Policy Simulation for New BRT and Area Pricing Alternatives Using an Opinion Survey in Jakarta

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1 Transportation Planning and Technology ISSN: (Print) (Online) Journal homepage: Policy Simulation for New BRT and Area Pricing Alternatives Using an Opinion Survey in Jakarta Sadayuki Yagi & Abolfazl Mohammadian To cite this article: Sadayuki Yagi & Abolfazl Mohammadian (2008) Policy Simulation for New BRT and Area Pricing Alternatives Using an Opinion Survey in Jakarta, Transportation Planning and Technology, 31:5, , DOI: / To link to this article: Published online: 16 Sep Submit your article to this journal Article views: 508 View related articles Citing articles: 8 View citing articles Full Terms & Conditions of access and use can be found at

2 Transportation Planning and Technology, October 2008 Vol. 31, No. 5, pp ARTICLE Policy Simulation for New BRT and Area Pricing Alternatives Using an Opinion Survey in Jakarta SADAYUKI YAGI & ABOLFAZL MOHAMMADIAN Department of Civil and Materials Engineering, University of Illinois at Chicago, Chicago, IL, USA (Received 28 February 2007; Revised 5 September 2007; In final form 28 March 2008) ABSTRACT An area pricing scheme for Jakarta, Indonesia, is currently under review as a transportation control measure along with the operation of new bus rapid transit (BRT) system. While this scheme may be effective for congestion reduction in the central business district (CBD), provision of alternative means of transportation for auto users that are pushed-out is of great importance to obtain public acceptance. Hence, it is necessary to simulate simultaneously the area pricing scheme and the BRT development which may serve as an alternative for assumed pushed-out auto users. Utilizing data from an opinion survey, this paper studies how BRT and auto ridership are likely to vary as a function of traveler and system attributes. Additionally, the study attempts to evaluate the way this new travel mode is distinguished from other existing conventional transportation alternatives in Jakarta. The survey data contains socioeconomic information of over 1000 respondents as well as details of to-work/school trips to the CBD including mode, travel cost, time, etc. Respondents were asked about their willingness to shift from their current mode to BRT to make the same travel for different BRT fare levels. Modeling efforts suggest that a mixed logit model performs better in explaining choice behavior. Therefore, this model was used for policy simulation. The simulation results brought about many implications as to the tested policies. While the developed models may be applied only to future BRT corridors in which the survey was conducted, they capture the key variables Correspondence Address: Abolfazl Mohammadian, Department of Civil and Materials Engineering, University of Illinois at Chicago, 842 W. Taylor St, Chicago, IL 60607, USA. kouros@uic.edu ISSN print: ISSN online # 2008 Taylor & Francis DOI: /

3 590 Sadayuki Yagi & Abolfazl Mohammadian that are significant in explaining mode choice behavior and present great potential for practical use in policy simulation and analysis in a large metropolitan area of the developing world. KEY WORDS: Area pricing; bus rapid transit; stated preference; policy simulation; Jakarta Introduction A study entitled The Study on Integrated Transportation Master Plan (BAPPENAS & JICA, 2004) was conducted in the Jakarta Metropolitan Area from November 2001 to March The overall objective was to identify possible policy measures and solutions to develop a sustainable transportation system in the Jakarta Metropolitan Area with a focus on encouraging public transport usage and improving mobility of people. As such, detailed transportation surveys and analyses were undertaken to prepare a comprehensive long-term transportation plan with the objective to develop and calibrate disaggregate travel demand models to simulate present and future interactions between land use and transportation in the region. The Household Travel Survey (HTS), among a variety of the surveys conducted, provides the largest and most comprehensive travel data in the region. The dataset covers as many as 166,000 households which correspond to 3% of the entire population, and provides daily travel patterns and detailed information on household sociodemographic characteristics. The ultimate goal of the authors research on Jakarta data was to develop a novel activity-based micro-simulation modeling system to test different transportation policy scenarios for the Jakarta Metropolitan Area. The study simulates the way individuals schedule their daily activities and travel in an urban region of the developing world. It is hoped that the proposed new models contributes to the improvement of the emerging travel demand forecasting techniques and provide an opportunity to evaluate urban transportation policy scenarios. A Stated Preference (SP) opinion survey on transport system (SP survey) was also conducted in Jakarta to obtain the information regarding people s preference among the existing and future transport modes under different conditions and policies such as costs and service levels. Utilizing the SP data, the main purpose of this study was to determine how demand for a new Bus Rapid Transit (BRT) is likely to vary as a function of attributes that distinguish this new travel mode from other existing conventional alternatives in the region. In particular, the study explores several methods of joint modeling such as multinomial, nested, and mixed logit (ML) discrete choice models to

