Dynamics of Vehicle Ownership in Singapore
By
Yunke Xiang
B.E in Urban Planning
B.A in Economics
Peking University (2011)
Submitted to the Department of Urban Studies and Planning
in partial fulfillment of the requirements for the degree of
Master in City Planning
and
Submitted to the Department of Civil and Environmental Engineering
ARO*NES
in partial fulfillment of the requirements for the degree of
Master of Science in Transportation
MASSACHUSETTS INS
OF TECHNOLOGY
at the
MAR 0 5 2014
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
2014
February
©2014 Massachusetts Institute of Technology. All rights reserved.
LIBRARIES
The author here by grants to MIT the permission to reproduce and to distribute publicly paper
and electronic copies of the thesis document in whole or in part in any medium now known or
hereafter created.
Author
U
nt of
ep
Civl and
Department
nS d sand Planning
ion ntal Engineering
n23. 2014
,,-
Certified by
AssiciateProfessor P. C istopher Zegras
dies and Planning
Dep mnt of
hesis Supervisor
Accepted by
Assoc ate Pr
t
as
CP Committee
Cha
Department of UrbyjStudies andflanning
Accepted by
ProfessorXieidi(M. Nepf
Chair, Departmental Committee for Graduate Students
Department of Civil and Environmental Engineering
E
Dynamics of Vehicle Ownership in Singapore
By Yunke Xiang
Submitted to the Department of Urban Studies and Planning on Jan 24, 2014 in partial fulfillment of the
requirements for the degree of Master in City Planning for the degree of Master of Science in
Transportation.
ABSTRACT
Cities around the world are trying out a wide range of transportation policy and
investment alternatives to reduce car-induced externalities. However, without a solid
understanding of how people behave within the constraints from these policies, it is
hard to tell which of these policies are really doing the job and which may be inducing
unintended problems. The focus of this paper is the determinants of vehicle ownership
in the motorized city-state context of Singapore.
Using survey data from 1997 to 2008, a discrete choice model of vehicle ownership
suggests that income dominates the household vehicle ownership decision. Further
modeling, attempting to detect preference change over the years, suggests that the
dynamics of income's influence on vehicle ownership is changing, perhaps reflecting a
combination of the nation's increasingly high ownership costs and expanding transit
system. All income groups have become less likely to own cars over time, with
households in the lowest income groups apparently being affected the most. For 2008,
the distance to rail transit stations had a discernible relationship with households'
likelihood of owning more than one car, and accessibility and relative travel costs also
influenced vehicle ownership. Including these variables, however, had very modest
influence on improving model fit.
Thesis Supervisor: Associate Professor P. Christopher Zegras
Title: Associate Professor of Department of Urban Studies and Planning and Department
of Civil and Environmental Engineering
2
Acknowledgments
This research was supported by the Singapore National Research Foundation under the
SMART Future Urban Mobility Research Group. The views expressed herein are those of
the author and are not necessarily those of the Singapore National Research Foundation.
I thank also Singapore Land Transport Agency for sharing with me the Travel Surveys
and the help and support in developing this project.
I also offer my heartfelt gratitude to:
Chris Zegras, whose dedication for land use and transportation issues has guided and
shaped this project at every stage. I feel motivated and assured after seeing your effort
in research and advocating the innovative solutions for problems in transportation
practice. I feel grateful for your countless comments and tireless commitment to editing
and revising my drafts. As my professor and advisor, you have been an inspirational and
motivating force and have profoundly shaped my understanding of urban planning and
transportation.
Joseph Ferreira, who has supported me with his insights and encouragement.
Yi Zhu, Jingsi Xu, Weixuan Li,who have offered generous help when I need it the most.
Shan Jiang, Miguel Andres Paredes, Diao Mi, Xiaosu Ma and all the members in the long
term group and SMART center for their help and support.
My friends, for brightening my life at MIT.
Mom, dad, and Yichuan for your continuous love and support that have brought me
where I am.
3
Contents:
ABSTRACT ............................................................................................................................
2
CHAPTER 1: INTRODUCTION .......................................................................................
7
Motivation .............................................................................................................................
7
Thesis Structure..............................................................................................................
8
CHAPTER 2: EMPIRICAL SETTING AND RESEARCH QUESTIONS
......................
Private Vehicle Ow nership Managem ent...............................................................
9
10
Additional Registration Fee ................................................................................................
11
Private Vehicle Usage M anagem ent ........................................................................
13
The Off-peak Car (OPC) scheme .......................................................................................
13
Public Transit Im provem ents.......................................................................................
15
Research Questions.......................................................................................................
19
CHAPTER 3: M ETHODS ..............................................................................................
20
Prior Research..............................................................................................................
20
Direct Precedents.........................................................................................................
22
M ultinom ial Logit M odel ............................................................................................
24
Preference Variation Test..........................................................................................
25
CHAPTER 4: DATA AND MODEL STRUCTURE .......................................................
28
Data sources.......................................................................................................................
28
Basic M odel Structure .................................................................................................
28
Dependent Variables ....................................................................................................
29
Explanatory Variables..................................................................................................
31
CHAPTER 5: MULTINOMIAL LOGIT MODELS.........................................................
37
1997,2004,2008 Evolution of Vehicle Ow nership.............................................
37
Interpretation of vehicle Ownership Models of 1997,2004, and 2008......... 38
2008 Model with Locational Variables and Transportation-/Accessibility Related
Variables .....................................................................................................................................
46
Interpretation of 2008 Model..................................................................................
CHAPTER 6: DISCUSSION AND CONCLUSION .......................................................
49
51
4
Discussion and Implication........................................................................................
51
Limitations and Further Research...........................................................................
53
Conclusion..........................................................................................................................
54
Appendix:........................................................................................................................56
References .........................................................................................................................
59
Tables:
Table 1 OPC restricted hours .......................................................................................
14
Table 2 Public transport ridership ..................................................................................
15
Table 3 Transit fares per ride .......................................................................................
18
Table 4 Vehicle availability by ownership type.......................................................
31
Table 5 Family types definition..................................................................................
33
Table 6 Variables in 1997 2004 2008 models .........................................................
36
Table 7 Model A: Separate models for 1997, 2004 and 2008............................
41
Table 8 Pooled models with different scale parameter (Model B) and same scale
param eter (M odel C) ...................................................................................................................
42
Table 9 Results for 1997-2004.....................................................................................
43
Table 10 Results for 2004-2008 ..................................................................................
44
Table 11 Relative ratios with residence in HDB parameter .................. 45
Table 12 Base 2008 Motor Vehicle Ownership Model with only social economic
va riab les ..........................................................................................................................................
Table 13 Final model with accessibility index.........................................................
48
48
Table 14 Private motor vehicle fleet and population growth 1961-2011........... 56
5
Figures:
Figure 1 Gini coefficient based on different methods (household income from work
including employer CPF contributions)................................................................................9
Figure 2 Vehicle fleet growth.......................................................................................
10
Figure 3 Mass Rapid Transit System Expansion Plan for 2030 .........................
16
Figure 4 MNL Model Structure .....................................................................................
29
Figure 5 Income coefficient comparison .................................................................
45
Figure 6 Income distribution of three-year data ...................................................
58
6
CHAPTER 1: INTRODUCTION
Motivation
Singapore is long known as a place where motor vehicle ownership and use has
been tightly controlled and highly priced. As a result it has relatively low motorization
rates (defined as vehicles per 1000 persons) and low growth rates in the motor vehicle
fleet. Singapore is often held up as a model for others to follow. Versions of its vehicle
restraint approach have even made it to China's megacities (e.g., Shanghai, Beijing).
In the past fifty years, Singapore has established a three-pillar transportation
management system which is among the strictest in the world and has been regarded as
effective and exemplary in addressing the land use and transportation problem: The
country implemented a vehicle ownership control system in 1972, adopted congestion
pricing in 1974, and has been expanding its public transit system since 1987.
The three-pillar system has curbed the rapid growth of the car fleet. From 1980 to
2004, Singapore's motorization rate grew from 63 to 100 cars per thousand people
compared to Taipei's motorization rate's growth from 55 to 245 cars per thousand
people in the same time period (AchARyA et al, 2007; Singapore Department of
Statistics, 1980, 2004). As for public transit, the results of the 2012 Household
Interview Travel Survey (HITS) showed the percentage share of public transport trips
during the morning and evening peak hours reached about 63 per cent (Land Transport
Authority, 2013).
However, criticism of the current transportation policy also exists. Cheong and Toh
(2010) found that Singapore's vehicle kilometers travelled per capita was still high
while public transport modal share barely improved given the country's firm
transportation demand management policies. One reason that explains such results, as
Barter (2013) argues, is that the objective of Singapore's congestion pricing system is
not to limit traffic, rather, is to optimize speed in order to maximize traffic flow at
congested times. Under such a system, users have the incentive to shift their departure
7
time of automobile trips and the routes they are taking, rather than shifting travel to
public transportation, per se.
To better understand the behavioral impacts of Singapore's vehicle ownership and
usage costs and restrictions, this thesis explores the vehicle ownership behavior among
Singaporean households, addressing the question: How have vehicle ownership
patterns changed over time and have the underlying preferences towards ownership
changed for different types of households. Finally, I examine the implications of these
results for urban system modeling.
Thesis Structure
This thesis follows the following structure:
* Chapter 2 presents the empirical setting of the study by reviewing the
transportation management policies in Singapore, including private vehicle
ownership management, private vehicle usage management, and public transit
improvements. It also presents the research questions.
* Chapter 3 reviews the existing vehicle ownership models for Singapore and
beyond. It also presents the methods adopted in this thesis.
" Chapter 4 describes the data and the model structures. It also outlines the
variables in the models.
* Chapter 5 models the determinants of household vehicle ownership decisions
and the evolution of these determinants over time.
* Finally, Chapter 6 concludes with a summary of the findings, recommendations,
limitations and areas for further work.
8
CHAPTER 2: EMPIRICAL SETTING AND RESEARCH QUESTIONS
Singapore is one of the wealthiest countries in Asia with a total population of 5.3
million and a GDP per capita of nearly US$51,000 in 2012. Measured in 1990 dollars, the
average household monthly income rose from S$3080 (US$2425) in 1990 to S$4170
(US$3280) in 2000 at an average annual rate of 2.8% (Appendix Table 13). Despite its
development, Singapore has among the highest income inequality in the world; its Gini
Coefficient of income inequality increased from 0.454 to 0.478 from 2002 to 2012 (Key
Household Income Trends, 2012) This income inequality may also induce lower vehicle
ownership than would otherwise be predicted by average income levels (Gakenheimer,
1999).
on Per Ilousehoki Member
-Based
Based on Modified OECD S&ale
"Based
on Squate Root Sale
0.482
0.478
0.471
0.47
04544
0.454
0.457
0.465
0.472
.
0.473
0.460
0.456
0.457
0.452
0.44g
.460.449
0.452
0.4240
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Figure 1 Gini coefficient based on different methods (household income from work
including employer CPF contributions)
Source: Singapore Department of Statistics
Devoted to solving housing and employment problems when the nation was
founded, Singapore placed less relative emphasis on transportation system
management until the young nation's continuing economic growth made congestion a
9
major concern by the early 1970s. The number of private motorcars in the country
doubled from 70,000 in 1961 to around 143,000 in 1974 (Appendix Table 13).
700000
140
600000
120
500000
---
-
-
----
100
400000
80
M300000
60
200000
40
------
z 100000
-T
-r-TF
20
-7'FTT-
T
r'T-
7T-TFT-7TT--T 'rlTT
-r7-7-
-r
0
=*=Private motor cars
Persons per prvate motor car (Vehicle/ thousand persons)
Figure 2 Vehicle fleet growth
Data Source: Singapore Department of Statistics
Singapore has taken an integrated approach to manage its vehicle fleet growth,
including strict control over private vehicle ownership as well as usage. Both measures
were initiated in the seventies and progressively increased since to alleviate congestion.
In the eighties, the country also started to develop travel options to encourage people to
shift to public transit options. The following part of this chapter will introduce the
three-pillars: ownership management, usage management, and provision of other travel
option.
Private Vehicle Ownership Management
Singapore's vehicle ownership management has two main components: the
Additional Registration Fee (ARF) and the Vehicle Quota System (VQS). The Additional
10
Registration Fee is a revenue-raising scheme, which also limits fleet growth, and the
Quota System aims to control the total number of cars that can hit the street every year.
Additional Registration Fee
In 1972, the Singapore government introduced the Additional Registration Fee
(ARF) as 15 percent of the Open Market Value (OMV) of the vehicle. The ARF increased
to 150 percent in February 1980, and 175 percent in October 1983 (Phang et al, 1990).
