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Reinforcement Learning to Reach Equilibrium Flow on Roads in Transportation System

dc.contributor.authorBaghcheband, Hajar
dc.date.accessioned2019-09-12T14:50:33Z
dc.date.available2019-09-12T14:50:33Z
dc.date.issued2019
dc.description.abstractTraffic congestion threats the vitality of cities and the welfare of citizens. Transportation systems are using various technologies to allow users to adapt and have a different decision on transportation modes. Modification and improvement of these systems affect commuters’ perspective and social welfare. In this study, the effect of equilibrium road flow on commuters’ utilities with a different type of transportation mode will be discussed. A simple network with two modes of transportation will be illustrated to test the efficiency of minority game and reinforcement learning in commuters’ daily trip decision making based on time and mode. The artificial society of agents is simulated to analyze the results.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.isbn978-972-752-243-9
dc.identifier.urihttp://hdl.handle.net/10400.22/14584
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationCISTER Research Unit (UID/CEC/04234)pt_PT
dc.relation.publisherversionhttps://web.fe.up.pt/~prodei/dsie19/assets/Proceedings/DSIE_Procedings2019.pdfpt_PT
dc.subjectTransportation systempt_PT
dc.subjectMinority gamept_PT
dc.subjectReinforcement learningpt_PT
dc.subjectMulti-agent systempt_PT
dc.subjectSimulationpt_PT
dc.titleReinforcement Learning to Reach Equilibrium Flow on Roads in Transportation Systempt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlacePorto, Portugalpt_PT
oaire.citation.endPage65pt_PT
oaire.citation.startPage60pt_PT
oaire.citation.titleProceedings of the 14th Doctoral Symposium in Informatics Engineeringpt_PT
person.familyNamebaghcheband
person.givenNamehajar
person.identifier.orcid0000-0003-1587-4635
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication87a2f63d-3e6b-4469-b80b-46a42811c23b
relation.isAuthorOfPublication.latestForDiscovery87a2f63d-3e6b-4469-b80b-46a42811c23b

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