Chaotic Prediction for Traffic Flow of Improved BP Neural Network

Abstract A prediction algorithm for traffic flow prediction of BP neural based on Differential Evolution (DE) is proposed to overcome the problems such as long computing time and easy to fall into local minimum by combing DE and neural network. In the algorithm, DE is used to optimize the thresholds and weights of BP neural network, and the BP neural network is used to search for the optimal solution. The efficiency of the proposed prediction method is tested by the simulation of two typical chaotic time series and real traffic flow. The simulation results show that the proposed method has higher precision compared with the traditional BP neural network, so prove it is feasible and effective in the practical prediction of traffic flow.