A rapid density method for taxi passengers hot spot recognition and visualization based on DBSCAN+

Abstract With the development of city size and vehicle interconnection, visual analysis technology is playing a very important role in the course of city calculation and city perception. A Reasonable visual model can effectively present the feature of city. In order to solve the problem of tradition...

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Autores principales:	Zihe Huang, Shangbing Gao, Chuangxin Cai, Hao Zheng, Zhigeng Pan, Wenting Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/89008aae052842b6add275306100fcf6
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spelling	oai:doaj.org-article:89008aae052842b6add275306100fcf62021-12-02T16:51:50ZA rapid density method for taxi passengers hot spot recognition and visualization based on DBSCAN+10.1038/s41598-021-88822-32045-2322https://doaj.org/article/89008aae052842b6add275306100fcf62021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88822-3https://doaj.org/toc/2045-2322Abstract With the development of city size and vehicle interconnection, visual analysis technology is playing a very important role in the course of city calculation and city perception. A Reasonable visual model can effectively present the feature of city. In order to solve the problem of traditional density algorithm that cluster the large scale data slowly and cannot find cluster centers to adapt taxi track data. The DBSCAN+ (density-based spatial clustering of applications with noise plus) algorithm that can split data and extract maximum density clusters under the large scale data was proposed in the paper. The passenger points should be cleaned from the original point of the passenger trajectory data firstly, and then the massive passenger points are sliced and clustered cyclically. In the clustering process, the cluster centers can be extracted based on maximum density, and finally the clustering results are visualized according to the results. The experimental results show that compared with other popular methods, the proposed method has significant advantages in clustering speed, precision and visualization for large-scale city passenger hotspots. Moreover, it provides important decisions for further urban planning and promotes the traffic efficiency.Zihe HuangShangbing GaoChuangxin CaiHao ZhengZhigeng PanWenting LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zihe Huang Shangbing Gao Chuangxin Cai Hao Zheng Zhigeng Pan Wenting Li A rapid density method for taxi passengers hot spot recognition and visualization based on DBSCAN+
description	Abstract With the development of city size and vehicle interconnection, visual analysis technology is playing a very important role in the course of city calculation and city perception. A Reasonable visual model can effectively present the feature of city. In order to solve the problem of traditional density algorithm that cluster the large scale data slowly and cannot find cluster centers to adapt taxi track data. The DBSCAN+ (density-based spatial clustering of applications with noise plus) algorithm that can split data and extract maximum density clusters under the large scale data was proposed in the paper. The passenger points should be cleaned from the original point of the passenger trajectory data firstly, and then the massive passenger points are sliced and clustered cyclically. In the clustering process, the cluster centers can be extracted based on maximum density, and finally the clustering results are visualized according to the results. The experimental results show that compared with other popular methods, the proposed method has significant advantages in clustering speed, precision and visualization for large-scale city passenger hotspots. Moreover, it provides important decisions for further urban planning and promotes the traffic efficiency.
format	article
author	Zihe Huang Shangbing Gao Chuangxin Cai Hao Zheng Zhigeng Pan Wenting Li
author_facet	Zihe Huang Shangbing Gao Chuangxin Cai Hao Zheng Zhigeng Pan Wenting Li
author_sort	Zihe Huang
title	A rapid density method for taxi passengers hot spot recognition and visualization based on DBSCAN+
title_short	A rapid density method for taxi passengers hot spot recognition and visualization based on DBSCAN+
title_full	A rapid density method for taxi passengers hot spot recognition and visualization based on DBSCAN+
title_fullStr	A rapid density method for taxi passengers hot spot recognition and visualization based on DBSCAN+
title_full_unstemmed	A rapid density method for taxi passengers hot spot recognition and visualization based on DBSCAN+
title_sort	rapid density method for taxi passengers hot spot recognition and visualization based on dbscan+
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/89008aae052842b6add275306100fcf6
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A rapid density method for taxi passengers hot spot recognition and visualization based on DBSCAN+

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