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 |
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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. |
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ISSN: | 0968-090X 1879-2359 |
DOI: | 10.1016/j.trc.2016.04.018 |