Multiscale Spatiotemporal Graph Convolutional Networks With Dynamic Delay Awareness for Traffic Forecasting

The spatiotemporal dynamics of traffic forecasting make it a challenging task. In recent years, by adapting to the topology of traffic networks where road segments serve as nodes, graph convolutional networks (GCNs) have been able to capture spatiotemporal dependencies, thereby improving traffic forecasting performance. However, there are two shortcomings of GCN-based methods: 1) existing methods treat the delays between nodes in the traffic network as equally important and fail to extract critical information effectively, leading to information redundancy, the introduction of irrelevant noise, and increased computational costs and 2) most methods overlook the issue that spatiotemporal correlations between nodes are inconsistent across different timescales. This article designs a new dynamic delay-aware multiscal spatiotemporal graph convolutional network (DDAMGCN) for traffic forecasting. Specifically, a dynamic delay-aware module is designed to identify key nodes and model the important delays from key nodes, so that the model focuses on key information and reduces computational cost. Additionally, a novel multiscale spatiotemporal graph convolution module is designed to achieve fine-grained modeling of the spatiotemporal correlation of different nodes at different timescales. Experiments on eight real traffic datasets verify the superiority of the proposed method compared to several state-of-the-art baselines.