The sprawling patterns of land development common to metropolitan areas of the US have been blamed for high levels of automobile travel, and thus for air quality problems. In response, smart growth programs-designed to counter sprawl-have gained popularity in the US. Studies show that, all else equal, residents of neighborhoods with higher levels of density, land-use mix, transit accessibility, and pedestrian friendliness drive less than residents of neighborhoods with lower levels of these characteristics. These studies have shed little light, however, on the underlying direction of causality-in particular, whether neighborhood design influences travel behavior or whether travel preferences influence the choice of neighborhood. The evidence thus leaves a key question largely unanswered: if cities use land use policies to bring residents closer to destinations and provide viable alternatives to driving, will people drive less and thereby reduce emissions? Here a quasi-longitudinal design is used to investigate the relationship between neighborhood characteristics and travel behavior while taking into account the role of travel preferences and neighborhood preferences in explaining this relationship. A multivariate analysis of crosssectional data shows that differences in travel behavior between suburban and traditional neighborhoods are largely explained by attitudes. However, a quasi-longitudinal analysis of changes in travel behavior and changes in the built environment shows significant associations, even when attitudes have been accounted for, providing support for a causal relationship.

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Abstract. Using a system of structural equations, this paper empirically examines the relationship of residential neighborhood type to travel behavior, incorporating attitudinal, lifestyle, and demographic variables. Data on these variables were collected from residents of five neighborhoods in the San Francisco Bay Area in 1993 (final N ¼ 515), including ''traditional'' and ''suburban'' as well as mixtures of those two extremes. A conceptual model of the interrelationships among the key variables of interest was operationalized with a nineequation structural model system. The nine endogenous variables included two measures of residential location type, three measures of travel demand, three attitudinal measures, and one measure of job location.In terms of both direct and total e¤ects, attitudinal and lifestyle variables had the greatest impact on travel demand among all the explanatory variables. By contrast, residential location type had little impact on travel behavior. This is perhaps the strongest evidence to date supporting the speculation that the association commonly observed between land use configuration and travel patterns is not one of direct causality, but due primarily to correlations of each of those variables with others. In particular, the results suggest that when attitudinal, lifestyle, and sociodemographic variables are accounted for, neighborhood type has little influence on travel behavior.