Real-Time Traffic Control Using IoT Nodes Based on Traffic Density Information

Fortunately, there is currently a Iot of research on traffic management systems and there is still ongoing investigation on intelligent traffic monitoring driven by new technologies involving the Internet of Things (IoT). This study can boost urban growth and enhance decision-making processes by integrating these technologies. The available traffic prediction methods focus primarily on traffic management in cities and highways. However, not much research has been done on closed campuses and collector roads. In addition, it is challenging to engage the public and forge meaningful connections to support their decision-making when customers do not use any smart devices. The goal is to increase mobility by putting roadside messaging devices in place to deliver real-time traffic reports about unexpected traffic events and gridlock. The general public will benefit from these time- saving alerts, particularly during rush hour. Additionally, the system transmits traffic updates from the regulatory authorities. The results of the experiments demonstrate a minimal relative error in road occupancy prediction and a high degree of vehicle recognition accuracy. The model's viability is assessed using a prototype. The project's focus is on real-time feedback for adaptive traffic signals.