Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
By Renhe Jiang, Zhaonan Wang, Jiawei Yong, P. Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Shintaro Fukushima, Toyotaro Suzumura. AAAI Conference on Artificial Intelligence, 2022
363 citations recorded by the source corpus.
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.
Subfields: Building and Construction, Computer Vision and Pattern Recognition, Transportation
View Paper: https://www.semanticscholar.org/paper/12dd3ea6cd7041aef237f63c03a3e90fdc16897b
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- Hangchen Liu
- Zheng Dong
- Jiewen Deng
- Jinliang Deng
- Quanjun Chen
- Xuan Song
- Renhe Jiang
Citations in the corpus
- Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting (2023, background): “Researchers have devoted considerable effort to developing fancy and complicated models for traffic forecasting, such as novel graph convolutions [2, 3, 8–10, 12, 17, 20, 27, 30, 31, 36, 40], learning graph structures [14, 23, 33, 34, 39], efficient attention mechanisms [5, 19, 32, 42, 43], and othe”