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Title	Application Of Generative Ai In Predictive Analysis Of Urban Energy Distribution And Traffic Congestion In Smart Cities
ID_Doc	9887
Authors	Lin Y.; Yao Y.; Zhu J.; He C.
Year	2025
Published	2025 IEEE International Conference on Electronics, Energy Systems and Power Engineering, EESPE 2025
DOI	http://dx.doi.org/10.1109/EESPE63401.2025.10987500
Abstract	This research explores the application of Generative Artificial Intelligence (GenAI) in smart cities, focusing on predictive analysis of urban energy distribution and traffic congestion. The study proposes a comprehensive framework that integrates edge computing, microservices architecture, and AI agents, utilizing Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) for data generation and analysis. Experimental results demonstrate significant improvements across key metrics, including a 22% reduction in traffic congestion, 1 2 % optimization in energy consumption, and 35 % improvement in public safety response time, while also achieving environmental benefits such as 1 8 % CO2 emissions reduction and 25 % increase in energy efficiency. The system implementation, based on a three-layer architecture (data layer, processing layer, and application layer) combined with the deployment of over 200 microservices, resulted in a 45% reduction in processing time and 9 9. 9 9 % system availability. Twelve months of field deployment data validates the framework's effectiveness in improving urban operations and promoting environmental sustainability, providing an innovative solution for data-driven decision-making in smart cities. © 2025 IEEE.
Author Keywords	Energy Management; Generative AI; Real-Time Data Analysis; Smart Cities; Traffic Optimization

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