Traffic Flow Prediction Based on Stack AutoEncoder and Long Short-Term Memory Network

Abstract

Accurate prediction traffic flow is one of the most critical works of the intelligent transport system (ITS). Accurate prediction results can provide better conditions for traffic guidance, management, and control. However, many existing traffic flow prediction methods are not particularly satisfactory in practical applications. In this paper, the stack auto-encoder (SAE) and long short-term memory network (LSTM) are combined for traffic flow prediction, in which SAE is used to obtain spatial features, while LSTM extracts temporal features of traffic flow. Then, the features from SAE and LSTM are combined to predict the traffic flow state. The real-time traffic flow data from Beijing is used to evaluate the performance of the proposed method. Experimental results show that the performance of the proposed method is better than some well-known prediction models.