Real-time optimization for train regulation and stop-skipping adjustment strategy of urban rail transit lines - Chen, Zebin; Li, Shukai; D’Ariano, Andrea; Yang, Lixing (Omega (Oxford), 2022)
•This work deals with the real-time optimization for train regulation and stop-skipping adjustment strategy.•A nonlinear programming model is proposed for the train regulation problem in urban rail transit lines.•A model predictive control method is designed to solve the collaborative model in a real-time manner.•The proposed strategy outperforms the current practical strategy in terms of operational efficiency and service quality. This paper addresses the real-time train regulation problem by taking the stop-skipping strategy into account in high-frequency urban rail transit lines. During rush hours, if the train regulation is not adopted properly and rapidly to deal with the frequent disturbances, the delays may further spread and result in a large number of passengers stranded in the stations. The purpose of this paper is to explore the possibility of dynamic train regulation and stop-skipping adjustment strategy to cope with the disturbance management in a real-time manner. By considering the impact of dynamic passenger flows, a nonlinear programming model for train regulation problem is proposed with the objective of minimizing the total train deviation and enhancing the passenger service quality, which is further converted into a mixed-integer quadratic programming model for ease to solve. In addition, the constraints related to the train rolling-stock plan are taken into account to provide a feasible scheme. Based on a customized model predictive control (MPC) method, the proposed model can be solved in a real-time manner, laying a theoretical groundwork for implementation of the train regulation strategy. Computational results based on the real-world data of Beijing Yizhuang metro line illustrate the superiority of the train regulation strategy in comparison with the practical strategy. The robustness of the method is examined through various scenarios of uncertain passenger demands, and the adjustment performance with different prediction horizons is explored to illustrate the added value brought by the updated information of the proposed MPC method.