APDS: A framework for discovering movement pattern from trajectory database

Abstract

Currently, the boosting of location acquisition devices makes it possible to track all kinds of moving objects, and collect and store their trajectories in database. Therefore, how to find knowledge from huge amount of trajectory data has become an attractive topic. Movement pattern is an efficient way to understand moving objects’ behavior and analyze their habits. To promote the application of spatiotemporal data mining, a moving object activity pattern discovery system is designed and implemented in this article. First of all, raw trajectory data are preprocessed using methods like data clean, data interpolation, and compression. Second, a simplified density-based trajectory clustering algorithm is implemented to find and group similar movement patterns. Third, in order to discover the trends and periodicity of movement pattern, a trajectory periodic pattern mining algorithm is developed. Finally, comprehensive experiments with different parameters are conducted to validate the pattern discovery system. The experimental results show that the system is robust and efficient to analyze moving object trajectory data and discover useful patterns.

A generation of synthesized trajectories of free moving objects for testing a set of continuous spatio‐temporal queries

“…However, the spatiotemporal relationship models did not consider topological relationships (e.g., approach, retreat, detour, U‐turn, and hovering turn) of free‐moving objects against a set of CQ regions. After storing the trajectory pattern in the DB, an attempt 4 to find a useful pattern by pattern discovery or pattern mining is the opposite of the approach of this article that creates a bound pattern.…”

“…Existing approaches related to data set creation of moving objects have been largely classified into network‐based or free movement. The network‐based data generation method 4,5 is a method of generating the distribution and trajectory of a moving object moving along a road network. Previous studies on the trajectory patterns of network‐constrained moving objects included a trajectory generator using various mobility patterns, 6 vehicle location prediction by training trajectory classification, 7 and generation of simulated trajectories 8 .…”