中国物理B ›› 2021, Vol. 30 ›› Issue (3): 34501-.doi: 10.1088/1674-1056/abc3b3

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  • 收稿日期:2020-08-01 修回日期:2020-09-26 接受日期:2020-10-22 出版日期:2021-02-22 发布日期:2021-03-05

Modeling and analysis of car-following behavior considering backward-looking effect

Dongfang Ma(马东方)1, Yueyi Han(韩月一)1, Fengzhong Qu(瞿逢重)1, and Sheng Jin(金盛)2,†   

  1. 1 Ocean College, Institute of Marine Information Science and Technology, Zhejiang University, Hangzhou 310058, China; 2 College of Civil Engineering and Architecture, Institute of Intelligent Transportation System, Zhejiang University, Hangzhou 310058, China
  • Received:2020-08-01 Revised:2020-09-26 Accepted:2020-10-22 Online:2021-02-22 Published:2021-03-05
  • Contact: †Corresponding author. E-mail: jinsheng@zju.edu.cn
  • Supported by:
    Project supported by the National Key Research and Development Program of China (Grant No. 2018YFB1601000), the National Natural Science Foundation of China (Grant Nos. 61773337, 61773338, and 61722113), and the Key Research and Development Program of Shandong Province, China (Grant No. 2019TSLH0203).

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Abstract: The car-following behavior can be influenced by its driver's backward-looking effect. Especially in traffic congestion, if vehicles adjust the headway by considering backward-looking effect, the stability of traffic flow can be enhanced. A model of car-following behavior considering backward-looking effect was built using visual information as a stimulus. The critical stability conditions were derived by linear and nonlinear stability analyses. The results of parameter sensitivity analysis indicate that the stability of traffic flow was enhanced by considering the backward-looking effect. The spatiotemporal evolution of traffic flow of different truck ratios and varying degrees of backward-looking effect was determined by numerical simulation. This study lays a foundation for exploring the complex feature of car-following behavior and making the intelligent network vehicles control rules more consistent with human driver habits.

Key words: traffic flow, car-following model, visual angle, backward-looking effect

中图分类号:  (Granular models of complex systems; traffic flow)

  • 45.70.Vn
89.40.-a (Transportation)