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

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.
Original languageEnglish
Title of host publicationProceedings of the 2021 International Conference on Computational Science
Subtitle of host publication(ICCS 2021)
PublisherSpringer Nature Switzerland AG
Publication statusAccepted/In press - 17 Mar 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
https://www.iccs-meeting.org/iccs2021/

Conference

Conference21st International Conference on Computational Science
Abbreviated titleICCS 2021
CityVirtual due to COVID-19
Period16/06/2118/06/21
Internet address

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