Dynamic Route Optimization and Emission Reduction System This Python-based application optimizes vehicle routes in real-time using traffic, weather, and vehicle-specific data. It calculates the most efficient route and estimates CO2 emissions to help reduce environmental impact. The system integrates APIs like TomTom, Google Maps, AQICN, and OSRM for dynamic routing and emission reduction.
Features Real-Time Route Optimization: Uses traffic and weather data to recommend the most efficient route.
Emission Estimation: Calculates CO2 emissions for each route based on vehicle details.
API Integration: Fetches data from TomTom, Google Maps, AQICN, and OSRM.
User-Friendly: Simple CLI interface for input and output.
Prerequisites Python 3.8 or higher
API keys for:
TomTom API
Google Maps API
AQICN API
Install required Python libraries.
Setup Instructions
- Clone the Repository bash Copy git clone https://github.com/your-username/dynamic-route-optimization.git cd dynamic-route-optimization
- Set Up a Virtual Environment Create and activate a virtual environment:
bash Copy
python -m venv venv venv\Scripts\activate
python3 -m venv venv source venv/bin/activate 3. Install Dependencies Install the required Python libraries:
bash Copy pip install requests numpy pandas 4. Add API Keys Create a .env file in the root directory and add your API keys:
plaintext Copy TOMTOM_API_KEY=your_tomtom_api_key GOOGLE_MAPS_API_KEY=your_google_maps_api_key AQICN_API_KEY=your_aqicn_api_key 5. Run the Application Execute the Python script:
bash Copy python main.py Usage Input Vehicle Details:
Provide vehicle details such as fuel type (gasoline, diesel, or electric) and fuel efficiency (km per liter or kWh).
Input Start and End Locations:
Enter the start and destination addresses.
View Results:
The system will output the optimal route and estimated CO2 emissions.
Example python Copy
start_location = "New York, NY" end_location = "Boston, MA" vehicle_details = { "fuel_type": "gasoline", "fuel_efficiency": 12 # km per liter }
optimal_route, emissions = optimize_route(start_location, end_location, vehicle_details)
print("Optimal Route:", optimal_route['summary']) print("Estimated Emissions (kg CO2):", emissions) Project Structure Copy dynamic-route-optimization/ ├── venv/ # Virtual environment ├── .env # Environment variables (API keys) ├── main.py # Main 5672 application script ├── README.md # Project documentation ├── requirements.txt # List of dependencies Dependencies requests: For making API calls.
numpy: For numerical calculations.
pandas: For data manipulation (if needed).
Contributing Contributions are welcome! Please follow these steps:
Fork the repository.
Create a new branch (git checkout -b feature/YourFeatureName).
Commit your changes (git commit -m 'Add some feature').
Push to the branch (git push origin feature/YourFeatureName).
Open a pull request.
License This project is licensed under the MIT License. See the LICENSE file for details.
Acknowledgments TomTom API for real-time traffic data.
Google Maps API for route generation.
AQICN API for weather and air quality data.
OSRM for open-source routing.
This README.md file provides a comprehensive guide for setting up and using the application. It also includes instructions for creating a virtual environment and installing dependencies, ensuring a smooth setup process for users.