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Traffic Prediction On Pemsd8

Métriques

12 steps MAE

12 steps RMSE

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	12 steps MAE	12 steps RMSE	Paper Title	Repository
Hierarchical-Attention-LSTM (HierAttnLSTM)	9.215	22.320	Network Level Spatial Temporal Traffic State Forecasting with Hierarchical-Attention-LSTM (HierAttnLSTM)
PM-DMNet(P)	13.55	23.35	Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction
STD-MAE	13.44	22.47	Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
PDG2Seq	13.60	23.37	PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction	-
STG-NRDE	15.32	24.72	Graph Neural Rough Differential Equations for Traffic Forecasting
FasterSTS	13.60	-	FasterSTS: A Faster Spatio-Temporal Synchronous Graph Convolutional Networks for Traffic flow Forecasting
STWave	13.42	-	When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks	-
DDGCRN	14.40	-	A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting	-
HTVGNN	13.24	-	A novel hybrid time-varying graph neural network for traffic flow forecasting	-
HAGCN	14.85	-	HAGCN : Network Decentralization Attention Based Heterogeneity-Aware Spatiotemporal Graph Convolution Network for Traffic Signal Forecasting	-
STG-NCDE	15.45	24.81	Graph Neural Controlled Differential Equations for Traffic Forecasting
PM-DMNet(R)	13.40	23.22	Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction
PDFormer	13.58	-	PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction

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