Traffic Flow Forecasting Research Based on Delay Reconstruction and GRU-SVR

Abstract

To improve the prediction accuracy of traffic flow, short-term traffic flow prediction based on delayed reconstruction and integrated GRU-SVR model using Stacking strategy is proposed to address the problems of nonlinearity, complexity and time dependence of traffic flow. By solving the phase space reconstruction parameters through the chaotic nature of source traffic to map the sequences into high-dimensional vector matrices, the integrated GRU-SVR model is optimized using iGridSearch CV for prediction. GRU alleviates the long dependence problem among data, can make full use of the before-and-after correlation information in the time dimension to sense the data causality, and the SVR introduction parameters are searched for optimality through improved grid search, and the global optimal solution is obtained in high time efficiency. The global optimal solution can ensure the generalizability of the integrated model. The results show that the RMSE, MAPE and R2 score of the integrated algorithm are better than the other three models. The experiments prove that the method can effectively improve the prediction accuracy and has better generalization ability.