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138 I N N O VAT I O N S I N T R AV E L D E M A N D M O D E L I N G , V O L U M E 2 including car ownership, number of trips, time of day, Schwanen and Mokhtarian 2005; Kitamura et al. 1997; route choice, travel mode choice, purpose of trips, and Handy et al. 2005; and Lund 2003). However, while so forth. A fundamental question is what dimension of there has been recognition that sensitivity to BE attri- BE impacts what dimension of travel--a seemingly butes can vary among decision makers (see Badoe and innocuous, but very complex, question. Many earlier Miller 2000), most studies have not examined the indi- research works have focused on the impact of selected rect effect of demographics on the sensitivity to BE BE characteristics on selected travel dimensions, but such attributes. And, to our knowledge, no study has recog- analyses provide a limited picture of the many interac- nized the potential effect of unobserved decision-maker tions leading up to travel impacts. In particular, the use characteristics on the response to BE attributes. It is pos- of a narrow set of BE measures may render the measures sible, though, that the varying levels and sometimes non- as proxies for other BE measures, making it difficult to intuitive effects of BE attributes on travel behavior found identify which element of the multidimensional package in earlier empirical studies (for example, in Bhat and of BE measures is actually responsible for the travel Gossen 2004) is, at least in part, a manifestation of vary- impact. Similarly, focus on the impacts of BE on narrow ing BE attribute effects across decision makers in the dimensions of travel does not provide the overall effect population. on travel. For instance, a denser environment may be associated with fewer pick-up or drop-off activity episodes, but more recreational episodes (see Bhat and Spatial Scale of Analysis Srinivasan 2005). The net impact on overall travel will depend on the aggregation across the effects on individ- The third element is the neighborhood shape and scale ual travel dimensions. Finally, most empirical analyses used to gauge BE measures. Most studies use predefined consider a trip-based approach to analysis, ignoring the spatial units based on census tracts, zip codes, or trans- chaining of activities and the interplay of the effect of BE port analysis zones as operational surrogates for neigh- attributes on the many dimensions characterizing activ- borhoods because urban form data are more readily ity participation and travel. available and easily matched to travel data at these scales. However, it is not clear how individuals perceive the neighborhood space and scale, and how they filter Moderating Influence of Decision-Maker spatial information when making spatial choice deci- Characteristics sions (see Golledge and Grling 2003; Krizek 2003; and Guo and Bhat 2004, 2007 for detailed discussions). Fur- The second element is the moderating influence of deci- ther, it is possible that different BE attributes have differ- sion makers' characteristics on travel behavior (individ- ent spatial extents of influence on travel choices, as uals and households). These characteristics may include illustrated by Guo and Bhat (2007) and Boarnet and sociodemographic factors (such as gender, income, and Sarmiento (1998). household structure), travel-related and environmental attitudes (such as preference for nonmotorized or motor- ized modes of transportation and concerns about mobile RESIDENTIAL SORTING BASED ON source emissions), and perceptions regarding BE attri- TRAVEL BEHAVIOR PREFERENCES butes (that is, cognitive filtering of the objective BE attributes). These may have a direct influence on travel The second major issue in BEtravel behavior relation- behavior (for example, higher-income households are ship is residential sorting based on travel behavior prefer- more likely to own cars) or an indirect influence by mod- ences. A fundamental assumption is that there is a ifying the sensitivity to BE characteristics (for example, it one-way causal flow from BE characteristics to travel may be that high-income households, wherever they live, behavior. Specifically, the assumption is that households own several cars and use them more than low-income and individuals locate themselves in neighborhoods and households; this creates a situation where high-income then, based on neighborhood attributes, determine their households are less sensitive to BE attributes in their car travel behaviors. Thus, if good land use mixing has a neg- ownership and use patterns than low-income house- ative influence on the number of motorized trips, the holds). Almost all individual and household-level analy- implication would be that building neighborhoods with ses of the effect of BE characteristics on travel behavior good land use mix would result in decreased motorized control for the direct influence of decision-maker attri- trips, which would reduce traffic congestion. A problem butes by incorporating sociodemographic characteristics with the theory is that it does not take a comprehensive as determinants of travel behavior. A handful of studies view of how individuals and households make residential also control for the direct impact of attitudes and per- choice and travel decisions. Households and individuals ceptions of decision makers on travel behavior (see who are auto-disinclined because of their demographics,