Grid and Road Expressions Are Complementary for Trajectory Representation Learning
Silin Zhou, Shuo Shang, Lisi Chen, Peng Han, Christian S. Jensen

TL;DR
This paper introduces GREEN, a multimodal trajectory representation learning method that combines grid and road trajectory data using contrastive and MLM losses, significantly improving downstream task performance.
Contribution
GREEN is the first to jointly utilize grid and road trajectory data with tailored encoders and a dual-modal fusion, enhancing trajectory representations.
Findings
GREEN outperforms 7 state-of-the-art methods across three tasks.
GREEN improves baseline accuracy by an average of 15.99%.
The multimodal approach effectively captures complementary trajectory information.
Abstract
Trajectory representation learning (TRL) maps trajectories to vectors that can be used for many downstream tasks. Existing TRL methods use either grid trajectories, capturing movement in free space, or road trajectories, capturing movement in a road network, as input. We observe that the two types of trajectories are complementary, providing either region and location information or providing road structure and movement regularity. Therefore, we propose a novel multimodal TRL method, dubbed GREEN, to jointly utilize Grid and Road trajectory Expressions for Effective representatioN learning. In particular, we transform raw GPS trajectories into both grid and road trajectories and tailor two encoders to capture their respective information. To align the two encoders such that they complement each other, we adopt a contrastive loss to encourage them to produce similar embeddings for the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNatural Language Processing Techniques · Data Management and Algorithms · Geographic Information Systems Studies
MethodsGreedy Policy Search · ALIGN · ADaptive gradient method with the OPTimal convergence rate
