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• W3121782708 abstract "Digital sensing provides an unprecedented opportunity to assess and understand mobility. However, incompleteness, missing information, possible inaccuracies, and temporal heterogeneity in the geolocation data can undermine its applicability. As mobility patterns are often repeated, we propose a method to use similar trajectory patterns from the local vicinity and probabilistically ensemble them to robustly reconstruct missing or unreliable observations. We evaluate the proposed approach in comparison with traditional functional trajectory interpolation using a case of sea vessel trajectory data provided by The Automatic Identification System (AIS). By effectively leveraging the similarities in real-world trajectories, our pattern ensembling method helps to reconstruct missing trajectory segments of extended length and complex geometry. It can be used for locating mobile objects when temporary unobserved as well as for creating an evenly sampled trajectory interpolation useful for further trajectory mining." @default.
• W3121782708 created "2021-02-01" @default.
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• W3121782708 date "2021-01-25" @default.
• W3121782708 modified "2023-09-27" @default.
• W3121782708 title "Pattern Ensembling for Spatial Trajectory Reconstruction" @default.
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• W3121782708 hasPublicationYear "2021" @default.
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