At least partly as a result, people tended to buy smaller cars with lower OMV to mitigate
the price effects (Tan et al, 2009). A tiered ARF rate was introduced in 2013. In the
tiered scheme, a car with an OMV of up to $20,000 will be taxed at the rate of 100 per
cent. The next $30,000 will be taxed at 140 per cent, and any OMV above $50,000 at 180
per cent.1 In addition to the ARF scheme, Singapore introduced the Vehicle Quota
System (VQS) in 1990 (Tan.L.H, 2001).
The VQS with its auctions of Certificates of Entitlement (COEs) aims to keep the
annual car fleet growth below a certain level, historically 3 percent. The quota is
determined by the actual number of vehicles taken off the roads (i.e. number of vehicles
de-registered), the allowable growth in the vehicle fleet, the unused quota from last
period, and adjustments arising from temporary certificates that have expired or were
cancelled. 2 A prospective car owner has to bid for a Certificate of Entitlement (COE)
under the VQS system, which allows the vehicle to be used for 10 years.
When the VQS was first introduced, the quota licenses were transferable which gave
rise to serious speculation (Tan.L.H, 2001). Since Oct. 1991, the resale of quota licenses
in all categories except "goods vehicles and buses" and "open" 3 was prohibited (Tan.L.H,
2001). However, once a COE is used to purchase a vehicle, it can technically be
transferred together with the vehicle, subject to restrictions.
1http://www.lta.gov.sg/content/ltaweb/en/roads-and-motoring/owning-a-vehicle/costs-of-owning
-a-vehicle/tax-structure-for- cars.html
2
http://www.lta.gov.sg/content/ltaweb/en/roads-and-motoring/owning-a-vehicle/vehicle-quota-s
ystem/overview-of-vehicle-quota-system.html.
3 Open Category in the name of individuals can be transferred once within the first 3 months.
11
The Vehicle Quota System has been effective in the sense that it has controlled the
overall number of vehicles that have been added to the road. For example, in 2011 only
around 8500 new private and company cars were added to the fleet (As shown in Table
13 in Appendix). However, it has basically made car ownership a luxury good in
Singapore.
The price of a COE is determined in a first seal bid process that happens twice a
month. The price has been ever-increasing since the start of the quota system, and has
boomed with the increasingly stringent cap on fleet growth in recent years. Since May
2009, when the Land and Transport Agency (LTA) lowered the vehicle growth rate from
3% to 1.5% as one of the measures to further control traffic congestion, the price of COE
for type A (Cars with engine capacity 1,600cc and below) and type B (Cars with engine
capacity 1,601cc and above) vehicles has risen rapidly, from S$5000(US$4040) to more
than S$80,000(US$64700). In August 2012, the vehicle growth rate was lowered to 1%
and further lowered to 0.5% in February 2013. By 2013, the COE price reached above
S$90,000 (US$71,208). Apart from the COE and the ARF, a registration fee of S$140
(US$110), an excise duty of 20% of the OMV and a 7% Goods and Services Tax has to be
paid. All these taxes and fees easily represent 80% of the full cost to the consumer of a
car. For example, Toyota Camry with an OMV of S$25,021 (US$19,700) costs
S$176,988(US$140,032) in 2013 after accounting for all the taxes and fees. 4
The only fiscal measure that has lowered the price of the COE is the financing
restriction that the Monetary Authority of Singapore imposed in 2013 March. The
restriction limits both the loan amount and tenure. It includes a 60% maximum
loan-to-value (LTV) for vehicles with an open market value (OMV) that does not exceed
$20,000 (including relevant taxes and the price of the Certificate of Entitlement). For a
motor vehicle with an OMV of more than $20,000, the maximum LTV is 50%. The tenure
of a motor vehicle loan is capped at 5 years. The rules took effect from 26 February
4 From the document "Cost (S$) For Cars Registered in Nov 2013" available at:
www.onemotoring.com.sg.
12
2013 and a drop of nearly S$30,000(US$24,000) in COE prices was observed for
February and March.
Private Vehicle Usage Management
The vehicle usage management and pricing system in Singapore evolved in three
phases: an original restriction on entering the restricted zone of the CBD; the restriction
of using expressways, and the incorporation of the two systems into a single one with
automated pricing measures. The ultimate goal behind levying the usage cost is to make
road users aware of the externality cost of driving and incorporating those costs into
the decision about whether or not to drive, the period during which to make a trip, and
the route to take.
In 1975, the Area Licensing Scheme (ALS) was introduced in Singapore as the first
urban traffic congestion pricing scheme to be successfully implemented in the world. As
the charge for drivers entering downtown went into place, the initial drop in traffic into
the CBD was 45% (Phang at el, 1990). The drop was not sustained over time, however,
due to increased employment in the CBD (Phang at el, 1990). In 1995, the government
implemented the Road Pricing Scheme (RPS) as a way to reduce the bottlenecks at
congested expressways and arterials outside the CBD. The initial drop in traffic volumes
along RPS-monitored expressways dropped by 41% from 12,400 to 7,300 vehicles
while public transportation travel speeds increased by 16% (Lam and Toan, 2006). The
Electronic Road Pricing (ERP) scheme, introduced in 1998, integrated the ALS and RPS
systems in an automated manner.
The Off-peak Car (OPC) scheme
The off-peak car (OPC) scheme was introduced to allow more affordable vehicles
for those owners willing to limit usage to off-peak periods. The scheme was first
implemented on October 1994 as a substitute for the Weekend Car (WEC) scheme that
was introduced in May 1991. The restricted hours for weekdays are 7.00am to 7.00pm
( OPC restricted hours). The user has the option to buy an Electronic Day License (e-Day
Licence) for S$20 (US$15) if he uses the OPC car during the restricted hours.
13
The OPC offers the option for people who are interested in using the car to save on
registration fee at the cost of not being able to use the car in peak hours. For newly
registered cars under the OPC scheme, there is an upfront rebate of
S$17,000(US$13385) off the COE and ARF in exchange for the reduced usage. For a
Toyota Camry 2.0 that costs around US$140,032 in 2013 after accounting for all the
taxes and fees, the rebate of OPC is almost 10% of the total cost.5 There is also an S$800
(US$629) discount per year from the normal car road tax of around S$1200 (US$945).
Table 1 OPC restricted hours
OPC scheme
OPC scheme (after
2010 revision)
Weekdays (except public holidays)
7.00am to 7.00pm
7.00am to 7.00 pm
Saturdays (except public holidays)
7.00am to 3.00pm
No restriction
Eve of New Year, Lunar New Year, Hari
7.00am to 3.00pm
No restriction
No restriction
No restriction
Raya Puasa, Deepavali and Christmas
Sundays and public holidays
Source: www.lta.gov.sg
However, in practice, the OPC has had limited reach. According to the 2008
Household Interview Survey, only 1% of all households, approximately 12,000, have
access to off-peak cars. Apparently few households have interest in owning cars with
restrictions on time of use, suggesting most households highly value the flexibility of
being able to use a car when they would like to.
To make the scheme more attractive, LTA revised it such that, from January 2010
onwards, existing cars converted to, or new cars registered under, the revised OPC
scheme can enjoy unrestricted use on Saturdays public holidays ().
s From www.onemotoring.com.sg._
14
Public Transit Improvements
Along with the strict controls over private vehicle ownership and use, the
government has aimed to expand public transportation options including the bus, Mass
Rapid Transit (MRT), and Light Rail Transit (LRT). Table 1 shows the daily passenger
trips travelled on these modes. About 63 percent of the total trips made in Singapore
during the AM peak hour are on public transport (Land Transport Authority, 2013).
However, in its 2030 vision the Singapore government aims to have 75 percent of all
journeys in peak hours undertaken on public transport (Land Transport Authority,
2013).
Table 1 Public transport ridership
Mode
Average Daily Ridership ('000 passenger-trips)
MRT
2525
LRT
124
Buses
3481
Source: Singapore Land Transport: Statistics in Brief 2013
15
Thomson Line
-~n
Oos Wand
Line
Region Line
NorthEast Line
Extension
Donun
nLine
TuisWest
Extension
Downtonn Line
Extension
Eastern Region
North-South Line
D~wnwnn irwExtension
Cinde line
Stage 6
---.
----
Existing Fadines
Land Tranmport Vasbwr Plan 2=0 RAI Lines
Land Tranport Master Plan 2013 Ru Lines
Nom: LTMP 2006 Rail lines include Thomson Line. Eastern Region Line, Tuns West Extension, and North-South Line Extension.
Figure 3 Mass Rapid Transit System Expansion Plan for 2030
Source: Singapore Land Transport Master Plan for 2013
16
Buses enjoy the highest ridership in Singapore among all public transit options.
There are two main bus operators in Singapore, namely, SBS Transit Ltd (the
previous Singapore Bus Services) and Trans-Island Bus Services Ltd (TIBS) which
took over the private bus companies that provided services in the early sixties and
seventies (Phang at el, 1990). Singapore Bus Services was renamed SBS Transit in
2001, which reflects SBS's move from being just a bus operator to a provider of
both bus and train services (Faishal, 2003).
The Singapore MRT system is another major component of the public transport
system. In August 1987, the government created a quasi-private company,
Singapore Mass Rapid Transit (SMRT) Limited, which is owned by the Mass Rapid
Transit Corporation, and which had responsibility for running the MRT system.
Part of the MRT system began operation in November 1987, and the initial system,
comprising 41 stations and a route length of some 66 km was completed by 1989. It
has since grown rapidly into the backbone of the public transport system in
Singapore, with an average daily ridership of 2.406 million in 2011, approximately
71% of the bus network's 3.385 million. Currently, SMRT operates the North-South
MRT line, East-West line (including the Changi branch), and Circle Line. SBS Transit
operates the North East MRT line, Downtown MRT Line, Sengkang LRT and Punggol
LRT lines (Figure 3).
MRT stations are reasonable indicators of easy public transport access to
destinations within the city. The routes are designed in such a way that the
placement of MRT stations provides the greatest accessibility to public
housing-the Housing Development Board (HDB) estates, which represent nearly
80% of the total housing in the country (SingStat 2013). The LTA's Land Transport
Master Plan for 2013 proposes the vision of having 8 in 10 households living within
a 10-minute walk from a train station.
Even though different companies run the various public transport services and
modes, the fares have been integrated via the TransitLink fare card. The price of
bus/MRT fare is calculated based on the distance between the origin and
17
destination stops/stations. Typical fares ranges from US$0.54 to US$1.54 per ride
(Table 2).
Transit Link was a private company set up in November 1987 to oversee the
integration of the MRT with existing bus services through the use of a common
bus-rail ticket. With integration, the fares for multi-modal trips involving both bus
and MRT receive discounts, giving the commuter rebates at each transaction. The
EZLink card, introduced in 2002, is a contactless smart card that speeds up
payment. The EZLink card quickly became the primary method of payment,
replacing the fare card (Lam and Toan, 2006). Table 2 outlines the average cost of a
public transport trip. Under the distance-based fare structure, people travelling the
same distance will pay the same fare for the same type of service, whether they
travel directly or make transfers.
Table 2 Transit fares per ride
(Numbers within parenthesis are fares on air-conditioned buses)
Transit Service
EZ Link fare (US dollars)
Maximum
Average
Trunk bus service
1.35
1.07
(1.54)
(1.25)
0.54
--
Feeder bus service
(0.57)
Express
2.02
1.73
North-East Line and
1.74
1.40
1.54
1.25
Circle Line
North-South and
East-West Lines, and LRT
Source: https://app.ptc.gov.sg/index.aspx (2013)
18
Research Questions
Singapore has certainly been at the forefront of charging, quite highly, for
vehicle ownership and use in an attempt to manage motorization. Yet, as the
country continues its economic growth and its residents become wealthier, private
ownership and use remains in reasonably high demand. Despite the high
ownership and usage costs and extensive public transit system, car ownership and
use continues increasing. For example, even though the numbers of public
transport trips has increased from 4.33 million in 2000 to 5.37 million in 2010, the
current level of mode share of public transport has not increased. The mode share
for public transport dropped from 63% in 1998 to 56% in 2008 but has reportedly
gone back up to 63% in 2012 (Choi and Toh, 2010; Land Transport Authority,
2013).
Clearly vehicle ownership and use remain highly prized among Singaporeans. A
perverse effect may be partly at work here; since the up-front cost of purchasing a
car is so high, sensitivity to road pricing may be lowered due to its relatively low
cost vis-A-vis the "sunk" costs in ownership. People who get a car tend to use the
car extensively. To further understand the results of Singapore's evolving
transportation and motorization management policies, I aim to examine whether
and how household vehicle ownership preferences have changed over time. I
expect that preferences have particularly changed for lower income groups. In
addition, I am to assess, given the nation's strong motorization management and
high densities and extensive public transport system, the built environment and
transportation levels of service factors relate to vehicle ownership. The next
Chapter introduces the methods I will use in this analysis.
