Nowadays, tremendous data are continuously gathering from the smart card in public transport domain. Such data, conveying two viable distinct information, can assist designing public transportation network. The first component of the data, provides the spatial feature, that indicates the geographical coordinates of bus stops or subway stations. The second component of the data deals with the temporal feature, which is the time of the trips that public transport is used. Hence, it is necessary to distill the data, in order to get the advantages of the data analysis techniques and extract the essential knowledge. More specifically, user behavior in a public transport system can be investigated as one of the data mining and machine learning applications. Extracting this information could lead analysts, engineers, managers, and strategists to excavate, design, decide, and plan more effectively. This makes the usage of the network practically efficient especially in large metropolitan cities. In this regard, we propose new methods of temporal data analysis to investigate pattern of user behavior in the public transport network.