Intelligent transportation has been emerged as one of the data mining and machine learning applications. The smart card data nowadays are continuously gathering in the public transport systems. Such data, usually convey two viable distinct information to investigate how users prefer to behave in a public transport system. The first component of the data, provides the spatial feature, which indicates the geographical coordinates of the bus stops or subway stations. The second component of the data, deals with the temporal feature that is the time of the trips that the public transport has been 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 out of the data. Due to massive data storage and diversity of the data analysis methods, various challenges are arisen in the process of exploring and exploiting the hidden patterns of the data. We review a couple of scenarios and suggest a solution to overcome the raised challenges.