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International Conference on Computational Science

ICCS 2021: Computational Science – ICCS 2021 pp 168–182Cite as

An Efficient Hybrid Planning Framework for In-Station Train Dispatching

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12742))

Abstract

In-station train dispatching is the problem of optimising the effective utilisation of available railway infrastructures for mitigating incidents and delays. This is a fundamental problem for the whole railway network efficiency, and in turn for the transportation of goods and passengers, given that stations are among the most critical points in networks since a high number of interconnections of trains’ routes holds therein . Despite such importance, nowadays in-station train dispatching is mainly managed manually by human operators.

In this paper we present a framework for solving in-station train dispatching problems, to support human operators in dealing with such task. We employ automated planning languages and tools for solving the task: PDDL+ for the specification of the problem, and the ENHSP planning engine, enhanced by domain-specific techniques, for solving the problem. We carry out a in-depth analysis using real data of a station of the North West of Italy, that shows the effectiveness of our approach and the contribution that domain-specific techniques may have in efficiently solving the various instances of the problem. Finally, we also present a visualisation tool for graphically inspecting the generated plans.

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Acknowledgements

This work has been partially funded by Hitachi Rail STS through the RAIDLab (Railway Artificial Intelligence and Data Analysis Laboratory), a joint laboratory between Hitachi Rail STS and University of Genoa. This work has been supported by RFI (Rete Ferroviaria Italiana) who provided the data for the analysis (we sincerely thank Renzo Canepa for his support). Mauro Vallati was supported by a UKRI Future Leaders Fellowship [grant number MR/T041196/1].

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Authors and Affiliations

  1. University of Genoa, Genova, Italy

    Matteo Cardellini, Marco Maratea, Gianluca Boleto & Luca Oneto

  2. University of Huddersfield, Huddersfield, UK

    Mauro Vallati

Authors
  1. Matteo Cardellini
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  2. Marco Maratea
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  3. Mauro Vallati
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  4. Gianluca Boleto
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  5. Luca Oneto
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Corresponding author

Correspondence to Mauro Vallati .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Cardellini, M., Maratea, M., Vallati, M., Boleto, G., Oneto, L. (2021). An Efficient Hybrid Planning Framework for In-Station Train Dispatching. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12742. Springer, Cham. https://doi.org/10.1007/978-3-030-77961-0_15

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  • DOI: https://doi.org/10.1007/978-3-030-77961-0_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77960-3

  • Online ISBN: 978-3-030-77961-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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