Optimizing High-Speed Railroad Timetable with Passenger and Station Service Demands: A Case Study in the Wuhan-Guangzhou Corridor

This paper aims to optimize high-speed railroad timetables for a corridor. We propose an integer programming model using a time-space network-based approach to consider passenger service demands, train scheduling, and station service demands simultaneously. A modified branch-and-price algorithm is used for the computation. This algorithm solves the linear relaxation of all nodes in a branch-and-bound tree using a column generation algorithm to derive a lower-bound value (LB) and derive an upper-bound value (UB) using a rapid branching strategy. The optimal solution is derived by iteratively updating the upper- and lower-bound values. Three acceleration strategies, namely, initial solution iteration, delayed constraints, and column removal, were designed to accelerate the computation. The effectiveness and efficiency of the proposed model and algorithm were tested using Wuhan-Guangzhou high-speed railroad data. The results show that the proposed model and algorithm can quickly reduce the defined cost function by 38.2% and improve the average travel speed by 10.7 km/h, which indicates that our proposed model and algorithm can effectively improve the quality of a constructed train timetable and the travel efficiency for passengers.