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

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Suggested Citation:"T57054 txt_138.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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including car ownership, number of trips, time of day, route choice, travel mode choice, purpose of trips, and so forth. A fundamental question is what dimension of BE impacts what dimension of travel— a seemingly innocuous, but very complex, question. Many earlier research works have focused on the impact of selected BE characteristics on selected travel dimensions, but such analyses provide a limited picture of the many interac- tions leading up to travel impacts. In particular, the use of a narrow set of BE measures may render the measures as proxies for other BE measures, making it difficult to identify which element of the multidimensional package of BE measures is actually responsible for the travel impact. Similarly, focus on the impacts of BE on narrow dimensions of travel does not provide the overall effect 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 Srinivasan 2005). The net impact on overall travel will depend on the aggregation across the effects on individ- ual travel dimensions. Finally, most empirical analyses consider a trip- based approach to analysis, ignoring the chaining of activities and the interplay of the effect of BE attributes on the many dimensions characterizing activ- ity participation and travel. Moderating Influence of Decision- Maker Characteristics The second element is the moderating influence of deci- sion makers’ characteristics on travel behavior (individ- uals and households). These characteristics may include sociodemographic factors (such as gender, income, and household structure), travel- related and environmental attitudes (such as preference for nonmotorized or motor- ized modes of transportation and concerns about mobile source emissions), and perceptions regarding BE attri - butes (that is, cognitive filtering of the objective BE attributes). These may have a direct influence on travel behavior (for example, higher- income households are more likely to own cars) or an indirect influence by mod- ifying the sensitivity to BE characteristics (for example, it may be that high- income households, wherever they live, own several cars and use them more than low- income households; this creates a situation where high- income households are less sensitive to BE attributes in their car ownership and use patterns than low- income house- holds). Almost all individual and household- level analy- ses of the effect of BE characteristics on travel behavior control for the direct influence of decision- maker attri - butes by incorporating sociodemographic characteristics as determinants of travel behavior. A handful of studies also control for the direct impact of attitudes and per- ceptions of decision makers on travel behavior (see Schwanen and Mokhtarian 2005; Kitamura et al. 1997; Handy et al. 2005; and Lund 2003). However, while there has been recognition that sensitivity to BE attri - butes can vary among decision makers (see Badoe and Miller 2000), most studies have not examined the indi- rect effect of demographics on the sensitivity to BE attributes. And, to our knowledge, no study has recog- nized the potential effect of unobserved decision- maker characteristics on the response to BE attributes. It is pos- sible, though, that the varying levels and sometimes non- intuitive effects of BE attributes on travel behavior found in earlier empirical studies (for example, in Bhat and Gossen 2004) is, at least in part, a manifestation of vary- ing BE attribute effects across decision makers in the population. Spatial Scale of Analysis The third element is the neighborhood shape and scale used to gauge BE measures. Most studies use predefined spatial units based on census tracts, zip codes, or trans- port analysis zones as operational surrogates for neigh- borhoods because urban form data are more readily 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 spatial information when making spatial choice deci- sions (see Golledge and Gärling 2003; Krizek 2003; and Guo and Bhat 2004, 2007 for detailed discussions). Fur- ther, it is possible that different BE attributes have differ- ent spatial extents of influence on travel choices, as illustrated by Guo and Bhat (2007) and Boarnet and Sarmiento (1998). RESIDENTIAL SORTING BASED ON TRAVEL BEHAVIOR PREFERENCES The second major issue in BE–travel behavior relation- ship is residential sorting based on travel behavior prefer- ences. A fundamental assumption is that there is a one- way causal flow from BE characteristics to travel behavior. Specifically, the assumption is that households and individuals locate themselves in neighborhoods and then, based on neighborhood attributes, determine their travel behaviors. Thus, if good land use mixing has a neg- ative influence on the number of motorized trips, the implication would be that building neighborhoods with good land use mix would result in decreased motorized trips, which would reduce traffic congestion. A problem with the theory is that it does not take a comprehensive view of how individuals and households make residential choice and travel decisions. Households and individuals who are auto- disinclined because of their demographics, 138 INNOVATIONS IN TRAVEL DEMAND MODELING, VOLUME 2

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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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