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.
Highlights
With the rapid development of urban traffic, urban vehicle surges and the pressure on traffic capacities are increasing sharply
The experimental results show that the proposed algorithm can effectively overcome the shortcomings of spectral clustering concerning the sensitivity of parameters
To obtain detailed knowledge for judging traffic congestion, comprehensive analysis of the three variables of traffic flows for determining the traffic flow state can be used to reflect the real conditions of the road for predicting traffic jams [14]
Summary
With the rapid development of urban traffic, urban vehicle surges and the pressure on traffic capacities are increasing sharply. In China, the conditions of roads and vehicles are quite inconvenient, and traffic congestion has caused substantial social and economic problems. In this case, traffic jams waste time, delay work, and reduce efficiency and cause a substantial waste of fuel, increase the probability of accidents and exacerbate the already serious difficulties facing traffic control and management. Since the 1980s, intelligent transportation systems (ITSs) consisting of integrated computer technology, automatic control technology, communication technology and information processing technology have achieved remarkable results worldwide. Many aspects of ITSs are based on traffic information. Traffic information processing has become an important aspect of ITSs [1].
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