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

EasyChair Preprint 9874

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

@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 9874},
  year      = {EasyChair, 2023}}