Abstract
Emissions from urban traffic pose a significant problem affecting the quality of cities. The high volume of vehicles moving through urban areas leads to a substantial amount of emissions. However, the waiting time of vehicles at traffic lights results in wasted emissions. Therefore, efficient coordination of traffic lights would help reduce vehicle waiting times and consequently, emissions. In this article, we propose a GPU-based simulator with an integrated genetic algorithm for traffic lights coordination. The key advantage of this genetic algorithm is its compatibility with an optimized urban traffic simulator designed specifically for calculating emissions of this nature. This, together with the efficiency of the simulator facilitates the processing of large amount, enabling simulation of large urban areas such as metropolitan cities.
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Acknowledgements
This work was partially supported with grant DIGITAL-2022 CLOUD-AI-02 funded by the European Commission; grant PID2021-123673OB-C31 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”; and Cátedra Telefónica Smart Inteligencia Artificial.
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Cubillas, C.H., Banquiero, M.M., Alberola, J.M., Sánchez-Anguix, V., Julián, V., Botti, V. (2023). An Urban Simulator Integrated with a Genetic Algorithm for Efficient Traffic Light Coordination. In: Quaresma, P., Camacho, D., Yin, H., Gonçalves, T., Julian, V., Tallón-Ballesteros, A.J. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2023. IDEAL 2023. Lecture Notes in Computer Science, vol 14404. Springer, Cham. https://doi.org/10.1007/978-3-031-48232-8_10
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