Clustering spatio-temporal trajectories based on compression of interesting places
Weixiang Xu · Journal of Beijing Jiaotong University · 2011
Discovering similar trajectories according to proximity in time and space can greatly affect many fields such as animal migration,weather forecasting,and the personal and vehicular mobile patterns in urban transportation.This paper researches clustering trajectories left behind moving objects according to the spatio-temporal similarity of local interesting places on the trajectories.Firstly,these interesting places on the trajectories are extracted and turned to Minimum Bounding Boxes(MBB),thus the original trajectories can be expressed by the smaller and less complex primitives(MBB) that are batter suited to storage and computation;and then a similarity measure formula is proposed with a combination of temporal and spatial properties of the compressed trajectory.Finally a hierarchical clustering experiment is performed in order to test the performance of the new similarity measure.The experimental results show that the proposed method not only can effectively cluster moving object trajectories,but also enables the clustering trajectory incrementally.