A hybrid model to improve the train running time prediction ability during high-speed railway disruptions.

• Machine learning models were proven to be incapable of unexpected situation prediction. • Support vector regression and Kalman filter were combined for train running time prediction. • The Kalman filter substantially improved the performance of the machine learning models. • The proposed method conforms to the timeliness and high accuracy requirements of real-time prediction. This study aims to propose a hybrid model that comprises support vector regression (SVR) and a Kalman filter (KF) to improve the train running time prediction accuracy of machine learning models during railway disruptions. The SVR was trained using offline data, whereas the KF updated the SVR prediction using real-time information. Thus, the hybrid model mitigates the time-consuming online training of machine learning models and their inability to reflect real-time information when using offline training. To obtain a high-performance prediction model, four key SVR parameters were first optimized based on cross-validation. Then, SVR predictions were evaluated using the mean absolute and percentage errors of the test datasets by considering the trains that suffered disruptions. The results from this evaluation show that the SVR notably outperformed other benchmark models but was unable to provide satisfactory predictions under unexpected situations. Next, we applied the KF to update the SVR prediction using real-time information and conducted model performance evaluation of the predictions based on the hybrid model. The corresponding results show that the KF significantly improved the SVR prediction accuracy under unexpected disruption situations. Furthermore, using offline training, along with the KF instead of online training, substantially reduced the computational time. [ABSTRACT FROM AUTHOR]