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A Novel Prediction Model for Trajectory-User Linking Based on Graph Attention Network

Author

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  • Haitao Zhang

    (Nanjing University of Posts and Telecommunications, China)

  • Yuxuan Ge

    (School of Internet of Things, Nanjing University of Posts and Telecommunications, China)

  • Huixiang Jiang

    (School of Internet of Things, Nanjing University of Posts and Telecommunications, China)

  • DeCheng Pan

    (School of Internet of Things, Nanjing University of Posts and Telecommunications, China)

  • Yuanjian Liu

    (School of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, China)

  • Yi Li

    (China Academy of Railway Sciences, China)

  • Yuhao Luo

    (China Academy of Railway Sciences, China)

Abstract

Trajectory-user linking (TUL) plays a vital role in multisource geospatial data analytics for behavioral pattern recognition and user identification. Addressing the limitations of conventional TUL approaches in computational efficiency and predictive accuracy, this study proposes a novel prediction model for TUL based on graph attention network (PMTULGAN) that harnesses graph attention networks to significantly enhance predictive performance of TUL tasks. PMTULGAN's key innovation lies in its dynamic attention mechanism, which adaptively allocates weights to nodes to facilitate more accurate extraction and interpretation of salient features within complex trajectory data. Extensive experimental evaluations reveal a substantial performance enhancement for the proposed method over conventional methods. These empirical results underscore the robustness and reliability of PMTULGAN in various data scenarios and substantiate its practical utility in real-world applications.

Suggested Citation

  • Haitao Zhang & Yuxuan Ge & Huixiang Jiang & DeCheng Pan & Yuanjian Liu & Yi Li & Yuhao Luo, 2026. "A Novel Prediction Model for Trajectory-User Linking Based on Graph Attention Network," International Journal of Data Warehousing and Mining (IJDWM), IGI Global Scientific Publishing, vol. 22(1), pages 1-20, January.
    • Handle: RePEc:igg:jdwm00:v:22:y:2026:i:1:p:1-20
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