An open data-driven approach for travel demand synthesis: an application to São Paulo

REFERENCES

• ActivitySim (2020). An open platform for activity-based travel modeling. URL https://activitysim.github.io/.
   Google Scholar
• Axhausen, K. W., & Gärling, T. (1992). Activity-based approaches to travel analysis: Conceptual frameworks, models, and research problems. Transport Reviews, 12(4), 323–341. https://doi.org/https://doi.org/10.1080/01441649208716826
   Web of Science ®Google Scholar
• Balac, M., Ciari, F., & Axhausen, K. W. (2015). Carsharing demand estimation: Case study of Zurich area. Transportation Research Record, 2536(1), 10–18. https://doi.org/https://doi.org/10.3141/2536-02
   Google Scholar
• Balać, M., & Hörl, S. (2021, January). Synthetic population for the state of California based on open-data: examples of San Francisco Bay area and San Diego County. 100th Annual Meeting of the Transportation Research Board, Washington, DC, USA.
   Google Scholar
• Bischoff, J., & Maciejewski, M. (2016). Simulation of city-wide replacement of private cars with autonomous taxis in Berlin. Procedia Computer Science, 83, 237–244. https://doi.org/https://doi.org/10.1016/j.procs.2016.04.121
   Google Scholar
• Bowman, J. L. (1995). Activity based travel demand model system with daily activity schedules [Doctoral dissertation]. Massachusetts Institute of Technology.
   Google Scholar
• Bowman, J. L. (1998). The day activity schedule approach to travel demand analysis [Doctoral dissertation]. Massachusetts Institute of Technology
   Google Scholar
• Chapin, F. S. (1974). Human activity patterns in the city: Things people do in time and in space, volume 13. Wiley-Interscience.
   Google Scholar
• Chapuis, K., Taillandier, P., Renaud, M., & Drogoul, A. (2018). Gen*: a generic toolkit to generate spatially explicit synthetic populations. International Journal of Geographical Information Science, 32(6), 1194–1210. https://doi.org/https://doi.org/10.1080/13658816.2018.1440563
   Web of Science ®Google Scholar
• Chu, Z., Cheng, L., & Chen, H. (2012, August 3–6). A review of activity-based travel demand modeling. In CICTP 2012: Multimodal transportation systems: Convenient, safe, cost-effective, efficient, Beijing, China (pp. 48–59).
   Google Scholar
• Coordenadoria de Informaço, Evidência, Tecnologia e Matrícula (CITEM) (2020). Endereços de escolas (addresses of the schools). URL https://dados.educacao.sp.gov.br/dataset/endere%C3%A7os-de-escolas.
   Google Scholar
• de Dios Ortúzar, J., & Willumsen, L. G. (2011). Modelling transport (4th ed.). John Wiley & Sons.
   Google Scholar
• Diogu, W. O. (2019). Towards the implementation of an activity-based travel demand model for emerging cities: Integrating TASHA and MATSim [Master's thesis]. University of Toronto, Canada. https://www.proquest.com/dissertations-theses/towards-implementation-activity-based-travel/docview/2323546171/se-2?accountid=27229
   Google Scholar
• D’Orazio, M., Di Zio, M., & Scanu, M. (2012, May, 29–June 1). Statistical matching of data from complex sample surveys. Proceedings of the European Conference on Quality in Official Statistics-Q2012, volume 29.
   Google Scholar
• Erath, A., Fourie, P. J., van Eggermond, M. A., Ordonez Medina, S. A., Chakirov, A., & Axhausen, K. W. (2012). Large-scale agent-based transport demand model for Singapore. Arbeitsberichte Verkehrs- und Raumplanung, 790.
   Google Scholar
• Hägerstrand, T. (1970). What about people in regional science? In Papers of the regional science association (Volume 24, pp. 6–21). John Wiley & Sons.
   Google Scholar
• Hörl, S., & Axhausen, K. W. (2020, January 12–16). Relaxation–discretization algorithm for spatially constrained secondary location assignment. 99th Annual Meeting of the Transportation Research Board.
   Google Scholar
• Hörl, S., & Balac, M. (2021a). Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data. Transportation Research Part C: Emerging Technologies, 130, 103291. https://doi.org/https://doi.org/10.1016/j.trc.2021.103291
   Web of Science ®Google Scholar
• Hörl, S., & Balac, M. (2021b). Introducing the eqasim pipeline: From raw data to agent-based transport simulation. Procedia Computer Science, 184, 712–719. https://doi.org/https://doi.org/10.1016/j.procs.2021.03.089
   Google Scholar
• Hörl, S., Balac, M., & Axhausen, K. W. (2018). A first look at bridging discrete choice modeling and agent-based microsimulation in matsim. Procedia Computer Science, 130, 900–907. https://doi.org/https://doi.org/10.1016/j.procs.2018.04.087
   Google Scholar
• Hörl, S., Becker, F., Dubernet, T., & Axhausen, K. W. (2019). Induzierter Verkehr durch autonome Fahrzeuge: Eine Abschtzung (traffic induced by autonomous vehicles: An estimation). SVI 2016/001, Schriftenreihe, 1650.
