research-article

Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks

Authors:

Chao Huang, Yanwei Yu,

Junyu DongAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 4

Article No.: 85, Pages 1 - 22

https://doi.org/10.1145/3635718

Published: 12 February 2024 Publication History

Abstract

Trajectory-User Linking (TUL) is crucial for human mobility modeling by linking different trajectories to users with the exploration of complex mobility patterns. Existing works mainly rely on the recurrent neural framework to encode the temporal dependencies in trajectories, have fall short in capturing spatial-temporal global context for TUL prediction. To fill this gap, this work presents a new hierarchical spatio-temporal attention neural network, called AttnTUL, to jointly encode the local trajectory transitional patterns and global spatial dependencies for TUL. Specifically, our first model component is built over the graph neural architecture to preserve the local and global context and enhance the representation paradigm of geographical regions and user trajectories. Additionally, a hierarchically structured attention network is designed to simultaneously encode the intra-trajectory and inter-trajectory dependencies, with the integration of the temporal attention mechanism and global elastic attentional encoder. Extensive experiments demonstrate the superiority of our AttnTUL method as compared to state-of-the-art baselines on various trajectory datasets. The source code of our model is available at https://github.com/Onedean/AttnTUL.

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Cited By

Li YSun TShao ZZhen YXu YWang F(2025)Trajectory-User Linking via Multi-Scale Graph Attention NetworkPattern Recognition10.1016/j.patcog.2024.110978158(110978)Online publication date: Feb-2025
https://doi.org/10.1016/j.patcog.2024.110978
Dong FCao YWang NZong TChen LLi JZhao WHan YHu X(2024)Trajectory-user linking via Complexed-Valued Multi-Layer Perception with Fast Fourier Transform2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC)10.1109/YAC63405.2024.10598759(1514-1519)Online publication date: 7-Jun-2024
https://doi.org/10.1109/YAC63405.2024.10598759
Cen KYang ZWang ZDong M(2024)A cross-domain user association scheme based on graph attention networks with trajectory embeddingMachine Language10.1007/s10994-024-06613-z113:10(7905-7930)Online publication date: 21-Aug-2024
https://dl.acm.org/doi/10.1007/s10994-024-06613-z

Index Terms

Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
      2. Neural networks
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 18, Issue 4

May 2024

707 pages

EISSN:1556-472X

DOI:10.1145/3613622

Editor:
Jian Pei
Duke University, USA

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Publication History

Published: 12 February 2024

Online AM: 04 December 2023

Accepted: 29 November 2023

Revised: 16 August 2023

Received: 10 July 2022

Published in TKDD Volume 18, Issue 4

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Cited By

Li YSun TShao ZZhen YXu YWang F(2025)Trajectory-User Linking via Multi-Scale Graph Attention NetworkPattern Recognition10.1016/j.patcog.2024.110978158(110978)Online publication date: Feb-2025
https://doi.org/10.1016/j.patcog.2024.110978
Dong FCao YWang NZong TChen LLi JZhao WHan YHu X(2024)Trajectory-user linking via Complexed-Valued Multi-Layer Perception with Fast Fourier Transform2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC)10.1109/YAC63405.2024.10598759(1514-1519)Online publication date: 7-Jun-2024
https://doi.org/10.1109/YAC63405.2024.10598759
Cen KYang ZWang ZDong M(2024)A cross-domain user association scheme based on graph attention networks with trajectory embeddingMachine Language10.1007/s10994-024-06613-z113:10(7905-7930)Online publication date: 21-Aug-2024
https://dl.acm.org/doi/10.1007/s10994-024-06613-z

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