Description:

The Parking Space Availability Predictor is a smart data analytics and IoT-based web application that forecasts the number of available parking spots in a given area using historical parking occupancy data, real-time IoT sensor feeds, and traffic patterns.
It helps drivers quickly find available parking and city authorities optimize parking space usage to reduce congestion.

By leveraging machine learning algorithms, the system predicts occupancy trends at different times of the day and recommends best parking options to users in real-time.

Key Features:

1. Real-Time Parking Updates – Shows live availability of parking spaces via IoT sensors or manual input.

2. Occupancy Prediction – Uses historical data and ML models to forecast future parking availability.

3. Interactive Map View – Displays parking spots, availability, and distance from the user’s location.

4. Route Optimization – Suggests the fastest route to the nearest available parking spot.

5. Peak Hour Analytics – Identifies high-demand time slots for parking zones.

6. Parking Fee Estimator – Calculates estimated parking cost based on location and time.

7. Mobile-Friendly Interface – Enables users to search for parking on the go.

8. Admin Dashboard – Allows parking lot operators to track occupancy and optimize pricing.

Technology Stack:

• Backend: Node.js / Java / PHP (for APIs, parking data management)

• Frontend: HTML, CSS, Bootstrap, JavaScript (Leaflet.js / Google Maps API for mapping)

• Database: MySQL / MongoDB (stores parking history, sensor readings, location data)

• Data Science Layer: Python (pandas, NumPy, scikit-learn, ARIMA or LSTM for time-series prediction)

• IoT Integration: Ultrasonic sensors or camera-based parking detection systems for live spot monitoring

• External APIs: Traffic APIs, GPS services, payment gateways

Example Use Case:

• In a shopping mall parking lot, ultrasonic sensors detect 80% occupancy at 6 PM.

• The ML model predicts that the lot will be fully occupied within 20 minutes based on historical weekend trends.

• Drivers using the app see a warning that the lot is almost full and receive alternate parking suggestions nearby.

• The mall management uses peak time analytics to adjust dynamic pricing for better space distribution.