Traffic Forecasting Model Based on Two-stage Stacked Graph Convolution Network

Abstract

Traffic forecasting is an important part of Intelligent Transportation System (ITS). The traffic prediction results provide data support for traffic guidance and traffic information release system. Research on traffic forecasting is of great significance to alleviate traffic congestion and increase traffic flow. However, the road network is complex and dynamic. The accuracy of multi-step traffic forecasting decreases rapidly with the increase of time steps, and other performances such as RMSE, MAE, ACC and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> are getting worse and worse, which seriously affects the prediction results. Therefore, we proposes a traffic prediction model based on two-stage stacked graph convolution network ( ED2GCN ) , in which EDGCN is used in both encoder and decoder stages. EDGCN includes a GRU and two stacked AGCN modules. A single AGCN module is composed of GCN, GLU and Attention mechanism. In order to capture the spatial dependence between distant points, two AGCNS are superimposed by residual connection to form a deep AGCN structure. In this paper, PEMS08 data set is used for experimental verification. The experimental results show that the prediction effect of ED2GCN proposed in this paper is better than that of the baselines. Among them, RMSE, MAE, ACC and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> are optimized by at least 7.8%, 10.8%, 0.9% and 0.7% respectively compared with A3T-GCN.