RED is a masked-autoencoder trajectory representation learner that preserves key road segments during masking and encodes spatial, temporal, and user information together, improving downstream task accuracy on three datasets.
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RED: Effective Trajectory Representation Learning with Comprehensive Information
RED is a masked-autoencoder trajectory representation learner that preserves key road segments during masking and encodes spatial, temporal, and user information together, improving downstream task accuracy on three datasets.