
Vehicle category and specifications for gasoline vehicles
Consumption lit/100 km
or
kWh/100 km* Annual Insurance
Annual License and Registration
5 year Depreciation and Maintenance
Fixed Monthly Costs
Gasoline
Compact/Subcompact 6.7 2,055 89 5,464 270
Midsize 7.7 2,075 89 6,725 292
Full size 9.3 2,075 89 7,154 300
Crossover/SUV 8.3 2,117 89 7,871 315
Pick-up Truck 11.3 2,200 89 8,097 326
HEV
Compact/Subcompact 4.8 2,055 89 5,464 270
Midsize 5.2 2,075 89 6,725 292
Full size 8.3 2,075 89 7,154 300
Crossover/SUV 7.8 2,117 89 7,871 315
Pick-up Truck 9.3 2,200 89 8,097 326
BEV*
Compact/Subcompact 17.8 2,055 89 5,464 270
Midsize N/A 2,075 89 6,725 292
Full size 21.9 2,075 89 7,154 300
Crossover/SUV N/A 2,117 89 7,871 315
Pick-up Truck N/A 2,200 89 8,097 326
117
Appendix D: House Pricing by Number of Bedrooms and Neighbourhood /City Scores
Table 23: Dwelling Type Price for One-bedroom Units by Neighborhood
Neighborhood/City
Name
house
sale
townhouse
sale
apartment
sale
house
rent
townhouse
rent
apartment
rent
Neighborhood/City
Name
house
sale
townhouse
sale
apartment
sale
house
rent
townhouse
rent
apartment
rent
Broadview North 1,050 Dufferin Grove 479,200 1,650Leaside-Bennington 362,400 1,600 High Park North 446,243 1,820O' Connor-Parkview 189,900 900 High Park-Swansea 303,600 1,200Old East York 329,000 1,175 Junction Area 449,000 312,633 1,500Woodbine-Lumsden 1,088 Kensington-Chinatown 403,320 1,769Islington 282,308 1,441 Lawrence Park North 284,900Kingsview Village-Richview 132,900 1,050 1,800 Lawrence Park South 323,200 1,650Kingsway 272,947 Little Italy-Palmerston 575,342 1,500 1,548 1,700Long Branch 305,980 1,375 Moss Park 344,900 394,077 1,700 1,745Mimico 311,259 1,566 Mount Pleasant 335,000 436,980 1,800 1,656New Toronto 929,000 229,900 Niagara 324,083 362,364 2,269 1,726Stonegate-Queensway 308,988 1,581 North Riverdale 659,900 353,920 487,137 1,297 1,323 1,486West Humber-Clairville 139,900 Roncesvalles 375,824 2,173Banbury-Don Mills 289,718 1,559 Rosedale-St.Claire 416,378 2,400Bathurst Manor 295,233 285,527 South Riverdale-Leslieville 376,776 1,800 1,504Bayview Village 329,233 1,476 St. James 341,100 1,630Bayview Woods-Steeles 327,604 963 1,489 The Beaches 383,440 1,592 1,200Black Creek 144,125 950 Trinity-Bellwoods 369,567 2,000Bridle Path 393,750 999 Waterfront Communities-The Island599,000 405,656 1,955Brookhaven-Amesbury 199,267 Wychwood 321,598 1,000Don Valley Village 281,450 1,398 Yonge 366,741 1,987Downsview 528,900 1,375 Fairbanks 449,900Englemount-Lawrence 97,400 800 Forest Hill 243,580 1,623Humber Summit 174,900 125,750 900 Humewood-Cedarvale 269,950 1,600Lansing-Westgate 449,900 950 Keelesdale-Eglinton West 229,900Nortown 296,133 1,025 Rockcliffe-Mount Dennis379,000Victoria Village 176,033 Ajax 169,900 848 800Weston 228,500 210,520 1,150 Clarington 119,900 153,000Willowdale 350,975 1,150 1,500 1,562 Oshawa 216,633 142,150 789Agincourt 257,130 Pickering 249,900 875Birchcliffe-Cliffside 384,000 381,950 1,000 Scugog 149,000Clairlea-Birchmount 245,267 Whitby 795 1,400Dorset Park 193,300 750 Burlington 369,000 479,900 249,732 1,798 1,466Highland Creek 750 Halton Hills 399,900 700Kennedy Park 157,975 Milton 1,308L' Amoreaux 279,900 196,653 1,330 Oakville 449,019 775 