Gendered Mobility: Insights From Large-Scale Behavioral Data

For decades, geographers and urban planners have documented gender differences in mobility: women rely more on flexible and public transport, travel shorter commutes, and more often combine multiple purposes within a single trip. These insights have been foundational but are often drawn from surveys with small samples or narrow contexts.
The rise of large-scale digital traces has transformed the study of mobility, with smartphone data enabling analysis at unprecedented scales and resolutions. These datasets reveal striking regularities in how people travel, but most lack sociodemographic detail—especially gender—holding back progress in understanding gendered mobility.
In this thesis, we draw on a unique large-scale smartphone dataset combining mobility traces with self-reported gender for more than half a million individuals across ten countries. With these data, we quantify gendered mobility across multiple dimensions and countries, revisiting established mobility measures, introducing new network-based approaches and developing robust methods to detect home and work locations.
Our analyses show that men are more active but more often make back-and-forth trips. Women, though more home-anchored, link destinations into tighter networks and add extra stops before returning home, making their travel more efficient even over longer distances. To understand the drivers of these differences, we examined the role of work and found that although work routines shape mobility for both genders, gaps persist even among those with similar work schedules, pointing to the added influence of family obligations, care-related travel and urban space.
These findings highlight the importance of large-scale, genderdisaggregated data to map gender differences at scale, uncover their drivers, and guide the design of more equitable and inclusive cities.