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Title	Ai-Driven Uav And Iot Traffic Optimization: Large Language Models For Congestion And Emission Reduction In Smart Cities
ID_Doc	7051
Authors	Moraga Á.; de Curtò J.; de Zarzà I.; Calafate C.T.
Year	2025
Published	Drones, 9, 4
DOI	http://dx.doi.org/10.3390/drones9040248
Abstract	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. © 2025 by the authors.
Author Keywords	AI-driven traffic control; CO<sub>2</sub> emission reduction; drone-assisted traffic management; IoT; large language models; smart mobility; SUMO; traffic optimization; UAV; urban congestion

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