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Traffic Prediction On Pemsd4

Métriques

12 steps MAE

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	12 steps MAE	Paper Title	Repository
STD-MAE	17.80	Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
PM-DMNet(R)	18.37	Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction
PDG2Seq	18.24	PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction
STWave	18.50	When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks
FasterSTS	18.49	FasterSTS: A Faster Spatio-Temporal Synchronous Graph Convolutional Networks for Traffic flow Forecasting	-
HTVGNN	17.99	A novel hybrid time-varying graph neural network for traffic flow forecasting	-
STG-NCDE	19.21	Graph Neural Controlled Differential Equations for Traffic Forecasting
DDGCRN	18.45	A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting
Hierarchical-Attention-LSTM (HierAttnLSTM)	9.168	Network Level Spatial Temporal Traffic State Forecasting with Hierarchical Attention LSTM (HierAttnLSTM)
PDFormer	18.32	PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction
STG-NRDE	19.13	Graph Neural Rough Differential Equations for Traffic Forecasting
HAGCN	18.70	HAGCN : Network Decentralization Attention Based Heterogeneity-Aware Spatiotemporal Graph Convolution Network for Traffic Signal Forecasting	-
PM-DMNet(P)	18.34	Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction

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Traffic Prediction On Pemsd4 | SOTA | HyperAI