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

Florian Hauck () and Natalia Kliewer
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Florian Hauck: Freie Universität Berlin
Natalia Kliewer: Freie Universität Berlin

A chapter in Operations Research Proceedings 2019, 2020, pp 741-747 from Springer

Abstract: 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.

Keywords: Delay prediction; Machine learning; Railway network (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-030-48439-2_90

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DOI: 10.1007/978-3-030-48439-2_90

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