MDSTGCN : Multi-Scale Dynamic Spatial-Temporal Graph Convolution Network With Edge Feature Embedding for Traffic Forecasting

The problem of traffic forecasting has received much attention as a central part of intelligent transportation systems. In recent years, many different models have been proposed to improve the performance of traffic forecasting. However, there are some problems with these models: they only focus on the dependencies between nodes and ignore the dependencies between edges; the highly dynamic spatial dependencies of traffic networks in time are not fully considered. In this paper, we propose a multi-scale dynamic spatial-temporal graph convolution network with edge feature embedding(MDSTGCN). In the spatial dimension, we construct the dynamic adjacency matrix and the hypergraph. Capturing spatial correlations using diffusion convolution. In the temporal dimension, we design a multi-scale temporal convolution module to capture the temporal dynamics of traffic data at different scales. We conducted experiments on four real datasets and the results show that our model outperforms the baseline models.