4 New BRT and Area Pricing Alternatives 591 predict the mode choice, using both Revealed Preference (RP) and SP data and takes a further step into simulation analysis of some policies using the most appropriate mode choice model. Stated Preference (SP) Data SP surveys refer to a wide array of possible ways of asking consumers about preferences, choices, ways of using options, frequencies of use, and so forth, while revealed RPs are associated only with actual choices (Louviere & Street, 2000). While the HTS has abundant RP data, the results of the SP survey contain both RP and SP data that can be used to establish discrete mode choice models for future forecasting. An SP survey was conducted in the central business district (CBD), targeting at the residents who live along the planned BRT network corridors and commute to the CBD of Jakarta, and interviewed transit (TR) users and car/motorcycle (MC) users with regard to their preference of BRT to the existing mode under different conditions. The area covered by the SP survey in CBD is shown in Figure 1. The survey targeted at the to-work, to-school, and shopping trips with Figure 1. Proposed BRT system. Black lines, planned BRT; gray lines, existing railways; shaded zones, target area for the SP survey in CBD. Source: BAPPENAS & JICA (2004)

5 592 Sadayuki Yagi & Abolfazl Mohammadian zones along the planned BRT corridors (shaded zones) as the origin and with zones in the CBD (darker shaded zones) as the destination. Information of sampled residents who make such trips was taken from the HTS database, and the survey was conducted by reinterviewing the persons who actually made trips from home to the CBD. The SP survey in the CBD first asked information on the socioeconomic details of the respondents and their households. Then, as the current travel behavior constitutes the RP data, the survey asked the respondents about details of to-work/school trips to the CBD that they made including the details such as mode(s) used, travel cost, and time. For the SP part, since there was no BRT in operation yet when the survey was conducted, detailed explanation and images of the planned BRT were presented to the respondents. Then, the survey asked respondents about their potential responses to different fare levels of the planned BRT. A total of 13 fare levels were prepared to ask the respondents whether they would be willing to shift from their current mode to BRT to make the same travel. With regard to the trips that fulfill the above origin-destination (OD)-zone criteria, effort was made to collect the samples so that the purpose and mode compositions would comply with those of the HTS database. In this way, the entire OD-zone pairs included in Figure 1 would be applied for mode choice modeling and policy simulation without using weights to the RP alternatives. Modeling Structure Random utility-based discrete choice models have found their ways in many disciplines including transportation, marketing, and other fields. Multinomial logit (MNL) model is the most popular form of discrete choice model in practical applications (Mohammadian & Doherty, 2005). It is based on several simplifying assumptions such as independent and identical Gumbel distribution (IID) of random components of the utilities and the absence of heteroscedasticity and autocorrelation in the model. As such, the MNL model belongs to a class of models that possesses the so-called independence of irrelevant alternatives (IIA) property, which is both one of the strengths of the MNL model and its major weakness (Meyer & Miller, 2001). Nested logit (NL) model is the model that has been developed in order to overcome the IIA limitation in the MNL model by modifying the choice structure into multiple tiers. NL models are very commonly used for modeling mode choice. The NL model permits covariance in random components among nests of alternatives. Alternatives in a nest exhibit an identical degree of increased

6 New BRT and Area Pricing Alternatives 593 sensitivity relative to alternatives in the nest (Williams, 1977; Daly & Zachary, 1978; McFadden, 1978). Each nest in the NL model has associated with it a log-sum parameter, or expected maximum utility associated with the lower-tier decision process, which determines the correlation in unobserved components among alternatives in the nest (Daganzo & Kusnic, 1993). The range of this log-sum parameter should be between 0 and 1 for all nests if the NL model is to remain globally consistent with the random utility maximizing principle. Furthermore, recent research works contribute to the development of closed form models which relax some of the above-mentioned simplifying assumptions to provide a more realistic representation of choice probabilities. Mixed logit (ML) model is an example of these alternative structures (Bhat, 2002). The ML model has been introduced by Ben-Akiva and Bolduc (1996) to bridge the gap between logit and probit models by combining the advantages of both techniques. In ML models, heterogeneity can be accounted for by letting certain parameters of the utility function differ across individuals. It has been shown that this formulation can significantly improve both the explanatory power of models and the precision of parameter estimates (Ben-Akiva & Bolduc, 1996; Bhat, 2000). There are a growing number of empirical studies implementing ML method. Joint SPRP Models Both RP and SP data have their strengths and weaknesses, namely that RP data are cognitively congruent with actual behavior while SP surveys can be collected in a tightly controlled choice environment and can provide richer information on preferences (Walker & Ben- Akiva, 2002). The strengths of both data sources could be exploited and weaknesses ameliorated by pooling both data sources as a joint SPRP model (Louviere et al., 2000). This data enrichment process should provide more robust parameter estimates and should increase confidence and accuracy in predictions (Verhoef & Franses, 2002). Techniques of joint SPRP models have been commonly used in different disciplines such as marketing, transportation, and environment for quite some time. In the context of activity-based modeling, current work includes development of an SPRP combined mode choice model as the lowest-level model for the primary tour within the entire activity-based model system, drawing on the various available RP and SP data sources for the city of Tel-Aviv (Shiftan et al., 2003).

