type is the flexible factors like transporting mode alternatives and routing alternatives. In this case the people who will work still have the flexib

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1 Performing Dynamic Modeling of Transport Modes Utilization Fraction in Urban Areas A Case of Bekasi City, Indonesia Nunu NOVIANDI a, Pradono PRADONO b, Muhammad TASRIF c, Iwan P KUSUMANTORO d a,b,c,d School of Architecture, Planning, and Policy Development, Institut Teknologi Bandung, Indonesia a Nunov69@gmail.com b Pradono@pl.itb.ac.id c Muhammadtasrif52@gmail.com d Ipkus@pl.itb.ac.id Abstract: The classical method of mode utilization prediction in urban scale commonly used the technique in the form of a mathematical model that is static. Modeling substances generally focused on efforts to find the best composition of mode utilization based on criteria that built from utility on each alternative mode. Relationships between variable that determine the utilization attractiveness of the mode treat as an exogenous component. As result the dynamic change of each determinant variable cannot automatically calculated in the model. Prediction of mode utilization fraction became constant and static. In the reality, in the context of urban dynamic, factors that influence the attractiveness of mode utilization like degree of road level of service, travel time, transport cost, etc. change in the high intensity. This modeling research tries to accommodate dynamic change in the determinant factors of the mode utilization attractiveness, especially road level of service factor, into mode utilization fraction model dynamically. The research question in this modeling research is how to build the model that can capturing the dynamics behavior of transport mode utilization. The methodology that used to answer the research question is system dynamics technique. Keywords: transport mode utilization, mode attractiveness, and road level of service, dynamic model 1. INTRODUCTION Congestion is the stand out phenomenon in the macro system of urban transportation. The loss came from urban traffic congestion in Indonesia almost reach billions rupiah for each year. For that reason effort to understand the congestion phenomenon became more importance, especially for urban and city planner. Congestion is a condition in which the flow of traffic passing on a road exceeded the road capacity that result a free speed of these roads approaching 0 km/h, this condition causing the queue. At the congestion, the road degree of saturation reaches above 0.5 ( IHCM, 1997). One indicator of congestion is the road level of service (LOS). Level of service (LOS) is a qualitative measure used to relate the quality of traffic service. LOS is used to analyze highways by categorizing traffic flow and assigning quality levels of traffic based on performance measure like speed, density,etc (HCM, 2010). Daily congestion is a result from growth travel demand that accumulate from yearly demand. Daily movement closely related with structure of decision making of individual in the choice of working place, working time, and trip route and transport mode. Determinant factors that influence the decision to choice mode transport can divide in two types. The first 814

2 type is the flexible factors like transporting mode alternatives and routing alternatives. In this case the people who will work still have the flexibility to change the selection of the specific factors, when the public transport services improving, the one who used to work with the private car will shift to the use of public transport. Likewise, the choice of routes, private car users will choose the most optimal route. In the context of planning theory, this condition reflecting the principle of maximum utility theory and the basis for modeling the behavior in transport (transport behavior). The second type is factors that are rigid which include work sites and work time. Both of these factors have relatively little flexibility for the people to choose, because it is associated with other factors that can not be determined solely as office policies and other agreements that have bind. The number of factors involved in the phenomenon of congestion reflects that the phenomenon is systemic and complex. Therefore an approach based on system thinking is required. Systems thinking have been defined as an approach to problem solving that attempts to balance holistic thinking and reductionist thinking. By taking the overall system as well as its parts into account systems thinking is designed to avoid potentially contributing to further development of unintended consequences (Cabrera, 2015). Systems thinking focuses on cyclical rather than linear cause and effect. This modeling research used the Bekasi City in Indonesia as a research area. Bekasi City is one of the cities that were part of Megapolitan Jabodetabek. Bekasi city can be a good object research because this city shown the great dynamic change in economic, population, land use and transportation compare to other city in megapolitan Jabodetabek. Table 1 shows the performance of road level of services in main road in research area. Table 1. Road level of service at research area Capacity V/C No Road Name and Type Volume (Veh/hour) Ratio 1 Sultan Agung ( Primary Artery) E 2 Sudirman ( Primary Artery) F 3 Juanda( Primary Artery) E 4 Ahmad Yani ( Primary Artery) F 5 Cut Meutia ( Primary Artery) C 6 Chairil Anwar ( Primary Artery) E 7 Joyomartono(Secondary Artery) F 8 Siliwangi( Primary Artery) B 9 Pengasinan (Secondary Artery) F 10 H.Djole (Secondary Artery) A 11 Kali Baru - Teluk Buyung F 12 Pd.Gede-Bekasi (Secondary Artery) C 13 KH.Noer Ali ( Primary Artery) F 14 Cibubur-Cileungsi( Primary Artery) E 15 Pd. Gede - LBuaya( Primary Artery) D 16 Narogong ( Primary Artery) D 17 A.Yani - Juanda( Primary Artery) F 18 Jl. R.Hankam (Secondary Artery) F 19 Jl R Jati Asih (Secondary Artery) D Source : (City Department of Transportation, 2013) LoS 815

