The effect of urban transportation infrastructures on the behavior of choosing a private car based on the system dynamics approach
Subject Areas : Industrial ManagementLadan Shahhosseini 1 , Reza Radfar 2 , Abbas Toloie Ashlaghi 3
1 - Ph.D. Candidate, Department of Industrial management, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Professor, Department of Industrial Management, Science and Research Unit, Islamic Azad University, Tehran, Iran
3 - Professor, Department of Industrial Management, Science and Research Unit, Islamic Azad University, Tehran, Iran
Keywords: public transportation, car-oriented, travel behavior, designing, system dynamics model,
Abstract :
People's travel behavior is reflected in their choice of transportation modes and is influenced by various factors. Travelling by private car often leads to numerous problems. Therefore, policymaking to shift citizens’ travel behavior from private car use to public bus transportation is important. Hence, the purpose of this research is to examine the travel behavior of Tehran’s residents using a system dynamics simulation model. Accordingly, after identifying the main variables affecting travel behavior through library studies and expert interviews, the hypotheses of the model were formulated. Subsequently, by drawing the cause-and-effect diagram and the stock and flow model, the relevant mathematical equations were derived and validated, and the model was then tested for accuracy and reliability. Subsequently, policies related to the three variables of the number of BRT buses, access to BRT buses and parking capacity were implemented through several scenarios. The results revealed that increasing the rate of parking construction does not lead to favorable results. Moreover, the increase in the number of BRT bus fleet alone cannot have an effective role either under current conditions or when combined with the scenarios involving reducing or increasing parking construction rates. Reducing the rate of parking alone has favorable results. Similarly, increasing the number of BRT stations yields positive results. Moreover, implementing both scenarios simultaneously- reducing the rate of parking development and expanding BRT stations- represents the most effective scenario among those analyzed.
Key Words:
public transportation, car-oriented, travel behavior, system dynamics model
1.Introduction
The use of private cars has become a major challenge for cities worldwide due to its negative externalities, such as traffic congestion and environmental pollution. Achieving sustainability in transportation and continuing economic development requires reducing the use of private cars and increasing dependence on public transportation. In Tehran, according to the obtained statistics, the demand for daily trips, the share of rides, the demand for daily car trips and the number of private cars used per day are all increasing. The current statistical situation indicates the important role of planners and policy makers in this area. In fact, travel planning seeks to create a balance between travel supply and demand, where the first depends on the capacity of the transportation network and the second on the amount of travel needs of users. Knowing the travel demand helps the planners of this area to develop the necessary infrastructure according to the actual demand or to use the maximum capacity of the existing transportation network. Understanding the factors affecting the choice of public transport travel method is very necessary for the purpose of transport planning. Therefore, this article specifically examines the long-term effect of travel supply policies (i.e., parking capacity, access to BRT stations and fleet) on the competitive behavior between choosing private cars and BRT buses in Tehran.
- Literature review
In recent years, numerous studies have been conducted to investigate the factors influencing on the decision-making regarding the choice of travel method. Zhou and his colleagues (2023) investigated the necessary policies to reduce the use of private vehicles in an urban area in the Netherlands using an activity-based travel demand model. The results indicate that the improvement of public transportation services and small transportation network increase the potential of displacement hubs in terms of stabilizing travel displacement patterns. Also, limiting parking capacity and increasing parking costs in city centers are especially useful strategies for reducing car use. McSlan and Sperry (2023) investigated the relationship between parking requirements and car ownership in Swedish municipalities. The results of this study showed that reducing parking minimums can be an effective policy to reduce car ownership. Khosravi et al. (2020) used system dynamics modeling to evaluate transportation demand management policies in the center of Isfahan. In this research, incentive and restriction policies were investigated in the central commercial area of Isfahan. Effective transportation policies were implemented for ten years and were ranked based on three indicators of air pollution, energy consumption and traffic flow. The results revealed that completing the metro network, developing the BRT network, improving bicycle facilities, implementing road pricing, increasing parking fees, improving bus and taxi services, enforcing the even and odd policy, and encouraging car sharing are among the most effective policies in the center of Isfahan.
