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Multi-Agent Transport Simulations and Economic Evaluation

Author

Listed:
  • Nagel Kai

    (TU Berlin, Transport Systems Planning and Transport Telematics, Sekr. SG 12, Salzufer 17–19, 10587 Berlin, Germany)

  • Grether Dominik

    (TU Berlin, Transport Systems Planning and Transport Telematics, Sekr. SG 12, Salzufer 17–19, 10587 Berlin, Germany)

  • Beuck Ulrike

    (TU Berlin, Transport Systems Planning and Transport Telematics, Sekr. SG 12, Salzufer 17–19, 10587 Berlin, Germany)

  • Chen Yu

    (TU Berlin, Transport Systems Planning and Transport Telematics, Sekr. SG 12, Salzufer 17–19, 10587 Berlin, Germany)

  • Rieser Marcel

    (TU Berlin, Transport Systems Planning and Transport Telematics, Sekr. SG 12, Salzufer 17–19, 10587 Berlin, Germany)

  • Axhausen Kay W.

    (ETH Zurich, Institute for Transport Planning and Systems, Wolfgang- Pauli-Str. 15, 8093 Zürich, Switzerland)

Abstract

Tolls are frequently discussed policies to reduce traffic in cities. However, road pricing measures are seldom implemented due to high investments and unpopularity. Transportation planning tools can support planning authorities by solving those problems if they take into account the following aspects:– Demographic attributes like income and time constraints– Time reactions to the policy– Schedule changes of population’s individuals during the whole dayOur approach uses multi-agent simulations to model and simulate full daily plans. Each of our agents has a utility function that appraises the performance of a typical, microscopically simulated day. The sum of all utility changes to a policy change can be interpreted as the change in the system’s welfare thus the economic evaluation of a measure straightforward.The approach is tested with travel behavior of the Zurich metropolitan region in Switzerland. Several tolling schemes are investigated. It is shown that the simulation can be used to model travelers’ reactions to time-dependent tolls in a way most existing transportation planning tools are not able to do. It is demonstrated that route adjustment only, as is done in many traditional transport planning packages, results in no economic gains from the tolls. As time-dependent tolls are a much-debated subject in transportation politics, the ability to fully model such tolls and the reactions of travelers may help to find better toll schemes. In a world where individuals have more and more freedom to schedule their daily plans, agent-based simulations offer an intuitive way to research complex topics with lots of interdependencies.

Suggested Citation

  • Nagel Kai & Grether Dominik & Beuck Ulrike & Chen Yu & Rieser Marcel & Axhausen Kay W., 2008. "Multi-Agent Transport Simulations and Economic Evaluation," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(2-3), pages 173-194, April.
    • Handle: RePEc:jns:jbstat:v:228:y:2008:i:2-3:p:183-194
    DOI: 10.1515/jbnst-2008-2-304
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    References listed on IDEAS

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    Cited by:

    1. Albert Solé-Ribalta & Sergio Gómez & Alex Arenas, 2018. "Decongestion of Urban Areas with Hotspot Pricing," Networks and Spatial Economics, Springer, vol. 18(1), pages 33-50, March.
    2. LeBaron Blake & Winker Peter, 2008. "Introduction to the Special Issue on Agent-Based Models for Economic Policy Advice," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(2-3), pages 141-148, April.
    3. Kaddoura, Ihab & Nagel, Kai, 2019. "Congestion pricing in a real-world oriented agent-based simulation context," Research in Transportation Economics, Elsevier, vol. 74(C), pages 40-51.
    4. Benjamin Kickhöfer & Dominik Grether & Kai Nagel, 2011. "Public acceptance and economic evaluation of transport policies (refereed paper)," ERSA conference papers ersa10p1022, European Regional Science Association.
    5. Benjamin Kickhöfer & Dominik Grether & Kai Nagel, 2011. "Income-contingent user preferences in policy evaluation: application and discussion based on multi-agent transport simulations," Transportation, Springer, vol. 38(6), pages 849-870, November.

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