A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels

Abstract

Supported by general revenues from the State of Texas. 16. Abstract In this report, we identify the research designs and methodologies that may be used to test the presence of "true" causality versus residential sorting-based "spurious" associations in the land-use transportation connection. The report then develops a methodological formulation to control for residential sorting effects in the analysis of the effect of built environment attributes on travel behavior-related choices. The formulation is applied to comprehensively examine the impact of the built environment, transportation network attributes, and demographic characteristics on residential choice and car ownership decisions. The model formulation takes the form of a joint mixed multinomial logit-ordered response structure that (a) accommodates differential sensitivity to the built environment and transportation network variables due to both demographic and unobserved household attributes and (b) controls for the self-selection of individuals into neighborhoods based on car ownership preferences stemming from both demographic characteristics and unobserved household factors. The analysis in the report represents, to our knowledge, the first instance of the formulation and application of a unified mixed multinomial logit-ordered response structure in the econometric literature. The empirical analysis in the report is based on the residential choice and car ownership decisions of San Francisco Bay area residents.vi ACKNOWLEDGEMENTS

The impact of residential density on vehicle usage and energy consumption

“…The restriction of a diagonal matrix also passes a Hausman test that compares the OLS estimates and the 3SLS estimates without the restriction. These results indicate that the results cannot reject the null hypothesis that all of the error correlations are zero, consistent with the findings of other studies (Brownstone andGolob, 2009, Bhat andGuo, 2007). However, it does not mean that there is no residential self-selection.…”

“…A weighting approach is considered to correct for the bias caused by non-random missing data, but unweighted estimation is presented as the final result. After controlling for various socio-demographic and geographic locations, the model cannot reject the null hypothesis that there is no significant self-selection effect, which is consistent with Bhat and Guo (2007) and Brownstone and Golob (2009).…”

“…Most of the previous studies that attempt to measure the influence of urban spatial structure on vehicle usage focus on specific regions in order to guarantee geographic homogeneity (Boarnet and Crane, 2001, Bhat and Guo, 2007, Brownstone and Golob, 2009). This study analyzes national level data, which requires that geographic heterogeneity is controlled for.…”

Reducing car use: changing attitudes or relocating? The influence of residential dissonance on travel behavior

“…Individuals with an affinity toward a certain kind of travel often choose a residential location that enables them to travel as much as possible with their preferred travel mode (Handy et al, 2005). A person who prefers public transit, for instance, often has an affinity for urban residential neighborhoods, as public transit is best organized in these urban areas (Bhat and Guo, 2007;Cao et al, 2007;Chen et al, 2008;Schwanen and Mokhtarian, 2005b;van Wee, 2009;van Wee et al, 2002). However, such a residential self-selection process is not always the case.…”

A new approach to specify and estimate non-normally mixed multinomial probit models

“…In many situations, such as in a path choice model (see Yai et al, 1997) or a model with spatial location alternatives (see Bhat and Guo, 2007), a specific parametric structure, based on theoretical considerations appropriate to the context, can be placed on .…”