Modeling the choice continuum: an integrated model of residential location,auto ownership,bicycle ownership,and commute tour mode choice decisions |
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Authors: | Abdul Rawoof Pinjari Ram M Pendyala Chandra R Bhat Paul A Waddell |
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Institution: | (1) Department of Civil and Environmental Engineering, University of South Florida, 4202 E. Fowler Ave, ENB 118 Tampa, FL, USA;(2) School of Sustainable Engineering and the Built Environment, Arizona State University, Room ECG252, Tempe, AZ 85287-5306, USA;(3) Department of Civil, Architectural & Environmental Engineering, The University of Texas at Austin, 1 University Station C1761, Austin, TX 78712-0278, USA;(4) Department of City and Regional Planning, University of California, Berkeley, 228 Wurster Hall #1850, Berkeley, CA 94720-1850, USA |
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Abstract: | The integrated modeling of land use and transportation choices involves analyzing a continuum of choices that characterize
people’s lifestyles across temporal scales. This includes long-term choices such as residential and work location choices
that affect land-use, medium-term choices such as vehicle ownership, and short-term choices such as travel mode choice that
affect travel demand. Prior research in this area has been limited by the complexities associated with the development of
integrated model systems that combine the long-, medium- and short-term choices into a unified analytical framework. This
paper presents an integrated simultaneous multi-dimensional choice model of residential location, auto ownership, bicycle
ownership, and commute tour mode choices using a mixed multidimensional choice modeling methodology. Model estimation results
using the San Francisco Bay Area highlight a series of interdependencies among the multi-dimensional choice processes. The
interdependencies include: (1) self-selection effects due to observed and unobserved factors, where households locate based
on lifestyle and mobility preferences, (2) endogeneity effects, where any one choice dimension is not exogenous to another,
but is endogenous to the system as a whole, (3) correlated error structures, where common unobserved factors significantly
and simultaneously impact multiple choice dimensions, and (4) unobserved heterogeneity, where decision-makers show significant
variation in sensitivity to explanatory variables due to unobserved factors. From a policy standpoint, to be able to forecast
the “true” causal influence of activity-travel environment changes on residential location, auto/bicycle ownership, and commute
mode choices, it is necessary to capture the above-identified interdependencies by jointly modeling the multiple choice dimensions
in an integrated framework. |
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