合理的城市轨道交通车站分类对车站的规划设计及客流预测有重要作用。基于刷卡数据提取出行时间、频次、车票类型等反映车站客流特性的若干变量，运用主成分分析法(PCA)和高斯混合模型(GMM)进行车站聚类。该聚类方法不仅可以识别车站类别，同时可以根据后验概率确定混合类型的车站。以北京地铁为例，将全网233个车站分为4类，利用地理信息系统(GIS)工具可视化分类结果，并叠加地理信息描述各类车站的特征，直观地展示了部分混合性质的车站。与K均值聚类结果比较显示，GMM方法可以更好地解释多种特性混合的车站类型。

Reasonable classification of urban rail transit stations is of great significance to station planning, designing and ridership forecasting. This research focused on the characteristics of station ridership and proposed travel time, frequency, ticket type and other variables extracted from smart card data. Accordingly, Principal Component Analysis (PCA) and the Gaussian Mixture Model (GMM) were used in clustering and classifying stations. The metro stations are classified into arbitrary types and mixed types using the posterior probability generated by GMM, which revealed to what extent and by which arbitrary types a mixed station was mixed. Beijing was selected as a case and 4 clusters were determined for the 233 stations on Beijing Metro network. The classification results were visualized by GIS and all types of stations were characterized by superposing geographic information, meanwhile, parts of mixed stations were presented intuitively. At last, comparative analysis was conducted between GMM and K-means algorithm and the results showed that GMM can explain mixed stations with various characteristics preferably.

U231.4

中央高校基本科研业务费专项资金资助（2016YJS102）