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@article{186773,
        author = {Miss. Babar.P.L and Miss.Bhadane A.D and Miss.Ugalmugale.S.B and Miss. Aware.S.P. and Mr. Abhale.B.A.},
        title = {AI-Based Smart Bus Passenger Counting and Alert System for Bus Capacity},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {1900-1905},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186773},
        abstract = {Effective management of public transportation is crucial for enhancing urban mobility, alleviating traffic congestion, and guaranteeing commuter safety. Conventional methods for counting passengers on buses, like manual monitoring or infrared sensors, often face issues such as inaccuracy, restricted coverage, and operational difficulties. This review paper explores AI-driven smart passenger counting technologies that leverage deep learning, video analysis, and IoT frameworks to track passenger entry and exit in real time. It emphasizes important methods including object detection models (like YOLO), tracking algorithms (such as OpenCV-based tracking), and embedded systems. Furthermore, this research investigates alert systems that inform drivers or authorities when bus capacity limits are surpassed, thereby improving operational efficiency and passenger safety, particularly in the context of post-pandemic smart cities. The review also addresses performance evaluation metrics, challenges such as occlusion and low visibility, and potential future research paths aimed at creating more resilient, privacy-conscious, and energy-efficient systems. In conclusion, AI- driven automated counting and alert systems offer a promising solution for optimizing public transport operations and improving the commuter experience.},
        keywords = {Artificial Intelligence (AI), YOLO, Object Detection, Public Transportation, Real Time Tracking, Crowd Density Analysis, Alert System.},
        month = {November},
        }