A predictive model of train delays on a railway line. (29th December 2019)

• Record Type: Journal Article
• Authors: Wen, Chao; Mou, Weiwei; Huang, Ping; Li, Zhongcan
• Is Part Of: Journal of forecasting. Volume 39:Number 3(2020)
• Page Start: 470
• Page End: 488
• Publication Date: 2019-12-29
• Subjects: delay prediction -- LSTM model -- railway -- real‐world data; Forecasting -- Periodicals; Forecasting -- Mathematical models -- Periodicals; 003.2
• DOI: 10.1002/for.2639
• Languages: English
• ISSNs: 0277-6693

Abstract

Abstract: Delay prediction is an important issue associated with train timetabling and dispatching. Based on real‐world operation records, accurate forecasting of delays is of immense significance in train operation and decisions of dispatchers. In this study, we established a model that illustrates the interaction between train delays and their affecting factors via train describer records on a Dutch railway line. Based on the main factors that affect train delay and the time series trend, we determined the independent and dependent variables. A long short‐term memory (LSTM) prediction model in which the actual delay time corresponded to the dependent variable was established via Python. Finally, the prediction accuracy of the random forest model and artificial neural network model was compared. The results indicated that the LSTM model outperformed the other two models.