Volume 40 Issue 3
Jun.  2022
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Article Navigation >  Journal of Transport Information and Safety  > 2022  >  40(3): 51-59
TAO Lefeng, SHI Jungang, YANG Jing, YANG Xiaoguang. A Safety-oriented Optimization Model for Train Skip-stop Strategy of Oversaturated Metro Lines[J]. Journal of Transport Information and Safety, 2022, 40(3): 51-59. doi: 10.3963/j.jssn.1674-4861.2022.03.006
Citation: TAO Lefeng, SHI Jungang, YANG Jing, YANG Xiaoguang. A Safety-oriented Optimization Model for Train Skip-stop Strategy of Oversaturated Metro Lines[J]. Journal of Transport Information and Safety, 2022, 40(3): 51-59. doi: 10.3963/j.jssn.1674-4861.2022.03.006
shu

A Safety-oriented Optimization Model for Train Skip-stop Strategy of Oversaturated Metro Lines

doi: 10.3963/j.jssn.1674-4861.2022.03.006
  • 1.

    College of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China

  • 2.

    College of Transportation Engineering, Tongji University, Shanghai 201804, China

  • Received Date: 2022-01-02
    Available Online: 2022-07-25
    Abstract
  • In order to alleviate the extreme congestion of oversaturated passenger flow of metro lines during peak hours, an optimization problem of skip-stop strategy for metro trains is studied from the respective of safety, which aims to minimize both risk of passenger congestion and their waiting time. Due to varying passenger demands over time, the number of waiting passengers under the train skip-stop strategy is estimated at each station by considering multiple constraints, including skip-stop operation, train tracking, and dynamic loading of passengers, and a specific evaluation function is formulated to measure the risk of passenger congestion. Based on an optimization method for the traditional train skip-stop strategy only considering passenger waiting time, a safety-oriented optimization model is proposed by integrating risk of passenger congestion into its objective function. Due to nonlinear characteristics of the proposed model, a variable neighborhood search algorithm (VNS) is designed to improve computation efficiency, where three types of novel neighborhood solutions are presented, and a penalty function is set for constraint violations. Taking Beijing Batong metro line as a case study, the proposed optimization model for train skip-stop strategy is tested for the downstream direction with 42 operating trains during morning peak hours and a part of off-peak hours (from 07:00 to 10:40 am). The experiment results show that the proposed algorithm can find high-quality train skip-stop schemes within 5 min, which can significantly relieve passenger congestion and improve service quality. Compared with the scenario where trains stop at all stations, the maximum number of waiting passengers with the train skip-stop strategy decreases from 5 299 to 2 495 over all the stations, and the risk of passenger congestion is reduced by 98.7%. At the same time, the average passenger waiting time decreases from 9.49 min to 9.15 min, reduced by 3.6%.

     

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