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Innovations in Travel Demand Modeling, Volume 2: Papers (2008)

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Suggested Citation:"T57054 txt_139.pdf." National Academies of Sciences, Engineering, and Medicine. 2008. Innovations in Travel Demand Modeling, Volume 2: Papers. Washington, DC: The National Academies Press. doi: 10.17226/13678.
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attitudes, or other characteristics, may search for loca- tions with high residential densities, good land use mix, and high public transit service levels, so they can pursue their activities using nonmotorized travel modes. If this were true, urban land use policies aimed at, for example, increasing density or land use mix, would not stimulate lower levels of auto use, but would simply alter the spa- tial residence patterns of the population based on motor- ized mode use desires. Ignoring this self- selection in residence choices can lead to a spurious causal effect of neighborhood attributes on travel, and potentially lead to misinformed BE design policies. The literature that has considered the self- selection issue (also referred to as the residential sorting issue) in assessing the impact of BE attributes on travel choices has done so in one of three ways. Controlling for Decision- Maker Attributes The first approach is to control for demographic and other travel- related attitudes and perceptions of decision makers that may impact the neighborhood type individ- uals choose. This can be accomplished by incorporating decision- making characteristics as explanatory variables in models of travel behavior. This is a creative, and sim- ple, way of tackling the self- selection problem, but its use is limited because most travel survey data sets do not col- lect attitudinal data. Further, it is unlikely that all the demographic and travel lifestyle attitudes that have any substantive impact on residential sorting can be collected in a survey because it is difficult to gauge how close the estimated BE effects are to the true causal effect. Instrumental Variables Approach The second approach to alleviate the residential sample selection effect is a two- stage instrumental variable approach in which the endogenous explanatory BE attributes are first regressed on instruments that are related to BE attributes, but have little correlation with the randomness in the primary travel behavior of inter- est. The predicted values of BE attributes from this first regression are next introduced as independent variables (along with other demographic attributes of the individ- ual) in the travel behavior relationship of interest. A problem with this approach, however, is that it is not applicable to the case in which the travel behavior equa- tion of interest has a nonlinear structure, such as a dis- crete choice or a limited or truncated variable. There are control functions and related approaches to deal with the case of endogenous explanatory variables in the con- text of discrete choice and other nonlinear models (see Berry et al. 1995; Lewbel 2004; Louviere et al. 2005), but these methods require tedious computations to rec- ognize the sampling variation in the predicted value of the endogenous BE attributes to obtain the correct stan- dard errors in the main equation of interest. Ignoring the sampling variance in the predicted values of BE attri - butes, as done by Boarnet and Sarmiento, can lead to incorrect conclusions about the statistical significance of the effects of BE attributes. Using Before–After Household Move Data The third approach is to examine the travel patterns of households immediately before and after a household relocation. The potential advantage of examining the same household in two different neighborhoods is that one can ostensibly control for the overall travel desires and attitudes of the members of a household, so that the before–after relocation changes in travel behavior may be attributed to the different BEs in the two neighbor- hoods. The idea in this approach is to consider the relo- cation as a treatment, with the associated travel behavior changes being the response variable. The assumption is that relocating households are in equilibrium in their pre- move neighborhood in terms of BE attributes, and moved because of factors unrelated to their preference of BE attributes (such as to upgrade the physical housing stock in response to higher incomes or a change in life- cycle). While such an approach can alleviate the self- selection problem, the relocating households are themselves a self- selected group, and may have moved because of dissonance in the pre- move neighborhood BE attributes. PROPOSED MODELING FRAMEWORK This section addresses some of the challenges discussed in the previous two sections. In particular, the authors propose a modeling framework that (a) accommodates differential sensitivity to BE 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 travel preferences. The framework can be used to con- trol for residential self- selection for any kind of travel behavior variable and provides the correct standard errors regarding the effect of BE attributes. It is geared toward cross- sectional analysis, recognizing that almost all existing data sources available for analysis of BE effects are cross- sectional in nature. Unlike earlier stud- ies, the methodology also models the residential location choice decision jointly with the travel behavior choice. The results of applying the model formulation to an empirical analysis of residential choice and car owner- 139AN INNOVATIVE METHODOLOGICAL FRAMEWORK

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TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 2: Papers includes the papers that were presented at a May 21-23, 2006, conference that examined advances in travel demand modeling, explored the opportunities and the challenges associated with the implementation of advanced travel models, and reviewed the skills and training necessary to apply new modeling techniques. TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 1: Session Summaries is available online.

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