Short-term traffic flow prediction based on adaptive BAS optimized RBF neural network

Abstract:

In order to improve the accuracy of short-term traffic flow prediction, an improved traffic flow predicting model was proposed by using the adaptive beetle antennae search (BAS) algorithm to optimize the radial basis function (RBF) neural network. This was done in consideration of the drawbacks of the traditional RBF neural network model for short-term traffic flow prediction, such as fixed center value and vulnerability to drift data interference. In this model, the adaptive step size was utilized to improve the iteration speed and optimization ability of BAS algorithm. The center of the RBF hidden layer was determined based on the DBSCAN cluster, and thus the neural network structure was optimized. Traffic flow datasets were collected from real road network for training, and the proposed model was compared with widely-used models, such as BP neural network, RBF neural network, and generalized RBF neural network. Results showed that in comparison with the BP neural network, the proposed method reduced the mean absolute error by 1.87, the mean absolute percentage error by 15.96, and the root mean square error by 3.24%; the fitting degree of the proposed method was improved by 3.96%. In comparison with the generalized RBF neural network, the proposed method reduced the mean absolute error by 1.36, the mean absolute percentage error by 5.01, and the root mean square error by 2.19%; the fitting degree of the proposed method was improved by 2.5%. The proposed short-term traffic flow prediction model can provide accurate predictions for intelligent traffic guidance.