多视角融合的时空动态图卷积网络城市交通流量预测

城市交通流量预测是构建绿色低碳、安全高效的智能交通系统的重要组成部分.时空图神经网络由于具有强大的时空数据表征能力,被广泛应用于城市交通流量预测.当前时空图神经网络在城市交通流量预测中仍存在以下两方面局限性:1)直接构建静态路网拓扑图对城市空间相关性进行表示,忽略了节点的动态交通模式,难以表达节点流量之间的时序相似性,无法捕获路网节点之间在时序上的动态关联.2)只考虑路网节点的局部空间相关性,忽略节点的全局空间相关性,无法建模交通路网中局部区域和全局空间之间的依赖关系.为打破上述局限性,本文提出了一种多视角融合的时空动态图卷积模型用于预测交通流量.首先,从静态空间拓扑和动态流量模式视角出发,构建路网空间结构图和动态流量关联图,并使用动态图卷积学习节点在两种视角下的特征,全面捕获城市路网中多元的空间相关性.其次,从局部视角和全局视角出发,计算路网的全局表示,将全局特征与局部特征融合,增强路网节点特征的表现力,发掘城市交通流量的整体结构特征.接下来,设计了局部卷积多头自注意力机制来获取交通数据的动态时间相关性,实现在多种时间窗口下的准确流量预测.最后,在四种真实交通数据上的实验结果证明了本文模型的有效性和准确性.

Multi-view Fused Spatial-temporal Dynamic Graph Convolutional Network for Urban Traffic Flow Prediction

Traffic flow prediction is an essential component of environmental, safe, and efficient intelligent transportation system. Due to the powerful spatial-temporal data representation ability, spatial-temporal graph neural network is widely used in traffic flow prediction. However, existing spatial-temporal graph neural network based traffic flow prediction models have two limitations: 1) The static topology graph constructed from city spatial correlation ignores the dynamic traffic patterns, which are unable to reflect the temporal dynamic correlation between nodes in road network. 2) Only considering the spatial correlation of local traffic areas lacks the spatial correlations between the local region and the global road network. To overcome the above limitations, this paper proposes a multi-view fused spatial-temporal dynamic graph convolutional network model for traffic flow prediction. Firstly, it constructs a road network spatial structure graph and a dynamic traffic-flow association graph from the perspectives of static spatial topology and dynamic traffic patterns, and uses dynamic graph convolution to learn the node features from both perspectives, comprehensively capturing the diverse spatial correlations in the road network. After that, from the local and global perspectives, it calculates the global representation of the road network and fuses global features with local features to enhance the expressiveness of node features and explore the global structural features of traffic flow. Finally, the model designs a local convolutional multi-head self-attention mechanism to obtain the dynamic temporal correlation of traffic data, achieving accurate traffic flow prediction under multiple time windows. The experimental results on four real traffic data demonstrate the effectiveness and universality of the proposed model.