Papers › Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

18 Jun 2022arXiv:2206.09112archive 2025-07-28

Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, Christian S. Jensen

We all depend on mobility, and vehicular transportation affects the daily lives of most of us. Thus, the ability to forecast the state of traffic in a road network is an important functionality and a challenging task. Traffic data is often obtained from sensors deployed in a road network. Recent proposals on spatial-temporal graph neural networks have achieved great progress at modeling complex spatial-temporal correlations in traffic data, by modeling traffic data as a diffusion process. However, intuitively, traffic data encompasses two different kinds of hidden time series signals, namely the diffusion signals and inherent signals. Unfortunately, nearly all previous works coarsely consider traffic signals entirely as the outcome of the diffusion, while neglecting the inherent signals, which impacts model performance negatively. To improve modeling performance, we propose a novel Decoupled Spatial-Temporal Framework (DSTF) that separates the diffusion and inherent traffic information in a data-driven manner, which encompasses a unique estimation gate and a residual decomposition mechanism. The separated signals can be handled subsequently by the diffusion and inherent modules separately. Further, we propose an instantiation of DSTF, Decoupled Dynamic Spatial-Temporal Graph Neural Network (D2STGNN), that captures spatial-temporal correlations and also features a dynamic graph learning module that targets the learning of the dynamic characteristics of traffic networks. Extensive experiments with four real-world traffic datasets demonstrate that the framework is capable of advancing the state-of-the-art.

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Code

zezhishao/d2stgnn officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Graph Learning Graph Neural Network Time Series Analysis Time Series Forecasting Traffic Prediction

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Traffic Prediction	METR-LA	D2STGNN	MAE @ 12 step	3.35	#7 of 20	Archive leaderboard	report
Traffic Prediction	METR-LA	D2STGNN	MAE @ 3 step	2.56	#7 of 20	Archive leaderboard	report
Traffic Prediction	PEMS-BAY	D2STGNN	MAE @ 12 step	1.85	#5 of 16	Archive leaderboard	report
Traffic Prediction	PEMS-BAY	D2STGNN	RMSE	4.30	#5 of 16	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Diffusion Graph Neural Network

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