Abstract
Predicting the delay time of trains is an important task in intelligent transport systems, as an accurate prediction can provide a reliable reference for passengers and dispatchers of the railway system. However, due to the complexity of the railway system, interactions of various spatio-temporal variables make it difficult to find the rules of delay propagation. We introduce a Sequential Precoding Spatial-Temporal Network (SPSTN) model to predict the delay of trains. SPSTN consists of a Transformer encoder that captures long-term dependencies in time series, and spatio-temporal graph convolution blocks that model delay propagation at both temporal and spatial levels. Experiments on a subset of the British railway network show that SPSTN performs favorably against the state-of-the-art, which verifies that the combination of sequential precoding and spatio-temporal convolution can effectively model delay propagation on railway networks.
J. Fu and L. Zhong—These authors contributed equally to this work and should be considered co-first authors.
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Acknowledgements
This work is supported by The Center of National Railway Intelligent Transportation System Engineering and Technology (Contract No. RITS2021KF08), China Academy of Railway Sciences (Contract No. 2021YJ195).
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Fu, J., Zhong, L., Li, C., Li, H., Kong, C., Shao, J. (2023). SPSTN: Sequential Precoding Spatial-Temporal Networks for Railway Delay Prediction. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13421. Springer, Cham. https://doi.org/10.1007/978-3-031-25158-0_37
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DOI: https://doi.org/10.1007/978-3-031-25158-0_37
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