In-Station Train Dispatching: A PDDL+ Planning Approach

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

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


In railway networks, stations are probably the most critical points for interconnecting trains’ routes: in a restricted geographical area, a potentially large number of trains have to stop according to an official timetable, with the concrete risk of accumulating delays that can then have a knockout effect on the rest of the network. In this context, in-station train dispatching plays a central role in maximising the effective utilisation of available railway infrastructures and in mitigating the impact of incidents and delays. Unfortunately, in-station train dispatching is still largely handled manually by human operators in charge of a group of stations. In this paper we make a step towards supporting the operator with some automatic tool, by describing an approach for performing in-station dispatching by means of automated planning techniques. Given the mixed discrete-continuous nature of the problem, we employ PDDL+ for the specification of the problem, and the ENHSP planning engine enhanced by domain-specific solving techniques. Results on a range of scenarios, using real-data of a station of the North West of Italy, show the potential of our approach.
Original languageEnglish
Title of host publicationProceedings of the 31st International Conference on Automated Planning and Scheduling
Subtitle of host publicationICAPS 2021
EditorsSusanne Biundo, Minh Do, Robert Goldman, Michael Katz, Qiang Yang, Hankz Hankui Zhou
PublisherAAAI press
Number of pages9
ISBN (Print)9781577358671
Publication statusPublished - 18 May 2021
Event31st International Conference on Automated Planning and Scheduling - Now to be held online due to COVID-19), Virtual from Guangzhou, China
Duration: 2 Aug 202113 Aug 2021
Conference number: 31

Publication series

NameProceedings of the International Conference on Automated Planning and Scheduling
PublisherAAAI Press
ISSN (Print)2334-0835
ISSN (Electronic)2334-0843


Conference31st International Conference on Automated Planning and Scheduling
Abbreviated titleICAPS 2021
CityVirtual from Guangzhou
Internet address


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