基于对智能交通卡数据的挖掘与分析能够为城市交通建设和城市管理提供有力支持,但现有研究数据大都仅包含公交或地铁这两方面数据,且主要关注群体性宏观出行规律.针对这一问题,以某城市交通卡数据为例,该数据包含着城市居民日常出行公交、地铁、出租车等多源数据,首先提出行程链的概念对居民出行行为建模,在此基础上给出不同维度的周期性出行特征;然后提出一种基于最长公共子序列的空间周期性特征提取方法,并对城市居民出行规律进行聚类分析;最后通过规则定义5个评价指标对该方法的有效性进行初步验证.结果表明引入该方法的聚类算法对聚类结果有6.8％的效果提升,有利于发现居民的行为模式.%The mining and analysis of smart traffic card data can provide strong support for urban traffic construction and urban management.However,most of the existing research data only include data about bus or subway,and mainly focus on macro-travel patterns.In view of this problem,taking a city traffic card data as the example,which contains the multi-source daily travel data of urban residents including bus,subway and taxi,the concept of tour chain was put forward to model the behavior of residents.On this basis,the periodic travel characteristics of different dimensions were given.Then a spatial periodic feature extraction method based on the longest common subsequence was proposed,and the travel rules of urban residents were analyzed by clustering analysis.Finally,the effectiveness of this method was verified by five evaluation indexesdefined by the rules,and the clustering result was improved by 6.8％ by applying the spatial periodic feature extraction method,which is helpful to discover the behavior pattern of residents.
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