AI-Based Traffic Density Analyzer & Smart Signal Controller

Problem statement

Fixed-time traffic lights do not adapt to real-time traffic volume, causing unnecessary waiting and congestion at some lanes.

Abstract

This project processes video from cameras placed at each road of a junction. Using computer vision techniques such as background subtraction or deep learning object detection, it estimates vehicle count or density. A controller then allocates green signal duration proportionally, improving throughput and reducing waiting time.

Components required

• CCTV or USB cameras (one per lane)
• PC or embedded board (Raspberry Pi / Jetson Nano)
• Python with OpenCV and ML frameworks
• Microcontroller/PLC or simulation for signal lights
• LED signal model
• Dataset of traffic scenarios

Block diagram

Working

The system captures frames from each camera and either applies classical background subtraction or YOLO-like detectors to count vehicles. Density values for each lane are fed into a scheduling algorithm that computes green time slices. The controller actuates model traffic lights or sends commands to a simulated junction.

Applications

• Smart city traffic management
• Realistic final-year AI + embedded project
• Simulation tool for transport planners
• Base for adaptive traffic signal deployment