Density based traffic monitoring system

The Density Based Traffic Monitoring System through IoT using ResNet is an intelligent framework designed to optimize traffic management and reduce congestion in urban areas through automated analysis of vehicular flow. Traditional traffic control systems rely on fixed signal timing, which often leads to unnecessary delays and traffic jams, especially during peak hours. To overcome these limitations, this system integrates Internet of Things (IoT) technology with deep learning to create a dynamic and adaptive traffic control mechanism. The Density Based Traffic Monitoring System through IoT using ResNet is an intelligent framework designed to optimize traffic management and reduce congestion in urban areas through automated analysis of vehicular flow. Traditional traffic control systems rely on fixed signal timing, which often leads to unnecessary delays and traffic jams, especially during peak hours. To overcome these limitations, this system integrates Internet of Things (IoT) technology with deep learning to create a dynamic and adaptive traffic control mechanism. IoT-enabled surveillance cameras are installed at various intersections to continuously capture live video feeds of the traffic. These video streams are transmitted to a processing unit, where image frames are extracted and analysed using the ResNet (Residual Network) model. ResNet, known for its high accuracy and efficient feature extraction capabilities, is used to detect and classify vehicles in each lane. By counting the number of vehicles, the system determines the traffic density in real time. Based on this density information, the system automatically adjusts the traffic signal duration to allow smoother vehicle movement in congested lanes, thus reducing waiting times and improving overall traffic flow. Additionally, the data collected from the IoT sensors and cameras can be stored and analysed for long-term traffic pattern prediction, road planning, and infrastructure management. The integration of deep learning and IoT enables continuous monitoring without human intervention and ensures real-time responsiveness to changing traffic conditions. This system contributes to the development of smart cities by enhancing traffic efficiency, reducing fuel consumption, minimizing carbon emissions, and improving commuter safety and convenience. Keywords: IoT, ResNet, Traffic Monitoring, Vehicle Detection, Traffic Density Estimation, Smart City, Deep Learning