Discovery of convoys in trajectory databases

Abstract

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 reallife 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 wellknown filter-refinement framework. In the filter step, we apply linesimplification 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.

Decentralized Monitoring of Moving Objects in a Transportation Network Augmented with Checkpoints

“…Longer term objectives are to be able to compute decentrally meaningful movement patterns, such as flocks [52,53], convoys [54] or leadership [55]. These works employ trajectory-based data but can be adapted to work with data from cordon-structured networks.…”

Visual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach

“…Of particular interest has been the development of approaches that can identify groups of moving objects whose members share a strong relationship by being present within a defined spatial region during a given time duration. Some examples of these kinds of approaches are moving cluster analyses (Kalnis et al 2005, Jensen et al 2007, convoy queries (Jeung et al 2008) and flock patterns (Gudmundsson and van Kreveld 2006, Benkert et al 2008, Vieira et al 2009). Vieira et al (2009) define moving flock patterns as groups of at least μ entities moving in the same direction while being close to one another during a given time interval δ (Figure 1).…”

Moving objects: Combining gradual rules and spatio-temporal patterns

“…A moving object cluster can be defined in both spatial and temporal dimensions: (1) a group of moving objects should be geometrically close to each other, (2) they should be together for at least some minimum numbers of certain timestamps. In this context, many recent studies are interested in mining moving object clusters including moving clusters [4], flocks [6,7,13], convoys [1,2,12] and trajectory [5,11,3,14].…”

“…For instance, let take a look at Fig 1, there are 12 objects moving independently in 10 minutes. Individual moving objects clusters techniques including moving cluster [4], flock [6,7], convoy [1,2,12] and trajectory [5,11,3] detecting are not efficient in this situation. In fact, these techniques require objects to be together for at least some minimum numbers of certain timestamps; therefore, they are not adapted to extract some interesting patterns in this context.…”

“…For instance, the flock notion, firstly introduced in [13] and further studied in [6,7], is defined as a group of moving objects moving together in a disc of a fixed radius for k consecutive timestamps. A recent study by Jeung [1,2] proposes the convoy concept, an extension of flock, where spatial clustering is based on density. In [4], authors propose to extract moving clusters, i.e.…”