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Integration of Different Mobility Behaviors and Intermodal Trips in MATSim

Author

Listed:
  • Johannes Müller

    (AIT Austrian Institute of Technology, Giefinggasse 6, 1210 Vienna, Austria)

  • Markus Straub

    (AIT Austrian Institute of Technology, Giefinggasse 6, 1210 Vienna, Austria)

  • Gerald Richter

    (AIT Austrian Institute of Technology, Giefinggasse 6, 1210 Vienna, Austria)

  • Christian Rudloff

    (AIT Austrian Institute of Technology, Giefinggasse 6, 1210 Vienna, Austria)

Abstract

MATSim is an open-source simulation framework for mesoscopic traffic simulations that has gained popularity in recent years. In this paper, we present a MATSim model for the city of Vienna, with a particular emphasis on the intermodal routing framework used to create agent trips, and the development of a utility function to specify different agents’ mode preferences. To create agent activity chains, we use mobility diaries from the national transportation survey in Austria and disaggregate the available geospatial information to best fit the reported travel times. The novelty of the intermodal framework is the ability to create trips that do not consist of only one mode of transportation, but to also include bicycle, car, and demand-responsive transport (e.g., cab, car sharing) trips in combination with public transportation. To represent the different mobility behaviors of agents, we divide the population into groups and assign them different utility functions for transportation modes according to their socio-demographic characteristics. After presenting the validation of the model, we discuss ways to improve the model.

Suggested Citation

  • Johannes Müller & Markus Straub & Gerald Richter & Christian Rudloff, 2021. "Integration of Different Mobility Behaviors and Intermodal Trips in MATSim," Sustainability, MDPI, vol. 14(1), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2021:i:1:p:428-:d:715497
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    References listed on IDEAS

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    1. Cuauhtemoc Anda & Alexander Erath & Pieter Jacobus Fourie, 2017. "Transport modelling in the age of big data," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(0), pages 19-42, August.
    2. Gunnar Flötteröd & Michel Bierlaire & Kai Nagel, 2011. "Bayesian Demand Calibration for Dynamic Traffic Simulations," Transportation Science, INFORMS, vol. 45(4), pages 541-561, November.
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