  Spatio-temporal trajectories play a vital role in various spatio-temporal
data mining tasks. Developing a versatile trajectory learning approach that can
adapt to different tasks while ensuring high accuracy is crucial. This requires
effectively extracting movement patterns and travel purposes embedded in
trajectories. However, this task is challenging due to limitations in the size
and quality of available trajectory datasets. On the other hand, pre-trained
language models (PLMs) have shown great success in adapting to different tasks
by training on large-scale, high-quality corpus datasets. Given the
similarities between trajectories and sentences, there is potential in
leveraging PLMs to enhance the development of a versatile and effective
trajectory learning method. Nevertheless, vanilla PLMs are not tailored to
handle the unique spatio-temporal features present in trajectories and lack the
capability to extract movement patterns and travel purposes from them.
  To overcome these obstacles, we propose a model called PLM4Traj that
effectively utilizes PLMs to model trajectories. PLM4Traj leverages the
strengths of PLMs to create a versatile trajectory learning approach while
addressing the limitations of vanilla PLMs in modeling trajectories. Firstly,
PLM4Traj incorporates a novel trajectory semantic embedder that enables PLMs to
process spatio-temporal features in trajectories and extract movement patterns
and travel purposes from them. Secondly, PLM4Traj introduces a novel trajectory
prompt that integrates movement patterns and travel purposes into PLMs, while
also allowing the model to adapt to various tasks. Extensive experiments
conducted on two real-world datasets and two representative tasks demonstrate
that PLM4Traj successfully achieves its design goals. Codes are available at
https://github.com/Zeru19/PLM4Traj.
