Spatio-Temporal Graph Convolutional Networks for Traffic Forecasting

Abstract

Traffic forecasting is an important and challenging problem for intelligent transportation systems due to the complex spatial dependencies among neighboring roads and changing road conditions in different time periods. Spatio-temporal graph convolutional networks (STGCNs) are usually adopted to forecast traffic features in a road network. Some STGCN models involves spatial layers first and then temporal layers and some other models involves these layers in a reverse order. This creates an interesting research question on whether the ordering of the spatial layers (or temporal layers) first in an existing STGCN model could improve the forecasting performance. To the best of our knowledge, we are the first to study this interesting research problem, which creates a deep insight as a guideline to the research community on how to design STGCN models. We conducted extensive experiments to study a number of representative STCGN models for this research problem. We found that these models with spatial layers constructed before temporal layers has a higher chance to outperform that with temporal layers constructed first, which suggests the future design principle of STGCN models. © 2021 ACM.