Disaggregating Train Delays into Primary and Secondary Components using Gated Graph Convolutional Networks

Abstract

This study presents a novel approach for disaggregating aggregated train delays into primary and secondary components using Gated Graph Convolutional Networks (GatedGCNs). We develop a graph-based representation of railway traffic that captures complex spatiotemporal relationships and long-range dependencies. Our method is applied to synthetic delay data generated from an agent-based simulation model of the Austrian railway network. We evaluate the model on classification and regression tasks, demonstrating high accuracy in distinguishing between primary and secondary delays. The classification task achieves 96% accuracy and 0.99 AUC, while the regression task attains an R-squared value of 0.86. These results significantly outperform a naive baseline model. The findings suggest that GatedGCN is a promising method for delay disaggregation and has potential for broader applications in capturing delay propagation processes. However, while the results on synthetic data demonstrate strong performance, further validation on real-world data is essential to confirm its practical applicability.