Stochastic prediction of train delays in real-time using Bayesian networks |
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| Institution: | 1. Institute for Transport Planning and Systems, ETH Zurich, Switzerland;2. Department of Science and Technology, Linköping University, Sweden;1. Center for Industrial Management, Catholic University of Leuven, Celestijnenlaan 300A - 3001 Heverlee, Belgium;2. Maritime and Transport Technology Department, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands;3. Dipartimento di Ingegneria, Università degli Studi Roma Tre, via della vasca navale, 79 - 00146 Roma, Italy;4. Dipartimento di Ingegneria dell''Informazione e Scienze Matematiche, Università degli Studi di Siena, via Roma, 56 - 53100 Siena, Italy;1. DTU Management, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark;2. Banedanmark, Carsten Niebuhrs Gade 49, 1577 Copenhagen, Denmark;1. Department of Transport and Planning, Delft University of Technology, The Netherlands;2. Delft Institute of Applied Mathematics, Delft University of Technology, The Netherlands;1. Norwegian University of Science and Technology, Dept. of Mechanical and Industrial Engineering, N-7491 Trondheim, Norway;2. SINTEF Technology and Society, Postboks 4760 Sluppen, N-7465 Trondheim, Norway |
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| Abstract: | In this paper we present a stochastic model for predicting the propagation of train delays based on Bayesian networks. This method can efficiently represent and compute the complex stochastic inference between random variables. Moreover, it allows updating the probability distributions and reducing the uncertainty of future train delays in real time under the assumption that more information continuously becomes available from the monitoring system. The dynamics of a train delay over time and space is presented as a stochastic process that describes the evolution of the time-dependent random variable. This approach is further extended by modelling the interdependence between trains that share the same infrastructure or have a scheduled passenger transfer. The model is applied on a set of historical traffic realisation data from the part of a busy corridor in Sweden. We present the results and analyse the accuracy of predictions as well as the evolution of probability distributions of event delays over time. The presented method is important for making better predictions for train traffic, that are not only based on static, offline collected data, but are able to positively include the dynamic characteristics of the continuously changing delays. |
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| Keywords: | Bayesian networks Prediction Railway traffic Stochastic processes Train delays |
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