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Found 4 papers, 3 papers with code
Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond
In this paper, we present a comprehensive review of the development and recent advances in deep learning for trajectory computing (DL4Traj).
Spatial-temporal Forecasting for Regions without Observations
Our key insight is to learn from the locations that resemble those in the region of interest, and we propose a selective masking strategy to enable the learning.
Urban Region Representation Learning with Attentive Fusion
Our model is powered by a dual-feature attentive fusion module named DAFusion, which fuses embeddings from different region features to learn higher-order correlations between the regions as well as between the different types of region features.
Contrastive Trajectory Similarity Learning with Dual-Feature Attention
Trajectory similarity measures act as query predicates in trajectory databases, making them the key player in determining the query results.
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