A clear knowledge about human travel patterns is significantly important in the domains of science as well as commerce. In this paper an approach is proposed to identify the travel patterns of individuals. More specifically, the travel patterns of individuals including students and professionals are investigated by analyzing their smartphone sensing data (e.g. GPS trajectories). In this work, the travel patterns are explored by performing staypoint detection and frequent sequence mining. The proposed approach is also evaluated with a real-world smartphone sensing dataset collected by volunteers of diverse demographic characteristics (e.g. occupation, age, gender, place of residence, etc).