TIGR combines grid, road network, and spatio-temporal branches with contrastive learning to produce trajectory embeddings that outperform single-modality baselines on similarity, travel time, and destination prediction.
Trajgat: A graph- based long-term dependency modeling approach for trajectory similarity computation,
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Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics
TIGR combines grid, road network, and spatio-temporal branches with contrastive learning to produce trajectory embeddings that outperform single-modality baselines on similarity, travel time, and destination prediction.