3 The composition of utilization transport mode is the one of component transport system that influences the road level of service. Fraction of the private car utilization is one of the factors that led to the high ratio between the volumes of vehicles with road capacity. The results of the primary survey in the research area that show the composition of transport mode that used to workplace, can be seen in Figure 1. The movement to workplace is done through a variety of alternative modes. Data from Indonesian train agency (2014) and central bureau of statistic (2014), during 2013 the number of passengers in the commuter stations in Kota Bekasi (research area) reached an average of people per day, or about 17% of commuters using public transport. While passenger bus to the external area of Bekasi City according to data from the transportation department Bekasi City in 2013 reached approximately 39.8 thousand people per day, or about 997 per day Bus. The remaining approximately 297 thousand people per day-movement to the working place area by private vehicle. The movement of commuters using private vehicles divided into two alternative modes that are cars and motorcycles. Results of the primary survey in 44 structural residential areas and covering 1340 households in Bekasi showed that about 37% of respondents do commuting by private car vehicle and the remaining approximately 15% using the motor to make commuting to work. Figure 1. Composition of transport mode that use to workplace Thus, the simple question of research is how to develop a model that can help policy simulation or simulation efforts to reduce the level of congestion in the area of research. This activity is important because based on empirical experience; policies or efforts to overcome bottlenecks that occur often generate more severe congestion or only temporary. Traditionally, modeling needs of the vehicle can be classified in two methods that are single aggregate equation model and the equations plural models elections disaggregate. Model aggregate closely linked to macroeconomic and social influence. While the discrete model associated with the effect of changes both on the price and characteristics of the vehicle as well as other factors. The utilization of modes in the urban transport system has become one of the main focuses in the modeling of urban transport related to sustainable urban development. Promotion of public transport use a lot done but the use of private vehicle ownership continues to grow high (This condition reflects the phenomenon of resistance to the policy (Steg, 2005; Kitamura, 1989). Related Research modal choice has much to do to understand the behavior of the public in determining the mode of movement. In general, Bajracharya (2016) identified the 816

4 methodology used in modeling modal choice is binary and multinomial logit method (Mintesnot and Takano, 2007), statistical method (Scheiner and Holz-Rau, 2010; Anable and Gatersleben, 2005; Lu and Pas, 1999) and qualitative methods (Beirao and Cabral, 2007; Stone et al., 2003; Hiscock et al., 2002). Several recent studies conducted by using data panel (Kitamura, 2009). These methods are used generally to understand the behavior of decision-making in selecting the mode to be used. Some of the literature discussing the weaknesses of the method commonly used in modeling modal choice as well as the direction of the better methods in the future. The static approach based on cross-sectional data can direct to the wrong results of the analysis (Kitamura, 2009). Urgency development of models to predict changes in the composition of vehicle use dynamically through this research is to provide technology that can help policy formulation at the local government level in the handling of congestion and planning of the transport system more holistic. The dynamic model is expected to provide a more realistic than a static model because it is built based on the understanding of the phenomenon that occurs systematically 2. MODEL DEVELOPMENT An important aspect of this modeling research is the complexity and dynamic aspect of the models. In theory modeling, dynamic model is a model that describes the behavior change of the objects modeled in a time dimension. The changes that occur in the system can be derived as a function of time. Dynamic models have the time as the main variables (Simatupang, 1995). The dynamic programming is a mathematical technique that is normally used to make a decision on a series of interrelated decisions. The dynamic program provides a systematic procedure to determine the optimal combination of decision. The main purpose of this model is to completion of the optimization problem that related to certain characteristics (Arga, 1985). Unlike linear programming, there is no standard mathematical form for the formulation of dynamic programming. However, dynamic programming is a common approach to solving specific problems and equations used in this approach must be formed in accordance with the situation of the problems encountered. One way to recognize a situation that can be formulated as a dynamic programming problem is aware of the basic structure of the problem (Dimyati, 2006). Dynamic models of transport mode utilization fraction in urban area was built using system dynamics approach There are several main activities that become stages in the development of the model with the approach of system dynamics: (1) defining the problem to be modeled, (2) formulation of dynamic hypotheses or theory about the causes of the problems, (3) formulation simulation model to test the dynamic hypothesis, (4) the test of models, (5) The design and evaluation of policies to increase system performance. The definition of the research problem have already discussed in the previous session, next session will explain about the formulation of dynamic hypotheses, formulation of causal loop diagram, formulation of quantitative models and testing of the model. 2.1 Dynamic Hypotheses of Transport Modes Utilization Phenomenon In systems theory perspective, all the components that exist in the real world related to one another (Almendinger, 2009). The relationship between these components produce system performance can be measured and perceived. In relation to land and urban transportation, some indicators of system performance that can be felt in everyday life include traffic congestion. Congestion is a condition in which the volumes of vehicles that have exceeded the capacity of the existing road network. In a simple effort to overcome congestion was done 817

