摘要： 针对城市交通运行状态模式分类研究实验对象单一、标准方法适用性较差的问题，提出一种基于高斯混合分等级随机抽样聚类的交通运行状态模式分类方法。采用相对速度作为聚类指标，利用分等级随机抽样的方法，从组成城市路网6种主要道路等级的路段中分别进行随机采样，在此基础上比较不同采样数下的聚类结果。实验结果表明：当采样路段数在3 000条以上时，该方法NMI指标维持在0.95以上，聚类结果基本保持稳定；当交通运行状态模式数为5时最为合理，与其他模式数的结果相比，此时聚类中心没有明显重合，DBI指标最小；与国标、FCM和K-means聚类方法相比，该方法的整体分类效果更优，符合聚类指标的分布特点，与聚类指标的相关性更高。

关键词: 智能交通系统, 相对速度, 交通运行状态, 模式分类, 高斯混合模型

Abstract: 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.

Key words: Intelligent Transportation System(ITS), relative speed, traffic operation status, mode classification, Gaussian Mixture Model(GMM)

中图分类号: 

• U491