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Effect of looking backward on traffic flow in a cooperative driving car following model

Author

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  • H. X. Ge
  • H. B. Zhu
  • S. Q. Dai

Abstract

An extended car following model is proposed by incorporating intelligent transportation system and the backward looking effect under certain condition in traffic flow. The neutral stability condition of this model is obtained by using the linear stability theory. The results show that anticipating the behavior of vehicles preceding and following one vehicle could lead to appreciable stabilization of traffic system. From the simulation of space-time evolution of the vehicle headways, it is shown that the traffic jam could be suppressed efficiently via taking into account the information about the motion of two preceding vehicles and one following vehicle, and the analytical result is consistent with the simulation one. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2006

Suggested Citation

  • H. X. Ge & H. B. Zhu & S. Q. Dai, 2006. "Effect of looking backward on traffic flow in a cooperative driving car following model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 54(4), pages 503-507, December.
    • Handle: RePEc:spr:eurphb:v:54:y:2006:i:4:p:503-507
    DOI: 10.1140/epjb/e2007-00014-x
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    Citations

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    Cited by:

    1. Zhu, Chenqiang & Zhong, Shiquan & Li, Guangyu & Ma, Shoufeng, 2017. "New control strategy for the lattice hydrodynamic model of traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 445-453.
    2. Hu, Yanmei & Ma, Tianshan & Chen, Jianzhong, 2021. "Multi-anticipative bi-directional visual field traffic flow models in the connected vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    3. Wang, Zihao & Ge, Hongxia & Cheng, Rongjun, 2018. "Nonlinear analysis for a modified continuum model considering driver’s memory and backward looking effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 18-27.
    4. Zheng, Liang & Jin, Peter J. & Huang, Helai, 2015. "An anisotropic continuum model considering bi-directional information impact," Transportation Research Part B: Methodological, Elsevier, vol. 75(C), pages 36-57.
    5. Gong, Siyuan & Du, Lili, 2018. "Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 116(C), pages 25-61.
    6. Subaih, Rudina & Tordeux, Antoine, 2024. "Modeling pedestrian single-file movement: Extending the interaction to the follower," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    7. Yi, Ziwei & Lu, Wenqi & Qu, Xu & Gan, Jing & Li, Linheng & Ran, Bin, 2022. "A bidirectional car-following model considering distance balance between adjacent vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    8. Maiti, Nandan & Chilukuri, Bhargava Rama, 2023. "Does anisotropy hold in mixed traffic conditions?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    9. Zeng, Youzhi & Ran, Bin & Zhang, Ning & Yang, Xiaobao & Shen, Jia-Jun & Deng, She-Jun, 2020. "Combined effects of drivers’ disturbance risk preference heterogeneity and the nearest following vehicle headway on traffic flow instability: Analytical studies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    10. Zong, Fang & Wang, Meng & Tang, Jinjun & Zeng, Meng, 2022. "Modeling AVs & RVs’ car-following behavior by considering impacts of multiple surrounding vehicles and driving characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).

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