INTRODUCTION
In view of the increased number of registered private cars and injuries
and deaths of its users in Malaysia, a shift away from car using towards
other safer modes is essential to increase road safety. The increasing
number of car users involved in crashes and the associated injury has
prompted the government of Malaysia to undertake various studies to address
the problem. One of these studies was the shift of transportation mode
from private car to public transportation (Bus and Train) in Malaysia
(Riza, 2004). The study targeted to evaluate policies and strategies than
can help to formulate, model shift of transportation mode from private
car to public transportation in Malaysia, to formulate the modeling of
possible model shift from private car to public transportation and to
predict the future model shift. The current study is a part of the research
that has focused on model shift initiatives. To date, research efforts
have focused primarily on modeling modal shift from private car to public
transport. Many cities have attempted to restrict the use of private cars
in favour of public transport Steg (2003). Such policies exist in France
(Harrison et al., 1998), Germany (FitzRoy and Smith, 1998), Britain
(Harrison et al., 1998), (Sayed Sharafuddin and Ata Khan, 2000),
Netherlands (Cheung and Hoen, 1996), Romania (Marshall and McLellan, 1998),
Australia (Black, 1996), Asian countries (Shimazaki et al., 1994;
Land Transport Authority, 1996) and Canada (Schimek, 1996).
Mode choice models have been widely used to predict mode choice for work
trips and other types of trips in the development of regional travel models
(Moshe and Lerman, 1985). This study describes modelling of transportation
policies to formulate, model shift from private car to public transport.
The purpose of this study is to consider car users’ receptiveness
to various policy changes, namely providing park and ride facilities,
raising the minimum driving age from 18 to 23 years and improvements in
public transport frequency and services. The explanatory variables included
in the models were demographic, socio-economic characteristics of individuals,
trip characteristics and mode attributes. A binary logit model was used
to identify factors that are significant in determining the choice of
transport and to predict the probability of a change in bus and train
ridership with respect to various travel times and cost.
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| Fig. 1: |
Effect of public transport travel time reduction on
car users mode choice probability |
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| Fig. 2: |
Effect of distance from home to public transport increases
on car users mode choice probability |
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| Fig. 3: |
Effect of distance from home to work increases on car
users mode choice probability |
The study examined
the probability of car users shifting to public transport based on a scenario
of a reduction in bus travel time and travel cost. This was done by solving the binary logit equation for probability using
the range of various travel time scenarios. The effects of other variables
were controlled by keeping them constant (giving them the values mainly
based on means). The mode share probabilities categorized by various levels
of travel time and distance from home and work to public transport are
shown in Fig. 1-3.
MATERIALS AND METHODS
To achieve the objectives of this study, survey was carried out in the
state of Kuala Lumpur city center over four months from (1 September to
1 February 2005), for users of three modes of transport: private car,
bus and urban train (n = 1200). Kuala Lumpur was chosen as the study locality
because it has high car ownership and use and public transport (bus and
rail) available. The SP and RP methods were adopted because of their successful
previous use (Kores and Sheldon, 1988).
The SP survey was designed to gather information on the choice of commuting
by private and public transport (bus and train) using a series of hypothetical
route choice questions. The questionnaire was in three parts. The first
contained nine questions on general information (personal characteristics
and socio-demographic influence): age, income, convenience, trip perception
and purpose, education, household size, car ownership and occupation.
The second part (12 questions) was on the trip characteristics and preference
for driving versus public transport, weather, comfort, satisfaction, flexibility
and prestige. The last part (11 questions) asked the respondents to consider
three policy tools in choosing his travel mode and to choose the factors
most likely to persuade him to use public transport:
| Policy 1: |
Providing park and ride facilities |
| Policy 2: |
Raise the legal age for driving from the current 18 to 23 years. |
| Policy 3: |
Transit improvement (frequency and services) |
In the survey, the respondents were asked to reflect on their last trip.
They were asked their destination, how they traveled and how much it cost.
Then they were asked for another way by which they could have traveled
instead, had their mode of travel not been Available. The answers provided
the RP data. The survey also hypothetically varied the Public transport
fares for the respondents’ current and alternative modes of travel
under a Series of pricing scenarios and asked what they would have done
in each situation. The responses were recorded as the mode of transport
they would have used and the fare.
