An efficient and intelligent traffic flow prediction method based on LSTM and variational modal decomposition

Abstract

Traffic flow prediction is a very important research field in intelligent transportation system. The traditional prediction methods have a very wide application in traffic flow prediction. However, in the short-term traffic flow prediction, due to the complexity of its influencing factors, the traditional prediction methods cannot predict the short-term traffic flow well. In this paper, the short-term traffic flow prediction model is constructed by using the short-term and short-term memory network, and the modal aliasing problem is solved by using the variational modal decomposition. From the experimental results, the method proposed in this paper is very suitable for short-term traffic flow prediction, and can achieve good prediction effect and accuracy.