Modeling the choice continuum: an integrated model of residential location, auto ownership, bicycle ownership, and commute tour mode choice decisions

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.

The role of location in residential location choice models: a review of literature

“…Guo and Bhat (2007) find a positive influence and also use the difference between household size and average zonal household size as a variable-which has a negative impact-underlining the previous results. is approach also has been mentioned in other studies: Weisbrod et al (1980), Pinjari et al (2009), Pinjari et al (2011), Zolfaghari et al (2012; all find a negative impact when household sizes differ. Waddell (2006) introduces two variables: households of similar age and similar size.…”

“…As mentioned previously, this type of household generally prefers urban areas, mixed land use and high-density dwelling units. Pinjari et al (2009Pinjari et al ( , 2011) find a general positive influence using a logarithmic formulation of population density, but a negative impact for senior households and households with children in the untransformed formulation. Zolfaghari et al (2012) report a positive influence of population density for all households.…”

“…e latter study additionally shows a negative influence of a high density of young households, which might represent correlation effects to the preference of young households for urban areas. Pinjari et al (2009Pinjari et al ( , 2011 and Weisbrod et al (1980) use the fraction of senior persons as explaining variables on residential choice of senior households and find a positive influence as well. Waddell (2006) uses density of same-age households instead of segments.…”

“…Kim et al (2005) find the travel cost factor (which we expect to be correlated to distance) increases the probability of moving and reduces the utility of a residential location utility. Pinjari et al (2009Pinjari et al ( , 2011 report on density of sport and recreational facilities variables for residential location choice and find the number of physical recreation centers significantly enhances location utility. e number of natural recreational centers has only a minor influence for households with bicycles.…”

Residential mobility and location choice: a nested logit model with sampling of alternatives

“…Some studies have linked short-and medium-term travel-related behaviors with residential mobility (e.g., Clark and Withers 1999;Prillwitz et al 2006;Prillwitz et al 2007) while numerous others made similar connections with residential location (e.g., Ben-Akiva and Bowman 1998; Eliasson and Mattsson 2000;Krizek 2003;Krizek 2006;Ng 2008;Pinjari et al 2008a;Pinjari et al 2008b;Sermons and Koppelman 2001;Waddell et al 2008). Few studies have, however, explicitly modeled these closely related decisions together; one example used a stated preference approach (Kim et al 2005).…”