Mode Classification of Urban Traffic Operation Status Based on Random Sampling GMM
摘要
The experiment subjects of existing studies on mode classification of urban road traffic operation status are not diverse,and the applicability of the standard method is poor.To address the problems,this paper proposes a mode classification method of traffic operation status based on Gaussian mixture graded random sampling clustering.The method uses relative speed as the clustering indicator.Meanwhile,it utilizes the graded random sampling method to conduct random sampling from the roads of six main road grades that make up the urban road network.Different sampling numbers are set to conduct multiple clustering experiments,and the clustering results are compared.The experimental results show that,when the number of sampled roads is more than 3 000,the NMI index generally remains at more than 0.95 and the clustering results are basically stable.The most reasonable number of traffic status mode is 5,under which there is no obvious coincidence of clustering centers and the DBI index is the smallest.Compared with the national standard,FCM and K-means clustering methods,the proposed method has better classification performance.It complies with the distribution characteristics of the clustering indicator and has stronger correlation with the clustering indicator.