Transformer-Based Macroscopic Regulation for High-Speed Railway Timetable Rescheduling
IEEE/CAA Journal of Automatica Sinica(2023)
摘要
Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway system. In such cases, train timetables need to be rescheduled. However, timely and efficient train timetable rescheduling is still a challenging problem due to its modeling difficulties and low optimization efficiency. This paper presents a Transformer-based macroscopic regulation approach which consists of two stages including Transformer-based modeling and policy-based decision-making. Firstly, the relationship between various train schedules and operations is described by creating a macroscopic model with the Transformer, providing the better understanding of overall operation in the high-speed railway system. Then, a policy-based approach is used to solve a continuous decision problem after macro-modeling for fast convergence. Extensive experiments on various delay scenarios are conducted. The results demonstrate the effectiveness of the proposed method in comparison to other popular methods.
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关键词
High-speed railway,reinforcement learning,train timetable rescheduling,Transformer
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