An Efficient Hybrid Planning Framework for In-Station Train Dispatching

Matteo Cardellini, Marco Maratea, Mauro Vallati, Gianluca Boleto, Luca Oneto

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Title of host publicationComputational Science – ICCS 2021
Subtitle of host publication21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part I
EditorsMaciej Paszynski, Dieter Kranzlmüller, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, Peter M. A. Sloot
Place of PublicationCham
PublisherSpringer Nature Switzerland AG
Number of pages15
VolumeLNCS/LNTCS 12742
ISBN (Electronic)9783030779610
ISBN (Print)9783030779603
Publication statusPublished - 16 Jun 2021
Event21st International Conference on Computational Science - Online due to COVID-19 (was due to take place in Krakow, Poland), Virtual due to COVID-19
Duration: 16 Jun 202118 Jun 2021
Conference number: 21

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Nature Switzerland AG
VolumeLNCS/LNTCS 12742
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st International Conference on Computational Science
Abbreviated titleICCS 2021
CityVirtual due to COVID-19
Internet address


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