Retrieving k-Nearest Neighboring Trajectories by a Set of Point Locations

Abstract

Abstr act. The advance of object tracking technologies leads to huge volumes of spatio-temporal data accumulated in the form of location trajectories. Such data bring us new opportunities and challenges in efficient trajectory retrieval.In this paper, we study a new type of query that finds the k Nearest Neighboring Trajectories (k-NNT) with the minimum aggregated distance to a set of query points. Such queries, though have a broad range of applications like trip planning and moving object study, cannot be handled by traditional k-NN query processing techniques that only find the neighboring points of an object. To facilitate scalable, flexible and effective query execution, we propose a k-NN trajectory retrieval algorithm using a candidate-generation-and-verification strategy. The algorithm utilizes a data structure called global heap to retrieve candidate trajectories near each individual query point. Then, at the verification step, it refines these trajectory candidates by a lower-bound computed based on the global heap. The global heap guarantees the candidate's completeness (i.e., all the k-NNTs are included), and reduces the computational overhead of candidate verification. In addition, we propose a qualifier expectation measure that ranks partial-matching candidate trajectories to accelerate query processing in the cases of non-uniform trajectory distributions or outlier query locations. Extensive experiments on both real and synthetic trajectory datasets demonstrate the feasibility and effectiveness of proposed methods.

Searching Trajectories by Regions of Interest

“…The availability of massive trajectory data enables novel mobile applications. Such applications may utilize trajectory search, which finds trajectories that are similar in some specific sense to query parameters (a set or sequence of locations [5], [16], [17], [19], [25], or regions).…”

“…Such experiences are captured in trajectories shared by previous visitors. In existing studies (e.g., [5], [16], [17], [19], [25]), all trajectories are treated the same, regardless of their frequencies of use. For example, some less traveled trajectories may be new or just less popular because the region they are in is less traveled.…”

“…In most existing studies on trajectory search [5], [16], [19], the query parameters are a set or sequence of locations. However, in some cases, a place may not be a point location, but may be a region of interest that contains several spatial objects (e.g., a scenic area, a commercial district, or a dining area, where spatial objects can be points of interest (POIs), geotagged photos, or geo-tagged tweets).…”

Exploiting large-scale check-in data to recommend time-sensitive routes

“…L.-A. Tang et al [10] finds the top-k nearest neighboring trajectories with the minimum aggregated distance to some query locations. L.-Y.…”

Multidimensional Sensor Data Analysis in Cyber-Physical System: An Atypical Cube Approach

“…The Cyber-Physical System (CPS) has been a focused research theme recently due to its wide applications in the areas of traffic monitoring, battlefield surveillance, and sensornetwork-based monitoring [1][2][3][4][5][6]. It is placed on the top of the priority list for federal research investment in the fiscal year report of US president's council of advisors on science and technology [7].…”