<![CDATA[One of the most complex challenges in urban management, particularly in developing countries, is traffic control. Traffic congestion has become a global issue, significantly affecting mobility, economic productivity, and quality of life. To address this problem, smart traffic systems are increasingly being adopted as adaptive and efficient solutions. This study aims to implement the You Only Look Once version 7 (YOLOv7) object detection model within a smart traffic system to calculate vehicle volume and monitor traffic conditions in real time. YOLOv7 is chosen for its high object detection accuracy, even in dynamic and complex environments where objects are fast-moving or overlapping in dense backgrounds. The methodology involves processing a 2-minute-30-second CCTV video recording taken from a street in New York City. Vehicle detection is conducted by applying bounding boxes over specific areas within the video frames, which serve as virtual counters for vehicles passing through. The experimental results demonstrate that the system effectively counts vehicles per second and identifies traffic conditions, which in this case remained smooth throughout the observation period. These findings highlight the potential of implementing YOLOv7 in smart traffic systems to support data-driven, automated, and real-time traffic management.]]>
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