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Effect of Five Driver’s Behavior Characteristics on Car-Following Safety

Author

Listed:
  • Junjie Zhang

    (Hefei Innovation Research Institute, Beihang University, Hefei 230012, China)

  • Can Yang

    (Hefei Innovation Research Institute, Beihang University, Hefei 230012, China)

  • Jun Zhang

    (Hefei Innovation Research Institute, Beihang University, Hefei 230012, China)

  • Haojie Ji

    (School of Electronic and Information Engineering, Beihang University, Beijing 100191, China)

Abstract

Driver’s behavior characteristics (DBCs) influence car-following safety. Therefore, this paper aimed to analyze the effect of different DBCs on the car-following safety based on the desired safety margin (DSM) car-following model, which includes five DBC parameters. Based on the Monte Carlo simulation method, the effect of DBCs on car-following safety is investigated under a given rear-end collision (RECs) condition. We find that larger subjective risk perception levels can reduce RECs, a smaller acceleration sensitivity (or a larger deceleration sensitivity) can improve car-following safety, and a faster reaction ability of the driver can avoid RECs in the car-following process. It implies that DBCs would cause a traffic wave in the car-following process. Therefore, a reasonable value of DBCs can enhance traffic flow stability, and a traffic control strategy can improve car-following safety by using the adjustment of DBCs.

Suggested Citation

  • Junjie Zhang & Can Yang & Jun Zhang & Haojie Ji, 2022. "Effect of Five Driver’s Behavior Characteristics on Car-Following Safety," IJERPH, MDPI, vol. 20(1), pages 1-13, December.
  • Handle: RePEc:gam:jijerp:v:20:y:2022:i:1:p:76-:d:1010038
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    References listed on IDEAS

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    1. Guangquan Lu & Bo Cheng & Yunpeng Wang & Qingfeng Lin, 2013. "A Car-Following Model Based on Quantified Homeostatic Risk Perception," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-13, November.
    2. Lanjun Wang & Hao Zhang & Huadong Meng & Xiqin Wang, 2011. "The Influence Of Individual Driver Characteristics On Congestion Formation," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 305-318.
    3. Taylor, Jeffrey & Zhou, Xuesong & Rouphail, Nagui M. & Porter, Richard J., 2015. "Method for investigating intradriver heterogeneity using vehicle trajectory data: A Dynamic Time Warping approach," Transportation Research Part B: Methodological, Elsevier, vol. 73(C), pages 59-80.
    4. Montanino, Marcello & Monteil, Julien & Punzo, Vincenzo, 2021. "From homogeneous to heterogeneous traffic flows: Lp String stability under uncertain model parameters," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 136-154.
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