Research On The Mining Methods Of Trajectory Data For Moving Objects

In recent years, with the rapid growth of GPS devices, sensor network, satellitesand wireless communication technologies, various kinds of moving objects can betraced all over the world. At the same time, more and more moving objectstrajectories are collected and stored in database. These data often contain a great dealof knowledge, which need an urgent analysis. This dissertation takes moving objectdata mining as research object and considers the discovery of moving objects'periodic activities as main goal. The main research works are listed as follows:1. Driven by the goals and tasks of trajectory data mining for moving object,and under analyzing the characteristics of current existing moving object miningmethodologies, a novel framework of moving object periodic activity mining ispresented. With the framework, moving object trajectory data can be furtheranalyzed and mined from different aspects, and full moving object activities can befound.2. A trajectory analysis method based on structure features is presented toovercome the shortages existing in current algorithms. This method analyzes movingobjects' movement patterns and trajectory features from microcosmic viewpoint. Bycomparing the extracted structure features from motion trajectory, this method cananalyze objects' motion features from different angles. Moreover, setting trajectorystructure weights makes the sensitive degree of trajectory structure more easilyadjusted, and motion trajectory of moving objects also can be analyzed faster, highefficient, comprehensive and more flexible.3. In order to analyze object's activity in deep view, an interesting activity ofmoving objects discovery algorithm based on collaborative filtering is put forward.This method discovers moving objects' interesting activities and interesting routesfrom macroscopic viewpoint. Firstly, hot regions discovery algorithm is given totransform sporadic and redundant trajectory data into activities sequence. Thenobjects' potential interesting activities are recommended on a basis of neighbors. Themethod also makes use of largest common sub-patterns to discover interestingactivity routes among neighbor objects, which lays a solid foundation for furtherresearching objects' activities.4. A periodic activities discovery method based on multiple granularities.Moving objects' activities sequence is multi-granularity modeled on a basis of objects' interesting activities discovery. By space priority algorithm of multiplegranularity activities discovery and time priority algorithm, the activities arerepresented using multiple granularities. A new periodic pattern discovery algorithmof single activity is proposed to find objects' activity period with unknown periods.In addition, Max Sub-pattern Tree is introduced to discover periodic pattern ofobjects' linked activities more flexible and high efficient.5. Finally, this dissertation designs and develops a trajectory data miningprototype system. Combining with the requirements of mine personnel position, themethods and theories are heuristic applied and verified in mine personnel positionsystem, which verifies the feasibility and effectiveness of correlation mining methodabout moving objects' activities. The proposed methods provide new ideas and waysto explore the theories and techniques in moving object data mining.