Understanding changing public transit travel patterns of urban visitors during COVID-19: A multi-stage study

COVID-19 has caused huge disruptions to urban travel and mobility. As a critical transportation mode in cities, public transit was hit hardest. In this study, we analyze public transit usage of urban visitors with a nearly two-year smart card dataset collected in Jeju, South Korea - a major tourism city in the Asia Pacific. The dataset captures transit usage behavior of millions of domestic visitors who traveled to Jeju between January 1, 2019 and September 30, 2020. By identifying a few key pandemic stages based on COVID-19 timeline, we employ ridge regression models to investigate the impact of pandemic severity on transit ridership. We then derive a set of mobility indicators - from perspectives of trip frequency, spatial diversity, and travel range - to quantify how individual visitors used the transit system during their stay in Jeju. By further employing time series decomposition, we extract the trend component for each mobility indicator to study long-term dynamics of visitors' mobility behavior. According to the regression analysis, the pandemic had a dampening effect on public transit ridership. The overall ridership was jointly affected by national and local pandemic situations. The time series decomposition result reveals a long-term decay of individual transit usage, hinting that visitors in Jeju tended to use the transit system more conservatively as the pandemic endured. The study provides critical insights into urban visitors' transit usage behavior during the pandemic and sheds light on how to restore tourism, public transit usage, and overall urban vibrancy with some policy suggestions.