ABSTRACT
We used a Bayesian structural time series (BSTS) model to evaluate the short- and long-term impacts of the coronavirus disease 2019 (COVID-19) pandemic on transit ridership. We accessed smart-card data from Miyazaki City, Japan. We defined attributes based on card types (commuters, students and elders) and aggregated attributes (high-frequency users and "frequently used bus-stop pairs”) and analyzed the differences between all users and the extracted groups. Among card types, the short-term impact on elders was almost identical to that of all users, however, the short-term impact of the pandemic on commuters was much smaller and that of students was much larger than that of all users. The long-term trend of commuters was less fluctuated than that of all users. The long-term ridership recovery of students was higher than that of all users. Among aggregated attributes, the short-term impact was smaller on "high-frequency users” than on all users: the decrease in ridership immediately after the appearance of COVID-19 was smaller among "high-frequency users” than among all users. The long-term recoveries in the riderships of the extracted subsets were slower than the recoveries of riderships of all users. © 2023 The Authors