# Breaking Data Silos: Towards Open and Scalable Mobility Foundation Models via Generative Continual Learning

arXiv: https://arxiv.org/abs/2506.06694 · HTML: https://mlsanity.com/paper/2506.06694/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in MoE for time series and graphs, one of 12 directions in Mixture-of-experts.
- Builds on 24 of its 69 references that are in the map.
- First posted on arXiv 7 Jun 2025.

## Placed in

- Mixture-of-experts / [MoE for time series and graphs](https://mlsanity.com/moe/moe-for-time-series-and-graphs/)

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [DeepSeek LLM: Scaling Open-Source Language Models with Longtermism](https://mlsanity.com/paper/2401.02954/) (2024)
- [Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models](https://mlsanity.com/paper/2402.17177/) (2024)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting](https://mlsanity.com/paper/1904.00310/) (2019)
- [Federated Continual Learning with Weighted Inter-client Transfer](https://mlsanity.com/paper/2003.03196/) (2020)
- [Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation](https://mlsanity.com/paper/2402.14744/) (2024)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction](https://mlsanity.com/paper/2402.11838/) (2024)
- [A Theoretical Study on Solving Continual Learning](https://mlsanity.com/paper/2211.02633/) (2022)
- [OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction](https://mlsanity.com/paper/2408.10269/) (2024)
- [LightTR: A Lightweight Framework for Federated Trajectory Recovery](https://mlsanity.com/paper/2405.03409/) (2024)
- [Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data](https://mlsanity.com/paper/1702.06270/) (2017)
- [TrajFM: A Vehicle Trajectory Foundation Model for Region and Task Transferability](https://mlsanity.com/paper/2408.15251/) (2024)
- [A Universal Model for Human Mobility Prediction](https://mlsanity.com/paper/2412.15294/) (2024)
- [Noise Matters: Diffusion Model-based Urban Mobility Generation with Collaborative Noise Priors](https://mlsanity.com/paper/2412.05000/) (2024)
- [UniFlow: A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction](https://mlsanity.com/paper/2411.12972/) (2024)
- [WorldMove, a global open data for human mobility](https://mlsanity.com/paper/2504.10506/) (2025)
- [Estimating On-road Transportation Carbon Emissions from Open Data of Road Network and Origin-destination Flow Data](https://mlsanity.com/paper/2402.05153/) (2024)

## Built on by (most cited first, 1 of 1)

- [TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generation](https://mlsanity.com/paper/2605.10020/) (2026)

## Abstract (arXiv)

Human mobility is a fundamental pillar of urban science and sustainability, providing critical insights into energy consumption, carbon emissions, and public health. However, the discovery of universal mobility laws is currently hindered by the ``data silo'' problem, where institutional boundaries and privacy regulations fragment the necessary large-scale datasets. In this paper, we propose MoveGCL, a transformative framework that facilitates collaborative and decentralized mobility science via generative continual learning. MoveGCL enables a distributed ecosystem of data holders to jointly evolve a foundation model without compromising individual privacy. The core of MoveGCL lies in its ability to replay synthetic trajectories derived from a generative teacher and utilize a mobility-pattern-aware Mixture-of-Experts (MoE) architecture. This allows the model to encapsulate the unique characteristics of diverse urban structures while mitigating the risk of knowledge erosion (catastrophic forgetting). With a specialized layer-wise progressive adaptation strategy, MoveGCL ensures stable convergence during the continuous integration of new urban domains. Our experiments on six global urban datasets demonstrate that MoveGCL achieves performance parity with joint training, a previously unattainable feat under siloed conditions. This work provides a scalable, privacy-preserving pathway toward Open Mobility Science, empowering researchers to address global sustainability challenges through cross-institutional AI collaboration. To facilitate reproducibility and future research, we have released the code and models at colorbluehttps://github.com/tsinghua-fib-lab/MoveGCL.