Spatial-Temporal Interplay in Human Mobility: A Hierarchical Reinforcement Learning Approach with Hypergraph Representation

• URL: http://arxiv.org/abs/2312.15717v1
• Date: Mon, 25 Dec 2023 13:00:05 GMT
• Title: Spatial-Temporal Interplay in Human Mobility: A Hierarchical Reinforcement Learning Approach with Hypergraph Representation
• Authors: Zhaofan Zhang, Yanan Xiao, Lu Jiang, Dingqi Yang, Minghao Yin, Pengyang Wang
• Abstract summary: "STI-HRL" framework captures interplay between spatial and temporal factors in human mobility decision-making. To complement the hierarchical decision setting, we construct a hypergraph to organize historical data. Our experiments on two real-world datasets validate the superiority of STI-HRL over state-of-the-art methods in predicting users' next visits.
• Score: 25.26148307071171
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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