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RED: Effective Trajectory Representation Learning with Comprehensive Information by Zhou, Silin; Shang, Shuo; Chen, Lisi; Jensen, Christian S.; Kalnis, Panos is a scholarly article available to read on EtoBox.

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Trajectory representation learning (TRL) maps trajectories to vectors that can then be used for various downstream tasks, including trajectory similarity computation, trajectory classification, and travel-time estimation. However, existing TRL methods often produce vectors that, when used in downstream tasks, yield insufficiently accurate results. A key reason is that they fail to utilize the comprehensive information encompassed by trajectories. We propose a self-supervised TRL framework, called RED, which effectively exploits multiple types of trajectory information. Overall, RED adopts the Transformer as the backbone model and masks the constituting paths in trajectories to train a masked autoencoder (MAE). In particular, RED considers the moving patterns of trajectories by employing a Road-aware masking strategy} that retains key paths of trajectories during masking, thereby preserving crucial information of the trajectories. RED also adopts a spatial-temporal-user joint Embedding scheme to encode comprehensive information when preparing the trajectories as model inputs. To conduct training, RED adopts Dual-objective task learning}: the Transformer encoder predicts the next seg

Author

Zhou, Silin; Shang, Shuo; Chen, Lisi; Jensen, Christian S.; Kalnis, Panos

Published

2024

Language

EN