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Stochastic prediction of train delays in real-time using Bayesian networks
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
Delft University of Technology, The Netherlands.
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
2015 (English)In: Conference on Advanced Systems in Public Transport: CASPT2015, 2015Conference paper (Refereed)
Abstract [en]

In this paper we present a stochastic model for predicting the propagation of train delays based on Bayesian networks. This method can eciently 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 of have a scheduled passenger transfer. The model is applied on a set of historical trac realisation data from the part of a busy corridor in Sweden.We present the initial 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 trac, that are not only based on static, oine collected data, but are able to positively include the dynamic characteristics of the continuously changing delays.

Place, publisher, year, edition, pages
2015.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-119749OAI: oai:DiVA.org:liu-119749DiVA: diva2:827030
Conference
Conference on Advanced Systems in Public Transport
Available from: 2015-06-26 Created: 2015-06-25 Last updated: 2015-08-13Bibliographically approved

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Kecman, PavlePeterson, AndersJoborn, Martin
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ReferencesLink to record
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