Summary of Grid and Road Expressions Are Complementary For Trajectory Representation Learning, by Silin Zhou et al.
Grid and Road Expressions Are Complementary for Trajectory Representation Learning
by Silin Zhou, Shuo Shang, Lisi Chen, Peng Han, Christian S. Jensen
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to trajectory representation learning (TRL), dubbed GREEN, which jointly utilizes grid and road trajectories for effective representation learning. The method transforms raw GPS trajectories into both grid and road trajectories and employs two encoders to capture their respective information. A contrastive loss is used to align the two encoders, while a mask language model (MLM) loss is designed to utilize grid trajectories to help reconstruct masked road trajectories. The final trajectory representation is learned using a dual-modal interactor that fuses the outputs of the two encoders via cross-attention. GREEN outperforms 7 state-of-the-art TRL methods for 3 downstream tasks, with an average improvement of 15.99% over the best-performing baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn about movements and paths using both map-like and free-form data. The current methods use either one or the other type of data, but this new approach combines both types to get better results. It uses special algorithms to analyze the data and create a single representation that captures both the location and movement patterns. The method is tested on three different tasks and outperforms the existing approaches by an average of 15.99%. |
Keywords
» Artificial intelligence » Contrastive loss » Cross attention » Language model » Mask » Representation learning
