Swarm intelligence for traffic light scheduling: Application to real urban areas

Autores
García Nieto, José M.; Alba, Enrique; Olivera, Ana Carolina
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Congestion, pollution, security, parking, noise, and many other problems derived from vehicular traffic are present every day in most cities around the world. The growing number of traffic lights that control the vehicular flow requires a complex scheduling, and hence, automatic systems are indispensable nowadays for optimally tackling this task. In this work, we propose a Swarm Intelligence approach to find successful cycle programs of traffic lights. Using a microscopic traffic simulator, the solutions obtained by our algorithm are evaluated in the context of two large and heterogeneous metropolitan areas located in the cities of Málaga and Sevilla (in Spain). In comparison with cycle programs predefined by experts (close to real ones), our proposal obtains significant profits in terms of two main indicators: the number of vehicles that reach their destinations on time and the global trip time.
Fil: García Nieto, José M.. Universidad de Málaga; España
Fil: Alba, Enrique. Universidad de Málaga; España
Fil: Olivera, Ana Carolina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina
Materia
CYCLE PROGRAM OPTIMIZATION
PARTICLE SWARM OPTIMIZATION
REALISTIC TRAFFIC INSTANCES
SUMO MICROSCOPIC SIMULATOR OF URBAN MOBILITY
TRAFFIC LIGHT SCHEDULING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/192649
Acceder
id CONICETDig_6a8f75722631570d5bcc435154ebffee
oai_identifier_str oai:ri.conicet.gov.ar:11336/192649
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Swarm intelligence for traffic light scheduling: Application to real urban areasGarcía Nieto, José M.Alba, EnriqueOlivera, Ana CarolinaCYCLE PROGRAM OPTIMIZATIONPARTICLE SWARM OPTIMIZATIONREALISTIC TRAFFIC INSTANCESSUMO MICROSCOPIC SIMULATOR OF URBAN MOBILITYTRAFFIC LIGHT SCHEDULINGhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Congestion, pollution, security, parking, noise, and many other problems derived from vehicular traffic are present every day in most cities around the world. The growing number of traffic lights that control the vehicular flow requires a complex scheduling, and hence, automatic systems are indispensable nowadays for optimally tackling this task. In this work, we propose a Swarm Intelligence approach to find successful cycle programs of traffic lights. Using a microscopic traffic simulator, the solutions obtained by our algorithm are evaluated in the context of two large and heterogeneous metropolitan areas located in the cities of Málaga and Sevilla (in Spain). In comparison with cycle programs predefined by experts (close to real ones), our proposal obtains significant profits in terms of two main indicators: the number of vehicles that reach their destinations on time and the global trip time.Fil: García Nieto, José M.. Universidad de Málaga; EspañaFil: Alba, Enrique. Universidad de Málaga; EspañaFil: Olivera, Ana Carolina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaPergamon-Elsevier Science Ltd2012-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/192649García Nieto, José M.; Alba, Enrique; Olivera, Ana Carolina; Swarm intelligence for traffic light scheduling: Application to real urban areas; Pergamon-Elsevier Science Ltd; Engineering Applications Of Artificial Intelligence; 25; 2; 3-2012; 274-2830952-1976CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.journals.elsevier.com/engineering-applications-of-artificial-intelligence/info:eu-repo/semantics/altIdentifier/doi/10.1016/j.engappai.2011.04.011info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:45:04Zoai:ri.conicet.gov.ar:11336/192649instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:45:04.75CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Swarm intelligence for traffic light scheduling: Application to real urban areas
title Swarm intelligence for traffic light scheduling: Application to real urban areas
spellingShingle Swarm intelligence for traffic light scheduling: Application to real urban areas
García Nieto, José M.
