摘要：

由于交通流的非平稳性, 在提取交通流动态时空特征方面存在一定的挑战。针对非平稳特性导致的不同交通模式和不同邻域范围内交通流的动态变化问题, 提出一种基于自适应图卷积的元图学习的交通流预测模型(Meta-AGC)。设计一种能自适应捕获不同交通模式下交通流各节点间的空间相关性和不同邻域范围内交通流动态变化的方法。该方法将AGC捕获的时空特征与元图学习中的元节点库进行模式匹配, 使得基于元节点库生成的时空元图能够自适应地表示不同交通模式下各节点间的空间相关性。同时, AGC由一组具有不同可学习尺度的图小波和上下文注意力机制组成, 以随时根据输入的交通流信息动态调整卷积感受野, 解决传统卷积接受域范围固定的局限性, 有效捕捉由于随机事件引发的不同邻域范围内交通流变化问题。实验结果表明, 在6步和12步预测步长中, 与较好的基线模型相比, Meta-AGC模型预测的准确性分别提高了5.2%和4.2%, 进一步证明了提出的Meta-AGC模型能更有效地建模交通流的非平稳性, 提高预测精度。

关键词: 交通流预测, 元图学习, 图神经网络, 注意力机制, 自适应图卷积

Abstract:

Owing to the non-stationary nature of traffic flow, extracting its dynamic spatial-temporal features is challenging. The non-smooth characteristics of traffic flow causes dynamic changes in different traffic modes and within different neighborhoods. To address this issue, this study proposes a Meta-graph learning traffic flow prediction model based on Adaptive Graph Convolution (Meta-AGC). Specifically, a method is designed to adaptively capture the spatial correlation between nodes in different traffic modes and the dynamic changes in traffic flow within different neighborhoods. The method pattern-matches the spatial-temporal features captured by AGC with a meta-node library in meta-graph learning, which enables the spatial-temporal meta-graph generated based on the meta-node library to adaptively represent the spatial correlations among nodes in different traffic modes. AGC consists of a set of graph wavelets with different learnable scales and a context attention mechanism to dynamically adjust the convolution receptivity field according to the input traffic flow information at any time. Consequently, the limitation of fixed acceptance domain in traditional convolution is overcome, and traffic flow variations within different neighborhoods triggered by random events are captured efficiently. Experimental results demonstrate that Meta-AGC enhances the prediction accuracy by 5.2% and 4.2% compared with the superior baseline model at the 6-step and 12-step prediction intervals, respectively. Additionally, the findings substantiate the assertion that Meta-AGC is more effective in modeling the non-stationarity of traffic flow and improving prediction accuracy.

Key words: traffic flow forecasting, meta-graph learning, graph neural networks, attention mechanism, Adaptive Graph Convolution (AGC)