Smart AI-Based Traffic Light Control System Using YOLO and Simulation

Abstract

Rapid urbanization and the increasing number of vehicles on roads have led to severe
traffic congestion in cities worldwide. Traditional traffic light systems operate on fixed
timing mechanisms, which are inefficient in handling dynamic traffic conditions. This
project presents a smart traffic control system that leverages Artificial Intelligence (AI)
and computer vision techniques to optimize traffic signal management. The system
integrates traffic simulation with real-time vehicle detection using the YOLO (You Only
Look Once) object detection algorithm.The proposed system consists of two primary
modules: a traffic simulation module and a YOLO-based vehicle detection and counting
module. The simulation module models traffic flow at intersections, allowing users to
observe how intelligent signal control can reduce congestion. The YOLO module
processes video input to detect and count vehicles in real time. Based on the number of
vehicles detected in each lane, the system dynamically adjusts traffic signal timings.
A graphical user interface (GUI) is developed using Python’s Tkinter library to provide
user interaction. The GUI allows users to load traffic videos, run detection algorithms,
and initiate simulations. This makes the system user-friendly and suitable for
demonstration and experimentation purposes.The YOLO algorithm is chosen due to its
high speed and accuracy in object detection tasks. It processes entire images in a single
pass, making it ideal for real-time applications like traffic monitoring. The detected
vehicles are classified and counted, providing essential data for intelligent decisionmaking.
The system aims to reduce waiting times at intersections, improve traffic flow
efficiency, and minimize fuel consumption and emissions. By adapting signal timings
based on real-time conditions rather than fixed intervals, the proposed approach
significantly enhances traffic management.This project demonstrates how AI can be
effectively applied to real-world problems in transportation