IMPROVING PREDICTION ACCURACY OF THE TRAFFIC SPEED USING THE FUZZY NEURAL NETWORK

Abstract

The accuracy of the predicted speed is the more important in the efficiency of the traffic management system.The
first-order Takagi–Sugeno system is used to complete the fuzzy inference. To train the evolving fuzzy neural network (EFNN),
two learning processes are proposed. First, a K-means method is employed to partition input samples into different clusters
and a Gaussian fuzzy membership function was designed for each cluster to measure the membership degree of the samples
to the cluster centers. When the number of the input samples increases, the cluster centers are modified and membership
functions are also updated. Second, a weighted recursive least squares estimator is used to optimize the parameters of the
linear functions in the Takagi–Sugeno type fuzzy rules. Furthermore, a trigonometric regression function is introduced to
capture the periodic component in the raw speed data. Specifically, the predicted performance between the proposed model
and six traditional models are compared, which are artificial neural network, support vector machine, autoregressive
integrated moving average model, and vector autoregressive model. The results suggest that the prediction performances of
EFNN are better than those of traditional models due to their strong learning ability. As the prediction time step increases,
the EFNN model can consider the periodic pattern and demonstrate advantages over other models with smaller predicted
errors and slow raising rate of errors