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All Journal Jurnal Teknologi Informasi dan Multimedia
Kurniadin Abd Latif
Program Studi Rekayasa Perangkat Lunak, Universitas Bumigora
Computer Science & IT

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Smart Traffic untuk Menghitung Volume Kendaraan dan Klasifikasi Kondisi Lalu Lintas Menggunakan Model YOLOv7 Kurniadin Abd Latif; Putri Tanisa Utami; Apriani Apriani; Fatimatuzzahra Fatimatuzzahra; Ria Rismayati
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 2 (2025): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i2.667

Abstract

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.
Co-Authors Apriani Apriani Fatimatuzzahra Fatimatuzzahra Putri Tanisa Utami Ria Rismayati