SeMiTri

Abstract

GPS devices allow recording the movement track of the moving object they are attached to. This data typically consists of a stream of spatio-temporal (x,y,t) points. For application purposes the stream is transformed into finite subsequences called trajectories. Existing knowledge extraction algorithms defined for trajectories mainly assume a specific context (e.g. vehicle movements) or analyze specific parts of a trajectory (e.g. stops), in association with data from chosen geographic sources (e.g. points-of-interest, road networks). We investigate a more comprehensive semantic annotation framework that allows enriching trajectories with any kind of semantic data provided by multiple 3rd party sources.This paper presents SeMiTri -the framework that enables annotating trajectories for any kind of moving objects. Doing so, the application can benefit from a "semantic trajectory" representation of the physical movement. The framework and its algorithms have been designed to work on trajectories with varying data quality and different structures, with the objective of covering abstraction requirements of a wide range of applications. Performance of SeMiTri has been evaluated using many GPS datasets from multiple sourcesincluding both fast moving objects (e.g. cars, trucks) and people's trajectories (e.g. with smartphones). These two kinds of experiments are reported in this paper.

A review of urban computing for mobile phone traces

“…Similar issues exist in the mobile phone data to certain extent, depending on the accuracy and resolution of the spatially triangulated locations of the anonymous phone users. Nonetheless, finding smart techniques to link the association rules of semanticenriched land use and POI information of the diverse areas that individuals visit is an open challenge for estimating the activity types that individuals engage in [45,46]. And this information is extremely valuable to put mobile phone data into the service of urban and transportation planning.…”

Semantic enrichment of spatio-temporal trajectories for worker safety on construction sites

“…The mapping of the POIs and ROIs with the trajectory points is performed using a topological correlation, i.e., Trajectory _ epidode⋈ θ Location. Here, the parameter θ is computed using the topological spatial relations such as distance and displacement [27,28]. After computing the value of θ, spatial joins are performed with the boundary of the trajectory.…”

“…Once the segmentation of trajectories is completed, annotation techniques are applied to transform GPS trajectories into semantic trajectories [14]. The annotation process involves enrichment of trajectory episodes with the meaningful information such as the mapping of the POIs that can be in the form of points, in the form of lines, or the geographical regions [27,28]. There are many annotation approaches present in the existing literature as provided in Table 1.…”

“…The ability of this framework of describing the spatial context of GPS trajectories can be used as a building block of future expert systems for trajectory exploration. Moreover, trajectories of slow-and fast-moving objects are also annotated at different levels of data abstraction using a multi-layer framework [27]. The locations where objects move provide information about their interests.…”

Segmentation techniques for the summarization of individual mobility data

“…The segmentation task partitions a trajectory into a series of subsequences—the segments—that are somehow homogeneous with respect to certain properties. The systematic use of segmentation for trajectory data summarization and representation has been first proposed in Ref as part of a methodological framework aiming at supporting the semantic trajectory discovery process. In this article, we overview major directions of recent research and highlight the methodological differences among these directions.…”