Both recognizing human behavior and understanding a user's mobility from sensor data are
critical issues in ubiquitous computing systems. As a kind of user behavior, the
transportation modes, such as walking, driving, etc., that a user takes, can enrich the user's
mobility with informative knowledge and provide pervasive computing systems with more
context information. In this paper, we propose an approach based on supervised learning to
infer people's motion modes from their GPS logs. The contribution of this work lies in the …