Integrated Optimization of Timetabling and Skip-Stop Patterns with Passenger Transfer Strategy in Urban Rail Transit

During peak hours, urban rail transit systems often face imbalanced spatial–temporal demands. Due to the limited transportation capacity, passengers departing from downstream stations often experience longer waiting times. Mostly traditional timetable and skip-stop strategies overlook passengers’ transfer behavior, which may impact the implementation of optimization strategies. This paper aims to take passengers’ transfer behavior into account and construct a coordinated optimization model of timetable and skip-stop patterns. We regulate passengers’ transfer strategies and design a genetic algorithm for solving the optimization model. In order to characterize feasible passenger travel patterns, strict FCFS rules and capacity constraints are incorporated into the model. Our result demonstrates that considering passengers’ transfer behavior, the coordinated optimization of timetable and skip-stop strategy can not only mitigate the unfairness of acquiring rail service among passengers but also reduce the average waiting time of the entire system. We validate the effectiveness of our algorithm using the dataset from Line 1 of Singapore’s urban rail transit system as a case study.