Hyungun Sung’s research while affiliated with Hanyang University and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (1)


Causal Impacts of the COVID-19 Pandemic on Daily Ridership of Public Bicycle Sharing in Seoul
  • Article

December 2022

·

30 Reads

·

25 Citations

Sustainable Cities and Society

Hyungun Sung

Public bicycle can be a disease-resilient travel mode during the coronavirus disease 2019 (COVID-19) pandemic. Nonetheless, its evidence on public bicycle sharing is still inconclusive. This study used Bayesian structural time series models and causal impact inference for the data on the daily ridership of public bicycles in Seoul, South Korea, for 1826 days from January 1, 2017, to December 31, 2021. The study found that the usage of public bicycles was robust against the COVID-19 pandemic even in densely populated Seoul. Compared with the pre-pandemic period, public bicycles' usage was unaffected on days when weather conditions, such as snow, rain, and wind speed were not as severe, as well as on days with non-seasonal event factors, such as weekdays, public holidays, and traditional Korean holidays. In addition, its robustness against the pandemic became more pronounced as the number of bicycle racks increased and the intensity of social distancing increased. However, public bicycles were in demand primarily for leisure and exercise, not for travel, during the pandemic. Public bicycle sharing can be a disease-resilient travel mode. Continuous investment in infrastructure such as bicycle paths and public bicycle is required to become a more resilient travel mode against infectious diseases.

Citations (1)


... It can also accommodate external regressors, which makes it possible to quantify the impacts of regressors on the response. Recent literature (Sung 2023;Zhang and Fricker 2021) has utilized the BSTS model to analyze COVID-19 data. Based on BSTS, the Multivariate BSTS (MBSTS) (Qiu, Jammalamadaka, and Ning 2018;Ning and Qiu 2023) was proposed as a novel tool for inferring and predicting multiple correlated time series. ...

Reference:

Modeling the impacts of governmental and human responses on COVID-19 spread using statistical machine learning
Causal Impacts of the COVID-19 Pandemic on Daily Ridership of Public Bicycle Sharing in Seoul
  • Citing Article
  • December 2022

Sustainable Cities and Society