19
CHAPTER 3: METHODS
Prior Research
There is a vast body of literature available on various types of auto ownership
modeling. Some literature on car ownership has focused on examining car
ownership at an aggregated level (Holtzclaw et al., 2002; Clark, 2007). These
studies analyze ownership decision at a regional or zonal level, but cannot readily
capture detailed household-level characteristics. On the other hand, disaggregated
vehicle ownership/availability models provide much more details and policy
relevant findings by examining car ownership decisions at the household level
(Train, 1986; Bhat and Pulugurta, 1998; Dargay and Gately, 1999). De Jong et al.
(2004) provide a comprehensive review of nine types of vehicle ownership models:
aggregate time series models, aggregate cohort models, aggregate car market
models, heuristic simulation models, static disaggregate ownership models,
indirect utility models of car ownership and use, static disaggregate car-type choice
models, panel models and pseudo-panel models and dynamic car transactions
models with models for the duration until replacement, acquisition or disposal, and
with conditional type choice. In this thesis, given my objective of understanding
factors related to vehicle ownership, and due to data limitation regarding vehicle
prices, types and transactions, I will use disaggregate models of household vehicle
ownership.
There are at least three kinds of disaggregated model structures that have been
used in modeling car ownership: Multinomial Logit Model (MNL), Ordered
Response Logit (ORL) and Nested Logit (NL) models. An MNL model of vehicle
ownership assumes that a household makes a one-time choice of the number of
vehicles to have. An ORL applies when the choices can be assumed as incrementally
taken; that is, that a household decides, first, whether to have zero or one or more
vehicles. If the one-or-more alternative is selected, the household decides again to
have one or two-or-more vehicles. This process continues until the households
have considered all alternatives. The NL model assumes that each household
considers all alternatives simultaneously, but groups some alternatives as being
20
more similar than others (e.g., electronic bike and motorcycle). Potoglou and Susilo
(2008) have tested the different assumptions about the behavioral choice
mechanism with two different modeling structures: the ordered and unordered.
The outcome showed that MNL models performed the best among all the models
they tested.
Household income and demographic characteristics are the oft-identified
dominant determinants of car ownership (Schimek, 1996; Kim and Kim, 2004;
Zegras, 2010; Potoglou and Kanaroglou, 2008; Salon, 2009). To further understand
the role of income, Nolan (2010) proposed a binary random effects model to
analyze the car ownership decision of Irish households for the period 1995 to 2001.
The paper reported that fixed income exerts greater influence on the ownership
level decision than current income.
As for the role of urban form and accessibility to public transportation or jobs,
some clear patterns emerge from the studies. Zegras (2010), using a multinomial
logit model with data for Santiago (Chile) in 2001, finds a number of transport
level-of-service, relative location, and built environment variables to be related to
vehicle ownership. A homogenous picture emerges in relation to public transport
supply. Schimek (1996), Kim and Kim (2004), Bento et al.(2005), Potoglou and
Kanaroglou(2008), Matas and Raymond(2008) and Salon (2009) provide evidence
that having higher accessibility to public transit is negatively associated with the
number of cars per household. Gao et al. (2008) and Chen et al. (2008) both showed
that having higher accessibility to employment reduces dependence on personal
vehicles. Matas and Raymond (2009) captured the effect of urban structure through
a measure of job accessibility to employment by public transport and found that
spatial variables play a significant role in explaining the probability of car
ownership. Guo (2013) investigated the impact of residential parking supply on
private car ownership with a nested logit model and found that, in a parking-scarce
place like New York City, parking supply can significantly determine household car
ownership decisions. The influence even exceeds the role of household income and
demographic characteristics. Due to limitations of parking data availability in
21
Singapore, this thesis will not include a variable capturing parking supply. However,
attention should be paid in the role of parking in the future studies.
One major problem of most studies is endogeneity, which in the vehicle
ownership case could include simultaneity bias, whereby residential location and
vehicle ownership decisions influence each other; and, omitted variable bias,
whereby unobserved variables-such as attitudes-produce the ownership outcome,
but these attitudes also correlate with other characteristics that may produce
incorrect associations (Zegras, 2010). Cao (2013) applied the statistical control
approach in a quasi-longitudinal research design and found that the Light Rail
Transit (LRT) in the Minneapolis-St.Paul metropolitan does not have an
independent/direct impact on auto ownership. His study suggests that the
observed impact of the LRT on auto ownership is a result of residential
self-selection, meaning that those who prefer to own fewer vehicles choose to live
near the LRT to better enable their vehicle ownership preferences.
Few studies have examined changing vehicle ownership behaviors over time in
a disaggregate way. Zegras and Hannan (2012) examine changes in household
vehicle ownership preferences from 1991 to 2001 in Santiago de Chile, using a MNL
models, finding that preferences changed from 1991 to 2001, suggesting that as
incomes rise and vehicle ownership becomes increasingly affordable, demographic
and land-use and other contextual variables change in their influence. Most notably,
the relationship between vehicle ownership and land use mix appears to weaken
over time, while the distance to CBD effect strengthens, and the residential density
effect varies in the apparent direction of change, depending on the vehicle
ownership category.
Direct Precedents
Few studies have focused on private car ownership in Singapore using
disaggregate models. Van Eggermond, M. et al. (2012) estimated a household
vehicle ownership model for Singapore using an MNL model with data from the
2008 HITS. They geo-located the household with the postal code information
22
(roughly equivalent to the building level) in the survey. Among their findings:
income increases the utility of owning a car; lower income groups tend to prefer a
car more if children are in a younger age group than in a higher age group; and the
number of driving licenses among household members increases the utility of
owning a vehicle. Including driver license possession in the model could, however,
introduce simultaneity bias in the model. They use an entropy index proposed by
Cervero and Kockelman (1997) and Chu (2002) to account for neighborhood
heterogeneity:
Es 50 0 =
-
p
(1)
In(k)
where pi is the proportion of developed land-use category in category k. In
total 5 land use types were considered: business, community area and education,
residential, commercial and parks within 500 meters radius of the residential
postal code. Values of El vary between 0 and 1, with one indicating even
distribution among all land-use types and zero implying single type of land-use
within radius of 500 meters (Van Eggermond, M. et al, 2012). Values of the entropy
index close to one imply ease of access to activities and therefore the parameter of
El is expected to be negative (Potoglou and Kanaroglou, 2008).
They found a higher measure of heterogeneity decreases the utility of car
ownership. Apart from the household characteristics and land use characteristics,
they also examined the influence of measurable costs, which may influence car
owernship-car parks and Electronic Road Pricing gantries. The variable capturing
whether there is a multi-story car park within 500 meters of the dwelling and
distance to nearest ERP gantry proved to be insignificant. By contrast, an MRT stop
within 500 meters to the dwelling and an MRT stop within 500 meters to the work
location (based on the furthest distance work location among workers in the
household) 6 both decrease the household's utility of owning a vehicle.
A household can contain multiple working members; therefore either a choice or aggregation
has to be made on work-side points of interest. The maximum distance within a household from the
work location to a MRT station was chosen.
6
23
In this thesis, I test the hypothesis of household vehicle ownership preference
changes using datasets from 1997, 2004 and 2008. The models include some
location-specific characteristics (e.g., distance to MRT stations) as explanatory,
exogenous, variables, although in practice they may be endogenous (e.g.,
households may jointly choose their residence and vehicle ownership).
Multinomial Logit Model
In the MNL model, decision-makers choose from a set of alternatives in the
discrete choice framework. Each choice can be characterized by a number of
attributes. Each attribute contributes to the utility of the choice. The overall utility
of a choice i to individual n is defined by a deterministic and a stochastic part:
(2)
Uin = Vin + ein
Vin is the systematic indirect utility expressed as a function of observable
variables with a vector
of taste parameters and the vector Xinof attributes of
fik
the choice,
in
and
=1
Ein
(3)
f W ink
as a random utility component.
An individual n will select the choice with the highest utility Uin from among
the options in the choice set
Pn(i) = Pr(Uin > Un, V]
=
Pr(Vin + Ein
= Pr(§cn
Vin
E Cn, * i)
Vn + Ein, V] E Cn,,]
i)
+ Ein, V] E Cn,J
i)
-Vn
=Pr(Ein ! Vin - Vn + Ein, V E CJ # i)
Letting f(Ein, E2n, ---,
'jcn)
(4)
denote the joint density function of the disturbance
terms and considering alternative i to be the first alternative in the choice set,
then:
24
Pa(1)
f
cx.
f,-V,+E1
f~1n=-oo fE2n=-oo
inn=--~
+81nf(Ein, E2 n,
Ejn)
d Ejn dEjnIn de1 n
(5)
The MNL is the most commonly used discrete choice model due to its ease of
estimation and simple mathematical structure (McFadden, 1974). It is based on the
assumption that the random terms, often-called error terms or disturbances, are
identically and independently (i.i.d.) Gumbel distributed. The choice probabilities
equal to:
pj)=
eVinV
ZjECn e6
(6)
Assuming Uin = Vin + Ein, for all i E Cn, and that the disturbances Cin are
i.i.d. Gumbel-distributed with a parameter and a scale parameter i > 0, then
P,(j)= el
v
EjECn e Avj
(7)
Preference Variation Test
As discussed in the previous chapter, my interest is in comparing vehicle
ownership preferences over time: we want to compare models from three different
time periods, 1997, 2004 and 2008. However, given that the models are estimated
on samples and the particular form of the MNL model, care must be taken in
comparing models across time to ensure that the observed differences from model
estimation do not come from inherent variances in the underlying data (i.e.,
surveys). When comparing parameters from models in different times, one must
account for the possible differences in error component variances. As noted by
Swait and Louviere (1993), failure to do so confounds differences in scales (or
magnitudes) of model parameters due to error variance differences with real
differences in model parameters.
To be more specific, the scale parameter y, in
Equation 7, cannot be identified and is arbitrarily assumed to be one. This
assumption will not have an effect on the utility order within one dataset. Between
datasets, however, the values of the scale parameters may differ significantly,
implying variability across the datasets.
Even though i cannot be identified in a
single data source, the ratio of the p's from different datasets can be estimated.
25
As an example Arnold et al (1983) reported variation in choice model
parameters over time and space in retailing, without considering the variation in
scale parameters. Severin et al (2001) point out that Arnold et al's (1983) result
was problematic because the retail choice model parameters for two time periods
(say 1997 and 2008) would appear to differ if their variance scale ratio was not
equal to 1; that is,
p1997 = 02008,
p1997 *
12008.
Variance scale ratio differences
suggest that differences in research contexts over time and space drive differences
in the effects of unobserved factors on choices, not the differences in effects of
common observed factors (Severin et al, 2001).
In our case, we want to test whether underlying preferences have changed over
time, utilizing models estimated on a sample taken at different points in time. For
example, for the 1997 and 2008 time periods, we want to know whether the model
estimation parameters are different, which would indicate underlying vehicle
ownership behavioral changes from 1997 to 2008, as reflected in the different
magnitude/significance of the same socio-economic and policy variables in the
models.
Following the methods suggested by Severin et al (2001) and taking the years
1997 and 2008 as examples, I will firstly estimate unrestricted MNL models to
allow different parameters in each data set as Model A. I then restrict the
parameters to be identical (W199 7
=
02008) but allow different variance-scale ratio
parameters for each data set (14997 *
112008)
and estimate Model B. Models A and B
will be compared using the likelihood ratio test statistic: -2[LN
g=1 LN_(g)],
the distribution of which is chi square. The degrees of freedom equal the difference
in the number of parameters between Models A and B.
If the likelihood ratio test statistic is not significant, it leads to the conclusion
that the model allowing different parameters (unrestricted) has less explanatory
power than the restricted model (Model B). In such a case, we could not reject the
assumption that
01997 = P2008.
The scale parameter ratio we get from estimating
Model B can be close to 1, which indicates a similar dataset context. It can also be
different from 1, which may be due to the unobserved factors that lead to more
26
variance in one dataset. If, after taking differences in random component variances
between the two data sources into account, the common parameters in the indirect
utility function do not differ significantly then the underlying choice process
revealed is stable across the time periods.
If the likelihood ratio test statistic is significant, it leads to the conclusion that
the unrestricted model, allowing different parameters, has more explanatory power
than the restricted model. Therefore, we can reject the assumption that P1997 =
P2008. The scale parameter ratio we get from estimating Model B won't indicate the
real ratio because P1997 #
12008.
