HFD-TM predicts turning movements with 2.49 MAE by hierarchically decomposing corridor flows and enforcing conservation, outperforming Transformer and GRU baselines on six months of Nashville LiDAR data.
Spatial–temporal graph transformer network for traffic network flow prediction using parallel training based on cloud computing,
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Hierarchical Flow Decomposition for Turning Movement Prediction at Signalized Intersections
HFD-TM predicts turning movements with 2.49 MAE by hierarchically decomposing corridor flows and enforcing conservation, outperforming Transformer and GRU baselines on six months of Nashville LiDAR data.