  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.
