Modeling railway disruption lengths with Copula Bayesian Networks

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, the authors 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. The authors 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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  • Accession Number: 01605310
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jun 13 2016 9:48AM