7 594 Sadayuki Yagi & Abolfazl Mohammadian Model Estimation Data Preparation The SP dataset comprises 797 effective samples. Excluding data records presenting modes with few samples such as railways and focusing only on to-work and to-school trips for the purpose of this study, a dataset containing 761 samples was established for mode choice analysis. Major characteristics of the respondents found to be:. workers constitute more than 80% of the total, and the remaining are students;. males comprise about two-thirds of the total respondents;. about 33 and 40% of the total respondents have automobile and MC driver s licenses, respectively, whereas 40% have neither license;. approximately 40% of their households own automobiles, while 60% own MCs; and. among private mode users, about 25% of workers receive some kind of transportation allowance from the employer. Alternative Setting For this study, three major motorized modes were included as existing modes: automobile (car), motor cycle (MC), and transit (TR). Furthermore, car trips were divided into two modes: drive alone (DA) and shared ride (SR). Since DA and SR were suspected to violate the IIA assumption after testing the utility functions with some major variables like travel cost and time as generic variables, these two alternatives were placed under the same nest in the NL model. As for non-motorized (NM) mode of transport, it tends to be omitted from the mode choice models of all but a few metropolitan areas; however, such an omission is problematic, not only because these trips are an important component of personal mobility, but because crosselasticities are quite high between non-motorized trips and automobile or TR trips, depending on cost, time, and so on (Harvey & Deakin, 1993). Although the share of NM in the SP survey was only 1.8%, it was included in the model as a major mode. As such, there are a total of five existing modes that have been set for this study: DA, SR, MC, TR, and NM. Shares of these representative modes in the dataset are 20.8%, 7.7%, 31.4%, 38.5%, and 1.7%, respectively. For the SP part of the model, BRT (BR) is added to these five existing modes. It is assumed that modes of TR, BR, and NM are available to all individuals, while the availability of modes of car (DA and SR) and MC are limited.

8 New BRT and Area Pricing Alternatives 595 Explanatory Variables The variables tested for modeling are attributes related to the travel as well as socioeconomic attributes of the household and the individual, and are listed as below. Travel-related variables. Travel cost, travel time, and travel distance. Household-related variables. Household income, and vehicle ownership (i.e. number of automobiles and MCs in the household) and Individual-related variable. Employment status (e.g. full-time, part-time, and student), school type, personal income, gender, age, vehicle availability, work/school location, and various types of commuting allowance provided by the employer. In addition, some composite variables such as travel cost divided by the household income and travel time multiplied by the household income were also tested as explanatory variables. Modeling Results For developing MNL and NL models, an SP model and an RP model were first estimated, respectively. While the number of observations in the RP model is exactly the same as the number of sampled individuals, the number of observations in the SP model is nearly double because most of the individuals chose more than one alternatives (i.e. BRT and non-brt modes) under different BRT fare settings. Meanwhile, a joint SPRP model was estimated in which one subset is labeled as the RP choice set and the other is labeled as the SP choice set. The RP choice set was placed just below the root of the tree with a scale (log-sum) parameter fixed at 1.0, while each alternative of the SP choice set was placed as a degenerate (single alternative) branch (except for the car nest in the NL model) with a free but common scale parameter. It is an artificial NL model, and the scaling parameter does not need to lie in the unit interval but can be greater than 1.0 because individuals are not modeled as choosing from the full set of RP and SP alternatives (Greene, 2002). In fact, choice has to be made equally from the SP and RP choice sets. As such, each observation of the SP choice has the corresponding observation of the RP choice. Hence, the total number of observations in the joint SPRP model is double the number of observations in the SP model. In the joint SPRP model, effort was made to have common coefficients in both of the RP and SP utility functions for the same alternative. This means that the marginal rates of substitution among some of the variables are the same in the SP and RP models. On the

