Executive orders or public fear: What caused transit ridership to drop in Chicago during COVID-19?
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.
Osorio, J., Liu, Y., & Ouyang, Y. (2022) .Executive orders or public fear: What caused transit ridership to drop in Chicago during COVID-19? Transportation Research Part D: Transport and Environment, Vol. 105, 103226.