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Robust Optimization of Train Timetable with Short-Turning Strategy Considering Uncertain Passenger Demand and Vehicle Selection

EasyChair Preprint no. 9874

23 pagesDate: March 17, 2023

Abstract

Considering the uncertainty of passenger demand and vehicle selection, this paper investigates a robust optimization approach for the train timetabling problem with short-turning strategy in urban rail transit system. With the scenario-based representation of passenger distribution, a mixed-integer linear programming (MILP) model is formulated that simultaneously integrates train timetabling, short-turning strategy and rolling stock circulation. The proportion of passengers who take the short-turning train services to the last station of the short-turning region and transfer to the full-length train services to their destination stations, is introduced to describe the passenger vehicle selection behavior under short-turning strategy. Finally, three experiments are designed for Xi’an Metro Line 3 to verify the solution quality and effectiveness of the proposed methods. The results indicate that the robust train timetable can more effectively satisfy multi-scenario passenger demand than the satisfactory train timetable generated by independent optimization of each demand scenario.

Keyphrases: robust optimization, short-turning strategy, train timetable, uncertain passenger demand, vehicle selection

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:9874,
  author = {Fangsheng Wang and Pengling Wang and Zixuan Zhu and Xiaofang Xiao and Ruihua Xu},
  title = {Robust Optimization of Train Timetable with Short-Turning Strategy Considering Uncertain Passenger Demand and Vehicle Selection},
  howpublished = {EasyChair Preprint no. 9874},

  year = {EasyChair, 2023}}
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