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Effect of pre-signals in a Manhattan-like urban traffic network

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  • Huang, Jian
  • Hu, Mao-Bin
  • Jiang, Rui
  • Li, Ming

Abstract

Pre-signal is an additional traffic light proposed in traffic system to improve the left-turning efficiency of intersections. The efficiency of pre-signals in a networked environment will be affected by the coordination of pre-signals and the intersection traffic lights. In this paper, we study the implementation of pre-signal in a Manhattan-like urban traffic system based on a microscopic cellular automaton model. The pre-signals are located before the normal traffic lights, and have two phases (left-turning phase and through phase) for the aim of segregating left-turning and through vehicles into separate sets of lanes. We propose a reliable method to coordinate the intersection traffic light period and phases with the pre-signal period and phases. Simulation results show that the pre-signals can improve the performance of the system, for both closed and open boundaries. The macroscopic fundamental diagram of the system shows a much higher traffic flow for a wide range of densities. Moreover, the effect of pre-signal area length also needs to be considered in a networked environment. We find that the optimal length of pre-signal area should be determined by the number of vehicles passing the traffic light in a left-turning phase at the intersection. The results can help the implementation and application of pre-signal in real traffic system.

Suggested Citation

  • Huang, Jian & Hu, Mao-Bin & Jiang, Rui & Li, Ming, 2018. "Effect of pre-signals in a Manhattan-like urban traffic network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 71-85.
  • Handle: RePEc:eee:phsmap:v:503:y:2018:i:c:p:71-85
    DOI: 10.1016/j.physa.2018.02.170
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    References listed on IDEAS

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    Cited by:

    1. Ding, Zhong-Jun & Dai, Zong & Chen, Xiqun (Michael) & Jiang, Rui, 2020. "Simulating on-demand ride services in a Manhattan-like urban network considering traffic dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    2. Zhao, Jing & Yan, Jiachao & Wang, Jiawen, 2019. "Analysis of alternative treatments for left turn bicycles at tandem intersections," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 314-328.
    3. Xu, Yuan-Hao & Guan, Xiao-Kui & Li, Li & Hu, Mao-Bin, 2024. "Selection-sort-based cooperative driving strategy for CAVs at non-signalized intersections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    4. Guo, Yajuan & Yang, Licai & Hao, Shenxue & Gu, Xinxin, 2021. "Perimeter traffic control for single urban congested region with macroscopic fundamental diagram and boundary conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    5. Yang, Bo & Wang, Chunsheng & Cao, Yuan & Yang, Qiaoli, 2024. "Modeling and evaluating the impact of variable bus lane on isolated signal intersection performance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).

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