5 by adding capacity through the construction of roads or decrease the volume of vehicles. But in reality, these efforts often do not lead to sustainable solutions. Some theories related to the phenomenon of congestion have already developed, such as Wardrop equilibrium theory (1952), kerner s tree phase congestion theory (1996) and 2002), braess paradox (Braess,2005)) and death spiral (Sterman, 2000). These theories suggest that efforts to handle congestion are not linear, but more complex, dynamic and systemic. Dynamic complexity that arises in the phenomenon of congestion in research area can be described as in the figure 2. One dominant characteristic of dynamic complexity in the case of this congestion is the relationship that is not linear, where the relationship between the components is not only one way but also it involves some interrelationship with other components. Physically congestion occurs due to the volume of the vehicle exceeds the capacity of the road network so that the level of service to achieve the degree of saturation (LoS E-F). Thus the factors that affect the congestion at first can be described consist of two (2) factors of road capacity and volume of vehicles. The fact that occurred in research area shows at rush hours the ratio between the volumes of vehicles with a capacity of road has reached a value above 0,8. This condition shows at that hour traffic conditions on the roads in research area have experienced congestion. Classically efforts to overcome this congestion by the government were increasing road capacity through the construction of new roads and increase road capacity by widening the existing road. The result of that effort in the short term may lower the V/C ratio so that LoS road congestion decreases. Some of the development activities undertaken in research area, such as build a new road network like flyovers to the north and increase the width of the local streets. In the period the increase in the road network in the city of Bekasi as a research area relatively high through improving the local road with addition new road about 1,200 Km in one year. However, in the period is relatively limited addition of new roads, upgrading of roads average only reaches 400 m per year. Figure 2. The first phase of dynamic hypotheses of congestion Increasing the capacity of the road network in the short term will lead to reduced congestion on some streets, but in the medium and long term tend to increase the attractiveness of the areas located around the streets. As a result, many developers choose these areas to build real estate or apartment. Data and information indicates that Kota Bekasi is the highest region in property investment in Indonesia. The occupancy rate of residential areas in the city of Bekasi in 2015 reached 92%, the value is above of the national average occupancy with 82% of occupancy rate. Increased residential area, which is not balanced with the provision of public transport, in turn will increase trip generation by private vehicle. The use of private vehicles is one of the major components that affect the road level of 818

6 service in the area of research. The increasing of vehicles utilization can cause from various factors, including the increase of the purchasing power, the regional economic growth, the location of residence (aspects of land use), the performance of public transport services and other factors. The second phase of dynamic hypotheses of congestion phenomenon that involving the utilization of transport modes can be seen in figure 3. Figure 3. The Second phase of dynamic hypotheses of the congestion phenomenon Bekasi city became inward migration destination for people seeking work and working in Jakarta as a center of the largest national economy. The high population growth that influence of in-migration resulted in the bigger trip generation not only in the area around the city center but also has expanded to suburban areas. This is possible because the access roads have been upgraded. Trip generation which generate from the housing combined with the increasing of private car ownership resulted in the increasing of traffic volume and in turn lead to the increasing of V/C ratio and congestion occur again. Facts show that from 1999 to 2014 Bekasi-Jakarta-travel time increased almost four times during rush hour. This condition reflects that in terms of reduction in congestion, new road network development efforts have not shown optimal results. The dynamic process of the relationship between the volume of vehicles, land use and the level of congestion as the third phase of dynamic hypotheses of congestion phenomenon can be seen in the figure

