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

• URL: http://arxiv.org/abs/2503.15779v1
• Date: Thu, 20 Mar 2025 01:41:28 GMT
• Title: MobiFuse: Learning Universal Human Mobility Patterns through Cross-domain Data Fusion
• Authors: Haoxuan Ma, Xishun Liao, Yifan Liu, Qinhua Jiang, Chris Stanford, Shangqing Cao, Jiaqi Ma,
• Abstract summary: We propose a cross-domain data fusion framework that integrates data of distinct nature andtemporal resolution.<n>This framework is demonstrated through two case studies in Los Angeles (LA) and Egypt.<n>Large-scale traffic simulations for LA County based on the generated synthetic demand align well with observed traffic.
• 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 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.

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