Trajectory User Linking via TrajTRoPE

Abstract

Trajectory-user linking (TUL) is a critical problem in spatio-temporal data mining that involves associating anonymous trajectories with the users who generated them. In

this thesis, we address the TUL challenge by proposing a Transformer-based model aug- mented with Rotary Positional Embedding (RoPE) encoder and enriched with semantic

context through Points of Interest (POI) features. Using the GeoLife dataset as the ex- perimental platform, we conduct extensive evaluations on user subsets with trajectory

lengths constrained to a maximum of 1000 points.

Our findings demonstrate that the incorporation of POI features substantially en- hances the model’s performance, with the full model achieving an Accuracy@1 of 68.33%

and a Macro F1 score of 51.34%. Ablation studies reveal that removing either the Trans- former structure or the RoPE encoder significantly degrades performance, highlighting

the importance of both sequential modeling and positional encoding. Furthermore, fea- ture importance analysis shows that the absence of POI information results in a dramatic

collapse of predictive accuracy and generalization, reaffirming the necessity of contextual features for effective user discrimination.

While the model achieves strong results, the study also acknowledges limitations, in- cluding reliance on static spatial features and a relatively narrow evaluation scope. Ethical

considerations regarding user privacy are discussed, emphasizing the need for responsi- ble data handling and privacy-preserving extensions. Overall, this thesis advances the

trajectory-user linking field by demonstrating the synergistic role of structural modeling and semantic enrichment, while paving the way for future research in robust, scalable, and ethically aligned mobility analytics.