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.
Highlights
As of 2018, approximately 4.2 billion people, or 55% of the world’s population, live in urban areas [1]
First, the conventional nearest car (NC) algorithm; second, the modified NC algorithm assuming an image recognition software that provides a deterministic number of people; and, a third probabilistic NC algorithm with Bayesian network (BN) generating an effective number of waiting “passengers/units” are compared
The time, measured in seconds, by a passenger maximum expected utility thattime counts theisprobability value of each state andspent in addition the utility travelling in an elevator car, starting from the moment of boarding the lift until the moment of stepping priority weight that is set by the designer of the BN
Summary
As of 2018, approximately 4.2 billion people, or 55% of the world’s population, live in urban areas [1]. In the study of [11], the authors propose an elevator control system that utilizes the information from hallway surveillance cameras and adjusts the dispatching function using a generic algorithm. The primary goal of this algorithm is to minimize consumption of electricity by the elevator system and reduce the passenger wait time Another elevator control algorithm utilizing the information obtained by the hallway cameras is proposed in [13]. The states of the passengers, in these studies, are represented as deterministic values, yet it is obvious that the predictions made by the proposed systems cannot be 100% accurate This problem can be tackled by representation of the passenger traffic using Bayesian networks where the number of passengers waiting for an elevator as well as their movement directions are represented as probabilistic variables. The rest of this paper is organized as follows: Section 2 describes the proposed methodology and provides some comparison between the existing and proposed EGC methods, Section 3 provides a discussion on the results of the case study, and Section 4 presents the conclusion
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