UniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces
Yuanshao Zhu, James Yu, Xiangyu Zhao, Xun Zhou, Liang Han, Xuetao Wei, Yuxuan Liang
Building a universal trajectory foundation model is a promising solution to address the limitations of existing trajectory modeling approaches, such as task specificity, regional dependency, and data sensitivity.Despite its potential, data preparation, pre-training strategy development, and architectural design present significant challenges in constructing this model.Therefore, we introduce **UniTraj**, a Universal Trajectory foundation model that aims to address these limitations through three key innovations.First, we construct **WorldTrace**, an unprecedented dataset of 2.45 million trajectories with billions of GPS points spanning 70 countries, providing the diverse geographic coverage essential for region-independent modeling. Second, we develop novel pre-training strategies---Adaptive Trajectory Resampling and Self-supervised Trajectory Masking---that enable robust learning from heterogeneous trajectory data with varying sampling rates and quality. Finally, we tailor a flexible model architecture to accommodate a variety of trajectory tasks, effectively capturing complex movement patterns to support broad applicability.Extensive experiments across multiple tasks and real-world datasets demonstrate that UniTraj consistently outperforms existing methods, exhibiting superior scalability, adaptability, and generalization. These findings also position UniTraj as a versatile solution for a wide array of trajectory-related applications, with WorldTrace serving as an ideal yet non-exclusive training resource. The implementation codes and dataset samples are available in the **Supplementary Material**.