Gender Differences in Travel and Everyday Life: a Data-Driven Approach to Address the Intersectional Nature of Gender as a Social Construct

Abstract

Gender differences in travel patterns have been extensively studied in the transportation realm. Recent studies have started to adopt an intersectional lens to acknowledge that the convergence of gender and other social identities can create unique travel needs and experiences. However, studies often focus on gender differences in trip characteristics instead of putting trips in the context of daily schedules. Further, existing studies often select one to two socio-demographic characteristics along with gender to define intersectional groups and compare trip characteristics of these groups. So, findings from these studies are largely influenced by the pre-selected socio-demographic characteristics and may neglect some key characteristics that significantly affect behaviors in a given region during specific periods. To address these gaps, this study first applies the sequence alignment method (SAM) to detect behavior patterns that can account for both trips and activities in daily schedules. Then, the study applies the Chi-square automatic interaction detection (CHAID) to identify key characteristics that have significant impacts on the behavior patterns. Last, the study defines intersectional groups using these identified key characteristics and gender and examines whether each group tends to have a unique set of behavior patterns. To demonstrate the methods, this study uses the travel survey data collected in Minnesota as a study case. The SAM results reveal that the behavior patterns on weekdays and weekends are different, and the CHAID analysis also identifies different key characteristics for weekdays and weekends. Moreover, we find several gender gaps that have rarely been addressed in the literature, such as women tending to lose their after-work personal time after having kids. These findings prove that our approach can derive intersectional groups directly from the data and provide novel insights into gender gaps in travel and activity participation in the context of everyday life.

Yaxuan (Sean) Zhang

PhD Candidate at UMN | MGIS Student | Computer Science Minor

My research interests include geospatial data science, transportation planning, and GeoAI.