构建可扩展的移动性基础模型，实现全球范围精准建模与泛化。
MoveGPT: Scaling Mobility Foundation Models with Spatially-Aware Mixture of Experts
- 用统一地理编码器将分散地点映射到共享语义空间，支持全局预训练。
- 设计空间感知专家混合架构，高效捕捉大规模移动模式多样性。
- 在亿级数据上训练，跨城市泛化能力强，平均性能提升达35%。
语言领域基础模型的成功激发了人类移动性通用模型的新浪潮。然而，现有方法因两个根本缺陷难以有效扩展：无法使用有意义的基本单元表示移动行为，且难以捕捉大规模数据中的丰富模式多样性。本文提出MoveGPT，一种专为克服这些障碍而设计的大规模基础模型。其核心创新包括：(1) 统一位置编码器，将地理上分散的地点映射至共享语义空间，支持全局规模预训练；(2) 空间感知专家混合变换器，通过专业化专家高效捕捉多样化移动模式。在亿级规模数据集上预训练后，MoveGPT在多种下游任务中达到新基准，平均性能提升最高达35%。同时展现出对未见城市的强泛化能力。本工作首次为人类移动性领域提供了可扩展性的实证支持，验证了构建更强大基础模型的可行路径。
原文摘要 · Abstract (English)
The success of foundation models in language has inspired a new wave of general-purpose models for human mobility. However, existing approaches struggle to scale effectively due to two fundamental limitations: a failure to use meaningful basic units to represent movement, and an inability to capture the vast diversity of patterns found in large-scale data. In this work, we develop MoveGPT, a large-scale foundation model specifically architected to overcome these barriers. MoveGPT is built upon two key innovations: (1) a unified location encoder that maps geographically disjoint locations into a shared semantic space, enabling pre-training on a global scale; and (2) a Spatially-Aware Mixture-of-Experts Transformer that develops specialized experts to efficiently capture diverse mobility patterns. Pre-trained on billion-scale datasets, MoveGPT establishes a new state-of-the-art across a wide range of downstream tasks, achieving performance gains of up to 35% on average. It also demonstrates strong generalization capabilities to unseen cities. Crucially, our work provides empirical evidence of scaling ability in human mobility, validating a clear path toward building increasingly capable foundation models in this domain.
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