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@article{174476,
        author = {K.Rachitha Sony and D.Sruthi and D.Harish and R.Harshitha and Shaik Ameerkhan and Yenugutala Suresh},
        title = {AI-based tarffic management system},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4708-4713},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174476},
        abstract = {In cities, traffic congestion is becoming a bigger problem that leads to delays, financial losses, and environmental issues. In this paper, object detection methods like YOLO and COCO SSD are used to investigate an AI-driven approach to traffic congestion analysis. Our objective is to develop an intelligent system that uses real-time traffic data, including vehicle count, lane distribution, signal time, and congestion levels, to assist transportation authorities in optimising traffic flow. The study looks at the literature on traffic management with reinforcement learning, deep learning, and the Internet of Things. The study also discusses key object detection methods, emphasising their application in monitoring and assessing traffic congestion. The effectiveness of the model is evaluated by considering performance metrics such as mAP, IoU, FPS, and latency. The findings demonstrate the importance of artificial intelligence and deep learning.},
        keywords = {COCO-SSD, Performance Metrics, Traffic Congestion Analysis, YOLO},
        month = {April},
        }