Spatial-Temporal Interplay in Human Mobility: A Hierarchical
  Reinforcement Learning Approach with Hypergraph Representation
Zhaofan Zhang Yanan Xiao Lu Jiang Dingqi Yang Minghao Yin Pengyang Wang
In the realm of human mobility, the decision-making process for selecting the
next-visit location is intricately influenced by a trade-off between spatial
and temporal constraints, which are reflective of individual needs and
preferences. This trade-off, however, varies across individuals, making the
modeling of these spatial-temporal dynamics a formidable challenge. To address
the problem, in this work, we introduce the "Spatial-temporal Induced
Hierarchical Reinforcement Learning" (STI-HRL) framework, for capturing the
interplay between spatial and temporal factors in human mobility
decision-making. Specifically, STI-HRL employs a two-tiered decision-making
process: the low-level focuses on disentangling spatial and temporal
preferences using dedicated agents, while the high-level integrates these
considerations to finalize the decision. To complement the hierarchical
decision setting, we construct a hypergraph to organize historical data,
encapsulating the multi-aspect semantics of human mobility. We propose a
cross-channel hypergraph embedding module to learn the representations as the
states to facilitate the decision-making cycle. Our extensive experiments on
two real-world datasets validate the superiority of STI-HRL over
state-of-the-art methods in predicting users' next visits across various
performance metrics.