1,604Malvern 158,331 650 1,050 Brampton 189,900 217,325 804 1,300Scarborough Village 141,900 Caledon 427,990 1,200West Hill 151,567 775 Mississauga 219,524 943 1,100 1,376Woburn 203,000 248,246 758 1,300 1,305 Aurora 349,600 950Annex ###### 419,900 731,607 2,100 3,045 East Gwillimbury 274,000 1,232Bay 554,066 1,650 2,189 Georgina 463,345Cabbagetown 329,900 319,850 King 579,267 1,190Casa Loma 799,900 239,450 Markham 282,030 844 1,378 1,314Corso Italia-Davenport 380,633 Newmarket 311,950Danforth Village 195,000 Richmond Hill 276,464 1,029 960 1,397Dovercourt 262,846 1,275 1,279 Vaughan 318,504 1,067 1,375 1,422
Whitchurch-Stouffville356,944
118
Table 24: Dwelling Type Price for Two-bedroom Units by Neighborhood
Neighborhood/City
Name
house
sale
townhouse
sale
apartment
sale
house
rent
townhouse
rent
apartment
rent
Neighborhood/City
Name house sale
townhouse
sale
apartment
sale
house
rent
townhouse
rent
apartment
rent
Broadview North 621,173 469,000 499,000 1,850 Dovercourt 562,000 421300 1,454Leaside-Bennington 1,250,000 849,000 2,300 2,000 Dufferin Grove 1,100,000O' Connor-Parkview 429,600 1,775 1,200 East End Danforth 399,990 419,000 1,725Old East York 838,000 1,400 High Park North 649,000 454,450 600,385 2,140Woodbine-Lumsden 497,213 High Park-Swansea 927,333 460,710 2,900 1,990Islington 518,000 374,600 345,803 1,714 Junction Area 459,000 649,900Kingsview Village-Richview 107,064 Kensington-Chinatown 374,990 764,450 580,370 1,900 2,317Kingsway 500,517 2,975 Lawrence Park North 409,000 450,400 2,000Long Branch 472,400 337,975 1,688 Lawrence Park South 609,000 587,667Mimico 549,900 486,727 1,475 2,400 1,989 Little Italy-Palmerston 1,043,838 2,317 1,950New Toronto 379,900 399,000 1,550 Moss Park 729,000 552,672 1,400 2,600 2,369Rexdale 419,900 149,900 Mount Pleasant 846,500 415,000 788,476 2,000 1,900 2,030Stonegate-Queensway 616,417 516,771 1,800 1,800 2,238 Niagara 645,000 591,056 573,666 2,294 2,097West Humber-Clairville 168,450 North Riverdale 565,000 503,100 556,050 2,567 2,145 1,800Banbury-Don Mills 384,169 1,875 Roncesvalles 1,199,000 555,793Bathurst Manor 931,167 479,900 414,743 Rosedale-St.Claire 4,800,000 1,897,000 994,622 2,000 2,459Bayview Village 599,000 456,457 2,599 1,891 South Riverdale-Leslieville599,000 339,900 597,198 5,995 1,800Bayview Woods-Steeles 412,277 2,100 1,988 St. James 439,000 505,979 2,110Black Creek 339,000 277,150 124,686 1,275 1,100 The Beaches 649,900 483,900 597,725 2,031 2,740Bridle Path 3,480,000 457,000 853,092 6,500 5,865 Trinity-Bellwoods 1,383,500 1,062,450 2,500 3,369Brookhaven-Amesbury 337,500 217,000 Waterfront Communities-The Island3,600,000 1,523,160 756,804 2,663 2,597Don Valley Village 699,000 264,233 427,177 1,047 2,071 Wychwood 579,900 329,900 2,280Downsview 534,983 333,425 235,786 1,350 1,575 Yonge 523,450 765,182 2,848 2,800 2,330Englemount-Lawrence 667,500 359,888 267,257 Fairbanks 436,260Humber Summit 479,900 284,900 133,300 1,017 Forest Hill 1,009,000 705,200 2,730Lansing-Westgate 978,778 437,347 1,743 1,950 Humewood-Cedarvale 799,900 1,299,000 406,300 1,900Nortown 984,422 575,843 2,000 Keelesdale-Eglinton West432,175Victoria Village 349,900 267,231 1,675 1,563 Rockcliffe-Mount Dennis406,607 292,900 242,725Weston 396,297 276,150 