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A new car-following model considering the driver's dynamic reaction time and driving visual angle on the slope

Author

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  • Chen, Yujiao
  • Zhang, Futao
  • Qian, Yongsheng
  • Zeng, Junwei
  • Li, Xin

Abstract

Based on the influence of the driver's driving visual angle and the gradient value on the car-following behavior, the two-way information feedback and time delay, a new car-following model considering dynamic reaction time and visual angle when the vehicle goes up and down the slope continuously is proposed, and the parameters are calibrated based on the actual vehicle driving data. On this basis, the linear stability condition of traffic flow is analyzed based on the long wave theory, and the evolution process of small disturbances of traffic flow and the emission rate fluctuation and total amount change of fuel consumption and CO2, NOX, PM10, and VOC are simulated and analyzed. Then, the driver is divided into aggressive type, conservative type, and neutral type, and the influence of driver characteristics and lane change behavior on stability and safety is studied by simulation. The results show that the model considering driving visual angle can give the vehicle better self-stabilizing. Bidirectional information feedback control has better stability control ability than unidirectional information feedback. The increased headway delay will aggravate the diffusion of minor disturbances, but the rise in velocity delay will help to enhance stability. The fluctuation trend of the instantaneous emission rate of fuel consumption and pollutant gas is consistent with the stability, but the total fuel consumption and the total emission of pollutant gas do not have a single correlation with the stability. Aggressive drivers contribute to improving stability but are not conducive to the safety of traffic flow. Lane-changing behavior will aggravate the speed difference, but it will help to improve safety. This study provides a new approach for constructing traffic flow-following models on mountainous roads. Further, it explores the correlation between stability, fuel consumption, and pollutant emissions, which is significant for formulating future traffic control strategies for mountainous roads and emission reduction strategies.

Suggested Citation

  • Chen, Yujiao & Zhang, Futao & Qian, Yongsheng & Zeng, Junwei & Li, Xin, 2025. "A new car-following model considering the driver's dynamic reaction time and driving visual angle on the slope," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 663(C).
    • Handle: RePEc:eee:phsmap:v:663:y:2025:i:c:s0378437125000603
    DOI: 10.1016/j.physa.2025.130408
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    3. Olusola O. Ajayi & Anish Kurien & Karim Djouani & Lamine Dieng, 2025. "Artificial Intelligence for Infrastructure Resilience: Transportation Systems as a Strategic Case for Policy and Practice," Sustainability, MDPI, vol. 17(20), pages 1-18, October.
    4. Peng, Guanghan & Zheng, Mengyao & Xia, Dongxue & Tan, Huili & Li, Xiaoqin, 2025. "Bifurcation and jamming transition in car-following model integrating PD control to curb overtaking behaviors under CAVs platform," Chaos, Solitons & Fractals, Elsevier, vol. 200(P3).

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