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.

[1]  R. L. Courtney.  A broad view of ITS standards in the US , 1997, Proceedings of Conference on Intelligent Transportation Systems.

[2]  Lily Elefteriadou,et al.  Expanded Transportation Performance Measures to Supplement Level of Service (LOS) for Growth Management and Transportation Impact Analysis , 2012 .

[3]  Zhang Feng.  The Traditional K-means Clustering Algorithm Research and Improvement , 2010 .

[4]  Kai Li,et al.  Fuzzy Clustering with Generalized Entropy Based on Neural Network , 2014 .

[5]  Arnold.  CONGESTION ON VIRGINIA'S URBAN HIGHWAYS , 1988 .

[6]  H. Wen,et al.  Study on Traffic Congestion Patterns of Large City in China Taking Beijing as an Example , 2014 .

[7]  Haiqiao Huang,et al.  A robust adaptive clustering analysis method for automatic identification of clusters , 2012, Pattern Recognit..

[9]  Panos M. Pardalos,et al.  On the minimization of traffic congestion in road networks with tolls , 2017, Ann. Oper. Res..

[10]  Hong Sun,et al.  Unsupervised Feature Learning Via Spectral Clustering of Multidimensional Patches for Remotely Sensed Scene Classification , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[11]  Yuhui Zheng,et al.  Image segmentation by generalized hierarchical fuzzy C-means algorithm , 2015, J. Intell. Fuzzy Syst..

[12]  Zhen Liu,et al.  Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy c-Mean Clustering and Advanced Neural Network , 2013, J. Appl. Math..