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dc.contributor.authorDusparic, Ivana
dc.contributor.authorGuériau, Maxime
dc.date.accessioned2020-03-04T16:59:57Z
dc.date.available2020-03-04T16:59:57Z
dc.date.issued2018
dc.date.submitted2018en
dc.identifier.citationGueriau, M., & Dusparic, I., SAMoD: Shared Autonomous Mobility-on-Demand using Decentralized Reinforcement Learning, The 21st IEEE International Conference on Intelligent Transportation Systems (ITSC2018), 2018en
dc.identifier.otherY
dc.identifier.urihttps://ieeexplore.ieee.org/document/8569608
dc.identifier.urihttp://hdl.handle.net/2262/91697
dc.descriptionPUBLISHEDen
dc.description.abstractShared mobility-on-demand systems can improve the efficiency of urban mobility through reduced vehicle ownership and parking demand. However, some issues in their implementations remain open, most notably the issue of rebalancing non-occupied vehicles to meet geographically uneven demand, as is, for example, the case during the rush hour. This is somewhat alleviated by the prospect of autonomous mobility-on-demand systems, where autonomous vehicles can relocate themselves; however, the proposed relocation strategies are still centralized and assume all vehicles are a part of the same fleet. Furthermore, ride-sharing is not considered, which also has an impact on rebalancing, as already occupied vehicles can also potentially be available to serve new requests simultaneously. In this paper we propose a reinforcement learning-based decentralized approach to vehicle relocation as well as ride request assignment in shared mobility-on-demand systems. Each vehicle autonomously learns its behaviour, which includes both rebalancing and selecting which requests to serve, based on its local current and observed historical demand. We evaluate the approach using data on taxi use in New York City, first serving a single request by a vehicle at a time, and then introduce ride-sharing to evaluate its impact on the learnt rebalancing and assignment behaviour.en
dc.language.isoenen
dc.rightsYen
dc.subjectUrban mobilityen
dc.subjectAutonomous vehiclesen
dc.subjectMobility-on-demanden
dc.titleSAMoD: Shared Autonomous Mobility-on-Demand using Decentralized Reinforcement Learningen
dc.title.alternativeThe 21st IEEE International Conference on Intelligent Transportation Systems (ITSC2018).en
dc.typeConference Paperen
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/duspari
dc.identifier.rssinternalid192395
dc.rights.ecaccessrightsopenAccess
dc.subject.TCDThemeSmart & Sustainable Planeten
dc.identifier.rssurihttps://www.scss.tcd.ie/Ivana.Dusparic/papers/ITSC2018.pdf
dc.status.accessibleNen


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