AI-Driven Optimization of Smart Urban Infrastructure for Sustainable Cities

Abstract

The need for sustainable urban management systems for transport, energy, and waste has grown as urbanization has increased. Traditional management systems do not dynamically adjust and manage systems in real time, leading to waste and negative environmental impacts. This paper proposes an AI optimization framework, using IoT and edge computing, combined with hybrid machine learning models, to enhance urban infrastructure management systems. This approach employs Long Short-Term Memory (LSTM) networks for time-series forecasting and Reinforcement Learning (RL) for real-time, dynamic, adaptive, iterative model-based decision-making. IoT edge computing also enhances rapid processing and control by reducing latency and enabling responses to changes in managed systems. Testing was conducted on a framework built on datasets from simulated smart city environments (50,000 records for urban traffic, energy consumption, and waste). The framework reduced traditional energy management systems by 25 % and traffic management by 30-33%, and waste management systems by 20%. AI has the potential to enhance the effective management systems and the efficiency of urban centers. The research determined that incorporating AI into smart systems makes urban management scalable, flexible, and data-driven. Furthermore, this framework helps integrate smart systems and cities to achieve SDGs and enhance urban dwellers' quality of life.