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dc.creatorHoklas, Megan M.en
dc.creatorHyun You, Daeen
dc.creatorPendyala, Ram M.en
dc.creatorVenu M., Garikapatien
dc.creatorBhat, Chandra R.en
dc.creatorDubey, Subodh K.en
dc.date.accessioned2014-09-29T20:37:39Zen
dc.date.available2014-09-29T20:37:39Zen
dc.date.issued2014-07en
dc.identifier.urihttp://hdl.handle.net/2152/26174en
dc.descriptionAt the time of publication M.M Hoklas, S.K. Dubey, and C.R. Bhat were at the University of Texas at Austin. V.M Garikapati at Arizona State University, R.M. Pendyala at Georgia Institute of Technology, and D. Hyun You at Arizona State University.en
dc.description.abstractThe health and well-being of individuals is related to their activity-travel patterns. Individuals who undertake physically active episodes such as walking and bicycling are likely to have improved health outcomes compared to individuals with sedentary auto-centric lifestyles. Activity-based travel demand models are able to predict activity-travel patterns of individuals at a high degree of fidelity, thus providing rich information for transportation and public health professionals to infer health outcomes that may be experienced by individuals in various geographic and demographic market segments. However, models of activity-travel demand do not account for the attitudinal factors and lifestyle preferences that affect activity-travel and mode use patterns. Such attitude and preference variables are virtually never collected explicitly in travel surveys, rendering it difficult to include them in model specifications. This paper applies Bhat’s (2014) Generalized Heterogeneous Data Model (GHDM) approach, whereby latent constructs representing the degree to which individuals are health conscious and inclined to pursue physical activities may be modeled as a function of observed socio-economic and demographic variables and then included as explanatory factors in models of activity-travel outcomes and walk and bicycle use. The model system is estimated on the 2005-2006 National Health and Nutrition Examination Survey (NHANES) sample, demonstrating the efficacy of the approach and the importance of including such latent constructs in model specifications that purport to forecast activity and time use patterns.en
dc.language.isoengen
dc.publisherThe University of Texas at Austinen
dc.subjectactivity-travel and healthen
dc.subjectgeneralized heterogeneous data modelen
dc.subjectattitudes and lifestyle preferencesen
dc.subjectphysical activity and health outcomesen
dc.subjectactivity-travel behavior modelsen
dc.titleAn Integrated Latent Construct Modeling Framework for Predicting Physical Activity Engagement and Health Outcomesen
dc.typeTechnical reporten
dc.description.departmentCivil, Architectural, and Environmental Engineeringen


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