SARBF neural networks fitting method for mending defective traffic flow data

Defective traffic flow data may occur because of sensor failure to collect urban traffic data. To solve the issue, a new data mending approach named SARBF neural network fitting is presented. It combines spatial autocorrelation based analysis method and RBF neural network fitting method. In our research, the relevant data-complete intersection need be determined according to the spatial autocorrelation of traffic grid. The historical traffic data of the data-complete intersections could be utilized as training samples of the RBF neural network model. The approach is to mend the defective traffic flow data of the intersections in Hangzhou city. Finally the experiment demonstrated that it can improve the mending precision and computing speed comparing with traditional regression analysis method.