Asymmetric Recovery Pathways of Seoul Subway Ridership After the COVID-19 Pandemic

This study examines patterns of ridership recovery across Seoul subway stations following the COVID-19 pandemic and identifies the factors associated with recovery stagnation. To this end, we apply cluster analysis, Markov transition analysis, and binary logistic regression using subway ridership data from 2019 to 2025. We classify station-level recovery into four types: Entrenched Low-Recovery, Partial Recovery, Improved Recovery, and Over-Recovery. The results reveal substantial variation in recovery trajectories across stations. Entrenched Low-Recovery stations display strong path dependence and state persistence, whereas Improved Recovery and Over-Recovery stations exhibit lower state stability and higher transition probabilities. In addition, recovery in morning commuting and evening travel, restaurant density, and average sales per store reduce the likelihood of recovery stagnation, whereas stations with stronger transfer functions are more likely to remain in a low-recovery state. These findings indicate that post-pandemic urban rail recovery is not a uniform return to pre-pandemic ridership levels, but a heterogeneous and dynamic process shaped by station functions and surrounding urban conditions. From a policy perspective, this heterogeneity highlights the need for station-specific recovery strategies that account for differences in transportation and local characteristics. More broadly, by revealing the persistence and transitions of station-level recovery states, this study provides a dynamic perspective on post-pandemic urban rail recovery beyond point-in-time comparisons of ridership change.