Automated Vehicle classification and counting for Smart City Traffic management using AI/ML and DL

Abstract

Traffic jams and safety on the road have become a serious problem in modern cities. They need smart and versatile solutions. This project is a proposal to use automated vehicles to count and classify real- time vehicles. The system will enhance the traffic monitoring and management in smart cities. It can identify and differentiate the various kinds of vehicles: cars, trucks, buses, and motorcycles by utilizing machine learning on real-time video feeds. The grouped information assists in the development of traffic flow statistics. These statistics may contribute to traffic control, congestion, and future urban planning. The system is made efficient and suited to various road conditions. It is also extendable to other places. This is a way of minimizing the use of manual traffic surveys. It is an affordable and valid alternative to the city officials that want to find smarter ways of addressing traffic issues. In addition, the proposed system supports real-time decision-making by enabling authorities to respond quickly to unusual traffic conditions such as accidents or sudden congestion. It can be integrated with existing smart infrastructure and traffic control systems to further enhance operational efficiency. The use of advanced analytics also allows long-term trend analysis, helping planners design better road networks and optimize resource allocation. Overall, this approach provides a scalable, reliable, and future-ready solution for modern traffic management challenges.