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根据交通流特性的相似性进行交通路段划分对城市交通管理和控制具有重要作用。交通流数据具有时间序列特征，相似性度量问题是时间序列聚类中的最基本的问题之一。本文为交通流数据聚类给出了一种基于灰色关联的相似性度量方法，通过比较试验确定了它具有较高的聚类精度。在每个时段时间序列间的相似性差异、在某一个时段的异常数据等会影响到在整个时间区间的交通流数据聚类,为此本文提出了一种基于时段划分的交通流数据聚类方法。这个方法首先对每个时段数据进行聚类，然后采用最大频繁项集方法得到最终聚类结果（即交通路段划分），实例证明了方法的有效性。

Division of Urban Traffic Road Section Based on Clustering Analysis

The division of traffic road section by the traffic flow similarity plays an important role in urban traffic control and management. Traffic flow has a characteristic of time series. Similarity measurement is one of the important problems in time series clustering problems. A similarity measurement method based on grey correlation is proposed for traffic flow dada clustering. The results of comparative experiments show that the proposed method has a high accuracy of clustering. As the differences of traffic flow time series in each time period can influence the accuracy of traffic flow data clustering in the whole time period, a clustering method of traffic flow data based on the division of time period is proposed in this paper. The clustering of traffic flow data for each time period is first studied, and a traffic road section is divided into several groups by maximum frequent itemsets method. The validity of this method is tested by the actual traffic flow data.