  As mobile devices with positioning capabilities continue to proliferate, data
management for so-called trajectory databases that capture the historical
movements of populations of moving objects becomes important. This paper
considers the querying of such databases for convoys, a convoy being a group of
objects that have traveled together for some time. More specifically, this
paper formalizes the concept of a convoy query using density-based notions, in
order to capture groups of arbitrary extents and shapes. Convoy discovery is
relevant for real-life applications in throughput planning of trucks and
carpooling of vehicles. Although there has been extensive research on
trajectories in the literature, none of this can be applied to retrieve
correctly exact convoy result sets. Motivated by this, we develop three
efficient algorithms for convoy discovery that adopt the well-known
filter-refinement framework. In the filter step, we apply line-simplification
techniques on the trajectories and establish distance bounds between the
simplified trajectories. This permits efficient convoy discovery over the
simplified trajectories without missing any actual convoys. In the refinement
step, the candidate convoys are further processed to obtain the actual convoys.
Our comprehensive empirical study offers insight into the properties of the
paper's proposals and demonstrates that the proposals are effective and
efficient on real-world trajectory data.
