SOTAVerified

Traffic Prediction

Traffic Prediction is a task that involves forecasting traffic conditions, such as the volume of vehicles and travel time, in a specific area or along a particular road. This task is important for optimizing transportation systems and reducing traffic congestion.

( Image credit: BaiduTraffic )

Papers

Showing 1–50 of 375 papers

TitleStatusHype
PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow PredictionCode2
Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management PerspectiveCode2
Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and RoadmapCode2
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series ForecastingCode2
BjTT: A Large-scale Multimodal Dataset for Traffic PredictionCode2
Spatio-Temporal Self-Supervised Learning for Traffic Flow PredictionCode2
Spatial-Temporal Large Language Model for Traffic PredictionCode2
Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic ForecastingCode2
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic PredictionCode2
Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic ForecastingCode2
LargeST: A Benchmark Dataset for Large-Scale Traffic ForecastingCode2
Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural NetworksCode2
FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic PredictionCode2
STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic ForecastingCode2
OpenCity: Open Spatio-Temporal Foundation Models for Traffic PredictionCode2
Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series ForecastingCode2
Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal ForecastingCode2
LibCity: An Open Library for Traffic PredictionCode2
Evaluation of Bio-Inspired Models under Different Learning Settings For Energy Efficiency in Network Traffic PredictionCode2
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal ModelingCode1
Long term 5G network traffic forecasting via modeling non-stationarity with deep learningCode1
MemDA: Forecasting Urban Time Series with Memory-based Drift AdaptationCode1
Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic ForecastingCode1
Graph Neural Controlled Differential Equations for Traffic ForecastingCode1
Graph WaveNet for Deep Spatial-Temporal Graph ModelingCode1
Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic PredictionCode1
LightCTS: A Lightweight Framework for Correlated Time Series ForecastingCode1
A Graph and Attentive Multi-Path Convolutional Network for Traffic PredictionCode1
A Graph-based U-Net Model for Predicting Traffic in unseen CitiesCode1
MA2GCN: Multi Adjacency relationship Attention Graph Convolutional Networks for Traffic Prediction using Trajectory dataCode1
Graph Neural Rough Differential Equations for Traffic ForecastingCode1
ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic ForecastingCode1
Federated Learning for 5G Base Station Traffic ForecastingCode1
Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic FrameworkCode1
Towards Explainable Traffic Flow Prediction with Large Language ModelsCode1
Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic PredictionCode1
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph LearningCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
Implementation and Experimental Validation of Data-Driven Predictive Control for Dissipating Stop-and-Go Waves in Mixed TrafficCode1
Few-Sample Traffic Prediction with Graph Networks using Locale as Relational Inductive BiasesCode1
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across CitiesCode1
Efficient Traffic Prediction Through Spatio-Temporal DistillationCode1
