A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Travel patterns of free-floating e-bike-sharing users before and during COVID-19 pandemic
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.
Highlights Travel patterns of FFEBSS users before and during COVID-19 were investigated. Nine different user-based behavioral features were extracted and investigated. Eight time frames in consideration of market cycle and COVID-19 were investigated. First-mile and last-mile usage exhibited constantly high values during the pandemic. The overall FFEBSS user patterns demonstrated stability, especially commuters.
Travel patterns of free-floating e-bike-sharing users before and during COVID-19 pandemic
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.
Highlights Travel patterns of FFEBSS users before and during COVID-19 were investigated. Nine different user-based behavioral features were extracted and investigated. Eight time frames in consideration of market cycle and COVID-19 were investigated. First-mile and last-mile usage exhibited constantly high values during the pandemic. The overall FFEBSS user patterns demonstrated stability, especially commuters.
Travel patterns of free-floating e-bike-sharing users before and during COVID-19 pandemic
Choi, Seung Eun (author) / Kim, Jinhee (author) / Seo, Dayoung (author)
Cities ; 132
2022-10-23
Article (Journal)
Electronic Resource
English
| DOAJ | 2023