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Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach

文献类型：期刊论文

作者Lu, Feng1,2,3; Liu, Kang2,4; Duan, Yingying2; Cheng, Shifen2,4; Du, Fei5
刊名PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
出版日期2018-07-01
卷号501页码:227-237
关键词Urban road system Spatial heterogeneity Traffic correlation Traffic-enhanced dual graph Community detection
ISSN号0378-4371
DOI10.1016/j.physa.2018.02.062
通讯作者Lu, Feng(luf@lreis.ac.cn) ; Duan, Yingying(duanyy@lreis.ac.cn)
英文摘要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 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.
WOS关键词COMPLEX NETWORKS ; FLOW ; PREDICTION ; FRAMEWORK
资助项目National 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]
WOS研究方向Physics
语种英语
WOS记录号WOS:000430027500022
出版者ELSEVIER SCIENCE BV
资助机构National Natural Science Foundation of China ; Key Project of the Chinese Academy of Sciences, China ; National Key Research and Development Program, China
源URL[http://ir.igsnrr.ac.cn/handle/311030/57372]  
专题中国科学院地理科学与资源研究所
通讯作者Lu, Feng; Duan, Yingying
作者单位1.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Fujian, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA
推荐引用方式
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.

入库方式： OAI收割

来源：地理科学与资源研究所

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