Hybrid AI and Big Data Solutions for Dynamic Urban Planning and Smart City Optimization

Abstract

Urban planning faces complex challenges, including efficient resource allocation, traffic management, and infrastructure optimization. Traditional methods often fall short in addressing these multifaceted issues, leading to inefficiencies and suboptimal outcomes. This study introduces a novel approach by combining Graph Neural Networks (GNNs) with Simulated Annealing (SA) to tackle these challenges in urban planning. GNNs are employed to extract meaningful features and relationships from urban infrastructure and social networks, providing a detailed understanding of patterns and interactions. SA is then used to optimize resource allocation, traffic routing, and scheduling tasks based on the insights derived from GNNs. This hybrid methodology allows for an iterative refinement process, where updated features from GNNs continuously enhance the optimization performed by SA. Key findings of the study reveal significant improvements. Traffic congestion was reduced by 25%, and average travel times decreased by 18%. Resource allocation efficiency improved by 30%, with a 20% reduction in resource wastage. Infrastructure optimization metrics showed a 22% gain in cost efficiency and a 15% increase in accessibility. The combined GNN-SA approach proved effective in addressing urban planning inefficiencies and optimizing various aspects of smart city management. The contributions of this study include a robust framework for integrating advanced AI techniques to solve complex urban planning problems, offering a scalable and adaptable solution for modern smart cities. The results highlight the potential of hybrid AI approaches in enhancing urban planning and provide a foundation for future research and application in this field.

Keywords

• Graph neural networks (GNNs)
• simulated annealing (SA)
• urban planning optimization
• smart city management
• resource allocation
• traffic management