NDLI: Method for defective traffic flow data mending based on SARBF neural networks

Content Provider	IEEE Xplore Digital Library
Author	Wu Jian Chen Ning
Copyright Year	2010
Description	Author affiliation: School of Mechanical and Automotive Engineering, Zhejiang University of Science and Technology, Hang Zhou 310023, China (Wu Jian; Chen Ning)
Abstract	Defective data to collect urban traffic flow message are always occurred due to the sensor failure. To mend the defective data, a new approach named SACRBF 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 is shown that the method is practicable.
Starting Page	5426
Ending Page	5429
File Size	425479
Page Count	4
File Format	PDF
ISBN	9781424462636
e-ISBN	9787894631046
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Association of Aut
Subject Keyword	Correlation Geographic Information Systems Mending Defective Traffic Flow Data Fitting Artificial neural networks Spatial Autocorrelation Electronic mail Indexes MATLAB SARBF Neural Network Fitting
Content Type	Text
Resource Type	Article

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Method for defective traffic flow data mending based on SARBF neural networks