Vehicle ownership in Singapore using revealed
preference data and spatial measures
Michael A.B. van Eggermond
Alexander Erath
Kay W. Axhausen
Future Cities Laboratory
Travel Behaviour Research: Current Foundations,
Future Prospects
13th International Conference on Travel Behaviour Research
Toronto 15-20, July 2012
Vehicle ownership in Singapore using revealed preference data and spatial measures
Alexander Erath
Michael A.B. van
Future Cities Laboratory
Eggermond
Singapore ETH Centre
Future Cities Laboratory
Singapore
Singapore ETH Centre
phone:
Singapore
fax:
phone:
[email protected]
fax:
[email protected]
Kay W. Axhausen
IVT
ETH Zurich
Switzerland
phone: +41-44-633 39 43
fax: +41-44-633 10 57
[email protected]
June 2012
Abstract
Vehicle ownership is a key determinant of household travel behavior and can lead
to negative externalities, such as air pollution and congestion. A recurring question
for policy-makers, transport planners and urban designers is therefore which factors
influence vehicle ownership. In this paper we present a vehicle ownership for the case
of Singapore. Models are estimated using the Household Interview Travel Survey 2008
which is enriched with spatial variables. Travel times by both private and public transport
have been calculated on a congested network between residence and work locations. We
find that household income positively influences vehicle ownership. Vicinity of train
stops decrease the utility of owning a vehicle. Distance to residential car parks is not
significant as is distance to the nearest electronic road pricing gantry. In addition to
these spatial variables, an entropy index has been calculated as well as the number of
opportunities within walking distance of a household’s residence.
Keywords
car ownership, spatial variables, urban form, Singapore, MNL
Preferred citation style
van Eggermond, M.A.B., A.L. Erath and K.W. Axhausen (2012) Vehicle ownership in
Singapore using revealed preference data and spatial measures, paper presented at the
13th International Conference on Travel Behaviour Research, Toronto, July 2012.
1
1 Introduction
Singapore is a small city state in Southeast Asia with a land area of 712 km2 , a permanent
residential population of 3.77 million and a total population of 5.08 million in 2010,
compared to respectively 697 km2 , 3.27 million and 4.03 million in 2000. GDP per
capita amounts to S$ 59,813 (US$ 45,200, 2010), which makes it one of the most
wealthy countries in (Southeast) Asia. Together with the increasing population and
wealth, vehicle ownership has increased. The number of cars has increased from 392,961
in 2000 to 597,746 in 2010 , or from 1 car per 10 households in 2004 to 1 car per 8.8
households in 2008 (Choi and Toh, 2010). The total number of motorized vehicles is
close to 1 million.
A set of policies has therefore been put in place to curb vehicle ownership and usage.
First, a set of policies has been devised to decrease the attractiveness of purchasing a
car by increasing the up-front costs. Vehicle growth is capped at 3% under the Vehicle
Quota System (VQS) until mid 2011 and will be decreased. In addition to the open
market value (OMV) of the vehicle, a registration fee (S$140), an additional registration
fee (ARF) of 100% the OMV, an excise duty of 20% of the OMV and a 7% Goods and
Services Tax has to be paid. Furthermore, the prospective car owner has to bid for a
Certificate of Entitlement (COE) under the VQS with a validity of 10 years and pay for
an annual road tax based on the engine capacity (Li et al., 2011) . Second, the usage of
car is being discouraged by tolling road users through Electronic Road Pricing (ERP).
The current scheme has different charges according to vehicle type, time of the day, and
location of the gantry (Santos et al., 2004).
Due to the high cost of vehicle ownership and the increasing wealth, Singaporeans
tend to use their car intensively, resulting in negative externalities, such as noise and
fine-particle emissions and dropping average speeds. The mode share of public transport
has fallen between 1998 and 2008 from 63% in 1998 to 56% in 2008. The absolut
number of public transport trips (excluding taxi) has increased from 4.33 million in 2000
to 5.37 million in 2010.
Household vehicle ownership models do not form a step in classic four-step transportation
model, but vehicle ownership and availability does serve as an input in mode choice
models, are commonly applied in agent-based transportation demand models and prove
to be relevant to urban planners and policy-makers alike. The field is well documented
and various type of models have been specified to estimate vehicle ownership varying
from linear regressions to ordererd probit, order logit and MNL-models. In this study
we will estimate models of the latter type.
In this study we follow a dual objective; on one hand, we want to investigate which
socio-demographic and spatial variables play a role in car ownership. Furthermore,
we intend to evaluate several measures for neighbourhood heterogeneity and include
2
home-based parking space availability. On other hand, we require a vehicle ownership
model of Singapore to apply to a synthetic population of Singapore (Erath et al., 2012)
to the agent-based transport model MATSim (e.g. Balmer, 2007).
