The accurate prediction of train delays can help to limit the negative effects of delays for passengers and railway operators. The aim of this paper is to develop an approach for training a supervised machine learning model that can be used as an online train delay prediction tool. We show how historical train delay data can be transformed and used to build a multivariate prediction model which is trained using real data from Deutsche Bahn. The results show that the neural network approach can achieve promising results.


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    Titel :

    Data Analytics in Railway Operations: Using Machine Learning to Predict Train Delays


    Weitere Titelangaben:

    Operations Research Proceedings


    Beteiligte:
    Neufeld, Janis S. (Herausgeber:in) / Buscher, Udo (Herausgeber:in) / Lasch, Rainer (Herausgeber:in) / Möst, Dominik (Herausgeber:in) / Schönberger, Jörn (Herausgeber:in) / Hauck, Florian (Autor:in) / Kliewer, Natalia (Autor:in)


    Erscheinungsdatum :

    25.09.2020


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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