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A New Coordinated Control Method on the Intersection of Traffic Region

Author

Listed:
  • Minghui Ma
  • Qingfang Yang
  • Shidong Liang
  • Yashi Wang

Abstract

Intersection regions widely exist in road networks as crucial connections, and the congestion effects on traffic in the networks cannot be ignored. In general, a conventional intersection region includes a merging region and more than two ramps, whose congestion brings a serious negative effect on traffic service quality and satisfaction of drivers. Specifically, intersection region congestion is caused by congestion in merging regions. However, current control methods regarding coordination of the variable speed limits and ramp metering in order to solve merging region congestion are insufficient to handle congestion occurring in the intersection region. Therefore, a new method of coordinated control is proposed in this paper based on mainline control using the variable speed limits and coordinated ramp metering. The coordinated control method is formulated considering the degree of crowdedness indexes and implemented based on the queue length, with the goal to relieve congestion in conventional intersection regions caused by merging region congestion. The coordinated control method is further evaluated based on an actual traffic network. The results show that the proposed method is effective in terms of improving traffic service quality and relieving intersection region congestion.

Suggested Citation

  • Minghui Ma & Qingfang Yang & Shidong Liang & Yashi Wang, 2016. "A New Coordinated Control Method on the Intersection of Traffic Region," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-10, May.
  • Handle: RePEc:hin:jnddns:5985840
    DOI: 10.1155/2016/5985840
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    Cited by:

    1. 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.
    2. Xie, Jiaohong & Yang, Zhenyu & Lai, Xiongfei & Liu, Yang & Yang, Xiao Bo & Teng, Teck-Hou & Tham, Chen-Khong, 2022. "Deep reinforcement learning for dynamic incident-responsive traffic information dissemination," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).

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