The data used for this study stem from several sources. As a basis, the Household
Interview Travel Survey 2008 (HITS) is taken. For the survey 1% of the population is
questioned on their travel behavior on a single workday. The survey contains 36,978
individuals in 10,641 households. The survey is conducted once every four years and is
commissioned by the Singaporean Land Transport Authority (LTA). HITS is enriched
with travel times based on a MATSim implementation of Singapore, a multi-agent
transport demand model. A limitation imposed by HITS is that it is not known what
type of vehicle households own and when the vehicle was purchased. Given the stark
fluctuations of the COE price this could lead to omitting relevant variables, varying from
S$23,801 in January 2004 to S$5,346 in January 2009 and S$51,380 in January 2012 for
the category under 1600 cc.
This paper continues with a literature review in section 2. Methodology is described in
section 3. In section 5 the model results are presented and discussed. Section 6 concludes
this paper.
2 Literature review
Vehicle ownership can be either modeled on an aggregate or disaggregate level. More
specifically, de Jong et al. (2004) make a distinction between nine model types: aggregate
time series models, aggregate cohort models, aggregate car market models, heuristic
simulation models, static disaggregate ownership models (explaining the number of
cars per household), indirect utility models of car ownership and car use (joint discretecontinuous models), static disaggregate car-type choice models (often with choice
of brand-model-vintage), 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). These model types were compared based on
16 criteria, ranging from the treatment of supply, through level of aggregation and data
requirements, to the treatment of scrappage.
For a comprehensive review and comparison of car ownership models the reader is
referred to their study; this study concerns static disaggregate ownership models as
we consider the household as basis unit of analysis and aim to improve our synthetic
population of Singapore with vehicle availability. Within this model type a distinction
can be made between modeling techniques applied, data used (revealed preference /
stated preference) and variables defined.
Lerman and Ben-Akiva (1976) introduced discrete choice models to automobile owner-
3
ship. In disaggregate models the number of cars owned by a household can either be
represented as an ordinal or nominal variable resulting in ordered or unordered choice
models respectively. Ordered models assume that households prefer to own more vehicles; unordered models assume that households will choose the number of vehicles that
maximizes their utility. Both model types can be found in literature to model vehicle
ownership; Potoglou and Kanaroglou (2008) provide a summary of studies applying
disaggregate models.
In addition to sociodemographic characteristics, Chu (2002) and Potoglou and Kanaroglou (2008) attempt to measure the influence of urban form in the vicinity of a
household’s residence in Hamilton, Canada. To measure urban form they use a mixed
density index (MDI) and a land-use entropy index (EI). The MDI is based on the employment and residence per acre per traffic zone; EI is the proportion of developed land
within a 500 meter walking distance of the household. Both indexes have negatively
affect car ownership. Also, Li et al. (2010) measure urban form with four attributes:
population density at the subdistrict level within the city, distance from the household
residential location to the CBD, distance from the residential location to the nearest bus
stop, residential location within Beijings fourth ring road, which is the citys urban fringe.
They find that households tend to have fewer private cars when they live further away
from the urban center.
Potoglou and Susilo (2008) compare three model types: Multinomial Logit (MNL)
models, Ordered Logit (ORL) models and Ordered Probit (ORP) models in three regions.
Variables differ per region, but mostly give an indication of dwelling type, household
income, race, household composition, number of workers and residential typology.
To compare the different models, Potoglou and Susilo (2008) use several indicators:
likelihood ratio, adjusted log likelihood ratio test (Ben-Akiva and Lerman, 1985), the
Akaike information criterion (AIC), Bayesian information criterion (BIC) and the Hannan
and Quinn information criterion (HQIC). In line with the recommendation provided by
Train (2003) they do not use the ”the percent correctly predicted”. Estimated parameters
showed that the MNL model is more flexible, allowing for alternative specific effects
of explanatory variables across different car ownership levels. Ordered models are
constrained by their nature to a unique coefficient per explanatory variable and assume a
parallel slope per response variable. The outcome of their comparison showed that MNL
models should be preferred to the ORL and ORP model.
Pinjari et al. (2011) propose an integrated model system of residential location choice,
auto ownership, bicycle ownership, and commute tour mode choice processes using a
mixed multidimensional choice modeling methodology. They argue that it is possible
that individuals and households make a multitude of choices including the choices to
live and work, the number of vehicles to own and the choice of their daily activities as
an overall package rather than as independent choices in a sequential fashion. In the car
ownership component of their model an ordered logit model is applied with stochastic
components. As main data source the 2000 San Francisco Bay Area Travel Survey
4
(BATS) is used. Variables include zonal characteristics, commute related variables,
attributes of employment zones, household characteristics, zonal household density,
residence-end street block density and employment-end street block density. In order to
aggregate variables relating to different members of the household, they sum or average
variables such as commuting time and street density respectively. Model estimation
results indicate: (1) individuals with certain modal preferences are found to self-select
into residential zones that support their preferences, (2) significant endogeneity effects
(e.g. auto ownership and bicycle ownership are endogenous to mode choice decisions),
(3) significant presence of common unobserved factors affecting multiple choice dimensions, for example, in the case of auto ownership and bicycle ownership, and (4)
significant unobserved heterogeneity, for example, where households showed significant
variance in sensitivity to commute travel time in making residential location choices
(due to unobserved factors). Ignoring any of these effects resulted in biased estimation
of the other effects.
