Unified Scheduling Model for High-Speed Train Timetable Optimization and Rescheduling Based on Deep Reinforcement Learning

Train schedule consists of two major phases, train timetable optimization (TTO) and train timetable rescheduling (TTR), which are interconnected with each other and aim to maintain the safety and punctuality of high-speed railway operations under ideal conditions and unexpected disturbances. However, current preparation and adjustment of train timetables face challenges in real-time responsiveness and poor performance on large-scale instances. To alleviate these problems, we propose a unified scheduling model based on deep reinforcement learning (DRL) for both TTO and TTR problems with similar formulations. The key components of our approach include a state representation utilizing the Markov decision process that captures global train and station characteristics, and a policy network that extracts information from this representation to sequentially construct the train departure order. The main benefits of our framework include adaptability to different stopping plans and delay scenarios, decoupling from the problem size, and ensuring the feasibility of generated schemes. Furthermore, to improve the solution quality, we integrate the learned decision policies with a local search method, enabling the scalability of the model with little additional computation cost. Experiments on extensive TTO and TTR instances of the Beijing-Shanghai high-speed railway line demonstrate the effectiveness and practicality of our approach. Our DRL-based method outperforms all the heuristic rules and commercial solvers without retraining the model on various problem sizes, especially on large-scale cases under limited calculation time.