Abstract
The constant growth of vehicles circulating in urban environments poses a number of challenges in terms of city planning and traffic regulation. A key aspect that affects the safety and efficiency of urban traffic is the configuration of traffic lights and junctions. Here, we propose a general framework, based on a realistic urban traffic simulator, SUMO, to aid city planners to optimize traffic lights, based on a customized version of NSGA-II. We show how different metrics -such as number of accidents, average speed of vehicles, and number of traffic jams- can be taken into account in a multi-objective fashion to obtain a number of Pareto-optimal light configurations. Our experiments, conducted on two city scenarios in Italy and different combinations of fitness functions, demonstrate the validity of this approach and show how evolutionary optimization is an effective tool for traffic light optimization.
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Notes
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Data collected by TomTom: https://ec.europa.eu/transport/facts-fundings/scoreboard/compare/energy-union-innovation/road-congestion_en.
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Vehicles on a low-priority edge have to wait until vehicles on a high-priority edge have passed the junction.
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By default, this generates vehicles with a constant period and arrival rate of 1/period per second. By using values below 1, multiple arrivals per second can be achieved. Routes are generates such that a new vehicle with a random path and destination is inserted at a certain starting position every period seconds, determining a certain average traffic density.
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Acknowledgments
We thank Andrea Ferigo for his contribution to a preliminary implementation of the framework proposed in the paper.
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Cacco, A., Iacca, G. (2020). Simulation-Driven Multi-objective Evolution for Traffic Light Optimization. In: Castillo, P.A., Jiménez Laredo, J.L., Fernández de Vega, F. (eds) Applications of Evolutionary Computation. EvoApplications 2020. Lecture Notes in Computer Science(), vol 12104. Springer, Cham. https://doi.org/10.1007/978-3-030-43722-0_7
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