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Analytical studies on an extended car following model for mixed traffic flow with slow and fast vehicles

Author

Listed:
  • Zhipeng Li

    (Information Processing and Intelligent Transportation System Laboratory, Department of Information and Communication Engineering, Tongji University, Shanghai 200092, P. R. China)

  • Xun Xu

    (Information Processing and Intelligent Transportation System Laboratory, Department of Information and Communication Engineering, Tongji University, Shanghai 200092, P. R. China)

  • Shangzhi Xu

    (Information Processing and Intelligent Transportation System Laboratory, Department of Information and Communication Engineering, Tongji University, Shanghai 200092, P. R. China)

  • Yeqing Qian

    (Information Processing and Intelligent Transportation System Laboratory, Department of Information and Communication Engineering, Tongji University, Shanghai 200092, P. R. China)

  • Juan Xu

    (Information Processing and Intelligent Transportation System Laboratory, Department of Information and Communication Engineering, Tongji University, Shanghai 200092, P. R. China)

Abstract

The car-following model is extended to take into account the characteristics of mixed traffic flow containing fast and slow vehicles. We conduct the linear stability analysis to the extended model with finding that the traffic flow can be stabilized with the increase of the percentage of the slow vehicle. It also can be concluded that the stabilization of the traffic flow closely depends on not only the average value of two maximum velocities characterizing two vehicle types, but also the standard deviation of the maximum velocities among all vehicles, when the percentage of the slow vehicles is the same as that of the fast ones. With increase of the average maximum velocity, the traffic flow becomes more and more unstable, while the increase of the standard deviation takes negative effect in stabilizing the traffic system. The direct numerical results are in good agreement with those of theoretical analysis. Moreover, the relation between the flux and the traffic density is investigated to simulate the effects of the percentage of slow vehicles on traffic flux in the whole density regions.

Suggested Citation

  • Zhipeng Li & Xun Xu & Shangzhi Xu & Yeqing Qian & Juan Xu, 2016. "Analytical studies on an extended car following model for mixed traffic flow with slow and fast vehicles," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 27(01), pages 1-20, January.
    • Handle: RePEc:wsi:ijmpcx:v:27:y:2016:i:01:n:s0129183116500042
    DOI: 10.1142/S0129183116500042
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    Citations

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    Cited by:

    1. Jin, Zhizhan & Li, Zhipeng & Cheng, Rongjun & Ge, Hongxia, 2018. "Nonlinear analysis for an improved car-following model account for the optimal velocity changes with memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 278-288.
    2. Changtao-Jiang, & Rongjun-Cheng, & Hongxia-Ge,, 2019. "Mean-field flow difference model with consideration of on-ramp and off-ramp," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 465-476.
    3. Vranken, Tim & Schreckenberg, Michael, 2022. "Modelling multi-lane heterogeneous traffic flow with human-driven, automated, and communicating automated vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    4. Jiang, Changtao & Cheng, Rongjun & Ge, Hongxia, 2018. "Effects of speed deviation and density difference in traffic lattice hydrodynamic model with interruption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 900-908.
    5. Qi, Xinyue & Ge, Hongxia & Cheng, Rongjun, 2019. "Analysis of a novel lattice hydrodynamic model considering density integral and “backward looking” effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 714-723.

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