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Traffic at the Edge of Chaos


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  • Kai Nagel
  • Steen Rasmussen
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    We use a very simple description of human driving behavior to simulate traffic. The regime of maximum vehicle flow in a closed system shows near-critical behavior, and as a result a sharp decrease of the predictability of travel time. Since Advanced Traffic Management Systems (ATMSs) tend to drive larger parts of the transportation system towards this regime of maximum flow, we argue that in consequence the traffic system as a whole will be driven closer to criticality, thus making predictions much harder. A simulation of simplified transportation network supports our argument.

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    Bibliographic Info

    Paper provided by Santa Fe Institute in its series Working Papers with number 94-06-032.

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    Date of creation: Jun 1994
    Date of revision:
    Handle: RePEc:wop:safiwp:94-06-032

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    Cited by:
    1. Kenneth A. Small & Xuehao Chu, 2003. "Hypercongestion," Journal of Transport Economics and Policy, London School of Economics and University of Bath, vol. 37(3), pages 319-352, September.
    2. van Ackere, Ann & Larsen, Erik R., 2004. "Self-organising behaviour in the presence of negative externalities: A conceptual model of commuter choice," European Journal of Operational Research, Elsevier, vol. 157(2), pages 501-513, September.
    3. Joshua M. Epstein, 2007. "Agent-Based Computational Models and Generative Social Science
      [Generative Social Science Studies in Agent-Based Computational Modeling]
      ," Introductory Chapters, Princeton University Press.
    4. Kelly, Terence, 1997. "Driver strategy and traffic system performance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 235(3), pages 407-416.
    5. Blue, Victor J. & Adler, Jeffrey L., 2001. "Cellular automata microsimulation for modeling bi-directional pedestrian walkways," Transportation Research Part B: Methodological, Elsevier, vol. 35(3), pages 293-312, March.


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