Autonomous Cyber-Physical Systems for Smart Cities: A Machine Learning Approach to Real- Time Traffic and Energy Optimization

Abstract

The rapid urbanization of modern societies has necessitated the development of intelligent infrastructures capable of managing complex urban systems efficiently. Smart cities leverage advanced technologies such as cyber-physical systems (CPS), Internet of Things (IoT), and machine learning to optimize critical services including transportation and energy management. This paper presents a comprehensive framework for autonomous cyber-physical systems designed to optimize real-time traffic flow and energy consumption in smart cities. The proposed system integrates distributed sensors, edge computing, and machine learning algorithms to enable dynamic decision-making and adaptive control. Traffic optimization is achieved through predictive modeling and intelligent signal control, while energy optimization focuses on demand forecasting and load balancing. Simulation results demonstrate significant improvements in traffic efficiency, reduced congestion, and enhanced energy utilization. The system achieves up to 35% reduction in traffic delays and 28% improvement in energy efficiency. The study also addresses challenges related to scalability, data privacy, and system integration. The findings highlight the potential of autonomous CPS in transforming urban infrastructure, contributing to sustainable and resilient smart city development.