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Investigation on changes in the usage patterns of Seoul Bike usage patterns owing to COVID-19 according to pass type.
Jung, Juhyeon; Kim, Kyoungok.
  • Jung J; Department of Data Science, Seoul National University of Science & Technology (SeoulTech), 232 Gongreungno, Seoul, 01811, Nowon-gu, Republic of Korea.
  • Kim K; Department of Industrial Engineering, Seoul National University of Science & Technology (SeoulTech), 232 Gongreungno, Seoul, 01811, Nowon-gu, Republic of Korea.
Heliyon ; 9(5): e16077, 2023 May.
Article in English | MEDLINE | ID: covidwho-2323931
ABSTRACT
Human mobility has been significantly impacted by varying degrees of social distancing and stay-at-home directives that have been implemented in many countries to prevent the spread of COVID-19; this effect was observed regardless of the mode of transportation. Several studies have indicated that bike-sharing is a relatively safe option in terms of COVID-19 infection, and more resilient than public transportation. However, previous studies on the effects of COVID-19 on bike-sharing, rarely considered the type of pass in their investigation of the pandemic-induced changes in usage patterns of shared bikes. To overcome this limitation, this study used trip records obtained from Seoul Bike to investigate the changes in usage patterns of shared bikes during the COVID-19 pandemic. The spatiotemporal usage patterns were characterized in this study based on the type of pass. Additionally, using t-tests and k-means clustering, we discovered significant factors that influenced changes in one-day pass usage rates and temporal usage patterns at the station level. Finally, we constructed spatial regression models to estimate changes in bike rentals caused by COVID-19 based on pass type. The findings provided a comprehensive understanding of how bike-sharing usage varies depending on pass type, which is closely related to shared bikes trip purposes.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Heliyon Year: 2023 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Heliyon Year: 2023 Document Type: Article