AI-Powered Smart Parking Reservation System: A Machine Learning-Enabled Solution to Urban Parking Challenges

This article introduces an AI-powered smart parking reservation system developed by an intern team, aiming to solve the increasingly severe parking problem in the process of urbanization. The system uses a full-stack architecture built with Django REST Framework and React.js, integrating machine learning models to implement core functions such as parking fee prediction, intelligent parking spot recommendation, and QR code verification, providing an efficient technical solution for urban parking management.

With the acceleration of urbanization and the growth of private car ownership, parking difficulty has become a common problem. Traditional parking lot management has pain points such as opaque parking spot information, inefficient and error-prone manual billing, congestion during peak hours, and lack of data-driven planning. To address these issues, the intern team developed this intelligent system, integrating web technologies and machine learning to improve parking efficiency, reduce congestion, and optimize user experience.

The system adopts a frontend-backend separation architecture: The backend is based on the Python ecosystem, using Django framework, DRF for API support, and SQLite for data storage; machine learning uses Scikit-learn, Pandas, NumPy, and Joblib. The frontend is based on React.js, built with Vite, using Axios for request handling, Tailwind CSS for styling, and Recharts for visualization, ensuring response speed and user experience.

The system's core functions include: 1. User authentication and permission management (registration, login, Token authentication); 2. Real-time parking spot reservation (query, duration selection, dynamic display); 3. Machine learning-driven fee prediction (based on duration, parking lot, and historical data); 4. Intelligent parking spot recommendation (considering availability, load, and user preferences); 5. QR code verification (contactless entry); 6. User dashboard and history records (statistical information, detail query).

Machine learning applications include: 1. Fee prediction model: Using Scikit-learn to train a regression model, learning influencing factors based on historical data, deploying via Joblib serialization to achieve millisecond-level inference; 2. Recommendation system algorithm: Combining rule-based filtering and collaborative filtering, balancing real-time availability and user preferences, and balancing recommendation quality and computational complexity.

The project provides local deployment documentation: Configure Python virtual environment, install dependencies, perform database migration, start frontend and backend services (backend default: http://127.0.0.1:8000/, frontend: http://localhost:5173/). The API follows RESTful specifications, with main endpoints including registration, login, fee prediction, parking spot recommendation, reservation, history query, and reservation cancellation.

As an internship project, this system demonstrates the transformation of theoretical knowledge into practical applications, covering the full-stack development process and providing a reference case for developers. Future expansion directions include: real-time parking map integration, IoT sensor support, online payment gateway, management analysis dashboard, mobile application, notification system, etc., which is expected to be upgraded into a complete smart parking ecosystem.

This system represents an innovative application of AI in urban management, integrating web technologies and machine learning to provide a feasible solution to parking difficulties. Although it is for educational internship purposes, its design concept provides a reference for similar projects. With the advancement of smart city construction, such solutions are expected to be widely applied to alleviate congestion and improve the quality of life for citizens.