GA-SVR Traffic Flow Prediction Based on Phase Space Reconstruction with Improved KNN Method

Traffic flow prediction plays an important role in intelligent traffic management. In order to solve the problem that the prediction model has low accuracy in traffic flow prediction when the amount of data is small. Considering the chaotic nature of the traffic flow time series data, the phase space reconstruction method is adopted to process the data, and then the SVR model is used to predict the processed data. The two parameters of phase space reconstruction, including embedding dimension and delay time, have a great impact on the final prediction accuracy. In this paper, the improved KNN method is used to select the parameters of phase space reconstruction and construct the KNN-GA-SVR model. Compared with the CC-GA-SVR model of phase space parameter selection based on C-C method, this model improves the prediction accuracy, is effective and feasible for the prediction of short-term traffic flow, and has strong applicability.