Modeling railway disruption lengths with Copula Bayesian Networks

Journal article (2016)

Authors

A.A. Zilko Applied Probability - EEMCS
D. Kurowicka Applied Probability - EEMCS
R.M.P. Goverde Transport and Planning - CEG
Research Group
Applied Probability (EEMCS) (TU Delft)
Prediction Dependence model Railway disruption
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Published Date

2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Delft Institute of Applied Mathematics

Research Group

Applied Probability

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.