CYCLE PROGRAM OPTIMIZATION
PARTICLE SWARM OPTIMIZATION
REALISTIC TRAFFIC INSTANCES
SUMO MICROSCOPIC SIMULATOR OF URBAN MOBILITY
TRAFFIC LIGHT SCHEDULING
title_short Swarm intelligence for traffic light scheduling: Application to real urban areas
title_full Swarm intelligence for traffic light scheduling: Application to real urban areas
title_fullStr Swarm intelligence for traffic light scheduling: Application to real urban areas
title_full_unstemmed Swarm intelligence for traffic light scheduling: Application to real urban areas
title_sort Swarm intelligence for traffic light scheduling: Application to real urban areas
dc.creator.none.fl_str_mv García Nieto, José M.
Alba, Enrique
Olivera, Ana Carolina
author García Nieto, José M.
author_facet García Nieto, José M.
Alba, Enrique
Olivera, Ana Carolina
author_role author
author2 Alba, Enrique
Olivera, Ana Carolina
author2_role author
author
dc.subject.none.fl_str_mv CYCLE PROGRAM OPTIMIZATION
PARTICLE SWARM OPTIMIZATION
REALISTIC TRAFFIC INSTANCES
SUMO MICROSCOPIC SIMULATOR OF URBAN MOBILITY
TRAFFIC LIGHT SCHEDULING
topic CYCLE PROGRAM OPTIMIZATION
PARTICLE SWARM OPTIMIZATION
REALISTIC TRAFFIC INSTANCES
SUMO MICROSCOPIC SIMULATOR OF URBAN MOBILITY
TRAFFIC LIGHT SCHEDULING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Congestion, pollution, security, parking, noise, and many other problems derived from vehicular traffic are present every day in most cities around the world. The growing number of traffic lights that control the vehicular flow requires a complex scheduling, and hence, automatic systems are indispensable nowadays for optimally tackling this task. In this work, we propose a Swarm Intelligence approach to find successful cycle programs of traffic lights. Using a microscopic traffic simulator, the solutions obtained by our algorithm are evaluated in the context of two large and heterogeneous metropolitan areas located in the cities of Málaga and Sevilla (in Spain). In comparison with cycle programs predefined by experts (close to real ones), our proposal obtains significant profits in terms of two main indicators: the number of vehicles that reach their destinations on time and the global trip time.
Fil: García Nieto, José M.. Universidad de Málaga; España
Fil: Alba, Enrique. Universidad de Málaga; España
Fil: Olivera, Ana Carolina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina
description Congestion, pollution, security, parking, noise, and many other problems derived from vehicular traffic are present every day in most cities around the world. The growing number of traffic lights that control the vehicular flow requires a complex scheduling, and hence, automatic systems are indispensable nowadays for optimally tackling this task. In this work, we propose a Swarm Intelligence approach to find successful cycle programs of traffic lights. Using a microscopic traffic simulator, the solutions obtained by our algorithm are evaluated in the context of two large and heterogeneous metropolitan areas located in the cities of Málaga and Sevilla (in Spain). In comparison with cycle programs predefined by experts (close to real ones), our proposal obtains significant profits in terms of two main indicators: the number of vehicles that reach their destinations on time and the global trip time.
publishDate 2012
dc.date.none.fl_str_mv 2012-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/192649
García Nieto, José M.; Alba, Enrique; Olivera, Ana Carolina; Swarm intelligence for traffic light scheduling: Application to real urban areas; Pergamon-Elsevier Science Ltd; Engineering Applications Of Artificial Intelligence; 25; 2; 3-2012; 274-283
0952-1976
CONICET Digital
CONICET
url http://hdl.handle.net/11336/192649
identifier_str_mv García Nieto, José M.; Alba, Enrique; Olivera, Ana Carolina; Swarm intelligence for traffic light scheduling: Application to real urban areas; Pergamon-Elsevier Science Ltd; Engineering Applications Of Artificial Intelligence; 25; 2; 3-2012; 274-283
0952-1976
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.journals.elsevier.com/engineering-applications-of-artificial-intelligence/
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.engappai.2011.04.011
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842268707918708736
score 13.13397

Publicaciones similares