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Optimal location of lane-changing warning point in a two-lane road considering different traffic flows

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  • He, Jia
  • He, Zhengbing
  • Fan, Bo
  • Chen, Yanyan

Abstract

The timely lane-changing (LC) warning information should be informed to vehicles upstream when some things (such as accident or vehicle breakdown) happen. To avoid secondary accidents or reduce traffic congestion, this paper investigates the optimal LC warning point in a two-lane road considering the different traffic condition. The Intelligent Driver car-following Model and MOBIL LC model are used to describe the vehicle’s longitudinal and lateral motion rules, and methods based on probability theory and numerical simulations are proposed to investigate the optimal warning point under different traffic scenarios. The numerical results show that: i) the successful LC probability is not monotone changing with velocity. ii) providing LC warning information can reduce the average travel time, especially in moderate or congested traffic conditions. iii) the appropriate LC warning point location range becomes smaller with the increase of flow rate. iv) the effective LC warning point is about 30–300 meters far from the event point under different traffic flows.

Suggested Citation

  • He, Jia & He, Zhengbing & Fan, Bo & Chen, Yanyan, 2020. "Optimal location of lane-changing warning point in a two-lane road considering different traffic flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    • Handle: RePEc:eee:phsmap:v:540:y:2020:i:c:s0378437119316954
    DOI: 10.1016/j.physa.2019.123000
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    References listed on IDEAS

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    Cited by:

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    2. Yang, Da & Jia, Bingmei & Dai, Liyuan & Jin, Jing Peter & Xu, Lihua & Chen, Fei & Zheng, Shiyu & Ran, Bin, 2022. "Optimization model for the freeway-exiting position decision problem of automated vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 159(C), pages 24-48.
    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. Guo, Yingshi & Zhang, Hongjia & Wang, Chang & Sun, Qinyu & Li, Wanmin, 2021. "Driver lane change intention recognition in the connected environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 575(C).
    5. He, Zhengbing, 2021. "Portraying ride-hailing mobility using multi-day trip order data: A case study of Beijing, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 146(C), pages 152-169.

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