9 596 Sadayuki Yagi & Abolfazl Mohammadian other hand, only a joint SPRP model was estimated for the ML model, in which both RP and SP choice sets were placed on the same tier. MNL model. The results of the MNL models that are estimated separately for the RP and SP choice sets and the joint SPRP MNL model are shown in Table 1. The adjusted r 2, as a measure of fit of the model, is for the RP model, which is better by far than its SP counterpart, This may be because RP data reflect actual choice behavior and hence have high reliability as to the choice among the existing alternatives as a current situation. In other words, under the current situation, few modes are actually available to people, and the mode is selected depending on their socioeconomic group. On the other hand, the adjusted r 2 of the joint SPRP model is It implies that the choice behavior including the new mode (i.e. BRT) is better estimated than the SP-only model, and it is one clear benefit of the joint SPRP model. The scale parameter of the SP data is 1.492, indicating that the SP error variance is smaller than the RP error variance. It may be contributed to by the fact that estimated parameters in the RP model have generally larger absolute values than those in the SP model. Modeling outcomes are summarized and discussed as below. All variables associated with the travel are included in the models as continuous variables. Travel cost per se did not work well in the models, but travel cost divided by household income was included in all the utility functions except for NM of the models. It implies that the magnitude of the negative impact of the travel cost on the choice of the motorized modes is larger for people in the lower-income household. Similarly, travel time did not work well in the models, but travel time multiplied by household income was included in some utility functions, that is, TR and BR of the SP model and TR of the joint SPRP model. It implies that the travel time by the public mode is an important factor to select this mode, especially for people in the higher-income household. It also shows that travel time does not affect the choice of the private modes; it can be inferred that car users are less likely to shift to the public mode whether there is serious congestion or not on the way to work/school. As for travel distance, its logarithm value is significant in NM (and MC in the RP model), implying that the longer travel distance reduces the utility of walking/biking all the way to work/school and it is also the case with the MC in the RP model. Variables related to the household and individual are all included in the models as dummy variables. There are two household-related variables involved in the models. One is a high-income household dummy, which is positively significant in SR and BR. In Jakarta, many people in the high-income household do not actually drive by themselves but have chauffeurs. Such trips with chauffeurs are

10 Table 1. MNL models: estimation of SP and RP mode choices SP model RP model Joint SP and RP model Variable Description Alternative Coefficients Alternative Coefficients Alternative SP coefficients RP coefficients Continuous variables C1 Travel cost (thousand Rp.) DA, SR, DA, SR, DA, SR, devided by household MC, TR, BR (2.85) MC, TR (2.92) MC, TR, BR (2.60) (2.60) income (mil. Rp./mo.) C3 Travel time (h) mutiplied TR, BR TR by household income (mil. (1.71) (2.02) (2.02) Rp./mo.) C4 Log of travel (line) NM MC, NM NM distance (km) (3.27) (3.44) (4.71) (4.71) Dummy Variables D1 High-income household (]4 mil. Rp./mo.) SR, BR (2.38) D3 Motorcycle-owning TR, NM household (2.52) D4 Male adult (age ]17) MC, BR (3.15) NM (1.76) D5 Having motorcycle TR, NM driver s license (3.67) D6 Work/school location within the 3-in-1 area D7 D8 Private mode allowance provided for the individual Free parking provided for the individual DA (2.30) SR (1.63) SR, BR (2.88) TR, NM TR, NM (1.87) (3.54) MC, NM MC, BR (1.81) (4.24) NM (3.02) TR, NM (4.45) DA (3.16) MC (1.76) DA (2.04) (2.88) (3.54) (4.24) (3.02) (4.45) (3.16) (2.04) New BRT and Area Pricing Alternatives 597

11 Table 1 (Continued) SP model RP model Joint SP and RP model Variable Description Alternative Coefficients Alternative Coefficients Alternative SP coefficients RP coefficients Alternative-specific constants Car (drive alone) DA DA DA Car (shared ride) SR (5.64) SR (4.76) SR (6.74) (7.64) Motorcycle MC (4.23) MC (1.70) MC (6.57) (6.55) Transit TR (4.06) TR (6.16) TR (6.89) (9.02) BRT BR BR (4.43) (7.36) Non-motorized transport NM (3.89) NM (6.94) NM (5.38) (8.53) Scale parameters Car (drive alone) DA (9.44) () Car (shared ride) SR (9.44) () Motorcycle MC (9.44) () Transit TR (9.44) () BRT BR (9.44) () Non-motorized transport NM (9.44) () Summary statisics 1465 Observations 761 Observations 2930 Observations L(0)2388 L(0)1117 L(0)7333 L(b)1165 L(b)100 L(b)3375 r r r Sadayuki Yagi & Abolfazl Mohammadian

