Railway Delay Prediction with Spatial-Temporal Graph Convolutional Networks

Jacob S.W. Heglund, Panukorn Taleongpong, Simon Hu, Huy T. Tran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728141497
DOIs
StatePublished - Sep 20 2020
Event23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 - Rhodes, Greece
Duration: Sep 20 2020Sep 23 2020

Publication series

Name2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020

Conference

Conference23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020
CountryGreece
CityRhodes
Period9/20/209/23/20

Keywords

  • Big Data
  • Deep Learning
  • Intelligent Transport Systems
  • Machine Learning
  • Railway

ASJC Scopus subject areas

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Information Systems and Management
  • Modeling and Simulation
  • Education

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