Prediction for short-term traffic flow based on modified PSO optimized BP neural network

In order to improve forecasting model accuracy of BP neural network,an improved prediction method of optimized BP neural network based on modified particle swarm optimization algorithm(PSO) was proposed.In this modified PSO algorithm,an adaptive mutation operator was proposed in PSO to change positions of the particles which plunged in the local optimization.The modified PSO was used to optimize the weights and thresholds of BP neural network,and then BP neural network was trained to search for the optimal solution.The availability of the modified prediction method was proved by predicting the time series of real traffic flow.The computer simulations have shown that the nonlinear fitting and accuracy of the modified prediction methods are better than other prediction methods.