Recognizing Discrepant Traffic Data Based on Least Square Support Vector Machine
Chen Yao, Jiuchun Gu
Journal of Digital Information Management, Vol. 13, No. 5, 2015.
DOI: 
Link: http://dline.info/fpaper/jdim/v13i5/v13i5_10.pdf
Abstract:
The real traffic databases are highly
susceptible to noisy, missing, and inconsistent data. As
reason of processing the discrepant data, a model is built
based on square support vector machine which is good
at dissolving the problems such as small samples,
nonlinear and pattern recognition. Utilizing of the SVM,
the inaccurate data can be detected by calculating the
difference value between the real and prediction data.
Comparing with the method of threshold theory, the model
is proved to be better for the accurate data detection online
and database cleaning.