Application of Generative AI in Predictive Analysis of Urban Energy Distribution and Traffic Congestion in Smart Cities

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, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 2 \%}$</tex> optimization in energy consumption, and 35 % improvement in public safety response time, while also achieving environmental benefits such as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 8 \%}$</tex> 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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 9. 9 9 \%}$</tex> 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.