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An Extended Car-Following Model Considering Generalized Preceding Vehicles in V2X Environment

Author

Listed:
  • Junyan Han

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Jinglei Zhang

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Xiaoyuan Wang

    (College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China)

  • Yaqi Liu

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
    College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China)

  • Quanzheng Wang

    (College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China)

  • Fusheng Zhong

    (College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China)

Abstract

Vehicle-to-everything (V2X) technology will significantly enhance the information perception ability of drivers and assist them in optimizing car-following behavior. Utilizing V2X technology, drivers could obtain motion state information of the front vehicle, non-neighboring front vehicle, and front vehicles in the adjacent lanes (these vehicles are collectively referred to as generalized preceding vehicles in this research). However, understanding of the impact exerted by the above information on car-following behavior and traffic flow is limited. In this paper, a car-following model considering the average velocity of generalized preceding vehicles (GPV) is proposed to explore the impact and then calibrated with the next generation simulation (NGSIM) data utilizing the genetic algorithm. The neutral stability condition of the model is derived via linear stability analysis. Numerical simulation on the starting, braking and disturbance propagation process is implemented to further study features of the established model and traffic flow stability. Research results suggest that the fitting accuracy of the GPV model is 40.497% higher than the full velocity difference (FVD) model. Good agreement between the theoretical analysis and the numerical simulation reveals that motion state information of GPV can stabilize traffic flow of following vehicles and thus alleviate traffic congestion.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:12:p:216-:d:452965
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    References listed on IDEAS

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    Cited by:

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    2. 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).
    3. Yadav, Sunita & Redhu, Poonam, 2024. "Impact of driving prediction on headway and velocity in car-following model under V2X environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    4. Junyan Han & Xiaoyuan Wang & Huili Shi & Bin Wang & Gang Wang & Longfei Chen & Quanzheng Wang, 2022. "Research on the Impacts of Vehicle Type on Car-Following Behavior, Fuel Consumption and Exhaust Emission in the V2X Environment," Sustainability, MDPI, vol. 14(22), pages 1-15, November.

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