An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification

Reliable and accurate real-time traffic flow state identification is crucial for an intelligent transportation system (ITS). This identification is a prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved traffic flow state i...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhanzhong Wang, Ruijuan Chu, Minghang Zhang, Xiaochao Wang, Siliang Luan
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Traffic flow state identification fuzzy C-means clustering random subspace algorithm selective ensemble learning machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/c0b35f36b65f42f68d982cfc4b93a26e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c0b35f36b65f42f68d982cfc4b93a26e
record_format	dspace
spelling	oai:doaj.org-article:c0b35f36b65f42f68d982cfc4b93a26e2021-11-19T00:05:34ZAn Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification2169-353610.1109/ACCESS.2020.3038801https://doaj.org/article/c0b35f36b65f42f68d982cfc4b93a26e2020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9261423/https://doaj.org/toc/2169-3536Reliable and accurate real-time traffic flow state identification is crucial for an intelligent transportation system (ITS). This identification is a prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved traffic flow state identification model that is based on selective ensemble learning (SEL). First, we adopted the fuzzy C-means (FCM) clustering method to divide the traffic flow data into three main kinds of traffic flow states and obtained the parameters that correspond to each kind of traffic flow state. Second, we applied the random subspace (RS) algorithm as the ensemble method and support vector machine (SVM) model as base learners to construct the RS-SVM ensemble model for traffic flow identification. Significantly, the discrete binary particle swarm optimization (BPSO) algorithm with global optimization search ability was employed to select the classifiers obtained by the random subspace training in the ensemble system. We experimentally validated the effectiveness of the proposed BPSO–RS-SVM-SEL approach. The research results reveal that compared with other classical traffic flow state identification methods, the proposed model has a higher maximum accuracy of 98.68%. It can be seen that our model improves the classification accuracy of traffic flow state identification and the difference in the ensemble system to a certain extent.Zhanzhong WangRuijuan ChuMinghang ZhangXiaochao WangSiliang LuanIEEEarticleTraffic flow state identificationfuzzy C-means clusteringrandom subspace algorithmselective ensemble learningmachine learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 212623-212634 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Traffic flow state identification fuzzy C-means clustering random subspace algorithm selective ensemble learning machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Traffic flow state identification fuzzy C-means clustering random subspace algorithm selective ensemble learning machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Zhanzhong Wang Ruijuan Chu Minghang Zhang Xiaochao Wang Siliang Luan An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification
description	Reliable and accurate real-time traffic flow state identification is crucial for an intelligent transportation system (ITS). This identification is a prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved traffic flow state identification model that is based on selective ensemble learning (SEL). First, we adopted the fuzzy C-means (FCM) clustering method to divide the traffic flow data into three main kinds of traffic flow states and obtained the parameters that correspond to each kind of traffic flow state. Second, we applied the random subspace (RS) algorithm as the ensemble method and support vector machine (SVM) model as base learners to construct the RS-SVM ensemble model for traffic flow identification. Significantly, the discrete binary particle swarm optimization (BPSO) algorithm with global optimization search ability was employed to select the classifiers obtained by the random subspace training in the ensemble system. We experimentally validated the effectiveness of the proposed BPSO–RS-SVM-SEL approach. The research results reveal that compared with other classical traffic flow state identification methods, the proposed model has a higher maximum accuracy of 98.68%. It can be seen that our model improves the classification accuracy of traffic flow state identification and the difference in the ensemble system to a certain extent.
format	article
author	Zhanzhong Wang Ruijuan Chu Minghang Zhang Xiaochao Wang Siliang Luan
author_facet	Zhanzhong Wang Ruijuan Chu Minghang Zhang Xiaochao Wang Siliang Luan
author_sort	Zhanzhong Wang
title	An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification
title_short	An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification
title_full	An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification
title_fullStr	An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification
title_full_unstemmed	An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification
title_sort	improved selective ensemble learning method for highway traffic flow state identification
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/c0b35f36b65f42f68d982cfc4b93a26e
work_keys_str_mv	AT zhanzhongwang animprovedselectiveensemblelearningmethodforhighwaytrafficflowstateidentification AT ruijuanchu animprovedselectiveensemblelearningmethodforhighwaytrafficflowstateidentification AT minghangzhang animprovedselectiveensemblelearningmethodforhighwaytrafficflowstateidentification AT xiaochaowang animprovedselectiveensemblelearningmethodforhighwaytrafficflowstateidentification AT siliangluan animprovedselectiveensemblelearningmethodforhighwaytrafficflowstateidentification AT zhanzhongwang improvedselectiveensemblelearningmethodforhighwaytrafficflowstateidentification AT ruijuanchu improvedselectiveensemblelearningmethodforhighwaytrafficflowstateidentification AT minghangzhang improvedselectiveensemblelearningmethodforhighwaytrafficflowstateidentification AT xiaochaowang improvedselectiveensemblelearningmethodforhighwaytrafficflowstateidentification AT siliangluan improvedselectiveensemblelearningmethodforhighwaytrafficflowstateidentification
_version_	1718420691318996992

An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification

Ejemplares similares