MobiFuse: Learning Universal Human Mobility Patterns through Cross-domain Data Fusion

Human mobility modeling is critical for urban planning and transportation management, yet existing datasets often lack the resolution and semantic richness required for comprehensive analysis. To address this, we proposed a cross-domain data fusion framework that 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. This framework is demonstrated through two case studies in Los Angeles (LA) and Egypt, where a domain adaptation algorithm ensures its transferability across diverse urban contexts. Quantitative evaluation shows that the generated synthetic dataset accurately reproduces mobility patterns observed in empirical data. Moreover, large-scale traffic simulations for LA County based on the generated synthetic demand align well with observed traffic. 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.

@article{ma2025_2503.15779,
  title={ MobiFuse: Learning Universal Human Mobility Patterns through Cross-domain Data Fusion },
  author={ Haoxuan Ma and Xishun Liao and Yifan Liu and Qinhua Jiang and Chris Stanford and Shangqing Cao and Jiaqi Ma },
  journal={arXiv preprint arXiv:2503.15779},
  year={ 2025 }
}