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

• URL: http://arxiv.org/abs/2511.20729v1
• Date: Tue, 25 Nov 2025 10:47:03 GMT
• Title: Spatio-Temporal Trajectory Foundation Model - Recent Advances and Future Directions
• Authors: Sean Bin Yang, Ying Sun, Yunyao Cheng, Yan Lin, Kristian Torp, Jilin Hu,
• Abstract summary: Foundation models (FMs) have emerged as a powerful paradigm, enabling a range of data analytics and knowledge discovery tasks.<n>This tutorial addresses a gap by offering a comprehensive overview of recent advances in foundation models (TFMs)
• Score: 11.997099077115957
• License: http://creativecommons.org/licenses/by/4.0/
• 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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