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

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

Transportation Research Part E: Logistics and Transportation Review, 2020, vol. 141, issue C

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.

Keywords: Train operation; Sequences; Delay prediction; Deep learning; Interactions (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.tre.2020.102022

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