In this case, we can further explore the reasons
for the statistically significant differences across the two dataset by comparing
individual coefficients between the two-year segments. Since there is only one scale
parameter for all the coefficients in each data set, if we observe that the coefficients
from 2008 are not proportionally different from the coefficients for 1997, we
should know the real mechanism has changed across years. For example, if
1distance,1997
=
2
*
Pistance,2008,
sincome,1997
=
3
*
Pincome,2008, even though we
cannot know the real number of y in this case, we still can know that the
underlying choice mechanism is not stable across the time periods because the
coefficients' change are not proportional.
27
CHAPTER 4: DATA AND MODEL STRUCTURE
The data I use come from several sources. The Household Interview Travel
Survey 2008 (HITS) and the Household Travel Surveys from 1997 and 2004 are
used as the foundation. The survey represents some 1% of the total households of
Singapore. In these surveys, carried out for LTA, randomly selected households are
questioned on their travel behavior for a single workday. The 2008 survey contains
10,641 households, the 2004 survey contains 9500 households, and the 1997
survey contains 7019 households.
The limitation of the available travel surveys is that they do not indicate
whether the household actually owns the vehicle or whether it is just available to
them temporarily; we also do not know when they purchased the vehicle nor any
details of the vehicle (e.g., make, model, etc.). I cannot, therefore, include aspects
such as the COE price into the model. Additional data for model estimation include:
network travel times and costs from the origin destination matrices provided from
a model run by the Singapore Land Transport Authority (LTA) and land use data
from the Urban Redevelopment Authority (URA).
Basic Model Structure
Figure 4 summarizes the automobile ownership model I specify and estimate.
28
Household Attributes
Locational
Variables for the
Vehicle
utilty
mum.+Portfolio
Accessibility
Figure 4 MNL Model Structure
Dependent Variables
Instead of just looking at whether a household has a car or not, I examined
various alternatives of vehicle holding, The different combinations of household
vehicle portfolios, based on 2008 HITS, 2004 and 1997 HIS, can be found in Table 5.
The number of vehicle holding alternatives to be included in the vehicle ownership
models is dependent on the numbers of households choosing each reported vehicle
availability level. Eighteen different combinations of ownership are included in the
table. Due to the constraints of the survey data, only eight of the combinations have
consistent data from 1997 to 2008. I consider the following four ownership
categories for the model estimation:
" No motorized vehicle;
* One normal car;
" Multiple normal cars;
" Other vehicles (off-peak cars, light -good etc.)
29
The survey data suggest little change in the share of households with no motor
vehicle available in the three years studied, with 49% in 1997 and 2008 and 52% in
2004.
Similarly, the share of one- and two-car owning households has varied little
over the 20 years, a relatively astounding stasis in the international context in the
face of Singapore's economic growth and clearly a product of the motorization
management path the country has taken. Motorcycle ownership has similarly
remained relatively static.
Another thing worth noticing is the rate of off-peak car access. In 2008, 1%
households had access to an Off-peak car. Among the Off-peak car owning
households, 8% of them have another normal car.
30
Table 3 Vehicle availability by ownership type
Type of Vehicle Available
No Vehicle
1 Normal car (only)
Motorcycle (only)
2+ Normal cars
Heavy/Light Goods Vehicle
Taxi
Normal car+ Motorcycle
Off Peak car
Normal car+ Heavy/light
Goods Vehicle (2004 van)
Off Peak car + Motorcycle
Normal car+ Taxi
Heavy/Light Goods Vehicle+
Motorcycle (2004 van)
Taxi +Motorcycle
Normal car + Off Peak car
Rental
Off Peak car+ Taxi
Normal car+Off Peak car+
Motorcycle
Other
Sub Total of
All Motorized Vehicles
The Expanded Total
1997
2004
2008
49
31.8
6.4
4.3
52
29.3
6.6
4.7
4.6
49.4
30.8
5
4.6
3.2
1.6
1.1
1.1
4.5
1.4
1
1
0.9
1.1
0.3
0.3
0
0.1
0.2
0.6
0.3
0.1
0.1
0.1
0.1
0
0
0
0.4
0.3
1.7
50.6
47.7
48.9
7020
1004132
1143718
1, Source: 1997 HIS, 2004 HIS, 2008 HITS.
2, 2004,2008 data are expanded with the household expansion factors in each survey. 1997
data is unexpanded.
Explanatory Variables
The following variables were considered in the estimation data set for the
vehicle ownership model. Each variable was tested for inclusion in the final model
based on how well each explains the observed household choice behavior, and
based on statistical considerations.
Household Variables
* Household income measures the resources available to the household for
the purchase of vehicles. Income in the earlier years was inflated to 2008
31
values, using CPI published by Singapore Department of Statistics .7 In the
surveys, income was reported as a categorical variable; to allow for the
comparison between years, nine categories for monthly household income
(in 2008 Singapore dollars) are defined: Not reported, SG$0, SG$0-1486,
SG$1486-3308, SG$3309-4128, SG$4129-5749, SG$5750-7540,
SG$7541-10446, and above SG$10446.
* The number of children under 14 years old in the household measures the
potential need for the purchase of vehicles to take children to school or
other activities. A vehicle offers the possibility of dropping and picking up
children at school and otherwise economizing on family trip-making.
* The number of workers in the household measures the potential need for
purchase of vehicles for home-based work trips.
e Family "type" aims to capture the life cycle of the household and its
potential implications for the purchase of vehicles. Eight family types have
been defined incorporating information about number of children under 14
years old, number of workers, and number of members older than 60:
Childless multi-worker family, Childless single-working family, Nuclear
family, Extended family, Worker and retired family, Grandparents family,
Retired family, and others. It is expected that worker and retired family,
retired family may have higher utility of a vehicle because private vehicles
offer better travel experience for the senior members of the families (Table
4).
7 http://www.singstatgov.sg/statistics/browseby
theme/prices.html.
32
Table 4 Family types definition
Family types
Childless multi-worker family
Childless single-worker
Nuclear family
Extended Family
Worker and retired family
Grandparent family
Retired family
Others
Description
More than 1 worker, no kids, no retired members
One worker, no kids, no retired members
Kids below 14 and workers, no retired members
Three generation family
Workers and retired members
Kids below 14 years and family member older than 60, but no workers
Only retired members
Others
LocationalVariablesfor the Residence
* Dwelling unit type (Whether HDB) measures the effect of dwelling type on
ownership. HDB residential complexes are designed with food courts and
shopping and services, therefore the people residing there may have less need
to drive to malls or restaurants.
Travel Time and Cost Variables
* The ratio of travel time using private vehicle versus public transit measures
the relative attractiveness of using a car for home to work trips versus using
public transit. The values are derived using the TAZ to TAZ travel time
estimates in the morning rush hours (7:30-9:30) from a network model
simulation result provided by LTA and using the household's residential
location and each working family member's reported work location.
* The ratio of travel cost using private vehicle versus public transit measures
the relative out-of-pocket costs of using car for home to work trips versus
using public transit. The private car usage cost is the ERP charge for
home-based work trips in the morning for all working family members using
highway. The total cost for public transit is from the simulated assignment of
the best public transit paths, taking into account both MRT/LRT and buses.
Again, both values are measured at the TAZ-to-TAZ level and are provided by
LTA.
33
Accessibility Variables
* Household proximity to an MRT station approximates the local
accessibility/convenience of public transport. Two types of measurements
have been used: the distance to the nearest MRT station and dummy variables
indicating distance less than 200 meters, 200-400 meters, 400-600 meters,
600-800 meters, 800-1000 meters, and above 1000 meters.
* A gravity-based measure of automobile accessibility from the home
location represents the potential ease of accessing opportunities across the
island. In total, six types of opportunities were used in the calculation:
Manufacturing, office, retail, hotel, port& airport and education institution. A
high accessibility value implies ease of access to these opportunities and
therefore the parameter estimates for the accessibility indicator are expected
to be negative (i.e., more accessible places, all else equal, are associated with
reduced vehicle ownership). Singapore's Urban Redevelopment Authority
(URA) provided the data. I use Hansen's (1959) gravity-based measure of
accessibility.
Aipv
-
01
f(Cijpv)
(8)
Where
A ipv is accessibility at the home zone i to jobs at zone j using private
vehicle
Oj
is the number of jobs at zone j
f(C1 pv) The impedance or cost function to travel between zone i and
zone j using private vehicle. I adopt the impedance function from LTA's
2008 trip distribution model, where they have defined the impedance
function of home-based work trips by private vehicle as:
for 0 < cij < 8, f(Cpv)=0
(9)
34
for 8 < ci; < 35, f(Cijpv) = 287 - 287(Cijpv - 168)/(360 - 168) (10)
for 35 < cij < 168, f(Cijpv) = 0.04040723(Cijpv - 6)4 3
3.4860416(Cjjv - 6) + 117.3 2876(Ci jp - 6)
2
-
2026.192(Cijpv - 6) + 19874.2 (11)
for ci > 168, f(Cijpv) = 0.00002639(Cijpv - 32)4 0.01031252(Cijpv - 32)3 + 1.516426(Cijpv - 32)2
-
106.161(ijy -
32) + 3589.5 (12)
35
Table 5 Variables in 1997 2004 2008 models
1997
2004
2008
53.0
35.5
56.4
33.3
Income <1496
1486-3305
4.6
6.9
9.1
15.0
14.6
3309-4128
4129-5749
5750-7540
17.8
6.2
8.9
4.1
6.2
8.1
11.9
21.9
11.0
8.5
11.2
7541-10446
>10446
7.8
7.8
3.5
8.6
49.8
36.9
5.4
7.9
0.0
7.2
19.6
11.3
14.4
12.8
9.6
8.9
No response
12.7
15.4
16.2
0 Worker
18.7
17.1
1 Worker
45.4
27.6
41.3
29.1
6.5
49.1
36.5
8.3
12.5
7.9
68.7
18.6
10.1
59.9
21.8
15.0
63.7
19.8
13.1
2.6
3.3
3.4
5.0
1.7
6.3
16.8
24.0
15.4
23.3
5.6
14.9
27.9
2.0
6.0
2.2
20.4
19.9
19.5
28.1
5.6
2.3
No Vehicle
Type of Vehicle
1 Normal car (Any)
Available for
2+ Normal car
Other Vehicle
Housbefod
,Household
income=O
Household
Income
Category
#Worker
2 Worker
>3
Worker
0 Child below 14
1 Child below 14
2 Child below 14
N Children
Below 14 years
>3 Child
Type
below
14
Retired family
Extended Family
Grandparent family
Childless multi-worker family
Childless single-worker
Worker and retired family
Nuclear family
of Family
Other
Household Living in HDB
Housing
Travel
Cost
Ratio
5.6
6.5
19.0
15.0
7.5
78.0
83.0
89.0
Mean (Standard Deviation)
Ratio of total travel time of private vehicle versus transit
--
-
0.59
(0.18)
Ratio of total travel cost of private vehicle versus transit
--
--
(0.73)
-
(484)
0.46
Distance to the nearest MRT
754
Gravity accessibility
7
To t s
Ttl7032
2
710
7
8675
(7)
3696
3696
(1 2 8 7
8365
36
CHAPTER 5: MULTINOMIAL LOGIT MODELS
This section presents the model estimation results.
First, I examine how
preferences may have evolved from 1997 to 2008. Then I present the 2008 models
with additional details on households' residential location, which may influence
ownership tendencies. Such models could not be estimated for the earlier years
because of data limitations, specifically the lack of land use data and transportation
system performance measures.
1997, 2004, 2008 Evolution of Vehicle Ownership
To understand whether household preferences for vehicle ownership have changed,
under the combination of increasing ownership costs and expanding supply of
alternatives, I first estimated unrestricted MNL models to allow different parameters
in each data set (Table 6) of the three years. Then, I restricted the parameters to be
identical but allowed for different variance-scale parameters for the data from the
three different years (Model B, in Table 7 Pooled models with different scale
parameter (Model B) and same scale parameter (Model C)
). We can compare Models A (unrestricted) and Model B (restricted) using the
likelihood ratio test, as described above. The result is that the unrestricted model
differs significantly from the restricted models at greater than 99% confidence level;
the vehicle ownership choice processes are not stable across the three years.
Model C (in Table 7 Pooled models with different scale parameter (Model B) and
same scale parameter (Model C)
) allows us to test whether we can combine the three data sets into a single
model. A test of the likelihood ratio of the pooled and scaled models (B and C) of
three years shows that the difference is significant at the 95% confidence level, so
the pooled model differs from rescaled models, suggesting that we can reject the
hypothesis of equal variance scale ratios of three years.
