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Identifying Flow Clusters Based on Density Domain Decomposition

文献类型：期刊论文

作者Song, Ci; Pei, Tao1; Shu, Hua
刊名IEEE ACCESS
出版日期2020
卷号8页码:5236-5243
关键词Origin-destination (OD) flow flow space flow clustering density domain decomposition point process
ISSN号2169-3536
DOI10.1109/ACCESS.2019.2963107
通讯作者Pei, Tao(peit@lreis.ac.cn)
英文摘要Flow clustering is one of the most important data mining methods for the analysis of origin-destination (OD) flow data, and it may reveal the underlying mechanisms responsible for the spatial distributions and temporal dynamics of geographical phenomena. Existing flow clustering approaches are based mainly on the extension of traditional clustering methods to points by redefining basic concepts or some spatial association indictors of flows and the implementation of classic clustering processes, such as aggregating, collecting or searching. However, current techniques still suffer from two main problems: poor identification accuracy and complicated parameter selection processes. To resolve these problems, a new clustering method is proposed in this study for arbitrarily shaped flow clusters based on the density domain decomposition of flows. Simulation experiments based on our method and existing methods show that our method outperforms the three most commonly used methods in terms of the overall identification rate and almost all F1 measures, and it does not require any manual adjustments during the parameter selection process. Finally, a case study is conducted on taxi trip data from Beijing. Several flow clusters are identified to represent different types of residents & x2019; travel behaviors, including daily commuting, return travel, tourism and behaviors on special days.
WOS关键词MOBILITY ; NETWORK ; AUTOCORRELATION ; TRAJECTORIES ; PATTERNS ; FEATURES ; SPACE
资助项目National Natural Science Foundation of China[41525004] ; National Natural Science Foundation of China[41421001] ; National Natural Science Foundation of China[41601430] ; Key Research Program of Frontier Science, Chinese Academy of Sciences[QYZDY-SSW-DQC007]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000524677500005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Key Research Program of Frontier Science, Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/133947]  
专题中国科学院地理科学与资源研究所
通讯作者Pei, Tao
作者单位1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 101408, Peoples R China
推荐引用方式
GB/T 7714		 Song, Ci,Pei, Tao,Shu, Hua. Identifying Flow Clusters Based on Density Domain Decomposition[J]. IEEE ACCESS,2020,8:5236-5243.
APA Song, Ci,Pei, Tao,&Shu, Hua.(2020).Identifying Flow Clusters Based on Density Domain Decomposition.IEEE ACCESS,8,5236-5243.
MLA Song, Ci,et al."Identifying Flow Clusters Based on Density Domain Decomposition".IEEE ACCESS 8(2020):5236-5243.

入库方式： OAI收割

来源：地理科学与资源研究所

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