交通流状态辨别在智能交通系统中起着十分重要的作用。本文根据对交通流状态辨别研究的分析,提出基于Hough变换方法和模糊C均值聚类方法的交通状态辨别方法。其中,基于Hough变换的图像识别方法用于交通畅通流的辨别,模糊C均值聚类方法用于其它交通状态分类。而且利用快速路固定型交通检测器实时数据进行了实证分析,且与模糊C均值聚类方法进行了对比分析。分析结果表明本文方法与FCM方法相比,更符合于三相交通流理论,且满足城市快速路交通流的特征。该方法可用于交通流状态分析。 Traffic flow status recognition plays an important role in ITS. Based on the analysis of traffic flow state identification, this paper proposes a traffic state identification method based on Hough transform and fuzzy C-means clustering. Among them, the image recognition method based on Hough transform is used to discriminate traffic flow, and fuzzy C-means clustering method is used for other traffic state classification. Moreover, the real-time data of express traffic fixed traffic detector is used for empirical analysis and compared with fuzzy C-means clustering method. The analysis results show that the proposed method is more in line with the three-phase traffic flow theory than the FCM method, and meets the characteristics of urban expressway traffic flow. The method can be used for traffic flow state analysis.