Automatic Passenger Counting System on Public Buses Using CNN YOLOv8 Model for Passenger Capacity Optimization

Ari Dian Prastyo; Sharfina Andzani Minhalina; Surya Agung; Denty Nirwana Bintang; Muhammad Yordi Septian; Endang Purnama Giri; Gema Parasti Mindara

doi:10.62951/ijies.v1i4.121

Authors

Ari Dian Prastyo Institut Pertanian Bogor
Sharfina Andzani Minhalina Institut Pertanian Bogor
Surya Agung Institut Pertanian Bogor
Denty Nirwana Bintang Institut Pertanian Bogor
Muhammad Yordi Septian Institut Pertanian Bogor
Endang Purnama Giri Institut Pertanian Bogor
Gema Parasti Mindara Institut Pertanian Bogor

DOI:

https://doi.org/10.62951/ijies.v1i4.121

Keywords:

Passenger Counting, YOLOv8, Public Transportation, Real-time Detection, Convolutional Neural Networks (CNN)

Abstract

This study presents the development of an automatic passenger counting system for public buses using YOLOv8 based on Convolutional Neural Networks (CNN). The system detects and counts passengers in real-time to optimize bus capacity and enhance operational efficiency. Results indicate that the system achieves high accuracy in the front camera view (confidence score of 0.82). However, in the rear camera view the accuracy slightly decreases (confidence score of 0.76) due to object overlap, emphasizing the importance of proper camera placement. The system offers potential improvements in bus capacity management, reduced operational costs, and enhanced passenger comfort. These findings contribute to advancing smarter and more efficient public transportation systems.

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