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

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

WoS Article

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.

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.

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

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

PÁLKOVÁ, M.; UHLÍK, O.; APELTAUER, T.

2025

18.01.2024

Public Library of Science

United States of America, California, San Francisco

1932-6203

PLoS One

19

1

United States of America

22

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

http://hdl.handle.net/11012/245484

journal.pone.0293679