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.