Real Time Traffic Analysis and Prediction Using Machine Learning

Urban traffic congestion is a growing challenge for developing smart and sustainable cities, leading to increased travel delays, fuel consumption, emissions, and safety issues. To address these concerns, this study proposes a Real-Time Traffic Analysis and Prediction System that applies machine learning (ML) and data-driven techniques to continuously monitor and forecast traffic conditions. Unlike conventional traffic management methods that rely on fixed signal schedules and manual observation, the proposed system dynamically adapts to variations in road activity. The framework integrates live traffic inputs collected from Kaggle datasets, GPS traces, and IoT-based sensors. These data streams are processed through a multi-model ML pipeline using algorithms such as Random Forest, Linear Regression, and Long Short-Term Memory (LSTM) networks to estimate traffic density, predict congestion levels, and analyze flow changes across different intersections. A React.js-enabled dashboard presents real-time insights through heatmaps, visual charts, and analytical metrics, enabling authorities to make informed and timely decisions. The backend, developed with Node.js and Python, ensures seamless communication between real-time data sources and prediction modules. An alerting component is also included to instantly notify administrators about unusualevents such as accidents or sudden traffic surges. Deployment on cloud platforms like AWS and Google Cloud enhances scalability, reliability, and continuous model updates as new data becomes available. Overall, this system showcases how the integration of ML-based predictive analytics can significantly improve traffic flow management, reduce congestion-related impacts, and support environmentally friendly urban mobility. The outcomes contribute to the advancement of intelligent transportation systems (ITS) and lay the groundwork for future smart city traffic solutions.