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An improved car-following model considering the influence of space gap to the response

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  • Li, Xiangchen
  • Luo, Xia
  • He, Mengchen
  • Chen, Siwei

Abstract

The Full velocity difference (FVD) model considers the velocity difference, but it cannot capture the acceleration/deceleration asymmetric characteristic. This paper points out that the response of drivers to the velocity difference is influenced by the space gap (which is the space headway minus the vehicle length), and improves the FVD model based on this view. The improved model indicates that the reason why the asymmetric characteristic exists in the car-following behavior is that the response of drivers to the velocity difference is in negative correlation to the space gap. Neutral stability curves of the improved model are asymmetry. And the stability analysis indicates that the visual angle model (one of the improved FVD models) is a special case of the improved model proposed in this paper. Results of numerical computer simulations confirm that the improved model can capture the asymmetric characteristic, and further indicate that the asymmetric characteristic is caused by the influence of space gap to the drivers’ response. And finally, parameters of the improved car-following model are calibrated by real traffic flow data.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:509:y:2018:i:c:p:536-545
    DOI: 10.1016/j.physa.2018.06.069
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    References listed on IDEAS

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    Cited by:

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    2. Yu, Lei, 2020. "A new continuum traffic flow model with two delays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    3. Peng, Yong & Liu, Shijie & Yu, Dennis Z., 2020. "An improved car-following model with consideration of multiple preceding and following vehicles in a driver’s view," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    4. Toan, Trinh Dinh & Lam, Soi Hoi & Wong, Yiik Diew & Meng, Meng, 2022. "Development and validation of a driving simulator for traffic control using field data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    5. Sun, Yuqing & Ge, Hongxia & Cheng, Rongjun, 2019. "An extended car-following model considering driver’s memory and average speed of preceding vehicles with control strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 752-761.
    6. Jinhua Tan & Xuqian Qin & Li Gong, 2020. "Using Vehicle-to-Vehicle Communication to Improve Traffic Safety in Sand-dust Environment," IJERPH, MDPI, vol. 17(4), pages 1-15, February.
    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. 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. 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.

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