To further understand where the preference change occurs, I have done a
pairwise comparison of the years 1997 versus 2004 and 2004 versus 2008 (Table 8,
37
Table 9). In both cases, the unrestricted models outperform the restricted models
at the 95% confidence level, with 2004 to 2008 models having more significant
improvement. This indicates that the choice preference changes happen both in the
1997-2004 period, as well as in 2004-2008 periods. But the latter period witnessed
bigger preference change. The model Cs (Table 8, Table 9)allow us to test the
hypothesis of equal variance scale ratios. It turns out that for the 1997-2004 model,
the pooled model doesn't differ from rescaled models, suggesting we cannot reject
the hypothesis of equal variance scale ratios from 1997 to 2004, however in
2004-2008, such hypothesis can be rejected.
To sum up, compared using the likelihood ratio test, for both 1997-2004 and
2004 -2008 periods, the models with different choice parameters for each year
(Model A), outperforms the models which restricts the parameter estimates to be
the same across years (Model B); hence the choice preferences have changed over
time, after allowing for variance-scale differences.
To compare the differences across the relevant components of the various
alternatives utility function across time, I choose a common significant variable and
divide each parameter in the respective utility functions by the chosen variable's
parameter estimate. Doing so provides us a relative ratio that is comparable given
the different scale parameters (Table 1011). Ideally this variable would be for a
continuous variable so that that would demonstrate a tradeoff, but in our case, we
don't have such a variable. I have chosen the residence in HDB as the variable and
divide each parameter by the absolute value of it here. Even though it is not
theoretically meaningful, it allows us to keep all the income estimates and thus be
able to compare them. The results of the comparison can be found in the following
section.
Interpretation of vehicle Ownership Models of 1997,2004, and 2008
A closer look at the data reveals that the socio-demographics and dwelling type
variables are statistically significant and have the expected signs across three years.
38
Income
The effect of increase in income is consistent with other studies. Looking at
model result from 2008, households with less than S$5750 income are less likely to
own any normal cars at all. Households earning more than S$7541 are more likely
to own a car or equally possible to own two cars, households in income group
S$10446 have the largest probability to have two or more cars.
From an evolution perspective, as shown with the comparable ratios in Table
10, almost all income groups have lower utility of having any car in 2008 as
compared to 1997 and 2004. Also, as can be seen in Figure 5, the 2008 curves for
one car and two or more cars are steeper than the curves from 1997 and 2004, in
other words, the gaps in low income groups are larger than the gaps in high income
groups. This indicates that, while the likelihood of vehicle ownership for all income
groups has been declining, the lower income groups are becoming even less likely
to own a car from 2004 to 2008. Such effect was not as clear in 1997 to 2004. This
result confirms with our conclusion that the mechanism of decision-making is
changing from 1997, 2004 to 2008 because otherwise we should observe a
proportional shift of coefficients due to the variance-scale difference or no shift at
all.
Family types
As for family types, the likelihood of having a car increases as the family moves
to the later stage of the life cycle chain: Childless families are less likely to own
vehicles, while grandparent families and retired families are more likely to own a
vehicle or even two. This finding contrasts Kim and Kim's (2004) study of the States.
They find that coupled households are likely to own more automobiles than both
senior household and single household. Children in a household are found not to be
a significant indicator of household automobile ownership.
A closer look at the evolution of the effect of family types shows that in 1997,
grandparents families also are more likely to own a vehicle, however the retired
39
families, without the presence of the children are less likely to own a vehicle. The
2004 model shows similar results.
The presence of children is a significant indicator of automobile ownership in
Singapore. The presence of children increases the likelihood of owning a vehicle for
all types of families in three years. For example, if we compare the effect of nuclear
family versus childless multi-worker family, we can see that across the three years,
the presence of children in the nuclear family makes it more likely to own a vehicle
than the childless multi-worker family or single-worker family.
Dwelling
Households residing in HDB estates have lower utility of owning a vehicle as
compared to household residing in condominiums or landed property across the
three years. Dwelling type here may provide an indication of amenities in the
residential area. Households residing in HDB may have better access to amenities
than households residing in the private apartment or landed property because of
the mixed-use development.
Distance to MRT
The farther away from the nearest MRT station, the higher the utility to the
household of owning one or more cars in 2004. However, as the MRT network
becomes more ubiquitous across the island, the strength of the relationship
between household auto ownership and distance to MRT is apparently weakening.
In 2008, distance to the nearest MRT station only significantly relates to the
household's utility of owning a second car.
40
Table 6 Model A: Separate models for 1997, 2004 and 2008
1997
7541-10446
10446
Household residing In HDB
Etended Family
Grandparent family
Childless mufti-worker family
Retired family
Childless single-worker
Worker and retired family
Nuclear family
Distance to MRT
2004
2+Cars
Other
1 Car
2+Cars
t-test Beta t-test Beta t-test Beta t-test Beta t-test
3.55 -0.89 -3.34 -2.37 -8.65 0.264 1.62 -1.36 -4.38
-1.59 -11.29 -1.96 4.14 -0.364 -2.06 -1.65 -13AS -3.31 -4.58
-0.859 -7.03 -1.74 -3.56 0.158 0.95 -0.732 -8.78 -1.78 -5.96
0.405 4.16 -0.367 1.44 0.038 -0.22 -0.206 -2.13 -0.976 -3.19
0.8
0.162 L62 -0.513 -1.82
0.19
0.578 4.25 0.279 0.8
0.933 7.82 0.163 0.58 0.092 0.39 0.751 8.01 0.329 156
1.13
7.58 0.967 3.49
4.44 0.103 0.37
L46
11.3
119
1.76 11.11 2.28
9.78
0.229 -0.52 L49 12.69 1.95 10.48
1.1
3.63
-1.3 -12.58 -2.35 -16.08
-1.14 -12.73 -2.A -14.97
0.375 1.38 1.13 2.49 -0.532 -0.97 0.563 3.42 0.87 2.39
0.7
3.04 0.752 4.69 0.762 2.09
0.504 3.22
1.71
0.59
-0.381 -2.79 -0.217 -0.2 -0.386 1.69 -0.065 -0.46 0.062 0.19
-1.2
-2.58
42 -3.86 -0.927 -5.17
-0.965 -5.05 -0.701 1A3
-0.058 -0A4 0.071 0.24 -0A1 -1.97 0.314 2.15 0.437 1.23
0.8
-0.604 -4.22 0.034 0.11 -0.299 1.37 0.053 0.37 0.271
0.58 424 0.383 118
0.304 2.37 -0.023 -0.08 0.095 0.47
0.256 4.53 0.648 6.5
* of estimated parameters.
Number of observations
Final log-Bkelhooda
Adjusted rho-square
48
6465
-5560
0.374
Variables
Constant
Income <1486
1486-3308
3309-4128
4129-5749
5750-7540
1 Car
Beta
0.461
2000
Other
Beta
-2.97
0.408
0.538
0.662
0.376
0.268
0.138
-0.153
0.592
-0.035
0.126
-0.192
-1.17
-0A4
-0.579
0.041
0.048
1 Car
2+Cars
Other
t-test Beta t-test
Beta t-test
-7.33
1.73 7.69 -0.008 -0.02 -1.14 -3.26
2.4
-5.37
-3.5 -14.43 -10A -62.52 -14A2
3.39
-1.3 -17.76 -3.53 -6.37 -0.078 -0.52
3.75 -0.89 -8.71 -2.23 -5.57 0.092 0.57
1.82 -0.253 -2.74 -1.15 4.71 0.052 0.32
0.09
1.23
0.95 -0.211 -1.08 0.003 0.02
0.52 4.84 0.436 2.37 -0.417 -1.73
0.35
-0.41
1.3
9.46
1.77 9.64 0.363 137
1.68 -1.06 -13.28 -2.23 -16.01 .0.164 -1.07
-0.13 -0.649 -2.79 -0.305 -0.75 -0A23 1.21
0.51 0.499 166 0.733 1.54 0.269 0.58
-0.86 -1.08 -5.04 -0.951 -2.58 -0.53
1.68
-3A4 0.189 0.62 0.668 137
0.427 0.86
-1.94 -1.06 4.94 -1.34 -3A8 -0.871 -2.75
-2A9 -0.904 -4.2 -0.401 1.09 -0.338 -2.64
0.2 -0.541 -2.56 -0.607 -1.67
0.2 -0.65
0.46 0.119 1.88 0.562 5.63 0.158 159
t-test
Beta
51
8657
-7417
51
7971
-7015
0.378
0.361
41
Table 7 Pooled models with different scale parameter (Model B) and same scale
parameter (Model C)
Model 8
ICar
:eta
0.491
N11e
Constant4
Constantas
0.503
0.639
ConstantM7
Income <1486
1486-3305
3309-4123
4129-5749
5750-7540
7541-10446
110446
Household residing in
HDB
Distance to MRT
Extended Family
Grandparent family
2+Cars
t-test
Beta
t-test
Beta
5.03
5.2
-6.18
7.65
-2L2
-16.8
-2.65
-1.26
-1.1
-0.687
-3.04
-2.21
-1.01
-6.1
-2.5
-. 89
-2.06
-0.257
0.215
0.269
-5.59
-3.92
-7.67
-9.61
-1.97
-1.04
-0.148
0.173
0.61
L01
1.52
2.86
-0.553
-3.38
0.235
9.93
13.02
17.05
0.167
0.842
1.33
6.38
2
16.1
0.162
-0.033
0.128
-1.19
-20.6
-2.33
-23.11
0.164
0.201
4.71
L73
0.611
8.31
0.13
0.66
-0.074
0.29
0.622
Childiess multi-worker family -0.251
Model C
iCar
Other
t-test
-12.38
-9.84
-12.88
-2.43
Beta
0.516
0.481
-0136
S
7.87
-23.29
-1.21
-2A7
0.153
3.05
0.626
LOS
10.86
-0.21
0.69
1.52
14.74
19.87
138
-1.21
1.8
0.206
0.3
0.619
2.42
2.75
2.13
1.41
0.688
-1.94
-1.02
-.
1
Other
t-test
Bet
-. 02
-6.36
-2A2
-2.03
-2.09
-0.232
0.226
0.279
0.242
0.165
-3.95
-7.77
-9.8
t-test
-13.7
-1LS5
-13.05
-2.22
2.54
-0.997
-0.357
0.17
0.865
-6.02
-3.37
2.01
17.98
0.107
.42
-0.17
0.57
-23.72
-2.35
-28.34
0.191
1.59
4.93
L85
8.93
0.126
1.74
3.21
-0.07
1.34
6.57
2.84
2.22
-0.028
5.68
0.716
3.19
3.32
0.355
-0.43
2.22
5.94
3.3
0.349
-0A41
2.22
-2.92
-0.108
-0.59
-0.221
-1.64
-0.251
-2.9S
-0.11
.0.6
-0.22
-1.64
-0.630
0.0586
0.24
0.188
-2.58
-1.18
-DAS
-0A59
-5.27
-3.46
-3.33
0.92
-0.852
-7.51
-0.28
-2A6
4.37
-0.65
-0.048
0.233
0.188
-2.64
-1.17
-0A64
-0A61
0.12
-5.35
-0.855
-0.0349
-7.34
-0.214
0.35
-243
4.19
Scale04
ScaleS
0.948
1.06
-1.4
1.47
Number of estimated
59
57
23093
23093
.20231
0.366
-0.4
pararsI
Chi-square
d.f
Chl-square critical value
(significance level 95%)
2+Cars
Seta
-. 14
-1.19
-0.68
-2.99
0.62
0.689
0.71
Retired family
Childless single-worker
Worker and retired family
Nuclear family
Number of observations
Final log-likellhood
Adjusted rho-square;
t-test
5.31
-20226
0.366
Unrestricted vs.
