3D-STGPCN: 3D Spatio-Temporal Graph Point-wise Convolutional Network for Traffic Forecasting

Traffic forecasting has been an important research topic in the intelligent transportation system for smooth and safe transportation. Among research directions, spatiotemporal-graph-based traffic forecasting has been known as one of the popular directions. Unfortunately, many requirements, such as lightweight architecture, low resource consumption, and flexible transfer learning, have not had satisfactory answers. Hence, we propose a new method called 3D Spatial Temporal Graph Point-wise Convolutional Network (3D-STGPCN) to address these requirements. Differing from others, we proceed with spatial and temporal information simultaneously (i.e., wrap them into one structure). We evaluate and compare our model on three public traffic network datasets, METR-LA, PEMS-BAY, and STREETS, with different methods in the same domain. The experimental results show the advantage of our model over others. We publish our source code at https://github.com/dophanh-26/3D-STGPCN.