AI-Driven UAV and IoT Traffic Optimization: Large Language Models for Congestion and Emission Reduction in Smart Cities

Moraga , Álvaro ; de Curtò, J. ; de Zarzà, I. (Universidad de Zaragoza) ; Calafate, Carlos T.

Resumen: 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.

Idioma: Inglés

DOI: 10.3390/drones9040248

Año: 2025

Publicado en: Drones (Basel) 9, 248 (2025), [28 pp.]
ISSN: 2504-446X

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)