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A multilane cellular automaton multi-attribute lane-changing decision model

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  • Deng, Jian-Hua
  • Feng, Huan-Huan

Abstract

Through exploring driver’s lane-changing decision mechanism, we divide the lane-changing decision-making process into two stages, i.e. lane-changing motivation and lane-changing decision. The influence attributes of decision-making are classified into two categories, i.e. internal decision attributes and external decision attributes. This paper presents a multi-attribute lane-changing decision model based on analytic hierarchy process after above work, introduces a newly-modified multilane traffic cellular automata model. By means of the numerical simulation under different traffic densities with various lane-line-markings as the external decision attributes the obtained conclusions show that, of a vehicle in system at each update time-step, the average lane-changing motivation probability is related directly to the internal decision attributes and the average lane-changing success probability is depend on both of internal and external decision attributes, the distribution of the lane-changing motivation probabilities and the lane-changing success probabilities fully response the variations of lane-line-marking. By and large, the proposed model has ability to analysis the legal implication of each pattern of lane-line-marking set in this paper. Due to the excellent expansibility of analytic hierarchy process frame structure, it should be improved to deal with more external decision attributes synchronously in future research.

Suggested Citation

  • Deng, Jian-Hua & Feng, Huan-Huan, 2019. "A multilane cellular automaton multi-attribute lane-changing decision model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 529(C).
    • Handle: RePEc:eee:phsmap:v:529:y:2019:i:c:s0378437119309094
    DOI: 10.1016/j.physa.2019.121545
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    Citations

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    Cited by:

    1. Ma, Changxi & Li, Dong, 2023. "A review of vehicle lane change research," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    2. Kuang, Xianyan & Chen, Ziru, 2022. "Trajectory research of Cellular Automaton Model based on real driving behaviour," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).
    3. Wang, Jinghui & Lv, Wei & Jiang, Yajuan & Qin, Shuangshuang & Li, Jiawei, 2021. "A multi-agent based cellular automata model for intersection traffic control simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    4. Bowen Gong & Zhipeng Xu & Ruixin Wei & Tao Wang & Ciyun Lin & Peng Gao, 2023. "Reinforcement Learning-Based Lane Change Decision for CAVs in Mixed Traffic Flow under Low Visibility Conditions," Mathematics, MDPI, vol. 11(6), pages 1-24, March.
    5. 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).
    6. Shang, Xue-Cheng & Li, Xin-Gang & Xie, Dong-Fan & Jia, Bin & Jiang, Rui, 2020. "Two-lane traffic flow model based on regular hexagonal cells with realistic lane changing behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).

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