UniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces

Yuanshao Zhu, James Jianqiao Yu, Xiangyu Zhao, Xun Zhou, Liang Han, Xuetao Wei, Yuxuan Liang

SUSTech· City University of Hong Kong· Hong Kong University of Science and Technology (Guangzhou)· Harbin Institute of Technology

Spatio-Temporal Data Mining Foundation Model Urban Computing

⋅ NeurIPS ⋅ Poster ⋅OpenReview ⋅Code

Abstract

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, with WorldTrace serving as an ideal yet non-exclusive training resource. The implementation codes and full dataset are available at https://github.com/Yasoz/UniTraj.