Smart Traffic Lights: A First Parallel Computing Approach

Optimal traffic light scheduling is a fundamental problem in modern urban areas. It has severe impact on traffic flow management, energy consumption and vehicular emissions, as well as on urban noise. The vast number of traffic lights in modern cities increases the complexity of the scheduling problem and, at the same time, urgently needs for efficient algorithms that optimize the light cycle programs. In this work, we propose a solution for the traffic light scheduling problem by using Differential Evolution, and investigate the benefits of parallelism on this complex problem. For understanding the impact in the city, the popular micro-simulator SUMO is used. We evaluate our approach on close-to-reality problem scenarios consisting of two large urban areas located in the cities of Málaga, Spain, and Paris, France. Our results are promising and encourage further investigation of parallel approaches to enable scalability.

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