Applied Machine Learning: Concepts, Algorithms, and Real-World Applications
Articles

Traffic Control System Using Machine Learning

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  • Jayanta Chowdhury
Jayanta Chowdhury
Swami Vivekananda University, Barrackpore, India

Published 2026-07-06

Keywords

  • Traffic control,
  • machine learning,
  • real-time systems,
  • traffic prediction,
  • congestion management,
  • signal optimization,
  • urban mobility,
  • intelligent transportation systems,
  • computer vision
    • ...More
    Less

How to Cite

Chowdhury, Jayanta. “Traffic Control System Using Machine Learning”. Swami Vivekananda University Press, July 2026, pp. 59-62, https://swamivivekanandauniversitypress.com/index.php/svup/article/view/654.

Copyright (c) 2026 Swami Vivekananda University Press

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

The Traffic Control System Using Machine Learning aims to enhance urban mobility and reduce traffic congestion through intelligent, data-driven systems. The system leverages machine learning models to analyze traffic patterns, predict congestion, and adjust traffic signal timings in real-time, optimizing traffic flow. By collecting and processing large datasets from traffic sensors, cameras, and GPS devices, the system can make dynamic adjustments, improving efficiency and reducing the likelihood of accidents. This paper discusses the design, implementation, challenges, and future potential of machine learning in traffic control, along with a detailed evaluation of the system’s performance.

DOI - https://doi.org/10.65525/SVUP.9788199963351.2026.59-62

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