Jointly optimized demand-oriented train timetable and passenger flow control strategy for a congested subway line under a short-turning operation pattern

Hongjiao Xue, Limin Jia, Jian Li and Jianyuan Guo

Physica A: Statistical Mechanics and its Applications, 2022, vol. 593, issue C

Abstract: With excessive passenger demand and uneven distribution of passenger flow, long-distance subway lines face oversaturated conditions during peak hours, leading to potential safety problems for subway systems. To improve travel efficiency and reduce safety problems, this study proposes a method of constructing a passenger flow control jointly optimized train timetable for an oversaturated subway line under a short-turning operation pattern. Considering the time-dependent and dynamic characteristics of passenger demand, an integer nonlinear programming model is formulated to minimize the total passenger waiting time. To solve the problem, the tabu search algorithm and the IBM ILOG CPLEX solver are combined to seek the approximate optimal solution. Finally, a series of numerical experiments are conducted on the Beijing Fangshan line to demonstrate the effectiveness of the proposed model and approach, which shows that the method of jointly optimizing the train timetable and passenger flow control can drastically decrease the total passenger waiting time and reduce congestion.

Keywords: Short-turning operation pattern; Demand-oriented train timetable; Passenger flow control; Integrated integer nonlinear programming (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:593:y:2022:i:c:s0378437122000619

DOI: 10.1016/j.physa.2022.126957

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