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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0293679&type=printable |
| _version_ | 1797349350932742144 |
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| author | Martina Pálková Ondřej Uhlík Tomáš Apeltauer |
| author_facet | Martina Pálková Ondřej Uhlík Tomáš Apeltauer |
| author_sort | Martina Pálková |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-08T12:29:07Z |
| format | Article |
| id | doaj.art-3b33f8fa3f0544e28df2e3dec6f2e45d |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-08T12:29:07Z |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-3b33f8fa3f0544e28df2e3dec6f2e45d2024-01-22T05:31:30ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-01191e029367910.1371/journal.pone.0293679Calibration of pedestrian ingress model based on CCTV surveillance data using machine learning methods.Martina PálkováOndřej UhlíkTomáš ApeltauerMachine 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0293679&type=printable |
| spellingShingle | Martina Pálková Ondřej Uhlík Tomáš Apeltauer Calibration of pedestrian ingress model based on CCTV surveillance data using machine learning methods. PLoS ONE |
| title | Calibration of pedestrian ingress model based on CCTV surveillance data using machine learning methods. |
| title_full | Calibration of pedestrian ingress model based on CCTV surveillance data using machine learning methods. |
| title_fullStr | Calibration of pedestrian ingress model based on CCTV surveillance data using machine learning methods. |
| title_full_unstemmed | Calibration of pedestrian ingress model based on CCTV surveillance data using machine learning methods. |
| title_short | Calibration of pedestrian ingress model based on CCTV surveillance data using machine learning methods. |
| title_sort | calibration of pedestrian ingress model based on cctv surveillance data using machine learning methods |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0293679&type=printable |
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