  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.
