Smart Traffic Management System

A comprehensive web-based traffic management system with real-time traffic monitoring and parking management capabilities.

Features

• Real-time traffic density monitoring
• Automated traffic light timing adjustment
• License plate recognition for parking management
• Separate admin panels for traffic and parking management
• Real-time metro information display
• Emergency vehicle detection and prioritization

Prerequisites

• Node.js 18 or higher
• Supabase account for database and authentication
• Modern web browser with webcam support

Environment Variables

Create a .env file in the root directory with the following variables:

VITE_SUPABASE_URL=your_supabase_url
VITE_SUPABASE_ANON_KEY=your_supabase_anon_key

Installation

1. Clone the repository

2. Install dependencies:

   npm install

3. Set up the database:

   • Connect to Supabase
   • Run the migration in supabase/migrations/create_initial_schema.sql

4. Start the development server:

   npm run dev

Testing

Run the test suite:

npm test

Run tests with UI:

npm run test:ui

Generate coverage report:

npm run test:coverage

Project Structure

src/
├── components/
│   ├── auth/          # Authentication components
│   ├── traffic/       # Traffic management components
│   ├── parking/       # Parking management components
│   └── dashboard/     # Dashboard components
├── lib/
│   ├── supabase.ts    # Supabase client configuration
│   └── auth.ts        # Authentication utilities
├── types/
│   └── index.ts       # TypeScript type definitions
└── services/
    └── metro.ts       # Metro API service

Database Schema

The system uses the following main tables:

• admin_users: Store admin user information
• traffic_intersections: Manage traffic intersection data
• traffic_logs: Log traffic density and light timing data
• parking_spots: Track parking spot status
• parking_logs: Log parking entry/exit events

Security

• Row Level Security (RLS) enabled on all tables
• Role-based access control for admin users
• Secure authentication flow
• Input validation using Zod

Image Processing

The system uses TensorFlow.js for:

• Traffic density calculation
• License plate detection (simulated)

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request