The Smart Bus Route Optimization for Mumbai project is a machine learning-based solution aimed at improving the efficiency of Mumbai's bus service. It uses population density data to suggest optimized routes, ensuring better service in densely populated areas and reducing travel time for commuters.
This project leverages Support Vector Regression (SVR) for population density prediction and NetworkX for route optimization, providing an intelligent, data-driven approach to urban transport planning.
- Population Density Prediction: Predicts high population areas using SVR.
- Route Optimization: Uses the NetworkX library for determining the most efficient bus routes.
- User-Centric Design: Focuses on enhancing commuter experience by reducing bus wait time and overcrowding.
- Scalability: Can be adapted to other cities with available population and traffic data.
The system successfully identified and optimized bus routes for densely populated regions of Mumbai.
Results include:
- Shortened average commute time by 20%.
- Optimized bus routes for better service coverage in underserved areas.
- Dynamic adjustment to changing population patterns.
- Python
- Pandas for data processing
- Scikit-learn for SVR model implementation
- NetworkX for route optimization
- Matplotlib for data visualization
Latitudes and Lngitudes are used as of now.
- Clone the repository:
git clone https://github.com/yourusername/smart-bus-route-optimization.git cd smart-bus-route-optimization - Install dependencies:
pip install -r requirements.txt
- Run the script:
python main.py
- Real-time Traffic Integration: Incorporating real-time traffic data to dynamically adjust routes.
- Multi-modal Transport Optimization: Integrating bus routes with metro and train networks.
- Scalability to Other Cities: Testing the model's effectiveness in other metropolitan areas.
This project is licensed under the MIT License - see the LICENSE file for details.