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.

翻译：交通预测作为多元时间序列预测的经典任务，一直是人工智能领域的重要研究课题。为了应对交通流中隐含的时空异质性和非平稳性，本文提出了一种新颖的图结构学习机制——时空元图学习。具体而言，我们通过将元节点库驱动的元图学习器嵌入GCRN编码器-解码器，将该思想实现为元图卷积循环网络（MegaCRN）。我们在两个基准数据集（METR-LA和PEMS-BAY）以及一个覆盖东京1843条高速公路路段的大规模交通速度数据集EXPY-TKY上进行了全面评估。我们的模型在所有三个数据集上均超越了现有最先进方法。此外，通过一系列定性评估，我们证明该模型能够明确解耦具有不同模式的路段和时间段，并对任何异常交通状况具有鲁棒的自适应能力。相关代码和数据集已公开于https://github.com/deepkashiwa20/MegaCRN。