Papers › Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting

Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting

24 Jun 2021arXiv:2106.12931archive 2025-07-28

Zheng Fang, Qingqing Long, Guojie Song, Kunqing Xie

Spatial-temporal forecasting has attracted tremendous attention in a wide range of applications, and traffic flow prediction is a canonical and typical example. The complex and long-range spatial-temporal correlations of traffic flow bring it to a most intractable challenge. Existing works typically utilize shallow graph convolution networks (GNNs) and temporal extracting modules to model spatial and temporal dependencies respectively. However, the representation ability of such models is limited due to: (1) shallow GNNs are incapable to capture long-range spatial correlations, (2) only spatial connections are considered and a mass of semantic connections are ignored, which are of great importance for a comprehensive understanding of traffic networks. To this end, we propose Spatial-Temporal Graph Ordinary Differential Equation Networks (STGODE). Specifically, we capture spatial-temporal dynamics through a tensor-based ordinary differential equation (ODE), as a result, deeper networks can be constructed and spatial-temporal features are utilized synchronously. To understand the network more comprehensively, semantical adjacency matrix is considered in our model, and a well-design temporal dialated convolution structure is used to capture long term temporal dependencies. We evaluate our model on multiple real-world traffic datasets and superior performance is achieved over state-of-the-art baselines.

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generate_dataset square-coder/STGODE/utils.py official repository unverified Apache-2.0 (permissive) · 2ac8a47ef9b63afb · report
get_normalized_adj square-coder/STGODE/utils.py official repository unverified Apache-2.0 (permissive) · cf24bb90005eb159 · report
train square-coder/STGODE/run_stode.py official repository unverified Apache-2.0 (permissive) · 4998ab382425190c · report

Tasks

Traffic Prediction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Traffic Prediction LargeST STGODE CA MAE 20.77 #5 of 6 Archive leaderboard report
Traffic Prediction LargeST STGODE GBA MAE 21.79 #5 of 6 Archive leaderboard report
Traffic Prediction LargeST STGODE GLA MAE 21.49 #5 of 6 Archive leaderboard report
Traffic Prediction LargeST STGODE SD MAE 19.55 #5 of 6 Archive leaderboard report
Traffic Prediction PeMS07 STGODE MAE@1h 22.99 #14 of 17 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

Convolution

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