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A new car-following model considering driver’s sensory memory

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  • Cao, Bao-gui

Abstract

This paper presents one kind of new car-following model (mean memory model, simplified as MMM) by introducing driver sensory memory (sensory buffer) term into the original optimal velocity (OV) function by Bando et al. (1995, 1998). The main improvement is that MMM can avoid the disadvantage of the sensory buffer time neglected in existing models. The stability condition of the proposed model is obtained by using linear stability theory. Results show that the stability region decreases when the driver’s sensory buffer time increases. Furthermore, the model is investigated in detail by numerical methods. The following conclusions are derived. (a) Numerical results of starting process for the car motion under a traffic signal accord with empirical traffic values; (b) the numerical simulations in the form of the space–time evolution of headway and velocity are also in good agreement with the theoretical analysis; (c) the size of hysteresis loops will be reduced with the sensing buffer time decreasing. Both analytical and simulation results show that the following car driver’s sensory buffer time plays an important role on the stability of traffic flow.

Suggested Citation

  • Cao, Bao-gui, 2015. "A new car-following model considering driver’s sensory memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 218-225.
    • Handle: RePEc:eee:phsmap:v:427:y:2015:i:c:p:218-225
    DOI: 10.1016/j.physa.2015.01.078
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    Citations

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    Cited by:

    1. Pei, Xin & Pan, Yan & Wang, Haixin & Wong, S.C. & Choi, Keechoo, 2016. "Empirical evidence and stability analysis of the linear car-following model with gamma-distributed memory effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 311-323.
    2. Yongjiang-Wang, & Han-Song, & Rongjun-Cheng,, 2019. "TDGL and mKdV equations for an extended car-following model with the consideration of driver’s memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 440-449.
    3. Qin, Shunda & He, Zhiting & Cheng, Rongjun, 2018. "An extended lattice hydrodynamic model based on control theory considering the memory effect of flux difference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 809-816.
    4. Sun, Yuqing & Ge, Hongxia & Cheng, Rongjun, 2019. "An extended car-following model considering driver’s memory and average speed of preceding vehicles with control strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 752-761.
    5. Zhang, Geng & Yin, Le & Pan, Dong-Bo & Zhang, Yu & Cui, Bo-Yuan & Jiang, Shan, 2020. "Research on multiple vehicles’ continuous self-delayed velocities on traffic flow with vehicle-to-vehicle communication," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    6. Ma, Guangyi & Ma, Minghui & Liang, Shidong & Wang, Yansong & Guo, Hui, 2021. "Nonlinear analysis of the car-following model considering headway changes with memory and backward looking effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    7. Lixia Duan & Shuangshuang Fan & Danyang Liu & Zhonghe He, 2022. "Two-parameter bifurcation and energy consumption analysis of the macro traffic flow model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(12), pages 1-12, December.
    8. Cheng, Rongjun & Ge, Hongxia & Sun, Fengxin & Wang, Jufeng, 2018. "An extended macro model accounting for acceleration changes with memory and numerical tests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 270-283.
    9. Qingtao, Zhai & Hongxia, Ge & Rongjun, Cheng, 2018. "An extended continuum model considering optimal velocity change with memory and numerical tests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 774-785.
    10. Heng Wang & Zehao Jiang & Tiandong Xu & Feng Li, 2021. "A Quantitative Approach of Subway Station Passengers’ Heterogeneity of Decision Preference Considering Personality Traits during Emergency Evacuation," Sustainability, MDPI, vol. 13(22), pages 1-14, November.

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