Search Results for author:
Found 1 papers, 0 papers with code
Jointly spatial-temporal representation learning for individual trajectories
The proposed ST-GraphRL consists of three compositions: (i) a weighted directed spatial-temporal graph to explicitly construct mobility interactions in both space and time dimensions; (ii) a two-stage jointly encoder (i. e., decoupling and fusion), to learn entangled spatial-temporal dependencies by independently decomposing and jointly aggregating space and time information; (iii) a decoder guides ST-GraphRL to learn explicit mobility regularities by simulating the spatial-temporal distributions of trajectories.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