229,549 2,131 Ajax 384,500 274,900 238,740 1,150Willowdale 788,800 545,789 1,525 2,020 1,916 Clarington 293,033 237,500 191,348 550 2,300Yorkdale 714,500 270,860 1,350 Oshawa 216,450 106,950 173,600 1,109Agincourt 337,742 931 1,455 Pickering 463,267 249,000 284,933 1,050Birchcliffe-Cliffside 486,210 464,225 1,640 Scugog 319,450 1,400Clairlea-Birchmount 475,000 291,740 312,117 1,300 Township of Brock 218,538 1,200Cliffcrest 438,967 Uxbridge 551,150 284,900 315,950 1,600Dorset Park 214,276 900 Whitby 695,000 259,920Highland Creek 599,900 Burlington 469,788 398,184 295,140 2,200 1,756 1,533Highland Creek 429,000 1,300 Halton Hills 391,596 208,950 299,600 1,200Kennedy Park 378,966 309,900 207,730 1,025 1,450 Milton 465,650 333,767 251,150 1,450 1,435L' Amoreaux 272,537 984 1,750 Oakville 702,100 568,927 682,042 1,600 1,594 1,927Malvern 231,129 158,016 944 450 1,375 Brampton 310,000 230,363 953 1,477Rouge 1,000 Caledon 522,767Scarborough Village 1,332,450 164,555 1,250 Mississauga 538,425 304,358 291,972 1,190 1,642 1,644West Hill 163,475 175,040 1,200 1,500 Aurora 563,098 950Wexford 449,300 169,000 925 East Gwillimbury 319,900 1,150Woburn 419,900 208,083 321,062 983 1,650 1,447 Georgina 249,789 199,700 224,900 1,150Annex 2,087,500 1,111,047 2,187,860 3,217 1,850 4,727 King 690,300 353,267 1,613 1,600Bay 724,900 973,293 3,554 Markham 432,913 350,827 1,181 1,544 1,629Cabbagetown 563,267 2,100 Newmarket 359,450 333,950 288,017 1,200Casa Loma 1,037,308 1,800 Richmond Hill 619,172 291,967 289,742 1,093 1,600 1,603Corso Italia-Davenport 526,500 424,900 1,400 Vaughan 241,450 438,518 380,158 1,137 1,750 1,806Danforth Village 476,725 2,534 1,450 1,400 Whitchurch-Stouffville 535,840 259,900 1,250
119
Table 25: Dwelling Type Price for Three-bedroom Units by Neighborhood
Neighborhood/City
Name
house
sale
townhouse
sale
apartment
sale
house
rent
townhouse
rent
apart.
rent
Neighborhood/City
Name
house
sale
townhouse
sale
apartment
sale
house
rent
townhouse
rent
apart.
rent
Broadview North 591,950 669,800 2,700 Danforth Village 627,610 437,450 2,450Leaside-Bennington 1,382,333 2,750 Dovercourt 679,733 649,000 422,450O' Connor-Parkview 678,743 Dufferin Grove 900,467 722,000 3,599Old East York 609,900 East End Danforth 576,056 543,000 3,995Woodbine-Lumsden 645,644 519,900 2,750 High Park North 3,060Islington 673,160 378,295 299,932 1,500 1,700 High Park-Swansea 1,342,000 511,300 2,500 2,369Kingsview Village-Richview613,934 658,950 119,980 1,936 2,500 Junction Area 531,071 612,633 1,900Kingsway 1,555,195 2,932 Kensington-Chinatown 946,333 480,000 849,000 2,800 2,850Long Branch 743,170 Lawrence Park North 929,000 3,072Mimico 739,000 727,667 899,900 2,000 2,723 2,557 Lawrence Park South 1,195,000 3,700New Toronto 576,950 2,300 1,925 Little Italy-Palmerston1,111,725 1,206,500Rexdale 484,523 159,900 1,450 Moss Park 1,182,000 4,390 2,662 3,399Stonegate-Queensway 854,628 692,309 1,142,355 2,100 2,085 3,040 Mount Pleasant 869,450 1,236,334 1,433,358 3,262 1,500 3,700West Humber-Clairville449,933 245,275 156,767 1,450 1,098 Niagara 1,062,000 723,233 974,450 6,200 3,200 2,768Banbury-Don Mills 928,429 459,000 2,363 1,950 2,200 North Riverdale 701,475 455,967 689,000 3,450Bathurst Manor 809,257 709,900 623,750 