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Towards network-wide safe and efficient traffic signal timing optimization based on costly stochastic simulation

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  • Zheng, Liang
  • Yang, Youpeng
  • Xue, Xinfeng
  • Li, Xiaoru
  • Xu, Chengcheng

Abstract

This study proposes a stochastic simulation-based network-wide signal timing optimization model with the balance consideration of traffic safety and efficiency, and solves it with a Bi-objective Stochastic Simulation-based Optimization (BOSSO) algorithm. During numerical experiments, an urban road network with 15 signalized and 5 non-signalized intersections in Changsha, China is modelled as an experimental scenario. The calibrated simulator VISSIM, Surrogate Safety Assessment Model (SSAM) and MATLAB are integrated to construct the VISSIM-SSAM-Matlab platform, based on which the network-wide signal timing optimization problems without and with coordination are solved by the BOSSO algorithm to trade-off traffic conflicts and total delay. Numerical results show that only hundreds of simulations are cost to obtain the ultimate non-dominated non-coordinated and coordinated signal timing plans, and validate a competing relationship between traffic conflicts and total delay. By the bi-objective comparison of three various signal plans from the overall and local aspects, the coordinated signal plan outperforms the non-coordinated one, which is followed by the field implemented one. It successfully demonstrates the effectiveness of the BOSSO method.

Suggested Citation

  • Zheng, Liang & Yang, Youpeng & Xue, Xinfeng & Li, Xiaoru & Xu, Chengcheng, 2021. "Towards network-wide safe and efficient traffic signal timing optimization based on costly stochastic simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    • Handle: RePEc:eee:phsmap:v:571:y:2021:i:c:s0378437121001230
    DOI: 10.1016/j.physa.2021.125851
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    References listed on IDEAS

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    1. Zheng, Liang & Xue, Xinfeng & Xu, Chengcheng & Ran, Bin, 2019. "A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 287-308.
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    Cited by:

    1. Wang, Yinpu & An, Chengchuan & Ou, Jishun & Lu, Zhenbo & Xia, Jingxin, 2022. "A general dynamic sequential learning framework for vehicle trajectory reconstruction using automatic vehicle location or identification data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).

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