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AI-Driven UAV and IoT Traffic Optimization: Large Language Models for Congestion and Emission Reduction in Smart Cities
dc.contributor.author | Moraga, Álvaro | es-ES |
dc.contributor.author | de Curtò i Díaz, Joaquim | es-ES |
dc.contributor.author | de Zarzà i Cubero, Irene | es-ES |
dc.contributor.author | Calafate, Carlos T. | es-ES |
dc.date.accessioned | 2025-03-26T15:51:08Z | |
dc.date.available | 2025-03-26T15:51:08Z | |
dc.date.issued | 2025-03-26 | es_ES |
dc.identifier.issn | 2504-446X | es_ES |
dc.identifier.uri | https://doi.org/10.3390/drones9040248 | es_ES |
dc.description | Artículos en revistas | es_ES |
dc.description.abstract | . | es-ES |
dc.description.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 Gemini2.0-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 | en-GB |
dc.format.mimetype | application/pdf | es_ES |
dc.language.iso | en-GB | es_ES |
dc.rights | Creative Commons Reconocimiento-NoComercial-SinObraDerivada España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | es_ES |
dc.source | Revista: Drones, Periodo: 1, Volumen: 9, Número: 4, Página inicial: 248, Página final: . | es_ES |
dc.title | AI-Driven UAV and IoT Traffic Optimization: Large Language Models for Congestion and Emission Reduction in Smart Cities | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.description.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.rights.holder | es_ES | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.keywords | . | es-ES |
dc.keywords | traffic optimization; IoT; large language models; SUMO; smart mobility; AI-driven traffic control; urban congestion; CO2 emission reduction; UAV; drone-assisted traffic management | en-GB |
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