2021/01/25 by Shivam Pathak, Mingyi He, Pathak, Shivam +5
Computer Science · Engineering · Social Sciences · #68W99 #Data Analysis #Data Management and Algorithms #FOS: Computer and information sciences #FOS: Physical sciences #Human Mobility and Location-Based Analysis #I.5 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Maritime Navigation and Safety #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2101.09844
openalex publication_date 2021/01/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.