Metadata only
Datum
2020-04Typ
- Journal Article
ETH Bibliographie
no
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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. Mehr anzeigen
Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
Journal of ForecastingBand
Seiten / Artikelnummer
Verlag
WileyThema
Delay prediction; LSTM model; Railway; Real-world dataOrganisationseinheit
09611 - Corman, Francesco / Corman, Francesco
02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG
ETH Bibliographie
no
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