# Where to Go Next Day: Multi-scale Spatial-Temporal Decoupled Model for Mid-term Human Mobility Prediction

arXiv: https://arxiv.org/abs/2501.06561 · HTML: https://mlsanity.com/paper/2501.06561/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 62 references that are in the map.
- First posted on arXiv 11 Jan 2025.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction](https://mlsanity.com/paper/2212.04475/) (2022)
- [GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation](https://mlsanity.com/paper/2303.04741/) (2023)
- [Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation](https://mlsanity.com/paper/2310.01224/) (2023)
- [A Universal Model for Human Mobility Prediction](https://mlsanity.com/paper/2412.15294/) (2024)
- [DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation](https://mlsanity.com/paper/2210.16591/) (2022)
- [Taming the Long Tail in Human Mobility Prediction](https://mlsanity.com/paper/2410.14970/) (2024)
- [Metropolitan Scale and Longitudinal Dataset of Anonymized Human Mobility Trajectories](https://mlsanity.com/paper/2307.03401/) (2023)
- [Learning Time Slot Preferences via Mobility Tree for Next POI Recommendation](https://mlsanity.com/paper/2403.12100/) (2024)
- [Mixing Individual and Collective Behaviours to Predict Out-of-Routine Mobility](https://mlsanity.com/paper/2404.02740/) (2024)
- [Modeling International Mobility using Roaming Cell Phone Traces during COVID-19 Pandemic](https://mlsanity.com/paper/2201.02470/) (2022)
- [Human Mobility Prediction with Causal and Spatial-constrained Multi-task Network](https://mlsanity.com/paper/2206.05731/) (2022)
- [Towards Effective Next POI Prediction: Spatial and Semantic Augmentation with Remote Sensing Data](https://mlsanity.com/paper/2404.04271/) (2024)

## Abstract (arXiv)

Predicting individual mobility patterns is crucial across various applications. While current methods mainly focus on predicting the next location for personalized services like recommendations, they often fall short in supporting broader applications such as traffic management and epidemic control, which require longer period forecasts of human mobility. This study addresses mid-term mobility prediction, aiming to capture daily travel patterns and forecast trajectories for the upcoming day or week. We propose a novel Multi-scale Spatial-Temporal Decoupled Predictor (MSTDP) designed to efficiently extract spatial and temporal information by decoupling daily trajectories into distinct location-duration chains. Our approach employs a hierarchical encoder to model multi-scale temporal patterns, including daily recurrence and weekly periodicity, and utilizes a transformer-based decoder to globally attend to predicted information in the location or duration chain. Additionally, we introduce a spatial heterogeneous graph learner to capture multi-scale spatial relationships, enhancing semantic-rich representations. Extensive experiments, including statistical physics analysis, are conducted on large-scale mobile phone records in five cities (Boston, Los Angeles, SF Bay Area, Shanghai, and Tokyo), to demonstrate MSTDP's advantages. Applied to epidemic modeling in Boston, MSTDP significantly outperforms the best-performing baseline, achieving a remarkable 62.8% reduction in MAE for cumulative new cases.