ENHANCING URBAN MOBILITY THROUGH MACHINE LEARNING-DRIVEN TRAFFIC MANAGEMENT IN SMART CITIES

Abstract [English]

Urbanization is on the rise, and smart cities are at the forefront of addressing the challenges of increased traffic congestion. This paper presents a novel approach to optimize traffic flow in smart cities using machine learning- powered traffic management systems. With urban populations growing rapidly, traditional traffic management methods have become insufficient, leading to increased congestion, longer commute times, and environmental concerns. Our proposed system leverages machine learning algorithms to analyze real-time traffic data from various sources, such as sensors, cameras, and GPS devices. By processing this data, the system can predict traffic patterns, identify congestion hotspots, and dynamically adjust traffic signals and routes. This data-driven approach allows for more efficient traffic management, reducing congestion, improving air quality, and enhancing overall urban mobility. Furthermore, our system is adaptable and can learn from historical data, continuously improving its performance over time. It can also integrate with other smart city initiatives, such as public transportation systems and smart infrastructure, to create a comprehensive and interconnected urban mobility ecosystem. we discuss the architecture and components of our machine learning-powered traffic management system and present case studies demonstrating its effectiveness in real-world smart city environments. We also address privacy and security considerations, as well as the potential for scalability and integration with emerging technologies like autonomous vehicles.