Vehicle Detection Using YOLOv11L to Estimate the Rush Hour Factor
DOI:
https://doi.org/10.35381/i.p.v8i15.5199Keywords:
Automation, transportation planning, motor vehicle, artificial intelligence, statistical analysis, (UNESCO Thesaurus)Abstract
The objective of this study was to evaluate the effectiveness of an automated vehicle counting system based on artificial intelligence for determining traffic volume and the peak-hour demand factor. Methodologically, a computer vision system using the YOLOv11l algorithm was implemented for vehicle detection and classification, processing video sequences of 12 hours per day (6:00 a.m.–6:00 p.m.) for 7 consecutive days. Regarding the results, the recorded VHMD was 1,698 vehicles (Day 2, between 5:00 PM–6:00 PM), with an FHP ranging from 0.65 to 0.84 depending on the day analyzed. In conclusion, the automated vehicle counting system is a reliable and efficient tool for determining traffic volume and the peak-hour factor, overcoming the limitations of traditional manual methods and providing detailed information on mobility patterns on different days of the week.
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