scaled
468.19
91
114.27
-0.31
1.31
1.05
0.118
0.0244
-0.214
0.36
-0.25
1.27
1.05
-3A7
-3.35
0.94
scaled vs.pooled
9.92
2
5.99
42
Ev
WE
'irP T Om 6'T
arleA IC314IJ3
3JEnbs-!q:)
S'ag
fitr
OLEO
flit'lLS90
TS
(9E'-I-)BWD
smawaird alex
usajAW
3 lapow
(papxW*sh paloS) aienbS-po
afqeA le3ll!J3
Om L616 = aienbSJ4:)
9LE*O
B'VZDETMST
UVO
6-EZOETMST
SS
(/V-O)iBVO*D (6**g)TWO
UCOWO
(/V*019TSO*O (E*9)SWO
(U*V)SilrO 19VOW110*0 (SS'S)M*O (IES'09WO
(OS'0)9ESO'C)
(t,6'0)EOZ'O
(uI)SEVO
(9'0)IEBBO'O
(E6*OWZ'D
U9"AtWO
(Z'O)BftO*O
(OT-I)ESIE'O
(trWwo
UVOIES60'O
(esz-)Tvo(gL,0)697*0 (zs*z-)Esz*o(wz-)LTre(St'O)ELT*O (6SZ-IUZ'O- (WZ-)GLS'C
(8,O)ILro (,tEG)ZESO*o
tLE-T-)66Z'O(T*T)Lsz,()
(ZWZ-)tWW
(SEVIEVO
(ZT'T)SSZ'O
(ZWZ-)Tfr*(E'TZl'()
(VG'T-WV(EZ*T)IEI,'G
IST*Z)VTE*O
(L6'T-)rgVO(tlZ-0)tlTLO'O
19TS-)BrT(TTIE-)TAYT(TSIL-kra(fT's-)ZET(we-)WT(6Vi-)T(1*'E-)LT'T(OSZ-)Z*T- (LrS-)LZrO(69'T-)S9rO- (EE*O-)LTLO*D(LV*Z-W"*O(TL-T-)SLZ-0- (EE*O-)TtLO'O(S*Z-)SZ'O- (99'0-)Z6T'O- (6T'O)ST90'0 (9V-O-)SWG(69-T-)99E'Cl(WE)co
(69'V)ZSi*O
(60'Z)Z91'0
(TS*0)9ZT'D
ILS'S)LEWO
(COM'D
(sz)gfiro
(zg"s*zro
(u-&)uro
lwz)sTro
lzvfligs*o
Wrzkiro
IZZ'E)LZVO (ET*G-)TSEO'CY
(WOEWO
(EZ'EbTVO (VVO-)V6LO'G(zz*f)tsro
(vfo-)Vuoc)(rf)ziro (gEzz-)SEZ(BT*91-)ZZ*TWEX-GrO
(BS'OZ-)TVZ- (LWLT-)VZ*T(89'1)Z6S'D (W91-)SE'Z49S'ZT-)E*T(E9'IE)T'l
(Irtl)wz
igrgz)6si
Q0'O-)6EZ'D46S'ET)VT'Z
19Z*ST)09'1 (T V-Olf ST-0(8*'OTIWT
(69*Zl)wl
IZS-O-)6ZZ*0(LOivslo
(Ovs)swl
(651606VT
(TcW91*0
(ws.)wT
ItT,60wT
(sc*o)Kio Wrf)mv
(WOET'T
(tE*O)Eol*o
(10*T)9T'O
(9*lwgzo
(ZZTz)vzro
(66'O)f97'O
(6cw ssiro
(EZ*TW9Z*O
(9S'TWE*O
(TOVITSCO
(GE'GIST690
(sg*T)Eszo (zol-)vzz*o(EvOuro
(E9*T)SSZ'O
(66'0-)SZZ-0(WEIZECO
(ZOIKWO (ZB'T-)ETS'O(Z9*T)Z9T*O
(9,0)6To
(TVZ)TWO
(5,"Ug*o(69,T)ITlo
(K'Z)6'0
(WE-WWO(SL'T)ZZT*O
ISCOZ99*0 (6T'E-)9L6*0- (ET*Z-)gU*D(WO-ABEVCY
(S6'0)BST*G
196'S-)U*T- (2L'l-)ZECO16E'E)IES*O
16L'9-)TV*T- (SVOT-IELL'(11(Lirz)TiEf*o
(6*9-)U*TIT6*Z)9ZFO
(WLT-)E9*T- (TO'O)ZSgDOO*O
WS'9-)BS*Z(80'0)ZSGCO'O
(S#'9-)E9*Z- (EW91-)99'T(VZ)OWO
(OS'10-)Tf*f- (@V'ET-)S9*T(9WZ-)"E'O(zgiMro
(Eri-kcz- (K**-)gvTftrzksro
WoOmfers-brilwz)Liro
(trtt-)wE- Iscs-bri(Truirrz- (9vv-)6wo(frOuro
(iE6'oT-)Erz(zrt-)gwo- (sr*)L6vo
(Sgv)LrzA410
silo 4z
A31
Ja4lO
U23 4Z
je3 1
A0410
silo +Z
A23 T
A410
sialmultied iqum F"JWVM LWm qpMV
tprA ups"
v=
JOI VrAvu
9 lapow
(poiarm Petvvamn) ambS-po
am*s-oLp pa**V
9'6SSSS909
: POWAWA14q pmw
suope^wW p ja"nN
and piqnuWa p mpnN
*= A4 al§n
ANN a%ammim
(90'0-)6ZZWDUE7)$WO
ftmiqjmpnN
iTT*O)TVEO'O
lZrt-)M*O- ANtuft POJPSA Puf JawOm
OVOILLSVOJgWDMB&P =DPW
(ES-T-)IOCO(SWS-)9WOAIPUF4 PluPaV
(ZCO-)LTZ*0- (6L7-)TU*O- UOJ Ja4iOm-ppm mmimPO
Anuw4 amedimaig
(IrIE)WO
(TL*1)6so
Aipual pWmvg
(K*T)S/E*O
(WZWTT
Wrfl-Wt(lV6)BrZ
(wt)"*T
(GS'O)f9T'O
(8,O)GIro
(6Ztt-)6S'T-
01'0961-
WH IR SUIPISW PMamW
('11309VT
9"'OTIC
(rubri
9MT-T"L
(WOEWO
Om-OSLS
fsz**)us*o
6US-RIV
(91*wo
OU101-ME
lKf438VT
(WOWD-
49S'E-)VL'T-
UK> summl
"PURM03
WOsroSilo +Z
(SS*E)TWO
ilo T
163u8tsLN*
"Popm
L661 A4IPMV
v Fqmw
tOOZ-L661 -ioj sllnsiH 9 alqel
flt
S'E z WjeA le3lVJ3
,Ip I 411m Zln - ejenbSI4:)
(Pap)od*m PSMS) aJwft-143
" = on IeA le3mia
sgfo
TEV'T6SIPTSZ99T
vs
TP Lf? talm L'66Z =aienbS-14:)
s9fo
SEE'ZsstTSZ99T
ss
(ScOn'T
(BV*T)90To
(91)LTWO
(s9,wm*o
(SST)EOTo
(6V'S)TSS*O
ftpcokwo
(Z6*0)WT'O
(SCO)TEVO
(96*0)EZZ'O
(8gskLf*o
(S6*Z-)TES'O- (IFT*T)TLZ*O (OL'O-)Z680*0- (WZ-)9ft'O- (91'T)SSZ'O
(89*Z-)UV'O- (SL'O-)T6T'O- (90'0-)9ZSO'O- (99*Z-)M'O- (LB'O-)toZ'Ofsrs-)90*1(86'0-)ELT*O-
("-&)Tgv(LV'O-)TTT'O-
(9**OXOT*O
(U7)SU'O
(LT'O-)KO'O(Sf'Z)U9V
(8'0-)IOT*O- (ZM-)$S'Z(SO*T)ZZ*O
(69**TWT
(68'0)WL*O
("'0-)60'0(T)EI'To
(Lg*T)gZZ*O
(az)66ro (sgt-)gu*o(WE)SWO
(S6*S-)Z*'T("*E)66ro
(V6-)Zf'Z(ZT-T-)9VT*O(TS*S-)6-6*V
(9*lr)LO*TUTIOT91(GroT-)n*z(TS*tl-)SO*TJa430
SJ83 +1
(ws-)"L*O(E6'T-)LTZ*O-
U9*91NO
(SVTJZTVO
(69*6T-)ZZ*T(9f**91)WT
(IM)6ffO
(gL*LIZTSo
(EB'O)SSSO*O
(fM)rlt*'O(zfrtT-)TT(U'*MWO
(Z6'*)M*O
J10 I
slo3awaied alem 1@3puaq 44m sUns"
(6L'i-)T(66'0-)"T*O-
(StrO)EIE60'0
(Z'O-)TBEO*O(EO'T-)EZT"O(EZ*T)gEZ'O
(LVO-)6S8O'O(fP9*T)EOZ'O
(6ZZ)MV
(WEILWO
(SSZ)%TO
(tS'T-)61*0(TE*L-)TS*T(COT41,17jaw
(16'T-)ZIPS'O(611*0-)BOT'o-
("ZU*O
(S*I)gs*o
(ZL*6T-)ZT*Z(BTVT)gL*l
WOZWO
(96*0)SZT'O
UT%-)60CO(6W9-)SE*T(EV&)TZ*Z(ws-)Tg*g(WE-)L$'O(91*06TISAO +1
(sg*o-)ZO-
U6*Z)VTf'O
(6S*T)OST'O
(TS'*)BLI'O
(6'0-)L960'0(U'O-)Bo'O(rS-)IU'O-
(wi-)Bu*o(SCON9*0
IfTZ)WO
(Zs*gg-)wT(sf'ST)WT
(t6'9)LSL*O
(06'9)SWO
(tS'O)ZEEO*O
U'9-)O'O(9VLlC-)WT(sroz-)Eoz(90*S)WO
(Z9'V)9fS*O
J10 T
(WZ-)MV(Si*z-)Ttsm(99'0-)LZIP'O-
(69,S)Zwo
(/9,1-)Iogo-
TWO
L6T'STOLU6L
OLVO
(PWX*m PvtWMun) aJmftf43
: auenbs-*W pmsnW
: pommo-lbi PUN
9OZILTviLS98
ONZ X4 Sion
(rt-)m*o-
(wi-)Tovo(LE*T)M'O
($T*T)ERE*O
(Z*Ommo
(6sgwgv
(9f?*0)6ti,0'0
(Z9*0)69T*O
5483+1
SM
suq3ffAjaw pmqumm
tied PKIWPSO 10 JOqUMN
is
(WT)61To
(m-)Tts*o(WIP-)90,T-
(ows-)K*T-
(99'0-)Z6T'O(89'T-)ES'O(IVZ-)TS6*0- (WS-)WT(Tso)gzTo
(OS'O-)69Z*O(tS'T)EEL'O
(99'T)66V'O
(TZ'T-)EZ'P'O(SL'O-)SOE*O- (6L*Z-)6"V- (ET'O-)TSEO*O(10*1-*gT*o(TW9*9-)U'Z- ftiru-)qoT(89*T)Z6S'O
(11,,o-)EST*O(9V6)VT
(LET)Egto
(f#9'6)U*T
(SE*O)RET*O
(EL'T-)LTf?'OUf Z)K"
(09**)zro
(ZO'O),tTEOO'O
(90'T-)TTZ'O- (S6'0)ZO60'0
(EZ*T)Vgzo
(%,L*z-)nro(ZB"T)9/E'O
(ZE'O)VZSO'O
(SL*t)ZM
(LS'O)IEZ60'0
Uss-)frz- (izv)sro(ZS'O-)SLOsO(mv-)Esf- (gi*Ll:-WT(GrOom
Urs-kri- (ZVM)V*OT.
(fM-)SlfOp"480"
(69*L)fi*T
(9rt-*T*T- (ZO'O-)T9LOO'O(ff*i-)L6*Z-
(6T'O)ST90'0
(60'Z)ZK*O
(6"kVO
(W9T-)SS'Z-
W00563
(60,019vo
(9S'T)6ZE'O
(Zg*l-)ETS'O(61*09WO(scs-)*LT(Inp-hre-
(KV-)gfTJa4lo
sislaumed slem jusi4W 44m s*nwv
m
T
impo
jW linsm
lvw 01 23unm
(KIOWO
Alpuq AspnN
UVO)ZESTO MPutl Pa4W Put JWIJPM
(STT*Jrro
imtxwmmsup ampo
AWAI% PaQRV
(Lrs-)LZ6,0(gt'o-)SWO- AM; JW4JW-PPM SSMPO
(69*01WO
Aftwel tuaindpUme
Anwel PQPU03X3
(ZVVMV
(am-)VT- GON ul Sumpm pp4a=wm
9vm-c
(69*nwl
61ds-mv
(19,T)ZgTo
9VM-TKL
OKL-OSLS
(OSIL)fl*T
(TOIB)TSL*O
(M -Morol
(BL's-)zuo(Iwtt-)59,T-
(zgl)vgzo
SM +Z
BUtfom
em-ml
9"T> OUNmq
smusmoo
mumsuto
J03 T
WqAA
mz M ung"
v lalow
slamw
3 M"
goOZ-tooZ -ioj sjjnsOH 6 alqel
.....
.......
.
11
.
.
..
.........
....
