GTR: A General, Multi-View, and Dynamic Framework for Trajectory Representation Learning

Xiangheng Wang, Ziquan Fang, Chenglong Huang, Danlei Hu, Lu Chen, Yunjun Gao
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:62825-62844, 2025.

Abstract

Trajectory representation learning aims to transform raw trajectory data into compact and low-dimensional vectors that are suitable for downstream analysis. However, most existing methods adopt either a free-space view or a road-network view during the learning process, which limits their ability to capture the complex, multi-view spatiotemporal features inherent in trajectory data. Moreover, these approaches rely on task-specific model training, restricting their generalizability and effectiveness for diverse analysis tasks. To this end, we propose GTR, a general, multi-view, and dynamic Trajectory Representation framework built on a pre-train and fine-tune architecture. Specifically, GTR introduces a multi-view encoder that captures the intrinsic multi-view spatiotemporal features. Based on the pre-train and fine-tune architecture, we provide the spatio-temporal fusion pre-training with a spatio-temporal mixture of experts to dynamically combine spatial and temporal features, enabling seamless adaptation to diverse trajectory analysis tasks. Furthermore, we propose an online frozen-hot updating strategy to efficiently update the representation model, accommodating the dynamic nature of trajectory data. Extensive experiments on two real-world datasets demonstrate that GTR consistently outperforms 15 state-of-the-art methods across 6 mainstream trajectory analysis tasks. All source code and data are available at https://github.com/ZJU-DAILY/GTR.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-wang25z, title = {{GTR}: A General, Multi-View, and Dynamic Framework for Trajectory Representation Learning}, author = {Wang, Xiangheng and Fang, Ziquan and Huang, Chenglong and Hu, Danlei and Chen, Lu and Gao, Yunjun}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {62825--62844}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/wang25z/wang25z.pdf}, url = {https://proceedings.mlr.press/v267/wang25z.html}, abstract = {Trajectory representation learning aims to transform raw trajectory data into compact and low-dimensional vectors that are suitable for downstream analysis. However, most existing methods adopt either a free-space view or a road-network view during the learning process, which limits their ability to capture the complex, multi-view spatiotemporal features inherent in trajectory data. Moreover, these approaches rely on task-specific model training, restricting their generalizability and effectiveness for diverse analysis tasks. To this end, we propose GTR, a general, multi-view, and dynamic Trajectory Representation framework built on a pre-train and fine-tune architecture. Specifically, GTR introduces a multi-view encoder that captures the intrinsic multi-view spatiotemporal features. Based on the pre-train and fine-tune architecture, we provide the spatio-temporal fusion pre-training with a spatio-temporal mixture of experts to dynamically combine spatial and temporal features, enabling seamless adaptation to diverse trajectory analysis tasks. Furthermore, we propose an online frozen-hot updating strategy to efficiently update the representation model, accommodating the dynamic nature of trajectory data. Extensive experiments on two real-world datasets demonstrate that GTR consistently outperforms 15 state-of-the-art methods across 6 mainstream trajectory analysis tasks. All source code and data are available at https://github.com/ZJU-DAILY/GTR.} }
Endnote
%0 Conference Paper %T GTR: A General, Multi-View, and Dynamic Framework for Trajectory Representation Learning %A Xiangheng Wang %A Ziquan Fang %A Chenglong Huang %A Danlei Hu %A Lu Chen %A Yunjun Gao %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-wang25z %I PMLR %P 62825--62844 %U https://proceedings.mlr.press/v267/wang25z.html %V 267 %X Trajectory representation learning aims to transform raw trajectory data into compact and low-dimensional vectors that are suitable for downstream analysis. However, most existing methods adopt either a free-space view or a road-network view during the learning process, which limits their ability to capture the complex, multi-view spatiotemporal features inherent in trajectory data. Moreover, these approaches rely on task-specific model training, restricting their generalizability and effectiveness for diverse analysis tasks. To this end, we propose GTR, a general, multi-view, and dynamic Trajectory Representation framework built on a pre-train and fine-tune architecture. Specifically, GTR introduces a multi-view encoder that captures the intrinsic multi-view spatiotemporal features. Based on the pre-train and fine-tune architecture, we provide the spatio-temporal fusion pre-training with a spatio-temporal mixture of experts to dynamically combine spatial and temporal features, enabling seamless adaptation to diverse trajectory analysis tasks. Furthermore, we propose an online frozen-hot updating strategy to efficiently update the representation model, accommodating the dynamic nature of trajectory data. Extensive experiments on two real-world datasets demonstrate that GTR consistently outperforms 15 state-of-the-art methods across 6 mainstream trajectory analysis tasks. All source code and data are available at https://github.com/ZJU-DAILY/GTR.
APA
Wang, X., Fang, Z., Huang, C., Hu, D., Chen, L. & Gao, Y.. (2025). GTR: A General, Multi-View, and Dynamic Framework for Trajectory Representation Learning. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:62825-62844 Available from https://proceedings.mlr.press/v267/wang25z.html.

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