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Stochastic prediction of train delays in real-time using Bayesian networks
Swiss Fed Inst Technol, Switzerland.
Linköping University, Department of Science and Technology. Linköping University, Faculty of Science & Engineering.
2018 (English)In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 95, p. 599-615Article in journal (Refereed) Published
Abstract [en]

In this paper we present a stochastic model for predicting the propagation of train delays based on Bayesian networks. This method can efficiently represent and compute the complex stochastic inference between random variables. Moreover, it allows updating the probability distributions and reducing the uncertainty of future train delays in real time under the assumption that more information continuously becomes available from the monitoring system. The dynamics of a train delay over time and space is presented as a stochastic process that describes the evolution of the time-dependent random variable. This approach is further extended by modelling the interdependence between trains that share the same infrastructure or have a scheduled passenger transfer. The model is applied on a set of historical traffic realisation data from the part of a busy corridor in Sweden. We present the results and analyse the accuracy of predictions as well as the evolution of probability distributions of event delays over time. The presented method is important for making better predictions for train traffic, that are not only based on static, offline collected data, but are able to positively include the dynamic characteristics of the continuously changing delays.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD , 2018. Vol. 95, p. 599-615
Keywords [en]
Bayesian networks; Prediction; Railway traffic; Stochastic processes; Train delays
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:liu:diva-152393DOI: 10.1016/j.trc.2018.08.003ISI: 000447112500035OAI: oai:DiVA.org:liu-152393DiVA, id: diva2:1259626
Note

Funding Agencies|FP7 European project Capacity4Rail; State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University [RCS2015K002, RCS2015K003]

Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2018-10-30

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Kecman, Pavle
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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf