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A continuous traffic flow model considering predictive headway variation and preceding vehicle’s taillight effect

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  • Zhai, Cong
  • Wu, Weitiao

Abstract

The car taillights in the deceleration process of preceding vehicle may greatly affect the acceleration and deceleration processes of the following vehicle. Additionally, the driver can predict the headway at the next time period from surrounding traffic information, and adjust the vehicle acceleration based on the difference between the predicted headway and the current headway information. To analyze these combined effects, we propose a new continuous traffic flow model taking into account the predictive headway variation and preceding vehicle’s taillight. The stability condition and KdV–Burgers equation of the continuum model are derived in linear and nonlinear stability analysis. The density wave solution obtained by solving the KdV–Burgers equation can be used to describe the formation and propagation mechanism of traffic jams near stable conditions. The complex traffic phenomena such as shock waves, rarefaction waves and local cluster effects are reproduced by simulation. Results show that the taillight information of preceding vehicle and the driver’s prediction time step both contribute greatly to the stability of the traffic flow and energy consumption.

Suggested Citation

  • Zhai, Cong & Wu, Weitiao, 2021. "A continuous traffic flow model considering predictive headway variation and preceding vehicle’s taillight effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    • Handle: RePEc:eee:phsmap:v:584:y:2021:i:c:s0378437121006373
    DOI: 10.1016/j.physa.2021.126364
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    Cited by:

    1. Zhai, Cong & Zhang, Ronghui & Peng, Tao & Zhong, Changfu & Xu, Hongguo, 2023. "Heterogeneous lattice hydrodynamic model and jamming transition mixed with connected vehicles and human-driven vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
    2. Kaur, Daljeet & Sharma, Sapna & Gupta, Arvind Kumar, 2022. "Analyses of lattice hydrodynamic area occupancy model for heterogeneous disorder traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    3. Zhai, Cong & Wu, Weitiao & Xiao, Yingping & Luo, Qiang & Zhang, Yusong, 2022. "Modeling bidirectional pedestrian flow with the perceived uncertainty of preceding pedestrian information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    4. Peng, Guanghan & Xu, Mingzuo & Tan, Huili, 2024. "Phase transition in a new heterogeneous macro continuum model of traffic flow under rain and snow weather environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    5. Zhai, Cong & Wu, Weitiao & Xiao, Yingping, 2023. "The jamming transition of multi-lane lattice hydrodynamic model with passing effect," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    6. Zhai, Cong & Wu, Weitiao, 2022. "A continuum model considering the uncertain velocity of preceding vehicles on gradient highways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    7. Peng, Guanghan & Luo, Chunli & Zhao, Hongzhuan & Tan, Huili, 2024. "Phase transitions of dual-lane lattice model incorporating cyber-attacks on lane change involving inflow and outflow under connected vehicles environment," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).

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