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

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...

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Bibliographic Details
Published in:PloS one 2024-01, Vol.19 (1), p.e0293679-e0293679
Main Authors: Pálková, Martina, Uhlík, Ondřej, Apeltauer, Tomáš
Format: Article
Language:English
Subjects:
Accuracy
Agent-based models
Algorithms
Analysis
Artificial intelligence
Biology and Life Sciences
Calibration
Cameras
Classification
Closed-circuit television
Computer and Information Sciences
Data mining
Detectors
Evacuations & rescues
Evaluation
Heart rate
Humans
Immunization
Information management
Internet of Things
Learning algorithms
Machine Learning
Medicine and Health Sciences
Methods
Neural networks
Pedestrians
Physical Sciences
Radio frequency identification
Research and Analysis Methods
Social networks
Vaccination
Waiting rooms
Walking
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0293679