Traffic Prediction System Using Machine Learning

Abstract

The rapid growth of urbanization has led to increased traffic congestion, posing significant challenges in efficient transportation management. This project proposes a machine learning-based intelligent traffic prediction system that utilizes both historical and real-time data to forecast traffic conditions accurately. By integrating data from Google Maps API, OpenWeatherMap API, and road sensors, the system analyzes key factors such as time, weather, vehicle density, and road type to predict congestion levels as low, moderate, or high. Advanced algorithms such as ARIMA, Regression, and Long Short-Term Memory (LSTM) are employed to model time-dependent traffic patterns and generate precise forecasts. The system’s results are visualized through an interactive web-based dashboard, providing real-time congestion insights, alerts, and alternative route suggestions for commuters and traffic authorities. This integrated and data-driven approach enhances urban mobility, reduces travel time and fuel consumption, and supports intelligent city planning by transforming traditional reactive systems into proactive, predictive traffic management solutions.