Optimizing integrated train rescheduling strategies for diverse disruption scenarios using reinforcement learning

Urban rail transit systems are crucial for efficient urban transportation, but unexpected disruptions pose significant challenges to maintaining optimal train schedules. Previous studies on train timetable rescheduling (TTR) have been limited by single disruption scenario and lack of comprehensive strategies, often relying on single or dual rescheduling tactics. This paper develops an innovative approach using reinforcement learning to optimize TTR under diverse disruption scenarios. Unlike previous studies, our approach incorporates a broader range of rescheduling strategies, including normal operations, train holding, short turning, reverse running, delayed departure from depots, and their combined strategies, enhancing the model’s flexibility and adaptability. To tackle the complexity of the proposed model, we further propose a novel reward mechanism with primary and secondary reward functions in reinforcement learning. The model’s effectiveness and adaptability are validated in various disruption scenarios using real-world cases on Beijing Metro Line 19. Experimental results demonstrate that, in a 30-minute disruption scenario, the integrated rescheduling strategy reduces total train delays by 46.6 % and computation time by 13.7 % compared to a single rescheduling strategy.