Railway Delay Prediction with Spatial-Temporal Graph Convolutional Networks
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Presented by Heglund, Jacob (University of Illinois Urbana-Champaign), Taleongpong, Panukorn (Center for Transport Studies, Department of Civil and Environment), Hu, Simon (Zhejiang University), Tran, Huy (University of Illinois at Urbana-Champaign) Abstract: Cascading delays that propagate from a primary source along a railway network are an immediate concern for British railway systems. Complex nonlinear interactions between various spatio-temporal variables govern the propagation of these delays which can quickly spread throughout railway networks, causing further severe disruptions. To better understand the effects of these nonlinear interactions, we present a novel, graph-based formulation of a subset of the British railway network. Using this graph-based formulation, we apply the Spatial-Temporal Graph Convolutional Network (STGCN) model to predict cascading delays throughout the railway network. We find that this model outperforms other statistical models which do not explicitly account for interactions on the rail network, thus showing the value of a Graph Neural Network (GNN) approach in predicting delays for the British railway system.