Traffic Forecast and Management Based on Dynamic Programming Neural Network

Abstract

The short-term traffic flow forecast is more affected by random disturbance than the long-term prediction. The uncertainty is stronger and the regularity is less obvious. On the basis of summarizing the existing prediction models and considering the nonlinearity, complexity and uncertainty of traffic itself, a short-term traffic flow prediction model based on dynamic programming neural network is proposed. The traffic impact model of dynamic programming neural network can quantitatively reflect the traffic impact on the surrounding road network after the completion of the project. The comparison between the experimental data and the prediction model based on BP neural network shows that the model is superior to the traditional prediction model in terms of accuracy, convergence time, generalization ability and optimality.