Li et al. (2011) estimate a vehicle ownership and mode choice model for Singapore using
MNL models to update key economic parameters related to urban transport, such as price
elasticity, income elasticity and value of travel time savings. In their car ownership model
they include variables such as income, dwelling type, distance to CBD and distance
to work. Estimation is carried out with data from stated preference survey carried out
by LTA in 1997. The data set contains 646 observations. The income elasticity of car
ownership is 0.5944, meaning that car owners treat cars as a necessity for commuting.
For every S$1000 increase in income, the likelihood of owning a car will increase by
8.3%. If the main income earner works in CBD, the likelihood of owning a car decreases
by 14%.
In line with Potoglou and Susilo (2008) we agree that there is no underlying behavioral
assumption for applying ordered logit models; opposed to Li et al. (2011) we aim to add
exact distance to bus and MRT stops as we believe these variables can not be only be
captured by dwelling type variables.
3 Methodology
3.1 Multinomial Logit Model
Within the discrete choice framework, a decision-maker chooses from a set of alternatives.
Each alternative is assumed to have a number of attributes. Each attribute has a level of
utility or disutility, which capture the costs and benefits of an alternative; the utility U of
an alternative i for a decision-maker q is defined by:
Uiq = Viq + εiq = f (βi xiq + εiq )
(1)
5
with a deterministic part Viq that consists of a function f of the vector βi of taste
parameters and the vector xiq of attributes of the alternative, the decision-maker and
the choice situation. In addition, socio-demographic attributes of decision-maker q
can be included in the deterministic part of the utility function. The non-deterministic,
non-observable part of the utility function is captured by εiq . Four types of errors can
be recognized: unobserved alternative attributes unobserved individual characteristics,
measurement errors and proxy variables. In order to reflect this fact uncertainty is
modeled as a random variable. Decision-maker q will choose the alternative from set C
with the highest utility:
P(i|Cq ) = P[Uiq ≥ U jq ∀ j ∈ Cq ] = P[Uiq max j∈Cq U jq ]
(2)
It can be derived that the level of utility is irrelevant both to the decision-maker and
the analyst, only differences in utility matter: P(i|Cq ) = P[Uiq − U jq ≥ ∀ j ∈ Cq ] . The
same holds for adding a constant to the utility of all alternatives, the alternative with
the highest utility does not change. If the utility is decomposed into the observed part
and the unobserved parts, the following equation is obtained: P(i|Cq ) = P[εiq − ε jq ≤
Uiq − U jq ∀ j ∈ Cq ]; the utility only depends on the differences.
The most commonly used discrete choice model is the Multinomial Logit (MNL) 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 probability of
each alternative can be calculated as:
eViq
P(i|Cq ) = X
eV jq
(3)
j
3.2 Model interpretation and evaluation
Model estimation results can be interpreted and evaluated in several ways (e.g. Louviere
et al., 2000, Train, 2003). First, the parameter estimate β̂ik of attribute k in expression Vi
of alternative i can be interpreted as the weight of the attribute in the utility expression
by multiplying β̂ik by the mean or median value of the attribute Xi .
Also, discrete choice models can be used to derive estimates of the willingness-to-pay
(WTP) or willingness to accept (WTA) of an individual to obtain a benefit or avoid a
cost. In a linear model, where each attribute is associated with a single parameter, the
ratio of two parameters is the WTP or WTA, holding all other constant. If one of the
attributes is measured in monetary units, the ratio can be interpreted as a valuation.
Finally, models can be evaluated by means of the responsiveness of market shares to
changes in each attribute. Under the assumption that utility is attribute ziq with βz a single
6
point elasticity for a continious variable in the MNL model can be calculated as:
Eiziq = βz ziq (1 − Piq )
(4)
Preferably, equation 4 is evaluated for each individual q and then aggregated, weighting
each individual’s estimated probability of choice (Louviere et al., 2000):
Q
X
E XP̄ijkq =
P
P̂iq E Xiqjkq
q=1
Q
X
(5)
P̂iq
q=1
For computing aggregate level elasticity effects of ordinal and dummy variables the
procedures outlined by Bhat and Pulugurta (1998) and Potoglou and Kanaroglou (2008)
are applied.
