Trajectory-User Linking (TUL) is crucial for human mobility modeling by linking diferent 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 ill 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. Speciically, our irst 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.

翻译：轨迹-用户关联（Trajectory-User Linking, TUL）通过探索复杂的移动模式将不同轨迹与用户关联，对人类移动性建模至关重要。现有研究主要依赖循环神经网络框架编码轨迹中的时间依赖性，但在捕捉用于TUL预测的时空全局上下文方面存在不足。为填补这一空白，本文提出一种新型分层时空注意力神经网络——AttnTUL，通过联合编码轨迹局部转移模式与全局空间依赖性实现TUL。具体而言，模型首个组件基于图神经网络架构构建，用于保留局部与全局上下文，增强地理区域与用户轨迹的表征范式。此外，我们设计了一种分层结构的注意力网络，通过融合时间注意力机制与全局弹性注意力编码器，同步编码轨迹内与轨迹间的依赖关系。大量实验表明，在多种轨迹数据集上，我们的AttnTUL方法相较于当前最优基线方法展现出显著优势。模型源代码已开源至https://github.com/Onedean/AttnTUL。