AI-DRIVEN TRAFFIC MANAGEMENT

  • Unique Paper ID: 186127
  • Volume: 12
  • Issue: 6
  • PageNo: 353-359
  • Abstract:
  • This paper reviews advancements in AI-driven traffic management systems, combining theory with real-world case studies. It covers adaptive signal control, machine learning for demand prediction, and reinforcement learning for signal optimization. Deployments in cities such as Pittsburgh, Los Angeles, and Singaporedemonstrate reduced travel times, lower emissions, and operational efficiency. Recommendations include deployment methods, evaluation metrics, ethical guidelines, and future research directions.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article 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.

BibTeX

@article{186127,
        author = {Mohammed Haroon Ali and Elzaan Syed and Mohammed Zubair Hussain and Abdul Ayaan and mr mohammed jalal uddin Asst prof and Dr khaja fareed uddin},
        title = {AI-DRIVEN TRAFFIC MANAGEMENT},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {353-359},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186127},
        abstract = {This paper reviews advancements in AI-driven traffic management systems, combining theory with real-world case studies. It covers adaptive signal control, machine learning for demand prediction, and reinforcement learning for signal optimization. Deployments in cities such as Pittsburgh, Los Angeles, and Singaporedemonstrate reduced travel times, lower emissions, and operational efficiency. Recommendations include deployment methods, evaluation metrics, ethical guidelines, and future research directions.},
        keywords = {},
        month = {October},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 12
  • Issue: 6
  • PageNo: 353-359

AI-DRIVEN TRAFFIC MANAGEMENT

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