Konferensartikel

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.

Railway, Real-world data, Delay prediction, LSTM model