The rapid urbanization and growing number of vehicles in cities have made traffic congestion a critical issue affecting productivity, energy consumption, and the environment. This paper presents the design and implementation of an Intelligent Real-Time Traffic Congestion Monitoring and Prediction System that integrates Machine Learning (ML) and the Internet of Things (IoT). The system gathers live traffic data using IoT sensors and cameras, processes it through a cloud-based analytical pipeline, and predicts congestion levels using ML algorithms. Results are visualized on a real-time dashboard to help authorities take proactive control actions such as signal optimization and route diversion. The framework aims to enhance road efficiency, reduce travel time, and promote sustainable urban mobility.
Jeevanantham * S (Sun,) studied this question.