To compute the elasticity of an ordinal variable (e.g. number of members in household)
the value of the ordinal variable is increased by one unit for each household and the
relative change of expected aggregate shares is computed as follows:
(i)
E XMS
=
k
MS S C (i) − MS (i)
MS (i)
(6)
where MS (i) and MS S C (i) are the shares of alternative i before and after the ordinal
variable are increased by one unit.
To compute the elasticity of a dummy variable (e.g. bus stop within 500 meters) the
value of the variable is changed to ne for the sub-sample of observations for which the
variable takes a value of zero and to zero for the sub-sample of observations for which
the variables takes a value of one. The shifts in expected aggregate shares in the two
sub-samples are summed after reversing the sign in the second sub-sample. Subsequently,
the effective proportional change in expected aggregate shares in the entire sample is
computed due to the change in the dummy variable from 0 to 1.
The aforementioned measures make it possible to compare not only models on estimated on the same data source but also model estimate from different studies and data
sources.
4 Data sources and variable specification
4.1 Data sources
Several data sources are available for this study for comparing models with and without
spatial variables and the availability of parking space. Vehicle ownership and household
7
information are given by the Household Interview Travel Survey (HITS) 2008. For
this survey 1% of the population is questioned on their travel behavior on a single
workday in person. The survey contains 36,978 individuals in 10,641 households. The
survey is conducted once every four years and is commissioned by the Singaporean
Land Transport Authority (LTA). HITS contains data on three levels of aggregation. The
highest level of aggregation contains household characteristics such as dwelling type,
postal code and quantity and type of vehicle available. Second, person characteristics
are available such as age, income, profession and employment type. On the lowest level
of aggregation information on trips is available such as mode, purpose, cost and time. In
1,533 of the households one or more members refused to disclose their monthly income.
As initial model estimations pointed to a high significance of this variable, income has
been imputed with a MNL model based on dwelling type, household size, profession and
age. For households employing a domestic worker, the salary of the domestic worker
has been substracted of the household income as this amounts to an household expense.
Spatial variables are collected from several sources. Geo-referenced train and bus
stops were obtained from LTA. Electronic Road Pricing (ERP) gantries, located along
major expressways, were collected online and geo-referenced. Approximately 80% of
building stock in Singapore consists of Housing Development Board (HDB) flats. Of the
remaining 20%, more than half consists of private condominiums.
HDB flats are mostly aggregated in so-called ”new towns” and are located on a ring
around the central water catchment area. Whereas landed property and condominiums
commonly have parking space available, this is not the case for HDB flats. Parking spaces
are available in car parks in addition to limited street-side parking. Information on multistorey car parks (MSCP) is collected online from Streetdirectory (2011). Unfortunately,
no capacity or average occupancy rate is available. In addition, information from one
private car park provider is collected with capacity figures. Visual inspection shows that
the latter car parks are mostly located in business areas and not so much in residential
areas. Malls and supermarkets are collected from several online sources and georeferenced. Distances between the households listed in HITS and the nearest point of
interest are calculated using PostGIS. In addition, the number of bus stops within 500
meters is calculated. Figure 2 shows the locations of the households and spatial variables
collected (excluding bus and train stops).
4.2 Variable specification
Values for the dependent variable in coded 0 and 1 for zero cars within a household and
1 for households with 1 car. In the survey there are no households that own more than
one car. Only households possessing 1 car have been considered in this study. Company
cars (0.8% of sample) and cars with an off-peak entitlement (1.1% of sample) have been
excluded. Other vehicles, such as light-good and passenger vehicles as well as heavy
good vehicles and motorcycles have been excluded. In total 8,846 cases remain for
estimation after controlling for availability of a driving license of which 5,436 households
have a driving license available.
8
Figure 1: Location of households and points of interest collected
To allow for a non-linear relationship between income and vehicle ownership and explore
interaction variables within income classes, four categories for monthly household
income are defined: below S$ 3,000 (38%), S$ 3,000 - S$ 6,000 (31%), S$ 6,000 S$ 10,000 (21%) and above S$ 10,000 (10%). The first category serves as a reference
variable; the three latter categories are entered into the utility function.
The number of driver licenses has been entered into the utility function as a dummy
variable indicating if the household has more than one driving license to indicate the
higher utility of a vehicle owning a single vehicle. Bhat and Pulugurta (1998) argue that
the number of licenses should be considered an exogenous variable and therefore should
not be include. Chu (2002) and Potoglou and Kanaroglou (2008) however both include
this variable as an indicator for the competition among households members between
the household members for a single vehicle and therefore increasing utility of multiple
vehicles. In our case, we expect that a family has a higher utility of a single vehicle if
more members of the household can use the vehicle.
To capture the life cycle of a household several age groups have been defined: households
with children until 10 years old (small children), households with children between 10
and 19 years old (children) and households with members above 50 years old The first
two categories are expected to increase the utility of vehicle ownership, whereas the
latter category is expected to have a lower utility of a vehicle.
