Using mobile phone data to explore gender and age gaps in urban mobility. Revealing the changes after COVID-19 in the metropolitan region of Madrid (Spain)

Gender differences in mobility are common in cities but analyzing them using data from travel surveys is limited by the sample size and the lack of detailed spatial and temporal patterns for different population groups. Big data sources, such as mobile phone data, offer almost ubiquitous data of daily mobility patterns and their changes while including basic socio-demographic characteristics. This paper studies differences in urban mobility from a gender and age perspective following the COVID-19 pandemic, comparing daily patterns of February 2020 and 2023 for the metropolitan region of Madrid. Urban activity, changes in mobility, and the gender gap are analyzed using different temporal and spatial aggregations based on Transport Analysis Zones (TAZ) and travel flows. The results reflect that the gender gap in trips decreases while remaining for the distance travelled for all age groups after COVID-19. Temporal patterns changed with more trips in the daytime and fewer at night, while the distance travelled evolves towards shorter journeys. Although women still make fewer trips and shorter distances, young women have reversed the gender gap between 2020 and 2023. This paper highlights how gender and age are longitudinal and transversal factors shaping mobility over an individual's life course and how COVID-19 seems to have accelerated pre-existing changing trends in urban mobility. Understanding the nature and relevance of the social dynamics and the differences between each population group is essential for supporting the study of urban segregation and helping authorities manage mobility in the territory. • The gender gap decreases in trips and remains in distances after COVID-19. • Young women have reversed the gender gap in the number of trips. • Travel flows create mesh structures that outline the gender gap in spatial terms. • Gender and age are longitudinal and transversal factors that influence urban mobility. • Phone data has proven to be more accurate for analyzing changes in urban mobility.