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An extended car-following model considering multi-anticipative average velocity effect under V2V environment

Author

Listed:
  • Kuang, Hua
  • Wang, Mei-Ting
  • Lu, Fang-Hua
  • Bai, Ke-Zhao
  • Li, Xing-Li

Abstract

Vehicle-to-vehicle (for short, V2V) communication technology is regarded as a promising technology to improve traffic efficiency and safety. In this paper, an extended car-following model is proposed to simulate traffic flow by considering multi-anticipative average velocity effect (including the average velocity and the mean expected velocity field effect of preceding vehicles group) under V2V environment. The stability condition of this model is obtained by applying the linear stability theory. The phase diagram comparison and analysis shows that the multi-anticipative average velocity effect can effectively enhance the stabilization of traffic system. In particular, the average velocity effect plays a more important role than that of the mean expected velocity field effect in improving the stability of traffic flow. The mKdV equation is derived to describe the evolution characteristics of traffic density waves by using the reductive perturbation method. Furthermore, the numerical simulation is carried out to validate the theoretical results, and indicates that the traffic jam can be suppressed efficiently via taking into account multi-anticipative average velocity effect.

Suggested Citation

  • Kuang, Hua & Wang, Mei-Ting & Lu, Fang-Hua & Bai, Ke-Zhao & Li, Xing-Li, 2019. "An extended car-following model considering multi-anticipative average velocity effect under V2V environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119307344
    DOI: 10.1016/j.physa.2019.121268
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    Citations

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    Cited by:

    1. Sun, Lu & Jafaripournimchahi, Ammar & Hu, Wusheng, 2020. "A forward-looking anticipative viscous high-order continuum model considering two leading vehicles for traffic flow through wireless V2X communication in autonomous and connected vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    2. Madaan, Nikita & Sharma, Sapna, 2021. "A lattice model accounting for multi-lane traffic system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    3. Hu, Yanmei & Ma, Tianshan & Chen, Jianzhong, 2021. "Multi-anticipative bi-directional visual field traffic flow models in the connected vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    4. 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).
    5. Yin, Yu-Hang & Lü, Xing & Jiang, Rui & Jia, Bin & Gao, Ziyou, 2024. "Kinetic analysis and numerical tests of an adaptive car-following model for real-time traffic in ITS," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    6. Jiang, Nan & Yu, Bin & Cao, Feng & Dang, Pengfei & Cui, Shaohua, 2021. "An extended visual angle car-following model considering the vehicle types in the adjacent lane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    7. Junyan Han & Jinglei Zhang & Xiaoyuan Wang & Yaqi Liu & Quanzheng Wang & Fusheng Zhong, 2020. "An Extended Car-Following Model Considering Generalized Preceding Vehicles in V2X Environment," Future Internet, MDPI, vol. 12(12), pages 1-15, November.
    8. Yu, Bin & Zhou, Huixin & Wang, Lin & Wang, Zirui & Cui, Shaohua, 2021. "An extended two-lane car-following model considering the influence of heterogeneous speed information on drivers with different characteristics under honk environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    9. Chang, Xin & Li, Haijian & Rong, Jian & Zhao, Xiaohua & Li, An’ran, 2020. "Analysis on traffic stability and capacity for mixed traffic flow with platoons of intelligent connected vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    10. 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).
    11. Xiaoyuan Wang & Junyan Han & Chenglin Bai & Huili Shi & Jinglei Zhang & Gang Wang, 2021. "Research on the Impacts of Generalized Preceding Vehicle Information on Traffic Flow in V2X Environment," Future Internet, MDPI, vol. 13(4), pages 1-17, March.
    12. Zhiyong Zhang & Wu Tang & Wenming Feng & Zhen Liu & Caixia Huang, 2024. "An Extended Car-Following Model Considering Lateral Gap and Optimal Velocity of the Preceding Vehicle," Sustainability, MDPI, vol. 16(14), pages 1-20, July.
    13. Zhang, Jing & Gao, Qian & Tian, Junfang & Cui, Fengying & Wang, Tao, 2024. "Car-following model based on spatial expectation effect in connected vehicle environment: modeling, stability analysis and identification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
    14. Melih Yildiz & Burcu Bilgiç & Utku Kale & Dániel Rohács, 2021. "Experimental Investigation of Communication Performance of Drones Used for Autonomous Car Track Tests," Sustainability, MDPI, vol. 13(10), pages 1-14, May.

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