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

• URL: http://arxiv.org/abs/2510.01262v1
• Date: Fri, 26 Sep 2025 01:52:52 GMT
• Title: RSTGCN: Railway-centric Spatio-Temporal Graph Convolutional Network for Train Delay Prediction
• Authors: Koyena Chowdhury, Paramita Koley, Abhijnan Chakraborty, Saptarshi Ghosh,
• Abstract summary: We propose the Railway-centric Spatio-Temporal Graph Convolutional Network (RSTGCN) to forecast average arrival delays of all the incoming trains at railway stations.<n>Our approach incorporates several architectural innovations and novel feature integrations, including train frequency-aware spatial attention.<n>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.
• Score: 6.818193314114963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate prediction of train delays is critical for efficient railway operations, enabling better scheduling and dispatching decisions. While earlier approaches have largely focused on forecasting the exact delays of individual trains, recent studies have begun exploring station-level delay prediction to support higher-level traffic management. In this paper, we propose the Railway-centric Spatio-Temporal Graph Convolutional Network (RSTGCN), designed to forecast average arrival delays of all the incoming trains at railway stations for a particular time period. Our approach incorporates several architectural innovations and novel feature integrations, including train frequency-aware spatial attention, which significantly enhances 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. Our work not only advances the modeling of average delay prediction in large-scale rail networks but also provides an open dataset to encourage further research in this critical domain.

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