Smart Tunnel Traffic Control System using IoT and Machine Learning

Smart Tunnel Traffic Control System Using IoT and Machine Learning” is designed to offer safety and effective management of traffic in single-lane tunnels. The conventional tunnel system does not prohibit big vehicles from entering the tunnel through either end, which may lead to congestion and increase the likelihood of accidents. The designed system uses an ESP32-CAM module to sense the images of the vehicles in real time at any entry point of the tunnel. Using a CNN model, the system classifies incoming vehicles by size, and if a large vehicle is detected, it triggers a red “STOP” signal and message at the opposite end. In this way, only one large vehicle occupies the tunnel at any time. The system further calculates the transit time in terms of the length of the tunnel and updates it dynamically to handle incoming vehicles. As soon as the tunnel becomes free, a green “GO” signal allows the next vehicle to move forward. Keywords: loT, ESP32-CAM, CNN, traffic, tunnel safety, automation, image processing, classification, monitoring, accident prevention, congestion, ML.