- Method
The present study aims to provide a dynamic simulation model of Tehran residents’ travel behavior using advanced modeling tools, in order to conduct a more detailed analysis of the residents' travel mode choices and their consequences, thereby helping policymakers improve the behavioral anomalies in the transportation sector. In this research, the method used was descriptive and modeling in its purpose. Additionally, the variables influencing travel mode choice were identified through a review of the research literature and experts' opinions. These variables were then incorporated into a system dynamics model, enabling simulation and examination of different policies over time. This research was conducted in Tehran, using the data collected from the Tehran City Transportation Organization on the share of Tehran residents’ trips made by private cars and BRT buses between 2011 to 2021. Since the system dynamics method consists of five steps, the model presented in this research was structured accordingly.
The first step in this process was to identify the problem and its boundaries. In this step, the reference variable and its past behavior were also examined. Based on this analysis, the number of private cars was identified as the main issue that this research aims to reduce. The second step focused onformulating dynamic hypotheses. In this step, the main variables affecting the problem were examined and the boundaries of the model were determined. In this regard, after reviewing the research literature and examining previous studies, a semi-structured questionnaire was used to get the experts' opinions. The experts were first asked the main questions, and during the interview, additional questions were posed as needed, based on the flow of the discussion. Through this process and using the opinions of subject matter experts, the research variables were identified and refined for use in the next stages. Drawing on the theoretical foundations of research and experts' opinions, and based on a clear understanding of the problem, cause-and-effect loops were designed and gradually a complete diagram of cause-and-effect loops was created, ultimately providing a simplified representation of the real-world system. In this regard, one of the influential factors contributing to the undesirable behavior of choosing a private car is its high level of attractiveness.
After formulating the hypotheses, the key variables were identified including parking capacity, number of BRT buses and number of BRT stations as the independent variables and the number of private cars used per day as the dependent variable. Then, how these variables affect each other were investigated and the cause-and-effect loops were drawn. The next step was to simulate the model in the relevant software. Once the main hypotheses and the system boundaries were formed, the model could be implemented. By entering the mathematical equations and identifying the stock, flow and auxiliary variables, the stock-flow diagram was then developed. Finally, the model was simulated and implemented. By analyzing the changes in the behavior of the model over time and comparing it with what actually happened in the past, the validation of the model was done to validate its ability to predict future behavior. In this research, the status of the error index and the coefficient of determination of 97% confirmed the validity of the model for predicting the future behavior of the model. Also, another required measure to validate the model was to analyze its sensitivity in the implementation of different scenarios. Other validation tests, including the structural evaluation test, system boundary adequacy test, dimensional consistency test, equation logic test, and model behavior prediction test were also performed and had acceptable results.
- Result
After simulating and examining the behavior of the model components over the desired thirty-year period, the values of the different variables of the model were adjusted and their effects were analyzed on the main variable, that is, the number of private cars used per day. In addition, the time step of model 1 and the time unit of the year were defined. By changing the values of parking construction rate, BRT bus purchase rate and the number of BRT stations, eight scenarios were compiled. The outputs of Vensim software regarding the first scenario or the increase in the rate of parking construction showed that the number of private cars has increased significantly with the increase in the rate of parking construction. In the second scenario which involves the reduction of the parking construction rate, the results indicated that the number of private cars will increase at a slower rate compared to the current situation. In the third scenario, that is, increasing the parking rate and increasing the BRT bus purchase rate at the same time, it was observed that the simultaneous application of increasing the parking rate and increasing the BRT bus purchase rate leads to an increase in the number of private car use. Notably, the increasing slope of the number of private cars in case of simultaneous application of the changes did not change significantly compared to the scenario in which only the parking construction rate was increased. In relation to the fourth scenario, addressing the increase in the purchase rate of BRT buses, the simulation results showed that the number of private cars after increasing the purchase rate of BRT buses was not different from the existing conditions, which means that with the increase in the purchase rate of BRT buses, the number of private cars will still be increasing with the same slope of the existing conditions. Regarding the fifth scenario, that is, reducing the parking rate and increasing the BRT bus purchase rate at the same time, it was observed that the simultaneous application of reducing the parking rate and increasing the BRT bus purchase rate led to an increase in the number of private cars but at a slower rate than under the existing conditions. In the sixth scenario regarding the increase in the number of BRT stations, the results also showed that with the increase in the number of BRT stations, the number of private cars increased but at a slower rate. Moreover, as regards to the seventh scenario, or increasing the rate of parking construction and the number of BRT bus stations at the same time, after performing the simulation, it was observed that simultaneous increase in the rate of parking construction as well as the number of BRT stations leads to an increase in the use of private cars. In this case, the increasing trend in the number of private cars, compared to the situation where only the rate of parking construction was increased, did not change significantly, showing a slight improvement. In the eighth scenario, the simulation results after reducing the rate of parking construction and increasing the number of BRT bus stations at the same time indicated a lower slope than under the existing conditions, leading to the best performance compared to the other seven scenarios.
