Smart Traffic: Integrating Machine Learning, and YOLO for Adaptive Traffic Management System

Authors

  • Nitin Sakhare Vishwakarma Institute of Information Technology, Pune
  • Mrunal Hedau Vishwakarma Institute of Information Technology, Pune
  • Gokul B. Vishwakarma Institute of Information Technology, Pune
  • Omkar Malpure Vishwakarma Institute of Information Technology, Pune
  • Trupti Shah Thakur College of Engineering and Technology, Mumbai
  • Anup Ingle Vishwakarma Institute of Information Technology, Pune

Keywords:

IoT-driven, lane-specific, considerably, environmentally, enumerating

Abstract

The growing number of vehicles has made traffic control a vital concern, rendering traditional manual solutions ineffective. This research proposes an innovative approach that makes use of the Internet of Things (IoT) and sophisticated image processing. Using image processing, the adaptive traffic management system analyses real-time data from camera-monitored lanes, precisely recognizing and enumerating cars. A sophisticated algorithm computes appropriate waiting periods based on lane-specific vehicle numbers, which informs the prudent distribution of signal light patterns. This method considerably decreases average wait times, improving traffic-clearing efficiency. Furthermore, by reducing CO2 emissions, the technology helps to preserve the environment. Its flexibility in emergency settings emphasizes its usefulness. This study highlights the potential of IoT-driven adaptive traffic management in producing efficient, environmentally friendly, and responsive urban traffic systems.

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References

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Published

12.01.2024

How to Cite

Sakhare, N. ., Hedau, M. ., B., G. ., Malpure, O. ., Shah, T. ., & Ingle, A. . (2024). Smart Traffic: Integrating Machine Learning, and YOLO for Adaptive Traffic Management System. International Journal of Intelligent Systems and Applications in Engineering, 12(12s), 347–355. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4520

Issue

Vol. 12 No. 12s (2024)

Section

Research Article

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