12 New BRT and Area Pricing Alternatives 599 considered as shared automobile rides of which utility is increased by this dummy variable. BRT is also regarded as a prospective alternative means of transport by people in the high-income household. As for the dummy variable indicating whether the household owns a MC, it proves to be significant in the models, but is not directly included in the utility of MC. It is rather included in the utilities of TR and NM with a negative parameter, implying that having a MC relatively increases the utilities of selecting the private modes in general. Similar tendencies can be found in one of the variables related to the individual, that is, a dummy of whether the individual has a MC driver s license. Although it is not included in the RP model, it reduces the utility to walk/bike or to use TR and relatively increases the utility to select a private mode of transport. Being a male adult considerably increases the utility to walk/bike. It also increases the utility to use MC, which seems to be reasonable in the case of Jakarta. Male adults also have higher utility to use BRT. Jakarta is famous for its unique transportation control measure (TCM) that has long been implemented in the CBD. It is called a 3-in- 1 regulation, in which only high-occupancy vehicles with three or more occupants are allowed to use the main corridor roads in the CBD of Jakarta during morning and evening peak periods. The variable which indicates whether work/school is located on these roads regulated by the 3-in-1 is included with a negative parameter in the DA utility in the RP and joint SPRP models, reducing the probability of driving alone to work/school because of the 3-in-1 regulation. Furthermore, existence of some travel allowances provided by the employer also has a significant impact on the utility function of some private modes, such as private mode allowance dummy, influencing on the MC (in the RP model only), and free parking dummy, influencing on driving a car alone (in the joint SPRP model only). It is another benefit of the joint SPRP model that parameters such as work/school location and free parking are significant and captured in the joint model. NL model. The results of the NL models estimated separately for the RP and SP choice sets and those of the joint SPRP NL model are shown in Table 2. As is the case with the MNL models, the adjusted r 2 is for the RP model, which is better by far than its SP counterpart, As implied in the MNL model, people are captive to the dominant mode in their socioeconomic group under the current situation. The adjusted r 2 of the joint SPRP model is 0.517, implying that the joint SPRP model is better estimated than the SP-only model. The scale parameter of the SP data is 1.258, again indicating that the SP error variance is smaller than the RP error variance. Furthermore, the logsum coefficients capturing the effect of expected utility from car

13 Variable Description Alternative C1 C3 C4 Continuous variables Travel cost (thousand Rp.) devided by household income (mil. Rp./mo.) Travel time (h) mutiplied by household income (mil. Rp./mo.) Log of travel (line) distance (km) Table 2. NL models: estimation of SP and RP mode choices DA, SR, MC, TR, BR SP model RP model Joint SP and RP model Coefficients (3.06) TR, BR (2.58) SR (1.52) NM (3.02) Alternative DA, SR, MC, TR Coefficients (2.90) Alternative DA, SR, MC, TR, BR SP coefficients (3.18) TR, BR (2.39) MC (2.76) NM (3.42) SR (3.02) NM (3.37) RP coefficient (3.18) (2.39) (3.02) (3.37) Dummy variables D1 High-income household (]4 mil. Rp./mo.) SR, BR (1.64) SR (1.69) SR, BR (2.99) (2.99) D2 Middle-income household (]1 &B4 mil. Rp./mo.) DA (1.48) DA (1.58) (1.58) D3 Motorcycle-owning household TR, NM (2.69) TR, NM (1.67) TR, NM (1.73) (1.73) D4 Male adult (age ]17) MC, BR (2.76) MC, NM (1.82) MC (1.69) (1.69) * NM (1.77) NM (2.87) (2.87) D5 Having motorcycle driver s license TR, NM (3.88) TR, NM (5.25) (5.25) D6 Work/school location within the 3-in-1 area DA (1.59) DA (2.18) DA (2.51) (2.51) D7 Private mode allowance provided for the individual (1.67) D9 Toll allowance provided for the individual SR (1.34) SR (1.90) (1.90) 600 Sadayuki Yagi & Abolfazl Mohammadian

14 Table 2 (Continued) Variable Description Alternative SP model RP model Joint SP and RP model Coefficients Alternative Coefficients Alternative SP coefficients RP coefficient Alternative-specific constants Car (drive alone) DA (3.26) DA (4.57) DA (4.82) (5.20) Car (shared ride) SR SR SR Motorcycle MC (0.85) MC MC (8.85) Transit TR TR TR (2.79) (4.01) BRT BR BR (0.65) Non-motorized transport NM (2.87) NM (3.99) NM (3.48) (4.36) Scale parameters Car (drive alone) DA (7.88) Car (shared ride) SR (7.88) Motorcycle MC (7.88) Transit TR (7.88) BRT BR (7.88) Non-motorized transport NM Log-sum: expected maximum utility from car Summary statisics DA, SR (2.35) DA, SR (1.92) (7.88) DA, SR (4.10) 1465 Observations 761 Observations 2930 Observations L(0)2267 L(0)1088 L(0)6979 L(b)1151 L(b)99 L(b)3367 r r r () () () () () () (5.06) New BRT and Area Pricing Alternatives 601

