Spatio-Temporal Meta-Graph Learning for Traffic Forecasting

• URL: http://arxiv.org/abs/2211.14701v1
• Date: Sun, 27 Nov 2022 01:56:58 GMT
• Title: Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
• Authors: Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Shintaro Fukushima, Toyotaro Suzumura
• Abstract summary: We propose Meta-Temporal Learning as a novel Graph Learning mechanism. We implement this idea into Meta-Graph Learner powered by a Meta-Node encoder into GCRN-decoder. Our model can explicitly disentangle time slots with different patterns and be robustly adaptive to any anomalous traffic situations.
• Score: 7.406501288721471
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 (METR-LA and PEMS-BAY) and a new large-scale traffic speed dataset in which traffic incident information is contained. Our model outperformed the state-of-the-arts to a large degree on all three datasets (over 27% MAE and 34% RMSE). 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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