AI-Based Smart Traffic Management System with Mobile Notification

Abstract

Traffic congestion is one of the most critical challenges faced by modern urban environments, leading to increased travel time, fuel consumption, environmental pollution, and economic loss. Conventional traffic signal systems operate on fixed time intervals without considering real-time traffic conditions, which results in inefficient utilization of road infrastructure. To overcome these limitations, this paper proposes an AI-based smart traffic management system that utilizes computer vision and deep learning techniques for real-time traffic monitoring and intelligent signal control. A camera captures continuous traffic video, which is processed using OpenCV [14] and analyzed using the YOLO (You Only Look Once) object detection model [1], [2] to detect and count vehicles. Based on vehicle density in each lane, the system dynamically adjusts traffic signal timings to optimize traffic flow. Additionally, emergency vehicles such as ambulances and fire trucks are detected and given immediate priority to ensure faster movement. The system also integrates the Telegram Bot API [13] to send real-time mobile notifications regarding traffic congestion and emergency situations. Experimental results demonstrate improved efficiency, reduced waiting time, and better traffic management. The proposed system provides a scalable, intelligent, and cost-effective solution for smart cities.

Introduction

The paper proposes an AI-based smart traffic management system to address urban traffic congestion caused by increasing vehicle usage. Traditional fixed-timer traffic signals are inefficient because they do not adapt to real-time traffic conditions, leading to delays, fuel wastage, and increased pollution.

The proposed system uses Artificial Intelligence and computer vision techniques, particularly YOLO, to detect and classify vehicles (cars, buses, trucks, motorcycles, and emergency vehicles) from live video captured at intersections. Using this data, the system calculates traffic density and dynamically adjusts signal timings—giving longer green signals to congested lanes. Emergency vehicles like ambulances are given the highest priority, triggering immediate green signals.

The system architecture includes modules for video capture, vehicle detection, traffic analysis, signal control (using Arduino or ESP32), and notifications via Telegram. The workflow involves capturing video → processing frames → detecting and counting vehicles → analyzing congestion → adjusting signals → sending alerts.

Conclusion

The AI-based smart traffic management system provides an intelligent and efficient solution for modern traffic problems. By integrating AI, computer vision, and IoT, the system dynamically controls traffic signals and reduces congestion. The system improves traffic flow, reduces waiting time, and enhances emergency response. It is scalable and suitable for smart city applications. Future enhancements include cloud integration, predictive analytics, and mobile applications [20].

References

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Copyright

Copyright © 2026 Mr. Mohan Raj K R., Akash R, Bryan Selvam V, Sathya Priya J. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.