ArticleTransport policy2022
Unraveling the dynamic impacts of COVID-19 on metro ridership: An empirical analysis of Beijing and Shanghai, China.
Article in Transport policy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 43 citations in OpenAlex.
- Article
- Investigating the potential of aggregated mobility indices for inferring public transport ridership changes.PloS one · 2024Article
- A multiple mediation analysis to untangle the impacts of COVID-19 on nationwide bus ridership in the United States.Transportation research. Part A, Policy and practice · 2023Article
- Metro travel and perceived COVID-19 infection risks: A case study of Hong Kong.Cities (London, England) · 2023Article
- Social and spatial heterogeneities in COVID-19 impacts on individual's metro use: A big-data driven causality inference.Applied geography (Sevenoaks, England) · 2023Article
- Systematic literature review on impacts of COVID-19 pandemic and corresponding measures on mobility.Transportation · 2023Article
- The Impact of Urban Rail Transit Epidemic Prevention Measures on Passengers' Safety Perception.International journal of environmental research and public health · 2023Article
- On the bumpy road to recovery: resilience of public transport ridership during COVID-19 in 15 European cities.Letters in spatial and resource sciences · 2023Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The outbreak of coronavirus disease 2019 (COVID-19) has had severely disruptive impacts on transportation, particularly public transit. To understand metro ridership changes due to the COVID-19 pandemic, this study conducts an in-depth analysis of two Chinese megacities from January 1, 2020, to August 31, 2021. Generalized linear models are used to explore the impact of the COVID-19 pandemic on metro ridership. The dependent variable is the relative change in metro ridership, and the independent variables include COVID-19, socio-economic, and weather variables. The results suggested the following: (1) The COVID-19 pandemic has a significantly negative effect on the relative change in metro ridership, and the number of cumulative confirmed COVID-19 cases within 14 days performs better in regression models, which reflects the existence of the time lag effect of the COVID-19 pandemic. (2) Emergency responses are negatively associated with metro system usage according to severity and duration. (3) The marginal effects of the COVID-19 variables and emergency responses are larger on weekdays than on weekends. (4) The number of imported confirmed COVID-19 cases only significantly affects metro ridership in the weekend and new-normal-phase models for Beijing. In addition, the daily gross domestic product and weather variables are significantly associated with metro ridership. These findings can aid in understanding the usage of metro systems in the outbreak and new-normal phases and provide transit operators with guidance to adjust services.
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