Dwelling type of a household provides an indication of the urban structure surrounding
a household and the availability of amenities. Households residing in a HDB town are
9
Figure 2: Alternative, income and members under 19 (top) and alternative, household
size and driving licenses (bottom)
expected to have more amenities than households either residing in a private apartment
or landed property. In addition, it is expected that within HDB towns a local social
networks exist, decreasing the need for extensive traveling by car. In addition to this
proxy variable for neighbourhood diversity two more indexes have been calculated as
indicators for the number of activity opportunities within a 500 meter walking distance
of the household.
The first indicator computed is an entropy index (EI500 ) as proposed by Cervero and
Kockelman (1997) and Chu (2002) has been calculated:
EI500 = −
k
X
pi ln(pi )
ln(k)
i=1
(7)
where pi is the proportion of developed land-use category in category k. In total 5 landuse types have been considered: business, community area and education, residential,
commercial and parks. Values of EI500 vary between 0 and 1, with one indicating even
10
distribution among all land-use categories and zero implying a single type of land-use
within the radius of 500 meters. Values of the entropy index close to one imply ease
of access to activities and therefore the parameter of EI500 is expected to be negative
(Potoglou and Kanaroglou, 2008). This has been confirmed in studies by Chu (2002)
and Potoglou and Kanaroglou (2008).
The second indicator computed contains the number of points of interest within 500
meters of the household. These points of interest were collected online in 2011; the
following categories were considered: supermarket, foodcentre, place of worship, fitness,
park, sports and mall.
Figure 3 shows a scatter plot of EI500 vs the number of opportunities within 500 meters
per dwelling type. First it can be seen that the entropy index has a value larger than
one whereas the number of opportunities is zero. On one hand, this is because certain
categories are excluded from the opportunities index, such as the broad category ’commercial’ as well as ’residential’. On other hand, this can indicate the points of interest
created online are still not complete. Second, it can be seen households residing in HDB
both have a higher number of opportunities within as a higher entropy index, which can
clearly been seen with an entropy index higher than 0.5.
Euclidean distances to points of interest have been computed. Instead of entering the
Figure 3: Entropy index 500 meters (EI500 ) vs opportunities within 500 meters, circles
indicating unique households
distances directly in the utility function and assuming a linear relationship between
distance and utility, dummy variables have been created. The following dummy variables
have been created: MRT station within 500 meters of dwelling, MRT station between
500 and 1000 meters of the dwelling, Multi-Storey Car Park within 500 meters of
dwelling and number of bus stops within 500 meter. A bus stop within 300 meters has
not been considered as each household residing in a HDB estate has a bus stop within
300 meters. Besides these transport related variables dummy variables indicating the
11
proximity of amenities as well: availability of a supermarket within 500 meters, the
number of primary schools within 500 meters and the number of secondary schools
within 500 meters. In addition the dwelling related points of interest, similar indicators
have been computed for work locations. 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 is chosen. Downtown areas such as the Central Business District, Orchard Road
and Outram all have a MRT stop within 500 meters and therefore correlates with the
distance to work-end MRT stop. Travel distances and times for both private transport
and public transport are calculated on a transport network of Singapore with congestion
to obtain realistic travel times for all commuting trips within a household. (Erath et al.,
2012) with a fixed work location. Pinjari et al. (2011) choose to sum the commute
time by auto and cost for a household. We specified two travel time bands by transit:
under 30 minutes and between 30 and 60 minutes and summed the number of household
members working within these travel time bands. Further considered specifications but
not developed worthwhile mentioning are the following: distance to nearest bus stop, as
all HDB dwellings have a bus stop within 300 meters. Also,
5 Model results
5.1 Parameter estimates
Table 1 shows the parameter estimates of MNL vehicle ownership models. Three models
are presented: a model containing only household socio-demographics, a model containing opportunites within 500 meters, distances to point of interest and travel times
and a model including the entropy index and travel times. All parameter estimates are
relative to the zero alternative of not owning a car. Model estimations have been carried
out with Biogeme (Bierlaire, 2003). The models performed well judging by the relative
high value of the adjusted rho-square. In addition to the MNL models, Ordered Logit
models have been estimated to reflect the propensity of owning a vehicle. However, the
parameter reflecting this propensity was insignificant. All individual coefficients are
significant at the 95% confidence level. Prior to model estimation correlations between
all variables have been computed. To avoid multicollinearity only variables where the
correlation are less than 0.35 were considered. Log-likelihood tests for all models were
performed to test that the null hypothesis that all parameters except for the constant are
zero.
