Research on Optimization of Traffic Flow Prediction Algorithm

To improve the accuracy of traffic flow prediction based on wavelet neural networks, an optimization algorithm for traffic flow prediction was proposed. The initial parameters of the wavelet neural network are optimized using the global optimization, fast convergence speed, and strong adaptive random search capabilities of the beetle antennae search algorithm, thereby overcoming the shortcomings of the wavelet neural network that is prone to fall into local optimization and cannot obtain optimal parameters. The BAS-WNN optimization algorithm is proposed and the traffic flow prediction model based on BAS-WNN is established. The experimental results show that the RMSE of the BAS-WNN traffic flow prediction algorithm is 8.0289, and the MAE is 5.3416. Compared with the WNN prediction algorithm, the prediction accuracy is significantly improved, with better prediction performance.