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Chinese Journal of Management Science ›› 2025, Vol. 33 ›› Issue (3): 256-263.doi: 10.16381/j.cnki.issn1003-207x.2022.1317

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Optimization of Train Skip-stop Operation on Tidal Overcrowded Metro Lines

Hui Liang1, Yun Jing1,3(), Zhiqiang Tian2,4, Qi Song2, Maowu Zhu2   

  1. 1.School of Traffic and Transportation,Beijing Jiaotong University,Beijing 100044,China
    2.School of Traffic and Transportation,Lanzhou Jiaotong University,Lanzhou 730070,China
    3.Frontiers Science Center for Smart High-speed Railway Systemss,Beijing Jiaotong University,Beijing 100044,China
    4.Key Laboratory of Railway Industry on Plateau Railway Transportation Intelligent Management and Control,Lanzhou Jiaotong University,Lanzhou 730070,China
  • Received:2022-06-16 Revised:2022-08-21 Online:2025-03-25 Published:2025-04-07
  • Contact: Yun Jing E-mail:yjing@bjtu.edu.cn

Abstract:

Due to the uneven distribution of tidal passenger flow in time and space of urban rail transit, the passengers in the direction of large passenger flow are seriously delayed in the station and the train capacity in the direction of small passenger flow is redundant. From the perspective of traffic demand, the minimum number of delayed passengers in the station and the minimum time of passengers in the train is taken as the optimization objectives respectively, and the urban rail transit timetable optimization model considering the skip-stop strategy is constructed and the simulated annealing algorithm is used to solve it. In order to verify the validity of the model and the algorithm, based on the actual operation data of an urban rail transit line, the results show that by using the cooperative optimization strategy of adjusting the train headway and train skip-stop pattern, the total number of delayed passengers in the station is reduced by 7.1%, and the total time of passengers in the train is reduced by 2.2%. Finally, the sensitivity analysis of the number of jumping stations can provide a variety of selection strategies for operators.

Key words: urban rail traffic, tidal passenger flow, bi-objective optimization, train timetable, skip-stop pattern

CLC Number: