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Modeling train operation as sequences: A study of delay prediction with operation and weather data

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  • Huang, Ping
  • Wen, Chao
  • Fu, Liping
  • Lessan, Javad
  • Jiang, Chaozhe
  • Peng, Qiyuan
  • Xu, Xinyue

Abstract

This paper presents a carefully designed train delay prediction model, called FCLL-Net, which combines a fully-connected neural network (FCNN) and two long short-term memory (LSTM) components, to capture operational interactions. The performance of FCLL-Net is tested using data from two high speed railway lines in China. The results show that FCLL-Net has significantly improved prediction performance, over 9.4% on both lines, in terms of the selected absolute and relative metrics compared to the commonly used state-of-the-art models. Additionally, the sensitivity analysis demonstrates that interactions of train operations and weather-related features are of great significance to consider in delay prediction models.

Suggested Citation

  • Huang, Ping & Wen, Chao & Fu, Liping & Lessan, Javad & Jiang, Chaozhe & Peng, Qiyuan & Xu, Xinyue, 2020. "Modeling train operation as sequences: A study of delay prediction with operation and weather data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    • Handle: RePEc:eee:transe:v:141:y:2020:i:c:s1366554520306736
    DOI: 10.1016/j.tre.2020.102022
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    References listed on IDEAS

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    Cited by:

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