Highlights 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.
Abstract 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.
Modeling railway disruption lengths with Copula Bayesian Networks
Tramsportation Research, Part C: Emerging Technologies ; 68 ; 350-368
2016-04-25
19 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Modeling railway disruption lengths with Copula Bayesian Networks
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