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.


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    Title :

    Modeling railway disruption lengths with Copula Bayesian Networks


    Contributors:


    Publication date :

    2016-04-25


    Size :

    19 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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