Modeling railway disruption lengths with Copula Bayesian Networks
mode - rail, place - europe, technology - intelligent transport systems
Railway disruption, Prediction, Dependence model
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.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Zilko, A.A., Kurowicka, D., & Goverde, R.M.P. (2016). Modeling railway disruption lengths with Copula Bayesian Networks. Transportation Research Part C: Emerging Technologies, Vol. 68, pp. 350–368.