Discovery of Loose Group Companion From Trajectory Data Streams

Thi Thi Shein, Sutheera Puntheeranurak, Makoto Imamura

Abstract: The general usability of location tracking devices has been generated a high volume of spatial-temporal data in the form of trajectory. Exploring useful knowledge from these trajectory data can contribute to understanding many real-world applications, such as traffic monitoring and weather forecasting. The main task of trajectory data analysis is the tracking of an object group movement pattern. Existing algorithms, studying the evolving structure of moving object trajectories, have high computational complexity, particularly when tracking loose group companions. To address this problem, we describe a loose group companion tracking framework over trajectory data streams in an incremental manner, which reduces computational time. Loose group companion is the moving objects group that travels together. However, some members are allowed to leave at some timestamp. A crucial part of our framework is the micro-group based loose group companion discovery. It follows a moving object group and then incrementally detects the loose group companions. We validated our techniques using two real vehicle data sets and one synthetic data set. Our approach was, on average, 45% faster than previous algorithms.