采用高斯混合模型GMM,同时以交通流量、平均速度和密度3种交通流宏观特征为指标,对交通流状态进行聚类和分类.和其他聚类分类方法比较,高斯混合模型是结构化的模型,适合于各种情形交通流参数.高斯混合模型中子类的个数通过Gap统计量结合交通流的领域知识加以确定,而模型的其他参数则由E-M算法进行估计.所建立的GMM模型可以作为实时交通流状态的分类器对新的观察值开展有效的分类识别和预报.同时,聚类分析和模式识别也可以用来对其他含有服务水平概念的设施进行聚类和分类分析,比如机场、停车场、交叉口等. Gaussian Mixture Model GMM is adopted to cluster and classify the traffic flow status based on macro-characteristics of traffic flow, average speed and density.Compared with other clustering methods, the Gaussian mixture model is a structured model , Which is suitable for all kinds of traffic flow parameters.The number of sub-classes in Gaussian mixture model is determined by Gap statistics combined with the field knowledge of traffic flow, while the other parameters of the model are estimated by EM algorithm.The established GMM model can As a real-time traffic flow status classifier to carry out effective classification of new observations and forecasting.At the same time, cluster analysis and pattern recognition can also be used to cluster and classify other facilities containing the concept of service level, such as the airport , Parking lot, intersection and so on.