15 602 Sadayuki Yagi & Abolfazl Mohammadian (i.e. DA and SR) fall within the theoretically acceptable range between 0 and 1 in the RP, SP, and joint SPRP models. Modeling outcomes which should be marked as compared to the MNL models are summarized and discussed as below. The logarithm value of travel distance is also significant in SR in the SP and joint SPRP models, with a positive sign. It implies that, as the travel distance becomes longer, it is more likely to share the ride with someone including a chauffeur rather than to drive such a long distance to work/school alone. This may make sense. As another variable related to the household, the middle-income dummy is added to the utility function of DA in the SP and joint SP RP models. In contrast to the high-income household which increases the utility of the SR, people in the middle-income household do not usually hire a chauffeur but drive by themselves, even if they can afford a car. Among travel allowances provided by the employer, the dummy of existence of expressway toll allowance for the individual is significant in the utility function of SR in the SP and joint SPRP models. Underlying reasons for this may be the same as the travel distance. That is, as the travel distance becomes longer, the utility of selecting the SR becomes greater, and so does the probability of taking an expressway to travel a long distance. ML model. For the ML modeling, 1000 repetitions are used to estimate the unconditional probability by simulation. This improves the accuracy of the simulation of individual log-likelihood functions and reduces simulation variance of the maximum simulated log-likelihood estimator. Random parameters for this ML model are estimated as normally distributed parameters in order to allow parameters to get both negative and positive values. Both observed attributes associated with the mode alternative, individual, and household (explanatory variables) and the unobserved attributes (alternative-specific constants) were tested by introducing random parameters. The results of the estimated joint SPRP ML model are shown in Table 3. The adjusted r 2 is 0.472, presenting a good model fit with statistically significant parameters. Furthermore, estimated standard deviations of the random parameters of the variable representing the travel cost and the constant specific to NM transport are statistically significant in the model at 80% confidence level or better. The significant t-statistics for these standard deviations indicate that these are likely to be statistically different from zero, confirming that parameters indeed vary across individuals. Features of the other variables and the implications are generally similar to those in the MNL and NL models. As travel cost has been introduced with random

16 New BRT and Area Pricing Alternatives 603 Table 3. ML model: joint estimation of SP and RP mode choices Joint SP and RP model Variable Description Alternative SP coefficients RP coefficients C2 C3 C4 Continuous variables Travel cost (thousand DA, SR, Rp.) MC, TR, BR (3.64) Standard deviation (5.20) Travel time (h) TR mutiplied by household (1.84) income (mil. Rp./mo.) Log of travel (line) distance (km) NM (2.66) (3.64) (5.20) (1.84) (2.66) Dummy variables D1 High-income household (]4 mil. Rp./mo.) SR, BR (3.21) D3 Motorcycle-owning TR, NM household (3.99) D4 Male adult (age ]17) MC, BR (4.64) NM (2.19) D5 Having motorcycle TR, NM driver s license (4.68) D6 Work/school location DA within the 3-in-1 area (1.60) (3.21) (3.99) (4.64) (2.19) (4.68) (1.60) Alternative-specific constants Car (drive alone) DA Car (shared ride) SR (8.48) (6.39) Motorcycle MC (8.33) (5.95) Transit TR (9.76) (7.06) BRT BR (10.60) Non-motorized transport NM (1.76) (1.55) Standard deviation (1.16) (1.24)

17 604 Sadayuki Yagi & Abolfazl Mohammadian Table 3 (Continued) Variable Description Alternative Summary statisics 2930 Observations L(0) 6393 L(b) 3370 r Joint SP and RP model SP coefficients RP coefficients parameters into the model, travel cost divided by household income is no more included in the model instead. Simulation of Mode Choice Current Policies under Review Since December 2003, the 3-in-1 regulation has been modified in terms of two major points. First, the corridor roads for the 3-in-1 have been extended, and it has been effective in the evening (4:00 7:00 p.m.) in addition to the morning period (7:0010:00 a.m.). Second, while the number of passengers in each vehicle was monitored only at the time of entering the designated road before December 2003, vehicles now must always have three or more occupants to pass through any section of the designated roads covered by the regulation. Along with the new 3-in-1 regulation, the city of Jakarta has initiated the first BRT operation on the same corridor since January Furthermore, the government of Jakarta has been trying to accelerate and move up the implementation schedule of the eight BRT corridors proposed by the Study on Integrated Transportation Master Plan for Jabodetabek (SITRAMP), though it was originally a phased plan and the entire BRT network would be completed in Their goal now is to complete the BRT network by Moreover, SITRAMP has proposed an area pricing scheme as an effective TCM to replace the existing 3-in-1 regulation, and the government of Jakarta is currently following the schedule and considering the implementation of the area pricing in Target area for this scheme has not been finalized yet, but it includes the existing 3-in-1 corridor and covers more spatially most of the CBD which the current vehicular trips are generated from and attracted to. It is an intention that the proposed pricing area should be served by

