Short-term traffic flow prediction of BP based on adaptive BWO optimization

Abstract

Accurate short-term traffic flow prediction is the basis and key to the intelligent transportation system. With the continuous development of machine learning algorithms and the latest swarm intelligence algorithms, a reasonable combination of the two will produce a good prediction effect. In this paper, BP neural network algorithm in the short-term traffic flow prediction problem accuracy is not high and easy to fall into the local minimum and so on. This paper established a BP based on adaptive BWO optimization short-term traffic flow prediction model, first of all, to carry on the data preprocessing the data set and divided into the training set and test set, and then the data for training, the best model to forecast practical optimization results, finally the model prediction results were compared with the rest of the 6 kinds of classical model. The experimental results show that the optimized BP model based on adaptive BWO can achieve a good traffic flow prediction effect in the short term, MAE is 7.357, MSE is 102.772, and <i>R²</i> is 0.889, which are better than the other six models.