Weiwei Mou
National Engineering Laboratory of Integrated Transportation Big Data Application, Technology, Chengdu Sichuan 610031, China
Zhaolan Cheng
National Engineering Laboratory of Integrated Transportation Big Data Application, Technology, Chengdu Sichuan 610031, China
Chao Wen
National Engineering Laboratory of Integrated Transportation Big Data Application, Technology, Chengdu Sichuan 610031, China / National United Engineering Laboratory of Integrated and Intelligent Transportation,
Southwest Jiaotong University, China
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Published in: RailNorrköping 2019. 8th International Conference on Railway Operations Modelling and Analysis (ICROMA), Norrköping, Sweden, June 17th – 20th, 2019
Linköping Electronic Conference Proceedings 69:59, p. 913-929
Published: 2019-09-13
ISBN: 978-91-7929-992-7
ISSN: 1650-3686 (print), 1650-3740 (online)
Delay prediction is an important issue associated with train timetabling and dispatching. Based on real-world operation records, accurate estimation of delays is of immense significance in train operation and decisions of dispatchers. In the study, we establish a model that illustrates the interaction between and the factors affecting the same via train operation records from a Dutch railway system. Based on the main factors that affect train delay and the time series trend, we identify the independent and dependent variables. A long short-term memory (LSTM) prediction model in which the actual delay time corresponded to the dependent variable is established via Python3.6. Finally, the prediction accuracy of the random forest model and artificial neural network model is compared. The results indicate that the LSTM model outperforms other models.
