The importance of mobile phone GPS trajectory data is widely recognized across many fields, yet the use of real data is often hindered by privacy concerns, limited accessibility, and high acquisition costs. As a result, generating pseudo-GPS trajectory data has become an active area of research. Recent diffusion-based approaches have achieved strong fidelity but remain limited in spatial scale (small urban areas), transportation-mode diversity, and efficiency (requiring numerous sampling steps). To address these challenges, we introduce TrajFlow, which to the best of our knowledge is the first flow-matching-based generative model for GPS trajectory generation. TrajFlow leverages the flow-matching paradigm to improve robustness and efficiency across multiple geospatial scales, and incorporates a trajectory harmonization and reconstruction strategy to jointly address scalability, diversity, and efficiency. Using a nationwide mobile phone GPS dataset with millions of trajectories across Japan, we show that TrajFlow or its variants consistently outperform diffusion-based and deep generative baselines at urban, metropolitan, and nationwide levels. As the first nationwide, multi-scale GPS trajectory generation model, TrajFlow demonstrates strong potential to support inter-region urban planning, traffic management, and disaster response, thereby advancing the resilience and intelligence of future mobility systems.

翻译：移动电话GPS轨迹数据的重要性已在众多领域得到广泛认可，然而真实数据的使用常受限于隐私问题、可获取性有限以及高昂的采集成本。因此，生成伪GPS轨迹数据已成为一个活跃的研究方向。近期基于扩散模型的方法虽实现了较高的保真度，但在空间尺度（小型城区）、交通方式多样性及生成效率（需大量采样步骤）方面仍存在局限。为应对这些挑战，我们提出了TrajFlow——据我们所知，这是首个基于流匹配的GPS轨迹生成模型。TrajFlow利用流匹配范式提升多地理尺度下的鲁棒性与效率，并结合轨迹协调与重建策略，协同解决可扩展性、多样性与效率问题。基于覆盖日本全国、包含数百万条轨迹的移动电话GPS数据集，我们证明TrajFlow及其变体在城市、都市圈及全国尺度上均持续优于基于扩散模型的方法及其他深度生成基线。作为首个全国范围、多尺度的GPS轨迹生成模型，TrajFlow展现出支持跨区域城市规划、交通管理与灾害响应的巨大潜力，从而推动未来移动系统的韧性与智能化发展。