✈️ EcoFlight AI - Carbon-Aware Flight Optimization System

An intelligent flight route optimization system that uses AI algorithms to minimize carbon emissions and fuel consumption while maintaining efficient flight times.

---

📸 Screenshots

---

🌟 Features

• 🤖 AI-Powered Optimization - A* pathfinding algorithm for optimal routes
• 🌍 Real-World Data - 7,000+ airports and 60,000+ flight routes
• 📊 Emission Tracking - Real-time CO₂ and fuel consumption calculations
• 🎯 Multi-Objective - Balance between eco-friendliness, time, and efficiency
• 📈 Visual Analytics - Interactive charts and route visualizations
• ⚡ Fast API - RESTful backend with comprehensive documentation

---

🏗️ Architecture

ecoflight-ai/
├── backend/                 # FastAPI Backend
│   ├── api/                # API endpoints
│   │   ├── routes/         # Route handlers
│   │   └── services/       # Business logic
│   ├── models/             # Database models
│   │   ├── database/       # SQLAlchemy schemas
│   │   └── ml/             # ML models
│   └── utils/              # Utilities
│
└── frontend/               # React Frontend
    ├── src/
    │   ├── components/     # React components
    │   ├── services/       # API integration
    │   └── App.jsx         # Main application
    └── public/

---

🚀 Tech Stack

Backend

• FastAPI - Modern Python web framework
• SQLAlchemy - Database ORM
• SQLite - Embedded database
• NetworkX - Graph algorithms for route optimization
• Scikit-learn - Machine learning utilities
• Geopy - Geographic calculations

Frontend

• React 18 - UI framework
• Vite - Build tool
• Tailwind CSS - Styling
• Recharts - Data visualization
• Lucide React - Icons
• Axios - HTTP client

---

📦 Installation

Prerequisites

• Python 3.10+
• Node.js 18+
• npm or yarn

Backend Setup

# Clone the repository
git clone <your-repo-url>
cd ecoflight-ai

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
cd backend
pip install -r requirements.txt

# Initialize database
python init_database.py

# Load airport and route data
python load_data.py

# Start the API server
uvicorn main:app --reload

The API will be available at http://localhost:8000

Frontend Setup

# Navigate to frontend
cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

The frontend will be available at http://localhost:5173

---

🎯 Usage

1. Select Airports

• Enter departure and arrival airports
• Autocomplete suggestions appear as you type

2. Choose Aircraft

• Select from 4 real aircraft models
• Each with different fuel efficiency ratings

3. Set Optimization Mode

• ECO: Minimize CO₂ emissions (70% weight on fuel)
• BALANCED: Balance time and emissions (50/50)
• FAST: Prioritize speed (70% weight on time)

4. View Results

• Total distance and flight time
• Fuel consumption and CO₂ emissions
• CO₂ savings vs. direct route
• Optimized waypoint path
• Visual route map and comparison charts

---

🧠 AI Optimization Algorithm

The system uses A pathfinding* with custom heuristics:

# Cost function
cost = w_fuel × fuel_consumption + w_time × flight_time

# Heuristic (great circle distance)
h(n) = geodesic_distance(current_node, goal_node)

# Total score
f(n) = g(n) + h(n)

Optimization Modes

Mode	Fuel Weight	Time Weight
ECO	0.7	0.3
BALANCED	0.5	0.5
FAST	0.3	0.7

---

📡 API Endpoints

Airports

• GET /api/v1/airports - List airports
• GET /api/v1/airports/{iata} - Get specific airport
• GET /api/v1/airports/search/autocomplete - Search airports

Flights

• POST /api/v1/flights/calculate-emissions - Calculate CO₂
• GET /api/v1/flights/aircraft - List aircraft

Optimization

• POST /api/v1/optimize/route - Optimize single route
• POST /api/v1/optimize/alternatives - Find alternatives
• GET /api/v1/optimize/history - View history

Interactive Documentation

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

---

📊 Database Schema

• airports - 7,000+ airports worldwide
• flight_routes - 60,000+ real routes
• aircraft - Aircraft specifications
• emission_data - Calculated emissions
• optimized_paths - AI-optimized routes

---

🌱 Environmental Impact

CO₂ Emission Factors

• Jet fuel: 3.16 kg CO₂ per kg fuel (ICAO standard)
• Based on real aircraft fuel efficiency data
• Considers takeoff/landing overhead (15%)

Sample Savings

• JFK → LAX (Eco Mode): ~8-12% CO₂ reduction
• Longer Routes: Up to 15-20% savings possible

---

🎨 UI Features

• Modern gradient design
• Smooth animations
• Responsive layout
• Real-time search
• Interactive visualizations
• Professional data presentation

---

🔜 Future Enhancements

• 3D Globe visualization with leaflet
• Weather data integration
• Historical flight data analysis
• User accounts and saved routes
• Carbon offset calculator
• Mobile app
• Real-time flight tracking
• Multi-leg journey optimization

---

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

---

👨‍💻 Author

Steve Thomas Mulamoottil

Built with ❤️ for a sustainable future in aviation

---

🙏 Acknowledgments

• OpenFlights.org for airport and route data
• ICAO for CO₂ emission standards
• Aircraft manufacturers for fuel efficiency data
• Open-Meteo for weather API
• Esri for satellite imagery

---

📞 Support

For issues or questions, please open an issue on GitHub.

Let's make aviation more sustainable, one optimized route at a time! 🌍✈️