7 Figure 4. The third phase of dynamic hypotheses of the congestion phenomenon Figure 5. Land use change and growth of private car number Furthermore the complexity of the congestion problem is also related to the ownership of vehicles, particularly private cars and motorcycles (Duranton, 2011). As mentioned in the study of the dynamics of the transport system in the region during research, almost 82% of households in housing regularly surveyed have a vehicle, 60% of that portion is moving out to external area (Jakarta) and the remainder circulating in the city. The high use of private cars reflects of the low level of utilization of public transport in the city of Bekasi. The utilization rate of public transport, which only reached an average of 47% also, reflects the attractiveness of public transport is low. The level of private vehicles ownership affected people's purchasing power, which the purchasing power of people affected by the economic conditions of Bekasi and Jakarta. With the proportion of Bekasi City workers who work in DKI Jakarta is about 60%, then the influence of Jakarta economic became more dominant compare with Bekasi city economy in the formation of purchasing power. The economic relations between Bekasi and Jakarta (as a core of metropolitan jabodetabek) also have the complexity of its own, which in turn affect the trip generation in Bekasi. Expansion of the dynamic hypotheses of Bekasi city congestion problem can be seen in Figure

8 A arc veness of Zone + Road LoS J + Popula on Growth + + Growth of Residen al Zone External Economic Influence Economic Growth + Growth of Commercialand services Zone Growth of Industrila Zonei Volume Of Vehicle Private car u liza on V/C Ra o - + Public transport u liza on - Increasing of road capacity - - Roadside impedance + + Purchasing Power Figure 6. The fourth phase of dynamic hypotheses of the congestion phenomenon Based on these descriptions can be concluded that the phenomenon of congestion as an indicator of the performance of the relationship between land use and transport in Bekasi city structurally has a high level of complexity. This research seeks to develop modeling approach wherever possible capture the phenomenon of the dynamic complexity of the congestion from the macro to the micro level. 2.2 Formulation of Model Dynamic Utilization of Transport Mode Based on the dynamic hypothesis, the formulation of the model in the study was done in three phases, which are; the model formulation of causal loop diagrams; flow charts and formulation of mathematical formulation. Model causal loop diagram is a picture of the structure of causal linkages between variables or component of a system, which is the object modeling. Model flow diagram is a translation of causal loop diagram in the language of system dynamics programming, while the mathematical formulation is a translation model of causal loop diagram on a mathematical formula for each relationships between variables Formulation of Causal Loop Diagram Model Models in modal choice have been developed in many varies, depending on the purpose of transportation planning. Every mode of transport is analyzed separately during the stages of process modeling, and socio-economic changes greatly affect the process mode choice. Each mode is considered to compete in the capture of passengers shared, so the attributes determinant of the mode attractiveness became the main factors affecting modal choice. In general, modal choice models can be grouped into two groups: the model using diversion curves and probability theory models. Models using diversion curves using the characteristics of the traveling public transportation system characteristics and the characteristics of the trip as the variables that affect the modal choice, for example, the percentage that use private transport plotted along with one variable, such as vehicle ownership, or income. Probabilistic 821

9 model is a model that uses the utility factor of each alternative modes that will be determine the chances to be chosen by the traveler. In the analysis of the mode choice, research developing a model based on the relationship between the accessibility of the region with the attractiveness of transport modes. The principle of dynamic models applied to the structural links between the performance of road level of services (LoS) with vehicle speed or travel time and transportation costs for each type of mode. Comparisons of attractiveness each mode will change dynamically follow changes in the determining factors in the form of travel time and transport costs. The output of this stage is predicted composition of utilization of transport modes. The concept of causal loop diagram illustrating the causal relationship between the LoS, travel time, transport costs and the use of transport modes (except railway mode) can be seen in Figure 7. The growth of private car use in cities affected by several factors, these factors through feedback mechanisms directly affect the growth rate of private vehicle use, which in turn result in changes to the trend of using the vehicle. In developing the model of the utilization of private cars, some of the key factors affecting the growth rate of private vehicle use, such as the city's transportation structures, city traffic conditions, population characteristics, social characteristics (Song, 2013; Cirilio, 2010). Figure 7. Causal loop diagram model of private car utilization In modeling the use of public transport, user satisfaction is a determinant factor of the people's choice in using public transport (Li, 2013). Naturally, the higher the satisfaction of users of public transport will result higher use public transportation (reinforcing loop). Instead of dissatisfaction with the performance of public transport will result in a decrease in demand for public transport, this condition produce a negative feedback (balancing loop). Some literature states that studies of the use of public transport majority focus on efforts to seek out the dominant factors that influence the perception of public transport users to continue use the 822

