Modeling railway disruption lengths with Copula Bayesian Networks

Aurelius Zilko, Dorota Kurowicka, Rob Goverde

Research output: Contribution to journalArticleScientificpeer-review

48 Citations (Scopus)


Decreasing the uncertainty in the lengths of railway disruptions is a major help to disruption management. To assist the Dutch Operational Control Center Rail (OCCR) during disruptions, we propose the Copula Bayesian Network method to construct a disruption length prediction model. Computational efficiency and fast inference features make the method attractive for the OCCR’s real-time decision making environment. The method considers the factors influencing the length of a disruption and models the dependence between them to produce a prediction. As an illustration, a model for track circuit (TC) disruptions in the Dutch railway network is presented in this paper. Factors influencing the TC disruption length are considered and a disruption length model is constructed. We show that the resulting model’s prediction power is sound and discuss its real-life use and challenges to be tackled in practice.
Original languageEnglish
Pages (from-to)350-368
Number of pages19
JournalTransportation Research. Part C: Emerging Technologies
Publication statusPublished - 2016


  • Railway disruption
  • Prediction
  • Dependence model


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