Evaluate the feasibility of implementing activitysim activity-based model for Vietnamese municipalities in term of the available input data sources
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Author
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Cao Thi Xuan MyThe University of Danang - University of Technology and Education, VietnamNguyen Van NoiTien Giang University, Tien Giang province, VietnamChu Cong MinhHo Chi Minh City University of Technology (HCMUT), VNU-HCM, Ho Chi Minh City, Vietnam
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Tóm tắt
Activity-based modeling (ABM) has emerged as a promising approach in urban travel demand forecasting, addressing the limitations of traditional models that have dominated the field for over 50 years. ABM offers a powerful framework for simulating traffic at the city scale, enabling a deeper understanding of the complex behavior of urban transportation systems under various scenarios. This paper concisely overviews recent advancements and challenges associated with applying activity-based models in travel demand forecasting. Additionally, the article explores the operational process of the ActivitySIM model, a specific ABM tool for traffic demand forecasting, by detailing the required input data and parameters. The potential for deploying this technology in Ho Chi Minh City was analyzed to highlight relevant data sources. Furthermore, the paper discusses potential solutions to improve data accuracy and enhance the consistency of ABMs across multiple days of the week, addressing critical challenges in implementing these models effectively.
Tài liệu tham khảo
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[1] Ben-Akiva, "Travel demand modeling”, Transportation Systems Analysis: Demand & Economics, 2008.
[2] Mladenovic and A. Trifunovic, "The Shortcomings of the Conventional Four Step Travel Demand Forecasting Process”, Journal of Road and Traffic Engineering, vol. 60, no. 1, pp. 5-12, 2014.
[3] Burns and T. Hanson, "Four-Step Modelling Active Transportation For Small Cities: Challenges And Opportunities”, in Canadian Transportation Research Forum 57th Annual Conference, 2022.
[4] Wang, B. Wu, and L. Li, Urban Redevelopment and Traffic Congestion Management Strategies. Singapore: Springer Nature Singapore, 2022, pp. 147-186.
[5] G. McNally, The activity-based approach in: Handbook of transport modelling, Elsevier, 2000, pp. 53-69.
[6] G. McNally and C. R. Rindt, "The activity-based approach”, in Handbook of transport modelling: Emerald Group Publishing Limited, 2007.
[7] Henson, K. Goulias, and R. Golledge, "An assessment of activity-based modeling and simulation for applications in operational studies, disaster preparedness, and homeland security”, Transportation Letters, vol. 1, no. 1, pp. 19-39, 2009.
[8] Vovsha, M. Bradley, and J. L. Bowman, Activity-based travel forecasting models in the United States: progress since 1995 and prospects for the future, Elsevier, 2005, pp. 389-414.
[9] Castiglione, M. Bradley, and J. Gliebe, Activity-based travel demand models: a primer, Transportation Research Board, SHRP 2 Report, 2015.
[10] S. Subbarao and K. Krishnarao, "Activity based approach to travel demand modelling: An overview”, European Transport-Trasporti Europei, no. 61, p. 6, 2016.
[11] Zhong, R. Shan, D. Du, and C. Lu, "A comparative analysis of traditional four-step and activity-based travel demand modeling: a case study of Tampa, Florida”, Transportation Planning and Technology, vol. 38, no. 5, pp. 517-533, 2015.
[12] S. Hasnine and K. Nurul Habib, "Tour-based mode choice modelling as the core of an activity-based travel demand modelling framework: A review of state-of-the-art”, Transport Reviews, vol. 41, no. 1, pp. 5-26, 2021.
[13] Adler and M. Ben-Akiva, "A theoretical and empirical model of trip chaining behavior”, Transportation Research Part B: Methodological, vol. 13, no. 3, pp. 243-257, 1979.
[14] R. Pinjari and C. R. Bhat, "Activity-based travel demand analysis”, in A handbook of transport Economics: Edward Elgar Publishing, 2011.
[15] Balmer, K. W. Axhausen, and K. Nagel, "Agent-based demand-modeling framework for large-scale microsimulations”, Transportation Research Record, vol. 1985, no. 1, pp. 125-134, 2006.
[16] Balmer, K. Meister, M. Rieser, K. Nagel, and K. W. Axhausen, "Agent-based simulation of travel demand: Structure and computational performance of MATSim-T”, Arbeitsberichte Verkehrs-und Raumplanung, vol. 504, 2008.
[17] Bellemans, B. Kochan, D. Janssens, G. Wets, T. Arentze, and H. Timmermans, "Implementation framework and development trajectory of FEATHERS activity-based simulation platform”, Transportation Research Record, vol. 2175, no. 1, pp. 111-119, 2010.
[18] A. Arentze, "Albatross: A Learning-Based Transportation Oriented Simulation System”, Via-Via, vol. 36, no. 3, pp. 49-51, 2005.
[19] Arentze and H. Timmermans, Albatross: a learning based transportation oriented simulation system. Citeseer, 2000.
[20] A. Arentze and H. J. Timmermans, "A learning-based transportation oriented simulation system”, Transportation Research Part B: Methodological, vol. 38, no. 7, pp. 613-633, 2004.
[21] Auld and A. Mohammadian, "Framework for the development of the agent-based dynamic activity planning and travel scheduling (ADAPTS) model”, Transportation Letters, vol. 1, no. 3, pp. 245-255, 2009.
[22] A. Auld and A. Mohammadian, "Planning Constrained Destination Choice Modeling in the Adapts Activity-Based Model”, in Proceedings of the Transportation Research Board 90th Annual Meeting, Washington, DC, USA, 2011, pp. 23-27.
[23] Auld and A. K. Mohammadian, "Activity planning processes in the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model”, Transportation Research Part A: Policy and Practice, vol. 46, no. 8, pp. 1386-1403, 2012.
[24] Adnan et al., "Simmobility: A multi-scale integrated agent-based simulation platform”, in 95th Annual Meeting of the Transportation Research Board Forthcoming in Transportation Research Record, 2016, vol. 2: The National Academies of Sciences, Engineering, and Medicine Washington, DC.
[25] Auld, M. Hope, H. Ley, V. Sokolov, B. Xu, and K. Zhang, "POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations”, Transportation Research Part C: Emerging Technologies, vol. 64, pp. 101-116, 2016.
[26] Gali, S. Eidenbenz, S. Mniszewski, L. Cuellar, and C. Teuscher, "ActivitySim: large-scale agent based activity generation for infrastructure simulation”, Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2008.
[27] Tajaddini, G. Rose, K. M. Kockelman, and H. L. Vu, Recent progress in activity-based travel demand modeling: rising data and applicability. IntechOpen, 2020.
[28] McFadden, "The measurement of urban travel demand”, Journal of public economics, vol. 3, no. 4, pp. 303-328, 1974.
[29] Chicago Metropolitan Agency For Planning, "Activitysim Activity-Based model technical description”, RSG, 2023. [Online]. Available: https://s.net.vn/AyjS
[30] T. Linh, M. Adnan, W. Ectors, B. Kochan, T. Bellemans, and V. A. Tuan, "Exploring the spatial transferability of FEATHERS–An activity based travel demand model–For Ho Chi Minh city, Vietnam”, Procedia Computer Science, vol. 151, pp. 226-233, 2019.
[31] H. T. Linh, V. A. Tuan, M. Adnan, and T. Bellemans, "Analyzing Potential Impacts of Motorcycle Travel Demand Management Using an Activity-Based Travel Demand Model for Ho Chi Minh City, Vietnam”, Procedia Computer Science, vol. 220, pp. 567-574, 2023.