Tunable and Transferable RBF Model for Short-Term Traffic Forecasting

The application of short-term traffic forecasting can guide the operation of traffic networks efficiently and reduce the traffic cost for travelers. On the basis of radial basis function (RBF) neural network, this paper introduces a tunable and transferable RBF (TT-RBF) model to conduct on-line forecasting and transfer forecasting. Considering the spatiotemporal correlation of traffic flows in a road network, a spatiotemporal state matrix formed by the detrended cross-correlation analysis is used for the model input. With the on-line forecasting process, an improved on-line structure and parameter adjustment are proposed to enhance the existing model. Thus, the TT-RBF model can be adaptive to time-varying traffic states, especially to deal with the difference between non-peak and peak hours. Moreover, the proposed model can be transferred from one road segment to act on other road segments. By this way, the traffic states of numerous road segments can be forecasted conveniently without complex model training processes. The floating car data of a typical road network in Beijing are used for the performance verification of the TT-RBF model, and some frequently used forecasting models are selected for comparisons. The numerical experiments show that the TT-RBF model can get more accurate results than those in single-step forecasting, multi-step forecasting, and transfer forecasting.