A New Ensemble Reinforcement Learning Recursive Network for Traffic Volume Forecasting in a Freeway Network

Abstract

While recent development, the freeways promote economic and traffic demand, but it also increases a lot of traffic congestion and accidents. The effective traffic volume forecasting technology can reduce traffic congestion, and improve traffic network planning and information management. In this research, a new ensemble forecasting model is proposed to forecast traffic volume. The framework of the model consists of the TCN model, the GRU model, and the ensemble SARSA algorithm. TCN and GRU are utilized as predictors to construct the prediction model for deep mining of the traffic volume data and SARSA can integrate the prediction results from these two predictors and achieve satisfactory results. We used two sets of the traffic data collected from a freeway in Changsha to verify the performance. The experiment results proved the effectiveness of the hybrid model that had the best accuracy in both cases, and the MAPEs of TCN-GRU-Sarsa are 7.8281% and 6.9015%.