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Modeling traffic on crossroads

Author

Listed:
  • Wastavino, L.A.
  • Toledo, B.A.
  • Rogan, J.
  • Zarama, R.
  • Muñoz, V.
  • Valdivia, J.A.

Abstract

A simplified traffic model is studied, consisting of two vehicles traveling through a sequence of crossroads regulated by yield signs. A car approaching a yield sign stops if, in the other street, there is a car closer than a certain distance xtol from the intersection. It is shown that the function which maps the state of the vehicles displays a period doubling transition to chaos. An interesting feature of the dynamics is that for extremely cautious drivers (xtol too large), the map turns chaotic, thus becoming a potential source of emergent jams. Complex behavior such as the one observed in this simple system seems to be an essential ingredient in traffic patterns, and could be of relevance in studying actual crossroads situations.

Suggested Citation

  • Wastavino, L.A. & Toledo, B.A. & Rogan, J. & Zarama, R. & Muñoz, V. & Valdivia, J.A., 2007. "Modeling traffic on crossroads," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 381(C), pages 411-419.
    • Handle: RePEc:eee:phsmap:v:381:y:2007:i:c:p:411-419
    DOI: 10.1016/j.physa.2007.03.052
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    References listed on IDEAS

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    1. Nagatani, Takashi, 2005. "Chaos and dynamical transition of a single vehicle induced by traffic light and speedup," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 561-571.
    2. Sasaki, Masashi & Nagatani, Takashi, 2003. "Transition and saturation of traffic flow controlled by traffic lights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 325(3), pages 531-546.
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    Citations

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    Cited by:

    1. Tang, Jinjun & Wang, Yinhai & Liu, Fang, 2013. "Characterizing traffic time series based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4192-4201.
    2. Ruru Hao & Tiancheng Ruan, 2024. "Advancing Traffic Simulation Precision and Scalability: A Data-Driven Approach Utilizing Deep Neural Networks," Sustainability, MDPI, vol. 16(7), pages 1-16, March.
    3. Tang, Jinjun & Wang, Yinhai & Wang, Hua & Zhang, Shen & Liu, Fang, 2014. "Dynamic analysis of traffic time series at different temporal scales: A complex networks approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 303-315.
    4. Pastén, D. & Muñoz, V. & Toledo, B. & Villalobos, J. & Zarama, R. & Rogan, J. & Valdivia, J.A., 2012. "Universal behavior in a model of city traffic with unequal green/red times," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(21), pages 5230-5243.

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