A Survey of Traffic Flow Prediction Methods Based on Long Short-Term Memory Networks

It is generally recognized that accurate and timely prediction of future traffic flow information is one of the important conditions for improving the utilization rate of road networks and relieving traffic congestion. Since long short-term memory (LSTM) networks have obvious advantages in dealing with time series data, LSTM-based prediction models, as well as the improved and hybrid models based on LSTM, have been widely used in the field of traffic flow prediction and have achieved a series of important results. However, although some existing reviews have provided a comprehensive summary of and prospects for traffic flow prediction methods, there is no recent review specifically summarizing the traffic flow prediction methods based on LSTM. Therefore, this article presents a comprehensive review of LSTM-based methods for traffic flow prediction. First, we briefly introduce the development history of traffic flow prediction, the advantages of using LSTM for traffic flow prediction, and the basic principles of LSTM. Then, the representative model structures, training methods, public datasets, attractive results, the advantages and disadvantages of different LSTM-based methods are presented and analyzed in detail. Furthermore, to illustrate the performance of various refinements and hybrid models based on LSTM for traffic flow prediction, we meticulously select several representative approaches to conduct comparative experiments and quantitative analysis. Finally, we discuss the current limitations and challenges of LSTM and traffic flow prediction and present potential areas of future research interest to the readers.