Classification of users' whereabouts patterns is impor- tant for many emerging ubiquitous computing applications. Latent Dirichlet Allocation (LDA) is a ...
PDF | On Jan 1, 2010, Laura Ferrari and others published Classification of Whereabouts Patterns From Large-scale Mobility Data.
CLASSIFICATION. LASSIFICATION OF WHEREABOUTS PATTERNS. FROM LARGE-SCALE MOBILITY DATA. LAURA FERRARI,. MARCO MAMEI. WOA 2010. Rimini, Settembre 2010. Agents and ...
Bibliographic details on Classification of Whereabouts Patterns From Large-scale Mobility Data.
Jun 15, 2024 · This study introduces an innovative algorithm for classifying transportation modes. It categorizes modes such as walking, biking, tram, bus, taxi, and private ...
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Classification of users' whereabouts patterns is important for many emerging ubiquitous computing applications. Latent Dirichlet Allocation (LDA) is a powerful ...
The analysis of longitudinal travel data enables investigating how mobility patterns vary across the population and identify the spatial properties thereof.
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We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data.
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In this paper we provide a systematic review of the state-of-the-art on identifying and clustering human mobility patterns from smart card data.
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Sep 3, 2021 � Globally, we reveal that (i) our metrics capture places that impact the number of visits in their neighborhood; (ii) cities in the same�...