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


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

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oai:doaj.org/article:86f40e3ebfe8483bbed914db9fea7ce5Last time updated on 10/13/2017

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