计算机科学 ›› 2023, Vol. 50 ›› Issue (4): 40-46.doi: 10.11896/jsjkx.220200079
尹恒1, 张凡1,2, 李天瑞1,2,3
YIN Heng1, ZHANG Fan1,2, LI Tianrui1,2,3
摘要: 交通流预测在智慧城市系统中占有重要地位,是许多交通方向应用的基石。该任务的难点在于如何有效地建模交通流的时空依赖。现有方法大都使用图卷积网络(Graph Convolution Networks,GCN)建模空间关系,使用卷积神经网络网络(Convolution Neural Network,CNN)或者循环神经网络(Recurrent Neural Network,RNN)建模时间关系,但在建模空间关系时往往只利用邻接矩阵建模了局部关系而忽略了全局空间信息。而在整个路网中存在一些道路,其周围的路网结构相似,这些道路在路网中承载的作用是相似的,这些相似道路的特征也可以作为流量预测的依据。因此,提出一种基于多邻接图与多头注意力机制的时空网络模型MA-STGCN,包括:1)利用node2vec算法计算路网中道路的向量表示,通过阈值计算出相似矩阵用于图卷积操作,抽取全局空间信息;2)利用多通道自注意力机制深入挖掘模型的时空特征。在公开数据集PEMS04与PEMS08上进行的实验验证了该模型的有效性,其准确率与主流的模型相比均有提高。
中图分类号:
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