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

arXiv: https://arxiv.org/abs/2312.15717 · HTML: https://mlsanity.com/paper/2312.15717/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 25 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation](https://mlsanity.com/paper/2303.04741/) (2023)
- [Reimagining City Configuration: Automated Urban Planning via Adversarial Learning](https://mlsanity.com/paper/2008.09912/) (2020)
- [Reinforced Imitative Graph Learning for Mobile User Profiling](https://mlsanity.com/paper/2203.06550/) (2022)

## Built on by (most cited first, 6 of 6)

- [MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question Complexity](https://mlsanity.com/paper/2412.01572/) (2024)
- [Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction](https://mlsanity.com/paper/2406.16992/) (2024)
- [Identifying and Characterising Higher Order Interactions in Mobility Networks Using Hypergraphs](https://mlsanity.com/paper/2503.18572/) (2025)
- [M-STAR: Multi-Scale Spatiotemporal Autoregression for Human Mobility Modeling](https://mlsanity.com/paper/2512.07314/) (2025)
- [Mobility-Aware Cache Framework for Scalable LLM-Based Human Mobility Simulation](https://mlsanity.com/paper/2602.16727/) (2026)
- [Hierarchical Decision Making with Structured Policies: A Principled Design via Inverse Optimization](https://mlsanity.com/paper/2606.28764/) (2026)

## Abstract (arXiv)

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.