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.

翻译：语言基础模型的成功激发了构建通用人类移动模型的新浪潮。然而，现有方法因两个根本性局限而难以有效扩展：一是未能使用有意义的基本单元来表示移动，二是无法捕捉大规模数据中存在的巨大多样性模式。本研究开发了MoveGPT，这是一个专门为克服这些障碍而构建的大规模基础模型。MoveGPT基于两项关键创新：(1) 统一位置编码器，将地理上不连续的位置映射到共享语义空间，实现全球范围的预训练；(2) 空间感知专家混合Transformer，通过开发专用专家模块高效捕捉多样化的移动模式。基于十亿级数据集预训练的MoveGPT在广泛的下游任务中确立了新的性能标杆，平均性能提升高达35%。该模型同时展现出对未见过城市的强大泛化能力。重要的是，我们的工作为人类移动领域的可扩展性提供了实证依据，验证了在该领域构建更强大基础模型的清晰路径。