Modeling railway disruption lengths with Copula Bayesian Networks

•We construct a dependence model for the railway disruption length in the Netherlands.•The model is used to make prediction of disruption length.•The model is validated against a test set.•The model appears to perform well in predicting the disruption length distribution.•We show how our model can b...

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Veröffentlicht in:Transportation research. Part C, Emerging technologies Emerging technologies, 2016-07, Vol.68, p.350-368
Hauptverfasser: Zilko, Aurelius A., Kurowicka, Dorota, Goverde, Rob M.P.
Format: Artikel
Sprache:eng
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Zusammenfassung:•We construct a dependence model for the railway disruption length in the Netherlands.•The model is used to make prediction of disruption length.•The model is validated against a test set.•The model appears to perform well in predicting the disruption length distribution.•We show how our model can be used in real-life application. 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.
ISSN:0968-090X
1879-2359
DOI:10.1016/j.trc.2016.04.018