A Fitting Approach to Mend Defective Urban Traffic Flow Information Based on SARBF Neural Networks
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.
Keywords: Defective-data Mending, Spatial Auto-correlation£¬SARBF Neural Network, Fitting Approach, Urban Traffic Flow Information
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ABOUT THE AUTHORS
Ning Chen
Dr. Ning Chen received the Ph.D. degree in mechanical engineering from Zhejiang University in 2004. Currently, he is an Associate Professor at Zhejiang University of Science and Technology, and the fellow of Chinese Association on Mechanical Engineering. His research interests include Intelligent Transport System(ITS) and Logistics Engineering. He has published 33 papers around mechanical engineering, transportation engineering, and logistics engineering. His work has been supported by the National Natural Science Foundation of China as well as other academic originations.
Weibing Weng
Dr. Weibing Weng received the Ph.D. degree in Logistics Engineering from Dortmund University of Technology in 2011. Currently, he is an assistant professor at Zhejiang University of Science and Technology. His interests are in logistics technologies and equipments.
Xing Xu
Dr. Xing Xu received the Ph.D. degree in mechanical engineering from Zhejiang Sci-Tech University in 2013. Currently, he is an assistant professor at Zhejiang University of Science and Technology. His interests are in urban transportation system control design and information processing.
Ning Chen
Dr. Ning Chen received the Ph.D. degree in mechanical engineering from Zhejiang University in 2004. Currently, he is an Associate Professor at Zhejiang University of Science and Technology, and the fellow of Chinese Association on Mechanical Engineering. His research interests include Intelligent Transport System(ITS) and Logistics Engineering. He has published 33 papers around mechanical engineering, transportation engineering, and logistics engineering. His work has been supported by the National Natural Science Foundation of China as well as other academic originations.
Weibing Weng
Dr. Weibing Weng received the Ph.D. degree in Logistics Engineering from Dortmund University of Technology in 2011. Currently, he is an assistant professor at Zhejiang University of Science and Technology. His interests are in logistics technologies and equipments.
Xing Xu
Dr. Xing Xu received the Ph.D. degree in mechanical engineering from Zhejiang Sci-Tech University in 2013. Currently, he is an assistant professor at Zhejiang University of Science and Technology. His interests are in urban transportation system control design and information processing.
