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Individual Decisions and Collective Effects in a Simulated Traffic System

Author

Listed:
  • Hani S. Mahmassani

    (The University of Texas at Austin, Austin, Texas)

  • Gang-Len Chang

    (University of Utah, Salt Lake City, Utah)

  • Robert Herman

    (The University of Texas at Austin, Austin, Texas)

Abstract

The interdependence of the time-dependent behavior of traffic flows and tripmakers' departure time decisions is central to the analysis of congestion phenomena. The study of the day-to-day dynamics of this interaction in the field is hampered by the inability to obtain adequate data. This paper presents an experimental approach involving real commuters, whereby a traffic simulation model is used to predict congestion patterns under given departure distributions resulting from the actual decisions of the individual participants. These decisions are updated daily, in response to information on prior system performance predicted by the simulation model. The results of this experiment are described in this paper, with emphasis on the system's evolution and dynamic properties, particularly convergence. In addition, concentration and congestion patterns as well as the associated travel time variability are addressed. Initial insights into the processes underlying user behavior in this system are also presented.

Suggested Citation

  • Hani S. Mahmassani & Gang-Len Chang & Robert Herman, 1986. "Individual Decisions and Collective Effects in a Simulated Traffic System," Transportation Science, INFORMS, vol. 20(4), pages 258-271, November.
    • Handle: RePEc:inm:ortrsc:v:20:y:1986:i:4:p:258-271
    DOI: 10.1287/trsc.20.4.258
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    Citations

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    Cited by:

    1. Sinuany-Stern, Z. & Stern, E. & Sfaradi, Z. & Holm, E., 1997. "The effect of information on commuters' behavior: A comparative micro-simulation approach," European Journal of Operational Research, Elsevier, vol. 96(3), pages 455-470, February.
    2. Chen, Roger B., 2018. "Models of count with endogenous choices," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 862-875.
    3. Golledge, Reginald G. & Zhou, Jianyu, 2001. "GPS-Based Tracking of Daily Activities," University of California Transportation Center, Working Papers qt9jb438r2, University of California Transportation Center.
    4. Golledge, Reginald G & Zhou, Jack, 1999. "A GPS-based Analysis of Household Travel Behavior," University of California Transportation Center, Working Papers qt3hg1f5nb, University of California Transportation Center.
    5. Mahmassani, Hani S. & Jou, Rong-Chang, 2000. "Transferring insights into commuter behavior dynamics from laboratory experiments to field surveys," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(4), pages 243-260, May.
    6. Wong, Jinn-Tsai, 1997. "Basic concepts for a system for advance booking for highway use," Transport Policy, Elsevier, vol. 4(2), pages 109-114, April.
    7. A. de Palma & F. Marchal, 2000. "Dynamic traffic analysis with static data: some guidelines with an application to Paris," THEMA Working Papers 2000-55, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    8. Zhou, Jack & Golledge, Reginald, 1999. "A GPS-based Analysis Household Travel Behavior," University of California Transportation Center, Working Papers qt3zf8h075, University of California Transportation Center.
    9. André de Palma & Robin Lindsey & Nathalie Picard, 2012. "Risk Aversion, the Value of Information, and Traffic Equilibrium," Transportation Science, INFORMS, vol. 46(1), pages 1-26, February.
    10. Terry E. Daniel & Eyran J. Gisches & Amnon Rapoport, 2009. "Departure Times in Y-Shaped Traffic Networks with Multiple Bottlenecks," American Economic Review, American Economic Association, vol. 99(5), pages 2149-2176, December.
    11. Wijayaratna, Kasun P. & Dixit, Vinayak V., 2016. "Impact of information on risk attitudes: Implications on valuation of reliability and information," Journal of choice modelling, Elsevier, vol. 20(C), pages 16-34.

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