Jiang Shixiong, Cai Canhuang
School of Urban Planning and Design, Peking University Shenzhen Graduate School, China.
College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, China.
Transp Policy (Oxf). 2022 Oct;127:158-170. doi: 10.1016/j.tranpol.2022.09.002. Epub 2022 Sep 8.
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.
2019年冠状病毒病（COVID-19）的爆发对交通运输业，尤其是公共交通，产生了严重的破坏性影响。为了解COVID-19大流行导致的地铁客流量变化，本研究对2020年1月1日至2021年8月31日期间的两个中国特大城市进行了深入分析。使用广义线性模型来探究COVID-19大流行对地铁客流量的影响。因变量是地铁客流量的相对变化，自变量包括COVID-19、社会经济和天气变量。结果表明：（1）COVID-19大流行对地铁客流量的相对变化有显著负面影响，且14天内的COVID-19累计确诊病例数在回归模型中表现更佳，这反映了COVID-19大流行存在时间滞后效应。（2）应急响应根据严重程度和持续时间与地铁系统使用呈负相关。（3）COVID-19变量和应急响应在工作日的边际效应大于周末。（4）输入性COVID-19确诊病例数仅在北京的周末和常态化阶段模型中对地铁客流量有显著影响。此外，每日国内生产总值和天气变量与地铁客流量显著相关。这些发现有助于了解疫情爆发和常态化阶段地铁系统的使用情况，并为公交运营商调整服务提供指导。
