Project Overview:

The Smart Parking System with ML Forecasting is an IoT + AI/ML-based application that enables real-time parking slot detection and predicts future parking availability using historical data. This system helps drivers find available parking slots quickly and allows authorities to manage parking resources efficiently.

It uses sensors or cameras to detect the occupancy of parking slots and applies Machine Learning algorithms to forecast slot availability based on past patterns, time of day, and traffic data.

Technologies Used:

Hardware:

• Microcontroller: ESP32 / NodeMCU / Raspberry Pi

• IR Sensors or Ultrasonic Sensors – To detect vehicle presence in each slot

• Wi-Fi Module – For sending slot data to the server

• (Optional) Camera for image-based vehicle detection

• (Optional) LED indicators for physical guidance in lots

Frontend:

• HTML, CSS, Bootstrap

• JavaScript (Leaflet.js / Google Maps API + Chart.js)

Backend:

• Option 1: Node.js + MongoDB

• Option 2: PHP + MySQL

• Option 3: Java Spring Boot + PostgreSQL

AI/ML:

• Python (pandas, scikit-learn, XGBoost, TensorFlow)

• Models:

  • Time-series forecasting for slot availability

  • Classification model for peak hour prediction

  • Clustering (K-Means) for optimal parking guidance (optional)

System Architecture:

1. Sensors detect whether a vehicle is occupying a slot (or camera does image-based detection).

2. Data is sent to the backend server over Wi-Fi.

3. The server stores live parking data and historical logs.

4. An AI/ML model forecasts:

   • Slot availability in the next 1–2 hours

   • Peak demand times

5. A web dashboard displays:

   • Real-time slot availability

   • Color-coded map of parking area (Green = free, Red = occupied)

   • Forecasted availability

   • Option to reserve a parking spot in advance

6. Notifications/alerts can be sent if all slots are full.

ML Model Description:

✅ Slot Availability Forecast (Time-Series Regression):

• Input: Timestamp, day of week, past occupancy status, event/holiday info

• Output: Estimated availability per slot or total free slots

• Algorithm: LSTM / ARIMA / Linear Regression / Random Forest Regressor

✅ Peak Time Classification (Optional):

• Input: Time, day, previous week traffic

• Output: Low / Medium / High parking demand

• Algorithm: Decision Tree or SVM

Web App Features:

✅ Real-time view of parking lot status
✅ Forecasted availability view (per hour/day)
✅ Reserve a slot in advance
✅ Admin panel for managing parking areas and sensors
✅ Notifications if lot is full or reserved
✅ Visual heatmap of historical demand
✅ Responsive and mobile-friendly UI

User Roles:

• Admin – Manages sensors, lots, users, and forecasts

• Driver/User – Finds parking, makes reservations, sees forecasts

• Parking Attendant (optional) – Views slot status in real time

Optional Add-ons:

• Mobile App for drivers to check parking on-the-go

• License Plate Recognition (LPR) using OpenCV

• Payment Gateway Integration for paid parking

• Smart entry gates controlled based on reservation

• Weather and event data integration to improve predictions

Benefits:

• Saves time and fuel for users

• Reduces congestion in parking lots

• Increases parking space utilization

• Provides predictive insights for parking management

• Enhances user experience through real-time and forecast-based features