Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization

Optimal staging of traffic lights, and in particular optimal light cycle programs, is a crucial task in present day cities with potential benefits in terms of energy consumption, traffic flow management, pedestrian safety, and environmental issues. Nevertheless, very few publications in the current...

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Detalhes bibliográficos
Autores: García Nieto, José Manuel, Olivera, Ana Carolina, Alba, Enrique
Formato: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2013
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/108851
Acesso em linha:https://hdl.handle.net/11441/108851
https://doi.org/10.1109/TEVC.2013.2260755
Access Level:acceso abierto
Palavra-chave:Particle Swarm Optimization
Programming cycles of traffic lights
simulator of urban mobility (SUMO)
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spelling Optimal Cycle Program of Traffic Lights With Particle Swarm OptimizationGarcía Nieto, José ManuelOlivera, Ana CarolinaAlba, EnriqueParticle Swarm OptimizationProgramming cycles of traffic lightssimulator of urban mobility (SUMO)Optimal staging of traffic lights, and in particular optimal light cycle programs, is a crucial task in present day cities with potential benefits in terms of energy consumption, traffic flow management, pedestrian safety, and environmental issues. Nevertheless, very few publications in the current literature tackle this problem by means of automatic intelligent systems, and, when they do, they focus on limited areas with elementary traffic light schedules. In this paper, we propose an optimization approach in which a particle swarm optimizer (PSO) is able to find successful traffic light cycle programs. The solutions obtained are simulated with simulator of urban mobility, a well-known microscopic traffic simulator. For this study, we have tested two large and heterogeneous metropolitan areas with hundreds of traffic lights located in the cities of Bah´ıa Blanca in Argentina (American style) and M´alaga in Spain (European style). Our algorithm is shown to obtain efficient traffic light cycle programs for both kinds of cities. In comparison with expertly predefined cycle programs (close to real ones), our PSO achieved quantitative improvements for the two main objectives: 1) the number of vehicles that reach their destination and 2) the overall journey time.Ministerio de Economía y Competitividad TIN2011-28194Ministerio de Economía y Competitividad BES-2009-018767IEEE Computer SocietyCiencias de la Computación e Inteligencia ArtificialMinisterio de Economía y Competitividad (MINECO). España2013info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/108851https://doi.org/10.1109/TEVC.2013.2260755reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésIEEE Transactions on Evolutionary Computation, 17 (6), 823-839.TIN2011-28194BES-2009-018767https://ieeexplore.ieee.org/document/6510532info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1088512026-06-17T12:51:07Z
dc.title.none.fl_str_mv Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization
title Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization
spellingShingle Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization
García Nieto, José Manuel
Particle Swarm Optimization
Programming cycles of traffic lights
simulator of urban mobility (SUMO)
title_short Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization
title_full Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization
title_fullStr Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization
title_full_unstemmed Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization
title_sort Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization
dc.creator.none.fl_str_mv García Nieto, José Manuel
Olivera, Ana Carolina
Alba, Enrique
author García Nieto, José Manuel
author_facet García Nieto, José Manuel
Olivera, Ana Carolina
Alba, Enrique
author_role author
author2 Olivera, Ana Carolina
Alba, Enrique
author2_role author
author
dc.contributor.none.fl_str_mv Ciencias de la Computación e Inteligencia Artificial
Ministerio de Economía y Competitividad (MINECO). España
dc.subject.none.fl_str_mv Particle Swarm Optimization
Programming cycles of traffic lights
simulator of urban mobility (SUMO)
topic Particle Swarm Optimization
Programming cycles of traffic lights
simulator of urban mobility (SUMO)
description Optimal staging of traffic lights, and in particular optimal light cycle programs, is a crucial task in present day cities with potential benefits in terms of energy consumption, traffic flow management, pedestrian safety, and environmental issues. Nevertheless, very few publications in the current literature tackle this problem by means of automatic intelligent systems, and, when they do, they focus on limited areas with elementary traffic light schedules. In this paper, we propose an optimization approach in which a particle swarm optimizer (PSO) is able to find successful traffic light cycle programs. The solutions obtained are simulated with simulator of urban mobility, a well-known microscopic traffic simulator. For this study, we have tested two large and heterogeneous metropolitan areas with hundreds of traffic lights located in the cities of Bah´ıa Blanca in Argentina (American style) and M´alaga in Spain (European style). Our algorithm is shown to obtain efficient traffic light cycle programs for both kinds of cities. In comparison with expertly predefined cycle programs (close to real ones), our PSO achieved quantitative improvements for the two main objectives: 1) the number of vehicles that reach their destination and 2) the overall journey time.
publishDate 2013
dc.date.none.fl_str_mv 2013
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/11441/108851
https://doi.org/10.1109/TEVC.2013.2260755
url https://hdl.handle.net/11441/108851
https://doi.org/10.1109/TEVC.2013.2260755
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Transactions on Evolutionary Computation, 17 (6), 823-839.
TIN2011-28194
BES-2009-018767
https://ieeexplore.ieee.org/document/6510532
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 IEEE Computer Society
publisher.none.fl_str_mv IEEE Computer Society
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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