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Simulating synchronized traffic flow and wide moving jam based on the brake light rule

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  • Xiang, Zheng-Tao
  • Li, Yu-Jin
  • Chen, Yu-Feng
  • Xiong, Li

Abstract

A new cellular automaton (CA) model based on brake light rules is proposed, which considers the influence of deterministic deceleration on randomization probability and deceleration extent. To describe the synchronized flow phase of Kerner’s three-phase theory in accordance with empirical data, we have changed some rules of vehicle motion with the aim to improve speed and acceleration vehicle behavior in synchronized flow simulated with earlier cellular automaton models with brake lights. The fundamental diagrams and spatial–temporal diagrams are analyzed, as well as the complexity of the traffic evolution, the emergence process of wide moving jam. Simulation results show that our new model can reproduce the three traffic phases: free flow, synchronized flow and wide moving jam. In addition, our new model can well describe the complexity of traffic evolution: (1) with initial homogeneous distribution and large densities, the traffic will evolve into multiple steady states, in which the numbers of wide moving jams are not invariable. (2) With initial homogeneous distribution and the middle range of density, the wide moving jam will emerge stochastically. (3) With initial mega-jam distribution and the density close to a point with the low value, the initial mega-jam will disappear stochastically. (4) For the cases with multiple wide moving jams, the process is analyzed involving the generation of narrow moving jam due to “pinch effect”, which leads to wide moving jam emergence.

Suggested Citation

  • Xiang, Zheng-Tao & Li, Yu-Jin & Chen, Yu-Feng & Xiong, Li, 2013. "Simulating synchronized traffic flow and wide moving jam based on the brake light rule," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5399-5413.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:21:p:5399-5413
    DOI: 10.1016/j.physa.2013.06.066
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    References listed on IDEAS

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    1. Rehborn, Hubert & Klenov, Sergey L. & Palmer, Jochen, 2011. "An empirical study of common traffic congestion features based on traffic data measured in the USA, the UK, and Germany," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4466-4485.
    2. Kerner, Boris S. & Klenov, Sergey L. & Hermanns, Gerhard & Schreckenberg, Michael, 2013. "Effect of driver over-acceleration on traffic breakdown in three-phase cellular automaton traffic flow models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4083-4105.
    3. Tian, Jun-fang & Jia, Bin & Li, Xin-gang & Jiang, Rui & Zhao, Xiao-mei & Gao, Zi-you, 2009. "Synchronized traffic flow simulating with cellular automata model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(23), pages 4827-4837.
    4. Hoogendoorn, Serge P. & Bovy, Piet H. L., 2001. "Generic gas-kinetic traffic systems modeling with applications to vehicular traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 35(4), pages 317-336, May.
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    Cited by:

    1. Meng, Y.C. & Lin, Z.Y. & Li, X.Y. & Qiao, D.L. & Guo, M.M. & Zhang, P., 2022. "A semi-discrete model of traffic flow in correspondence with a continuum model under Lagrange coordinate system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    2. Qi, Le & Zheng, Zhongyi & Gang, Longhui, 2017. "Marine traffic model based on cellular automaton: Considering the change of the ship’s velocity under the influence of the weather and sea," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 480-494.

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