期刊
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
卷 105, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trd.2022.103226
关键词
COVID-19; Transit ridership; Bayesian structural time series; Dynamics model; Telecommute; Remote work; Regression analysis; Ridership recovery; Mobility
资金
- Illinois Department of Transportation via Illinois Center for Transportation project [ICT R27-SP45]
The study reveals that remote learning/working accounts for the majority of transit ridership losses during the COVID-19 pandemic, and these impacts heavily depend on socio-demographic characteristics.
The COVID-19 pandemic has induced significant transit ridership losses worldwide. This paper conducts a quantitative analysis to reveal contributing factors to such losses, using data from the Chicago Transit Authority's bus and rail systems before and after the COVID-19 outbreak. It builds a sequential statistical modeling framework that integrates a Bayesian structural time series model, a dynamics model, and a series of linear regression models, to fit the ridership loss with pandemic evolution and regulatory events, and to quantify how the impacts of those factors depend on socio-demographic characteristics. Results reveal that, for both bus and rail, remote learning/working answers for the majority of ridership loss, and their impacts depend highly on socio-demographic characteristics. Findings from this study cast insights into future evolution of transit ridership as well as recovery campaigns in the post-pandemic era.
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