RL-based scheduling of an AAM traffic network

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dc.contributor.authorAltun, Arinc Tutku
dc.contributor.authorXu, Yan
dc.contributor.authorInalhan, Gokhan
dc.contributor.authorHardt, Michael W.
dc.date.accessioned2023-09-15T11:10:03Z
dc.date.available2023-09-15T11:10:03Z
dc.date.issued2023-08-02
dc.description.abstractThis study presents an approach for pre-flight planning process to be used in the future Advanced Air Mobility (AAM) system especially after contingency situations and relevant activities take place. The methodology for scheduling is modeled as a reinforcement learning (RL) agent that resolves potential conflicts for the traffic and balances the demand and capacity at vertiports. The reason behind to use RL is that specific problem requires a very quick response since it also deals with resolving conflicts that are observed between the flights that are about to take-off and the contingent flights that diverted for an emergency landing. The main objective of this work is to develop a pre-flight planning service to work compatible with contingency management activities for enhancing the contingency management process for the AAM system.en_UK
dc.identifier.citationAltun AT, Xu Y, Inalhan G, Hardt MW. (2023) RL-based scheduling of an AAM traffic network. In: 2023 IEEE Conference on Artificial Intelligence (CAI 2023), 5-6 June 2023, Santa Clara, USA, pp. 87-88en_UK
dc.identifier.eisbn979-8-3503-3984-0
dc.identifier.isbn979-8-3503-3985-7
dc.identifier.urihttps://doi.org/10.1109/CAI54212.2023.00045
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20216
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectAAMen_UK
dc.subjectUTMen_UK
dc.subjectpre-flight planningen_UK
dc.subjectpotential conflict resolutionen_UK
dc.subjectdemand capacity balancingen_UK
dc.subjectcontingency managementen_UK
dc.subjectreinforcement learningen_UK
dc.titleRL-based scheduling of an AAM traffic networken_UK
dc.typeConference paperen_UK

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