Prediction of Bus Passenger Flow Based on CEEMDAN-BP Model 2020-01-5166
The prediction of passenger flow is of great significance to facilitate the decision-making processes for local authorities and transport operators to provide an effective bus scheduling. In this work, a backpropagation neural network (BPNN) was adopted to predict the bus passenger flow. To reduce the prediction error and improve the prediction accuracy, a combined model CEEMDAN-BP, which combines CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) method and BPNN, has been proposed. CEEMDAN is an improved method based on EEMD, which has been widely applied to signal smoothing and de-noising. Experimental results show that this combined model can exactly achieve an excellent prediction effect and improve the prediction accuracy of the network greatly. Compared with the single BPNN model, the combined model CEEMDAN-BP2 with the best prediction effect has reduced the root mean square error (RMSE) by 80.80% and mean absolute error (MAE) by 84.48%, and the maximum relative error of which is also less than 7%, which fully confirmed the usability and effectiveness of the combined model in improving the effect of passenger flow prediction.