Residential locations, the jobs–housing relationship, and commuting patterns are key elements to understand urban spatial structure and how city dwellers live. Their successive interaction is important for various fields including urban planning, transport, intraurban migration studies, and social science. However, understanding of the long-term trajectories of workplace and home location, and the resulting commuting patterns, is still limited due to lack of year-to-year data tracking individual behavior. With a 7-y transit smartcard dataset, this paper traces individual trajectories of residences and workplaces. Based on in-metro travel times before and after job and/or home moves, we find that 45 min is an inflection point where the behavioral preference changes. Commuters whose travel time exceeds the point prefer to shorten commutes via moves, while others with shorter commutes tend to increase travel time for better jobs and/or residences. Moreover, we capture four mobility groups: home mover, job hopper, job-and-residence switcher, and stayer. This paper studies how these groups trade off travel time and housing expenditure with their job and housing patterns. Stayers with high job and housing stability tend to be home (apartment unit) owners subject to middle- to high-income groups. Home movers work at places similar to stayers, while they may upgrade from tenancy to ownership. Switchers increase commute time as well as housing expenditure via job and home moves, as they pay for better residences and work farther from home. Job hoppers mainly reside in the suburbs, suffer from long commutes, change jobs frequently, and are likely to be low-income migrants.

To better understand how public transport use varied during the first year of COVID-19, we define and measure travel behavior resilience. With trip records between November 2019 and September 2020 in Kunming, China, we identify people who relied on traveling by subway both before and after the first pandemic wave. We investigate whether and how travelers recover to their pre-pandemic mobility level. We find that public transport use recovered slowly, as urban mobility is a result of urban functionality, transport supply, social context, and inter-personal differences. In general, urban mobility represents a strengthened revisiting tendency during COVID-19, as individual’s trips occur within a more limited space. We confirm that travel behavior resilience differs by groups. Commuters recover travel frequency and length, while older people decrease frequency but retain activity space. The study suggests that policymakers take group heterogeneity and travel behavior resilience into account for transport management and city restoration.