Papers › Spatio-Temporal Meta-Graph Learning for Traffic Forecasting

Spatio-Temporal Meta-Graph Learning for Traffic Forecasting

27 Nov 2022arXiv:2211.14701archive 2025-07-28

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

DecoderGraph LearningGraph structure learningMultivariate Time Series ForecastingTime SeriesTime Series AnalysisTime Series ForecastingTraffic Prediction

Datasets

Introduced by this paper, per the archive.

EXPY-TKY

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

SPEED

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