They would have liked to pay for the SP data. A binary logit model was
developed for three alternatives namely, bus, train and car, with the
aim of comparing the utility of these travel modes and to identify the
factors that would influence car users to move from traveling by car to
choosing the public transport alternative. In these models, model car
and public transport, the dependent variable was 1 if the commuters traveled
by public transport and 0 for car use and other three models the dependent
variable was 1 switch to public transport and 0 if the commuters were
not agree to switch. The explanatory variables were: age, gender, income,
travel time, travel cost, household size, distance from home to public
transport, distance from home to work and car ownership. Some of the explanatory
variables such as age, income per month household size and gender were
categorized. For instance, the income was categorized as; <RM 1000,
RM 1001-2000, RM2001-3000, RM 3001-4000, >4001 (1USD = RM3.65) while
gender was categorized as 0 for male and 1 for female. Age was also categorized
as; 16-20, 21-25, 26-30, 31-35, 36-40, 41-45, 46-50, 51-55 and >56.
Household size was categorized as; 2 person 3-5 person and >6 persons.
RESULTS
Modal choice models
Modal 1. (Car and public transport): The estimated coefficients
for gender for the public transport mode (Table 1) came
out positive, implying that if the gender was female, the preference would
be for commuting by public transport instead of driving the car. The odds
ratio increased by approximately 2.622 times for females compared to males.
The estimated coefficients for travel time and travel cost for the public
transport mode (Table 1) were negative, implying that
an increase in travel time and travel cost for the public transport mode
was likely to increase the probability of car users to continue choosing
the car as the preferred transport mode. The likelihood of shifting car
users to public transport was likely if reductions in travel time and
cost could be achieved. In the model, demographic variables such as age
and income were found to significantly explain mode choice behavior. In
terms of age, older people were more likely to use the public transport
as opposed to driving. The odds ratio increased about 93% for older people
compared to younger commuters.
| Table 1: |
Estimation results for binary mode
choice model (n = 1200) |
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| Table 2: |
Estimation results for binary ligit model (n = 1200) |
|
The models explanatory power, the two R2 values indicate the
model’s strong explanatory power. The factors included in the model
account for 90.4% of the variation for the Negelkerke, while Cox and Snellt
explained 63.9%. Classification matrices were calculated to assess if
the model fits the data and it was found that the model correctly classified
about 97.7% of carcases and about 93.4% of bus mode cases. The overall
accuracy of the prediction model was 96.4%.
The mode choice probabilities were categorized by various levels of travel
time (Fig. 1). Mode choice probabilities ranged from
60% likelihood of car use with current public transport total travel time
per trip (60 min) to 20% likelihood of car use with a reduction in public
transport total travel time per trip (10 min). At the same time, the probability
of public transport ridership increased from 10% with current public transport
total travel time per trip (60 min) to 80% of likelihood with a 10 min
reduction in public transport total travel time per trip. A 50: 50 split
may be achieved when the travel time are set at 30 min per trip for public
transport travel.
Modeling of proposed policies
Model 2. (Policy 1: Providing park and ride facilities): The estimated
coefficients for gender for the public transport mode (Table
2) came out positive, implying that if the gender was female, the
preference would be for commuting by public transport instead of driving
the car. The odds ratio increased by approximately 1.721 times for females
compared to males. The estimated coefficients for Distance from home to
public transport and Distance from home to work for the public transport
mode (Table 2) were negative, implying that an increase
in Distance from home to public transport and Distance from home to work
for the public transport mode was likely to increase the probability of
car users to continue choosing the car as the preferred transport mode.
The likelihood of shifting car users to public transport was likely if
reductions in Distance from home to public transport and Distance from
home to work could be achieved.
In the model, age was found to significantly explain mode choice behavior.
Older people were more likely to use the public transport as opposed to
driving. The odds ratio increased about 96.7% for older people compared
to younger commuters.
The models explanatory power, the two R2 values indicate the
model’s strong explanatory power. The factors included in the model
account for 94% of the variation for the Negelkerke, while Cox and Snellt
explained 72.4%.
The mode choice probabilities were categorized by various levels of Distance
from home to public transport (Fig. 2) and Distance
from home to work. Mode choice probabilities ranged from 34% likelihood
of car use with current Distance from home to public transport (100 m)
to 68% likelihood of car use with a increases in Distance from home to
public transport (700 m) At the same time, the probability of public transport
ridership decrease from 66% with current Distance from home to public
transport (100 m) to 32% of likelihood with (700 m). A 50:50 split may
be achieved when the Distance from home to public transport are set at
350 m.
