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

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 indispen...

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Detalles Bibliográficos
Autores: García Nieto, José Manuel, Alba, Enrique, Olivera, Ana Carolina
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2012
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/109031
Acceso en línea:https://hdl.handle.net/11441/109031
https://doi.org/10.1016/j.engappai.2011.04.011
Access Level:acceso abierto
Palabra clave:Traffic Light Scheduling
Particle Swarm Optimization
SUMO Microscopic Simulator of Urban Mobility
Cycle program optimization
Realistic traffic instances
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spelling Swarm intelligence for traffic light scheduling: Application to real urban areasGarcía Nieto, José ManuelAlba, EnriqueOlivera, Ana CarolinaTraffic Light SchedulingParticle Swarm OptimizationSUMO Microscopic Simulator of Urban MobilityCycle program optimizationRealistic traffic instancesCongestion, 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.Ministerio de Ciencia, Innovación y Universidades TIN2008-06491-C04-01Ministerio de Ciencia, Innovación y Universidades BES-2009-018767Junta de Andalucía P07-TIC-03044ElsevierCiencias de la Computación e Inteligencia ArtificialMinisterio de Ciencia, Innovación y Universidades (MICINN). EspañaJunta de Andalucía2012info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/109031https://doi.org/10.1016/j.engappai.2011.04.011reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésEngineering Applications of Artificial Intelligence, 25 (2), 274-283.TIN2008-06491-C04-01BES-2009-018767P07-TIC-03044https://www.sciencedirect.com/science/article/pii/S0952197611000777info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1090312026-06-17T12:51:07Z
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 Manuel
Traffic Light Scheduling
Particle Swarm Optimization
SUMO Microscopic Simulator of Urban Mobility
Cycle program optimization
Realistic traffic instances
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 Manuel
Alba, Enrique
Olivera, Ana Carolina
author García Nieto, José Manuel
author_facet García Nieto, José Manuel
Alba, Enrique
Olivera, Ana Carolina
author_role author
author2 Alba, Enrique
Olivera, Ana Carolina
author2_role author
author
dc.contributor.none.fl_str_mv Ciencias de la Computación e Inteligencia Artificial
Ministerio de Ciencia, Innovación y Universidades (MICINN). España
Junta de Andalucía
dc.subject.none.fl_str_mv Traffic Light Scheduling
Particle Swarm Optimization
SUMO Microscopic Simulator of Urban Mobility
Cycle program optimization
Realistic traffic instances
topic Traffic Light Scheduling
Particle Swarm Optimization
SUMO Microscopic Simulator of Urban Mobility
Cycle program optimization
Realistic traffic instances
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
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/109031
https://doi.org/10.1016/j.engappai.2011.04.011
url https://hdl.handle.net/11441/109031
https://doi.org/10.1016/j.engappai.2011.04.011
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Engineering Applications of Artificial Intelligence, 25 (2), 274-283.
TIN2008-06491-C04-01
BES-2009-018767
P07-TIC-03044
https://www.sciencedirect.com/science/article/pii/S0952197611000777
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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