Robust optimization of high-speed railway train stop plan and ticket allocation considering passenger transfer

With the rapid increase of the passenger volume in China, the high spatial-temporal fluctuation and a huge number of cross-line passengers put forward higher requirements for the reasonable high-speed railway train stop plan and ticket allocation scheme. This paper constructs a conditional probability based uncertainty set to characterize the uncertain passenger travel demands, and proposes a robust joint stop plan and ticket allocation model considering the passenger transfer behaviors to enhance the robustness and flexibility of high-speed railway operation plans. This paper further designs a row-and-column generation algorithm to solve the model, where an exact algorithm based on Kuhn-Tuck conditions and an approximation algorithm based on the problem structural property are proposed for the subproblems. Numerical experiments are conducted based on real-world data from Beijing-Shanghai line to validate the effectiveness of the proposed model and algorithms. Experimental results show that the proposed model can reduce the total stop time and unsatisfied passenger flow, and mitigate the conservativeness of the traditional uncertainty set based robust model. The proposed algorithms solve the problem in a few iterations with high accuracy. In practice, the railway department should take into account the uncertain passenger demands, passengers transfer behaviors, and the trade-off between service efficiency and service level to obtain the system's optimal high-speed railway train stop plan and ticket allocation scheme. © 2022 Systems Engineering Society of China. All rights reserved.