PURPOSES : This study aimed to explore crowding impedance for high-risk travelers on various modes of public transit during the COVID-19 pandemic and develop a transport policy to encourage the proper use of public transport. METHODS : A stated preference survey was conducted to investigate the behaviors of travelers on various modes of public transit, with special emphasis on crowding inside vehicles. Multinomial logit-based modeling was used to estimate the explanatory variables identified as parameters based on the surveyed data. A crowding multiplier was adopted to represent the behavioral differences for the high-risk travelers on various modes of public transit. RESULTS : The established model was solved using the ‘mlogit’ R package program to estimate the identified parameters. The results demonstrated significant behavioral difference for the high-risk travelers on public transit during the COVID-19 pandemic. The proposed crowding multiplier successfully captured the reduced likelihood of high-risk travelers to be sensitive to crowding on the subway; furthermore, it revealed that non-crowding travelers on the subway are less sensitive to crowding than bus travelers. CONCLUSIONS : This study estimated crowding impedance for high-risk travelers on various modes of public transit during the COVID-19 pandemic and suggested an appropriate transport policy for those travelers.