A FittingApproach toMend Defective Urban Traffic Flow Information Based on SARBFNeural Networks
نویسندگان
چکیده
Data-defectives are always occurred during collecting urban traffic flow information due to all kinds of sensors’ failures. To mend the defective urban traffic flow information data, a new approach named SARBF neural network fitting is presented. It combines analysis based on spatial autocorrelation and RBF neural network fitting method. The complete data is determined to mend the defective data according to the spatial autocorrelation of traffic grid. Not only the mending precision is improved and also the limitation of regression analysis is avoided by using RBF neural network. Finally, the experiment to mend the defective traffic flow data in Hangzhou City is shown that the method is practicable.
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