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arXiv · 2511.20729

Spatio-Temporal Trajectory Foundation Model - Recent Advances and Future Directions

Sean Bin Yang·Ying Sun·Yunyao Cheng·Yan Lin·Kristian Torp·Jilin Hu
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Abstract

Foundation models (FMs) have emerged as a powerful paradigm, enabling a diverse range of data analytics and knowledge discovery tasks across scientific fields. Inspired by the success of FMs, particularly large language models, researchers have recently begun to explore spatio-temporal foundation models (STFMs) to improve adaptability and generalization across a wide spectrum of spatio-temporal (ST) tasks. Despite rapid progress, a systematic investigation of trajectory foundation models (TFMs), a crucial subclass of STFMs, is largely lacking. This tutorial addresses this gap by offering a comprehensive overview of recent advances in TFMs, including a taxonomy of existing methodologies and a critical analysis of their strengths and limitations. In addition, the tutorial highlights open challenges and outlines promising research directions to advance spatio-temporal general intelligence through the development of robust, responsible, and transferable TFMs.

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Sean Bin Yang, Ying Sun, Yunyao Cheng, Yan Lin, Kristian Torp, Jilin Hu. 2025-11-25. Spatio-Temporal Trajectory Foundation Model - Recent Advances and Future Directions. https://arxiv.org/abs/2511.20729

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