10 public transport. Lai and Chen (2011) states that the factor that is important is the user's perception of the quality of public transport services. Hensher et al. (2003) and Konig (2002) state that timeliness is a key factor influencing the perception of public transport users to continue use the modes. Furthermore, Eboli and Mazzulla (2012) suggests several factors that are expected to be the dominant factor affecting the use of public transport such the guarantees of the availability of transportation, reliability, comfort, cleanliness, security, safety, tariff and environmental factors. Beirao and Cabral (2007) stated that compared with tariff, the timeliness, travel time and the convenience have a higher weight in influencing the decision to use public transport. Causal loop diagram, which are representing the structural linkage between determinant factors that influence the utilization of public transport, can be seen in Figure 8. Figure 8. Causal loop diagram model of city public transport utilization The level of service (LOS) is a qualitative measure used to assess the quality of service traffic. LOS is used to analyze the performance of highways by classifying traffic flow and assess the quality level of the traffic based on performance measures such as speed, density, etc. LOS concept was first developed for the highway in an era of rapid expansion in the use of private cars. The main concern is the congestion. In this modeling process, the performance of road level of service use the terminology that from Indonesian Highway Capacity (1997). Modifying the partial does development of the calculation formula and static formula becomes systemic and dynamic formula. The process of developing a systemic and dynamic method is done by developing a causal relation between variables, commonly treated with exogenous to other variables that are dynamic. One component of the model developed in an effort to build a dynamic model of the road level of service is a dynamic model of roadside impedance. 823

11 Figure 9. Causal loop diagram of road capacity and LoS Formulation of Quantitative Model Model causal loop diagrams are qualitative and became the foundation of the relationship between the variables that used in the model. To make the model can be used for policy simulations; the next step is to build a flow diagram, which is a translation of a model of causal loop diagrams on quantitative models. Flow diagram model for calculations of transport mode utilization fraction can be seen in Figure 10. Figure 10. Flow diagram of transport mode utilization model Model dynamics of the transport modes utilization fraction were developed for each type of mode, so overall there are five model that includes private vehicle models, the motorcycles models, the city's public transport models, bus, truck and train model. Mathematical Formulation of the flow chart is as follows: Mathematical formulation for vehicle utilization Where: Mu(t) = (1) Mu(t) : number of mode utilization at time t 824

12 RtMu(t) : rate of mode utilization at time t Mu(to) : initial number of mode utilization at time to Mathematical formulation for a variable fraction of the utilization of vehicles FrPgMobK(t) = BPgMk(t)/TBPgKndk(t) (2) Where: FrPgMobK(t) BPgMk(t) TBPgKndk(t) : fraction of mode utilization at time (t) : weight of mode utilization at time (t) : total weight of mode utilization at time (t) Mathematical formulation for weight of mode utilization BPgMk(t) = BDTMobK(t)*FrPgMobK(t0) (3) Where : BDTMobK(t) : weight of mode attractiveness at time (t) FrPgMobK(t0) : initial fraction of mode utilization Mathematical formulation for weight of mode utilization attractiveness Where: BDTMobK(t) = DTMobK(t)*T DTKnd(t) (4) DTMobK(t) : attractiveness mode utilization at time (t) T DTKnd(t) : total attractiveness mode utilization at time (t) Mathematical formulation for mode utilization attractiveness Where: DTMobK(t) = BWkMobK(t) *BByMobK(t) (5) BWkMobK(t) : weight of mode travel time at time (t) BByMobK(t) : weight of mode transport cost at time (t) Mathematical formulation for weight of mode travel time Where: BWkMobK(t) = AvWkMobK(t)*TAvWkKnd(t) (6) BWkMobK(t) : weight of mode travel time AvWkMobK(t) : average moe travel time( t) TAvWkKnd(t) : total average mode travel time (t) Mathematical formulation for mode travel time Where: AvWkMobK(t) = Jrk/VMobK(t)*EfLoS(t) (7) Jrk : distance to workplace VMobK(t) : average of mode speed (t) EfLoS(t) : effect of road LoS to speed (t) Mathematical formulation for weight of mode transport cost 825

