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While the paths humans take play out in social as well as physical space,measures to describe and compare their trajectories are carried out inabstract, typically Euclidean, space. When these measures are applied totrajectories of actual individuals in an application area, alterations that areinconsequential in abstract space may suddenly become problematic once overlaidwith geographic reality. In this work, we present a different view ontrajectory similarity by introducing a measure that utilizes logicalentailment. This is an inferential perspective that considers facts as triplestatements deduced from the social and environmental context in which thetravel takes place, and their practical implications. We suggest aformalization of entailment-based trajectory similarity, measured as theoverlapping proportion of facts, which are spatial relation statements in ourcase study. With the proposed measure, we evaluate LSTM-TrajGAN, aprivacy-preserving trajectory-generation model. The entailment-based modelevaluation reveals potential consequences of disregarding the rich structure ofgeographic space (e.g., miscalculated insurance risk due to regional shifts inour toy example). Our work highlights the advantage of applying logicalentailment to trajectory-similarity reasoning for location-privacy protectionand beyond.

Here Is Not There: Measuring Entailment-Based Trajectory Similarity for Location-Privacy Protection and Beyond