Abstract: With the continuous development of smart city,vehicle track can be acquired automatically based on traffic bayonet,which lays a foundation for vehicle behavior analysis based on track.However,since the bayonet position is fixed,the vehicle tra-jectory is expressed as bayonet sequence.Therefore,the bayonet and trajectory are first mapped into words and sentences respectively,and the semantic similarity method is used to calculate the trajectory similarity.Then,based on the similarity of tracks,track entropy is proposed to measure the regularity of all tracks of a vehicle.Finally,the trajectory entropy is used to analyze the behavioral characteristics of vehicles.For example,vehicles with low trajectory entropy mean that the driving is particularly regular,which is likely to be commuter vehicles.To facilitate users in-depth analysis,this paper further provides a visual analysis system with more linkage view,which allows the user to compare the vehicle trajectory entropy,and combines clustering analysis and related interaction,to help users find meaningful vehicle behavior,such as commuting a commuter has a low trajectory entropy,following the model of taxi path entropy is very high.By analyzing the bayonet data set of Kunming city in February 2019,the vehicle travel behavior and its characteristics in different trajectory entropy intervals can be found effectively,which proves the effectiveness of the proposed method.

Key words: Clustering, Deriving track, Semantic similarity, Trajectory of entropy, Visual analysis