Abstract
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.
Abstract (translated)
轨迹建模指的是描述人类运动行为的过程，它在理解移动模式中是一个关键的步骤。然而，现有研究通常忽视地理上下文的影响，导致获得伪相关性和有限的泛化能力。为了弥合这一空白，我们首先提出了一个结构因果模型（SCM），从因果视角解码轨迹表示学习过程。在此基础上，我们进一步提出了一个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一种干预工具来消除地理上下文与轨迹之间的伪相关性。在两个真实世界数据集上的广泛实验证实，TrajCL在轨迹分类任务中的性能明显增强，同时表现出卓越的泛化和可解释性。
URL
https://arxiv.org/abs/2404.14073
