Elevator traffic flow prediction using artificial intelligence

Elevator traffic flow prediction is essential part of the modern elevator group control system to enable controller apply the best dispatching strategy based on predicted traffic flow data to achieve optimum operation with the aim to reduce average waiting time of passenger for arrival of elevator t...

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Main Author:	Lee, Choo Yong
Format:	Thesis
Language:	English
Published:	2008
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/11481/1/LeeChooYongMFKE2008.pdf
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id	my-utm-ep.11481
record_format	uketd_dc
spelling	my-utm-ep.114812018-10-14T07:23:39Z Elevator traffic flow prediction using artificial intelligence 2008-04 Lee, Choo Yong TK Electrical engineering. Electronics Nuclear engineering Elevator traffic flow prediction is essential part of the modern elevator group control system to enable controller apply the best dispatching strategy based on predicted traffic flow data to achieve optimum operation with the aim to reduce average waiting time of passenger for arrival of elevator to serve them. Generally, elevator traffic flow has high complexity and passenger flow possesses nonlinear feature which is difficult to be expressed by a certain functional style. In this thesis, artificial intelligent technique radial basis function neural network (RBF NN) is used to develop elevator traffic flow prediction model. RBF NN is selected because it is suitable to model nonlinear system and can be trained using fast 2 stages training algorithm assures fast convergence. The past interval traffic flow data and traffic flow data at same time on previous days are used to train RBF NN so that it could predict traffic flow ahead. Neural network toolbox that incorporates newrbe and newrb functions in matlab software is employed to develop algorithm and program of RBF NN. Optimum spread constant that will yield minimum mean square error is obtained and become input to the RBF NN. Ten cases with different k and p are studied to evaluate performance of RBF NN. Given training data collected from field, RBF NN is able to predict elevator up peak traffic flow occur at 8:15 a.m. (in 5 minutes interval) which is short term traffic fairly accurate. Mean square errors from simulation results are small and some of them could be neglected. The maximum mean square error is 2.82 for case that use past 3 interval data on 4th day and past 3 days (1st,2nd and 3rd day) data to predict traffic flow on 5th day executed by using newrb function. It is concluded that RBF NN is an effective artificial intelligent technique to build elevator traffic flow prediction model. 2008-04 Thesis http://eprints.utm.my/id/eprint/11481/ http://eprints.utm.my/id/eprint/11481/1/LeeChooYongMFKE2008.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Lee, Choo Yong Elevator traffic flow prediction using artificial intelligence
description	Elevator traffic flow prediction is essential part of the modern elevator group control system to enable controller apply the best dispatching strategy based on predicted traffic flow data to achieve optimum operation with the aim to reduce average waiting time of passenger for arrival of elevator to serve them. Generally, elevator traffic flow has high complexity and passenger flow possesses nonlinear feature which is difficult to be expressed by a certain functional style. In this thesis, artificial intelligent technique radial basis function neural network (RBF NN) is used to develop elevator traffic flow prediction model. RBF NN is selected because it is suitable to model nonlinear system and can be trained using fast 2 stages training algorithm assures fast convergence. The past interval traffic flow data and traffic flow data at same time on previous days are used to train RBF NN so that it could predict traffic flow ahead. Neural network toolbox that incorporates newrbe and newrb functions in matlab software is employed to develop algorithm and program of RBF NN. Optimum spread constant that will yield minimum mean square error is obtained and become input to the RBF NN. Ten cases with different k and p are studied to evaluate performance of RBF NN. Given training data collected from field, RBF NN is able to predict elevator up peak traffic flow occur at 8:15 a.m. (in 5 minutes interval) which is short term traffic fairly accurate. Mean square errors from simulation results are small and some of them could be neglected. The maximum mean square error is 2.82 for case that use past 3 interval data on 4th day and past 3 days (1st,2nd and 3rd day) data to predict traffic flow on 5th day executed by using newrb function. It is concluded that RBF NN is an effective artificial intelligent technique to build elevator traffic flow prediction model.
format	Thesis
qualification_level	Master's degree
author	Lee, Choo Yong
author_facet	Lee, Choo Yong
author_sort	Lee, Choo Yong
title	Elevator traffic flow prediction using artificial intelligence
title_short	Elevator traffic flow prediction using artificial intelligence
title_full	Elevator traffic flow prediction using artificial intelligence
title_fullStr	Elevator traffic flow prediction using artificial intelligence
title_full_unstemmed	Elevator traffic flow prediction using artificial intelligence
title_sort	elevator traffic flow prediction using artificial intelligence
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2008
url	http://eprints.utm.my/id/eprint/11481/1/LeeChooYongMFKE2008.pdf
_version_	1747814860872220672

Elevator traffic flow prediction using artificial intelligence

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