Structured Processing And Behavioral Semantic Perception Of Spatiotemporal Trajectory Data

In the age of big data and artificial intelligence,with the widespread use of wireless sensor positioning equipment,moving objects trajectory data shows an explosive growth.Trajectory data includes vehicle trajectory,human activity trajectory,animal migration trajectory,natural phenomenon trajectory and so on,which has many advantages,such as big data,high value,and crowdsourcing and so on.Due to its huge research and application value,many research achievements have sprung up,and it is widely used in intelligent transportation,urban planning,service recommendation,behavior analysis,environmental monitoring,public safety,urban computing,social sensing and other fields.However,the 5V characteristics of big data bring new challenges to trajectory data mining and knowledge discovery,which makes it face the predicament of “rich data and poor knowledge” or “knowledge is common sense”.Given this problem,establishing a fast and efficient structured processing model and method to extract high-value,structured knowledge from trajectory data has become an urgent problem to be solved.Taking trajectory data as research object and taking road information extraction and update and activity behavior modeling and place semantic perception as research issues,this paper studies the structured processing model and method of trajectory big data.The main research work are listed as follows:1.Aiming at the problem of fast extraction of structured road information,a new method of road boundary and road line extraction based on low frequency vehicle trajectory data is proposed.Firstly,an adaptive interpolation method is presented to solve the problems of high noise and sparse sampling.Then,the constrained Delaunay triangulation and Voronoi model are introduced to model vehicle trajectory data at the level of geometric details,and the road boundary detection model is constructed by integrating the trajectory movement features and boundary identification indicators.Finally,the model and algorithm of road information extraction based on Delaunay triangulation are presented.Experimental results showed that the proposed method is suitable for GPS traces with complex road structure,disparity density and different time interval,and the structured trajectory data modeling based on the data processing flow of “trajectory-road polygon-road line-road map” is realized.2.Aiming at the problem of fast updating of road data,a method of road change information detection and updating based on crowdsourcing vehicle trajectory data is proposed.Firstly,the relationship between road change and trajectory data is deeply analyzed to provide theoretical support for this method.Then,a model of road change information detection and classification is constructed by integrating movement features and semantic information.Finally,after fine processing of the track geometric features,a new method that taking road segment buffer as basic update unit is presented to detect road change information,identify the type of road change and update the road data.Compared with the existing method,our method can identify local changes,direction changes and semantic changes of road network in finer granularity,and road change detection and update based on the data processing flow of “local analysis-type inference-change extraction-incremental update” is realized.3.Aiming at the problem of fine road map construction,a method of fine road information acquisition and map reconstruction based on crowdsourcing trajectory data is proposed.Firstly,the features of activity trajectory data and the process of fine road map reconstruction are analyzed deeply to provide theoretical support for this method.Secondly,according to the relationship of “trajectory data-turning point-cluster center-road node”,the turning point detection algorithm is proposed to extract turning point set,and the ASCDT algorithm is used to cluster turning points to model road node.Finally,according to the processing flow of “trajectory segmentation-incremental fusion-network construction-optimization processing”,a series of methods,such as trajectory segmentation,incremental fusion,optimization processing,are proposed to build fine road map.The presented method can quickly acquire finer road information,complete the fine modeling of road network geometry,topology,and semantic and construct multi-thematic map,and the fine road map reconstruction based on the data processing flow of "trajectory-road node-road edge-road map" is realized.4.Aiming at the problem of activity behavior recognition and place semantic perception,a method of activity behavior modeling and semantic feature information extraction based on spatiotemporal trajectory data is proposed.Based on the geometric and semantic features of track,the method uses trajectory movement parameters to model activity behavior and extract the semantic features of typical behavior at the individual level.Then,Delaunay triangulation model and semantic enhancement method are used to analyze the coupling features between behavior events and places and to extract the location semantic information of places at the aggregation level.Taking refueling stop behavior and jogging circling behavior as cases,the framework of trajectory semantic perception from “behavior pattern mining” to “place semantic perception” is established.The framework extracts structured geometry and semantic features to model activity behavior,identify behavior patterns,mine behavior semantics and complete deep understanding of place semantics through the strategy of structured divide and conquer,and the mining and analysis of activity place based on the data processing process of “spatiotemporal trajectory data-behavior modeling-activity detection-place perception” is realized.5.Lastly,this paper develops a trajectory data mining prototype system.And a series of mining,processing and analysis of spatio-temporal trajectory data are realized,and the validity and feasibility of the related models and algorithms are verified.In summary,this paper carries out some works on spatio-temporal trajectory data mining,including spatio-temporal heterogeneity analysis of track density features,road information acquisition and updating through fusion geometric computing and spatial statistics,typical semantic feature extraction from spatial-temporal context,minging of the coupling features between behavior events and places,etc.And a series of structured processing models and algorithms for trajectory data are presented,and modeling processing and semantic understanding of trajectory data from geometric detail level to semantic generalization level are realized.However,trajectory data processing as a complex and systematic work,there are still many problems to be studied in depth.