RoncesvallesBayview Village 744,500 2,400 4,325 2,224 Rosedale-St.Claire 1,899,667 2,362,667 3,925Bayview Woods-Steeles 575,967 2,850 2,202 South Riverdale-Leslieville644,719 541,600 2,350 2,650Black Creek 522,736 241,959 147,966 1,295 1,417 1,650 St. James 670,000 2,800Bridle Path 2,258,111 1,196,950 906,225 4,310 3,000 2,723 The Beaches 974,980 599,000 662,900 3,657Brookhaven-Amesbury 677,200 469,000 186,825 Trinity-Bellwoods 895,667 699,450 2,600 3,775 5,800Don Valley Village 652,404 596,772 777,800 2,038 1,540 2,418 Waterfront Communities-The Island1,136,300 1,170,400 2,995 4,425 3,951Downsview 570,563 415,000 158,450 1,600 Wychwood 702,022 428,800 2,700 2,267Englemount-Lawrence 749,000 199,000 1,850 Yonge 1,399,000 712,997 3,350 2,873Humber Summit 469,750 305,999 170,900 Fairbanks 602,060 449,900 2,150Lansing-Westgate 1,134,829 312,900 2,379 Forest Hill 1,749,000 873,000 3,500 3,450Nortown 1,284,875 3,228 Humewood-Cedarvale 1,099,633 2,395 2,300St. Andrew- Windfields1,371,250 1,800 2,248 Keelesdale-Eglinton West544,700 564,900Victoria Village 637,500 170,950 226,053 1,823 1,698 Rockcliffe-Mount Dennis457,120 207,900 1,767Weston 559,528 470,974 1,900 Ajax 365,827 295,350 274,596 1,550 1,567Willowdale 1,077,667 807,000 671,063 2,128 3,038 2,487 Clarington 303,513 252,763 1,600 1,600Yorkdale 959,180 239,000 1,789 Oshawa 277,765 180,110 1,400 1,600Agincourt 599,050 363,000 217,600 1,366 1,456 1,614 Pickering 455,713 382,380 301,300 2,000Birchcliffe-Cliffside 730,113 2,300 Scugog 436,468 549,000 1,699Clairlea-Birchmount 489,360 579,000 1,650 Township of Brock 299,560 189,520 159,900Cliffcrest 1,112,950 289,900 3,500 Uxbridge 740,608 378,000Dorset Park 470,600 361,888 174,875 1,250 Whitby 703,609 297,000 236,400 1,620 1,649Highland Creek 555,120 1,200 Burlington 628,161 411,165 330,761 2,400 1,910 1,833Highland Creek 606,467 348,800 1,525 Halton Hills 466,812 319,663 1,675 1,775Kennedy Park 501,293 174,297 146,500 1,475 Milton 516,000 409,083 374,200 1,704 1,582 1,763L' Amoreaux 693,565 333,382 285,903 1,350 1,765 1,930 Oakville 933,999 525,306 719,359 2,662 2,801 2,938Malvern 399,538 283,845 174,500 1,350 Brampton 407,348 338,181 270,308 1,477 1,580 1,587Rouge 482,633 438,300 1,700 Caledon 547,577 413,100 1,900 1,917Scarborough Village 537,500 320,250 173,186 1,500 Mississauga 616,661 370,247 366,427 1,875 1,821 1,770West Hill 487,376 265,645 145,000 1,350 Aurora 547,350 365,083 570,898 1,743 1,700Wexford 504,173 195,000 1,650 East Gwillimbury 479,543 393,180 1,618 1,760Woburn 461,095 336,736 239,900 1,487 2,150 1,810 Georgina 442,365 274,600 1,287 1,600Annex 2,112,000 1,459,725 2,488,498 5,833 5,625 5,865 King 807,054 537,633 425,567 1,850Bay 1,914,786 2,841 Markham 773,343 462,590 481,041 1,918 1,910 1,966Cabbagetown 2,200,000 869,000 Newmarket 473,661 398,538 1,557 1,675Casa Loma 829,000 3,000 4,750 Richmond Hill 811,999 523,498 350,154 1,758 1,819 1,879Corso Italia-Davenport 483,730 492,000 2,200 Vaughan 717,637 568,126 431,517 2,206 2,132 1,990
Whitchurch-Stouffville 806,948 348,500 437,933 1,623 1,600 1,800
120
Table 26: Dwelling Type Price for Four-bedroom Units by Neighborhood
Neighborhood/City
Name house sale
townhouse
sale
apartment
sale
house
rent
townhouse
rent
apart.
