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

• URL: http://arxiv.org/abs/2503.15779v2
• Date: Thu, 17 Jul 2025 02:52:37 GMT
• Title: Learning Universal Human Mobility Patterns with a Foundation Model for Cross-domain Data Fusion
• Authors: Haoxuan Ma, Xishun Liao, Yifan Liu, Qinhua Jiang, Chris Stanford, Shangqing Cao, Jiaqi Ma,
• Abstract summary: We present a foundation model framework for universal human mobility.<n>We leverage cross-domain data fusion and large language models to address limitations.<n>Our framework demonstrates adaptability through domain transfer techniques.
• Score: 11.332722237426987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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