13 where : ByMobK(t) = ByBBMMobK(t)+ ByPark+ ByToll (8) ByBBMMobK(t) : cost for fuel ByPark : parking tariff ByToll : toll tariff Mathematical formulation for mode transport cost where : ByBBMMobK(t) = By BBM per/km* Jrk* EfLoS(t) (9) ByBBMper km(t) : cost for fuel per km Jrk : distance to workplace EfLoS(t) : effect of road level of service to fuel cost (t) Dynamics models of transport modes utilization fraction associated with the model of road network capacity. The linkage with these road capacity models has become cause of dynamic behavior of the mode. The model focuses on developing interrelation between the factors, which have an effect on the dynamics of the roadside impedance, especially the dynamics changes of land use. Flow diagram model of the road network capacity can be seen in Figure 11. Figure 11. Flow diagram of dynamic road capacity model 826

14 Dynamic model of transport mode utilization fraction and the dynamics model of road capacity is part of the development model of land use and urban transport (ULTRANS). Until this paper made the model development process as a whole is still in the process of completion. 2.3 Data Used or The Model The data of land use system and transportation system is the main data needed in the modeling. Data related to land use system and transportation system in the research area used in modeling can be seen in Table 2. The data obtained through survey activities that use international standard. (IHCM, 2007). Table 2 Data Used for Modeling NO DATA VARIABEL SOURCE Land Use a. Land use type of road Sattelite Imageinery corridor b. Growth of land use of road corridor c. Intensity of land use d. Land use attactiveness Land use map Spatial Plan Agency of Planning of Bekasi City e. Land use Policy 2. Road a. Road Class and Function Road Survey b. Road Specificatiom Intersection Public and Private Transportatiom c. Quality Of Road d. Road Capacity e. Road Sign f. Road Used Intensity g. Road Developmant Plan h. Policy of Road Development Agency of Tranportation Bekasi City a. Location Intersection survey b. Physical Dimension Intersection survey c. Operation Mechanism Intersection survey d. Sign and Mark Intersection survey e. Capacity Intersection survey f. Policy Agency of Tranportation of Bekasi City a. Terminal and transit b. Utility c. Operation Mechanism d. Tarrif e. Operational or Transport Cost f. Quality of services g. Policy 827

15 3. MODEL TESTING AND VALIDATION Model testing was done with secondary and primary data collected during the research process. The initial step is to construct a model test scenarios to be simulated. Model testing at this stage is to find out the difference of simulation result between the trend-based scenarios and scenario-based policies. Models testing are also intended to identify the difference between the simulation results of the static model and dynamic model, which has developed. The simulation results are a prediction of transport mode utilization fraction in the area of research. Trend scenario is a scenario that build based on the conditions that occurred in the last 5 years without making any changes to the policy of developments. Policy scenarios built based on a policy change that is applied to some components of the system such as changes to the city public transport fares, toll rates, fuel costs, and changes in regional accessibility. Scenario parameters to be simulated can be seen in Table 3. In principle, the simulation is done in two sides of an urban transport policy that is, policies aimed at the demand side and supply-side policies. On the demand side, there is several kind of policy instrument such an increasing of transportation costs (such as fuel costs, toll fees and parking fees). While on the supply side, increasing of the road network capacity to be one of the policy instruments that are simulated. No Parameters Table 3. Parameters of model testing Initial condition Scenario Trend Scen-1 Scen-2 Scen-3 Scen-4 Scen-5 1 Parkir Tariff* Growth policy Private Car % 50% 0% 0% 0% 0% Motorcycles % 50% 0% 0% 0% 0% 2 Toll Tariff* Private Car % 15% 0% 0% 0% 0% Bus and Truck % 15% 0% 0% 0% 0% 3 Fuel Costs* Car 800 0% 0% 15% 0% 0% 0% Motorcycles 525 0% 0% 15% 0% 0% 0% Bus and truck 600 0% 0% 15% 0% 0% 0% 4 Public Transport Fare* City car (angkot) % 0% 0% 15% 0% 0% Bus % 0% 0% 15% 0% 0% Train (railway) % 0% 0% 15% 0% 0% 5 Accessibility** : Private car 15 0% 0% 0% 0% 0% 0% Motorcycles 20 0% 0% 0% 0% 0% 0% City car (angkot) 25 0% 0% 0% 0% 10% 0% Bus 30 0% 0% 0% 0% 10% 0% Train (raiway) 35 0% 0% 0% 0% 10% 0% 6 Road Capacity *** % 0% 0% 0% 0% 10% * in rupiah; ** in Km ; *** car per hour The resulting simulation outputs include the following: 828

