为了探究公共交通乘客的活动模式和规律性，文中利用北京市2020年10月份3周的公共交通智能卡数据，构建乘客多天的出行活动序列，利用PrefixSpan算法挖掘乘客的频繁活动模式序列，并基于最长公共子序列的方法定义活动模式相似性度量方法，分别计算了个体乘客日活动序列相似度和不同乘客之间的活动模式相似度，基于乘客间的相似度，利用层次聚类算法对乘客进行分类。结果表明：工作日或非工作日之内的相似度明显高于工作日与非工作日之间的相似度；在工作日中，周五与其他天的活动序列相似度较低；在不同周次的同一天，乘客的活动序列相似度更高。层次聚类结果显示，乘客有4类典型的活动模式导向，分别为娱乐购物导向、生活外出导向、工作通勤导向和个人事务外出导向，并且活动模式为工作通勤导向的乘客个体的日活动序列相似度高于其他活动模式的乘客。文中研究结果可有助于科学制定精准化的公共交通运营管理和服务政策。

In order to explore the activity pattern and regularity of public transport passengers, this study constructed multi-day passenger travel activity sequences using three weeks smart card data in Beijing in October 2020. The frequent activity pattern sequences of passengers were mined through the PrefixSpan algorithm, and the similarity measure method of activity patterns was defined based on the longest common subsequence. The day-to-day activity sequence similarity of individual and activity pattern similarities among different passengers were calculated respectively, and passengers were classified according to activity pattern similarities among passengers by using the hierarchical clustering algorithm. The results show that the similarity between workdays and weekends is significantly lower than that within workdays or weekends. In workdays, the activity sequence similarity between Friday and the other days is low. Meanwhile, the activity sequence similarity of the same days in different weeks is high. The result of hierarchical clustering shows that there are four typical activity patterns, including entertainment and shopping orientation, life orientation, work orientation and personal affair orientation. Moreover, the day-to-day activity sequence similarity of passenger with work orientation pattern is higher than that of passenger with other activity patterns. The research results in this paper are helpful to scientifically formulate accurate public transport operation management and service policies.