Swarm intelligence for traffic light scheduling: Application to real urban areas |
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| Authors: | J García-Nieto E Alba A Carolina Olivera |
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| Affiliation: | 1. Dept. de Lenguajes y Ciencias de la Computación, University of Málaga, ETSI Informática, Campus de Teatinos, Málaga 29071, Spain;2. Departamento de Ciencias e Ingeniería de la Computación, Universidad Nacional del Sur, Av. Alem 1253, 8000 Bahía Blanca, Argentina;1. School of Transportation and Logistics, Dalian University of Technology, Dalian, Liaoning 116024, China;2. Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL 32611, USA;3. Department of Computer & Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA;1. Department of Civil and Environmental Engineering, Imperial College London, United Kingdom;2. Department of Industrial and Manufacturing Engineering, Pennsylvania State University, USA;3. Department of Civil and Environmental Engineering, Pennsylvania State University, USA;1. School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran;2. Institute of Informatics, University of Federal Rio Grand do Sul, Porto Alegre, Brazil;1. School of Business Administration, Hubei University of Economics, Wuhan 430205, China;2. School of Management, Shanghai University, Shanghai 200444, China;3. Business College, Huanggang Normal University, Huanggang, Hubei 43800, China;4. School of Mathematics and Physics, Nanyang Institute of Technology, Nanyang 473004, China |
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| 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. |
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