Propose a vehicle trajectory learning model capable of transferring across regions and tasks without retraining.

We propose a novel trajectory representation learning approach that efficiently learns travel semantics, including movement patterns and travel purposes, from vehicle trajectories.

We propose a trajectory recovery model based on PLM to address the challenge of the availability of dense trajectories is limited and to generalize across sparse trajectories with varying sampling intervals.