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dc.creatorBapin Y., Alimanov K., Zarikas V.en
dc.date.accessioned2023-01-31T07:35:55Z
dc.date.available2023-01-31T07:35:55Z
dc.date.issued2020
dc.identifier10.3390/en13236161
dc.identifier.issn19961073
dc.identifier.urihttp://hdl.handle.net/11615/71117
dc.description.abstractA fast and reliable vertical transportation system is an important component of modern office buildings. Optimization of elevator control strategies can be easily done using the state-of-the-art artificial intelligence (AI) algorithms. This study presents a novel method for optimal dispatching of conventional passenger elevators using the information obtained by surveillance cameras. It is assumed that a real-time video is processed by an image processing system that determines the number of passengers and items waiting for an elevator car in hallways and riding the lifts. It is supposed that these numbers are also associated with a given uncertainly probability. The efficiency of our novel elevator control algorithm is achieved not only by the probabilistic utilization of the number of people and/or items waiting but also from the demand to exhaustively serve a crowded floor, directing to it as many elevators as there are available and filling them up to the maximum allowed weight. The proposed algorithm takes into account the uncertainty that can take place due to inaccuracy of the image processing system, introducing the concept of effective number of people and items using Bayesian networks. The aim is to reduce the waiting time. According to the simulation results, the implementation of the proposed algorithm resulted in reduction of the passenger journey time. The proposed approach was tested on a 10-storey office building with five elevator cars and traffic size and intensity varying from 10 to 300 and 0.01 to 3, respectively. The results showed that, for the interfloor traffic conditions, the average travel time for scenarios with varying traffic size and intensity improved by 39.94% and 19.53%, respectively. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en
dc.language.isoenen
dc.sourceEnergiesen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85101225797&doi=10.3390%2fen13236161&partnerID=40&md5=2320f7ee04c72dcc2c12f0e3458ab813
dc.subjectArtificial intelligenceen
dc.subjectBayesian networksen
dc.subjectBehavioral researchen
dc.subjectCamerasen
dc.subjectImage processingen
dc.subjectOffice buildingsen
dc.subjectSecurity systemsen
dc.subjectTraffic controlen
dc.subjectTravel timeen
dc.subjectAverage travel timeen
dc.subjectImage processing systemen
dc.subjectOptimal dispatchingen
dc.subjectPassenger elevatorsen
dc.subjectPassenger journey timeen
dc.subjectProbabilistic algorithmen
dc.subjectSurveillance camerasen
dc.subjectVertical transportation systemsen
dc.subjectElevatorsen
dc.subjectMDPI AGen
dc.titleCamera-driven probabilistic algorithm for multi-elevator systemsen
dc.typejournalArticleen


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