Learning Generalized and Flexible Trajectory Models from Omni-Semantic Supervision
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913854999494656 |
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| author | Zhu, Yuanshao Yu, James Jianqiao Zhao, Xiangyu Han, Xiao Liu, Qidong Wei, Xuetao Liang, Yuxuan |
| author_facet | Zhu, Yuanshao Yu, James Jianqiao Zhao, Xiangyu Han, Xiao Liu, Qidong Wei, Xuetao Liang, Yuxuan |
| contents | The widespread adoption of mobile devices and data collection technologies has led to an exponential increase in trajectory data, presenting significant challenges in spatio-temporal data mining, particularly for efficient and accurate trajectory retrieval. However, existing methods for trajectory retrieval face notable limitations, including inefficiencies in large-scale data, lack of support for condition-based queries, and reliance on trajectory similarity measures. To address the above challenges, we propose OmniTraj, a generalized and flexible omni-semantic trajectory retrieval framework that integrates four complementary modalities or semantics -- raw trajectories, topology, road segments, and regions -- into a unified system. Unlike traditional approaches that are limited to computing and processing trajectories as a single modality, OmniTraj designs dedicated encoders for each modality, which are embedded and fused into a shared representation space. This design enables OmniTraj to support accurate and flexible queries based on any individual modality or combination thereof, overcoming the rigidity of traditional similarity-based methods. Extensive experiments on two real-world datasets demonstrate the effectiveness of OmniTraj in handling large-scale data, providing flexible, multi-modality queries, and supporting downstream tasks and applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_17437 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Generalized and Flexible Trajectory Models from Omni-Semantic Supervision Zhu, Yuanshao Yu, James Jianqiao Zhao, Xiangyu Han, Xiao Liu, Qidong Wei, Xuetao Liang, Yuxuan Computer Vision and Pattern Recognition Artificial Intelligence The widespread adoption of mobile devices and data collection technologies has led to an exponential increase in trajectory data, presenting significant challenges in spatio-temporal data mining, particularly for efficient and accurate trajectory retrieval. However, existing methods for trajectory retrieval face notable limitations, including inefficiencies in large-scale data, lack of support for condition-based queries, and reliance on trajectory similarity measures. To address the above challenges, we propose OmniTraj, a generalized and flexible omni-semantic trajectory retrieval framework that integrates four complementary modalities or semantics -- raw trajectories, topology, road segments, and regions -- into a unified system. Unlike traditional approaches that are limited to computing and processing trajectories as a single modality, OmniTraj designs dedicated encoders for each modality, which are embedded and fused into a shared representation space. This design enables OmniTraj to support accurate and flexible queries based on any individual modality or combination thereof, overcoming the rigidity of traditional similarity-based methods. Extensive experiments on two real-world datasets demonstrate the effectiveness of OmniTraj in handling large-scale data, providing flexible, multi-modality queries, and supporting downstream tasks and applications. |
| title | Learning Generalized and Flexible Trajectory Models from Omni-Semantic Supervision |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2505.17437 |