Household socio-demographics
An increase of income increase the utility of owning a car and is line with expectations
and other studies regarding vehicle ownership. Similar preferences for the middle two
12
Table 1: Model estimation results for vehicle ownership (all estimates for alternative
single vehicle)
No
spatial
variables
Opportunities
Entropy index
Constant for alternative car
0.44 (4.33)
1.08 (8.16)
1.45 (7.68)
More than 1 driving license in household
1.07 (14.54)
1.13 (14.95)
1.12 (14.85)
Household consisting of 2 members or less with member(s) above 50
-0.25 (-2.32)
-0.34 (-3.09)
-0.33 (-2.97)
Monthly household income between S$3,000 and S$
6,000
0.41 (4.2)
0.59 (5.79)
0.58 (5.71)
Number of members under age of 10 for households with
income between S$3,000 and S$6,000
0.84 (2.49)
0.57 (3.15)
0.58 (3.04)
Number of members between age 10 and 19 for households with income between S$3,000 and S$6,000
0.63 (2.44)
0.39 (2.63)
0.4 (2.51)
Monthly household income between S$6,000 and
S$10,000
0.38 (6.75)
0.52 (8.65)
0.52 (8.59)
Number of members under age of 19 for households with
income between S$6,000 and S$10,00
1.38 (4.01)
0.91 (4.22)
0.92 (4.21)
Monthly household income above S$10,000
1.95 (12.37)
2.33 (13.51)
2.33 (13.55)
Household residing in HDB estate
-1.19 (-13.08)
-1.02 (-10.47)
-0.98 (-9.98)
Entropy index 500m
-0.9 (-2.88)
Number of opportunities facilities within 500m
-0.01 (-1)
Number of primary schools within 1000m of residence
-0.06 (-2.41)
-0.07 (-2.91)
MRT station within 500m of residence
-0.41 (-4.7)
-0.42 (-5.01)
MRT station between 500m and 1000m of residence
-0.17 (-2.03)
-0.18 (-2.09)
MRT station within 500m of work location (highest value
within household)
-0.26 (-3.49)
-0.26 (-3.51)
Number of members working within 30 minutes by transit
-0.55 (-7.9)
-0.54 (-7.82)
Number of members working between 30 and 60 minutes
by transit
-0.41 (-8.11)
-0.41 (-8.08)
Number of observations
8846
Number of observations with both alternatives available
0
Initial log-likelihood
-3767.95
Constant only model
-3425.13
Final log-likelihood
-2723.032
-2651.347
-2647.879
Rho-squared
0.277
0.296
0.297
Adjusted rho-squared
0.274
0.292
0.293
13
income groups can be observed. However it is chosen to keep these groups separated as
different within group preferences can be observed.
Households with a monthly income between S$3,000 and S$6,000 have a higher utility
of owning a car when the children are below 10 than with children between 10 and 19.
A vehicle increases the possibilities of performing multiple activities with children on
a single day and offers the possibility of dropping and picking up children at school.
Households with an income between S$6,000 and S$10,000 have a higher utility of
owning vehicle when having children in both age groups. Parameter estimates for both
age groups for this income category are in the same range.
Households containing two or less members, where all members are above 50, show
a negative utility of owning a vehicle. More than one driving license in the household
increases the utility of owning a vehicle as expected.
Further model specifications included model specifications with the number of adult
members, total household size and one and two person households. These however led
to correlation with other estimates and were therefore not developed further. Also, the
number of full-time and part-time workers correlated strongly with income.
Dwelling and mixed use development
Households residing in HDB estates have a lower utility of owning a vehicle as compared
to households residing in condominiums or landed property. The entropy index has a
negative parameter as expected. This indicates that a higher heterogeneity within a 500
meter radius decreases the utility of owning a vehicle. The parameter estimate for the
number of opportunities however is insignificant in combination with the variable for
HDB estate.
Distances and travel times
The number of members household working within 30 minutes by transit decreases the
utility of owning a vehicle. To a lesser extent this is also the case for the number of
household members working between 30 and 60 minutes by transit.
Points of interest
The number of primary schools within 1000 meters decreases the utility of owning a
vehicle. A MRT station within 500m of residence decreases the probability of owning a
car and to a lesser extent a MRT station between 500m and 1000m of residence. These
distances were chosen based on previous studies. The number of bus stops within 500
meters did not yield a significant parameter estimate as was expected as the number and
reliability of transport options would increase. Also, distance to the nearest Light Rapid
Transit (LRT) stop did not yield a significant parameter estimate.
Several model specifications have been experimented with regard to multi-storey car
parks and car parks. All specifications did not yield significant parameter estimates,
including those where distance was only considered for HDB households. It can be
that parking space is abundantly available and thus not an issue. Also, it might be that
parking costs play a role which are not considered. The same holds for supermarkets
and malls; no significant parameter estimates could be obtained.
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Distance to nearest ERP gantry did not yield a significant parameter result. It is thought
that ERP is perceived as a variable cost and not a fixed cost when considering purchase of
vehicle. Furthermore, ERP rates change per quarter and time of day, making it hard for a
decision-maker to calculate ERP costs in advance. Initial models contained variables
indicating the postal sector of residence of the household. These variables lost their
significance when including distance to MRT stop and density indicators.
