AI Powered Traffic Management and Signal Monitoring System

Abstract

This project is about creating a smart web application to control traffic signals at a busy four-way intersection. Instead of using fixed timers, this system uses artificial intelligence (AI) to adjust the green light time based on how many vehicles are waiting on each road. Cameras at each road capture live video, which the AI processes to count the number of vehicles and understand the traffic density.When one road has more vehicles waiting, the system automatically gives it more green light time, helping to reduce waiting time and traffic buildup. The web application lets users see real-time traffic information and monitor signal changes. Over time, the system also collects data that helps traffic authorities better understand peak traffic times and patterns. This smart traffic control system aims to make driving smoother, reduce idle time, and lower fuel emissions by managing traffic more efficiently.

Introduction

This project develops a smart web application for traffic light control at a busy four-way intersection using artificial intelligence (AI). Unlike traditional fixed-timer traffic signals, the system dynamically adjusts green light durations based on real-time vehicle counts captured via cameras on each road. AI-powered computer vision processes live video to detect traffic density, prioritizing heavily congested roads with longer green lights to reduce waiting times, congestion, and vehicle emissions.

The web app provides users and traffic authorities with live traffic data, signal statuses, and historical analytics to understand peak traffic patterns and optimize traffic flow. Key objectives include minimizing idle times, improving road efficiency, and supporting data-driven traffic management decisions.

The methodology involves:

• Installing cameras for continuous video feeds.

• Using deep learning models (e.g., YOLO, SSD) for vehicle detection and counting.

• Dynamically adjusting traffic signal timings based on traffic density.

• Displaying real-time traffic info on an intuitive web interface.

• Collecting and analyzing historical traffic data for ongoing optimization.

• Alerting authorities in case of signal or camera malfunctions.

The system is designed to be secure, reliable, scalable, and user-friendly, with potential expansion to integrate other smart city services like public transportation and emergency systems.

Conclusion

In conclusion, the smart traffic control system using AI and real-time data analysis offers a promising solution to reduce traffic congestion and improve the flow of vehicles at busy intersections. By dynamically adjusting signal timings based on vehicle density, the system can minimize waiting times, lower fuel consumption, and reduce emissions. It also provides valuable insights into traffic patterns, which can help authorities plan better for future traffic management. While there are challenges like high setup costs and camera dependencies, the system’s benefits for urban mobility and the environment make it a valuable tool for smart cities. Overall, it contributes to safer, more efficient, and sustainable traffic management.

References

[1] M. Khan and K. Srinivas, ”Helmet, Number Plate and Vehicle Speed Detection Using Python and OpenCV,” International Journal of Advanced Research in Computer Science, vol. 11, no. 1, pp. 232-239, 2020. [2] H. B. Han, L. Zhang, and L. N. Tan, ”Real-time Traffic Flow Prediction Using Deep Learning,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 15-24, 2021. [3] A. S. Agarwal, ”AI-based Traffic Management System for Smart Cities,” IEEE Access, vol. 8, pp. 89523-89531, 2020. [4] M. Kumar, A. S. Rathi, and R. D. Sharma, ”Vehicle Detection and Counting System Using Computer Vision for Smart Traffic Signals,” International Journal of Computer Applications, vol. 178, no. 7, pp. 22-29, 2020.

Copyright

Copyright © 2025 Andhale Mayur Nathu, Pawar Siddhesh Navnath , Mapari Darshan Arun , Wamane Devendra Suryabhan, Miss. Shital Aher. 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.