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Car-following modeling based on Morse model with consideration of road slope in connected vehicles environment

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  • Yin, Jiacheng
  • Li, Zongping
  • Cao, Peng
  • Li, Linheng
  • Ju, Yanni

Abstract

To develop connected and automated transportation system, it is essential to model the car-following behavior of connected vehicles (CVs). In recent years, car-following models based on potential fields have attracted increasing attention owing to their objectivity, universality, variability, and measurability. However, existing potential-field-based car-following models do not consider unified gravity and repulsion between vehicles, and they lack scalability to other driving behaviors, including lane-changing and overtaking. In this study, we proposed a multiple risk potential-field-based car-following model (MRPFM) for traffic flow in CVs environment, which integrated multiple risk potential fields of traffic subjects, including road markings, road longitudinal slopes, and vehicle interactions. This model revealed the relationships between the potential field, interaction force, and driving risk. In particular, the Morse model was applied to formulate the risk potential field of vehicle interaction, and the risk potential field of the road longitudinal slope was derived using force analysis. The experiments were conducted based on the Zen Traffic Data dataset, which contained precise and complete trajectories of all vehicles along a 2 km freeway segment of longitudinal slope. We conducted a comparative analysis between the MRPFM and four prevailing car-following models—that is, the optimal velocity model, the full velocity difference model, the intelligent driver model, and the driving risk potential-field model. The results showed that the MRPFM achieved the best performance in terms of accuracy and stability.

Suggested Citation

  • Yin, Jiacheng & Li, Zongping & Cao, Peng & Li, Linheng & Ju, Yanni, 2023. "Car-following modeling based on Morse model with consideration of road slope in connected vehicles environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    • Handle: RePEc:eee:phsmap:v:622:y:2023:i:c:s0378437123003825
    DOI: 10.1016/j.physa.2023.128827
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    References listed on IDEAS

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    1. Komada, Kazuhito & Masukura, Shuichi & Nagatani, Takashi, 2009. "Effect of gravitational force upon traffic flow with gradients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2880-2894.
    2. Yulei Jiao & Rongjun Cheng & Hongxia Ge, 2020. "A New Continuum Model considering Driving Behaviors and Electronic Throttle Effect on a Gradient Highway," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-22, May.
    3. Ye, Lanhang & Yamamoto, Toshiyuki, 2018. "Impact of dedicated lanes for connected and autonomous vehicle on traffic flow throughput," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 588-597.
    4. Nishi, Ryosuke & Watanabe, Takashi, 2022. "System-size dependence of a jam-absorption driving strategy to remove traffic jam caused by a sag under the presence of traffic instability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    5. Sharma, Anshuman & Zheng, Zuduo & Bhaskar, Ashish, 2019. "Is more always better? The impact of vehicular trajectory completeness on car-following model calibration and validation," Transportation Research Part B: Methodological, Elsevier, vol. 120(C), pages 49-75.
    6. Zhu, Wen-Xing & Yu, Rui-Ling, 2012. "Nonlinear analysis of traffic flow on a gradient highway," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 954-965.
    7. Zhu, Wen-Xing & Yu, Rui-Ling, 2014. "A new car-following model considering the related factors of a gyroidal road," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 101-111.
    8. Sharma, Anshuman & Zheng, Zuduo & Bhaskar, Ashish & Haque, Md. Mazharul, 2019. "Modelling car-following behaviour of connected vehicles with a focus on driver compliance," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 256-279.
    9. Jia, Yanfeng & Qu, Dayi & Song, Hui & Wang, Tao & Zhao, Zixu, 2022. "Car-following characteristics and model of connected autonomous vehicles based on safe potential field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    10. Li, Linheng & Gan, Jing & Zhou, Kun & Qu, Xu & Ran, Bin, 2020. "A novel lane-changing model of connected and automated vehicles: Using the safety potential field theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
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