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。

交通预测的时空交互动态图卷积网络

Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting

Recent works have made great progress in semantic segmentation by exploiting
contextual information in a local or global manner with dilated convolutions,
pyramid pooling or self-attention mechanism. In order to avoid potential
misleading contextual information aggregation in previous works, we propose a
class-wise dynamic graph convolution (CDGC) module to adaptively propagate
information. The graph reasoning is performed among pixels in the same class.
Based on the proposed CDGC module, we further introduce the Class-wise Dynamic
Graph Convolution Network(CDGCNet), which consists of two main parts including
the CDGC module and a basic segmentation network, forming a coarse-to-fine
paradigm. Specifically, the CDGC module takes the coarse segmentation result as
class mask to extract node features for graph construction and performs dynamic
graph convolutions on the constructed graph to learn the feature aggregation
and weight allocation. Then the refined feature and the original feature are
fused to get the final prediction. We conduct extensive experiments on three
popular semantic segmentation benchmarks including Cityscapes, PASCAL VOC 2012
and COCO Stuff, and achieve state-of-the-art performance on all three
benchmarks.

该论文提出基于类别的动态图卷积网络 CDGCNet，采用类别动态图卷积（CDGC）模块自适应地传递信息，获得了在 Cityscapes，PASCAL VOC 2012 和 COCO Stuff 等三个流行的语义分割基准上的 state-of-the-art 表现。