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http://dx.doi.org/10.9717/kmms.2022.25.2.287

A Generation and Accuracy Evaluation of Common Metadata Prediction Model Using Public Bicycle Data and Imputation Method  

Kim, Jong-Chan (Dept. of Computer Engineering, Sunchon National University)
Jung, Se-Hoon (School of Creative Convergence, Andong National University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.2, 2022 , pp. 287-296 More about this Journal
Abstract
Today, air pollution is becoming a severe issue worldwide and various policies are being implemented to solve environmental pollution. In major cities, public bicycles are installed and operated to reduce pollution and solve transportation problems, and operational information is collected in real time. However, research using public bicycle operation information data has not been processed. This study uses the daily weather data of Korea Meteorological Agency and real-time air pollution data of Korea Environment Corporation to predict the amount of daily rental bicycles. Cross- validation, principal component analysis and multiple regression analysis were used to determine the independent variables of the predictive model. Then, the study selected the elements that satisfy the significance level, constructed a model, predicted the amount of daily rental bicycles, and measured the accuracy.
Keywords
Big Data; Imputation Method; Public Bicycle; Prediction Model; Regression;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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