18 New BRT and Area Pricing Alternatives 605 improved public TR including BRT. The objective is to reduce the current vehicular traffic in the CBD as much as possible so that at least the current level of congestion will not deteriorate even in the future. It is also envisaged that the area for pricing will be expanded toward While this area pricing scheme may be effective for congestion reduction in the CBD, provision of alternative means of transportation for the pushed-out users by the area pricing is of great importance to obtain public acceptance. Hence, it is necessary to consider simultaneously the area pricing scheme and the BRT development which may serve as an alternative for assumed pushed-out vehicle users. As such, in this study these two major policies are simulated in the mode choice model. Model to be Used for Simulation The joint SPRP ML model was applied for simulation due to the following reasons:. either SP or joint SPRP models are necessary to simulate the future BRT mode share. Since SP data are hypothetical and experience difficulty taking into account certain types of real market constraints, estimating and applying a stand-alone SP model for simulation is not recommended but a joint SPRP model is preferred (Louviere et al., 2000);. the existing mode shares were used as the benchmark to compare the three joint SPRP models, and the mode shared derived from the ML model best matched with the observed data as shown in Table 4;. NL models are essentially better than MNL models when the IIA property is suspected to be violated. However, a problem with the NL model is that it requires a priori specification of the nesting structure. The actual competition structure among alternatives may be a continuum that cannot be accurately represented by partitioning the alternatives into mutually exclusive nests (Bhat, 2003); and. in combining SP and RP data, scaling differences and the correlation in unobserved attributes across repeated choices by the same decision makers are often an issue. Simple ML specifications easily incorporate unobserved correlation and scaling differences, and are statistically superior to the standard joint scaled logit models (Brownstone et al., 2000).

19 606 Sadayuki Yagi & Abolfazl Mohammadian Table 4. Comparison of surveyed and simulated mode shares Mode Survey result (%) MNL model (%) NL model (%) ML model (%) Car (drive alone) Car (shared ride) Motorcycle Transit Non-motorized Total Assumptions The assumptions employed for the simulation are as follows:. the operation hours of area pricing include at least those of the current 3-in-1 regulation, that is, morning and evening peak hours. As all the samples in the dataset are either to-work or to-school car trips with CBD zones as the destination, all the trips are affected by the area pricing scheme;. a variety of fare levels were tested for the BRT, ranging from Rp to Rp per ride with an interval of Rp (where Rp. 10,000US$ 1.08). The BRT service frequency is every three minutes in all the cases;. six cases of levy rate were tested for area pricing, namely, Rp. 0 (i.e. no area pricing), Rp. 4000, Rp. 8000, Rp , Rp , and Rp per trip;. all vehicles passing/driving in the target area are to be charged under this area pricing scheme, and it is different from the cordon pricing scheme in which only vehicles entering the area are to be charged; and. analyses are made as if changes took place now. Most of the transportation-related costs such as TR fares, expressway tolls, parking prices, and fuel cost have been raised since the time that the SP survey was conducted (August 2003). Although the rates of increase vary depending on the items, an average increase rate of 10% was assumed for simulation. As of July 2005, the new BRT is operated with a fare of Rp. 2500, while the regular bus costs around Rp per trip depending on the bus size and the existing air-conditioned express bus costs Rp per trip. The initial taxi fare is Rp Prices of this range may not be so painful for high-income people but may be significant

