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Active lane-changing model of vehicle in B-type weaving region based on potential energy field theory

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  • Ma, Yanli
  • Zhang, Peng
  • Hu, Baoyu

Abstract

To define lane-changing time accurately and quantitatively, the influence of vehicle spacing on lane-changing time was studied using cars. Taking into account the influence of surrounding vehicles’ spacing on lane-changing, a vehicle lane-changing time model on an urban road B-type weaving section based on a potential energy field theory was proposed. The model quantitatively illustrated the influence of surrounding vehicles’ spacing on lane-changing lateral acceleration and then deduced the relationship between the lateral displacement, longitudinal displacement and time of the lane-changing vehicle. By using the aerial data of passenger cars lane-changing in the weaving area of an urban trunk road, the parameters in the model were calibrated and the lane-changing trajectory of vehicles simulated by the model was tested. It is found that the accuracy of the lane-changing model to quantitatively define the transverse acceleration while changing lanes and the spacing between constrained regions reaches 90%, and the fitting degree between simulated lane change trajectory and actual lane change trajectory is over 90%. The results show that the vehicle lane-change model based on the potential energy field theory can effectively simulate the relationship between vehicle lane-change time and lateral and longitudinal displacement and can provide theoretical and technical support for traffic simulation and multi-vehicle interaction.

Suggested Citation

  • Ma, Yanli & Zhang, Peng & Hu, Baoyu, 2019. "Active lane-changing model of vehicle in B-type weaving region based on potential energy field theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    • Handle: RePEc:eee:phsmap:v:535:y:2019:i:c:s0378437119313275
    DOI: 10.1016/j.physa.2019.122291
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    References listed on IDEAS

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    1. Gipps, P. G., 1986. "A model for the structure of lane-changing decisions," Transportation Research Part B: Methodological, Elsevier, vol. 20(5), pages 403-414, October.
    2. Tang, Jinjun & Liang, Jian & Zhang, Shen & Huang, Helai & Liu, Fang, 2018. "Inferring driving trajectories based on probabilistic model from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 566-577.
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    Citations

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    Cited by:

    1. Chen, Tianyi & Shi, Xiupeng & Wong, Yiik Diew, 2021. "A lane-changing risk profile analysis method based on time-series clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    2. Song Fang & Linghong Shen & Jianxiao Ma & Chubo Xu, 2022. "Study on the Design of Variable Lane Demarcation in Urban Tunnels," Sustainability, MDPI, vol. 14(9), pages 1-12, May.
    3. 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).
    4. Ma, Changxi & Li, Dong, 2023. "A review of vehicle lane change research," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    5. Yuning Wang & Shuocheng Yang & Jinhao Li & Shaobing Xu & Jianqiang Wang, 2023. "An Emergency Driving Intervention System Designed for Driver Disability Scenarios Based on Emergency Risk Field," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
    6. Ma, Yanli & Lv, Zhiliang & Zhang, Peng & Chan, Ching-Yao, 2021. "Impact of lane changing on adjacent vehicles considering multi-vehicle interaction in mixed traffic flow: A velocity estimating model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).

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