5.2 Market shares & elasticities
In Table 2 the market shares of both the sample and the estimated models can be seen.
Demand to own a car is overestimated by 3%. Several model specifications have been
investigated to better capture market share, but all resulting market shares remained
in a similar range. Spatial variables and adding distances improved the market shares.
Another possible explanation is that household budgets and expenses are not covered
by this survey. The costs of owning a car would be too high for a household, but is
not reflected in the model. Predicted results have been plotted on a map to visually
validate if market shares were underestimated or overestimated on both residence-end
and work-end.
Table 2: Market shares for estimated models
Simulated choice
Sample
No spatial variables
Opportunities
Entropy index
No vehicle
58.48%
53.23%
55.17%
55.15%
One vehicle
41.52%
46.77%
44.83%
44.85%
In Table 3 elasticities are presented for selected variables. More than 1 one driving
license within a household has the largest influence on aggregate market shares, followed
by households with children. Phang (1992) and Li et al. (2011) respectively report
an elasticity of 0.496 respectively 0.594 for income. Further investigation is required
to calculate income elasticities with the given set of dummy variables representing
income.
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Table 3: Selected elasticities for model with entropy index
Variable
Type
Number of vehicles
0
1
More than 1 driving license in household
Dummy
0.687
0.844
Number of members under age of 10 for households with income between S$3,000 and S$6,000
Ordinal
-0.580
0.714
Number of members between age 10 and 19 for
households with income between S$3,000 and
S$6,000
Ordinal
-0.559
0.688
Number of members under age of 19 for households with income between S$6,000 and S$10,00
Ordinal
-0.531
0.653
Household residing in HDB estate
Dummy
0.514
0.632
Entropy index 500m
Continious
0.254
-0.130
MRT station within 500m of residence
Dummy
0.257
0.316
MRT station between 500m and 1000m of residence
Dummy
0.041
0.050
MRT station within 500m of work location (highest
value within household)
Dummy
0.155
0.191
Number of members working within 30 minutes
by transit
Ordinal
-0.269
0.331
Number of members working between 30 and 60
minutes by transit
Ordinal
-0.335
0.412
6 Discussion and outlook
6.1 Discussion
A recurring question for policy-makers, transport planners and urban designers is which
factors influence vehicle ownership. Vehicle ownership is a key determinant of household
travel behavior and leads to negative externalities, such as air pollution and congestion.
In this paper we present a MNL for vehicle ownership for the case of Singapore. 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. Higher income groups
prefer a car when having children under 19. As models were estimated on a crosssectional data set these attributes cannot be interpreted as key turning points in life to
own a vehicle. Beige and Axhausen (2012) find that an increase of number of members
of a household is such a turning point based on mobility biographies. An implication for
policy-makers is that child-friendly transit (access to bus) and amenities within walking
distance, such as child-care can help curb vehicle ownership. The number of driving
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licenses increases the utility of owning a vehicle. The high impact and significance of
this variable leads to the question if the choice for driving license and car ownership
should be modeled simultaneously.
Neighbourhood heterogeneity has been accounted for by using an entropy index. A
higher heterogeneity decreases the utility of a car. A similar effect could not be observed
for the number of opportunities within walking distance. This can be because both
variables have measured slightly different. The first is calculated based on general
categories in the Singapore Master Plan, the latter on listings obtained online; here a
selection needs to be made which categories to include. This will be further investigated.
Also, these indexes have been calculated based on a simple Euclidean distance measure.
Guo and Bhat (2007) consider different spatial boundaries for residential location choice
and finds that these increase model performance. A similar approach is envisaged for
vehicle ownership. Car parks and Electronic Road Pricing gantries prove not to be
significant; these represent variable costs of owning a car. Also, the availability of a
parking space at work can influence vehicle ownership and is not accounterd for. A
MRT stop within 500 meters decreases the utility of owning a vehicle on both residence
and work side of the trip. Access to public transport is therefore an important factor to
consider for urban planners.
The significance of travel time traveled as well as the significance of locational variables
indicates that the generation of the synthetic population needs to be reviewed; distance
traveled is an outcome of a MATSim simulation of Singapore and not a input.
6.2 Outlook
In the near future, we intend to better capture the urban structure of Singapore by including high resolution accessibility indicators (Nicolai and Nagel, 2011). An interesting
question that arises is how car ownership influence mode choice and car usage. Mode
choice is possible to model with the current data set; however alternative would need to
be generated as the data is revealed preference. For car usage a separate survey would
need to be conducted.
One of the reasons for high purchase costs of vehicle is the Certificate of Entitlement
(COE). The price for a COE depands on vehicle type, demand and supply (based on
vehicle scrapping and allowed growth rate). In order to model vehicle population and
not only demand, not only insight in vehicle demand is necessary but also the decision
to sell or scrap.
