TUL-IB: Enhancing Explainability in Trajectory User Linking with Information Bottleneck

Deep trajectory modeling has garnered significant attention across various applications, particularly in trajectory user linking (TUL), which aims to associate trajectories with specific users by analyzing complex mobility patterns. Despite its advancements, the lack of explainability remains a critical challenge. In this paper, we propose a general Information Bottleneck framework, TUL-IB, designed to enhance the explainability of TUL models for both sequence and graph data, with tractable optimization bounds to solve the TUL-IB objective. We further demonstrate that TUL-IB can be effectively applied to two distinct types of trajectory data: (1) waypoint trajectories, for which we extend TUL-IB into a dual-view approach, TUL-DV-IB, integrating both driving behavior sequences and trajectory road graphs. To ensure temporal continuity in sub-sequence selection, we employ dynamic programming during post-processing; (2) staypoint trajectories, for which we adapt TUL-IB to the graph node level and apply it to global trajectory graph model, resulting in TUL-GTG-IB. This adaptation identifies key neighboring trajectories that significantly contribute to explaining the user-linking results. Experimental results on three real-world datasets demonstrate that our method outperforms existing explainable approaches, providing deeper insights into trajectory user linking models.