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Effect of front two adjacent vehicles’ velocity information on car-following model construction and stability analysis

Author

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  • Wang, Xiaoning
  • Liu, Minzhuang
  • Ci, Yusheng
  • Wu, Lina

Abstract

Herein, an extended car-following model is proposed considering the anticipative velocity and average velocity information of front adjacent and sub-adjacent vehicles based on manual driving and V2V environments. The traffic flow stability, traffic flow characteristics, and vehicle operation benefits of this model are analyzed to provide a reference for traffic flow modeling. The stability condition of this model is obtained via linear stability analysis, and the mKdV equation describing the evolution characteristics of the traffic density wave of this model is obtained via nonlinear analysis. Theoretical and numerical simulation results show that the extended model can effectively improve the traffic flow stability, the stability increases with the sum of the action intensity of the anticipative velocity information, and the change in the action intensity relationship is negligible. The average velocity information does not contribute significantly to the traffic flow stability but its superposition with the anticipative velocity information is beneficial. The extended model affords smoother vehicle starting and braking at a signalized intersection, thereby reducing fuel consumption and emissions.

Suggested Citation

  • Wang, Xiaoning & Liu, Minzhuang & Ci, Yusheng & Wu, Lina, 2022. "Effect of front two adjacent vehicles’ velocity information on car-following model construction and stability analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    • Handle: RePEc:eee:phsmap:v:607:y:2022:i:c:s0378437122007543
    DOI: 10.1016/j.physa.2022.128196
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    References listed on IDEAS

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    Cited by:

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    2. Cui, Ziyu & Wang, Xiaoning & Ci, Yusheng & Yang, Changyun & Yao, Jia, 2023. "Modeling and analysis of car-following models incorporating multiple lead vehicles and acceleration information in heterogeneous traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    3. Cheng-Ju Song & Hong-Fei Jia, 2022. "Car-Following Model Optimization and Simulation Based on Cooperative Adaptive Cruise Control," Sustainability, MDPI, vol. 14(21), pages 1-12, October.
    4. Ting, Samson & Lymburn, Thomas & Stemler, Thomas & Sun, Yuchao & Small, Michael, 2024. "Parameter estimation for Gipps’ car following model in a Bayesian framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
    5. Qin, Yanyan & Liu, Mingxuan & Hao, Wei, 2024. "Energy-optimal car-following model for connected automated vehicles considering traffic flow stability," Energy, Elsevier, vol. 298(C).
    6. Tanaka, Masaki & Tanimoto, Jun, 2024. "A new microscopic traffic-flow model based on the spatiotemporal continuous system concept considering nonlinear human response," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 648(C).

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