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This study investigated ridership losses on the Chicago Transit Authority’s (CTA) rail and bus systems since the COVID-19 outbreak. It aimed at identifying key factors influencing transit ridership to inform effective policies and efficient service planning in future pandemics. The study developed a statistical modeling framework to reveal temporal variations and spatial disparity of ridership loss for rail stations and bus routes. The temporal analysis included a Bayesian structural time-series model to predict counterfactual ridership and a lag variable regression model to analyze the impacts of temporal factors on ridership loss. The spatial analysis adopted a regression model to connect ridership loss with local sociodemographic and land-use characteristics. The results revealed that workplace occupancy rates significantly affected ridership at all CTA rail stations and bus lines, partly because of the CTA’s high market share of commuter trips. It highlighted the significant impacts of remote-work policies on transit ridership recovery for the near future. Fare-discount programs were found to be significantly negatively correlated with ridership losses at 84% of rail stations and 67% of bus lines, indicating their strong effectiveness in helping recover transit ridership. The impacts of various factors on transit ridership were spatially heterogeneous over different population groups and different land-use types. For example, fare discounts were especially effective for male riders, people living in poverty, employed individuals, and trips linked to residential areas and open spaces. However, they were less effective in commercial zones, suggesting that transit agencies should collaborate with specific industries to support ridership recovery.
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