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.

提出了一种 STIDGCN 神经网络的拓扑交互动态图卷积模型，可以有效地进行交通预测，具有更好的性能。