For current trajectory clustering algorithms

most of them group full trajectories as basic units

and lead the low efficient results.Aiming at this problem

a trajectory clustering algorithm based on structural similarity was proposed.By introducing a new concept of trajectory structure and presenting structural similarity function

the internal and external features of trajectories were analyzed.The algorithm first partitioned trajectories into trajectory segments according to corner;then computed the matching degree between every trajectory segment pairs by comparing their structure features;finally grouped trajectories into clusters.Experiment results on real data set demonstrate not only the efficiency and effectiveness of the algorithm

but also the flexibility that feature sensitivity can be adjusted by different parameters.