Greenify 🌱

An intelligent routing system that helps users make environmentally conscious travel decisions by predicting and minimizing carbon emissions for different routes.

Features

🗺️ Smart Route Analysis

• Real-time route calculation using HERE Maps API
• Distance and travel time estimation
• Carbon emission predictions for different routes
• Intelligent route recommendations based on environmental impact

🤖 Machine Learning Capabilities

• Neural network model for accurate emission predictions
• Considers multiple factors:
  • Distance
  • Traffic conditions
  • Weather conditions
• Trained on real-world emissions data
• Continuous learning and improvement

📊 Environmental Impact Metrics

• Carbon emission calculations in grams/kilograms
• Comparative analysis between routes
• Historical data tracking
• Environmental impact visualization

Technology Stack

• Frontend: React.js
• Backend: Python
• Machine Learning: TensorFlow
• Maps API: HERE Maps
• Data Processing: NumPy, scikit-learn

Installation

1. Clone the repository:

git clone https://github.com/yourusername/greenify.git
cd greenify

2. Install Python dependencies:

pip install -r requirements.txt

3. Install Node.js dependencies:

cd frontend
npm install

4. Set up environment variables:

# Create .env file in root directory
HERE_MAPS_API_KEY=your_api_key

Usage

1. Start the backend server:

python app.py

2. Start the React frontend:

cd frontend
npm start

3. Access the application at http://localhost:3000

API Documentation

Route Calculation

GET /api/routes
Parameters:
- origin: string (latitude,longitude)
- destination: string (latitude,longitude)
- mode: string (car, bicycle, walking)

Response:
{
    "distance": float,
    "duration": float,
    "carbon_emissions": float,
    "route_options": [...]
}

Carbon Prediction

POST /api/predict
Body:
{
    "distance": float,
    "traffic_level": int (1-10),
    "weather_condition": int (1-10)
}

Response:
{
    "predicted_emissions": float,
    "confidence_level": float
}

Machine Learning Model

The system uses a neural network with:

• Input features: distance, traffic level, weather conditions
• Hidden layers: 64 and 32 neurons with ReLU activation
• Output: predicted carbon emissions
• Training metrics: Mean Absolute Error (MAE)

Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a new branch: git checkout -b feature/your-feature
3. Make your changes
4. Submit a pull request

Environment Variables

Required environment variables:

HERE_MAPS_API_KEY=your_api_key
ML_MODEL_PATH=path/to/model
DEBUG=True/False

Future Enhancements

• Add support for public transportation routes
• Implement real-time weather data integration
• Add user accounts and route history
• Develop mobile application
• Include carbon offset recommendations
• Add support for electric vehicles

License

This project is licensed under the GNU General Public v3.0 License - see the LICENSE file for details.

Acknowledgments

• HERE Maps for their routing API
• TensorFlow team for the machine learning framework
• Contributors and beta testers

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🌍 Help reduce carbon emissions, one route at a time.