A Correlation Information-based Spatiotemporal Network for Traffic Flow ForecastingCode1
Dynamic Causal Graph Convolutional Network for Traffic PredictionCode1
A Decomposition Dynamic graph convolutional recurrent network for traffic forecastingCode1
FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched GraphCode1
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic ForecastingCode1
A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic PredictionCode1
Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic ForecastingCode1
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic PredictionCode1
Show:102550
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1STGCNMAE @ 12 step4.45—Unverified
2DCRNNMAE @ 12 step3.6—Unverified
3ST-UNetMAE @ 12 step3.55—Unverified
4Graph WaveNetMAE @ 12 step3.53—Unverified
5GWNET-CovMAE @ 12 step3.5—Unverified
6DCGCNMAE @ 12 step3.48—Unverified
7Finetune from t1-6 checkpointMAE @ 12 step3.47—Unverified
8STAWnetMAE @ 12 step3.44—Unverified
9ADNMAE @ 12 step3.42—Unverified
10STD-MAEMAE @ 12 step3.4—Unverified
#ModelMetricClaimedVerifiedStatus
1ASTGCNMAE@1h28.05—Unverified
2STGCNMAE@1h25.38—Unverified
3STSGCNMAE@1h24.26—Unverified
4STGODEMAE@1h22.99—Unverified
5STFGNNMAE@1h22.07—Unverified
6ADNMAE@1h21.62—Unverified
7STWaveMAE@1h19.94—Unverified
8PDFormerMAE@1h19.83—Unverified
9DDGCRNMAE@1h19.79—Unverified
10CorrSTNMAE@1h19.62—Unverified
#ModelMetricClaimedVerifiedStatus
1STBayesianRMSE 4.44—Unverified
2DCRNNMAE @ 12 step2.07—Unverified
3SLCNNMAE @ 12 step2.03—Unverified
4Graph Wave-NetMAE @ 12 step1.95—Unverified
5GMANMAE @ 12 step1.92—Unverified
6STAEformerMAE @ 12 step1.91—Unverified
7GWNET-CovMAE @ 12 step1.91—Unverified
8STAWnetMAE @ 12 step1.89—Unverified
9MegaCRNMAE @ 12 step1.88—Unverified
10RGDANMAE @ 12 step1.86—Unverified
#ModelMetricClaimedVerifiedStatus
1LightCTSFLOPs(M)70—Unverified
2DDGCRNMAE@1h14.4—Unverified
3CorrSTNMAE@1h14.27—Unverified
4PDG2SeqMAE@1h13.6—Unverified
5FasterSTSMAE@1h13.6—Unverified
6PDFormerMAE@1h13.58—Unverified
7PM-DMNet(P)MAE@1h13.55—Unverified
8Cy2MixerMAE@1h13.53—Unverified
9STAEformerMAE@1h13.46—Unverified
10STWaveMAE@1h13.42—Unverified
#ModelMetricClaimedVerifiedStatus
1STG-NCDE12 steps MAE19.21—Unverified
2STG-NRDE12 steps MAE19.13—Unverified
3HAGCN12 steps MAE18.7—Unverified
4STWave12 steps MAE18.5—Unverified
5FasterSTS12 steps MAE18.49—Unverified
6DDGCRN12 steps MAE18.45—Unverified
7PM-DMNet(R)12 steps MAE18.37—Unverified
8PM-DMNet(P)12 steps MAE18.34—Unverified
9PDFormer12 steps MAE18.32—Unverified
10PDG2Seq12 steps MAE18.24—Unverified
#ModelMetricClaimedVerifiedStatus
1STG-NCDE12 steps MAE15.45—Unverified
2STG-NRDE12 steps MAE15.32—Unverified
3HAGCN12 steps MAE14.85—Unverified
4DDGCRN12 steps MAE14.4—Unverified
5PDG2Seq12 steps MAE13.6—Unverified
6FasterSTS12 steps MAE13.6—Unverified
7PDFormer12 steps MAE13.58—Unverified
8PM-DMNet(P)12 steps MAE13.55—Unverified
9STD-MAE12 steps MAE13.44—Unverified
10STWave12 steps MAE13.42—Unverified
#ModelMetricClaimedVerifiedStatus
1LightCTSFLOPs(M)147—Unverified
2AGCRN12 Steps MAE19.83—Unverified
3IDCN12 Steps MAE19.33—Unverified
4FasterSTS12 Steps MAE18.49—Unverified
5DDGCRN12 Steps MAE18.45—Unverified
6PDFormer12 Steps MAE18.32—Unverified
7PDG2Seq12 Steps MAE18.24—Unverified
8STAEformer12 Steps MAE18.22—Unverified
9Cy2Mixer12 Steps MAE18.14—Unverified
10DTRformer12 Steps MAE18—Unverified
#ModelMetricClaimedVerifiedStatus
1StemGNN1 step MAE6.08—Unverified
2DCRNN1 step MAE6.04—Unverified
3AGCRN1 step MAE5.99—Unverified
4PM-MemNet1 step MAE5.94—Unverified
5GWNet1 step MAE5.91—Unverified
6MTGNN1 step MAE5.86—Unverified
