Differential Dynamics of Transit Use Resilience During the COVID-19 Pandemic Using Multivariate Two-Dimensional Functional Data Analysis

Abstract

This study delves into the nuanced patterns of shock and recovery in transit ridership during and after the COVID-19 pandemic, aiming to illuminate the resilience exhibited by various geographic areas. This resilience is measured by the ability of transportation systems to withstand, adapt to, and bounce back from unforeseen shocks. In this research, smart card big data were exploited to track real-time mobility dynamics and economic activity within the city of Seoul, Korea. The approach employed multivariate two-dimensional functional data analysis and a hierarchical clustering method to examine both boarding and alighting patterns, taking into account multi-scalar temporal units, monthly and hourly demand fluctuations. The findings present distinct varied shock-and-recovery patterns across areas in transit ridership based on the socioeconomic characteristics of specific areas. These characteristics encompass factors such as industry and land-use composition, income levels, population density, and proximity to points of interest. Additionally, this methodology proves effective in identifying abnormal surges in demand linked to local large-scale development projects.