Human trajectory modeling is essential for deciphering movement patterns and supporting advanced applications across various domains. However, existing methods are often tailored to specific tasks and regions, resulting in limitations related to task specificity, regional dependency, and data quality sensitivity. Addressing these challenges requires a universal human trajectory foundation model capable of generalizing and scaling across diverse tasks and geographic contexts. To this end, we propose UniTraj, a Universal human Trajectory foundation model that is task-adaptive, region-independent, and highly generalizable. To further enhance performance, we construct WorldTrace, the first large-scale, high-quality, globally distributed dataset sourced from open web platforms, encompassing 2.45 million trajectories with billions of points across 70 countries. Through multiple resampling and masking strategies designed for pre-training, UniTraj effectively overcomes geographic and task constraints, adapting to heterogeneous data quality. Extensive experiments across multiple trajectory analysis tasks and real-world datasets demonstrate that UniTraj consistently outperforms existing approaches in terms of scalability and adaptability. These results underscore the potential of UniTraj as a versatile, robust solution for a wide range of trajectory analysis applications, with WorldTrace serving as an ideal but non-exclusive foundation for training.

翻译：人类轨迹建模对于解析运动模式及支持跨领域高级应用至关重要。然而，现有方法通常针对特定任务和区域设计，存在任务特异性、区域依赖性和数据质量敏感性等局限。应对这些挑战需要一种能够泛化并适应多样化任务与地理背景的通用人类轨迹基础模型。为此，我们提出UniTraj——一种任务自适应、区域无关且具有高度泛化能力的通用人类轨迹基础模型。为提升性能，我们构建了WorldTrace数据集，这是首个基于开放网络平台的大规模、高质量、全球分布式轨迹数据集，涵盖70个国家245万条轨迹、数十亿个轨迹点。通过为预训练设计的多重重采样与掩码策略，UniTraj有效克服了地理与任务限制，并能适应异构数据质量。在多个轨迹分析任务及真实数据集上的大量实验表明，UniTraj在可扩展性与适应性方面持续优于现有方法。这些结果凸显了UniTraj作为广泛轨迹分析应用的通用鲁棒解决方案的潜力，而WorldTrace可作为（非排他性的）理想训练基础。