  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.
