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http://dx.doi.org/10.17703/JCCT.2022.8.6.861

Predicting Determinants of Seoul-Bike Data Using Optimized Gradient-Boost  

Kim, Chayoung (경기대학교 교양학부)
Kim, Yoon (국립한국복지대학교 컴퓨터정보보안과)
Publication Information
The Journal of the Convergence on Culture Technology / v.8, no.6, 2022 , pp. 861-866 More about this Journal
Abstract
Seoul introduced the shared bicycle system, "Seoul Public Bike" in 2015 to help reduce traffic volume and air pollution. Hence, to solve various problems according to the supply and demand of the shared bicycle system, "Seoul Public Bike," several studies are being conducted. Most of the research is a strategic "Bicycle Rearrangement" in regard to the imbalance between supply and demand. Moreover, most of these studies predict demand by grouping features such as weather or season. In previous studies, demand was predicted by time-series-analysis. However, recently, studies that predict demand using deep learning or machine learning are emerging. In this paper, we can show that demand prediction can be made a little better by discovering new features or ordering the importance of various features based on well-known feature-patterns. In this study, by ordering the selection of new features or the importance of the features, a better coefficient of determination can be obtained even if the well-known deep learning or machine learning or time-series-analysis is exploited as it is. Therefore, we could be a better one for demand prediction.
Keywords
Gradient-Boost; Feature Pattern; Coefficient of Determinant; Seoul Public Bike; Bicyle Rearrangement;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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