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.
A comprehensive survey of deep learning-based traffic flow prediction models,
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
-
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.