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The Dynamical Decision Model of Intersection Congestion Based on Risk Identification

Author

Listed:
  • Xu Sun

    (School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
    Institute of Transportation Engineering, Tsinghua University, Beijing 100084, China)

  • Kun Lin

    (School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Pengpeng Jiao

    (School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Huapu Lu

    (Institute of Transportation Engineering, Tsinghua University, Beijing 100084, China)

Abstract

The paper focuses on the problem of traffic congestion at intersection based on the mechanism of risk identification. The main goal of this study is to explore a new methodology for identifying and predicting the intersection congestion. Considering all the factors influencing the traffic status of intersection congestion, an integrated evaluation index system is constructed. Then, a detailed dynamic decision model is proposed for identifying the risk degree of the traffic congestion and predicting its influence on future traffic flow, which combines the traffic flow intrinsic properties with the basic model of the Risking Dynamic Multi-Attribute Decision-Making theory. A case study based on a real-world road network in Baoji, China, is implemented to test the efficiency and applicability of the proposed modeling. The evaluation result is in accord with the actual condition and shows that the approach proposed can determine the likelihood and risk degree of the traffic congestion occurring in the intersection, which can be used as a tool to help transport managers make some traffic control measures in advance.

Suggested Citation

  • Xu Sun & Kun Lin & Pengpeng Jiao & Huapu Lu, 2020. "The Dynamical Decision Model of Intersection Congestion Based on Risk Identification," Sustainability, MDPI, vol. 12(15), pages 1-16, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:5923-:d:388453
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    References listed on IDEAS

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    Cited by:

    1. Shuanfeng Zhao & Chao Wang & Pei Wei & Qingqing Zhao, 2020. "Research on the Deep Recognition of Urban Road Vehicle Flow Based on Deep Learning," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    2. Chuanwei Zhang & Xibo Xue & Peilin Qin & Lingling Dong, 2023. "Research on a Speed Guidance Strategy for Mine Vehicles in Three-Fork Roadways Based on Vehicle–Road Coordination," Sustainability, MDPI, vol. 15(21), pages 1-17, October.
    3. Binghong Pan & Shangru Liu & Zhenjiang Xie & Yang Shao & Xiang Li & Ruicheng Ge, 2021. "Evaluating Operational Features of Three Unconventional Intersections under Heavy Traffic Based on CRITIC Method," Sustainability, MDPI, vol. 13(8), pages 1-30, April.

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