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A Spatio-Temporal Entropy-based Framework for the Detection of Trajectories Similarity

Type
Articles dans des revues avec comité de lecture
Author
HOSSEINPOOR MILAGHARDAN, Amin
548146 School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran [SSGE]
ABBASPOUR, Rahim Ali
548146 School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran [SSGE]
CLARAMUNT, Christophe
13094 Institut de Recherche de l'Ecole Navale (EA 3634) [IRENAV]

URI
http://hdl.handle.net/10985/13797
DOI
10.3390/e20070490
Date
2018
Journal
ENTROPY

Abstract

The rapid proliferation of sensors and big data repositories offer many new opportunities for data science. Among many application domains, the analysis of large trajectory datasets generated from people’s movements at the city scale is one of the most promising research avenues still to explore. Extracting trajectory patterns and outliers in urban environments is a direction still requiring exploration for many management and planning tasks. The research developed in this paper introduces a spatio-temporal framework, so-called STE-SD (Spatio-Temporal Entropy for Similarity Detection), based on the initial concept of entropy as introduced by Shannon in his seminal theory of information and as recently extended to the spatial and temporal dimensions. Our approach considers several complementary trajectory descriptors whose distribution in space and time are quantitatively evaluated. The trajectory primitives considered include curvatures, stop-points, self-intersections and velocities. These primitives are identified and then qualified using the notion of entropy as applied to the spatial and temporal dimensions. The whole approach is experimented and applied to urban trajectories derived from the Geolife dataset, a reference data benchmark available in the city of Beijing.

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