A Similarity Measurement Method for Clustering Spatio-Temporal Trajectories of Moving Objects

A similarity measurement formula is proposed based on the density of data points inside the boxes in order to effectively cluster the spatio-temporal trajectories of moving objects which are compressed into minimum bounding boxes (MBBs).The similarity measurement of the raw trajectories is translated into the similarity measurement of the MBB sequences with time overlapping in two trajectories firstly,which reduces data storage volume to a great extent.Then some factors affecting the similarity of MBB sequences are analyzed,including the time duration,the space distance and the density of data points inside the boxes.Through analyzing the influence of the three factors on the trajectory similarity,a formula of the spatio-temporal trajectories compressed into MBB is compressed into MBB is proposed.Experiments show that the formula can improve the value of validity index Dunn when it is used to cluster the spatio-temporal trajectories of moving objects.