SAGMTL decomposes dynamic sparse OD demand prediction into joint structural state modeling and flow intensity estimation via node-edge collaborative graph representations.
Short-term origin- destination demand prediction in urban rail transit systems: A channel- wise attentive split-convolutional neural network method,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Structure-Aware Graph Multi-Task Learning for Dynamic Sparse OD Demand Prediction
SAGMTL decomposes dynamic sparse OD demand prediction into joint structural state modeling and flow intensity estimation via node-edge collaborative graph representations.