TRAFFIC PREDICTION AND CONTROL SYSTEM FOR SMART CITIES

Electronics and Communication Engineering

The rapid increase in vehicle density within urban areas has led to severe traffic congestion, longer travel times, and higher fuel consumption, contributing significantly to air pollution and environmental degradation. Traditional traffic control systems operate with fixed signal timings that cannot adapt to real-time variations in traffic flow. To address this challenge, this project proposes a sustainable traffic prediction and control system designed for smart cities. The system utilizes image processing and machine learning algorithms to analyze live video feeds from intersections and estimate vehicle density in real time. Based on this data, it dynamically adjusts traffic signal durations to optimize flow and reduce congestion. Hardware implementation is achieved using Arduino Uno, cameras, and RFID modules for emergency vehicle detection, enabling automatic priority clearance. The proposed system not only minimizes vehicle idle time and fuel wastage but also enhances road safety and overall traffic efficiency. This adaptive, data- driven approach contributes toward developing intelligent and eco-friendly urban transportation infrastructures.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.