Open Access
Scientific Reports, volume 12, issue 1, publication number 5506
Intelligent escalator passenger safety management
Publication type: Journal Article
Publication date: 2022-04-01
PubMed ID:
35365721
Multidisciplinary
Abstract
This article addresses an approach to intelligent safety control of passengers on escalators. The aim is to improve the accuracy of detecting threatening situations on escalators in the subway to make decisions to prevent threats and eliminate the consequences. The novelty of the approach lies in the complex processing of information from three types of sources (video, audio, sensors) using machine learning methods and recurrent neural networks with controlled elements. The conditions and indicators of safety assurance efficiency are clarified. New methods and algorithms for managing the safety of passengers on escalators are proposed. The architecture of a promising safety software system is developed, and implementation of its components for cloud and fog computing environments is provided. Modeling results confirm the capabilities and advantages of the proposed technological solutions for enhancing the safety of escalator passengers, efficiency of control decision making, and system usability. Due to the proposed solutions, it has become possible to increase the speed of identifying situations 3.5 times and increase the accuracy of their determination by 26%. The efficiency of decision making has increased by almost 30%.
Citations by journals
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1
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Computers, Materials and Continua
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Computers, Materials and Continua, 1, 12.5%
Computers, Materials and Continua
1 publication, 12.5%
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Informatics and Automation
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Informatics and Automation
1 publication, 12.5%
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Scientific Reports
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Scientific Reports
1 publication, 12.5%
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Future Generation Computer Systems
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Future Generation Computer Systems
1 publication, 12.5%
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1
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Citations by publishers
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1
2
3
4
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IEEE
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IEEE
4 publications, 50%
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Tech Science Press
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Tech Science Press, 1, 12.5%
Tech Science Press
1 publication, 12.5%
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SPIIRAS
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SPIIRAS
1 publication, 12.5%
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Springer Nature
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Springer Nature
1 publication, 12.5%
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Elsevier
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Elsevier
1 publication, 12.5%
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1
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3
4
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Osipov V. et al. Intelligent escalator passenger safety management // Scientific Reports. 2022. Vol. 12. No. 1. 5506
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Osipov V., Zhukova N., Subbotin A., Glebovskiy P., Evnevich E. Intelligent escalator passenger safety management // Scientific Reports. 2022. Vol. 12. No. 1. 5506
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TY - JOUR
DO - 10.1038/s41598-022-09498-x
UR - https://doi.org/10.1038%2Fs41598-022-09498-x
TI - Intelligent escalator passenger safety management
T2 - Scientific Reports
AU - Osipov, Vasily
AU - Zhukova, Nataly
AU - Subbotin, Alexey
AU - Glebovskiy, Petr
AU - Evnevich, Elena
PY - 2022
DA - 2022/04/01 00:00:00
PB - Springer Nature
IS - 1
VL - 12
PMID - 35365721
SN - 2045-2322
ER -
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@article{2022_Osipov,
author = {Vasily Osipov and Nataly Zhukova and Alexey Subbotin and Petr Glebovskiy and Elena Evnevich},
title = {Intelligent escalator passenger safety management},
journal = {Scientific Reports},
year = {2022},
volume = {12},
publisher = {Springer Nature},
month = {apr},
url = {https://doi.org/10.1038%2Fs41598-022-09498-x},
number = {1},
doi = {10.1038/s41598-022-09498-x}
}