# Learning Universal Human Mobility Patterns with a Foundation Model for Cross-domain Data Fusion

arXiv: https://arxiv.org/abs/2503.15779 · HTML: https://mlsanity.com/paper/2503.15779/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 20 Mar 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks](https://mlsanity.com/paper/1903.05987/) (2019)
- [What Happens To BERT Embeddings During Fine-tuning?](https://mlsanity.com/paper/2004.14448/) (2020)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model](https://mlsanity.com/paper/2304.11582/) (2023)
- [Semantic Trajectory Data Mining with LLM-Informed POI Classification](https://mlsanity.com/paper/2405.11715/) (2024)
- [NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks](https://mlsanity.com/paper/2409.03024/) (2024)
- [AttnMove: History Enhanced Trajectory Recovery via Attentional Network](https://mlsanity.com/paper/2101.00646/) (2021)
- [Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis](https://mlsanity.com/paper/2405.17468/) (2024)
- [TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs](https://mlsanity.com/paper/2406.10310/) (2024)
- [Reconsidering utility: unveiling the limitations of synthetic mobility data generation algorithms in real-life scenarios](https://mlsanity.com/paper/2407.03237/) (2024)
- [Reconstructing Human Mobility Pattern: A Semi-Supervised Approach for Cross-Dataset Transfer Learning](https://mlsanity.com/paper/2410.03788/) (2024)

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

- [AgentSUMO: An Agentic Framework for Interactive Simulation Scenario Generation in SUMO via Large Language Models](https://mlsanity.com/paper/2511.06804/) (2025)
- [Beyond 9-to-5: A Generative Model for Augmenting Mobility Data of Underrepresented Shift Workers](https://mlsanity.com/paper/2507.19510/) (2025)
- [Uncertainty-Aware Trip Purpose Inference from GPS Trajectories via POI Semantic Zones and Pareto Calibration](https://mlsanity.com/paper/2605.01257/) (2026)
- [GPS-Enhanced Tourist Mobility Modeling with Seasonal Spatial Priors and LLM-Based Activity Chain Generation](https://mlsanity.com/paper/2605.29578/) (2026)
- [GenWorld: Empirically Grounded Urban Simulation Infrastructure for Scalable LLM-Agent Studies](https://mlsanity.com/paper/2606.27650/) (2026)
- [Do LLMs Know Your Neighborhood? Auditing LLM Priors for Neighborhood-Level Mobility Prediction and Structural Alignment](https://mlsanity.com/paper/2609.00345/) (2026)

## Abstract (arXiv)

Human mobility modeling is critical for urban planning and transportation management, yet existing approaches often lack the integration capabilities needed to handle diverse data sources. We present a foundation model framework for universal human mobility patterns that leverages cross-domain data fusion and large language models to address these limitations. Our approach integrates multi-modal data of distinct nature and spatio-temporal resolution, including geographical, mobility, socio-demographic, and traffic information, to construct a privacy-preserving and semantically enriched human travel trajectory dataset. Our framework demonstrates adaptability through domain transfer techniques that ensure transferability across diverse urban contexts, as evidenced in case studies of Los Angeles (LA) and Egypt. The framework employs LLMs for semantic enrichment of trajectory data, enabling comprehensive understanding of mobility patterns. Quantitative evaluation shows that our generated synthetic dataset accurately reproduces mobility patterns observed in empirical data. The practical utility of this foundation model approach is demonstrated through large-scale traffic simulations for LA County, where results align well with observed traffic data. On California's I-405 corridor, the simulation yields a Mean Absolute Percentage Error of 5.85% for traffic volume and 4.36% for speed compared to Caltrans PeMS observations, illustrating the framework's potential for intelligent transportation systems and urban mobility applications.