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SOTA
Traffic Prediction
Traffic Prediction On Pems07
Traffic Prediction On Pems07
Metrics
MAE@1h
Results
Performance results of various models on this benchmark
Columns
Model Name
MAE@1h
Paper Title
ASTGCN
28.05
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
STGCN
25.38
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
STSGCN
24.26
Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
STGODE
22.99
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
STFGNN
22.07
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting
ADN
21.62
Structured Time Series Prediction without Structural Prior
STWave
19.94
When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks
PDFormer
19.83
PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction
DDGCRN
19.79
A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting
ADCSD
19.62
Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting
CorrSTN
19.62
A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting
Cy2Mixer
19.45
Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks
PM-DMNet(P)
19.35
Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction
PDG2Seq
19.28
PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction
PM-DMnet(R)
19.18
Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction
STAEformer
19.14
STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
STD-MAE
18.31
Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
0 of 17 row(s) selected.
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HyperAI
HyperAI
Home
Console
Docs
News
Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
Terms of Service
Privacy Policy
English
HyperAI
HyperAI
Toggle Sidebar
Search the site…
⌘
K
Command Palette
Search for a command to run...
Console
Home
SOTA
Traffic Prediction
Traffic Prediction On Pems07
Traffic Prediction On Pems07
Metrics
MAE@1h
Results
Performance results of various models on this benchmark
Columns
Model Name
MAE@1h
Paper Title
ASTGCN
28.05
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
STGCN
25.38
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
STSGCN
24.26
Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
STGODE
22.99
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
STFGNN
22.07
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting
ADN
21.62
Structured Time Series Prediction without Structural Prior
STWave
19.94
When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks
PDFormer
19.83
PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction
DDGCRN
19.79
A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting
ADCSD
19.62
Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting
CorrSTN
19.62
A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting
Cy2Mixer
19.45
Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks
PM-DMNet(P)
19.35
Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction
PDG2Seq
19.28
PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction
PM-DMnet(R)
19.18
Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction
STAEformer
19.14
STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
STD-MAE
18.31
Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
0 of 17 row(s) selected.
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