Spatiotemporal Graph Convolutional Network for Multi-Scale Traffic Forecasting

Benefiting from the rapid development of geospatial big data-related technologies, intelligent transportation systems (ITS) have become a part of people's daily life. Traffic volume forecasting is one of the indispensable tasks in ITS. The spatiotemporal graph neural network has attracted attention from academic and business domains for its powerful spatiotemporal pattern capturing capability. However, the existing work focused on the overall traffic network instead of traffic nodes, and the latter can be useful in learning different patterns among nodes. Moreover, there are few works that captured fine-grained node-specific spatiotemporal feature extraction at multiple scales at the same time. To unfold the node pattern, a node embedding parameter was designed to adaptively learn nodes patterns in adjacency matrix and graph convolution layer. To address this multi-scale problem, we adopted the idea of Res2Net and designed a hierarchical temporal attention layer and hierarchical adaptive graph convolution layer. Based on the above methods, a novel model, called Temporal Residual II Graph Convolutional Network (Tres2GCN), was proposed to capture not only multi-scale spatiotemporal but also fine-grained features. Tres2GCN was validated by comparing it with 10 baseline methods using two public traffic volume datasets. The results show that our model performs good accuracy, outperforming existing methods by up to 9.4%.

Automated summary

Benefiting from the rapid development of geospatial big data-related technologies, intelligent transportation systems (ITS) have become a part of people's daily life. Researchers proposed a novel model called Temporal Residual II Graph Convolutional Network (Tres2GCN) to capture multi-scale spatiotemporal and fine-grained features in traffic volume forecasting. The model incorporates node embedding parameter, hierarchical temporal attention layer, and hierarchical adaptive graph convolution layer to address the limitations in existing works.