Semantic Trajectories: Mobility Data Computation and Annotation

doi:10.1145/2483669.2483682

Semantic Trajectories: Mobility Data Computation and Annotation

2013-07-01

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DOI：10.1145/2483669.2483682

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With the large-scale adoption of GPS equipped mobile sensing devices, positional data generated by moving objects (e.g., vehicles, people, animals) are being easily collected. Such data are typically modeled as streams of spatio-temporal (x,y,t) points, called trajectories. In recent years trajectory management research has progressed significantly towards efficient storage and indexing techniques, as well as suitable knowledge discovery. These works focused on the geometric aspect of the raw mobility data. We are now witnessing a growing demand in several application sectors (e.g., from shipment tracking to geo-social networks) on understanding the semantic behavior of moving objects. Semantic behavior refers to the use of semantic abstractions of the raw mobility data, including not only geometric patterns but also knowledge extracted jointly from the mobility data and the underlying geographic and application domains information. The core contribution of this article lies in a semantic model and a computation and annotation platform for developing a semantic approach that progressively transforms the raw mobility data into semantic trajectories enriched with segmentations and annotations. We also analyze a number of experiments we did with semantic trajectories in different domains.

Keywords:

Algorithms

Design

Experimentation

Spatio-temporal/structured/semantic trajectory

trajectory computing

trajectory annotation

trajectory segmentation

spatial join

map matching

hidden Markov model

Semantic Trajectories: Mobility Data Computation and Annotation

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