Papers › Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Shintaro Fukushima, Toyotaro Suzumura
Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity and non-stationarity implied in the traffic stream, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional Recurrent Network (MegaCRN) by plugging the Meta-Graph Learner powered by a Meta-Node Bank into GCRN encoder-decoder. We conduct a comprehensive evaluation on two benchmark datasets (i.e., METR-LA and PEMS-BAY) and a new large-scale traffic speed dataset called EXPY-TKY that covers 1843 expressway road links in Tokyo. Our model outperformed the state-of-the-arts on all three datasets. Besides, through a series of qualitative evaluations, we demonstrate that our model can explicitly disentangle the road links and time slots with different patterns and be robustly adaptive to any anomalous traffic situations. Codes and datasets are available at https://github.com/deepkashiwa20/MegaCRN.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Traffic Prediction | EXPY-TKY | MegaCRN | 1 step MAE | 5.81 | #2 of 8 | Archive leaderboard | report |
| Traffic Prediction | EXPY-TKY | MegaCRN | 3 step MAE | 6.44 | #2 of 8 | Archive leaderboard | report |
| Traffic Prediction | EXPY-TKY | MegaCRN | 6 step MAE | 6.83 | #2 of 8 | Archive leaderboard | report |
| Traffic Prediction | METR-LA | MegaCRN | MAE @ 12 step | 3.38 | #9 of 20 | Archive leaderboard | report |
| Traffic Prediction | METR-LA | MegaCRN | MAE @ 3 step | 2.63 | #9 of 20 | Archive leaderboard | report |
| Traffic Prediction | PEMS-BAY | MegaCRN | MAE @ 12 step | 1.88 | #8 of 16 | Archive leaderboard | report |
| Traffic Prediction | PEMS-BAY | MegaCRN | RMSE | 4.42 | #8 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
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