【目的】交通流预测对于城市交通系统的有效管理和运行至关重要。交通网络中不同路段或路口的流量随时间动态变化,空间邻近路段或路口的流量也会相互影响。为了更好地从交通流序列中学习不同路段或路口流量的时空相关性,提升交通流短时预测性能,提出基于多头注意力动态图卷积网络(dynamicgraphconvolutionnetworkwithmulti-headattention,DGCNMA)的交通流预测方法。【方法】DGCNMA模型在Transformer框架中首先引入图卷积网络学习交通流序列的空间嵌入并融入交通流序列,进而采用多头注意力机制从多个角度同时捕捉交通流序列的时间相关性和空间相关性;其次引入交互动态图卷积网络,通过卷积网络和动态图卷积网络交互学习以及交通流奇偶子序列特征交互融合,同时学习交通流序列的局部时空相关性和全局时空相关性。【结果】通过在高速公路交通流数据集(PEMS03、PEMS04、PEMS08)和地铁人群流量数据集(HZMEinflowandHZMEoutflow)上的大量实验,验证了所提出的DGCNMA模型的交通流预测性能优于基线模型。

【Purposes】 Traffic flow prediction is crucial for the effective management and oper-ation of urban transportation systems. The flows of different road sections or intersections in a traffic network change dynamically with time, meanwhile the flows of spatially neighboring road sections or intersections affect each other. In order to better learn the spatial and temporal corre-lation of the traffic flow of different road sections or intersections from the traffic flow sequences, and to improve the performance of short-term prediction of traffic flow, in this paper we propose a traffic flow prediction method based on Dynamic Graph Convolution Network with Multi-head Attention (DGCNMA). 【Methods】 The DGCNMA model first introduces graph convolution networks into the Transformer framework to learn the spatial embedding of traffic flow sequences and incorporate them into the traffic flow sequences, and then adopts the mechanism of Multi-head Attention to capture the temporal and spatial correlation of the traffic flow sequences from multiple perspectives at the same time; second, the Interactive Dynamic Graph Convolution Net-work is introduced to simultaneously learn the local and global spatial-temporal correlations of traffic flow sequences through the interactive learning of convolutional network and dynamic graph convolutional network, and the interactive fusion of parity subsequence features. 【Find-ings】 Experiments on highway traffic flow datasets (PEMS03, PEMS04, PEMS08) and subway crowd flow datasets (HZME inflow and HZME outflow) show that the proposed DGCNMA mod-el has better traffic flow prediction performance than the baseline models.