Robust metro train scheduling integrated with skip-stop pattern and passenger flow control strategy under uncertain passenger demands

Abstract

As the passenger demands, especially commuters, in the megacities increase dramatically, the congestion problem of metro lines becomes severe and affects passengers’ travel experience. More and more metro stations have adopted passenger flow control measures to ensure operational security or implemented skip-stop patterns to ease transportation pressure. This paper investigates the collaborative optimization problem of timetable scheduling, passenger flow control, and skip-stop pattern on a metro line. A mixed integer nonlinear programming (MINLP) model is proposed to require a performance balance between service level and operation costs. The collaborative optimization model is constructed to optimize the train timetable by determining arrival and departure times with bounded dwell time and running time at intervals. Specifically, time-dependent passenger arrival rates are regarded as uncertain parameters here. The scenario-based robust optimization (SBRO) model with nonlinear constraints is further transformed into a model with linear constraints, which can be directly solved by commercial solvers. Finally, several sets of numerical experiments according to the actual data about the Beijing Changping metro line are performed to demonstrate the validity of the proposed model and method. The optimized train timetable integrated with the skip-stop pattern and passenger flow control strategy can effectively ease the congestion of stations and improve the safety and service quality of the metro system. The tradeoff between solution robustness and model robustness is obtained with critical practical insights.