20 New BRT and Area Pricing Alternatives 607 enough to low- and middle-income class people. On the other hand, the highest area pricing levy assumed, Rp. 20,000, is something that high-income car users can still afford, but it is not negligible even for them. Simulation Results For each individual, the model simulates the mode choice decision to go to work/school in the CBD. As such, shares of the six representative modes, that is, DA, SR, MC, TR, BRT, and non-motorized are simulated under each combination of the BRT fare and the area pricing levy rate are shown in Table 5. No BRT and no area pricing cases are also included in the table. Furthermore, changes of the mode shares of BRT and car (DA and SR) are graphically depicted in Figure 2. Major changes of the mode shares can be summarized as below. When a new mode, that is, BRT is introduced, it is expected to play an important role in terms of the mode share. It will have the largest share in place of TR. Figures show that the majority of the prospective BRT passengers will come from the current TR users, that is, within the public modes. A significant portion of the current MC users are also expected to shift to BRT, implying that MC users are rather flexible in mode choice. Some of the current car users (both DA and SR) are expected to shift to BRT as well; however, such portions are relatively small. As the BRT fare rises, the share of BRT naturally decreases significantly. All the other transport modes will increase the shares accordingly, and it is especially remarkable in TR which is a mode previously used by many BRT passengers. Shares of car users (both DA and SR) will also increase, but only marginally. All these mode share changes caused by the BRT fare increase are more striking under the area pricing scheme, especially with a higher levy rate. If the area pricing scheme is applied to the CBD, the share of car users are expected to drop as the levy rate increases. This impact is greater for car users who DA rather than those who share the ride. This is because the impact could be alleviated by sharing the cost, or because the impact is relatively small for higher-income car users who tend to hire chauffeurs, forming ridesharing. It seems that the area pricing with a higher levy rate makes car users shift to BRT the most. Increase in shares of other modes is smaller. All these influences of the area pricing levy rates are more noticeable under the lower-fare cases of BRT. It implies that, as BRT becomes more affordable in terms of cost, it is expected to better serve as an alternative mode for cars under the area pricing scheme.

21 608 Sadayuki Yagi & Abolfazl Mohammadian Table 5. Simulation results using joint SPRP ML model With BRT: fare level (Rp.) Mode Without BRT No area pricing (%) DA SR MC TR BR NM Total Area pricing: Rp per entry (%) DA SR MC TR BR NM Total Area pricing: Rp. 12,000 per entry (%) DA SR MC TR BR NM Total Area pricing: Rp. 20,000 per entry (%) DA SR MC TR BR NM Total Conclusions This study has developed MNL, NL, and ML models to simulate the mode choice using RP and SP data obtained in Jakarta, Indonesia. Although the models established in this study may be difficult to be incorporated into a practical activity-based micro-simulation

22 New BRT and Area Pricing Alternatives 609 Figure 2. Simulated changes of mode shares modeling system due to the sample size limitation, purpose, and location, it captured the key variables that are significant for modeling mode choices in Jakarta and analyzed some of the policy scenarios that are currently under review, through the simulation using the best model derived.

23 610 Sadayuki Yagi & Abolfazl Mohammadian Interpretation of the effects of each explanatory variable in the developed models led to several interesting insights. A wide variety of different types of variables contributed significantly to the models, including basic travel characteristics (cost, time, and distance), household characteristics (income and vehicle ownership), and individual characteristics (gender/age, vehicle availability, driver s license, work/ school location, and allowance provided by the employer). Thus, it appears that only the characteristics associated with the trip may not suffice to fully explain mode choice; rather, several household and individual factors play an important role. Moreover, as demonstrated in this paper, the choice model that has incorporated such factors enables better analysis of policy scenarios through the simulation. Although the dataset consists of only from-home trips to the CBD, mode choice in this first segment of the individual s travel tour is important because these trips constrain the modes of the subsequent segments such as returning home trips and work-based sub-tours. Furthermore, according to the results of the SP survey, more than 80% of the shopping tours are made by the same mode that are selected to go to work/school, whether it is on weekdays or weekends. As such, one extension of the study will be to develop a model that determines modes of the subsequent trips in a tour, focusing on the mode transition. In addition, chauffeur-driven trips were mentioned in this paper and it would be interesting to distinguish chauffeur-driven trips from other SR trips and study different behaviors and characteristics of these modes. Furthermore, the authors further effort includes establishment of a comprehensive mode choice model that is applicable to the activitybased micro-simulation modeling system. Although a variety of variables proved to be significant in this study, activity patterns were not included as explanatory variables in the mode choice model, because the SP survey data lacked such information. Using the abundant RP data source available from the HTS, a full-scale mode choice model that includes activity-related variables as input and returns full information to the upper-level choice of the modeling system should be developed. The prototype mode choice model presented in this study will still be useful in predicting the mode shift caused by some new policies such as BRT and area pricing. Acknowledgements The authors would like to express their gratitude to the National Development Planning Agency (BAPPENAS) of Indonesia for permitting use of the survey data in this study. This study was partially funded by the Foundation for Advanced Studies on International Development (FASID) of Japan.

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