🚗 AI-SmartPark Gandhinagar
Predicting urban parking availability with AI to build a greener, smarter Gandhinagar.
AI-SmartPark is a predictive solution designed for the smart city of Gandhinagar. By analyzing historical occupancy and real-time traffic trends, it eliminates the "search time" for drivers, reducing city congestion and carbon footprints.
- AI Confidence: Provides a % likelihood of finding a spot (not just a Yes/No).
- Zone-Wise Mapping: Interactive map covering Sector 21, Infocity, and more.
- Sustainability Driven: Focused on reducing fuel wastage and urban idling.
- Problem ID: Solving the "30% traffic surge" caused by parking searches.
- Data Sync: Merging historical occupancy with local event calendars.
- Feature Tuning: Identifying demand spikes (e.g., peak office hours in Gandhinagar).
- AI Engine: Powered by Random Forest Regressors for non-linear prediction.
- Inference Layer: Real-time processing for instant user updates.
- Scalability: API-first design ready for integration with existing city apps.
| Layer | Technology |
|---|---|
| Frontend | React.js, Tailwind CSS |
| Backend | Python (FastAPI / Flask) |
| AI/ML Engine | Scikit-Learn, XGBoost, Pandas |
| Mapping | Leaflet.js / OpenStreetMap |
The AI calculates availability using:
- Green: High Availability (> 70%)
- Yellow: Moderate/Filling Fast (30% - 70%)
- Red: Nearly Full (< 30%)
git clone [https://github.com/your-username/smart-parking-gandhinagar.git](https://github.com/your-username/smart-parking-gandhinagar.git)
cd backend
pip install -r requirements.txt
python app.py
2. Launch Frontend
Bash
cd frontend
npm install
npm start
🌏 The Impact
For Commuters: Save ~15 mins of search time per trip.
For the Environment: Lower CO2 emissions in Gandhinagar's green belts.
For City Planning: Data-backed insights for future parking infrastructure.
📝 License
Distributed under the MIT License. See LICENSE for more information.