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An extended car-following model incorporating the effects of driver’s memory and mean expected velocity field in ITS environment

Author

Listed:
  • Hua Kuang

    (College of Physical Science and Technology, Guangxi Normal University, Guilin 541004, P. R. China)

  • Fang-Hua Lu

    (College of Physical Science and Technology, Guangxi Normal University, Guilin 541004, P. R. China)

  • Feng-Lan Yang

    (College of Physical Science and Technology, Guangxi Normal University, Guilin 541004, P. R. China)

  • Guang-Han Peng

    (College of Physical Science and Technology, Guangxi Normal University, Guilin 541004, P. R. China)

  • Xing-Li Li

    (School of Applied Science Taiyuan University of Science and Technology, Taiyuan 030024, P. R. China)

Abstract

In this paper, an extended car-following model is proposed to simulate traffic flow with consideration of incorporating the effects of driver’s memory and mean expected velocity field in ITS (i.e. intelligent transportation system) environment. The neutral stability condition of the new model is derived by applying the linear stability theory. Compared with the optimal velocity model and the full velocity difference model, the stability region of the new model can be significantly enlarged on the phase diagram, and the anticipating motion information of more vehicles ahead can further enhance traffic stability. Furthermore, the mean expected velocity field effect plays a more important role than that of driver’s memory effect in improving the stability of traffic flow. Nonlinear analysis is also conducted by using the reductive perturbation method, and the mKdV equation near the critical point is obtained to describe the evolution properties of traffic density waves. Numerical simulation results show that the coupling effect of driver’s memory and the mean expected velocity field can suppress the traffic jam effectively, which is in good agreement with the analytical result.

Suggested Citation

  • Hua Kuang & Fang-Hua Lu & Feng-Lan Yang & Guang-Han Peng & Xing-Li Li, 2021. "An extended car-following model incorporating the effects of driver’s memory and mean expected velocity field in ITS environment," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 32(07), pages 1-17, July.
    • Handle: RePEc:wsi:ijmpcx:v:32:y:2021:i:07:n:s0129183121500959
    DOI: 10.1142/S0129183121500959
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