This research aims to investigate the well-being implications of changes in activity-travel and time-use patterns brought about by the COVID-19 pandemic. The study uses American Time Use Survey (ATUS) data from 2019 and 2020 to assess changes in activity-travel and time-use patterns. It applies two methods—a well-being scoring method and a time-poverty analysis method—to evaluate the impacts of these changes on society. The results show that individuals experienced diminished well-being during the pandemic even when their time-poverty statistics showed an improvement; this is because the pandemic did not allow individuals to pursue activities in a way that would enhance well-being. In general, well-being is positively associated with the pursuit of discretionary activities in the company of others in favored out-of-home locations. This explains why people have rapidly embraced traveling again in a post-pandemic era. At the same time, people desire more discretionary time (less time poverty); because the elimination of the commute contributes to this, workers are reluctant to return fully to the workplace. Planning processes need to account for a new normal in which activity-travel patterns will be increasingly shaped by the human desire to accumulate positive life experiences.

This paper presents an examination of the interrelationship between household vehicle ownership and ridehailing use frequency. Both variables constitute important mobility choices with significant implications for the future of transport. Although it is generally known that these two behavioral phenomena are inversely related to one another, the direction of causality is rather ambiguous. Do vehicle ownership levels affect ridehailing use frequency, or does the adoption and use of ridehailing services affect vehicle ownership? If ridehailing services affect vehicle ownership, then it is plausible that a future of mobility-as-a-service would be characterized by lower levels of vehicle ownership. To explore the degree to which these causal relationships are prevalent in the population, a joint latent segmentation model system was formulated and estimated on a survey data set collected in four automobile-oriented metropolitan areas of the United States. The latent segmentation model system recognized that the causal structures driving the mobility choices of individuals were not directly observable. Model estimation results showed that 58% of the survey sample followed the causal structure in which ridehailing use frequency affected vehicle ownership. This finding suggests that there is considerable structural heterogeneity in the population with respect to causal structures and that ridehailing use does indeed hold considerable promise to effect changes in private vehicle ownership in the future.