Short-Term Traffic Flow Prediction Based on I-SAWOA-Deep Echo State Network

Abstract

In recent years, the phenomenon of road congestion has occurred in all cities around the world, and this situation has become more and more severe, which has affected the travel of residents and restricted the development of cities. Short term traffic flow prediction is one of the key technologies of Intelligent Transportation System. It can predict the traffic flow in the future for a period of time through historical data, and then provide key information for traffic management personnel to make decisions. Therefore, researchers in various fields pay attention to it, and gradually propose a variety of prediction methods.In this paper, the Deep Echo State Network is selected as the basic prediction method, and the Improved-Whale Optimization Algorithm is used to optimize the super parameters of the network, which solves the problem that it is difficult to reasonably set the super parameters of the network. Finally, the experiment shows that the algorithm can follow the change trend of traffic flow data and has a good prediction effect.