Human mobility discovering and movement intention detection with GPS trajectories
In this paper, we aim to mine the interesting locations and the frequent travel sequences in a given geo-spatial region, by taking into account the users' historic travel experiences as well as the correlation between locations. First, a new partition method is proposed to divide the trajectories into a set of line segments (which contains the stationary moving sequence), the start and end points of which are collected as characteristic points. Then some common clustering methods are introduced to cluster the geographical-similar endpoints into groups for fixed territories detecting reason. Finally an abstract path network is generated which shows the link relations between the mined fixed territories. The proposed method can be used to detect a user's frequent movement paths as well as fixed territories for a better personalized geographical recommendation.
movement intention clustering path network stationary sub-trajectory