Abstract
Free-floating micro-mobility as a mobility solution is becoming increasingly popular in cities. In this study, the travel patterns of free-floating electric bike-sharing service (FFEBSS) users before and during the COVID-19 pandemic were explored using big data and data mining. Existing real-time data studies provide a limited understanding of trip patterns and the characteristics of each user. Interpretations concerning the occurrence of life-changing events such as the COVID-19 pandemic are important. This study aimed to understand each user over 13 months comprising multiple time frames of market trends, seasonal change, and the COVID-19 pandemic outbreak. Multiple features were extracted from each user to explain the hidden data characteristics, and a data mining method was employed for clustering and evaluating user similarities with the extracted features. The results showed that FFEBSS users demonstrated a moderately stable travel pattern despite the COVID-19 pandemic, indicating the possibility of micro-mobilities being well adoptedas our future urban transportation.
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