Deep Learning to Identify the Spatio-Temporal Cascading Effects of Train Delays in a High-Density Network
Year: 2025
Type: conference-paper
Abstract: The operational efficiency of railway networks, a cornerstone of modern economies, is persistently undermined by the cascading effects of train delays. Accurately forecasting this delay propagation is a critical challenge for real-time traffic management. While recent research has leveraged Graph Neural Networks (GNNs) to model the network structure of railways, a significant gap remains in developing frameworks that provide multi-step autoregressive forecasts at a network-wide scale, while simultaneously offering the live, interpretable explanations needed for decision support.
Authors: Vu Duc Anh Nguyen, Ziyue Li
Institutions: University of Cologne, Technical University of Munich
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4417129309