From Lockdown to Recovery: Modeling the Long-Term Effects of COVID-19 on Station-Level Ridership of Xi’an Metro

The outbreak of COVID-19 and the lockdown policies led to a significant reduction in metro ridership, which gradually recovered during the reopening phase. However, how different COVID-19 policies affected ridership over time and how ridership has recovered during the post-pandemic period remain unclear. Understanding these impacts and recovery patterns is valuable for analyzing policy effectiveness in similar future situations. A panel dataset covering the full cycle of the COVID-19 pandemic (from January 2019 to September 2023) was collected for Xi’an, China. Multiple panel econometric models were constructed to measure the long-term impact of various determinants (i.e., building environment, socio-economics, transportation accessibility, and pandemic-related policies) on metro ridership. The results reveal that supply-side intervention (station closure) has the strongest negative impacts, followed by comprehensive lockdown restricting large-scale mobility, while restrictions targeting public places and school closures have relatively smaller effects. This reflects the stability of demand for essential trips during this period. Following the lifting of pandemic control measures, ridership exhibits a recovery pattern characterized by initial slow growth, followed by an accelerated recovery, ultimately reaching pre-pandemic levels. Meanwhile, the increased accessibility from network expansion is associated with decreased ridership at existing stations, as stations on existing lines are more likely to experience ridership diversion to new nearer stations. Despite this diversion effect, the recovery to pre-pandemic levels suggests particularly strong underlying recovery momentum. Moreover, individual travel decisions are primarily driven by health risk assessments rather than economic incentives. These findings provide empirical support for future transportation management and decision-making in response to similar public health events.