Pattern Ensembling for Spatial Trajectory Reconstruction
This addresses the issue of data quality in mobility analysis for applications like tracking and trajectory mining, though it is incremental as it builds on existing interpolation methods.
The paper tackles the problem of incomplete and unreliable geolocation data in mobility studies by proposing a pattern ensembling method that uses similar trajectory patterns from the local vicinity to reconstruct missing segments, achieving robust reconstruction for extended and complex trajectories as demonstrated on sea vessel AIS data.
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.