- Discussion
The practical findings of the current research show that, under current situation, improving access to the BRT bus stations is more critical than expanding the BRT bus fleet. Additionally, simultaneously purchasing BRT buses and building new parking facilities cannot contribute to the reduction of private car use. This finding is important for urban planners as the simultaneous implementation of these two policies fails to encourage a shift toward BRT use. As long as the time of searching for parking decreases due to the construction of new parking construction and private cars remain attractive, merely buying BRT buses will not significantly change travel behavior toward public transportation. Finally, the practical findings reveal that choosing the BRT bus travel mode compared to a private car is only possible when, in addition to strengthening the BRT bus infrastructure, we overlook expanding car infrastructure. Helping the managers of different areas of the municipality to observe the effects of independent policies is another practical finding of this research since the results showed that contradictory decisions can lead to the loss of desirable results and the imposition of heavy costs.
Conflict of interests: none
Ahmadvand, A.M., Mohammadiani, Z., & Khodadadi Abiazani, Hadith. (2016). Urban transportation system modeling using the system dynamics approach: policies to reduce traffic. Rahor scientific and promotional quarterly, year 14, number 37,1-28. [In Persian]. magiran.com/ p2105402
Andrew, L., Edes Kitali, A., Sando, Th., & Musagasa, J. (2022). Operational evaluation of the bus rapid transit system: Case study of Dar es Salaam city. Journal of Public Transportation, 24(6), 1-8. doi:10.1016/j.jpubtr.2022.100020
Axhausen, K.W., Polak, J. & Boltze, M. (1993). Effectiveness of parking guidance and information systems: Recent evidencefrom Nottingham and Frankfurt/Main. Transport Studies Unit, Oxford University-Publications, 1-7. https://www.researchgate.net/publication/238101931
Cheng, Y.-H., et al., (2015). Urban transportation energy and carbon dioxide emission reduction Strategies. Applied Energy, 157(1), 953-973. doi:10.1016/j.apenergy.2015.01.126
Cherchi, E., (2019). Our IATBR: 45years contributing to travel behavior research in Mapping the Travel Behavior Genome. K.G.a.A.W.D. Goulias, Editor. Elsevier, 17-28. https://www.everand.com/book/ 432661558/Mapping-the-Travel-Behavior-Genome
Chiou, Y.C., Jou, R.C., & Yang, C.H. (2015). Factors Affecting Public Transportation Usage Rate: Geographically Weighted Regression. Transportation Research Part A:Policy and Practice, 78(1), 161-177. doi:10.1016/j.tra.2015.05.016
Elahi, M. (2012). Dynamic analysis of factors affecting the development of the wire and cable industry in Iran. Master's thesis. Faculty of Engineering. Yazd University [In Persian]. https://elmnet.ir/doc/ 10641940-26471
Gabbe, C.J., Pierce, G., & Clowers, G. (2020). Parking policy: The effects of residential minimum parking requirements in Seattle. Land Use Pol, 91, 104053. doi:10.1016/j.landusepol.2019.104053
Grazi, F., & Van den Bergh, J.C. (2008). Spatial Organization, Transport and Climate Change: Comparing Instruments of Spatial Planning and Policy. Ecological Economics, 67(4), 630-639. doi:10.1016/j.ecolecon.2008.01.014
Habibiyan, M., & Kermanshah, M. (2012). Evaluation of the contribution of transportation management policies on the choice of alternative methods of personal riding in daily business trips. Transportation Engineering Quarterly, 3(3), 181-197[In Persian]. https://civilica.com/doc/244927
