Travel Patterns of Free-Floating E-Bike-Sharing Users Before and During Covid-19 Pandemic

40 Pages Posted: 11 Jan 2022

See all articles by Seung Eun Choi

Seung Eun Choi

Yonsei University

Jinhee Kim

Yonsei University

Dayoung Seo

affiliation not provided to SSRN

Abstract

Free-floating micro-mobility as a mobility solution is becoming increasingly popular and common in cities. Here, 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 machine learning. Existing real-time-data studies provide a limited understanding of trip patterns and the characteristics of each user. Interpretations with respect to the occurrence of life-changing events such as the pandemic are important. This study aimed to understand each user over 13 months comprising multiple time frames of market trend, seasonal change, and the pandemic outbreak. Multiple features were extracted from each user to explain the hidden data characteristics, and an unsupervised learning 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 pandemic, indicating the possibility of micro-mobilities being well adopted as our future urban transportation.

Keywords: Free-Floating Micro-Mobility, Electric-Bike Sharing, Pandemic, Unsupervised learning, clustering, Big Data

Suggested Citation

Choi, Seung Eun and Kim, Jinhee and Seo, Dayoung, Travel Patterns of Free-Floating E-Bike-Sharing Users Before and During Covid-19 Pandemic. Available at SSRN: https://ssrn.com/abstract=4003288 or http://dx.doi.org/10.2139/ssrn.4003288

Seung Eun Choi

Yonsei University ( email )

Seoul
Korea, Republic of (South Korea)

Jinhee Kim (Contact Author)

Yonsei University ( email )

Seoul
Korea, Republic of (South Korea)

Dayoung Seo

affiliation not provided to SSRN ( email )

No Address Available

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