SimpliFly: A Methodology for Simplification and Thematic Enhancement of Trajectories

Vrotsou, Katerina; Janetzko, Halldór; Navarra, Carlo; Fuchs, Georg; Spretke, David; Mansmann, Florian; Andrienko, Natalia; Andrienko, Gennady

doi:10.1109/tvcg.2014.2337333

Cited by 25 publications

(10 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, this kind of clustering can be used for tasks in which the integrity of original trajectories is not important, e.g., those that focus on the variation of the overall movement characteristics across a territory. Several approaches to clustering of consecutive trajectory segments according to their features are compared in paper [51]. Such clustering can be used for discriminating different parts of trajectories, e.g., flight phases.…”

Section: Trajectory Clusteringmentioning

confidence: 99%

Clustering Trajectories by Relevant Parts for Air Traffic Analysis

Andrienko

Fuchs

et al. 2018

IEEE Trans. Visual. Comput. Graphics

Self Cite

View full text Add to dashboard Cite

Fig. 1. Different approach routes to London airports are represented in a 3D view by central trajectories of density-based clusters of relevant parts of flight trajectories. The images show the routes that were used in two days with different wind parameters.Abstract-Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering.

show abstract

Section: Trajectory Clusteringmentioning

confidence: 99%

Clustering Trajectories by Relevant Parts for Air Traffic Analysis

Andrienko

Fuchs

et al. 2018

IEEE Trans. Visual. Comput. Graphics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Data abstraction can transform the raw data samples into a simple format while at the same time preserving significant features that are important for the user [31,110,118]. Large data sets containing billions of raw data samples can be pre-processed using standard data abstraction techniques and can be converted into a simple format.…”

Section: Data Abstractionmentioning

confidence: 99%

Data Abstraction and Pattern Identification in Time-series Data

Muthumanickam

2019

Linköping Studies in Science and Technology. Dissertations

View full text Add to dashboard Cite

show abstract

“…A few geometrical algorithms have been considered for detecting self-intersecting trajectories such as Douglas-Peucker. However, the resulting geometric simplification is not efficient enough and does not provide sufficient geometric simplification [64]. The second aim of this study is to select a few trajectory parameters that also identify the number and locations of POIs.…”

Section: Parameters Related Workmentioning

confidence: 99%

A Geometric Framework for Detection of Critical Points in a Trajectory Using Convex Hulls

Milaghardan

Abbaspour

Claramunt

2018

IJGI

View full text Add to dashboard Cite

Large volumes of trajectory-based data require development of appropriate data manipulation mechanisms that will offer efficient computational solutions. In particular, identification of meaningful geometric points of such trajectories is still an open research issue. Detection of these critical points implies to identify self-intersecting, turning and curvature points so that specific geometric characteristics that are worth identifying could be denoted. This research introduces an approach called Trajectory Critical Point detection using Convex Hull (TCP-CH) to identify a minimum number of critical points. The results can be applied to large trajectory data sets in order to reduce storage costs and complexity for further data mining and analysis. The main principles of the TCP-CH algorithm include computing: convex areas, convex hull curvatures, turning points, and intersecting points. The experimental validation applied to Geolife trajectory dataset reveals that the proposed framework can identify most of intersecting points in reasonable computing time. Finally, comparison of the proposed algorithm with other methods, such as turning function shows that our approach performs relatively well when considering the overall detection quality and computing time.

show abstract

SimpliFly: A Methodology for Simplification and Thematic Enhancement of Trajectories

Cited by 25 publications

References 45 publications

Clustering Trajectories by Relevant Parts for Air Traffic Analysis

Clustering Trajectories by Relevant Parts for Air Traffic Analysis

Data Abstraction and Pattern Identification in Time-series Data

A Geometric Framework for Detection of Critical Points in a Trajectory Using Convex Hulls

Contact Info

Product

Resources

About