  Human mobility prediction is a fundamental task essential for various
applications, including urban planning, location-based services and intelligent
transportation systems. Existing methods often ignore activity information
crucial for reasoning human preferences and routines, or adopt a simplified
representation of the dependencies between time, activities and locations. To
address these issues, we present Hierarchical Graph Attention Recurrent Network
(HGARN) for human mobility prediction. Specifically, we construct a
hierarchical graph based on all users' history mobility records and employ a
Hierarchical Graph Attention Module to capture complex time-activity-location
dependencies. This way, HGARN can learn representations with rich human travel
semantics to model user preferences at the global level. We also propose a
model-agnostic history-enhanced confidence (MAHEC) label to focus our model on
each user's individual-level preferences. Finally, we introduce a Temporal
Module, which employs recurrent structures to jointly predict users' next
activities (as an auxiliary task) and their associated locations. By leveraging
the predicted future user activity features through a hierarchical and residual
design, the accuracy of the location predictions can be further enhanced. For
model evaluation, we test the performances of our HGARN against existing SOTAs
in both the recurring and explorative settings. The recurring setting focuses
on assessing models' capabilities to capture users' individual-level
preferences, while the results in the explorative setting tend to reflect the
power of different models to learn users' global-level preferences. Overall,
our model outperforms other baselines significantly in all settings based on
two real-world human mobility data benchmarks. Source codes of HGARN are
available at https://github.com/YihongT/HGARN.
