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Predictive modelling of running and dwell times in railway traffic

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Abstract

Accurate estimation of running and dwell times is important for all levels of planning and control of railway traffic. The availability of historical track occupation data with a high degree of granularity inspired a data-driven approach for estimating these process times. In this paper we present and compare the accuracy of several approaches to model running and dwell times in railway traffic. Three global predictive model approaches are presented based on advanced statistical learning techniques: LTS robust linear regression, regression trees and random forests. Also local models are presented for a particular train line, station or block section, based on LTS robust linear regression with some refinements. The models are validated and compared using a test set independent from the training set. The applicability of the proposed data-driven approach for real-time applications is proved by the accuracy of the obtained estimates and the low computation times. Overall, the local models perform best both in accuracy and computation time.

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Acknowledgments

This work is partially funded by the Dutch Technology Foundation STW, research project: Model-Predictive Railway Traffic Management (Project No. 11025).

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Authors and Affiliations

  1. Department of Science and Technology, Linköping University, Linköping, Sweden

    Pavle Kecman

  2. Department of Transport and Planning, Delft University of Technology, Delft, The Netherlands

    Rob M. P. Goverde

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  1. Pavle Kecman
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  2. Rob M. P. Goverde
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Correspondence to Pavle Kecman.

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Kecman, P., Goverde, R.M.P. Predictive modelling of running and dwell times in railway traffic. Public Transp 7, 295–319 (2015). https://doi.org/10.1007/s12469-015-0106-7

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