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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Bibliographic Details
Published in:Engineering applications of artificial intelligence 2012-03, Vol.25 (2), p.274-283
Main Authors: García-Nieto, J., Alba, E., Carolina Olivera, A.
Format: Article
Language:English
Subjects:
Algorithms
Cycle program optimization
Metropolitan areas
Particle swarm optimization
Proposals
Realistic traffic instances
Scheduling
SUMO microscopic simulator of urban mobility
Swarm intelligence
Traffic engineering
Traffic flow
Traffic light scheduling
Urban areas
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Summary: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.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2011.04.011