Executive orders or public fear: What caused transit ridership to drop in Chicago during COVID-19?

Document Type

Journal Article

Publication Date


Subject Area

place - north america, place - urban, mode - bus, mode - rail, ridership - behaviour, ridership - modelling


COVID-19, Transit ridership, Bayesian structural time series, Dynamics model, Telecommute, Remote work, Regression analysis, Ridership recovery, Mobility


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.


Permission to publish the abstract has been given by Elsevier, copyright remains with them.


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