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

In: Operations Research Proceedings 2019

Author

Listed:
  • Florian Hauck

    (Freie Universität Berlin)

  • Natalia Kliewer

    (Freie Universität Berlin)

Abstract

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.

Suggested Citation

  • Florian Hauck & Natalia Kliewer, 2020. "Data Analytics in Railway Operations: Using Machine Learning to Predict Train Delays," Operations Research Proceedings, in: Janis S. Neufeld & Udo Buscher & Rainer Lasch & Dominik Möst & Jörn Schönberger (ed.), Operations Research Proceedings 2019, pages 741-747, Springer.
    • Handle: RePEc:spr:oprchp:978-3-030-48439-2_90
    DOI: 10.1007/978-3-030-48439-2_90
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