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

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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.

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Notes

  1. In the whole paper the real names of the stations have been anonymized because of confidentiality issues.

  2. In the whole paper, the real identifiers of the trains have been anonymized because of confidentiality issues.

  3. http://www.rfi.it/rfi/SERVIZI-E-MERCATO/Accesso-alla-rete/Prospetto-informativo-della-rete.

  4. The detailed and complete structure of the top-level decision tree cannot be reported because of confidentiality issues.

  5. Google Compute https://cloud.google.com/products/.

  6. Because of confidentiality issues, we cannot report the results and the ids for all the sections and all the checkpoints available.

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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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Authors and Affiliations

  1. DIBRIS, University of Genova, Via Opera Pia 13, 16145, Genoa, Italy

    Luca Oneto, Irene Buselli, Alessandro Lulli & Davide Anguita

  2. Rete Ferroviaria Italiana S.p.A., Via Don Vincenzo Minetti 6/5, 16126, Genoa, Italy

    Renzo Canepa & Simone Petralli

Authors
  1. Luca Oneto
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  2. Irene Buselli
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  3. Alessandro Lulli
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  4. Renzo Canepa
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  5. Simone Petralli
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  6. Davide Anguita
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Correspondence to Luca Oneto.

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This paper is an extended version of the DSAA’2018 Application Track paper titled “Large-Scale Railway Networks Train Movements: a Dynamic, Interpretable, and Robust Hybrid Data Analytics System” [27].

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Oneto, L., Buselli, I., Lulli, A. et al. A dynamic, interpretable, and robust hybrid data analytics system for train movements in large-scale railway networks. Int J Data Sci Anal 9, 95–111 (2020). https://doi.org/10.1007/s41060-018-00171-z

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