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http://dx.doi.org/10.12815/kits.2015.14.2.001

Considering of the Rainfall Effect in Missing Traffic Volume Data Imputation Method  

Kim, Min-Heon (ICT Convergence and Integration Research Division, Korea Institute of Civil Engineering and Building Technology)
Oh, Ju-Sam (ICT Convergence and Integration Research Division, Korea Institute of Civil Engineering and Building Technology)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.14, no.2, 2015 , pp. 1-13 More about this Journal
Abstract
Traffic volume data is basic information that is used in a wide variety of fields. Existing missing traffic volume data imputation method did not take the effect on the rainfall. This research analyzed considering of the rainfall effect in missing traffic volume data imputation method. In order to consider the effect of rainfall, established the following assumption. When missing of traffic volume data generated in rainy days it would be more accurate to use only the traffic volume data of the past rainy days. To confirm this assumption, compared for accuracy of imputed results at three kinds of imputation method(Unconditional Mean, Auto Regression, Expectation-Maximization Algorithm). The analysis results, the case on consideration of the rainfall effect was more low error occurred.
Keywords
Traffic volume data imputation; Rainfall; Cluster Analysis; T-test; MAPE; Unconditional Mean; Auto Regression; Expectation-Maximization Algorithm;
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Times Cited By KSCI : 4  (Citation Analysis)
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