Short-term traffic flow prediction of expressway based on CEEMD-GRU combination model

In order to improve the accuracy of short-term traffic flow prediction,a short-term traffic flow prediction method of expressway based on the combined model of complementary ensemble empirical mode decomposition (CEEMD) and gated recurrent unit (GRU) was proposed.Firstly,the unstable original traffi...

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Autores principales:	Fuxin SHEN, Qichun BING, Weijian ZHANG, Yanran HU, Peng GAO
Formato:	article
Lenguaje:	ZH
Publicado:	Hebei University of Science and Technology 2021
Materias:	road transportation management; urban expressway; short-term traffic flow prediction; complementary ensemble empirical mode decomposition; gated recurrent unit neural network Technology T
Acceso en línea:	https://doaj.org/article/69825497e03840d8b34a3ee01da6a3a8
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spelling	oai:doaj.org-article:69825497e03840d8b34a3ee01da6a3a82021-11-23T07:09:07ZShort-term traffic flow prediction of expressway based on CEEMD-GRU combination model1008-154210.7535/hbkd.2021yx05003https://doaj.org/article/69825497e03840d8b34a3ee01da6a3a82021-10-01T00:00:00Zhttp://xuebao.hebust.edu.cn/hbkjdx/ch/reader/create_pdf.aspx?file_no=b202105003&flag=1&journal_https://doaj.org/toc/1008-1542In order to improve the accuracy of short-term traffic flow prediction,a short-term traffic flow prediction method of expressway based on the combined model of complementary ensemble empirical mode decomposition (CEEMD) and gated recurrent unit (GRU) was proposed.Firstly,the unstable original traffic flow time series data were decomposed into relatively stable multiple modal components by complementary ensemble empirical mode decomposition algorithm.Then,a GRU model was established for each decomposed modal component sequence for one-step prediction.Finally,the predicted value of each component was superimposed to obtain the final prediction result,and the measured traffic flow data of north-south elevated expressway in Shanghai was used to verify and analyze the model.The experimental results show that the prediction effect of CEEMD-GRU combination model is superior to GRU neural network model,EMD-GRU combination model and EEMD-GRU combination model,and the average prediction accuracy is improved by [BF]33.4%[BFQ],[BF]25.6%[BFQ] and [BF]18.3%[BFQ],respectively.CEEMD-GRU combination model can effectively extract the characteristic components of traffic flow data and improve the prediction accuracy,which provides scientific decision-making basis for traffic control management.[HQ]Fuxin SHENQichun BINGWeijian ZHANGYanran HUPeng GAOHebei University of Science and Technologyarticleroad transportation management; urban expressway; short-term traffic flow prediction; complementary ensemble empirical mode decomposition; gated recurrent unit neural networkTechnologyTZHJournal of Hebei University of Science and Technology, Vol 42, Iss 5, Pp 454-461 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	road transportation management; urban expressway; short-term traffic flow prediction; complementary ensemble empirical mode decomposition; gated recurrent unit neural network Technology T
spellingShingle	road transportation management; urban expressway; short-term traffic flow prediction; complementary ensemble empirical mode decomposition; gated recurrent unit neural network Technology T Fuxin SHEN Qichun BING Weijian ZHANG Yanran HU Peng GAO Short-term traffic flow prediction of expressway based on CEEMD-GRU combination model
description	In order to improve the accuracy of short-term traffic flow prediction,a short-term traffic flow prediction method of expressway based on the combined model of complementary ensemble empirical mode decomposition (CEEMD) and gated recurrent unit (GRU) was proposed.Firstly,the unstable original traffic flow time series data were decomposed into relatively stable multiple modal components by complementary ensemble empirical mode decomposition algorithm.Then,a GRU model was established for each decomposed modal component sequence for one-step prediction.Finally,the predicted value of each component was superimposed to obtain the final prediction result,and the measured traffic flow data of north-south elevated expressway in Shanghai was used to verify and analyze the model.The experimental results show that the prediction effect of CEEMD-GRU combination model is superior to GRU neural network model,EMD-GRU combination model and EEMD-GRU combination model,and the average prediction accuracy is improved by [BF]33.4%[BFQ],[BF]25.6%[BFQ] and [BF]18.3%[BFQ],respectively.CEEMD-GRU combination model can effectively extract the characteristic components of traffic flow data and improve the prediction accuracy,which provides scientific decision-making basis for traffic control management.[HQ]
format	article
author	Fuxin SHEN Qichun BING Weijian ZHANG Yanran HU Peng GAO
author_facet	Fuxin SHEN Qichun BING Weijian ZHANG Yanran HU Peng GAO
author_sort	Fuxin SHEN
title	Short-term traffic flow prediction of expressway based on CEEMD-GRU combination model
title_short	Short-term traffic flow prediction of expressway based on CEEMD-GRU combination model
title_full	Short-term traffic flow prediction of expressway based on CEEMD-GRU combination model
title_fullStr	Short-term traffic flow prediction of expressway based on CEEMD-GRU combination model
title_full_unstemmed	Short-term traffic flow prediction of expressway based on CEEMD-GRU combination model
title_sort	short-term traffic flow prediction of expressway based on ceemd-gru combination model
publisher	Hebei University of Science and Technology
publishDate	2021
url	https://doaj.org/article/69825497e03840d8b34a3ee01da6a3a8
work_keys_str_mv	AT fuxinshen shorttermtrafficflowpredictionofexpresswaybasedonceemdgrucombinationmodel AT qichunbing shorttermtrafficflowpredictionofexpresswaybasedonceemdgrucombinationmodel AT weijianzhang shorttermtrafficflowpredictionofexpresswaybasedonceemdgrucombinationmodel AT yanranhu shorttermtrafficflowpredictionofexpresswaybasedonceemdgrucombinationmodel AT penggao shorttermtrafficflowpredictionofexpresswaybasedonceemdgrucombinationmodel
_version_	1718416831502352384

Short-term traffic flow prediction of expressway based on CEEMD-GRU combination model

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