Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.20729 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912729550290944 |
|---|---|
| author | Yang, Sean Bin Sun, Ying Cheng, Yunyao Lin, Yan Torp, Kristian Hu, Jilin |
| author_facet | Yang, Sean Bin Sun, Ying Cheng, Yunyao Lin, Yan Torp, Kristian Hu, Jilin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20729 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spatio-Temporal Trajectory Foundation Model - Recent Advances and Future Directions Yang, Sean Bin Sun, Ying Cheng, Yunyao Lin, Yan Torp, Kristian Hu, Jilin Machine Learning Artificial Intelligence 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. |
| title | Spatio-Temporal Trajectory Foundation Model - Recent Advances and Future Directions |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2511.20729 |