Solving the Railway Timetable Rescheduling Problem with Graph Neural Networks

• • The GNN-based approach predicts train dwelling and running times with satisfactory accuracy, enabling precise rescheduling decisions under delay scenarios. • • The proposed method reduces train delays by up to 20% compared to three rule-based benchmarks in disturbed train groups, demonstrating operational superiority. • • The model achieves high computational efficiency, with inference times under 1 second for real-time rescheduling scenarios, supporting practical deployment. • • The Overtaking Identification Algorithm (OIA) effectively identifies train overtaking events, enhancing the interpretability of rescheduling actions.