rent
Neighborhood/City
Name
house
sale
townhouse
sale
apartment
sale
house
rent
townhouse
rent
apart.
rent
Broadview North 852,500 Dovercourt 809,000 2,850Leaside-Bennington 1,750,580 Dufferin Grove 837,450 2,300O' Connor-Parkview 829,450 3,200 East End Danforth 2,500Old East York 1,080,480 High Park North 1,030,950 3,500Woodbine-Lumsden 929,000 4,100 High Park-Swansea 2,003,286Islington 1,390,327 259,995 2,550 2,090 Junction Area 529,000 1,650Kingsview Village-Richview582,952 2,148 Kensington-Chinatown 675,000Kingsway 2,034,349 494,000 3,700 Lawrence Park North 1,999,200 799,000 3,695Long Branch 749,900 Lawrence Park South 1,622,200 14,900Mimico 2,780 4,500 Litt le Italy-Palmerston 982,450 3,475 4,317New Toronto 899,999 4,400 Moss Park 2,900Rexdale 538,950 2,000 Mount Pleasant 1,123,500 934,900 4,519Stonegate-Queensway 1,200,502 729,900 3,745 5,000 4,688 Niagara 1,099,000 2,800West Humber-Clairville 494,120 258,933 North Riverdale 1,121,967 3,900Banbury-Don Mills 1,362,600 2,500 Roncesvalles 829,000Bathurst Manor 1,339,909 729,000 1,600 Rosedale-St.Claire 2,668,100 6,128 4,300Bayview Village 2,748 2,390 South Riverdale-Leslieville743,972 3,600Bayview Woods-Steeles St. JamesBlack Creek 567,833 211,333 1,650 The Beaches 1,349,981 3,772Bridle Path 2,660,259 828,000 4,981 Trinity-Bellwoods 967,900 4,500 4,200Brookhaven-Amesbury 968,333 185,000 Waterfront Communities-The Island 3,975,000 4,875Don Valley Village 901,160 358,567 2,333 1,850 1,999 Wychwood 700,000Downsview 852,773 198,500 1,900 Yonge 2,400Englemount-Lawrence 1,259,000 769,000 2,900 Fairbanks 731,000Humber Summit 799,075 299,000 Forest Hill 2,526,049 4,000Lansing-Westgate 1,509,500 2,995 Humewood-Cedarvale 1,361,975Nortown 2,046,566 4,000 Keelesdale-Eglinton West719,297St. Andrew- Windfields1,318,800 2,356 Rockcliffe-Mount Dennis542,450 1,950Victoria Village 788,950 694,500 286,600 2,400 Ajax 499,918 359,450 274,596 2,109 1,795Weston 862,450 2,198 Clarington 453,867 1,550Willowdale 1,967,160 2,450 2,783 Oshawa 395,200 224,900 1,719Yorkdale 1,118,338 159,000 Pickering 783,692 356,600 2,657 1,745Agincourt 1,052,136 1,814 1,950 1,900 Scugog 500,764 3,000Birchcliffe-Cliffside 944,245 1,900 Township of Brock 413,373Clairlea-Birchmount 607,000 1,788 Uxbridge 899,058 3,800Cliffcrest 1,009,360 569,000 Whitby 496,310 2,148Dorset Park 455,000 Burlington 850,893 355,312 2,549Highland Creek 760,518 Halton Hills 547,653 2,263Highland Creek 705,711 2,025 Milton 698,381 2,180 1,638Kennedy Park 605,950 Oakville 1,188,809 832,562 3,131 1,879L' Amoreaux 687,672 2,495 Brampton 731,009 372,387 228,233 1,939 1,653Malvern 479,200 350,172 159,000 1,617 1,550 1,750 Caledon 834,657 1,898 1,750Rouge 703,054 1,983 Mississauga 755,799 417,524 400,100 2,492 1,867 4,500Scarborough Village 817,857 Aurora 824,057 637,967 2,265West Hill 592,057 164,950 East Gwillimbury 950,811 1,800 2,225Wexford 791,333 Georgina 541,499 369,999Woburn 508,400 283,475 King 1,082,002 549,850 3,067Annex 1,355,667 1,217,000 3,600,000 3,767 10,000 Markham 1,087,014 585,180 623,725 2,322 1,920 9,800Bay 4,050 Newmarket 599,200 459,450 2,031 2,200Cabbagetown 1,799,000 Richmond Hill 889,756 547,151 522,320 2,331 2,113Casa Loma 2,774,667 1,349,000 5,500 Vaughan 1,021,638 589,800 2,643 1,800 3,600Corso Italia-Davenport 931,300 449,000 Whitchurch-Stouffville 850,886 1,800
121
Table 27: Dwelling Type Price for Five-bedroom Units by Neighborhood
Neighborhood/City
Name house sale
townhouse
sale
apartment
sale
house
rent
townhouse
rent
apart.
