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Traffic at the Edge of Chaos

Author

Listed:
  • Kai Nagel
  • Steen Rasmussen

Abstract

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.

Suggested Citation

  • Kai Nagel & Steen Rasmussen, 1994. "Traffic at the Edge of Chaos," Working Papers 94-06-032, Santa Fe Institute.
  • Handle: RePEc:wop:safiwp:94-06-032
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    Cited by:

    1. Kelly, Terence, 1997. "Driver strategy and traffic system performance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 235(3), pages 407-416.
    2. Joshua M. Epstein, 2007. "Agent-Based Computational Models and Generative Social Science," Introductory Chapters,in: Generative Social Science Studies in Agent-Based Computational Modeling Princeton University Press.
    3. 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.
    4. Kenneth A. Small & Xuehao Chu, 2003. "Hypercongestion," Journal of Transport Economics and Policy, University of Bath, vol. 37(3), pages 319-352, September.
    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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