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Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach
Lu, Feng1,2,3; Liu, Kang1,4; Duan, Yingying1; Cheng, Shifen1,4; Du, Fei5
2018-07-01
Source PublicationPHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
ISSN0378-4371
Volume501Pages:227-237
Corresponding AuthorLu, Feng(luf@lreis.ac.cn) ; Duan, Yingying(duanyy@lreis.ac.cn)
AbstractA better characterization of the traffic influence among urban roads is crucial for traffic control and traffic forecasting. The existence of spatial heterogeneity imposes great influence on modeling the extent and degree of road traffic correlation, which is usually neglected by the traditional distance based method. In this paper, we propose a traffic-enhanced community detection approach to spatially reveal the traffic correlation in city A better characterization of the traffic influence among urban roads is crucial for traffic control and traffic forecasting. The existence of spatial heterogeneity imposes great influence on modeling the extent and degree of road traffic correlation, which is usually neglected by the traditional distance based method. In this paper, we propose a traffic-enhanced community detection approach to spatially reveal the traffic correlation in city road networks. First, the road network is modeled as a traffic-enhanced dual graph with the closeness between two road segments determined not only by their topological connection, but also by the traffic correlation between them. Then a flow-based community detection algorithm called Infomap is utilized to identify the road segment clusters. Evaluated by Moran's I, Calinski-Harabaz Index and the traffic interpolation application, we find that compared to the distance based method and the community based method, our proposed traffic-enhanced community based method behaves better in capturing the extent of traffic relevance as both the topological structure of the road network and the traffic correlations among urban roads are considered. It can be used in more traffic-related applications, such as traffic forecasting, traffic control and guidance. (C) 2018 Elsevier B.V. All rights reserved.
KeywordUrban road system Spatial heterogeneity Traffic correlation Traffic-enhanced dual graph Community detection
DOI10.1016/j.physa.2018.02.062
WOS KeywordCOMPLEX NETWORKS ; FLOW ; PREDICTION ; FRAMEWORK
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41631177] ; Key Project of the Chinese Academy of Sciences, China[ZDRW-ZS-2016-6-3] ; National Key Research and Development Program, China[2016YFB0502104]
Funding OrganizationNational Natural Science Foundation of China ; Key Project of the Chinese Academy of Sciences, China ; National Key Research and Development Program, China
WOS Research AreaPhysics
WOS SubjectPhysics, Multidisciplinary
WOS IDWOS:000430027500022
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/57373
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLu, Feng; Duan, Yingying
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Fujian, Peoples R China
3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA
Recommended Citation
GB/T 7714
Lu, Feng,Liu, Kang,Duan, Yingying,et al. Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach[J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS,2018,501:227-237.
APA Lu, Feng,Liu, Kang,Duan, Yingying,Cheng, Shifen,&Du, Fei.(2018).Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach.PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS,501,227-237.
MLA Lu, Feng,et al."Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach".PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 501(2018):227-237.
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