Trajectory Recovery via Global Spatial Dependencies and Local Multi-Factor Semantics

Abstract

Trajectory data may suffer from missing locations due to environmental and equipment constraints, which impact the performance of downstream tasks. Trajectory recovery aims to enhance the utility of mobility data by reconstructing high-quality trajectories. However, existing methods face two significant limitations. First, most existing methods rely on individual historical trajectories, making it difficult to exploit global spatial dependencies across the population. Second, current approaches ignore the role of local multi-factor semantics in personalized trajectory recovery. To address the above challenges, we propose a joint trajectory recovery method named GLTrajRec, which integrates global spatial dependencies and local multi-factor semantics. Specifically, global trajectory flow graph modeling is proposed to capture shared mobility patterns from all users’ trajectories, which can provide effective spatial transition constraints even when individual historical data is insufficient. Then, multi-factor local semantics embedding is designed to comprehensively encode multi-dimensional personalized mobility information from multiple aspects, enabling more accurate personalized recovery results. In addition, a Transformer-based encoder–decoder framework is developed to bidirectionally encode the embedded global dependencies and local semantics and to decode multi-class information. Finally, we construct a transition matrix based on the global trajectory flow graph to improve the accuracy of the recovery output. Extensive experiments conducted on two real-world datasets demonstrate that the proposed method achieves mean average precision (MAP) scores of 0.7181 and 0.7345, respectively, representing an improvement of approximately 5% to 7% over traditional trajectory recovery methods.