为了充分利用实际高速公路路段交通拥堵信息，更合理地聚类交通拥堵的内在规律和特征变化，提出自适应确定聚类中心C和类别K值（adaptive center and K-means value, ACK-Means）的聚类算法，进行高速公路拥堵路段聚类。ACK-Means算法借助簇类密度、簇类间距以及簇类强度，同时又考虑到数据样本的偶然性，对离群点进行合理分配，ACK-Means算法可实现自适应确定聚类中心C和类别K值。基于实际交通拥堵信息构建数据集，Python编程实现高速公路拥堵路段ACK-Means聚类，巧妙解决了高速公路拥堵路段聚类数目K和聚类中心C设定问题。聚类结果表明，ACK-Means算法实现高速公路拥堵路段无监督聚类，聚类结果完全基于实际的高速公路交通拥堵信息，具有更高的实用性。

In order to make full use of the actual expressway traffic congested information and to cluster the internal laws and characteristic changes of the traffic congestion more reasonably, an adaptive center and K-Means value (ACK-Means) clustering algorithm to cluster congested segments of expressways was proposed. ACK-Means algorithm assigns outliers reasonably by means of cluster density, cluster spacing and cluster strength, and considering the contingency of data samples. The ACK-Means algorithm determine the initial cluster center C and the number of clusters K-value adaptively. The data set was constructed based on the actual expressway congested information, and the ACK-Means algorithm was implemented by Python programming to cluster the expressway congested segments. The problem of clustering number K and initial clustering center C setting for the expressway congested segments was solved skillfully. Clustering results show that the ACK-Means algorithm realizes the unsupervised clustering of expressway congested segments, and the clustering results are based on the actual expressway congested information, which has higher practicability.