Horridge, M. (1994). A computable general equilibrium model of urban transport demand. Journal of policy modeling, 16, 427-457. doi:10.1016/0161-8938(94)90037-X
Khosravi, Sh., Haghshenas, H., & Salehi, v. (2020). Macro-Scale Evaluation of Urban Transportation Demand Management Policies in CBD by Using System Dynamics Case Study: Isfahan CBD. Transportation Research Procedia, 48(1), 2671-2689. doi:10.1016/j.trpro.2020.08.246
Kimpton, A., Pojani, D., Ryan, C., Ouyang, L., Sipe, N., & Corcoran, J. (2021). Contemporary parking policy, practice, and outcomes in three large Australian cities, Progress in planning, 153, 1-25. doi:10.1016/j.progress.2020.100506
Lin, J., Kang, J., Khanna, N., Shi, L., Zhao, X., & Liao, J. (2018). Scenario analysis of urban GHG peak and mitigation co-benefits: A case study of Xiamen City, China. Journal of Cleaner Production, 171, 972–983. doi:10.1016/j.jclepro.2017.10.040
Liu, Q., Wang, J., Chen, P., & Xiao, Z. (2016). How does parking interplay with the built environment and affect automobile commuting in high-density cities? A case study in China. Urban Stud, 54, 3299–3317. doi:10.1177/0042098016667040
McAslan, D., & Sprei, F. (2023). Minimum parking requirements and car ownership: An analysis of Swedish municipalities. Transport Policy, 135, 45–58. doi:10.1016/j.tranpol.2023.03.003
Ostadi Jafari, M., & Rasafi, A.A. (2013). Environmental model of urban transportation planning using dynamic system models. Scientific research quarterly of environmental science and technology, 14(54), 11 – 28 [In Persian]. https://sid.ir/paper/486515/fa
Ostadi Jafari, M., & Habibian, M. (2013). Long-term evaluation of the combined effect of transportation demand management policies using the system dynamics model (case study: Mashhad metropolis). Transportation Engineering, 6th year, 1st issue, [In Persian]. https://civilica.com/doc/489352
Rivasplata, C. R. (2013). Congestion pricing for Latin America: Prospects and constraints. Research in Transportation Economics, 40(1): 56-65 doi: 10.1016/j.retrec.2012.06.037
Rizzi, L.I., & Maza., C. (2017). The external costs of private versus public road transport in the Metropolitan Area of Santiago, Chile. Transportation Research Part A: Policy and Practice, 98, 123–140. doi:10.1016/j.tra.2017.02.002
Saputra, H,Y., & F. Radam, I. (2023).Accessibility model of BRT stop locations using Geographically Weighted regression (GWR): A case study in Banjarmasin, Indonesia.International Journal of Transportation Science and Technology, 12(3), 779-792. doi:10.1016/j.ijtst.2022.07.002
Yang, X., Lin, W., Gong, R., Zhu, M., & Springer, C. (2021). Transport decarbonization in big cities: An integrated environmental co-benefit analysis of vehicles purchases quota-limit and new energy vehicles promotion policy in Beijing. Sustainable Cities and Society, 71(5), 102976. doi:10.1016/j.scs.2021.102976
Yin, C., Shao, C., & Wang, X. (2018). Built Environment and Parking Availability: Impacts on Car Ownership and Use. Sustainability, 10(7), 1-15. doi:10.3390/su10072285
Zheng, Z., Washington, S., Hyland, P., Sloan, K. & Liu, Y. (2016). Preference Heterogeneity in Mode Choice Based on a Nationwide Survey With a Focus on Urban Rail. Transportation Research Part A: Policy and Practice, 91, 178-194. doi:10.1016/j.tra.2016.06.032
Zhou, H., Dorsman, J.L., Mandjes, M., & Snelder, M. (2023). Sustainable mobility strategies and their impact: a case study using a multimodal activity based model. Case Studies on Transport Policy, 11, 1-19. doi:10.1016/j.cstp.2022.100945
