MoGERNN uses a mixture-of-graph-experts module and encoder-decoder structure to predict traffic states at unobserved locations and remain effective when the sensor network changes.
Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting,
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SAGMTL decomposes dynamic sparse OD demand prediction into joint structural state modeling and flow intensity estimation via node-edge collaborative graph representations.
citing papers explorer
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MoGERNN: An Inductive Traffic Predictor for Unobserved Locations
MoGERNN uses a mixture-of-graph-experts module and encoder-decoder structure to predict traffic states at unobserved locations and remain effective when the sensor network changes.
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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.