Capstone Project (2024-2025)
Smart Traffic Light System
Overview
The Smart Traffic Light System is an intelligent traffic management solution that dynamically adjusts signal timings based on real-time vehicle detection. The system aims to optimize traffic flow, reduce congestion, and improve road safety by utilizing computer vision and deep learning techniques. The project focuses on integrating YOLO-based object detection with Raspberry Pi and USB cameras to monitor vehicle presence at an intersection. The detection data is processed on a Cloud Computing server (PC) to determine traffic density, and signals are adjusted accordingly.
The entire system could be adjusted by setup window, used to set the variables, such as maximum green time, safe time, and base green time.
This project is designed for intersections, where each traffic light is equipped with a camera to monitor the traffic. The system aims to be implemented in real-time and aims to be scalable for future smart city applications.
Approach
Capturing Traffic Data
2-4 cameras are installed at the intersection to monitor traffic.
A small computer (Raspberry Pi) captures video.
Only useful information (like the number of vehicles and their position) is sent to a more powerful computer for further processing.
Processing & Decision Making
A smart program uses artificial intelligence (AI) to analyze the video and count vehicles.
If a road is too crowded, the system adjusts the lights to reduce waiting times.
Real-Time Control & Monitoring
The system continuously adapts to changing traffic conditions, making roads more efficient.
Testing & Improvements
Simulated traffic is used to test how well the system works.
The system is fine-tuned to ensure fast and accurate responses to traffic changes.
Nawaf S. Al-Mughathawi
Project #1