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.
Highlights
Predicting customer demand accurately in the data-driven business environment of the mobility industry is a key factor of success (Sohrabi et al, 2020; Wessel, 2020)
We implemented a prediction model for 70% of the total dataset and applied 30% dataset to the prediction model to calculate the performance of the actual prediction accuracy of the prediction model via root mean squared logarithmic error (RMSLE)
Bike sharing systems are in operation in many countries around the world
Summary
Predicting customer demand accurately in the data-driven business environment of the mobility industry is a key factor of success (Sohrabi et al, 2020; Wessel, 2020). In the data technology period, it is very easy to find multinational companies that provide their various services to customers based on demand prediction results. Companies have achieved success because they have sufficiently predicted demand based on internal and external features. Digital transformation is occurring in most industries around the world based on machine learning and deep learning algorithms (Veres & Moussa, 2019; Wang et al, 2019). Rapidly growing start-ups are often making their important business decisions based on data and algorithms, not on management experience or intuition.
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