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ISPRS Int. J. Geo-Inf. 2016, 5(5), 71; doi:10.3390/ijgi5050071

A Method for Traffic Congestion Clustering Judgment Based on Grey Relational Analysis

1
,
1,2,†,* , 3,†
and
4,*
1
Department of Computer, Nanjing University of Post and Telecommunications, Nanjing 210003, China
2
Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China
3
Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education, Nanjing University of Post and Telecommunications, Nanjing 210003, China
4
Departamento de Ingeniería Informática, Universidad de Santiago de Chile, Av. Ecuador, Santiago 3659, Chile
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editors: Chi-Hua Chen, Kuen-Rong Lo and Wolfgang Kainz
Received: 17 January 2016 / Revised: 2 May 2016 / Accepted: 9 May 2016 / Published: 18 May 2016
(This article belongs to the Special Issue Applications of Internet of Things)
View Full-Text   |   Download PDF [3409 KB, uploaded 18 May 2016]   |  

Abstract

Traffic congestion clustering judgment is a fundamental problem in the study of traffic jam warning. However, it is not satisfactory to judge traffic congestion degrees using only vehicle speed. In this paper, we collect traffic flow information with three properties (traffic flow velocity, traffic flow density and traffic volume) of urban trunk roads, which is used to judge the traffic congestion degree. We first define a grey relational clustering model by leveraging grey relational analysis and rough set theory to mine relationships of multidimensional-attribute information. Then, we propose a grey relational membership degree rank clustering algorithm (GMRC) to discriminant clustering priority and further analyze the urban traffic congestion degree. Our experimental results show that the average accuracy of the GMRC algorithm is 24.9% greater than that of the K-means algorithm and 30.8% greater than that of the Fuzzy C-Means (FCM) algorithm. Furthermore, we find that our method can be more conducive to dynamic traffic warnings. View Full-Text
Keywords: urban traffic; grey relational membership degree; traffic congestion judgment urban traffic; grey relational membership degree; traffic congestion judgment
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Zhang, Y.; Ye, N.; Wang, R.; Malekian, R. A Method for Traffic Congestion Clustering Judgment Based on Grey Relational Analysis. ISPRS Int. J. Geo-Inf. 2016, 5, 71.

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