rent
Neighborhood/City
Name
house
sale
townhouse
sale
apartment
sale
house
rent
townhouse
rent
apart.
rent
Leaside-Bennington 11,900 Waterfront Communities-The Island2,858,000 2,399,900Islington 2,046,663 Forest Hill 2,969,167 6,500Kingsview Village-Richview1,170,000 Humewood-Cedarvale 1,590,000Kingsway 3,324,725 4,023 Ajax 505,820 2,150Stonegate-Queensway 1,198,967 2,380 2,100 Clarington 660,591Banbury-Don Mills 2,988,800 2,350 Oshawa 504,293Bathurst Manor 1,083,725 Pickering 1,001,247Bayview Village 3,050 Scugog 745,540 2,400Bayview Woods-Steeles1,349,800 Township of Brock 797,967Black Creek 499,900 Uxbridge 1,117,280Bridle Path 3,423,875 8,062 Whitby 560,376 2,300Don Valley Village 1,308,000 Burlington 973,010 2,775Downsview 1,041,333 3,200 Halton Hills 708,879 2,800Englemount-Lawrence 1,495,000 Milton 1,091,813 2,413Lansing-Westgate 1,469,725 Oakville 1,589,243 5,856Nortown 2,421,000 2,975,000 Brampton 729,833 389,933 2,615 2,050St. Andrew- Windfields1,469,667 4,500 Caledon 1,157,029 1,875Victoria Village 1,700 Mississauga 1,009,810 466,950 3,172 2,200Willowdale 1,550,000 3,171 Aurora 1,575,857 3,000Yorkdale 1,524,500 East Gwillimbury 840,243Agincourt 1,733 Georgina 723,660Cliffcrest 1,887,000 1,650 King 1,591,356 2,100Dorset Park 649,000 1,825 Markham 1,579,587 865,998 2,629Highland Creek 1,799,000 Newmarket 611,029 3,300Highland Creek 879,450 Richmond Hill 1,152,787 1,250,000 2,896Kennedy Park 575,967 Vaughan 1,598,693 512,450 6,475L' Amoreaux 751,633 1,925 Whitchurch-Stouffville1,057,670Malvern 1,600Rouge 749,000West Hill 1,900Wexford 1,185,000 2,000Woburn 2,500 1,599Annex 1,514,000 4,875Casa Loma 2,999,500 15,000Danforth Village 798,800 4,495Dufferin Grove 1,069,633 4,000High Park North 1,170,283High Park-Swansea 2,588,000Junct ion Area 379,900Kensington-Chinatown 849,000Lawrence Park South 2,458,333 8,800Moss Park 719,000 7,500Mount Pleasant 1,599,000 3,383North Riverdale 1,274,500 649,000Roncesvalles 1,299,000 4,000Rosedale-St .Claire 2,810,500 12,500The Beaches 1,368,527 4,209
122
Table 28: Qualitative Characteristics of Neighbourhood
Municipality Neighborhood/City Namewalk
scorecrime score
school
scoreMunicipality Neighborhood/City Name walk score crime score school score
East York Broadview North 8 8 0 Toronto Midtown Annex 10 1 3East York Leaside-Bennington 9 8 8 Toronto Downtown Bay 10 1 0
East York O' Connor-Parkview 8 4 7 Toronto Downtown Cabbagetown 10 7 6East York Old East York 7 9 5 Toronto Midtown Casa Loma 8 7 9East York Woodbine-Lumsden 9 10 0 Toronto Midtown Corso Italia-Davenport 9 6 5Etobicoke Islington 6 1 0 Toronto East End Danforth Village 9 6 6Etobicoke Kingsview Village-Richview 6 5 5 Toronto Midtown Dovercourt 9 1 8Etobicoke Kingsway 6 9 0 Toronto West End Dufferin Grove 9 5 7Etobicoke Long Branch 9 7 7 Toronto East End East End Danforth 10 2 8Etobicoke Mimico 9 2 8 Toronto West End High Park North 8 6 7Etobicoke