Short-term Forecasting Model of Traffic Flow Based on GRNN

Urban traffic flow has the characteristics of nonlinearity and time-variation, and how to accurately forecast short-term traffic flow has been an essential part in traffic field. Taking advantage of the Generalized Regression Neural Network (GRNN), the paper establishes the short-term forecasting model of traffic flow based on GRNN. The GRNN model selects the cross validation algorithm to train the network, takes the root mean square of forecasting error as the evaluation criterion of the network to determine the smoothing factor and uses the method of rolling forecasting to forecast the traffic flow. Compared with the forecasting models of RBF and BP neural network, GRNN has stronger approximation capability and higher forecasting accuracy.