Identifying elderly travel time disparities using a correlated grouped random parameters hazard-based duration approach

Abstract Populations in countries throughout the world are ageing. Within the United States, baby boomers – those born between the years 1946 and 1964 – are living longer and are desiring more active and mobile lifestyles than prior generations. It is well established that the onset of chronic diseases and mobility impairments increase with age. Unmitigated mobility gaps threaten well-being, social interaction, and overall quality of life. As a result, transportation policy makers and planners should anticipate, identify, plan, and address transport disadvantages impacting increasingly vulnerable and possibly underserved population segments such as the elderly. Our study reveals potential mobility gaps by quantifying travel time disparities associated with the various household, traveler, travel mode, and trip purpose characteristics. In doing so, business opportunities with respect to unmet transportation needs may be uncovered. To analyze elderly trip durations, this paper extracts data from the 2009 National Household Travel Survey for the New York Consolidated Metropolitan Statistical Area. Specifically, the elderly are divided into two cohorts; those 65 through 74 years of age and those 75 and older. We apply a correlated grouped random parameters hazard-based duration model. This specification accounts for unobserved heterogeneity in the underlying hazard function and across observations, as well as unobserved effects due to the correlation between random parameters. To the authors' knowledge, this is the first study to use this statistical modeling framework to analyze travel times. Results suggest that the use of a correlated grouped random parameters provides a superior statistical fit to several established comparison models. The findings also reveal that the elderly population is not a homogeneous group and that the underlying distribution characterizing the hazard function for each age group is different. To that end, separate models are estimated for each age cohort. Furthermore, the study reveals apparent disparities in elderly travel times associated with birth nationality, ethnicity, education level, and public/private travel modes.

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