New Toronto 10 5 5 Toronto West End High Park-Swansea 9 6 7Etobicoke Rexdale 6 9 3 Toronto West End Junction Area 8 2 4Etobicoke Stonegate-Queensway 8 3 6 Toronto Downtown Kensington-Chinatown 10 0 6Etobicoke West Humber-Clairville 7 1 5 Toronto Uptown Lawrence Park North 7 7 8North York Banbury-Don Mills 7 4 9 Toronto Uptown Lawrence Park South 7 7 9North York Bathurst Manor 7 7 8 Toronto Downtown Little Italy-Palmerston 9 3 5North York Bayview Village 6 8 0 Toronto Downtown Moss Park 10 3 2North York Bayview Woods-Steeles 9 2 6 Toronto Uptown Mount Pleasant 10 7 7North York Black Creek 7 2 0 Toronto Downtown Niagara 9 3 2North York Bridle Path 5 9 10 Toronto East End North Riverdale 9 5 9North York Brookhaven-Amesbury 7 2 0 Toronto West End Roncesvalles 9 3 0North York Don Valley Village 7 3 10 Toronto Midtown Rosedale-St.Claire 7 5 9North York Downsview 8 0 4 Toronto East End South Riverdale-Leslieville 9 0 7North York Englemount-Lawrence 7 4 7 Toronto Downtown St. James 10 1 0North York Humber Summit 8 2 0 Toronto East End The Beaches 10 5 8
North York Lansing-Westgate 6 7 10 Toronto Downtown Trinity-Bellwoods 10 4 7North York Nortown 7 5 8 Toronto Downtown Waterfront Communities 10 0 0North York St. Andrew- Windfields 5 5 9 Toronto Midtown Wychwood 9 5 8
North York Victoria Village 7 4 3 Toronto Downtown Yonge 10 1 8North York Weston 8 2 4 York Fairbanks 7 9 0North York Willowdale 8 6 0 York Forest Hill 7 8 9
North York Yorkdale 8 1 10 York Humewood-Cedarvale 7 9 9Scarborough Agincourt 7 4 8 York Keelesdale-Eglinton West 6 5 5Scarborough Birchcliffe-Cliffside 8 3 6 Rockcliffe-Mount Dennis 7 4 0
Scarborough Clairlea-Birchmount 8 2 0Scarborough Cliffcrest 6 6 8 Durham Ajax 5 n/a 6Scarborough Dorset Park 6 2 5 Durham Clarington 1 n/a 6
Scarborough Highland Creek 5 9 7 Durham Oshawa 5 n/a 5Scarborough Highland Creek 7 8 0 Durham Pickering 5 n/a 7Scarborough Kennedy Park 6 2 0 Durham Scugog 2 n/a 6
Scarborough L' Amoreaux 8 3 7 Durham Township of Brock 0 n/a 4Scarborough Malvern 8 0 0 Durham Uxbridge 8 n/a 7Scarborough Rouge 4 3 4 Durham Whitby 4 n/a 7
Scarborough Scarborough Village 8 3 0 Halton Burlington 5 n/a 7Scarborough West Hill 8 1 4 Halton Halton Hills 4 n/a 7Scarborough Wexford 8 1 6 Halton Milton 4 n/a 7
Scarborough Woburn 7 0 6 Halton Oakville 5 n/a 8York East Gwillimbury 3 n/a Peel Brampton 5 n/a 6York Georgina 3 5 Peel Caledon 1 n/a 7
York King 7 7 Peel Mississauga 6 n/a 7York Markham 5 8 York Aurora 5 n/a 7York Newmarket 5 7
York Richmond Hill 5 8York Vaughan 5 7York Whitchurch-Stouffville 3 7
123
Appendix E: Transit Fare O-D Matrix for Scenario 1
Table 29a: Transit Fares for SP1
Burlington Oakville Milton Halton Hills Brampton Missisauga Calendon Vaughan Richmond Hill Markham King Aurora
Burlington 97 196 247 536 379 230 554 441 441 452 462 481
Oakville 196 105 237 468 295 167 496 371 371 395 393 414
