Home Console Docs News Papers Tutorials Datasets Wiki SOTA LLM Models GPU Leaderboard Events

English

Traffic Prediction On Pemsd8

Metrics

12 steps MAE

12 steps RMSE

Results

Performance results of various models on this benchmark

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

0 of 13 row(s) selected.

Traffic Prediction On Pemsd8 | SOTA | HyperAI