Computer Science ›› 2021, Vol. 48 ›› Issue (9): 86-94.doi: 10.11896/jsjkx.200900040

Special Issue: Intelligent Data Governance Technologies and Systems

• Intelligent Data Governance Technologies and Systems • Previous Articles     Next Articles

Historical Driving Track Set Based Visual Vehicle Behavior Analytic Method

LUO Yue-tong, WANG Tao, YANG Meng-nan, ZHANG Yan-kong   

  1. School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601,China
  • Received:2020-09-04 Revised:2020-11-27 Online:2021-09-15 Published:2021-09-10
  • About author:LUO Yue-tong,born in 1990,Ph.D,professor.His main research interests include digital image processing and scientific visualization.
    ZHANG Yan-kong,born in 1978,Ph.D.His main research interests include data mining and visual analytics.
  • Supported by:
    National Natural Science Foundation of China(61602146),National Basic Research Program of China(2017YFB1402200) and Key Science and Technology Program of Anhui Province,China(1604d0802009)

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

CLC Number: 

  • TP391.41
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