Application of traffic flow prediction based on Back Propagation neural network

In order to improve the neural network structure and parameters setting method a foundation of the bee algorithm and BP neural network was used to create an artificial bee colony algorithm training BP neural network to make model prediction to traffic flow. The artificial algorithm can obtain better network initial weight value and threshold value to compensate the random defect of BP neural network on choosing connecting weight value and threshold value. This can exert the generated mapping ability of BP neural network and cause BP neural network to have faster convergence and greater learning ability. The algorithm was applied to real traffic flow for verification and compared with BP neural network. The simulation results demonstrate that the algorithm has better prediction accuracy, which can verify the feasibility and effectiveness of the algorithm in the prediction realm.