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...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/192649 |
| Acceso en línea: | http://hdl.handle.net/11336/192649 |
| Access Level: | acceso abierto |
| Palabra clave: | CYCLE PROGRAM OPTIMIZATION PARTICLE SWARM OPTIMIZATION REALISTIC TRAFFIC INSTANCES SUMO MICROSCOPIC SIMULATOR OF URBAN MOBILITY TRAFFIC LIGHT SCHEDULING https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| Sumario: | 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. |
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