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The COVID-19 pandemic has significantly disrupted urban transportation systems , revealing the necessity for resilient and adaptable mobility solutions. This study examines the evolving roles of bikeshare systems (BSS) relative to public transit (PT) at micro, meso , and macro levels across four major U.S. cities during different pandemic phases, by employing a multilevel analytical framework that incorporates advanced statistical and machine learning methods. Results indicate remarkable resilience and adaptability of BSS, with usage surpassing pre-pandemic levels and the post-restrictions, contrasting with notable declines and slow recovery of PT. The analysis identifies short-term synergistic effects, long-term seasonal fluctuations, and the influence of weather, government policies, and pandemic severity on BSS-PT relationships. While weather conditions and city-specific factors alone do not exhibit significant correlations, their interactive terms with other variables significantly impact BSS-PT dynamics. Key pandemic-related variables are illustrated as critical determinants, with the initial outbreak and lockdown phase and COVID-19 death tolls exhibiting a pronounced negative association with BSS-PT interactions. Conversely, COVID-19 case counts display a notably positive correlation. This research provides valuable insights into the adaptation of transit systems to future disruptions, fostering more resilient and integrated mobility networks.
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