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A car-following model accounting for probability distribution

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  • Ou, Hui
  • Tang, Tie-Qiao
  • Zhang, Jian
  • Zhou, Jie-Ming

Abstract

Various stochastic factors (e.g., the driver’s individual properties) widely exist in the real traffic system, but the existing studies cannot completely describe the impacts of various stochastic factors on traffic flow (especially the driving behavior). In this paper, we introduce the driver’s three perceived errors into the car-following model, and construct a car-following model with the probability distributions of the three perceived errors to explore the effects of the three perceived errors on the driving behavior under three typical situations (i.e., uniform flow, shock and rarefaction waves, and a small perturbation). The numerical results show that the three perceived errors have significant impacts on the evolution of traffic flow (including the headway distribution), i.e., the distribution of density does not prominently change under the three traffic states. In addition, the impacts are directly related to the initial condition. The results can help drivers reasonably adjust their driving behaviors based on their current traffic state (especially when some stochastic factors exist).

Suggested Citation

  • Ou, Hui & Tang, Tie-Qiao & Zhang, Jian & Zhou, Jie-Ming, 2018. "A car-following model accounting for probability distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 105-113.
    • Handle: RePEc:eee:phsmap:v:505:y:2018:i:c:p:105-113
    DOI: 10.1016/j.physa.2018.03.072
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    References listed on IDEAS

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    Cited by:

    1. Wang, Jufeng & Sun, Fengxin & Ge, Hongxia, 2018. "Effect of the driver’s desire for smooth driving on the car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 96-108.
    2. Sun, Fengxin & Wang, Jufeng & Cheng, Rongjun, 2019. "An improved anisotropic continuum model considering the driver’s desire for steady driving," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1449-1462.
    3. Junyan Han & Xiaoyuan Wang & Gang Wang, 2022. "Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review," Sustainability, MDPI, vol. 14(13), pages 1-27, July.
    4. 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.
    5. 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).
    6. Mondal, Satyajit & Gupta, Ankit, 2021. "Speed distribution for interrupted flow facility under mixed traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).

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