Spatio-Temporal Feature Extraction for Predicting Large-Scale Train Delay Propagation in High-Speed Railway Networks

The safe, punctual, and reliable operation of high-speed railway (HSR) networks is crucial for ensuring system efficiency and enhancing passenger experience. However, due to the complexity of HSR systems and long operation routes, failures in system components can lead to unexpected incidents, causing deviations from the scheduled operations and, subsequently, delays. These delays, particularly large delays, significantly impact the overall performance and passenger experience of the network. Thus, accurate prediction of train delays, especially in large-delay scenarios, is essential for optimizing train scheduling and restoring normal operations in a timely manner. To address this challenge, this study proposes a deep learning architecture based on spatio-temporal feature extraction for accurate train delay prediction. A graph attention network-long short-term memory block is introduced to capture the spatio-temporal evolution features of different trains. In addition, a sequence forecasting approach and a mixture of experts module are integrated to model the complex relationships between the target train’s delay and its previous states. The delay evolution features are incorporated into the loss function, allowing for more accurate predictions. Experimental results show that the proposed model outperforms the baseline models, achieving at least an improvement of 50.62% and 27.89% in root-mean-squared error and mean absolute error, respectively. When the error tolerance is set within 3 min, the prediction accuracy reaches 96.68%. Experimental results demonstrate the superior performance of the proposed model.