LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing

Lane-level road cluster is a most representative phenomenon in road networks and is vital to spatial data mining, cartographic generalization, and data integration. In this article, a lane-level road cluster recognition method was proposed. First, the conception of lane-level road cluster and our mo...

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Autores principales:	Gong Xianyong, Wu Fang, Xing Ruixing, Du Jiawei, Liu Chengyi
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	cartographic generalization spatial data mining spatial cluster lane-level road cluster distance measurement Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/cba31085fd07428a99ef08ecdb66ad99
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id	oai:doaj.org-article:cba31085fd07428a99ef08ecdb66ad99
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spelling	oai:doaj.org-article:cba31085fd07428a99ef08ecdb66ad992021-12-05T14:10:49ZLCBRG: A lane-level road cluster mining algorithm with bidirectional region growing2391-544710.1515/geo-2020-0271https://doaj.org/article/cba31085fd07428a99ef08ecdb66ad992021-07-01T00:00:00Zhttps://doi.org/10.1515/geo-2020-0271https://doaj.org/toc/2391-5447Lane-level road cluster is a most representative phenomenon in road networks and is vital to spatial data mining, cartographic generalization, and data integration. In this article, a lane-level road cluster recognition method was proposed. First, the conception of lane-level road cluster and our motivation were addressed and the spatial characteristics were given. Second, a region growing cluster algorithm was defined to recognize lane-level road clusters, where constraints including distance and orientation were used. A novel moving distance (MD) metric was proposed to measure the distance of two lines, which can effectively handle the non-uniformly distributed vertexes, heterogeneous length, inharmonious spatial alignment, and complex shape. Experiments demonstrated that the proposed method can effectively recognize lane-level road clusters with the agreement to human spatial cognition.Gong XianyongWu FangXing RuixingDu JiaweiLiu ChengyiDe Gruyterarticlecartographic generalizationspatial data miningspatial clusterlane-level road clusterdistance measurementGeologyQE1-996.5ENOpen Geosciences, Vol 13, Iss 1, Pp 835-850 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cartographic generalization spatial data mining spatial cluster lane-level road cluster distance measurement Geology QE1-996.5
spellingShingle	cartographic generalization spatial data mining spatial cluster lane-level road cluster distance measurement Geology QE1-996.5 Gong Xianyong Wu Fang Xing Ruixing Du Jiawei Liu Chengyi LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing
description	Lane-level road cluster is a most representative phenomenon in road networks and is vital to spatial data mining, cartographic generalization, and data integration. In this article, a lane-level road cluster recognition method was proposed. First, the conception of lane-level road cluster and our motivation were addressed and the spatial characteristics were given. Second, a region growing cluster algorithm was defined to recognize lane-level road clusters, where constraints including distance and orientation were used. A novel moving distance (MD) metric was proposed to measure the distance of two lines, which can effectively handle the non-uniformly distributed vertexes, heterogeneous length, inharmonious spatial alignment, and complex shape. Experiments demonstrated that the proposed method can effectively recognize lane-level road clusters with the agreement to human spatial cognition.
format	article
author	Gong Xianyong Wu Fang Xing Ruixing Du Jiawei Liu Chengyi
author_facet	Gong Xianyong Wu Fang Xing Ruixing Du Jiawei Liu Chengyi
author_sort	Gong Xianyong
title	LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing
title_short	LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing
title_full	LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing
title_fullStr	LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing
title_full_unstemmed	LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing
title_sort	lcbrg: a lane-level road cluster mining algorithm with bidirectional region growing
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/cba31085fd07428a99ef08ecdb66ad99
work_keys_str_mv	AT gongxianyong lcbrgalanelevelroadclusterminingalgorithmwithbidirectionalregiongrowing AT wufang lcbrgalanelevelroadclusterminingalgorithmwithbidirectionalregiongrowing AT xingruixing lcbrgalanelevelroadclusterminingalgorithmwithbidirectionalregiongrowing AT dujiawei lcbrgalanelevelroadclusterminingalgorithmwithbidirectionalregiongrowing AT liuchengyi lcbrgalanelevelroadclusterminingalgorithmwithbidirectionalregiongrowing
_version_	1718371689932259328

LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing

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