A new ensemble deep graph reinforcement learning network for spatio-temporal traffic volume forecasting in a freeway network

Abstract

Spatio-temporal traffic volume forecasting technologies can effectively improve freeway traffic efficiency and the travel comfort of humans. To construct a high-precision traffic volume forecasting model, this study proposed a new ensemble deep graph reinforcement learning network. The modeling process of the spatio-temporal prediction model mainly included three steps. In step I, raw spatiotemporal traffic network datasets (traffic volumes, traffic speeds, weather, and holidays) were preprocessed and the adjacency matrix was constructed. In step II, a graph attention network (GAT) and graph convolution network (GCN) were used as the main predictors to build the spatio-temporal traffic volume forecasting model and obtain the forecasting results, respectively. In step III, deep reinforcement learning was used to effectively analyze the correlations between the forecasting results from these two neural networks and the final results, so as to optimize the weight coefficient. The final result of the proposed model was obtained by combining the forecasting results from the GAT and GCN with the weight coefficient. Based on summarizing and analyzing the experimental results, it can be concluded that: (1) deep reinforcement learning can effectively integrate the two different graph neural networks and achieve better results than traditional ensemble methods; and (2) the presented ensemble model performs better than twenty-one models proposed by other researchers for all studied cases.