🚦 Smart Control of Traffic Light Using Artificial Intelligence

An AI-driven traffic management system that dynamically controls traffic lights based on real-time traffic density. This project combines computer vision (YOLO) with a traffic simulation engine (Pygame) to demonstrate intelligent and adaptive traffic signal control.

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📌 Problem Statement

Traditional traffic signal systems rely on:

• Manual traffic control (high manpower)
• Fixed-time signals (inefficient for varying traffic)
• Sensor-based systems (high deployment cost)

These approaches fail to adapt efficiently to real-time traffic density.

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💡 Proposed Solution

This project uses traffic cameras and AI-based vehicle detection to:

• Detect and count vehicles in each lane using YOLO (You Only Look Once)
• Estimate traffic density dynamically
• Adjust traffic signal timing automatically

The system supports:

• Simulated traffic control using Pygame
• Real-world traffic video analysis using YOLO

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Key Features

• Real-time vehicle detection & counting
• Adaptive green/red signal timing
• Traffic simulation using Pygame
• YOLO-based video processing
• User-friendly GUI using Tkinter

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Technologies Used

• Python 3
• YOLOv3 (OpenCV DNN)
• Pygame – Traffic simulation
• Tkinter – GUI
• OpenCV – Video processing
• NumPy, SciPy – Computation

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Project Structure

├── Main.py                     # Main GUI application
├── traffic_simulation.py       # Pygame traffic simulation logic
├── yolo_traffic.py             # YOLO vehicle detection & counting
├── run.bat                     # One-click project launcher
├── requirements_instructions.txt
├── output.mp4                  # YOLO processed output video
├── images/                     # Simulation assets (signals, vehicles)
├── yolo-coco/                  # YOLO config, weights, class names
└── Smart_traffic_SCREENS.docx  # Screenshots & documentation

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Installation & Setup

1️ Prerequisites

• Python 3.x
• Windows OS (recommended)

2️ Install Dependencies

pip install pygame==2.0.0
pip install keras==2.3.1
pip install tensorflow==1.14.0
pip install opencv-python imutils numpy scipy

Note: YOLO runs slowly on systems without GPU support.

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How to Run the Project

Option 1: Using Batch File (Recommended)

Double-click run.bat

Option 2: Manual Execution

python Main.py

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Modules Explanation

Run Traffic Simulation

• Launches a Pygame-based traffic intersection
• Vehicles are generated randomly
• Signal timings adjust automatically based on density

Run YOLO Traffic Detection & Counting

• Upload a traffic video (e.g., traffic2.mp4)
• YOLO detects & counts vehicles frame-by-frame
• Output saved as output.mp4

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Screenshots & Demo

Screenshots and execution flow are available in:

Smart_traffic_SCREENS.docx

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Output

• Live traffic simulation window

• Processed video with:

  • Bounding boxes
  • Vehicle IDs
  • Total vehicle count overlay

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Future Enhancements

• GPU acceleration (CUDA)
• Lane-wise signal optimization
• Emergency vehicle prioritization
• IoT-based live camera integration
• Cloud-based traffic analytics dashboard

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