Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks

TL;DR

This paper introduces AttnTUL, a hierarchical spatio-temporal attention neural network that effectively captures local and global context in trajectories for improved user linking, surpassing existing methods.

Contribution

The work presents a novel hierarchical attention model combining graph neural networks and temporal attention for trajectory-user linking, addressing limitations of prior recurrent-based approaches.

Findings

AttnTUL outperforms state-of-the-art baselines on multiple datasets.

The model effectively captures both local and global spatio-temporal dependencies.

Source code is publicly available for reproducibility.

Abstract

Trajectory-User Linking (TUL) is crucial for human mobility modeling by linking diferent trajectories to users with the exploration of complex mobility patterns. Existing works mainly rely on the recurrent neural framework to encode the temporal dependencies in trajectories, have fall short in capturing spatial-temporal global context for TUL prediction. To ill this gap, this work presents a new hierarchical spatio-temporal attention neural network, called AttnTUL, to jointly encode the local trajectory transitional patterns and global spatial dependencies for TUL. Speciically, our irst model component is built over the graph neural architecture to preserve the local and global context and enhance the representation paradigm of geographical regions and user trajectories. Additionally, a hierarchically structured attention network is designed to simultaneously encode the intra-trajectory and inter-trajectory dependencies, with the integration of the temporal attention mechanism and global elastic attentional encoder. Extensive experiments demonstrate the superiority of our AttnTUL method as compared to state-of-the-art baselines on various trajectory datasets. The source code of our model is available at https://github.com/Onedean/AttnTUL.