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Published in:
Analysis and Optimization of Urban Energy Systems Read chapter Read first chapter

2020 | OriginalPaper | Chapter

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

Authors : Florian Hauck, Natalia Kliewer

Published in: Operations Research Proceedings 2019

Publisher: Springer International Publishing

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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.

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Metadata
Title
Data Analytics in Railway Operations: Using Machine Learning to Predict Train Delays
Authors
Florian Hauck
Natalia Kliewer
Copyright Year
2020
Book
Operations Research Proceedings 2019

Print ISBN: 978-3-030-48438-5

Electronic ISBN: 978-3-030-48439-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-48439-2_90

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