152188 20251017144555.0 doi 10.3390/drones9040248 sideral 143411 ART-2025-143411 eng Moraga , Álvaro AI-Driven UAV and IoT Traffic Optimization: Large Language Models for Congestion and Emission Reduction in Smart Cities 2025 Access copy available to the general public Unrestricted Traffic congestion and carbon emissions remain pressing challenges in urban mobility. This study explores the integration of UAV (drone)-based monitoring systems and IoT sensors, modeled as induction loops, with Large Language Models (LLMs) to optimize traffic flow. Using the SUMO simulator, we conducted experiments in three urban scenarios: Pacific Beach and Coronado in San Diego, and Argüelles in Madrid. A Gemini-2.0-Flash experimental LLM was interfaced with the simulation to dynamically adjust vehicle speeds based on real-time traffic conditions. Comparative results indicate that the AI-assisted approach significantly reduces congestion and CO2 emissions compared to a baseline simulation without AI intervention. This research highlights the potential of UAV-enhanced IoT frameworks for adaptive, scalable traffic management, aligning with the future of drone-assisted urban mobility solutions. info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion de Curtò, J. de Zarzà, I. Universidad de Zaragoza (orcid)0000-0002-5844-7871 Calafate, Carlos T. 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 9, 248 (2025), [28 pp.] Drones (Basel) Drones (Basel) 2504-446X 20794100 http://zaguan.unizar.es/record/152188/files/texto_completo.pdf Versión publicada 2420165 http://zaguan.unizar.es/record/152188/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:152188 articulos driver 2025-10-17-14:12:53 ARTICLE