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Non-lane-based full velocity difference car following model

Author

Listed:
  • Jin, Sheng
  • Wang, Dianhai
  • Tao, Pengfei
  • Li, Pingfan

Abstract

In order to describe car following behavior in real world, this paper presents a non-lane-based car following model by incorporating the effects of the lane width in traffic. The stability condition of the model is obtained by using the linear stability theory. And numerical simulation is carried out to validate the analytic results. The property of the model is investigated, and it is found that the proposed model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion. The results implied that incorporating the lane width effects in car following model not only stabilize traffic flow and suppress the traffic jam, but also lower critical headway and increase capacity. Thus, the lateral separation effects greatly enhance the realism of car following models.

Suggested Citation

  • Jin, Sheng & Wang, Dianhai & Tao, Pengfei & Li, Pingfan, 2010. "Non-lane-based full velocity difference car following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4654-4662.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:21:p:4654-4662
    DOI: 10.1016/j.physa.2010.06.014
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    Citations

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    Cited by:

    1. Fu, Chuanyun & Lu, Zhaoyou & Ding, Naikan & Bai, Wei, 2024. "Distance headway-based safety evaluation of emerging mixed traffic flow under snowy weather," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 642(C).
    2. 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.
    3. Ming Ye & Yitao Long & Yi Sui & Yonggang Liu & Qiao Li, 2019. "Active Control and Validation of the Electric Vehicle Powertrain System Using the Vehicle Cluster Environment," Energies, MDPI, vol. 12(19), pages 1-21, September.
    4. Tie-Qiao Tang & Yun-Peng Wang & Xiao-Bao Yang & Hai-Jun Huang, 2014. "A Multilane Traffic Flow Model Accounting for Lane Width, Lane-Changing and the Number of Lanes," Networks and Spatial Economics, Springer, vol. 14(3), pages 465-483, December.
    5. Wang, Jufeng & Sun, Fengxin & Ge, Hongxia, 2018. "Effect of the driver’s desire for smooth driving on the car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 96-108.
    6. Jiang, Nan & Yu, Bin & Cao, Feng & Dang, Pengfei & Cui, Shaohua, 2021. "An extended visual angle car-following model considering the vehicle types in the adjacent lane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    7. Li, Yongfu & Li, Kezhi & Zheng, Taixiong & Hu, Xiangdong & Feng, Huizong & Li, Yinguo, 2016. "Evaluating the performance of vehicular platoon control under different network topologies of initial states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 359-368.
    8. Li, Yongfu & Zhao, Hang & Zhang, Li & Zhang, Chao, 2018. "An extended car-following model incorporating the effects of lateral gap and gradient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 177-189.
    9. Yang, Da & Jin, Peter (Jing) & Pu, Yun & Ran, Bin, 2014. "Stability analysis of the mixed traffic flow of cars and trucks using heterogeneous optimal velocity car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 371-383.
    10. Yu, Yuewen & Luo, Xia & Su, Qiming & Peng, Weikang, 2023. "A dynamic lane-changing decision and trajectory planning model of autonomous vehicles under mixed autonomous vehicle and human-driven vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    11. Chen, Xiaolong & Hu, Manjiang & Xu, Biao & Bian, Yougang & Qin, Hongmao, 2022. "Improved reservation-based method with controllable gap strategy for vehicle coordination at non-signalized intersections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    12. Li, Yongfu & Kang, Yuhao & Yang, Bin & Peeta, Srinivas & Zhang, Li & Zheng, Taixong & Li, Yinguo, 2016. "A sliding mode controller for vehicular traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 38-47.
    13. Ponnu, Balaji & Coifman, Benjamin, 2015. "Speed-spacing dependency on relative speed from the adjacent lane: New insights for car following models," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 74-90.
    14. Ma, Guangyi & Ma, Minghui & Liang, Shidong & Wang, Yansong & Guo, Hui, 2021. "Nonlinear analysis of the car-following model considering headway changes with memory and backward looking effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    15. Zhiyong Zhang & Wu Tang & Wenming Feng & Zhen Liu & Caixia Huang, 2024. "An Extended Car-Following Model Considering Lateral Gap and Optimal Velocity of the Preceding Vehicle," Sustainability, MDPI, vol. 16(14), pages 1-20, July.
    16. 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.
    17. Rong, Ying & Wen, Huiying, 2018. "Non-lane-discipline-based car-following model under honk environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 278-293.
    18. Zhang, Xuan & Jia, Bin & Jiang, Rui, 2018. "Impact of safety assistance driving systems on oscillation magnitude, fuel consumption and emission in a car platoon," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 995-1007.

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