Abstract
Moving object data, in particular of mobile users, is becoming widely available. A GPS trajectory of a moving object is a time-stamped sequence of latitude and longitude coordinates. The analysis and extraction of knowledge from GPS trajectories is important for a range of applications. Existing studies have extracted knowledge from trajectory patterns for both single and multiple GPS trajectories. However, few works have taken into account the unreliability of GPS measurements for mobile devices or focused on the extraction of fine-grained events from a user's GPS trajectory, such as waiting in traffic, at an intersection, or at a bus stop. In this paper, we develop and experimentally evaluate a novel algorithm that analyses a mobile user's bearing change distribution, together with speed and acceleration, to extract significant places of events from their GPS trajectory.
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