Improving Grey Prediction Model and Its Application in Predicting the Number of Users of a Public Road Transportation System

The recent increase in the road transportation necessitates scheduling to reduce the adverse impacts of the road transportation and evaluate the effectiveness of previous actions taken in this context. However, it is impossible to undertake the scheduling and evaluation tasks unless previous informa...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Balochian Saeed, Baloochian Hossein
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	grey model parametrization particle swarm optimization (pso) road transportation users Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/e317266724654870bb870aa638f60d6f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e317266724654870bb870aa638f60d6f
record_format	dspace
spelling	oai:doaj.org-article:e317266724654870bb870aa638f60d6f2021-12-05T14:10:51ZImproving Grey Prediction Model and Its Application in Predicting the Number of Users of a Public Road Transportation System2191-026X10.1515/jisys-2019-0082https://doaj.org/article/e317266724654870bb870aa638f60d6f2020-07-01T00:00:00Zhttps://doi.org/10.1515/jisys-2019-0082https://doaj.org/toc/2191-026XThe recent increase in the road transportation necessitates scheduling to reduce the adverse impacts of the road transportation and evaluate the effectiveness of previous actions taken in this context. However, it is impossible to undertake the scheduling and evaluation tasks unless previous information are available to predict the future. The grey model requires a limited volume of data for estimating the behavior of an unknown system. It provides high-accuracy predictions based on few data points. Various grey prediction models have been proposed so far, in which three different approaches are followed to increase the accuracy: (1) data preprocessing, (2) improved equation models, and (3) error improvement or error balancing. In this paper, firstly, a theorem is proposed and proved to recognize the parameters affecting two grey models, namely GM(1, 1) and FGM(1, 1). Then, the effective parameters are adjusted through particle swarm optimization (PSO) to formulate two adjusted models, namely IGM(1, 1) and IFGM(1, 1). According to the simulation results of the proposed models, accuracy of the modeling improved by a minimum of 14.24% and a maximum of 82.39%. Finally, the number of users of a public road transportation system was predicted using the proposed models. The results showed enhanced accuracy (by 7.7%) of the proposed models for predicting the number of users of the public road transportation system.Balochian SaeedBaloochian HosseinDe Gruyterarticlegrey modelparametrizationparticle swarm optimization (pso)road transportation usersScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 104-114 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	grey model parametrization particle swarm optimization (pso) road transportation users Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	grey model parametrization particle swarm optimization (pso) road transportation users Science Q Electronic computers. Computer science QA75.5-76.95 Balochian Saeed Baloochian Hossein Improving Grey Prediction Model and Its Application in Predicting the Number of Users of a Public Road Transportation System
description	The recent increase in the road transportation necessitates scheduling to reduce the adverse impacts of the road transportation and evaluate the effectiveness of previous actions taken in this context. However, it is impossible to undertake the scheduling and evaluation tasks unless previous information are available to predict the future. The grey model requires a limited volume of data for estimating the behavior of an unknown system. It provides high-accuracy predictions based on few data points. Various grey prediction models have been proposed so far, in which three different approaches are followed to increase the accuracy: (1) data preprocessing, (2) improved equation models, and (3) error improvement or error balancing. In this paper, firstly, a theorem is proposed and proved to recognize the parameters affecting two grey models, namely GM(1, 1) and FGM(1, 1). Then, the effective parameters are adjusted through particle swarm optimization (PSO) to formulate two adjusted models, namely IGM(1, 1) and IFGM(1, 1). According to the simulation results of the proposed models, accuracy of the modeling improved by a minimum of 14.24% and a maximum of 82.39%. Finally, the number of users of a public road transportation system was predicted using the proposed models. The results showed enhanced accuracy (by 7.7%) of the proposed models for predicting the number of users of the public road transportation system.
format	article
author	Balochian Saeed Baloochian Hossein
author_facet	Balochian Saeed Baloochian Hossein
author_sort	Balochian Saeed
title	Improving Grey Prediction Model and Its Application in Predicting the Number of Users of a Public Road Transportation System
title_short	Improving Grey Prediction Model and Its Application in Predicting the Number of Users of a Public Road Transportation System
title_full	Improving Grey Prediction Model and Its Application in Predicting the Number of Users of a Public Road Transportation System
title_fullStr	Improving Grey Prediction Model and Its Application in Predicting the Number of Users of a Public Road Transportation System
title_full_unstemmed	Improving Grey Prediction Model and Its Application in Predicting the Number of Users of a Public Road Transportation System
title_sort	improving grey prediction model and its application in predicting the number of users of a public road transportation system
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/e317266724654870bb870aa638f60d6f
work_keys_str_mv	AT balochiansaeed improvinggreypredictionmodelanditsapplicationinpredictingthenumberofusersofapublicroadtransportationsystem AT baloochianhossein improvinggreypredictionmodelanditsapplicationinpredictingthenumberofusersofapublicroadtransportationsystem
_version_	1718371694623588352

Improving Grey Prediction Model and Its Application in Predicting the Number of Users of a Public Road Transportation System

Ejemplares similares