16 Prediction of dynamics changes of the use of vehicles for each type of transport mode. The prediction of the number of private cars that used is one of the important components, which determine the level of service roads can be seen in the figure 12. Figure 12. Prediction of private car utilization Prediction of dynamics change of transport modes attractiveness for each scenario can be seen in table 4. Table 4. Attractiveness index of transport modes Attractiveness index of transport mode utilization Transport Modes Trend Scen-1 Scen-2 Scen-3 Scen-4 Scen-5 Private car Motorcycles City freight (angkot) Bus Train (railway) Prediction of dynamics change of transport modes utilization fraction for each scenario can be seen in table 5. Tabel 5. Prediction of transport modes utilization fraction Transport mode utilization fraction Transport Modes Trend Scen-1 Scen-2 Scen-3 Scen-4 Scen-5 Private car Motorcycles City freight (angkot) Bus Train (railway)

17 Prediction of dynamics change of utilization fraction change for each type transport mode. This output shown the difference between initial fractions of transport mode utilization with fraction, which produce from scenario simulation. The output from simulation can be seen in table 6. Table 6. Prediction of fraction change of transport modes utilization Transport Modes Fraction change of transport mode utilization Trend Scen-1 Scen-2 Scen-3 Scen-4 Scen-5 Private car Motorcycles City freight (angkot) Bus Train (railway) Prediction of V/C ratio as a result of scenario simulation represents the dynamics change of road level of services at Main Street in research area. Simulation result for V/C ratio for each scenario can be seen in table 7. Year Table 7. Prediction of V/C Ratio V/C ratio Trend Scen-1 Scen-2 Scen-3 Scen-4 Scen The result of testing model showed that the model was able to give a prediction of the dynamics change of the components of the city's transportation system especially the dynamics change of transport modes utilization. The results of this testing model furthermore need validation process to provide an overview proximity simulation results with the actual conditions. Method validations for modeling system dynamics have many kinds. Validation process in this stage performs by using principles statistical validation. The indicator of that used to value the simulation result is R 2 value. Linier regression used to Asses proximity between distribution data of simulation results with historical data. The simulation results were used, as a measure of validation is the simulation result of use of private cars. Based on the results of statistical analysis conducted, it was found that the proximity of the simulation results with 830

18 the data History been pretty close. R 2 obtained is at with a standard deviation of The tests can be seen in the picture. An important aspect of concern in the validation process for this dynamic model is a similarity of behavior patterns between the simulation results and historical data. Figure 13 indicates that the behavior pattern of the simulation results with the historical data already unidirectional although at some period there are gap. The validation results show that the model was able to mimic the behavior of historical data quite well. 4. DISCUSSION Figure 13. Behavior patterns of simulation result and historical data The results of model testing using a dynamic model shows that efforts which oriented towards the reduction of private vehicles in the area of research requires an integrated policy instrument between policies on the demand side and the supply side. Policy instrument aimed at the demand side in the form of increased cost of parking cars and motorcycles as well as an increase in toll fees, have enough to affect change in the fraction of use of private vehicles, but have not been able to change the fraction of the use of public transport significantly. Increased fuel costs are also not able to change Increased fuel costs are also significantly not able to change the composition of the fraction of the use of modes of transport from the domination of private vehicles to public transport use the composition of the fraction of the use of modes of transport from the domination of private vehicles to public transport use. The increases in public transport fares are almost always happens in the area of research, showed a decrease in the fraction of the use of public transport and the city bus transit systems significantly, and otherwise improve the use of private vehicles higher. Policy instruments improving access to the location of the transit city transport, bus and KRL (commuter train) can significantly increase the use of KLR and buses. This instrument policy was shortening the distance or travel time from the residence to the location of transit or road network, which has public transport route. The most significant fractional change from policy scenarios, which increased access, is the use of KRL. Fraction increased from 17% to 24%. The fractional change is a condition that is believed to help reduces the level of congestion in the area of research significantly. While the policy of building roads to improve road capacity, in the short term can reduce congestion, but in the medium and long term will increase the fraction of the use of private vehicles (refer back to Figure 13, Table 5 and Table 6). The simulation results with various policy scenarios shown the result theoretically reinforces the theory Braess Paradox (1968) and the theory of Death Spiral (Sterman, 2000) 831

