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.

翻译：数字遥感为评估和理解流动性提供了前所未有的机会。然而,不完全、缺失信息、可能的不准确性和地理定位数据的时间差异性会损害其适用性。由于流动性模式经常重复,我们建议一种方法,从当地附近使用相似的轨迹模式,并可能合用这些模式,以强有力地重建缺失或不可靠的观测结果。我们利用自动识别系统(自动识别系统)提供的海洋船只轨迹数据,对拟议方法与传统功能性轨迹内插法进行比较。通过有效地利用现实世界轨迹的相似之处,我们的模式组合方法有助于重建漫长和复杂的几何测量缺失的轨迹段,可用于在暂时无观测时寻找移动物体,以及建立对进一步轨迹采矿有用的均衡抽样轨迹间插法。