Travel patterns of free-floating e-bike-sharing users before and during COVID-19 pandemic

Seung Eun Choi; Jinhee Kim; Dayoung Seo

doi:10.1016/j.cities.2022.104065

Travel patterns of free-floating e-bike-sharing users before and during COVID-19 pandemic

Cities. 2023 Jan:132:104065. doi: 10.1016/j.cities.2022.104065. Epub 2022 Oct 27.

Authors

Seung Eun Choi¹, Jinhee Kim², Dayoung Seo³

Affiliations

¹ School of Civil and Environmental Engineering, Transportation Systems Engineering, Georgia Institute of Technology, 790 Atlantic Drive, Atlanta, GA 30332, United States of America.
² Department of Urban Planning and Engineering, Transportation Planning, Yonsei University, Seoul 03722, South Korea.
³ Product Team Lead, Elecle, Seoul 06611, South Korea.

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.

Keywords: Big data; Clustering; Dock-less; Electric-bike sharing; Free-floating micro-mobility; Pandemic.