Tang, R., Liu, R. and Lin, Z. (2025) Predicting primary delay of train services using graph-embedding based machine learning. Journal of Rail Transport Planning and Management, 34. 100518. ISSN 2210-9706
Abstract
Train delays can cause huge economic loss and passenger dissatisfaction. The Train Delay Prediction Problem has been investigated by a large number of studies. How to best represent certain features of a train is key to successful prediction. For instance, due to its complex topological nature, a train's route (i.e., origin, intermediate stations and destination) is one of the most difficult features to effectively represent. This study introduces graph embedding to understand and model the complex structure of a railway network which is able to capture a comprehensive collection of features including network topology, infrastructure and train profile. In particular, for the first time, we propose an approach to embed a train's route in a network topology perspective based on Structural Deep Network Embedding (SDNE) and Singular Value Decomposition (SVD). Compared to a conventional advanced method, Principle Component Analysis (PCA), our route embedding not only significantly reduces feature vector length and computational effort, but is also highly accurate and reliable in terms of capturing network topology as evidenced by K-means clustering. Computational experiments based on real-world cases from a UK train operator (TransPennine Express) show our graph-embedding based models are competitive in prediction accuracy and F1-score while are substantially computationally efficient compared to PCA.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY-NC 4.0). |
Keywords: | Machine learning, Structural deep network embedding, Train delay prediction, Graph embedding, Singular value decomposition, Principle component analysis |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) |
Funding Information: | Funder Grant number EU - European Union 881782 |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Mar 2025 11:32 |
Last Modified: | 24 Mar 2025 14:28 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.jrtpm.2025.100518 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:223919 |