Self-Supervised Trajectory Representation Learning with Multi-Scale Spatio-Temporal Feature Exploration

Trajectory representation learning transforms the complex spatio-temporal features of trajectories into a dense, low-dimensional embedding, which supports various downstream analytics tasks such as trajectory classification, travel time estimation, and similar trajectory search. Existing trajectory representation learning methods treat trajectories merely as general point sequences and use sequence models to learn the correlations between points. However, the complex spatio-temporal features of trajectories are multi-scale, meaning they are not only reflected in the correlations between trajectory points but also in the correlations between trajectory segments. Moreover, most existing methods do not sufficiently capture the multi-faceted temporal features within trajectories. To fill these gaps, we propose a novel self-supervised Trajectory$R$epresentation$L$earning model with multi-scale spatio-temporal features exploration called TrajRL. Specifically, we utilize trajectory augmentation to generate two views to achieve self-supervised pre-training exploiting multiple self-supervisory signals. In each view, we can learn the multi-scale spatio-temporal correlations both within and between road segments and road segment sequences in trajectories through the proposed multi-scale trajectory encoder. Additionally, we perform multi-faceted temporal information encoding, especially leveraging time intervals to learn multi-scale context-aware time patterns within the trajectories. Extensive experiments demonstrate the superiority of our TrajRL as compared to state-of-the-art baselines on two real-world datasets across various downstream tasks. The source code of our model is available at https://github.com/Xfc30/TrajRL.