Authors: Suman Mitra*, University of Arkansas
Topics: Gender, United States, Quantitative Methods
Keywords: Elderly mobility, gender, cluster analysis, SEM
Session Type: Paper
Presentation File: No File Uploaded
This study examines gender differences in mobility patterns of elderly people (aged 65 years or over) in the US by analyzing data from the 2017 National Household Travel Survey. A K-prototype algorithm was employed first to classify elderly respondents into seven clusters based on their socio-demographic characteristics. A Structural Equation Model (SEM) was then estimated and showed that gender gaps existed in the mobility patterns of the elderly, and the differences were diverse across the different clusters. The most substantial gender gap was found in the Senior Elder Urban Male Middle-income cluster, followed by the Urban Worker cluster and the Senior Elder with Medical Condition(s) cluster. In contrast, elderly females in the Low-Income Single Elder cluster enjoyed positive mobility differences with their male counterparts. Our results also found that female elderly in the Senior Elder with Medical Condition(s) and the Senior Elder Urban Male Middle-income clusters suffered most after the cessation of driving, with the largest gender gap in the Low-Income Single Elder cluster. The results of this study will help transportation planners and policymakers understand gender and other socio-demographic differences in elderly mobility. Thereby, it will hopefully facilitate the development of measures to more effectively improve elderly mobility and reduce gender gaps by recognizing and addressing the mobility characteristics and needs of specific target groups, rather than treating the elderly as a single potential user group.