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Modeling train operation as sequences: A study of delay prediction with operation and weather data.
- Source :
-
Transportation Research Part E: Logistics & Transportation Review . Sep2020, Vol. 141, pN.PAG-N.PAG. 1p. - Publication Year :
- 2020
-
Abstract
- • 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. 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13665545
- Volume :
- 141
- Database :
- Academic Search Index
- Journal :
- Transportation Research Part E: Logistics & Transportation Review
- Publication Type :
- Academic Journal
- Accession number :
- 145698188
- Full Text :
- https://doi.org/10.1016/j.tre.2020.102022