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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
DB Group. (2018). Integrated Report 2018. Retrieved 11 Aug 2019, from https://www.deutschebahn.com/resource/blob/4045194/462384b76cf49fe8ec715f41e4a3202a/19-03-IB_en-data.pdf
Marković, N., Milinković, S., Tikhonov, K., Schonfeld, P.: Analyzing passenger train arrival delays with support vector regression. Transp. Res. C Emerg. Technol. 56, 251–262 (2015)
Corman, F., Kecman, P.: Stochastic prediction of train delays in real-time using Bayesian networks. Transp. Res. C Emerg. Technol. 95, 599–615 (2018)
Gorman, M.: Statistical estimation of railroad congestion delay. Transp. Res. E Logist. Transp. Rev. 45, 446–456 (2009)
Goverde, R.: Punctuality of railway operations and timetable stability analysis. Dissertation, Delft University of Technology (2005)
Berger, A., Gebhardt, A., Müller-Hannemann, M., Ostrowski, M.: Stochastic delay prediction in large train networks. In: 11th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems. OASIcs-OpenAccess Series in Informatics, vol. 20, pp. 100–111 (2011)
Yaghini, M., Khoshraftar, M., Seyedabadi, M.: Railway passenger train delay prediction via neural network model. J. Adv. Trans. 47, 355–368 (2013) .
Oneto, L., Fumeo, E., Clerico, G., Canepa, R., Papa, F., Dambra, C., Mazzino, N., Anguita, D.: Train delay prediction systems: a big data analytics perspective. Big Data Res. 11, 54–64 (2018)
Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE International Conference on Neural Networks (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hauck, F., Kliewer, N. (2020). Data Analytics in Railway Operations: Using Machine Learning to Predict Train Delays. In: Neufeld, J.S., Buscher, U., Lasch, R., Möst, D., Schönberger, J. (eds) Operations Research Proceedings 2019. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-030-48439-2_90
Download citation
DOI: https://doi.org/10.1007/978-3-030-48439-2_90
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-48438-5
Online ISBN: 978-3-030-48439-2
eBook Packages: Business and ManagementBusiness and Management (R0)