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Jodrian, Oey Joshua
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Computer Science & IT Library & Information Science

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Estimating Passenger Density in Trains through Crowd Counting Modeling Tjandra, Bryan; Jodrian, Oey Joshua; Handoko, Nyoo Steven Christopher; Wicaksono, Alfan Farizki
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 1 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i1.1314

Abstract

The Greater Jakarta Commuter Rail, also known as the KRL Commuter Line, is one of the primary transportation choices for many people due to its comfort and efficiency. However, the level of user dissatisfaction is still relatively high, particularly regarding the frequent and unpredictable overcrowding of trains. To address this issue, our research develops an Artificial Intelligence-based model to predict train passenger density through crowd counting. By utilizing the proposed k-F1 metric and a constructed dataset of train density, we compare three object detection approaches: bounding box prediction (YOLOv5), density map (CSRNet), and proposal point (P2PNet). Our results show that P2PNet excels in estimating the number of people and predicting their locations in crowded situations. However, for situations that have fewer people and larger object sizes, YOLOv5 demonstrates the best performance. To estimate the density of space, we propose a method that takes into account the region of interest, image perspective transformation, and masking. The proportion between the masked area and the total area provides an estimation of the density level within the train. This method can be applied to real-time image-based CCTV systems in predicting train congestion and facilitating transportation management decisions aligned with Indonesia's sustainable development goals.
Co-Authors Handoko, Nyoo Steven Christopher Tjandra, Bryan Wicaksono, Alfan Farizki