Who left riding transit? Examining socioeconomic disparities in the impact of COVID-19 on ridership

Document Type

Journal Article

Publication Date


Subject Area

place - north america, ridership - behaviour


COVID-19, Transit ridership, Socioeconomic disparity, Bayesian structural time series, Partial least square regression


The COVID-19 pandemic has led to a globally unprecedented decline in transit ridership. This paper leveraged the 20-years daily transit ridership data in Chicago to infer the impact of COVID-19 on ridership using the Bayesian structural time series model, controlling confounding effects of trend, seasonality, holiday, and weather. A partial least square regression was then employed to examine the relationships between the impact of ridership and various explanatory factors. Results suggested: (1) COVID-19 pandemic exerted significant effects on 95% of transit stations, leading to an average 72.4% drop in ridership. (2) Ridership declined more in regions with more commercial lands and higher percentages of white, educated, and high-income individuals. (3) Regions with more jobs in trade, transportation, and utility sectors presented smaller declines. (4) Regions with more COVID-19 cases/deaths presented smaller declines in transit ridership. Findings provide a timely understanding of the significantly reduced ridership during the pandemic and help transit agencies adjust services across different socioeconomic groups and space to better constrain virus transmission.


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


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