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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/122748
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dc.contributor.authorDezani, Henrique-
dc.contributor.authorBassi, Regiane Denise Solgon-
dc.contributor.authorMarranghello, Norian-
dc.contributor.authorGomes, Luis Filipe dos Santos-
dc.contributor.authorDamiani, Furio-
dc.contributor.authorSilva, Ivan Nunes da-
dc.date.accessioned2015-04-27T11:56:00Z-
dc.date.accessioned2016-10-25T20:46:58Z-
dc.date.available2015-04-27T11:56:00Z-
dc.date.available2016-10-25T20:46:58Z-
dc.date.issued2013-
dc.identifierhttp://www.sciencedirect.com/science/article/pii/S0925231213007571-
dc.identifier.citationNeurocomputing, v. 124, p. 162-167, 2013.-
dc.identifier.issn0925-2312-
dc.identifier.urihttp://hdl.handle.net/11449/122748-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/122748-
dc.description.abstractThis paper describes a new methodology adopted for urban traffic stream optimization. By using Petri net analysis as fitness function of a Genetic Algorithm, an entire urban road network is controlled in real time. With the advent of new technologies that have been published, particularly focusing on communications among vehicles and roads infrastructures, we consider that vehicles can provide their positions and their destinations to a central server so that it is able to calculate the best route for one of them. Our tests concentrate on comparisons between the proposed approach and other algorithms that are currently used for the same purpose, being possible to conclude that our algorithm optimizes traffic in a relevant manner.en
dc.format.extent162-167-
dc.language.isoeng-
dc.sourceCurrículo Lattes-
dc.subjectAlgoritmos Genéticospt
dc.subjectRedes de Petript
dc.subjectEmbedded Systemspt
dc.subjectSistemas de Tempo Realpt
dc.subjectSistemas Inteligentespt
dc.subjectUrban trafficen
dc.subjectGenetic Algorithmen
dc.subjectPetri neten
dc.subjectOptimizationen
dc.titleOptimizing urban traffic flow using genetic algorithm with petri net analysis as fitness functionen
dc.typeoutro-
dc.contributor.institutionFaculdade de Tecnologia de Rio Preto (FATEC)-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationUniversidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Ciência da Computação e Estatística, Instituto de Biociências Letras e Ciências Exatas de São José do Rio Preto, Sao Jose do Rio Preto, Rua Cristóvão Colombo, 2265, Jd. Nazareth, CEP 15054-000, SP, Brasil-
dc.description.affiliationUnespUniversidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Ciência da Computação e Estatística, Instituto de Biociências Letras e Ciências Exatas de São José do Rio Preto-
dc.identifier.doihttp://dx.doi.org/10.1016/j.neucom.2013.07.015-
dc.rights.accessRightsAcesso aberto-
dc.relation.ispartofNeurocomputing-
dc.identifier.orcid0000-0003-1086-3312pt
dc.identifier.lattes2098623262892719-
dc.identifier.lattes2663276714773913-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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