Go to NeurIPS 2025 Conference homepageUniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces
Keywords: Spatio-Temporal Data Mining, Foundation Model, Urban Computing
TL;DR: We built the first worldwide trajectory dataset and trained a universal trajectory foundation model.
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.
Primary Area: Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)
Flagged For Ethics Review: true
Submission Number: 6533
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