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A dynamic, interpretable, and robust hybrid data analytics system for train movements in large-scale railway networks

  • Luca OnetoEmail author
  • Irene Buselli
  • Alessandro Lulli
  • Renzo Canepa
  • Simone Petralli
  • Davide Anguita
Regular Paper
  • 38 Downloads

Abstract

We investigate the problem of analysing the train movements in large-scale railway networks for the purpose of understanding and predicting their behaviour. We focus on different important aspects: the Running Time of a train between two stations, the Dwell Time of a train in a station, the Train Delay, the Penalty Costs associated to a delay, and the Train Overtaking between two trains which are in the wrong relative position on the railway network. Two main approaches exist in the literature to address these problems. One is based on the knowledge of the network and the experience of the operators. The other one is based on the analysis of the historical data about the network with advanced data analytics methods. In this paper, we will propose a hybrid approach in order to address the limitations of the current solutions. In fact, experience-based models are interpretable and robust but not really able to take into account all the factors which influence train movements resulting in low accuracy. From the other side, data-driven models are usually not easy to interpret nor robust to infrequent events and require a representative amount of data which is not always available if the phenomenon under examination changes too fast. Results on real-world data coming from the Italian railway network will show that the proposed solution outperforms both state-of-the-art experience-based and data-driven systems in terms of interpretability, robustness, ability to handle nonrecurring events and changes in the behaviour of the network, and ability to consider complex and exogenous information.

Keywords

Railway network Train movements Running time Dwell time Train delays Penalty costs Train overtaking Experience-based models Data-driven models Hybrid models 

Notes

Acknowledgements

This research has been supported by the European Union through the projects IN2DREAMS (European Union’s Horizon 2020 research and innovation programme under grant agreement 777596) and In2Rail (European Union’s Horizon 2020 research and innovation programme under grant agreement 635900).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.DIBRISUniversity of GenovaGenoaItaly
  2. 2.Rete Ferroviaria Italiana S.p.A.GenoaItaly

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