Project Overview:

The Real-Time Smart Traffic Management System is an IoT and AI/ML-based solution that dynamically manages traffic signals based on real-time traffic density at intersections. It uses sensors or live camera feeds to detect traffic flow, processes the data using ML algorithms, and automatically adjusts the signal timing to minimize congestion and improve road efficiency.

This system also provides a web-based dashboard for traffic authorities to monitor live intersections, receive congestion alerts, and view historical traffic data and trends.

Technologies Used:

Hardware:

• Raspberry Pi / Arduino

• IR Sensors / Ultrasonic Sensors – To count vehicles at each lane

• Camera (optional) – For AI-based vehicle detection

• LEDs / Signal Lights – To simulate traffic signals

• Wi-Fi Module – For data transmission

Frontend:

• HTML, CSS, Bootstrap

• JavaScript (Google Maps API, Chart.js for real-time visualizations)

Backend:

• Option 1: Node.js + MongoDB

• Option 2: PHP + MySQL

• Option 3: Java Spring Boot + PostgreSQL

AI/ML:

• Python (OpenCV + scikit-learn / TensorFlow)

• Algorithms:

  • Vehicle count using image processing or sensors

  • Predict signal timing using ML classification/regression

  • Congestion prediction using historical data (Time-Series)

System Architecture:

1. IR sensors or cameras installed at each road lane count the number of vehicles.

2. Data is collected and sent to the server via Raspberry Pi/Wi-Fi.

3. The backend processes this data and feeds it into an ML model.

4. The ML model predicts optimal green-light duration based on traffic density.

5. The server sends timing control signals to traffic lights.

6. A web dashboard displays:

   • Real-time traffic status at intersections

   • Signal timing changes

   • Historical trends and congestion heatmaps

7. Optional alerts for emergency vehicles, accidents, or overload conditions.