An Ordinal Optimization-Based Method for the Collaborative Optimization of Train Scheduling and Passenger Flow Guidance in Urban Rail Transit

This study primarily investigates the collaborative optimization of train scheduling and passenger flow guidance in urban rail transit systems. Initially, the mathematical model of the optimization problem is briefly described from three perspectives: optimization objectives, decision variables, and constraints. Aiming to reduce system traction energy consumption and the average passenger waiting time, an ordinal optimization method is employed to solve the model, thereby determining the Pareto layers for the studied problem. Based on the obtained decision variables, a complete departure timetable is formulated. Finally, using Chongqing Metro Lines 1, 2, and 3 as the scene of verification, a numerical case study is given, and the experimental results demonstrate that compared to the baseline departure scheme, this method reduces system traction energy consumption by 0.68% and the average passenger waiting time by 7.79%. Additionally, the study utilizes ordinal optimization for solution quality assessment, showing that by select nine best solutions in the first two layers of all Pareto layers generated by the rough model within the representative set, it is possible to select nine solutions from the first three layers of all Pareto layers generated by the detailed model with a confidence level of α = 95%. These results fully validate the effectiveness of the proposed optimization method.