Acknowledgements
The research conducted at the Future Cities Laboratory is funded by the Singaporean
National Research Fund. We wish to express our gratitude to the Land Transport
17
Authority for providing us invaluable data sets on transport in Singapore. Also we
are very thankful to the Urban Redevelopment Authority for providing us with the
Masterplan 2008.
References
Balmer, M. (2007) Travel demand modeling for multi-agent traffic simulations: Algorithms and systems, Ph.D. Thesis, ETH Zurich, Zurich, May 2007.
Beige, S. and K. W. Axhausen (2012) Interdependencies between turning points in life
and long-term mobility decisions, Transportation, 39 (4) 857–872.
Ben-Akiva, M. E. and S. R. Lerman (1985) Discrete Choice Analysis: Theory and
Application to Travel Demand, MIT Press, Cambridge.
Bhat, C. R. and V. Pulugurta (1998) A comparison of two alternative behavioral choice
mechanisms for household auto ownership decisions, Transportation Research Part B:
Methodological, 32 (1) 61–75.
Bierlaire, M. (2003) BIOGEME: A free package for the estimation of discrete choice
models, paper presented at the 3rd Swiss Transport Research Conference, Ascona,
March 2003.
Cervero, R. and K. Kockelman (1997) Travel demand and the 3Ds: Density, diversity, and
design, Transportation Research Part D: Transport and Environment, 2 (3) 199–219.
Choi, C. C. and R. Toh (2010) Household interview surveys from 1997 to 2008: A
decade of changing travel behaviours, Journeys, 5 (May 2010) 52–61.
Chu, Y.-L. (2002) Automobile ownership analysis using ordered probit models, Transportation Research Record, 1805, 60–67.
de Jong, G., J. Fox, A. J. Daly, M. Pieters and R. Smit (2004) Comparsion of car
ownership models, Transport Reviews, 24 (4) 379–508.
Erath, A., A. Chakirov, P. J. Fourie, S. A. Ordóñez Medina, M. Shah, M. A. B. van
Eggermond and K. W. Axhausen (2012) A large-scale agent-based transport travel
demand model for Singapore: The implementation of MATSim, Working Paper,
Future Cities Laboratory, Singapore-ETH Centre (SEC), Singapore.
Guo, J. Y. and C. R. Bhat (2007) Operationalizing the concept of neighborhood: Application to residential location choice analysis, Journal of Transport Geography, 15 (1)
31–45.
Lerman, S. R. and M. E. Ben-Akiva (1976) Disaggregate behavioural model of automobile ownership, Transportation Research Record, 569, 34–51.
18
Li, J., J. L. Walker, S. Srinivasan and W. P. Anderson (2010) Modeling private car ownership in China: Investigation of urban form impact across megacities, Transportation
Research Record, 2193, 76–84.
Li, M. Z. F., D. C. B. Lau and D. W. M. Seah (2011) Car ownership and urban transport
demand in Singapore, International Journal of Transport Economics, 38 (1) 47–70.
Louviere, J. J., D. A. Hensher and J. Swait (2000) Stated Choice Methods - Analysis and
Application, Cambridge University Press, Cambridge.
McFadden, D. (1974) Conditional logit analysis of qualitative choice-behaviour, in
P. Zarembka (ed.) Frontiers in Econometrics, 105–142, Academic Press, New York.
Nicolai, T. and K. Nagel (2011) Investigating accessibility indicators for feedback from
a travel to a land use model, paper presented at the 51st Congress of the European
Regional Science Association, Barcelona, September 2011.
Phang, S.-Y. (1992) Housing Markets and Urban Transportation: Economic Theory,
Econometrics and Policy Analysis for Singapore, McGraw-Hill, Singapore.
Pinjari, A. R., R. M. Pendyala, C. R. Bhat and P. A. Waddell (2011) Modeling the
choice continuum: an integrated model of residential location, auto ownership, bicycle
ownership, and commute tour mode choice decisions, Transportation, 38 (6) 933–958.
Potoglou, D. and P. S. Kanaroglou (2008) Modelling car ownership in urban areas: A
case study of Hamilton, Canada, Journal of Transport Geography, 16 (1) 42–54.
Potoglou, D. and Y. O. Susilo (2008) Comparison of vehicle-ownership models, Transportation Research Record, 2076, 97–105.
Santos, G., W. W. Li and W. T. Koh (2004) 9. Transport Policies in Singapore, Research
in Transport Economics, 9 (1) 209–235.
Streetdirectory (2011) Streetdirectory Singapore,
streetdirectory.com. Accessed on 10/04/2011.
webpage,
http://www.
Train, K. E. (2003) Discrete Choice Methods with Simulation, Cambridge University
Press, New York.
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