Intelligent Traffic Management System using Artificial Intelligence and Computer Vision: Review
Keywords:
Intelligent Traffic Management, Artificial Intelligence, Computer Vision, Smart Cities, Congestion ControlAbstract
Urbanization and rapid motorization have intensified traffic congestion, causing economic loss, pollution, and safety challenges. Traditional static traffic systems fail to cope with today’s dynamic urban conditions, creating an urgent need for intelligent, adaptive, and real-time solutions. This review highlights Artificial Intelligence (AI) and Computer Vision (CV) as transformative technologies in Intelligent Traffic Management Systems (ITMS). Through deep learning, machine learning, and computer vision techniques, ITMS enables real-time vehicle detection, traffic density estimation, dynamic signal control, violation monitoring, and predictive analytics. Research studies across India and abroad demonstrate significant improvements in reducing travel time, fuel consumption, and accident rates while supporting environmental sustainability and smart city goals. Although implementation requires strong policy frameworks, interdisciplinary collaboration, and infrastructure investment, AI-driven ITMS holds immense potential for developing and developed nations alike. Overall, these systems represent a paradigm shift toward safer, greener, and more efficient urban mobility.
References
Nigam N, Singh DP, Choudhary J. A review of different components of the intelligent traffic management system (ITMS). Symmetry. 2023 Feb 23;15(3):583.
Kastrinaki V, Zervakis M, Kalaitzakis K. A survey of video processing techniques for traffic applications. Image and vision computing. 2003 Apr 1;21(4):359-81.
Asch M, Moore T, Badia R, Beck M, Beckman P, Bidot T, Bodin F, Cappello F, Choudhary A, De Supinski B, Deelman E. Big data and extreme-scale computing: Pathways to convergence-toward a shaping strategy for a future software and data ecosystem for scientific inquiry. The International Journal of High Performance Computing Applications. 2018 Jul;32(4):435-79.
Hartzog W, Conti G, Nelson J, Shay LA. Inefficiently automated law enforcement. Mich. St. L. Rev.. 2015:1763.
Djahel S, Doolan R, Muntean GM, Murphy J. A communications-oriented perspective on traffic management systems for smart cities: Challenges and innovative approaches. IEEE Communications Surveys & Tutorials. 2014 Jul 17;17(1):125-51.
Alam T, Gupta R, Nasurudeen Ahamed N, Ullah A, Almaghthwi A. Smart mobility adoption in sustainable smart cities to establish a growing ecosystem: Challenges and opportunities. MRS Energy & Sustainability. 2024 Sep;11(2):304-16.
Geels FW. Understanding system innovations: a critical literature review and a conceptual synthesis. System innovation and the transition to sustainability: Theory, evidence and policy. 2004;52:19-47.
Dilek E, Dener M. Computer vision applications in intelligent transportation systems: a survey. Sensors. 2023 Mar 8;23(6):2938.
Azfar T, Li J, Yu H, Cheu RL, Lv Y, Ke R. Deep learning-based computer vision methods for complex traffic environments perception: A review. Data Science for Transportation. 2024 Apr;6(1):1.
Ansh Sakhuja(2023).Intelligent Traffic Management System using Computer Vision and Machine Learning.Innovative Research Thoughts Refereed | Peer Reviewed | Indexed ISSN : 2454 – 308X | Volume : 09 , Issue : 05 | October - December 2023
Mujahid Issam Ashquer (2024). Real-Time Traffic Density Estimation Using Various Connected Vehicle Penetration Rates: A New Predictive Approach. DOI :10.21203/rs-4449927/V1| MAY 2024
Gupta, A., et al. (2020). YOLO-Based Vehicle Detection in Indian Traffic. International Journal of Computer Applications.
Syed Konain Abbas and Muhammad Umair Hassan (2024). Vision based intelligent traffic light management system using Faster R-CNN. | April 2024
Shruti Mishra (2029). An Improved Smart Traffic Signal using Computer Vision and Artificial Intelligence | DOI :103594/ijrte.C5098.118419 | November 2019
Kumar, S., & Singh, R. (2021). ‘Traffic Signal Control via Reinforcement Learning: A Review on Applications and Innovations’ DOI: 10.3390/infrastruactures10050114. | May 2025
Dr. Megha Kadam, 2Aarti Uttam Sutar, 3Vedika Vilas Kakad, 4Sarika AppasahebDubale, 5 Shamli Satish Vaidya. ‘Traffic Management Using Artificial Intelligence’| ISSN: 2456-4184 || Volume 10, Issue 6 June 2025
Vitthal B Kamble1, Onkar N Mundhe2, Chaitanya M Walunjkar3, Gaurav A Kale4 ‘AI-Driven Smart Traffic Management System: An Adaptive Approach Using YOLO and OpenCV’|Doi:10.71141/30485037|30 April 2025
Rizama Victor Samuel ‘Computer Vision for Intelligent Traffic Monitoring and Control’| SSN: 2456-8880 | IRE Journals | Volume 8 Issue 5 | NOV 2024
Ravina Dnyaneshwar Chavhan1 , Dr. G.B. Sambare2 ‘AI-Driven Traffic Management Systems In Smart Cities: A Review’ | Educational Administration: Theory and Practice| ISSN:2148-2403| 2024
Aryan Khare ‘Assessing the Impact of Artificial Intelligence on Resolving Traffic Issues in India’ | International Journal of Engineering, Management and Humanities (IJEMH)|pp: 127-133 | Volume 4, Issue 5, Sep.-Oct., 2023.
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