Also mode choice probabilities were categorized by various levels of
Distance from home to work (Fig. 3). Mode choice probabilities
ranged from 25% likelihood of car use with current Distance from home
to public transport (1 km) to 47% likelihood of car use with a increases
in Distance from home to work (30 km) At the same time, the probability
of public transport ridership decreased from 75% with current Distance
from home to work (1 km) to 53% of likelihood with (30 km).
Model 3. (Policy 2: Raising the minimum driving age from 18 to 23
years): Table 3 showed the effect of license age
increase on car users switching behavior against age and Gender. The results
showed that age is significant in explaining mode switching behavior.
The odds ratio increased by approximately 0.360% for each two years (1
unit). In other words, the older riders are more likely to switch to public
transport compared to the younger riders. The estimated coefficients for
gender, Table 3 showed the effect of license age increase
on car users switching behavior against gender, the results indicated
that resistance to switching is higher among female car users compared
to females car users. Cross tabulation results are also in agreement with
the model results.
| Table 3: |
License age increase switching behavior in relation
to age and gender (cross tabulation and estimated parameter) |
|
| Table 4: |
Estimation results for binary ligit model (n = 1200) |
|
Classification matrices were also calculated to assess the fit of the
model to the data. The model was found to correctly classify about 92.6%
of switching cases and about 89.5% of no switching cases. The prediction
model was 92% accurate. The factors included in the model account for
87% of the variation for the Negelkerke, while Cox and Snellt explained
77.16%.
Model 4. (Policy 3: Transit improvement (Frequency and services)):
Three factors were found to significantly explain mode choice behavior.
Age, gender and household size. In terms of age, older people were more
likely to use the public transport opposed to driving. The odds ratio
increased about 98% for older people compared to younger commuters. The
estimated coefficients for gender for the public transport mode (Table
4) came out positive, implying that if the gender was female, the
preference would be for commuting by bus instead of driving the car. The
odds ratio increased by approximately 5% for females compared to males.
Table 4 showed the effect of public transport improvement
on car users switching behavior against Household size. The results showed
that Household size is significant in explaining mode switching behavior.
The odds ratio increased by approximately 77% for each one person in the
household.
The model for Transit improvement was found to correctly classify about
96% of switching cases and about 88.0% of the not switching cases. The
prediction model was 89.7% accurate. In this model explanatory power,
the two R2 values indicate the model’s strong explanatory
power. The factors included in the model account for 87.6% of the variation
for the Negelkerke, while Cox and Snellt explained 90.55%. Thus, by promoting
the appropriate policy, in relation to providing Park and Ride Raise the
legal age for driving from the current 18 to 23 years and Transit improvement,
one could provide opportunities for mode shifts among car users, which
in return, will reduce their exposures and therefore, the risk of injury.
CONCLUSION
The study attempted to conduct mode choice behavior of travelers of tow
modes of transport namely car, public transport and determined the trade-offs
travelers make when considering choice of their mode of transport. Utility
of the two modes were compared to determine the important reasons behind
the choice of a particular mode and the circumstances, which might cause
travelers to change their choice for the car. In order to promote greater
use of public transport, this study examined the effect on car use if
total bus and train travel time, travel costs Distance from home to public
transport and Distance from home to work for the public were reduced and
the results suggest that travel time and travel cost are characteristics
that determine why car use is a favored modal choice.
This was understood by solving the binomial logit equation for probability
using several options of travel time and distances scenarios. In order
to promote greater use of public transport and less dependence on car,
an efficient public transport system is clearly needed. Higher capacity
transit systems, use of bus lanes, bus gates and ITS systems are among
initiatives that could be implemented to improve the public transport
system. The use of traffic restraint policies such as in France (Harrison
et al., 1998), Australia (Black, 1996), Area Licensing in Singapore
(Geok, 1981) or London Road Pricing (Litman, 2005) could further enhance
a policy that promotes public transport; a policy that is moving towards
a more sustainable transport system compared with total dependence on
private vehicles. The findings of this research can be concluded that
the travel time, travel cost, Distance from home to public transport and
Distance from home to work are the contributing factors that influence
the model shift from car to public transport in Malaysia.
ACKNOWLEDGMENTS
Support of this research was provided by grants from faculty of engineering,
university Kebangsaan Malaysia. Mr. Kamba would like to thank professor
Riza Atiq O.K. Rahmat and associate professor Amiruddin Ismail for
their guidance.
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