DEVELOPMENT FOR SMART TRAFFIC MANAGEMENT SYSTEMS FOR URBAN AREAS USING MACHINE LEARNING

The advancement of technology has led to the emergence of smart cities, and a crucial aspect of a smart city is efficient traffic management. In this context, machine learning has been proven to be a promising approach to develop smart traffic management systems for urban areas. This paper presents a review of the current state in smart traffic management systems, including the technologies and methods employed in this field. The paper also highlights the challenges faced in developing such systems and proposes a machine learning-based approach to overcome these challenges. The proposed system involves the use of real-time traffic data from various sources, such as cameras, sensors, and GPS devices, to predict traffic patterns and optimize traffic flow. The machine learning algorithms used in this system are capable of learning from historical data to predict traffic flow, detect anomalies, and suggest optimal routes. The system also includes a user interface that allows users to interact with the system. The demonstrates the potential of machine learning to develop smart traffic management systems for urban areas, and highlights the importance of further research in this field to improve traffic management and enhance the quality of life in smart cities.