Trajectory modeling refers to characterizing human movement behavior, serving as a pivotal step in understanding mobility patterns. Nevertheless, existing studies typically ignore the confounding effects of geospatial context, leading to the acquisition of spurious correlations and limited generalization capabilities. To bridge this gap, we initially formulate a Structural Causal Model (SCM) to decipher the trajectory representation learning process from a causal perspective. Building upon the SCM, we further present a Trajectory modeling framework (TrajCL) based on Causal Learning, which leverages the backdoor adjustment theory as an intervention tool to eliminate the spurious correlations between geospatial context and trajectories. Extensive experiments on two real-world datasets verify that TrajCL markedly enhances performance in trajectory classification tasks while showcasing superior generalization and interpretability.

翻译：轨迹建模是指表征人类移动行为，是理解移动模式的关键步骤。然而，现有研究通常忽略地理空间上下文的混淆效应，导致学习到伪相关关系且泛化能力有限。为填补这一空白，我们首先构建了一个结构因果模型（SCM），从因果视角解析轨迹表示学习过程。基于该SCM，我们进一步提出了基于因果学习的轨迹建模框架（TrajCL），该框架利用后门调整理论作为干预工具，消除地理空间上下文与轨迹之间的伪相关关系。在两个真实世界数据集上的大量实验证明，TrajCL在轨迹分类任务中显著提升了性能，同时展现出卓越的泛化能力和可解释性。