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Prediction of Bike Share Demand by Machine Learning: Role of Vehicle Accident as the New Feature

Prediction of Bike Share Demand by Machine Learning: Role of Vehicle Accident as the New Feature

Tae You Kim, Min Jae Park, Jiho Shin, Sungwon Oh
Copyright: © 2022 |Volume: 9 |Issue: 1 |Pages: 16
ISSN: 2334-4547|EISSN: 2334-4555|EISBN13: 9781683182870|DOI: 10.4018/IJBAN.288513
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MLA

Kim, Tae You, et al. "Prediction of Bike Share Demand by Machine Learning: Role of Vehicle Accident as the New Feature." IJBAN vol.9, no.1 2022: pp.1-16. http://doi.org/10.4018/IJBAN.288513

APA

Kim, T. Y., Park, M. J., Shin, J., & Oh, S. (2022). Prediction of Bike Share Demand by Machine Learning: Role of Vehicle Accident as the New Feature. International Journal of Business Analytics (IJBAN), 9(1), 1-16. http://doi.org/10.4018/IJBAN.288513

Chicago

Kim, Tae You, et al. "Prediction of Bike Share Demand by Machine Learning: Role of Vehicle Accident as the New Feature," International Journal of Business Analytics (IJBAN) 9, no.1: 1-16. http://doi.org/10.4018/IJBAN.288513

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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.