International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
www.irjet.net
AI–ENABLED SMART TRAFFIC LIGHT CONTROL SYSTEM 1Aman S. Dhemare, 2Dhruv N. Vetal , 3 Rushikesh S. Kangane , 4th Pradnesh V. Phadtare 1,2,3,4 Electronics and telecommunication
Jayawantrao Sawant polytechnic Pune, India -----------------------------------------------------------------------***-------------------------------------------------------------------ABSTRACT -This paper presents the design and implementation of an AI enabled Smart Traffic Light Control System for a four-lane road junction using Raspberry Pi 4. The proposed system focuses on reducing traffic congestion, improving emergency response time, and enhancing pedestrian safety through automation. A single camera connected to the Raspberry Pi is used for real-time video monitoring and ambulance detection using image processing techniques. Traffic density is measured using ultrasonic and IR sensors installed on each lane, and green signal timing is adjusted dynamically based on vehicle flow. Emergency vehicle priority is provided using AI-based detection, V2I communication through ESP32 modules, and authorized manual override buttons. A Flask-based web dashboard displays live video feed, lane status, traffic density, and emergency alerts. The system reduces manual traffic control, minimizes waiting time, and improves overall traffic management efficiency. The proposed solution is cost-effective, scalable, and suitable for smart city applications and academic prototype demonstrations. I. INTRODUCTION Traffic congestion has become a major problem in urban areas due to the rapid increase in the number of vehicles on roads. Traditional traffic signal systems mostly operate on fixed timing and do not consider the actual traffic density at intersections. This leads to longer waiting time, fuel wastage, and increased pollution. Emergency vehicles such as ambulances also face delays because there is no automatic priority system. Therefore, there is a need for an intelligent traffic control system that can manage signals based on real-time conditions. The AI-enabled smart traffic light control system proposed in this project uses image processing and automation techniques to improve traffic management. A camera captures traffic images, and a Raspberry Pi analyzes vehicle density to adjust signal timing dynamically. The system also includes emergency vehicle detection and a web-based dashboard for monitoring. The developed prototype demonstrates how artificial intelligence and embedded systems can improve traffic flow efficiency.
II. Problem Definition Traffic management at road intersections is becoming increasingly difficult due to the continuous growth in the number of vehicles. Most of the existing traffic light systems operate on fixed time intervals without considering the actual number of vehicles present on each lane. This results in inefficient signal control, longer waiting times, and unnecessary traffic congestion, especially during peak hours. In addition, emergency vehicles such as ambulances and fire brigades often face delays because there is no automatic mechanism to provide signal priority at intersections. Manual traffic control by authorities is also not always practical, as it requires continuous human effort and monitoring. There is a need for a smart and automated traffic control system that can detect real-time vehicle density and adjust signal timing accordingly. Therefore, the main problem addressed in this project is to design and implement an intelligent traffic light control system that reduces congestion, improves traffic flow, and provides priority for emergency vehicles using AIbased techniques.
III. Objectives of the Project The main objective of this project is to design and develop an AI-enabled smart traffic light control system that can manage traffic signals efficiently based on real-time vehicle density. The system aims to reduce traffic congestion at road intersections by automatically adjusting signal timing according to the number of vehicles present on each lane. Another important objective is to minimize unnecessary waiting time and improve overall traffic flow efficiency compared to traditional fixed-time traffic signal systems.
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