The maximum trajectory coverage query in spatial databases

Abstract

With the widespread use of GPS-enabled mobile devices, an unprecedented amount of trajectory data has become available from various sources such as Bikely, GPS-wayPoints, and Uber. The rise of smart transportation services and recent breakthroughs in autonomous vehicles increase our reliance on trajectory data in a wide variety of applications. Supporting these services in emerging platforms requires more efficient query processing in trajectory databases. In this paper, we propose two new coverage queries for trajectory databases: (i) k Best Facility Trajectory Search (kBFT); and (ii) k Best Coverage Facility Trajectory Search (kBCovFT). We propose a novel index structure, the Trajectory Quadtree (TQtree) that utilizes a quadtree to hierarchically organize trajectories into different nodes, and then applies a z-ordering to further organize the trajectories by spatial locality inside each node. This structure is highly effective in pruning the trajectory search space, which is of independent interest. By exploiting the TQ-tree, we develop a divide-and-conquer approach to efficiently process a kBFT query. To solve the kBCovFT, which is a non-submodular NP-hard problem, we propose a greedy approximation. We evaluate our algorithms through an extensive experimental study on several real datasets, and demonstrate that our algorithms outperform baselines by two to three orders of magnitude.

Contact Tracing With District-Based Trajectories

“…To handle the large number of trajectories, several indexing methods have been proposed to improve the processing performance, such as R Tree-based in MVR Tree (Hadjieleftheriou et al, 2006) or R Query (Zheng, 2011), Quad Tree-based in TQ Tree (Ali et al, 2018), TPR*-Tree (Li & Shin, 2018), and a combination of R-Tree and Quad Tree in QR-Tree (Ali et al, 2018) as shown in Figure 2. Other methods that can be used are the grid system using MGRS (Mariescu-Istodor & Fränti, 2017), or polynomial approximations (Ni & Ravishankar, 2007).…”

Experiments and Analyses of Anonymization Mechanisms for Trajectory Data Publishing

“…In this section, we introduce the measurements for privacy and utility. Following [1][2][3], we adopt unicity as the metric for measuring the privacy, and we evaluate the utility in terms of two classic applications: travel time estimation [53,54] and window range queries [6,55,56] .…”

“…Spatio-temporal queries are fundamental operations for trajectory data [6,38,55,56,65] , among which we choose window range queries to evaluate the utility of anonymized trajectory data. Window range queries are in particular useful for vehicle flow monitoring that explores the traffic flow information to help make better travel decisions, alleviate traffic congestion, and improve the urban planning [66] , and have been commonly used for the utility evaluation [10,31] .…”

Services and surveillance during the pandemic lockdown: Residents’ committees in Wuhan and Beijing

“…However, how to find unionable spatial datasets has not been fully explored. Considering the spatial dataset search scenario, spatial proximity (or connectivity) [7,25,43] and the size of unionable (or extensible) [7,25] data points are two key factors indicating the unionability of two spatial datasets. Thus, we propose a new class of spatial union search based on the set coverage problem and connectivity constraint.…”

“…1(b) we can find that the subway line Q and bus line D 1 are highly overlapping, so it is possible to consider merging the two lines to reduce the transport costs. (2) To improve the completeness of public transport, we need to expand the coverage region of access to public transport to serve more people [7,25]. It should be noted that it is inappropriate for the user to commute too long when switching transportation.…”