Detail publikace

Calibration of pedestrian ingress model based on CCTV surveillance data using machine learning methods

PÁLKOVÁ, M. UHLÍK, O. APELTAUER, T.

Originální název

Calibration of pedestrian ingress model based on CCTV surveillance data using machine learning methods

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

Machine learning methods and agent-based models enable the optimization of the operation of high capacity facilities. In this paper, we propose a method for automatically extracting and cleaning pedestrian traffic detector data for subsequent calibration of the ingress pedestrian model. The data was obtained from the waiting room traffic of a vaccination center. Walking speed distribution, the number of stops, the distribution of waiting times, and the locations of waiting points were extracted. Of the 9 machine learning algorithms, the random forest model achieved the highest accuracy in classifying valid data and noise. The proposed microscopic calibration allows for more accurate capacity assessment testing, procedural changes testing, and geometric modifications testing in parts of the facility adjacent to the calibrated parts. The results show that the proposed method achieves state-of-the-art performance on a violent-flows dataset. The proposed method has the potential to significantly improve the accuracy and efficiency of input model predictions and optimize the operation of high-capacity facilities.

Klíčová slova

pedestrian modelling, agent-based models, machine learning, random forest, calibration, surveillance

Autoři

PÁLKOVÁ, M.; UHLÍK, O.; APELTAUER, T.

Vydáno

18. 1. 2024

Nakladatel

Public Library of Science

Místo

United States of America, California, San Francisco

ISSN

1932-6203

Periodikum

PLOS ONE

Ročník

19

Číslo

1

Stát

Spojené státy americké

Strany počet

22

URL

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0293679

BibTex

@article{BUT187059,
  author="Martina {Pálková} and Ondřej {Uhlík} and Tomáš {Apeltauer}",
  title="Calibration of pedestrian ingress model based on CCTV surveillance data using machine learning methods",
  journal="PLOS ONE",
  year="2024",
  volume="19",
  number="1",
  pages="22",
  doi="10.1371/journal.pone.0293679	",
  issn="1932-6203",
  url="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0293679"
}