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An asymmetric full velocity difference car-following model

Author

Listed:
  • Gong, Huaxin
  • Liu, Hongchao
  • Wang, Bing-Hong

Abstract

This paper presents a car-following model that considers the asymmetric characteristic of the velocity differences of the vehicles in a traffic stream. The problems of the prevalent general force (GF) model and the full velocity difference (FVD) model were solved. Furthermore, the optimal velocity (OV) model, the GF model, and the FVD model are proved to be the special cases of the proposed asymmetric full velocity difference (AFVD) model. The mathematical model is presented, followed by simulation analysis which demonstrates the properties of the AFVD model.

Suggested Citation

  • Gong, Huaxin & Liu, Hongchao & Wang, Bing-Hong, 2008. "An asymmetric full velocity difference car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(11), pages 2595-2602.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:11:p:2595-2602
    DOI: 10.1016/j.physa.2008.01.038
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    Citations

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    Cited by:

    1. Xin, Qi & Yang, Nan & Fu, Rui & Yu, Shaowei & Shi, Zhongke, 2018. "Impacts analysis of car following models considering variable vehicular gap policies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 338-355.
    2. Shi, Kunsong & Wu, Yuankai & Shi, Haotian & Zhou, Yang & Ran, Bin, 2022. "An integrated car-following and lane changing vehicle trajectory prediction algorithm based on a deep neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    3. Yu, Shaowei & Huang, Mengxing & Ren, Jia & Shi, Zhongke, 2016. "An improved car-following model considering velocity fluctuation of the immediately ahead car," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 1-17.
    4. 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.
    5. Pan, Weixiu & Zhang, Jing & Tian, Junfang & Cui, Fengying & Wang, Tao, 2023. "Analysis of car–following behaviors based on data–driven and theory–driven car–following models: Heterogeneity and asymmetry," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    6. Xu, Xihua & Pang, John & Monterola, Christopher, 2015. "Asymmetric optimal-velocity car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 565-571.
    7. Kang, Chengjun & Qian, Yongsheng & Zeng, Junwei & Wei, Xuting & Zhang, Futao, 2024. "Analysis of stability, energy consumption and CO2 emissions in novel discrete-time car-following model with time delay under V2V environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).
    8. 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.
    9. Wei, Dali & Liu, Hongchao, 2013. "Analysis of asymmetric driving behavior using a self-learning approach," Transportation Research Part B: Methodological, Elsevier, vol. 47(C), pages 1-14.
    10. Wen Huan Ai & Ming Ming Wang & Da Wei Liu, 2023. "Analysis of macroscopic traffic flow model considering throttle dynamics," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(6), pages 1-18, June.

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