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Effects of a Simple Mode Choice Model in a Large-Scale Agent-Based Transport Simulation

In: Complexity and Spatial Networks

Author

Listed:
  • Dominik Grether

    (Technical University Berlin)

  • Yu Chen

    (Technical University Berlin)

  • Marcel Rieser

    (Technical University Berlin)

  • Kai Nagel

    (Technical University Berlin)

Abstract

The traditional transportation planning forecasting process is the four-step process, consisting of the following four steps (for example, Ortúzar and Willumsen 1995):1. Trip generation, where sources and sinks of travel are computed 2. Destination choice, where sources and sinks are connected to trips. This results in the so-called origin–destination (OD) matrix 3. Mode choice, where the trips are differentiated by mode 4. Assignment, where routes are found for the trips, taking into account that much-used streets become slower (“congested assignment”). It has been clear for quite some time now that this approach is at odds with anything that is time dependent. At best, separate runs of the four step process are made for, say, morning peak, mid-day, evening peak, and night. Within the periods, everything is “static” (or steady-state), in the sense flow rates are constant throughout the periods.

Suggested Citation

  • Dominik Grether & Yu Chen & Marcel Rieser & Kai Nagel, 2009. "Effects of a Simple Mode Choice Model in a Large-Scale Agent-Based Transport Simulation," Advances in Spatial Science, in: Aura Reggiani & Peter Nijkamp (ed.), Complexity and Spatial Networks, chapter 0, pages 167-186, Springer.
    • Handle: RePEc:spr:adspcp:978-3-642-01554-0_13
    DOI: 10.1007/978-3-642-01554-0_13
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    Citations

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    Cited by:

    1. Gunnar Flötteröd & Michel Bierlaire & Kai Nagel, 2011. "Bayesian Demand Calibration for Dynamic Traffic Simulations," Transportation Science, INFORMS, vol. 45(4), pages 541-561, November.
    2. Witsarut Achariyaviriya & Yoshitsugu Hayashi & Hiroyuki Takeshita & Masanobu Kii & Varameth Vichiensan & Thanaruk Theeramunkong, 2021. "Can Space–Time Shifting of Activities and Travels Mitigate Hyper-Congestion in an Emerging Megacity, Bangkok? Effects on Quality of Life and CO 2 Emission," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
    3. Gunnar Flötteröd & Yu Chen & Kai Nagel, 2012. "Behavioral Calibration and Analysis of a Large-Scale Travel Microsimulation," Networks and Spatial Economics, Springer, vol. 12(4), pages 481-502, December.
    4. Oskar Blom Västberg & Anders Karlström & Daniel Jonsson & Marcus Sundberg, 2020. "A Dynamic Discrete Choice Activity-Based Travel Demand Model," Transportation Science, INFORMS, vol. 54(1), pages 21-41, January.
    5. Cats, Oded, 2013. "Multi-agent transit operations and assignment model," Working papers in Transport Economics 2013:24, CTS - Centre for Transport Studies Stockholm (KTH and VTI).

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