Traffic Flow Prediction Based on the Optuna–CNN–BiLSTM–GBRT Model

Traffic flow prediction is very important in traffic-related fields, and increasing prediction accuracy is the primary goal of traffic prediction research. This study proposes a new traffic flow prediction method, which uses the CNN–BiLSTM model to extract features from traffic data, further models these features through GBRT, and uses Optuna to tune important hyperparameters of the overall model. The main contribution of this study is to propose a new combination model with better performance. The model integrates two deep learning models that are widely used in this field and creatively uses GBRT to process the output features of the front-end model. On this basis, the optimal hyperparameters and the robustness of the model are deeply explored, providing an effective and feasible solution to the difficult problems in traffic flow prediction. This model is experimentally studied using three different data transformation methods (original data, wavelet transform, Fourier transform). Compared with other models using similar data, the evaluation index performance of this model under wavelet transform is better, RMSE, MAE, MAPE, and R ² values are 53.879, 40.641, 11.13%, and 0.97, respectively, which are better than other comparison models. The results show that the proposed Optuna–CNN–BiLSTM–GBRT model can significantly improve the accuracy of traffic flow prediction and provide an effective means to solve problems in the field of traffic prediction.