Modeling train operation as sequences: A study of delay prediction with operation and weather data. (September 2020)

• Record Type: Journal Article
• Authors: Huang, Ping; Wen, Chao; Fu, Liping; Lessan, Javad; Jiang, Chaozhe; Peng, Qiyuan; Xu, Xinyue
• Is Part Of: Transportation research. Volume 141(2020)
• Publication Date: 2020-09
• Subjects: Train operation -- Sequences -- Delay prediction -- Deep learning -- Interactions; Logistics -- Periodicals; Transportation -- Periodicals; 388.011
• DOI: 10.1016/j.tre.2020.102022
• Languages: English
• ISSNs: 1366-5545

Abstract

Highlights: Deep learning models were employed to predict train delays. Train operations were modeled as sequences. Interactions were captured from train groups in the prediction model. The proposed model shows satisfactory performance on different railway lines. 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.