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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F24%3APU150136" target="_blank" >RIV/00216305:26110/24:PU150136 - isvavai.cz</a>

Result on the web

<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0293679" target="_blank" >https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0293679</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pone.0293679" target="_blank" >10.1371/journal.pone.0293679</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20101 - Civil engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

PLOS ONE

ISSN

1932-6203

e-ISSN

—

Volume of the periodical

19

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

22

Pages from-to

„“-„“

UT code for WoS article

001174325200016

EID of the result in the Scopus database

2-s2.0-85182600088