# Human Mobility Prediction with Causal and Spatial-constrained Multi-task Network

arXiv: https://arxiv.org/abs/2206.05731 · HTML: https://mlsanity.com/paper/2206.05731/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 12 Jun 2022.

## Builds on (most cited first, 8 of 8)

- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information](https://mlsanity.com/paper/2012.00489/) (2020)
- [A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks](https://mlsanity.com/paper/1811.06031/) (2018)
- [MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction](https://mlsanity.com/paper/2110.01401/) (2021)
- [Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction](https://mlsanity.com/paper/2111.03465/) (2021)
- [Data-driven generation of spatio-temporal routines in human mobility](https://mlsanity.com/paper/1607.05952/) (2016)
- [Modeling International Mobility using Roaming Cell Phone Traces during COVID-19 Pandemic](https://mlsanity.com/paper/2201.02470/) (2022)

## Built on by (most cited first, 5 of 5)

- [Beyond Regularity: Modeling Chaotic Mobility Patterns for Next Location Prediction](https://mlsanity.com/paper/2509.11713/) (2025)
- [Activity-aware Human Mobility Prediction with Hierarchical Graph Attention Recurrent Network](https://mlsanity.com/paper/2210.07765/) (2022)
- [Sim-MSTNet: sim2real based Multi-task SpatioTemporal Network Traffic Forecasting](https://mlsanity.com/paper/2601.21384/) (2026)
- [TrajGEOS: Trajectory Graph Enhanced Orientation-based Sequential Network for Mobility Prediction](https://mlsanity.com/paper/2412.19092/) (2024)
- [Where to Go Next Day: Multi-scale Spatial-Temporal Decoupled Model for Mid-term Human Mobility Prediction](https://mlsanity.com/paper/2501.06561/) (2025)

## Abstract (arXiv)

Modeling human mobility helps to understand how people are accessing resources and physically contacting with each other in cities, and thus contributes to various applications such as urban planning, epidemic control, and location-based advertisement. Next location prediction is one decisive task in individual human mobility modeling and is usually viewed as sequence modeling, solved with Markov or RNN-based methods. However, the existing models paid little attention to the logic of individual travel decisions and the reproducibility of the collective behavior of population. To this end, we propose a Causal and Spatial-constrained Long and Short-term Learner (CSLSL) for next location prediction. CSLSL utilizes a causal structure based on multi-task learning to explicitly model the "textitwhenrightarrowwhatrightarrowwhere", a.k.a. "textittimerightarrowactivityrightarrowlocation" decision logic. We next propose a spatial-constrained loss function as an auxiliary task, to ensure the consistency between the predicted and actual spatial distribution of travelers' destinations. Moreover, CSLSL adopts modules named Long and Short-term Capturer (LSC) to learn the transition regularities across different time spans. Extensive experiments on three real-world datasets show promising performance improvements of CSLSL over baselines and confirm the effectiveness of introducing the causality and consistency constraints. The implementation is available at https://github.com/urbanmobility/CSLSL.