Accurate traffic forecasting is essential for smart cities to achieve traffic control, route planning, and flow detection. Although many spatial-temporal methods are currently proposed, these methods are deficient in capturing the spatial-temporal dependence of traffic data synchronously. In addition, most of the methods ignore the dynamically changing correlations between road network nodes that arise as traffic data changes. We propose a neural network-based Spatial-Temporal Interactive Dynamic Graph Convolutional Network (STIDGCN) to address the above challenges for traffic forecasting. Specifically, we propose an interactive dynamic graph convolution structure, which divides the sequences at intervals and synchronously captures the traffic data's spatial-temporal dependence through an interactive learning strategy. The interactive learning strategy makes STIDGCN effective for long-term prediction. We also propose a novel dynamic graph convolution module to capture the dynamically changing correlations in the traffic network, consisting of a graph generator and fusion graph convolution. The dynamic graph convolution module can use the input traffic data and pre-defined graph structure to generate a graph structure. It is then fused with the defined adaptive adjacency matrix to generate a dynamic adjacency matrix, which fills the pre-defined graph structure and simulates the generation of dynamic associations between nodes in the road network. Extensive experiments on four real-world traffic flow datasets demonstrate that STIDGCN outperforms the state-of-the-art baseline.
翻译:准确的交通预报对于智能城市实现交通控制、路线规划和流量检测至关重要。虽然目前提出了许多空间时位方法,但这些方法在同步地捕捉交通数据的空间时空依赖性方面尚有缺陷。此外,大多数方法忽视了交通数据变化时产生的公路网络节点之间动态变化的相互关系。我们建议建立一个基于神经网络的空间-时空交互式动态动态动动动动动图变动网络(STIDGCN)以应对上述交通预测挑战。具体地说,我们提议了一个交互式动态动态动态动态图形变动结构,通过互动学习战略将间隔序列分隔开来,同步地捕捉到交通数据的空间时空依赖性。互动学习战略使STIDGCN对长期预测具有效力。我们还提出一个新的动态图形变动模块,以捕捉交通网络动态变化变化的动态生成器和组合图变动。动态图形变动模块可以使用输入流量数据和预定义的状态图形结构生成一个图表结构。然后将固定的流量流量与固定的动态网络流和动态数据库矩阵整合起来,从而填补了固定的动态数据库中流流动矩阵。