19 which states that the construction of a new road network will increase the length of travel and in turn will worsen the congestion that occurs in urban areas. Compare to the others scenario, scenario 5 (increasing road capacity through physical development of road network) in middle and long term caused utilization of private car higher. The policy, which provides good prospects for changing fraction of dominance vehicle use to public transport, especially rail-based public transport, is a policy to increase access to transit location or route busing or KRL station. In the context of interaction land use and transportation in urban system, this model has shown the significant relation between them. This model gives facility to evaluate the land use change impact to utilization of transport mode through simulation of dynamic roadside impedance. Roadside impedance has many determinant factors such as dimension of physical road, social behavior and land use in the road corridor. In this model dynamics change of land use in the road corridor treat as an endogenous variable, which gave significance influence toward road impedance. This endogenous process of dynamic change of land use in the context of transport mode utilization modeling had become the one of distinction of this model compare to the other. 5. Conclusion Model dynamics of transport mode utilization fraction is a part of Urban Land use and Transportation Dynamics Model (ULTRANS DM ) which still in the process of completion. Focus of this model is to make model, which could accommodate the dynamic changes of determinant factors that affect the utilization of transport mode in urban area. In practical, these model developments try to minimize the number of exogenous variable and transform these variables in to endogenous variable. One of the innovations that introduce in this modeling research is the integration concept of dynamic land use model in to concept of modal choice modeling. Recently, trend in modal choice modeling is use the discrete choice model as the method to predict individual probability to choice an alternative transport mode in the specific boundary of the model (Acheampong,2015). In the perspective of system, many of modal choice models tend to use the unidirectional feedback, especially in the city level. The used of unidirectional feedback can identify from mathematic formulation which consist of two kind of variable which are dependent and independent variable. As a result these models used many variable dependent, which are exogenous. In the model that was built through this research, the variable that used is interrelation one to another. Relation between variables built in causal relationship not correlation. Model treat variable as dependent variable, as a consequence of this concept, model used two-way feedback as core business of model structure. From simulation result with this model, there are some conclusion can provide as follow: This model has been able to describe the dynamics behavior of utilization of transport mode in research area in qualitative and quantitative form. Qualitative form has shown as causal loop diagram, which represent of interrelationship between variable and became basic of model structure. Quantitative form has shown as numerical outputs of simulation result as a basic to Asses or evaluate the policy scenario. Simulation result of each policy scenario strengthens the theory, which state that congestion is not linier phenomenon, but is a complex phenomenon. Solution for congestion needs integration policy both from demand side and supply side policy. 832

20 In the context to reduction of private car use as a dominant factor of congestion, simulation result has shown that increasing road capacity, as a single policy instrument could not solve the problem sustainably. Simulation result shown that in order to increase the road capacity, beside development of physical road dimension, it is need policy in land use development. Land use change has proven have strong impact to roadside impedance, even in research area this land use has become dominant factor of decreasing of road capacity. The priority policy scenario which have good prospect to reduce the use of private car and increase the use of public transport especially commuter train (KRL) is increasing access from residential to bus and KRL transit location. This policy can be done through built priority transport system that support on reduction of travel time and cost to that location. Validation process with statistical method shown that model has able to produce prediction number of private car in research area, which have proximity to historical data. Base on this validation result, at this stage this model is ready to be an alternative tool for transport policy evaluation. ACKNOWLEDGEMENTS This modeling research has get some helps from some institutions, that are Ministry of Research and Higher Education for research fund, Agency of Planning of Bekasi City for data and primary survey, Agency of Geospatial Information for using the system dynamics software, and School of Architecture, Planning, and Policy Development, Institute Technology Bandung, Indonesia for counseling, information and source of references. REFERENCES Acheampong, A.Ransford., Silva, A. Elisabete. (2015). Land use transport interaction modeling: A review of the literature and future research directions. The Journal of transport and land use, vol. 8 no. 3, pp Allmendinger, P.(2009) Planning theory. Macmillan, UK. Anable, J., Gatersleben, B.(2005) All works and no play? the roles of instrumental and affective factors in work and leisure journeys by different travel modes. Transportation Research Part A, Policy and Practice, 39(2-3), Arga, W.(1985) Dynamic and integer programming, BPFE, Yogyakarta. (in Indonesian) Bajracharya,A. (2016). Public transportation and private car: A system dynamics approach in understanding the mode choice, International Journal of System Dynamics Application, 5(2), Beirao, G., & Cabral, J. A. S. (2007) Understanding attitudes towards public transport and private car: A qualitative study. Transport Policy, 14(6), Boris, S. Kerner.(1999) Congested traffic Ffow: observations and theory, Transportation Research Record, 1678, Braess, D., Wakolbinger,T.(2005) On a paradox of traffic planning, journal Transportation Science, 39, 2005, Cabrera, D. and Cabrera, L. (2015) Systems Thinking Made Simple: New Hope for Solving Wicked Problems. Ithaca, NY: Odyssean Press. ISBN Center Bureou of Statistic, (2014) Bekasi city in figure, BPS press, Bekasi, Indonesia. 833

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