Milton 247 237 70 517 294 252 542 384 384 451 409 429
Halton Hills (Georgetown) 536 468 517 0 186 445 265 396 396 459 422 441
Brampton 379 295 294 186 115 289 205 342 342 407 364 387
Missisauga(Erindale) 230 167 252 445 289 120 472 347 347 371 369 390
Calendon 554 496 542 265 205 472 115 422 422 481 448 467
Vaughan(Rutherford) 441 371 384 396 342 347 422 132 167 361 172 194
Richmond Hill 441 371 384 396 342 347 422 167 132 361 172 194
Markham 452 395 451 459 407 371 481 361 361 132 384 404
King 462 393 409 422 364 369 448 172 172 384 177 167
Aurora 481 414 429 441 387 390 467 194 194 404 167 177
Newmarket 496 430 445 457 403 406 483 218 218 420 180 167
Whitchurch 496 438 494 502 451 414 525 404 404 183 427 448
East Gwilimbury 504 438 452 465 411 414 491 226 226 429 188 175
Uxbridge 544 486 542 550 499 462 573 452 452 220 475 496
Brock(Port Perry) 557 499 541 554 497 475 574 420 420 484 446 465
Scugog (Port Perry) 557 499 541 554 497 475 574 420 420 484 446 465
Pickering 448 390 433 446 391 366 472 308 308 380 339 358
Ajax 468 411 452 465 411 387 491 327 327 401 358 377
Whitby 491 433 475 488 433 409 513 348 348 424 380 400
Oshawa 513 456 497 510 456 432 536 369 369 446 403 422
Clarington 600 541 584 597 544 517 618 465 465 531 488 509
City of Toronto 314 249 313 321 262 225 343 207 207 239 237 258
124
Table 29b: Transit Fares for SP1
Newmarket Whitchurch East Gwilimbury Uxbridge Brock Scugog Pickering Ajax Whitby Oshawa Clarington City of Toronto
Burlington 496 496 504 544 557 557 448 468 491 513 600 314
Oakville 430 438 438 486 499 499 390 411 433 456 541 249
Milton 445 494 452 542 541 541 433 452 475 497 584 313
Halton Hills (Georgetown) 457 502 465 550 554 554 446 465 488 510 597 321
Brampton 403 451 411 499 497 497 391 411 433 456 544 262
Missisauga(Erindale) 406 414 414 462 475 475 366 387 409 432 517 225
Calendon 483 525 491 573 574 574 472 491 513 536 618 343
Vaughan(Rutherford) 218 404 226 452 420 420 308 327 348 369 465 207
Richmond Hill 218 404 226 452 420 420 308 327 348 369 465 207
Markham 420 183 429 220 484 484 380 401 424 446 531 239
King 180 427 188 475 446 446 339 358 380 403 488 237
Aurora 167 448 175 496 465 465 358 377 400 422 509 258
Newmarket 177 464 167 512 481 481 374 393 416 438 525 282
Whitchurch 464 177 472 191 528 528 424 445 467 489 576 291
East Gwilimbury 167 472 177 520 489 489 382 401 424 446 531 291
Uxbridge 512 191 520 103 576 576 472 493 515 538 624 339
Brock(Port Perry) 481 528 489 576 103 0 250 226 199 228 347 347
Scugog (Port Perry) 481 528 489 576 0 103 250 226 199 228 347 347
Pickering 374 424 382 472 250 250 103 167 183 204 329 239
Ajax 393 445 401 493 226 226 167 103 167 172 305 260
Whitby 416 467 424 515 199 199 183 167 103 167 286 291
Oshawa 438 489 446 538 228 228 204 172 167 103 255 313
Clarington 525 576 531 624 347 347 329 305 286 255 103 416
City of Toronto 282 291 291 339 347 347 239 260 291 313 416 130
125
ThE End
