Optimizing urban traffic flow using genetic algorithm with petri net analysis as fitness function

Detalhes bibliográficos
Autor(a) principal: Dezani, Henrique
Data de Publicação: 2013
Outros Autores: Bassi, Regiane Denise Solgon, Marranghello, Norian [UNESP], Gomes, Luis Filipe dos Santos, Damiani, Furio, Silva, Ivan Nunes da
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://www.sciencedirect.com/science/article/pii/S0925231213007571
http://hdl.handle.net/11449/122748
Resumo: This 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.
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spelling Optimizing urban traffic flow using genetic algorithm with petri net analysis as fitness functionAlgoritmos GenéticosRedes de PetriEmbedded SystemsSistemas de Tempo RealSistemas InteligentesUrban trafficGenetic AlgorithmPetri netOptimizationThis 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.Universidade 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, BrasilUniversidade 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 PretoFaculdade de Tecnologia de Rio Preto (FATEC)Universidade Estadual Paulista (Unesp)Dezani, HenriqueBassi, Regiane Denise SolgonMarranghello, Norian [UNESP]Gomes, Luis Filipe dos SantosDamiani, FurioSilva, Ivan Nunes da2015-04-27T11:56:00Z2015-04-27T11:56:00Z2013info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article162-167http://www.sciencedirect.com/science/article/pii/S0925231213007571Neurocomputing, v. 124, p. 162-167, 2013.0925-2312http://hdl.handle.net/11449/12274810.1016/j.neucom.2013.07.015209862326289271926632767147739130000-0003-1086-3312Currículo Lattesreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengNeurocomputing3.2411,073info:eu-repo/semantics/openAccess2021-10-23T22:04:27Zoai:repositorio.unesp.br:11449/122748Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:10:16.852052Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Optimizing urban traffic flow using genetic algorithm with petri net analysis as fitness function
title Optimizing urban traffic flow using genetic algorithm with petri net analysis as fitness function
spellingShingle Optimizing urban traffic flow using genetic algorithm with petri net analysis as fitness function
Dezani, Henrique
Algoritmos Genéticos
Redes de Petri
Embedded Systems
Sistemas de Tempo Real
Sistemas Inteligentes
Urban traffic
Genetic Algorithm
Petri net
Optimization
title_short Optimizing urban traffic flow using genetic algorithm with petri net analysis as fitness function
title_full Optimizing urban traffic flow using genetic algorithm with petri net analysis as fitness function
title_fullStr Optimizing urban traffic flow using genetic algorithm with petri net analysis as fitness function
title_full_unstemmed Optimizing urban traffic flow using genetic algorithm with petri net analysis as fitness function
title_sort Optimizing urban traffic flow using genetic algorithm with petri net analysis as fitness function
author Dezani, Henrique
author_facet Dezani, Henrique
Bassi, Regiane Denise Solgon
Marranghello, Norian [UNESP]
Gomes, Luis Filipe dos Santos
Damiani, Furio
Silva, Ivan Nunes da
author_role author
author2 Bassi, Regiane Denise Solgon
Marranghello, Norian [UNESP]
Gomes, Luis Filipe dos Santos
Damiani, Furio
Silva, Ivan Nunes da
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Faculdade de Tecnologia de Rio Preto (FATEC)
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Dezani, Henrique
Bassi, Regiane Denise Solgon
Marranghello, Norian [UNESP]
Gomes, Luis Filipe dos Santos
Damiani, Furio
Silva, Ivan Nunes da
dc.subject.por.fl_str_mv Algoritmos Genéticos
Redes de Petri
Embedded Systems
Sistemas de Tempo Real
Sistemas Inteligentes
Urban traffic
Genetic Algorithm
Petri net
Optimization
topic Algoritmos Genéticos
Redes de Petri
Embedded Systems
Sistemas de Tempo Real
Sistemas Inteligentes
Urban traffic
Genetic Algorithm
Petri net
Optimization
description This 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.
publishDate 2013
dc.date.none.fl_str_mv 2013
2015-04-27T11:56:00Z
2015-04-27T11:56:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://www.sciencedirect.com/science/article/pii/S0925231213007571
Neurocomputing, v. 124, p. 162-167, 2013.
0925-2312
http://hdl.handle.net/11449/122748
10.1016/j.neucom.2013.07.015
2098623262892719
2663276714773913
0000-0003-1086-3312
url http://www.sciencedirect.com/science/article/pii/S0925231213007571
http://hdl.handle.net/11449/122748
identifier_str_mv Neurocomputing, v. 124, p. 162-167, 2013.
0925-2312
10.1016/j.neucom.2013.07.015
2098623262892719
2663276714773913
0000-0003-1086-3312
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Neurocomputing
3.241
1,073
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 162-167
dc.source.none.fl_str_mv Currículo Lattes
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808128904371634176

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