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Microscopic state evolution model of mixed traffic flow based on potential field theory

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  • Li, Linheng
  • Wang, Can
  • Zhang, Ying
  • Qu, Xu
  • Li, Rui
  • Chen, Zhijun
  • Ran, Bin

Abstract

To investigate the microscopic-state evolution mechanism of mixed traffic flow composed of Connected and Automated Vehicles (CAVs) and Human Driven Vehicles (HDVs) in the intelligent and connected environment, this paper proposed a safety potential field-NaSch (SPF-NS) model for mixed traffic flow based on potential field theory. The model introduces the safety potential field theory into the cellular automata model, reformulates the cellular automata rules and realizes the discretization of the potential field. In addition, a comparative numerical simulation experiment between NaSch model and SPF-NS model was designed. The results show that SPF-NS model can realize the real-time change of vehicle’s acceleration according to the potential field distribution, which is more precise for the actual car-following state description than NaSch model. At the same time, the road traffic flow stability under SPF-NS model is higher and the traffic capacity is increased. Besides, the microscopic-state evolution process of mixed traffic flow is emphatically studied, and the simulation experiments of different CAV penetration conditions are designed. The simulation results show that the stability of mixed traffic flow increases with the increase of CAV penetration. Compared with the HDV environment, when the CAV penetration of mixed traffic flow reaches 100%, The maximum traffic capacity of the road was increased by 2.2 times, and the congestion ratio was reduced by 96.60%. Therefore, this model can reflect the driving risk faced by vehicles in the process of car-following and simulate the microscopic-state evolution process of mixed traffic flow. The research results can provide theoretical support for future research on vehicle lane changing behavior, mixed traffic flow management and control, macroscopic state prediction in traffic flow and so on.

Suggested Citation

  • Li, Linheng & Wang, Can & Zhang, Ying & Qu, Xu & Li, Rui & Chen, Zhijun & Ran, Bin, 2022. "Microscopic state evolution model of mixed traffic flow based on potential field theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    • Handle: RePEc:eee:phsmap:v:607:y:2022:i:c:s0378437122007439
    DOI: 10.1016/j.physa.2022.128185
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    References listed on IDEAS

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    Cited by:

    1. Wang, Zhengwu & Chen, Tao & Wang, Yi & Li, Hao, 2024. "A cellular automaton model for mixed traffic flow considering the size of CAV platoon," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).
    2. Ai, Yi & Li, Yueyang & Han, Xun & Yao, Zhihong & Li, Zongping, 2024. "Real-time risk assessment method for multi-aircraft interaction based on potential field theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    3. He, Yongming & Feng, Jia & Wei, Kun & Cao, Jian & Chen, Shisheng & Wan, Yanan, 2023. "Modeling and simulation of lane-changing and collision avoiding autonomous vehicles on superhighways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).

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