AdapGL: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks

计算机科学 利用 邻接矩阵 图形 算法 人工神经网络 人工智能 最大化 机器学习 理论计算机科学 数学优化 数学 计算机安全
作者
Wei Zhang,Fenghua Zhu,Yisheng Lv,C.Y. Tan,Wen Luo,Xin Zhang,Fei‐Yue Wang
出处
期刊:Transportation Research Part C-emerging Technologies [Elsevier]
卷期号:139: 103659-103659 被引量:35
标识
DOI:10.1016/j.trc.2022.103659
摘要

With well-defined graphs, graph convolution based spatiotemporal neural networks for traffic prediction have achieved great performance in numerous tasks. Compared to other methods, the networks can exploit the latent spatial dependencies between nodes according to the adjacency relationship. However, as the topological structure of the real road network tends to be intricate, it is difficult to accurately quantify the correlations between nodes in advance. In this paper, we propose a graph convolutional network based adaptive graph learning algorithm (AdapGL) to acquire the complex dependencies. First, by developing a novel graph learning module, more possible correlations between nodes can be adaptively captured during training. Second, inspired by the expectation maximization (EM) algorithm, the parameters of the prediction network module and the graph learning module are optimized by alternate training. An elaborate loss function is leveraged for graph learning to ensure the sparsity of the generated affinity matrix. In this way, the expectation maximization of one part can be realized under the condition that the other part is the best estimate. Finally, the graph structure is updated by a weighted sum approach. The proposed algorithm can be applied to most graph convolution based networks for traffic forecast. Experimental results demonstrated that our method can not only further improve the accuracy of traffic prediction, but also effectively exploit the hidden correlations of the nodes. The source code is available at https://github.com/goaheand/AdapGL-pytorch.
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