Discovery of evolving companion from trajectory data streams

Abstract

The widespread use of position-tracking devices leads to vast volumes of spatial-temporal data aggregated in the form of the trajectory data streams. Extracting useful knowledge from moving object trajectories can benefit many applications, such as traffic monitoring, military surveillance, and weather forecasting. Most of the knowledge gleaned from the trajectory data illustrates different kinds of group patterns, i.e., objects that travel together for some time. In the real world, the trajectory of the moving objects can change with time. Thus, existing approaches can miss a new pattern because they have a stringent requirement for moving object participators in a group movement pattern. To address this issue, we introduced a new type of moving object group pattern called an evolving companion. It allows some members of the group to leave and join anytime if some participators stay connected for all time intervals. In this pattern discovery, we model an incremental discovery solution to retrieve the evolving companion efficiently over the data stream. We evaluated the efficiency and effectiveness of our approach on two real vehicles and one synthetic dataset. Our method performed well compared with existing pattern discovery methods; for example, it was about 50% faster than Tang et al.'s buddy-based clustering method.

Mining truck platooning patterns through massive trajectory data

“…Liang et al [32] regarded coupled trucks within a 100 m flock of the current truck and traveling at the same segment as the instantaneous codriving sets, while Larson et al [33] applied a search radius ranging from 0.5 to 5 km to estimate the coordination potentials for spontaneous truck platooning in a transportation network in Germany. On the basis of Liang et al [32], Shein et al [34] combined flock sets with a central distance less than the search radius at each timestamp. Therefore, the enhanced algorithm mitigates the insufficient adaptability of fixed radius and avoids excessive density connection caused by density clustering algorithms.…”

“…The majority of iterative approaches apply the fixed radius to find co-driving trucks and cannot meet the address the issue of dynamic spacings between trucks. Shein et al [34] alleviated this issue to some extent but still cannot eliminate the errors of choosing the initial trucks. In addition, almost all bidirectional trunk roads are represented as single lines in OSM.…”

An experimental evaluation of grouping definitions for moving entities

“…Many different definitions have been suggested to model the collective movement of a "sufficiently large" set of entities that travel "together" for a "sufficiently long" period of time: flocks [4,13,44], mobile groups [16], moving clusters [21], moving micro-clusters [27], herds [15], convoys [19], swarms [28], gatherings [55], traveling companions [42], platoons [26], groups [5], refined groups [43], crews [29], and evolving companions [37].…”