TrajMoE: Spatially-Aware Mixture of Experts for Unified Human Mobility Modeling

• URL: http://arxiv.org/abs/2505.18670v1
• Date: Sat, 24 May 2025 12:17:47 GMT
• Title: TrajMoE: Spatially-Aware Mixture of Experts for Unified Human Mobility Modeling
• Authors: Chonghua Han, Yuan Yuan, Kaiyan Chen, Jingtao Ding, Yong Li,
• Abstract summary: We propose TrajMoE, a unified and scalable model for cross-city human mobility modeling.<n>TrajMoE addresses two key challenges: (1) inconsistent spatial semantics across cities, and (2) diverse urban mobility patterns.<n>Extensive experiments demonstrate that TrajMoE achieves up to 27% relative improvement over competitive mobility foundation models.
• Score: 10.338272381612112
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modeling human mobility across diverse cities is essential for applications such as urban planning, transportation optimization, and personalized services. However, generalization remains challenging due to heterogeneous spatial representations and mobility patterns across cities. Existing methods typically rely on numerical coordinates or require training city-specific models, limiting their scalability and transferability. We propose TrajMoE, a unified and scalable model for cross-city human mobility modeling. TrajMoE addresses two key challenges: (1) inconsistent spatial semantics across cities, and (2) diverse urban mobility patterns. To tackle these, we begin by designing a spatial semantic encoder that learns transferable location representations from POI-based functional semantics and visit patterns. Furthermore, we design a Spatially-Aware Mixture-of-Experts (SAMoE) Transformer that injects structured priors into experts specialized in distinct mobility semantics, along with a shared expert to capture city-invariant patterns and enable adaptive cross-city generalization. Extensive experiments demonstrate that TrajMoE achieves up to 27% relative improvement over competitive mobility foundation models after only one epoch of fine-tuning, and consistently outperforms full-data baselines using merely 5% of target city data. These results establish TrajMoE as a significant step toward realizing a truly generalizable, transferable, and pretrainable foundation model for human mobility.

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