IoT-Based Vehicle Class Detection for Smart Traffic Control

Traffic control is one of the most challenging fields for smart city planning. Recent automatic traffic controllers are static, and in emergency conditions, they are shifted to manual mode for better management. The authors suggest an IoT and machine learning-based, dynamic in nature, traffic controller that can be an efficient solution for traffic flow management. The proposed algorithm determines the number of vehicles belonging to one of the three groups employed in this approach, light, medium, and heavy vehicles. The traffic light’s dynamic character can be enhanced by using the numbers derived from this method to calculate densities in real time. Making traffic lights dynamic can assist in alleviating the problem of traffic congestion and lead to smoother flow.