7MegaCRN1 step MAE5.81—Unverified
8STD-MAE1 step MAE5.73—Unverified
#ModelMetricClaimedVerifiedStatus
1STG-NCDE12 steps MAE20.53—Unverified
2STG-NRDE12 steps MAE20.45—Unverified
3PDFormer12 steps MAE19.83—Unverified
4DDGCRN12 steps MAE19.79—Unverified
5PM-DMNet(P)12 steps MAE19.35—Unverified
6PM-DMNet(R)12 steps MAE19.18—Unverified
7STAEformer12 steps MAE19.14—Unverified
8STD-MAE12 steps MAE18.31—Unverified
#ModelMetricClaimedVerifiedStatus
1STGM12 steps MAE3—Unverified
2STG-NCDE12 steps MAE2.68—Unverified
3STG-NRDE12 steps MAE2.66—Unverified
4PM-DMNet(P)12 steps MAE2.61—Unverified
5PM-DMNet(R)12 steps MAE2.6—Unverified
6DDGCRN12 steps MAE2.59—Unverified
7STD-MAE12 steps MAE2.52—Unverified
#ModelMetricClaimedVerifiedStatus
1RPMixerSD MAE25.25—Unverified
2STGODESD MAE19.55—Unverified
3STWaveSD MAE18.22—Unverified
4STIDSD MAE17.86—Unverified
5GWNETSD MAE17.74—Unverified
6PatchSTGSD MAE16.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Graph WaveNet12 steps MAE4.99—Unverified
2AGCRN12 steps MAE4.99—Unverified
3MTGNN12 steps MAE4.9—Unverified
4GMAN12 steps MAE4.8—Unverified
5DGCRN12 steps MAE4.79—Unverified
6RGDAN12 steps MAE4.68—Unverified
#ModelMetricClaimedVerifiedStatus
1STG-NCDE12 steps MAE15.57—Unverified
2STG-NRDE12 steps MAE15.5—Unverified
3STWave12 steps MAE14.93—Unverified
4DDGCRN12 steps MAE14.63—Unverified
5PDG2Seq12 steps MAE14.62—Unverified
6STD-MAE12 steps MAE13.8—Unverified
#ModelMetricClaimedVerifiedStatus
1STG-NCDE12 steps MAE2.87—Unverified
2STG-NRDE12 steps MAE2.85—Unverified
3PM-DMNet(P)12 steps MAE2.81—Unverified
4DDGCRN12 steps MAE2.79—Unverified
5PM-DMNet(R)12 steps MAE2.79—Unverified
6STD-MAE12 steps MAE2.64—Unverified
#ModelMetricClaimedVerifiedStatus
1SimVP+SVQ (Learnable)MAE @ in14.64—Unverified
2STFGNNMAE @ in13.83—Unverified
3STSGCNMAE @ in12.72—Unverified
4AGCRNMAE @ in12.3—Unverified
5ST-SSLMAE @ in11.31—Unverified
#ModelMetricClaimedVerifiedStatus
1ADCSDMAE @ in16.99—Unverified
2STFGNNMAE @ in16.25—Unverified
3STSGCNMAE @ in13.69—Unverified
4AGCRNMAE @ in12.13—Unverified
5ST-SSLMAE @ in11.99—Unverified
#ModelMetricClaimedVerifiedStatus
1T-UNetMAE (60 min)7.04—Unverified
2FC-LSTMMAE (60 min)4.16—Unverified
3STGCNMAE (60 min)4.02—Unverified
43D-TGCNMAE (60 min)3.65—Unverified
5ST-UNetMAE (60 min)3.38—Unverified
#ModelMetricClaimedVerifiedStatus
1T-GCNMAE @ 15min2.71—Unverified
2GRUMAE @ 15min2.68—Unverified
3MHAST-GCNMAE @ 15min2.66—Unverified
4BSTGCNMAE @ 15min2.65—Unverified
5factorized ST-TGCNMAE @ 15min2.02—Unverified
#ModelMetricClaimedVerifiedStatus
1STFGNNMAE @ in6.53—Unverified
2STSGCNMAE @ in5.81—Unverified
3AGCRNMAE @ in5.17—Unverified
4ST-SSLMAE @ in4.94—Unverified
#ModelMetricClaimedVerifiedStatus
1STFGNNMAE @ in5.8—Unverified
2STSGCNMAE @ in5.25—Unverified
3AGCRNMAE @ in5.18—Unverified
4ST-SSLMAE @ in5.04—Unverified
#ModelMetricClaimedVerifiedStatus
1MemDAMAE3.19—Unverified
#ModelMetricClaimedVerifiedStatus
1CorrSTNMAE@1h11.2—Unverified
#ModelMetricClaimedVerifiedStatus
1CorrSTNMAE@1h17.26—Unverified
#ModelMetricClaimedVerifiedStatus
1DASTNetMAE19.25—Unverified
#ModelMetricClaimedVerifiedStatus
1DASTNetMAE20.67—Unverified
#ModelMetricClaimedVerifiedStatus
1DASTNetMAE22.82—Unverified
#ModelMetricClaimedVerifiedStatus
1DASTNetMAE20.91—Unverified
#ModelMetricClaimedVerifiedStatus
1DASTNetMAE22.96—Unverified
#ModelMetricClaimedVerifiedStatus
1DASTNetMAE26.88—Unverified
#ModelMetricClaimedVerifiedStatus
1DASTNetMAE15.26—Unverified
#ModelMetricClaimedVerifiedStatus
1DASTNetMAE16.41—Unverified
#ModelMetricClaimedVerifiedStatus
1DASTNetMAE18.84—Unverified
#ModelMetricClaimedVerifiedStatus
1hybrid Seq2SeqMAPE8.63—Unverified