Table 10 Relative ratios with residence in HDB parameter
2008
2004
997
1 Car 2+Cars Other 1 Car 2+Cars Other 1 Car 2+Cars Other
0.00 -6.95
1.63
-0.58
5.02
2.61
0.20
0.37
0.40
Constant
-466 4.66
-1.41
4.69 -3.30
A.2
0.33 -1.27
-L39
Income <1486
-0.48
-L58
-1.70
.91
4.76
-0.4 -0.56
0.73
4.75
1486-3308
0.56
-1.00
0.42 -LZ 4.84
0.03 -0.16
-0.15
0.36
3309-4128
0.32
0.52
.24
-0.22
-0.64
0.12
0.12
-0.17
0.51
4129-5749
0.02
0.09
-0.09
-0.45
0.58
0.14
0.07
-0.08
0.32
5750-7540
0.20
-2.54
-0.23
0.49
0.41
-0.09
0.37
1.23
0.50
7541-10446
2.21
0.79
0.26
1.23
0.83
1.15
0.95
0.21
1.54
>10446
-1.00
-1.00
-1.0
-L00
-1.00
-1.00 -1.00
-1.00
-1.00
Household residing in HDB
-2.58
-0.14
0.06 0.61
0.37
0.48
0.43
0.47
0.33
Extended Family
-1.64
0.33
-0.21 0.47
0.32
0.58
0.25 -0.64
0.44
Grandparent family
-3.23
-0.43
0.03
0.32 -1.02
-0.05
0.35
-0.09
Childless multi-worker family -0.33
0.30 -2.60
1.98 0.18
4.51
-0.29
1.29 0.71
-0.85
Retired family
Childless single-worker
Worker and retired family
Nuclear family
Distance to MRT
-0.05
4.53
0.27
0.03
0.37
0.24
0.19
0.74
-1.00
-0.60
-5.31
0.01
-0.01
0.27
-. 09
0.04
0.45
0.12
0.16
0.28
0.96
-0.07
0.08
-0.85
-0.51
0.11
-0.18
-0.27
0.25
-5.11
-1.22
0.96
0.20
2.00
g 1.00
S0.00
-1.00
-- 997 one car
one car
z
e2004
+2008 one car
-'
£
5 -2.00
i-3.00
2.00
1.00
0.001 00
.
1-1.
-0
--1997 two cars
.2008
two cars
-3.00
-4.00
-5.00
4.00
2.00
0.00
1997 other
2004other
-4.
2008 other
-2.00
-
-6.00
-8.00
-10.00
Figure 5 Income coefficient comparison
45
2008 Model with Locational Variables and Transportation-/Accessibility
Related Variables
Tables 12 and 13 show the estimates of 2008 MNL vehicle ownership models.
Two models are presented: a model containing only household socio-demographics
and residential location (Table 11) and a second model (Table 12)which includes a
number of location-specific and transportation-/accessibility-related variables.
Similar to the previous models, the base alternative was specified as no vehicles
available.
To compare models, I use the likelihood ratio test to ultimately determine
the best model fit among the range of possibilities. For example, a model with
alternative specific coefficients performs better than generic coefficients.
Table 11 shows the base model, only including the socio-demographic and
residential location variables: income, family types, and the dummy variable
indicating whether the household lives in HDB residence. To test whether the
distance to the nearest MRT station has the same impact for people living or not
living in HDB housing, I added an interaction term between household distance to
the nearest MRT station and the dummy variable indicating whether the household
lives in HDB. In Singapore, the HDB estates are mostly aggregated in the so-called
"new towns" and are designed with a package of food court as well as relatively
easy access to MRT stations. Walking to a MRT station from a HDB estate may be a
much more enjoyable experience than from a condominium or landed property.
However, the interaction term is not significant, indicating the distance to MRT has
the same impact for people living in or not in HDB.
The final expanded model includes the travel cost ratio variable and the
private car gravity-based accessibility measure (Table 12). The great share of the
variation in ownership preference is explained by the household characteristics.
While several locational and transport-related variables are significant, they have a
46
relatively small influence on the overall goodness of fit of the model. This suggests
that not representing these variables in vehicle household ownership forecasts in
Singapore would not be overly problematic.
47
Table 11 Base 2008 Motor Vehicle Ownership Model with only social
Table 12 Final model with accessibility index
economic variables
1 Car
Variablies
Constant
1.85 (8.55)
Income <146
-3.5 (-14.41)
1486-3305
-1.79 (-17.73)
3309412B
-0.89 (-6.71)
-0.251 (-2.72)
4129-5749
5750-7540
0.0902 (0.95)
7541-10446
0.516(4.81)
1.29(9.45)
3-10446
Extended Famly
-4.655(-2.82)
Grandparent family
0.494 (1.65)
Chiless multi-worker family -1.08 (-5.07)
Retired family
0.182(0.6)
Chlides single-worker
-1.07 (4.93)
Worker and retired famly
-0.909 (-4.24)
Nudear fanly
4.547 (-2.59)
Household residing In HDB -1.1(-14.06)
Number of estimated paraer.
Number of observations:
Final log-ikelihood:
A4usted rho-square:
48
7971
-7031.5
0.359
2+ Cars
Other
0.611(1.72)
-10.7(45.74)
-3.62(-7.04)
-2.31 (-5.79)
-1.21 (-5.01)
-0.302 (-1.57)
0.342 (1.87)
1.7(9.35)
-0.294 (-0.73)
0.775 (162)
-0.95 (-2.57)
0.657 (1.34)
-1.34 (-3.46)
-0.375 (-1.02)
-0.559 (-1.53)
-2.44 (-18A6)
-0.995 (-3)
-1A1 (-5.37)
-0.0749 (-0.5)
0.091 (0.56)
0.0529 (0.32)
0.00066(0)
-0.424 (-1.76)
0.358 (1.35)
-0.428 (-1.23)
-0.277 (-0.6)
-0.532 (-1.69)
-. 431 (-0.87)
-0.878 (-2.78)
-0.42 (-2.66)
-0.203 (-0.66)
-0.207 (-1.37)
Variables
Constant
Income <1456
1486-3308
3309-4128
4129-5749
5750-7540
7541-10446
>10446
Extended Family
Grandparent family
Childles muti-worker family
Retired family
Childess single-worker
Worker and retired family,
Nuclear family
Household residing in HDO
Distance to MRT
I Car
2+ Cars
Other
2.31(9.27)
-3.55 (-14.65)
-0.293 (-3.14)
0.0784 (0.82)
0.544 (1.27)
-11.9 (-70.89)
-3.6(-6.98)
-2.27 (-5.64)
-1.2(4.92)
-0219 (-1.11)
0.532(4.96)
0A67(2.54)
1.33(9.68)
4.658 (-2.8)
0.497 (1-67)
1.82(9.83
-0.283 (-0.7)
0.773 (1.63)
-0.923 (-2.5)
0.739 (1-51)
-1.23 (-3.31)
-0.324 (-0.88)
-0.81 (-1.6)
-2.35 (-16.71)
0.552(5.5)
-0.721(-1.89)
-1A7 (-5.53)
-0.123 (-0.81)
0.0581(0.36)
0.0246 (0.15)
0.000746 (0)
-0.397 (-1.65)
0.406 (153)
-0.433 (-1.24)
-0278 (-0.6)
-0.53 (-168)
-0.409 (-0.83)
4.5 (-2.69)
-0.796 (-2.51)
-0.208 (-0.67)
-0251(-1.62)
0.143 (1.44)
-0.0601 (-1.78)
-1.55
(-18.15)
4.934 (-9.06)
-1.0
(4.98)
0.209 (0.68)
-1.03 (-4.74)
-0.557 (-3.94)
-0.549 (-2.57)
-1.17 (-14.31)
0.117 (1-83)
4.0939(4.4)
Accessibility
Travel cost ratio of PV PT
-0.237 (-5.76)
Number of estimated parame
57
Number of observations:
7971
-6982.55
Final log-likellhood:
0.363
Afusted rho-square:
-0.0816 (-1.67)
-0.378 (-4.62)
4.247 (-3.53)
48
Interpretation of 2008 Model
Most of the statistically significant coefficients in the model have the expected
signs.
Travel costs and times
The ratio of estimated ERP cost compared to transit cost from home to work of
all the household members does have a significant association with the household
likelihood of owning a car. In zones where the variable cost of driving is high
compared to using transit, households have a lower likelihood of buying cars.
However, the ratio of the time to use private vehicle versus public transit is not
statistically related with the household's ownership choice. This is an interesting
finding, which indicates that households may factor in the anticipated relative daily
travel costs when they are deciding whether or not to own a car; relative travel
time, on the other hand, does not play a role.
Accessibility:
The higher the estimated automobile accessibility from a household's
residential zone, the less likely they own a single car; no relationship is detected for
the multiple car ownership decision. This implies some need to include relative
location in vehicle ownership forecasts, although incorporating the relative
accessibility (i.e., public transport relative to automobile) may be a more
meaningful indicator (e.g., Zegras, 2010).
The distance to MRT
A household's distance to the nearest MRT station does not have a significant
relationship with the ownership of one car, but the nearer a household lives to an
MRT station, the lower the likelihood of it having a second car. The role of distance
49
to MRT station does not vary for households living in HDB estates versus those
living in condos or landed properties.
50
CHAPTER 6: DISCUSSION AND CONCLUSION
Discussion and Implication
Vehicle ownership is a key determinant of household travel behavior. In this
thesis I present MNL models of vehicle ownership in Singapore, testing for changes
in preferences over a 21-year period, and examining specific locational and
accessibility measures in 2008.
Unsurprisingly, the results show that income increases the utility of owning a
car. Generally, income had a stronger effect for a household in either low-income
brackets or high-income brackets. Its effect on the middle class is not as strong. The
influence of income changes from 1997 to 2008, with lower income groups
particularly less likely to own a car in 2008 compared to 2004 and 1997.
Looking at all three years' results, the presence of children in the family is a
significant indicator of car ownership in Singapore. This is consistent with Van
Eggermond et al's (2012) research in Singapore and Zegras and Hannan (2012)'s
research in Chile, but is in contrast to Kim and Kim (2004) who find in the United
States that having children is not an indicator for owning car. Family types affect
the utility of ownership. The families at the later stage of the life cycle (e.g., the
grandparents families and the retired families) are more likely to own a car. The
implication for simulation modeling is that instead of considering only the number
of workers and children in the family, family types that capture the structure of the
household needs to be taken into account. However, the effect of the family types in
1997 and 2004 are slightly different, so it is worth further study to understand the
role of family types in household's car ownership decision. The limited variables
available to capture the built environment and transport systems for 1997 and
2004 might partially explain the differences. For application of simulation models,
these results suggest potential problems with assuming temporal transferability of
cross-sectional models estimated for a given year. Understanding how the
51
preference changes detected in this thesis could be incorporated into models
predicting future vehicle ownership levels (and the importance of doing so) is an
area of future research.
Households living in HDB estates are less likely to own any cars after
controlling other social economic variables. One explanation is that HDB towns are
designed with food courts and a range of retail and services in Singapore. People
residing in HDB may have less need to drive to shopping malls or restaurants. Other
reasons may have to do with the parking cost, which were not captured in my
models, however, found to be significant in various studies (Wu et al. 2009; Guo,
2013). Which explanation really stands still needs further study by adopting more
variables to capture point of interests and parking supply. For example, I do not
include, due to lack of data, measures of local land uses and the built environment,
which have been shown to correlate with household vehicle ownership in other
places (e.g., Zegras, 2010).
Household proximity to an MRT station decreases the utility of owning a
second car. A similar effect could not be observed for the first car. This effect does
not vary between households living in HDBs and those living in private
developments. As mentioned earlier, the Singapore government has ambitious MRT
expansion plans, with the LTA's Land Transport Master Plan for 2013 aiming to
have 8 in 10 households living within a 10-minute walk from a train station (Land
Transport Authority, 2013). The plan may not have much impact on vehicle
ownership.
Accessibility to a range of opportunities is accounted for by using the
accessibility index. A higher car accessibility to various opportunities decreases the
utility of owning a car. This implies that measures of accessibility should be
included in forecasts of vehicle ownership and related simulation models, although
the overall effect on explaining ownership is low, so that the resulting errors of
excluding this variable may not be great.
52
A higher travel cost ratio of home-based work trips of all family members by
car versus by transit decreases the utility of owning cars. However such a
relationship is not observed for the travel time ratio of home-based work trips by
car versus by transit. The expected ERP charges, and/or public transport fares, may
be influencing households' car purchasing decisions. One strategy might, therefore,
be making public transit more price competitive, or imposing higher ERP charges to
decrease the utility of owning cars.
