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An extended car-following model accounting for the average headway effect in intelligent transportation system

Author

Listed:
  • Kuang, Hua
  • Xu, Zhi-Peng
  • Li, Xing-Li
  • Lo, Siu-Ming

Abstract

In this paper, an extended car-following model is proposed to simulate traffic flow by considering average headway of preceding vehicles group in intelligent transportation systems environment. The stability condition of this model is obtained by using the linear stability analysis. The phase diagram can be divided into three regions classified as the stable, the metastable and the unstable ones. The theoretical result shows that the average headway plays an important role in improving the stabilization of traffic system. The mKdV equation near the critical point is derived to describe the evolution properties of traffic density waves by applying the reductive perturbation method. Furthermore, through the simulation of space–time evolution of the vehicle headway, it is shown that the traffic jam can be suppressed efficiently with taking into account the average headway effect, and the analytical result is consistent with the simulation one.

Suggested Citation

  • Kuang, Hua & Xu, Zhi-Peng & Li, Xing-Li & Lo, Siu-Ming, 2017. "An extended car-following model accounting for the average headway effect in intelligent transportation system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 778-787.
    • Handle: RePEc:eee:phsmap:v:471:y:2017:i:c:p:778-787
    DOI: 10.1016/j.physa.2016.12.022
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    Citations

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    Cited by:

    1. Sun, Lu & Jafaripournimchahi, Ammar & Hu, Wusheng, 2020. "A forward-looking anticipative viscous high-order continuum model considering two leading vehicles for traffic flow through wireless V2X communication in autonomous and connected vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    2. Jinhua Tan & Li Gong & Xuqian Qin, 2019. "Global Optimality under Internet of Vehicles: Strategy to Improve Traffic Safety and Reduce Energy Dissipation," Sustainability, MDPI, vol. 11(17), pages 1-16, August.
    3. Junyan Han & Jinglei Zhang & Xiaoyuan Wang & Yaqi Liu & Quanzheng Wang & Fusheng Zhong, 2020. "An Extended Car-Following Model Considering Generalized Preceding Vehicles in V2X Environment," Future Internet, MDPI, vol. 12(12), pages 1-15, November.
    4. Junyan Han & Xiaoyuan Wang & Huili Shi & Bin Wang & Gang Wang & Longfei Chen & Quanzheng Wang, 2022. "Research on the Impacts of Vehicle Type on Car-Following Behavior, Fuel Consumption and Exhaust Emission in the V2X Environment," Sustainability, MDPI, vol. 14(22), pages 1-15, November.
    5. Li, Xiangchen & Luo, Xia & He, Mengchen & Chen, Siwei, 2018. "An improved car-following model considering the influence of space gap to the response," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 536-545.
    6. 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).
    7. Shuaiyang Jiao & Shengrui Zhang & Bei Zhou & Zixuan Zhang & Liyuan Xue, 2020. "An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    8. Cui, Ziyu & Wang, Xiaoning & Ci, Yusheng & Yang, Changyun & Yao, Jia, 2023. "Modeling and analysis of car-following models incorporating multiple lead vehicles and acceleration information in heterogeneous traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    9. Tan, Jin-hua, 2019. "Impact of risk illusions on traffic flow in fog weather," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 216-222.
    10. Chang, Yinyin & He, Zhiting & Cheng, Rongjun, 2019. "An extended lattice hydrodynamic model considering the driver’s sensory memory and delayed-feedback control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 522-532.
    11. Wang, Xiaoning & Liu, Minzhuang & Ci, Yusheng & Wu, Lina, 2022. "Effect of front two adjacent vehicles’ velocity information on car-following model construction and stability analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    12. Chang, Yinyin & He, Zhiting & Cheng, Rongjun, 2019. "Analysis of the historical time integral form of relative flux and feedback control in an extended lattice hydrodynamic model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 326-334.
    13. Yao, Zhihong & Gu, Qiufan & Jiang, Yangsheng & Ran, Bin, 2022. "Fundamental diagram and stability of mixed traffic flow considering platoon size and intensity of connected automated vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    14. Xiaoyuan Wang & Junyan Han & Chenglin Bai & Huili Shi & Jinglei Zhang & Gang Wang, 2021. "Research on the Impacts of Generalized Preceding Vehicle Information on Traffic Flow in V2X Environment," Future Internet, MDPI, vol. 13(4), pages 1-17, March.

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