As the development of ICT has made it easier to collect various traffic information, research on creating new traffic attributes is drawing attention. Estimation and forecasts of demand and traffic volume are one of the essential indicators of traffic operation, assuming that the traffic pattern at a particular node or link is repeated. Traditionally, a survey method has been used to demonstrate this similarity of trip behavior. Recently, as traffic data have become easier to gather through automatic record collection systems such as DSRC techniques and smart cards, various studies are being performed to examine the regularity of trips. In this study, sparse trajectory data collected in Daegu metropolitan city, South Korea were analyzed to confirm that a traveler forms both spatially and temporally similar trip chain over several days. For this purpose, using dynamic time warping and an inter-spike interval algorithm, we newly define the concept of trip regularity and assess the difference between daily trip chains. Based on the degree of individual regularity, this paper defines a tendency between spatial and temporal regularity. In addition, using estimated individual activity attributes, factors that can affect the degree of trip similarity will be produced.