【意义】人类移动与交通、传染病、安全等密切相关，使得轨迹分析与建模成为持续的研究热点。目前，学界与业界已发展了大量以机器学习/深度学习为主流的轨迹专门模型，如轨迹插值模型、轨迹预测模型、轨迹分类模型等。然而，这些模型大多针对专门任务设计、基于局部区域数据训练，难以泛化应用于其他任务、其他区域乃至其他类型的轨迹。近年来，随着生成式人工智能发展，通用基础模型在自然语言处理、计算机视觉等领域得到显著应用。在这一技术发展趋势下，构建轨迹基础模型，使其学习到大规模轨迹数据的通用特征，以适用于不同区域与多种下游任务，成为轨迹建模的迫切需求。【方法】本文首先系统综述了各类轨迹专门模型的研究进展与发展脉络，然后将轨迹建模任务分为常规任务（轨迹相似性计算、插值、预测、分类等）与生成任务（轨迹生成），阐述了近年来面向这两类任务的轨迹基础模型前沿研究进展。【结论】本文认为，面向常规任务的轨迹基础模型除了具备任务泛化能力，还应进一步强化其空间泛化与数据泛化能力；面向生成任务的轨迹基础模型还需攻克空间泛化难题，能够基于易获取的目标城市宏观数据或特征，“从无到有”生成城市级大规模轨迹数据。此外，将轨迹数据与其他类型数据（如文本、地图、其他地理空间数据）联合构建多模态地理基础模型，以及构建面向交通管理、传染病传播、公安寻人等业务场景的轨迹基础模型，也是未来值得探讨的研究方向。

[Significance] Human mobility is closely tied to transportation, infectious disease spread, and public safety, making trajectory analysis and modeling a long-standing research focus. While numerous specialized trajectory models, such as interpolation, prediction, and classification models, have been developed using machine learning or deep learning, most are task-specific and trained on localized datasets, limiting their generalizability across tasks, regions, or trajectory data. Recent advances in generative AI have demonstrated the potential of foundation models in NLP and computer vision, motivating the need for a trajectory foundation model capable of learning universal patterns from large-scale mobility data to support diverse downstream applications. [Methods] This paper first reviews the research progress of various specialized trajectory models. It then categorizes trajectory modeling tasks into conventional tasks (e.g., trajectory similarity computation, interpolation, prediction, and classification) and generation task (i.e., trajectory generation), and elaborates on recent advances in trajectory foundation models for these two types of tasks. [Conclusions] The paper argues that trajectory foundation models for conventional tasks should enhance not only task generalization but also spatial and data generalization. Trajectory foundation models for generation task must address the challenge of spatial generalization, enabling the generation of large-scale trajectory data "from scratch" based on easily obtainable macro-level urban data or features. Furthermore, integrating trajectory data with other data types (e.g., text, maps, and other geospatial data) to construct multimodal geographic foundation models, as well as developing application-oriented trajectory foundation models for fields such as transportation, public health, and public safety, are promising research directions worthy of future exploration.

利益冲突：Conflicts of Interest 所有作者声明不存在利益冲突。

All authors disclose no relevant conflicts of interest.