   Google Scholar
• Horni, A., Nagel, K., & Axhausen, K. W. (2016). The multi-agent transport simulation MATSim. Ubiquity Press.
   Google Scholar
• Ilahi, A., & Axhausen, K. W. (2019). Integrating Bayesian network and generalized raking for population synthesis in Greater Jakarta. Regional Studies, Regional Science, 6(1), 623–636. https://doi.org/https://doi.org/10.1080/21681376.2019.1687011
   Web of Science ®Google Scholar
• Instituto Brasileiro de Geografia e Estatística (IBGE). (2016). Principais destaques da evoluç ao do mercado de trabalho nas regi oes metropolitanas abrangidas pela pesquisa (Main highlights of the evolution of the labor market in the metropolitan regions covered by the survey). URL ftp://ftp.ibge.gov.br/Trabalho_e_Rendimento/Pesquisa_Mensal_de_Emp%rego/Evolucao_Mercado_Trabalho/retrospectiva2003_2011.pdf.
   Google Scholar
• Instituto Brasileiro de Geografia e Estatística (IBGE). (2017). Pesquisa nacional por amostra de domicílios contínua – quarto trimestre de 2017 (national household sample survey – fourth quarter 2017). URL https://biblioteca.ibge.gov.br/visualizacao/periodicos/2421/pnact_2017%_4tri.pdf.
   Google Scholar
• Kitamura, R. (1988). An evaluation of activity-based travel analysis. Transportation, 9–34.
   Web of Science ®Google Scholar
• Lee, Y., Hickman, M., & Washington, S. (2007). Household types and structure, time–use pattern, and trip-chaining behavior. Transportation Research Part A: Policy and Practice, 41(10), 1004–1020. https://doi.org/https://doi.org/10.1016/j.tra.2007.06.007
   Web of Science ®Google Scholar
• Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., & Wießner, E. (2018, November 4–7). Microscopic traffic simulation using sumo. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 2575–2582). IEEE.
   Google Scholar
• Mallig, N., Kagerbauer, M., & Vortisch, P. (2013). Mobitopp – A modular agent-based travel demand modelling framework. Procedia Computer Science, 19, 854–859. https://doi.org/https://doi.org/10.1016/j.procs.2013.06.114
   Google Scholar
• Recker, W. W. (1995). The household activity pattern problem: General formulation and solution. Transportation Research Part B: Methodological, 29(1), 61–77. https://doi.org/https://doi.org/10.1016/0191-2615(94)00023-S
   Web of Science ®Google Scholar
• Scheiner, J., & Holz-Rau, C. (2017). Women’s complex daily lives: A gendered look at trip chaining and activity pattern entropy in Germany. Transportation, 44(1), 117–138. https://doi.org/https://doi.org/10.1007/s11116-015-9627-9
   Web of Science ®Google Scholar
• Viegas, J. M., & Martínez, L. M. (2010, July 11–15). Generating the universe of urban trips from a mobility survey sample with minimum recourse to behavioural assumptions. Proceedings of the 12th World Conference on Transport Research, Lisbon.
   Google Scholar
• Wen, C.-H. (1998). Development of stop generation and tour formation models for the analysis of travel/activity behavior.
   Google Scholar
• Zanatta, R. A., & Kira, B. (2018). Regulation of Uber in Sao Paulo: From conflict to regulatory experimentation. International Journal of Private Law, 9(1–2), 83–94. https://doi.org/https://doi.org/10.1504/IJPL.2018.097332
   Google Scholar
• Ziemke, D., Kaddoura, I., & Agarwal, A. (2019b). Entwicklung eines regionalen, agentenbasierten Verkehrssimulationsmodells zur Analyse von Mobilitätsszenarien für die Region Ruhr. In Mobilität in Zeiten der Veränderung (pp. 383–410). Springer.
   Google Scholar
• Ziemke, D., Kaddoura, I., & Nagel, K. (2019a). The MATSim Open Berlin Scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science, 151, 870–877. https://doi.org/https://doi.org/10.1016/j.procs.2019.04.120
   Google Scholar
• Ziemke, D., Nagel, K., & Bhat, C. (2015). Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record, 2493(1), 117–125. https://doi.org/https://doi.org/10.3141/2493-13
   Google Scholar