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Delay Prediction System for Large-Scale Railway Networks Based on Big Data Analytics

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Advances in Big Data (INNS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 529))

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Abstract

State-of-the-art train delay prediction systems do not exploit historical train movements data collected by the railway information systems, but they rely on static rules built by expert of the railway infrastructure based on classical univariate statistic. The purpose of this paper is to build a data-driven train delay prediction system for large-scale railway networks which exploits the most recent Big Data technologies and learning algorithms. In particular, we propose a fast learning algorithm for predicting train delays based on the Extreme Learning Machine that fully exploits the recent in-memory large-scale data processing technologies. Our system is able to rapidly extract nontrivial information from the large amount of data available in order to make accurate predictions about different future states of the railway network. Results on real world data coming from the Italian railway network show that our proposal is able to improve the current state-of-the-art train delay prediction systems.

L. Oneto—This research has been supported by the European Union through the projects Capacity4Rail (European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement 605650) and In2Rail (European Union’s Horizon 2020 research and innovation programme under grant agreement 635900).

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Author information

Authors and Affiliations

  1. DIBRIS - University of Genoa, Via Opera Pia 11A, 16145, Genova, Italy

    Luca Oneto, Emanuele Fumeo, Giorgio Clerico & Davide Anguita

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

    Renzo Canepa

  3. Ansaldo STS S.p.A., Via Paolo Mantovani 3-5, 16151, Genoa, Italy

    Federico Papa, Carlo Dambra & Nadia Mazzino

Authors
  1. Luca Oneto
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  2. Emanuele Fumeo
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  3. Giorgio Clerico
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  4. Renzo Canepa
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  5. Federico Papa
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  6. Carlo Dambra
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  7. Nadia Mazzino
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  8. Davide Anguita
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Corresponding author

Correspondence to Luca Oneto .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University , Lancaster, United Kingdom

    Plamen Angelov

  2. Data Engineering Lab, Dept. of Informatics, Aristotle University of Thessaloniki , Thessaloniki, Greece

    Yannis Manolopoulos

  3. Lab of Forest Informatics (FiLAB), Democritus University of Thrace , Orestiada, Greece

    Lazaros Iliadis

  4. WPC Information Systems Faculty, Arizona State University , Tempe, Arizona, USA

    Asim Roy

  5. Electrical Engineering Dept, (ICA), Pontifical Catholic Univ of Rio de Janei , Rio de Janeiro, Rio de Janeiro, Brazil

    Marley Vellasco

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Oneto, L. et al. (2017). Delay Prediction System for Large-Scale Railway Networks Based on Big Data Analytics. In: Angelov, P., Manolopoulos, Y., Iliadis, L., Roy, A., Vellasco, M. (eds) Advances in Big Data. INNS 2016. Advances in Intelligent Systems and Computing, vol 529. Springer, Cham. https://doi.org/10.1007/978-3-319-47898-2_15

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  • DOI: https://doi.org/10.1007/978-3-319-47898-2_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47897-5

  • Online ISBN: 978-3-319-47898-2

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