RSTGCN: Railway-centric Spatio-Temporal Graph Convolutional Network for Train Delay Prediction

Abstract:Accurate prediction of train delays is critical for efficient railway operations. While earlier approaches have largely focused on forecasting the exact delays of individual trains, studies on station-level delay prediction are somewhat sparse. To address this gap, we propose the Railway-centric Spatio-Temporal Graph Convolutional Network (RSTGCN), designed to forecast average arrival delays of all the incoming trains at a particular station for a particular time period. Our approach incorporates several architectural innovations and novel feature integrations, including train frequency-aware spatial attention, which significantly enhance predictive performance. To support this effort, we curate and release a comprehensive dataset for the entire Indian Railway Network (IRN), spanning 4,735 stations across 17 zones - the largest and most diverse railway network studied to date. We conduct extensive experiments using multiple state-of-the-art baselines, demonstrating consistent improvements across standard metrics. Specifically, RSTGCN outperforms the best baseline by 18% in MAE, 14% in MAPE, and 1-8% in RMSE on the IRN.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2510.01262 [cs.LG]

(or arXiv:2510.01262v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2510.01262 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1109/TITS.2026.3726308 Focus to learn more DOI(s) linking to related resources

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