Limitations and Further Research
My analysis faces a number of limitations. Most notably, I used very few
indicators representing the built environment. Currently, a relationship between
vehicle ownership and HDB residence is detected, however, this may be capturing
the effect of omitted variables such as mixed land uses. I did not account for the
potential role of parking in my models due to the lack of data. However parking
may be an additional area that Singapore can leverage as a powerful tool in
motorization management.
Another variable not included is the cost of vehicles. The Certificate of
Entitlement (COE) is one of the reasons for high purchase costs of vehicle. With
better data on vehicle types and purchase year, we could derive purchase costs and
thereby build transaction models that would not only provide insights into demand
for vehicles, but also the decision to sell or scrap the vehicle. Other interesting
paths of research include the relationship between car ownership and travel
behavior (e.g., mode choice) and car ownership and location choice.
Finally, the finding that the coefficients are not consistent in time raises the
question about using a model calibrated for one year to forecast for future years. It
may be problematic to assume that the future is the same as the past when there
are changing consumer preferences. This opens up an interesting area of research
possibilities- the temporal variability in transportation models.
53
Conclusion
I examined whether household vehicle ownership preferences have changed
over 1997, 2004 to 2008 in Singapore, long known as a place where motor vehicle
ownership and use have been tightly controlled and highly priced.
The results demonstrate that preferences influencing vehicle choice have
changed, suggesting variations in relative influences among socio-economic and
demographic factors. Income has played a consistently strong role in influencing
vehicle ownership. Among all income groups, but especially the lower income
groups, the models shows a decreasing utility for owning vehicle, possibly due to
the improvements in public transportation and the strict control of vehicle
ownership. Families in the later stage of the life cycle have higher utility of owning
vehicles, as do families with children. Over time, the relationship between family
type and vehicle ownership remains consisten, except for nuclear families and
extended families. The nuclear families and extended families have higher utility of
owning cars in 1997 and 2004, however a lower utility of owning cars in 2008. But
a closer examination informs us that, compared to other family types, the extended
family and nuclear family are still more likely to have cars than the childless
families.
As for locational variables, land use and travel cost variables; the model in 2008
shows us that households residing in HDB estates, the travel cost ratio of using
private vehicle versus public transit, accessibility to opportunities -are all
negatively correlated with vehicle ownership, while the travel time ratio doesn't
appear to be significant. Distance to the nearest MRT only reduces the utility of
owning a second car.
The results suggest that household preferences for vehicle ownership are
dynamic over time, responding, at least in part, to the strict transportation policies
in Singapore.
Nonetheless, the research raises more questions than answers and
54
points to areas for further investigation such as including more detailed built
environment measures, developing more sophisticated behavioral models (e.g.,
joint residential and vehicle choice) and understanding implications for forecasting
models.
55
Appendix:
Table 13 Private motor vehicle fleet and population growth 1961-2011
Private motor cars
Population
Persons per private motor car
Vehicle
1961
70108
1702.4
41.2
1965
104729
1886.9
55.5
10.6
1970
142568
2074.5
68.7
6.4
1974
142674
2229.8
64.0
0.0
1980
152574
2413.9
63.2
1.1
1985
221279
2558
86.5
7.7
1988
237801
2586.2
91.9
2.4
1992
300000
3230.7
92.9
6.0
1993
310000
3313.5
93.6
3.3
1994
350000
3419
102.4
12.9
1995
380000
3524.5
107.8
8.6
1996
385000
3670.7
104.9
1.3
1997
385000
3796
101.4
0.0
1998
390000
3927.2
99.3
1.3
1999
390000
3958.7
98.5
0.0
2000
395000
4027.9
98.1
1.3
2001
405354
4138
98.0
2.6
2002
404274
4176
96.8
-0.3
2003
405328
4114.8
98.5
0.3
2004
417103
4166.7
100.1
2.9
2005
438194
4265.8
102.7
5.1
2006
472308
4401.4
107.3
7.8
2007
514685
4588.6
112.2
9.0
2008
550455
4839.4
113.7
6.9
2009
576988
4987.6
115.7
4.8
2010
595185
5076.7
117.2
3.2
2011
603723
5183.7
116.5
1.4
Source: Data before 1988 are from Phang, Sock-Yong, and Anthony Chin. "An
Evaluation of Car-Ownership and Car-Usage Policies in Singapore." (1990): B105-B117.
Data after 1988 are from Singapore Department of Statistics. Yearbook of Statistics.
Various years.
56
Table 14 Household income from work
Year
Average income (SG$)
Median income (SG$)
1990
3,076
2,296
1995
4,107
3,135
1997
4,745
3,617
1998
4,822
3,692
1999
4,691
3,500
2000
4,943
3,607
2010
8,726
5,600
2011
9,618
6,307
Source: Singapore Department of Statistics. Measured in 1990 dollars, the average
household monthly income rose from SGD$3080 in 1990 to SGD$4170 in 2000 at an
average annual rate of 2.8%.
57
2500
2000
1500
1000
1997
500
2004
2008
2008
1997
r
dree
Figure 6 Income distribution of three-year data
58
References
1. AchARyA, Surya Raj, and Shigeru MoRichi. (2007). Motorization and role of
mass rapid transit in East Asian Megacities." IATSS research 31, no. 2
2. Arnold, S. J., Oum, T. H., & Tigert, D. J. (1983). Determinant attributes in
retail patronage: seasonal, temporal, regional, and international
comparisons. Journal of Marketing Research, 149-157.
3.
Barter, P. A. (2013). Singapore's Mobility Model: Time for an Update?. In
Megacity Mobility Culture 22 5-242. Springer Berlin Heidelberg.
4. Ben-Akiva, M., Bradley, M., Morikawa, T., Benjamin, J., Novak, T., Oppewal,
H., & Rao, V. (1994). Combining revealed and stated preferences
data.Marketing Letters, 5(4), 335-349.
5. Ben-Akiva, M.,& Lerman, S. R. (1985). Discrete choice analysis: theory and
application to predict travel demand ,vol. 9. The MIT press.
6. Bento, A. M., Cropper, M. L., Mobarak, A. M., &Vinha, K. (2005). The effects
of urban spatial structure on travel demand in the United States. Review of
Economics and Statistics, 87(3), 466-478.
7. Bhat, C. R., & Pulugurta, V. (1998). A comparison of two alternative
behavioral choice mechanisms for household auto ownership
decisions. Transportation Research Part B: Methodological, 32(1), 61-75.
8. Bierlaire, M. (2003). BIOGEME: a free package for the estimation of discrete
choice models.
9. Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: density,
diversity, and design. Transportation Research Part D: Transport and
Environment, 2(3), 199-219.
10. Chakirov, A. and Erath, A (2013)."Putting a price on travel"
59
http://www.todayonline.com/commentary/putting-price-travel
11. Cheong, C. C., & Toh, R. (2010). Household interview surveys from 1997 to
2008-A decade of changing travel behaviours. Editorial Team, 52.
12. Chin, A, & Smith, P. (1997). Automobile ownership and government policy:
The economics of Singapore's vehicle quota scheme. Transportation
Research Part A: Policy and Practice, 3 1(2), 129-140.
13. Chu, Y. L. (2002). Automobile ownership analysis using ordered probit
models.Transportation Research Record: Journal of the Transportation
Research Board, 1805(1), 60-67.
14. Clark, S. D. (2007). Estimating local car ownership models. Journal of
Transport Geography, 15(3), 184-197.
15. De Jong, G. C., &Kitamura, R. (2009). A review of household dynamic
vehicle ownership models: holdings models versus transactions
models.Transportation, 36(6), 733-743.
16. Gakenheimer, R. (1999). Urban mobility in the developing
world. Transportation Research Part A: Policy and Practice, 33(7), 671-689.
17. Hansen, W. G. (1959). How accessibility shapes land use. Journal of the
American Institute of Planners, 25(2), 73-76.
18. Hartung, R. (2013). "Why not a 'drive less, pay less' system?"
http://www.todayonline.com/commentary/why-not-drive-less-pay-less-sy
stem
19. Hocherman, I., Prashker, J.N., &Ben-Akiva, M. (1983). Estimation and use of
dynamic transaction models of automobile ownership. Transportation
Research Record, 944.
20. Holtzclaw, J., Clear, R., Dittmar, H., Goldstein, D., &Haas, P. (2002). Location
efficiency: Neighborhood and socio-economic characteristics determine
60
auto ownership and use-studies in Chicago, Los Angeles and San
Francisco.Transportation Planning and Technology, 25(1), 1-27.
21. http://www.smrt.com.sg/
22. http://www.transitlink.com.sg/
23. Ibrahim, M. F. (2003). Improvements and integration of a public transport
system: the case of Singapore. Cities, 20(3), 205-216.
24. Jong, G. D., Fox, J., Daly, A., Pieters, M., & Smit, R. (2004). Comparison of car
ownership models. Transport Reviews, 24(4), 379-408.
25. Key Household Income Trends, (2012). Singapore Department of Statistics.
http://www.singstat.gov.sg/Publications/publications-and-papers/househ
oldincomeandexpenditure/pp-s19.pdf
26. Kim, H. S., &Kim, E. (2004). Effects of public transit on automobile
ownership and use in households of the USA. Review of Urban &Regional
Development Studies, 16(3), 245-262.
27. Lam, S. H., &Toan, T. D. (2006). Land transport policy and public transport
in Singapore. Transportation, 33(2), 171-188.
28. Li, M. Z., Lau, D. C., &Seah, D. W. (2011). Car Ownership and Urban
Transport Demand in Singapore. International Journal of Transport
Economics= Rivista Internazionale de Economia dei Trasporti, 38(1).
29. Matas, A., Raymond, J.L., &Roig, J. L. (2009). Car ownership and access to
jobs in Spain. Transportation Research Part A: Policy and Practice, 43(6),
607-617.
30. Nolan, A. (2010). A dynamic analysis of household car
ownership.Transportation research part A: policy and practice, 44(6),
446-455.
31. Potoglou, D., &Susilo, Y. 0. (2008). Comparison of vehicle-ownership
61
models.Transportation Research Record: Journal of the Transportation
Research Board, 2076(1), 97-105.
32. Salon, D. (2009). Neighborhoods, cars, and commuting in New York City: A
discrete choice approach. Transportation Research Part A: Policy and
Practice,43(2), 180-196.
33. Schimek, P. (1996). Household motor vehicle ownership and use: How
much does residential density matter?. Transportation Research Record:
Journal of the Transportation Research Board, 1552(1), 120-125.
34. Severin, V., Louviere, J.J., & Finn, A. (2001). The stability of retail shopping
choices over time and across countries. Journal of Retailing, 77(2), 185-202.
35. Singapore Land Transport Master Plan 2013 Report, Land Transport
Authority, 2013.
36. Singapore Land Transport: Statistics In Brief 2013. www.lta.gov.sg
37. Singapore National Accounts:
http://www.singstat.gov.sg/statistics/browse-bytheme/nationalaccount
s.html
38. SingStat. (2013). 'Construction and Real Estate'. In Yearbook of Statistics
Singapore, 2013. Singapore: Singapore Department of Statistics.
39. Swait, J., & Louviere, J.(1993). The role of the scale parameter in the
estimation and comparison of multinomial logit models. Journal of
Marketing Research, 305-314.
40. Two new rail lines and three new extensions to expand rail network by
2030, Land Transport Authority,
http://app.ta.gov.sg/apps/news/page.aspx?c=2&id=38dc4ca3-5e70-4bf897bc-87f78e6303e7
41. Van Eggermond, M. A., Erath, A., &Axhausen, K. W. (2012). Vehicle
62
ownership in Singapore using revealed preference data and spatial
measures. In Proceedings of the 13th International Conference on Travel
Behaviour Research, Toronto, Canada.
42. Wu, G., Yamamoto, T., &Kitamura, R. (1999). Vehicle ownership model that
incorporates the causal structure underlying attitudes toward vehicle
ownership. Transportation Research Record: Journal of the Transportation
Research Board, 1676(1), 61-67.
43. Yuan, L. L. (1997). A Case Study on Urban Transportation Development and
Management in Singapore. In Second International Expert Panel Meeting on
Urban Infrastructure Development, Bangkok, Thailand
44. Zegras, C. (2010). The Built Environment and Motor Vehicle Ownership and
Use: Evidence from Santiago de Chile. Urban Studies, 47(8), 1793-1817.
45. Zegras, C., & Hannan, V. (2012). The Dynamics of Automobile Ownership
Under Rapid Growth: The Santiago de Chile Case. Transportation Research
Record: Journal of the Transportation Research Board, 80-89.
63