Camera-Driven Probabilistic Algorithm for Multi-Elevator Systems

Yerzhigit Bapin, Kanat Alimanov and Vasilios Zarikas
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Yerzhigit Bapin: School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan 010000, Kazakhstan
Kanat Alimanov: School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan 010000, Kazakhstan
Vasilios Zarikas: School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan 010000, Kazakhstan

Energies, 2020, vol. 13, issue 23, 1-16

Abstract: A 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.

Keywords: smart building; smart city; Bayesian networks; elevator control algorithm; intelligent elevator system; decision theory; decision support systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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