Research on High-Speed Railway Timetable Rescheduling Based on Reinforcement Learning
The rapid expansion of China's high-speed railway network,coupled with an increase in the number and speed of trains,has significantly strengthened the interdependence of trains,presenting challenges in adjusting operations.To address these issues,this paper introduces a train operation adjustment model focused on minimizing deviations between adjusted train timetable and planned timetable.It encompasses the design of an interactive environment,along with components such as agents,states,actions,reward functions,and more.The study employs the classical Q-learning algorithm within a reinforcement learning framework to resolve the aforementioned operational challenges.Finally,an illustrative example is provided to validate the effectiveness of both the proposed model and algorithm.The findings reveal that the algorithm's solution is 43.87%lower in total deviation than the First-Come-First-Serve(FCFS)approach.
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