Abstract: Vehicle trajectories provide crucial movement information for various traffic service applications.To better utilize vehicle trajectories,it is essential to develop trajectory representation learning methods that can effectively and efficiently extract tra-vel semantics,including movement behaviors and travel purposes,to support accurate downstream applications.However,this task presents two major challenges:1) movement behaviors are inherently spatio-temporally continuous,making them difficult to extract effectively from discrete trajectory points;2) travel purposes are related to the functionalities of areas and road segments traversed by vehicles,but these functionalities cannot be directly obtained from the raw spatio-temporal trajectory features,nor can they be extracted from the relevant complex textual features.To address these challenges,this paper proposes an efficient semantic-aware trajectory representation learning method called ESTRL.Firstly,a Mamba-based trajectory encoder is introduced.It uses high-order movement features to parameterize the trajectory state space model(Traj-SSM),which effectively and efficiently models continuous movement behaviors of vehicles.Secondly,a travel purpose-aware pre-training procedure is proposed.It integrates travel purposes into the learned trajectory embeddings through contrastive learning without introducing extra overhead to embedding calculation process.Extensive experiments on real-world datasets demonstrate that the proposed method outperforms state-of-the-art baseline models in both efficiency and accuracy.

Key words: Vehicle trajectories, Trajectory representation learning, State space model, Contrastive learning, Self-supervised pre-training