dc.creator | García Nieto, José Manuel | es |
dc.creator | Alba, Enrique | es |
dc.creator | Olivera, Ana Carolina | es |
dc.date.accessioned | 2021-05-14T07:27:26Z | |
dc.date.available | 2021-05-14T07:27:26Z | |
dc.date.issued | 2012 | |
dc.identifier.citation | García Nieto, J.M., Alba, E. y Olivera, A.C. (2012). Swarm intelligence for traffic light scheduling: Application to real urban areas. Engineering Applications of Artificial Intelligence, 25 (2), 274-283. | |
dc.identifier.issn | 0952-1976 | es |
dc.identifier.uri | https://hdl.handle.net/11441/109031 | |
dc.description.abstract | 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. | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades TIN2008-06491-C04-01 | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades BES-2009-018767 | es |
dc.description.sponsorship | Junta de Andalucía P07-TIC-03044 | es |
dc.format | application/pdf | es |
dc.format.extent | 10 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Engineering Applications of Artificial Intelligence, 25 (2), 274-283. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Traffic Light Scheduling | es |
dc.subject | Particle Swarm Optimization | es |
dc.subject | SUMO Microscopic Simulator of Urban Mobility | es |
dc.subject | Cycle program optimization | es |
dc.subject | Realistic traffic instances | es |
dc.title | Swarm intelligence for traffic light scheduling: Application to real urban areas | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | TIN2008-06491-C04-01 | es |
dc.relation.projectID | BES-2009-018767 | es |
dc.relation.projectID | P07-TIC-03044 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0952197611000777 | es |
dc.identifier.doi | 10.1016/j.engappai.2011.04.011 | es |
dc.journaltitle | Engineering Applications of Artificial Intelligence | es |
dc.publication.volumen | 25 | es |
dc.publication.issue | 2 | es |
dc.publication.initialPage | 274 | es |
dc.publication.endPage | 283 | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |
dc.contributor.funder | Junta de Andalucía | es |