{"title":"Prediction of Bike Share Demand by Machine Learning","authors":"Tae You Kim, M. Park, J. Shin, Sung-Baik Oh","doi":"10.4018/ijban.288513","DOIUrl":null,"url":null,"abstract":"In the fourth industrial revolution period, multinational companies and start-ups have applied a sharing economy concept to their business and have attempted to better serve customer demand by integrating demand prediction results into their business operations. For survival amongst today’s fierce competition, companies need to upgrade their prediction model to better predict customer demand in a more accurate manner. This study explores a new feature for bike share demand prediction models that resulted in an improved RMSLE score. By applying this new feature, the number of daily vehicle accidents reported in the Washington, D.C. area, to the Random Forest, XGBoost, and LightGBM models, the RMSLE score results improved. Many previous studies have primarily focused on feature engineering and regression techniques within given dataset. However, this study is meaningful because it focuses more on finding a new feature from an external data source.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Business Analytics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijban.288513","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 1
Abstract
In the fourth industrial revolution period, multinational companies and start-ups have applied a sharing economy concept to their business and have attempted to better serve customer demand by integrating demand prediction results into their business operations. For survival amongst today’s fierce competition, companies need to upgrade their prediction model to better predict customer demand in a more accurate manner. This study explores a new feature for bike share demand prediction models that resulted in an improved RMSLE score. By applying this new feature, the number of daily vehicle accidents reported in the Washington, D.C. area, to the Random Forest, XGBoost, and LightGBM models, the RMSLE score results improved. Many previous studies have primarily focused on feature engineering and regression techniques within given dataset. However, this study is meaningful because it focuses more on finding a new feature from an external data source.
期刊介绍:
The main objective of the International Journal of Business Analytics (IJBAN) is to advance the next frontier of decision sciences and provide an international forum for practitioners and researchers in business and governmental organizations—as well as information technology professionals, software developers, and vendors—to exchange, share, and present useful and innovative ideas and work. The journal encourages exploration of different models, methods, processes, and principles in profitable and actionable manners.