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Coherent moving states in highway traffic

Author

Listed:
  • Dirk Helbing

    (II Institute of Theoretical Physics, University of Stuttgart)

  • Bernardo A. Huberman

    (Xerox PARC)

Abstract

Advances in multiagent simulation techniques1,2,3 have made possible the study of realistic highway traffic patterns and have allowed theories3,4,5,6 based on driver behaviour to be tested. Such simulations display various empirical features of traffic flows7, and are used to design traffic controls that maximize the throughput of vehicles on busy highways. In addition to its intrinsic economic value8, vehicular traffic is of interest because it may be relevant to social phenomena in which diverse individuals compete with each other under certain constraints9,10. Here we report simulations of heterogeneous traffic which demonstrate that cooperative, coherent states can arise from competitive interactions between vehicles. As the density of vehicles increases, their interactions cause a transition into a highly correlated state in which all vehicles move with approximately the same speed, analogous to the motion of a solid block. This state is safer because it has a reduced lane-changing rate, and the traffic flow is high and stable. The coherent state disappears when the vehicle density exceeds a critical value. We observe the effect also in real Dutch traffic data.

Suggested Citation

  • Dirk Helbing & Bernardo A. Huberman, 1998. "Coherent moving states in highway traffic," Nature, Nature, vol. 396(6713), pages 738-740, December.
  • Handle: RePEc:nat:nature:v:396:y:1998:i:6713:d:10.1038_25499
    DOI: 10.1038/25499
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    Citations

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    Cited by:

    1. Guan, Wei & He, Shuyan, 2008. "Statistical features of traffic flow on urban freeways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 944-954.
    2. Lv, Wei & Song, Wei-guo & Fang, Zhi-ming & Ma, Jian, 2013. "Modelling of lane-changing behaviour integrating with merging effect before a city road bottleneck," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 5143-5153.
    3. Yin, Yi & Shang, Pengjian & Ahn, Andrew C. & Peng, Chung-Kang, 2019. "Multiscale joint permutation entropy for complex time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 388-402.
    4. Guan, Lin & Fang, Yuwen & Li, Kongzhai & Zeng, Chunhua & Yang, Fengzao, 2018. "Transport properties of active Brownian particles in a modified energy-depot model driven by correlated noises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 716-728.
    5. Mehdi Moussaïd & Elsa G Guillot & Mathieu Moreau & Jérôme Fehrenbach & Olivier Chabiron & Samuel Lemercier & Julien Pettré & Cécile Appert-Rolland & Pierre Degond & Guy Theraulaz, 2012. "Traffic Instabilities in Self-Organized Pedestrian Crowds," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-10, March.
    6. Kai Nagel & Peter Wagner & Richard Woesler, 2003. "Still Flowing: Approaches to Traffic Flow and Traffic Jam Modeling," Operations Research, INFORMS, vol. 51(5), pages 681-710, October.
    7. Juan Francisco Sánchez-Pérez & Santiago Oviedo-Casado & Gonzalo García-Ros & Manuel Conesa & Enrique Castro, 2024. "Understanding Complex Traffic Dynamics with the Nondimensionalisation Technique," Mathematics, MDPI, vol. 12(4), pages 1-14, February.
    8. Le-le Cao & Xiao-xue Li & Fen-ni Kang & Chang Liu & Fu-chun Sun & Ramamohanarao Kotagiri, 2015. "The Quantitative and Qualitative Evaluation of a Multi-Agent Microsimulation Model for Subway Carriage Design," International Journal of Microsimulation, International Microsimulation Association, vol. 8(3), pages 6-40.
    9. Combinido, Jay Samuel L. & Lim, May T., 2010. "Modeling U-turn traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3640-3647.
    10. Sujai Kumar & Sugata Mitra, 2006. "Self-Organizing Traffic at a Malfunctioning Intersection," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(4), pages 1-3.
    11. Ai, Wen-Huan & Shi, Zhong-Ke & Liu, Da-Wei, 2015. "Bifurcation analysis of a speed gradient continuum traffic flow model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 418-429.
    12. Lv, Wei & Song, Wei-guo & Fang, Zhi-ming, 2011. "Three-lane changing behaviour simulation using a modified optimal velocity model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(12), pages 2303-2314.
    13. He, Shuyan & Guan, Wei & Song, Liying, 2010. "Explaining traffic patterns at on-ramp vicinity by a driver perception model in the framework of three-phase traffic theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 825-836.
    14. Helbing, Dirk & Hennecke, Ansgar & Shvetsov, Vladimir & Treiber, Martin, 2001. "MASTER: macroscopic traffic simulation based on a gas-kinetic, non-local traffic model," Transportation Research Part B: Methodological, Elsevier, vol. 35(2), pages 183-211, February.

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