AI Traffic Management System

Overview

The AI Traffic Management System is an end-to-end solution designed to reduce urban congestion by dynamically adjusting traffic signal timings based on real-time vehicle density. The system utilizes computer vision for vehicle detection and a high-performance genetic algorithm to optimize traffic flow at four-way intersections.

Core Workflow

1. Data Ingestion: The system receives video feeds from four cameras (one per lane) or processes uploaded video files.
2. Vehicle Detection: YOLOv4-tiny identifies and counts vehicles in each frame, categorizing them to estimate lane pressure.
3. Traffic Optimization: The vehicle counts are passed to a Genetic Algorithm implemented in C++17. This algorithm simulates various timing scenarios to find the configuration that minimizes total waiting time.
4. Signal Implementation: The optimized green-light durations are returned to the dashboard for monitoring and implementation.

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Technical Features

• Object Detection: Implements YOLOv4 (You Only Look Once) via OpenCV's DNN module for high-speed vehicle identification.
• Optimization Engine: Uses a Genetic Algorithm (GA) written in C++ for performance. The GA evolves a population of signal timing solutions to find a near-optimal balance for the intersection.
• Real-time Analytics: A React-based web interface provides live visualizations of traffic density and system performance.
• Flexible Deployment: Support for Docker and Docker Compose ensures the system can be deployed on edge devices or central servers with minimal configuration.
• RTSP Support: Capable of streaming directly from IP cameras for production environments.

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Technology Stack

Layer	Technologies
Frontend	React
Backend API	Flask (Python 3.10)
Vision Processing	OpenCV, Darknet (YOLOv4)
Optimization Core	C++17, OpenMP [parallel..]
Infrastructure	Docker, Docker Compose, Nginx

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Installation and Setup

Important

Prerequisites Make sure your system has

• Docker Engine 20.10 or higher
• Docker Compose 2.0 or higher
• Minimum 8GB RAM recommended for AI model inference

Automated Setup (Recommended)

The project includes a Makefile to automate common deployment tasks. For detailed information regarding containerization and orchestration, please refer to the Docker Documentation.

1. Clone the repository

   git clone https://github.com/udaykiriti/TrafficManagementSystem
   cd TrafficManagementSystem

2. Initialize the environment This command downloads the required YOLO weights and builds the Docker containers.

   make setup

3. Start the services

   make up

Application Access

Web Dashboard
http://localhost:3000
User interface for monitoring and control.

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Backend API
http://localhost:5000
REST API endpoints.

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API Health Check
http://localhost:5000/health
Verify backend service status.

Documentation

Full project documentation is available in the docs/ directory.

• Project Overview
• Docker Guide
• Backend Documentation
• Frontend Documentation

Docker Deployment

The application is fully containerized using Docker and Docker Compose. This ensures a consistent environment for both development and production. For comprehensive instructions on building, running, and troubleshooting the Docker containers, see the Detailed Docker Guide.

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Project Management Commands

Command	Description
make setup	Downloads model weights and builds Docker images.
make up	Starts all services in the background.
make dev	Starts services in development mode with hot-reloading enabled.
make logs	Displays real-time output from all service containers.
make down	Stops and removes all running containers.
make clean	Removes containers, volumes, and local images.
make help	Lists all available Makefile targets.

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Manual Installation

For environments where Docker is not available, follow these steps:

Backend Configuration

1. Install Python dependencies: pip install -r backend/requirements.txt
2. Download YOLO weights: cd backend && bash download.sh
3. Compile the optimization engine: g++ -std=c++17 -O3 -fopenmp -o backend/Algo1 backend/Algo.cpp
4. Start the Flask server: python backend/app.py

Frontend Configuration

1. Install Node dependencies: cd frontend && npm install
2. Start the development server: npm start

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Contributing

1. Fork the project.
2. Create a feature branch (git checkout -b <feature/OptimizationUpdate>).
3. Commit your changes (git commit -m "Improve GA convergence speed").
4. Push to the branch (git push origin feature/OptimizationUpdate).
5. Open a Pull Request.

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Acknowledgments

• AlexeyAB/darknet: For the YOLOv4 implementation[not done yet.].
• OpenCV Team: For the vision processing libraries.
• Genetic Algorithm: Core logic based on standard evolutionary strategy principles.