Prediction of train arrival delays considering route conflicts at multi-line stations

Zhongcan Li, Ping Huang, Chao Wen*, Xi Jiang, Filipe Rodrigues

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Multi-line stations (MLSs) are the intersections of different railway lines; they are crucial for delay propagation in railway networks. Therefore, the precise prediction of train arrival delays at the MLSs can efficiently support train operation rescheduling plans and reduce delay propagation in the railway network. The arrival routes of trains at the MLSs are critical factors for managing train arrival delays, since there may be latent route conflicts with forward arrival/departure trains. However, route conflicts will not occur at single-line stations (SLSs) that are traversed by only one railway line. Existing train delay prediction studies have considered the ways that trains arrive at/depart from stations as black boxes, but have not considered the latent route conflicts from a microscopic view. This study considers the arrival routes of predicted trains and route conflicts with forward trains, for contemplating the gap (not considering the route conflicts from other railway lines) in the existing studies. The influencing factors are separated into three categories according to the data attributes, namely, route-related variables, delay-related variables, and environment-related variables. Then, an architecture called LLCF-net is proposed, with a one-dimensional convolutional neural network (CNN) block for route-related variables, two long short-term memory (LSTM) networks for delay-related variables, and a fully connected neural network (FCNN) block for environment-related variables. Compared with the methods in exiting studies, this architecture showed the best performance for both two MLSs—GuangzhouSouth(GZS) and ChangshaSouth (CSS)—on the Chinese high-speed railway network, regardless of the consideration of route-related variables. In addition, LLCF-net is proven to have a strong predictive effectiveness and a robust performance for different delay lengths.

Original languageEnglish
Article number103606
JournalTransportation Research Part C: Emerging Technologies
Volume138
ISSN0968-090X
DOIs
Publication statusPublished - 2022

Bibliographical note

Funding Information:
This work was supported by the National Nature Science Foundation of China (grant numbers 71871188 and U1834209 ), the Research and development project of China National Railway Group Co., Ltd [grant number P2020X016], the Fundamental Research Funds for the Central Universities (grant number 2682021CX051), and the China Scholarship Council [grant number 202007000149]. We are grateful for the contributions made by our project partners.

Publisher Copyright:
© 2022 Elsevier Ltd

Keywords

  • Deep learning
  • Multi-line stations
  • Route conflicts
  • Train delay prediction
  • Word embedding

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