@inproceedings{zhu2025learninggen,
    author = {Zhu, Yuanshao and Yu, James Jianqiao and Zhao, Xiangyu and Han, Xiao and Liu, Qidong and Wei, Xuetao and Liang, Yuxuan},
title = {Learning Generalized and Flexible Trajectory Models from Omni-Semantic Supervision},
    year = {2025},
    isbn = {9798400714542},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3711896.3737019},
    doi = {10.1145/3711896.3737019},
    abstract = {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.},
    booktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2},
    pages = {4214–4225},
    numpages = {12},
    keywords = {gps trajectory analysis, multi-modality retrieval, spatio-temporal data mining},
    location = {Toronto ON, Canada},
    series = {KDD '25}
}