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People’s attitudinal shifts toward an epidemic at different stages of the epidemic affect their travel behavior. Non-commuting travel behavior is more variable than commuting, as non-commuters have more travel options. However, few studies explored the changes in non-commuting travel and its influencing factors across different stages of sudden and localized COVID-19 outbreaks. Using survey data collected in Nanjing, China, where there was a sudden and localized outbreak of COVID-19, this research adopted the random parameter ordered logit model with heterogeneity in means and variances (HMV) to explore the factors influencing non-commuting travel in the early, middle, and late epidemic stages. The model results revealed that considering the HMV would improve the model fitness. In addition, the temporal stability of factors was investigated via a likelihood ratio test, which confirms traveler behavioral differences across different stages. The results showed that “e-bike ownership” and “the number of PCR (polymerase chain reaction) tests” is positively correlated with the number of non-commuting trips over three epidemic stages. The variables “people who live together have a red health code,”“mask replacement frequency,” and “risk-free areas” are significant in the early-stage and middle-stage models. The variables “people who live together have a green health code all the time” only become significant in the late-stage model. Research findings contribute to the understanding of non-commuting behaviors and targeted management needs during local outbreaks, and can help the government address travel issues under future major health events.
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