A hybrid model of neural network with VMD–CNN–GRU for traffic flow prediction

Abstract

An effective traffic flow prediction can serve as a foundation for control decisions on intelligent transportation. However, in view of the nonstationarity and complexity of traffic flow sequences, it is impossible to fully extract the dynamic change laws of time-series based on traditional forecasting models. Traffic flow data are often disturbed by noise during the collection. The existence of noise data may affect the features of the sequence itself or cover the real change trend of the series, resulting in the decline of prediction reliability. A hybrid prediction model based on variational mode decomposition–convolutional neural network–gated recurrent unit (VMD–CNN–GRU) is presented to increase the predictability of traffic flow, which is combined by VMD, CNN and GRU. First, the original time-series is decomposed into K components by VMD, and the noise part is eliminated to improve the modeling accuracy. Next, the time characteristics of traffic flow are mined by constructing the CNN–GRU network in Keras, a deep learning framework. Each sub-sequence is trained and predicted separately as an input vector. The total expected value of traffic flow is then calculated by superimposing the predicted value of each subsequence. The model performance is verified by the open-source dataset of actual England highways. The results show that compared with other models, the hybrid model established in this paper significantly raises the precision of traffic flow forecasting. The results could offer some useful insights for predicting traffic flow.