Assesing the impact of urban form measures in nonwork trip mode choice after controlling for demographic and level-of-service effects
The increasingly adverse effects of automobile use on traffic congestion and air pollution, combined with the limited financial ability to continually invest in transportation infrastructure, has led to the consideration of non-transportation strategies for managing and influencing travel demand. The paradigm shift toward non-transportation strategies to manage travel demand gained momentum, in particular, with the advent of the New Urbanism movement in the early 1990s (Duany and Plater-Zyberk, 1991). The New Urbanism movement is a manifestation of environmental determinism, wherein the urban planner’s role is to engineer and encourage socially-vibrant and environmentally-friendly modes of transportation such as walking and bicycling.
The consideration of non-transportation strategies to manage demand, spurred by the New Urbanism movement, has led to a burgeoning literature at the interface of land use and transportation. In particular, there have been several studies in the past decade focusing on the influence of urban form and the built environment on travel behavior. While these studies have contributed substantially to our understanding of the interactions between urban form and travel behavior, there is still considerable research to be done in this area. The next few sections discuss some of the issues characterizing earlier studies, and position the current study in the broader context of the earlier studies.
1.1 Work versus Nonwork Travel Mode Choice
The association between aspects of the built environment at the employment site or residence and workers’ commute choices has been studied by many researchers (for example, see Cervero, 1989; Cambridge Systematics, 1994; Kockelman, 1995; Cervero, 1996; Messenger and Ewing, 1996; Cervero and Wu, 1997; Levinson and Kumar, 1997). In contrast to the focus on the effect of the built environment on commute travel, there has been relatively lesser attention on the influence of the built environment on nonwork travel (for example, see Handy, 1992; Bhat et al., 1999; Boarnet and Sarmiento, 1998; Boarnet and Crane, 2001; Reilly, 2002). Nonwork trips constitute about three-quarters of urban trips and represent an increasingly large proportion of peak period trips (FHWA, 1995). Thus, it is important to analyze the impact of land use on nonwork travel. This study contributes toward this objective by examining the impact of the built environment on nonwork mode choice. The focus on the modal dimension of nonwork trips is motivated by the observation that the few earlier studies examining land use impact on nonwork travel have not focused on this dimension (for example, Handy, 1992 examines land use impacts on shopping trip frequency; Bhat et al., 1999 study land use and other variable impacts on shopping trips; Boarnet and Sarmiento, 1998 and Boarnet and Crane, 2001 examine land use impacts on nonwork automobile trips). Reilly’s (2002) study is the closest to the current research paper, although the empirical settings are different between his paper and ours. In addition, Reilly (2002) uses qualitative measures such as a Transit Access Index and proxies for streetscape in his San Francisco study, while the current paper attempts to use more direct measures of urban form.
1.2 Urban Form Measures
Earlier research studies have used various kinds of urban form measures to capture the effect of the built environment on travel behavior. But in any particular study, it has been quite typical to consider only a handful of measures of urban form (and in most cases, just one measure). For example, a single measure of density has been used in several studies including Bhat and Singh (2000), Spillar and Rutherford (1990) and Dunphy and Fisher (1996). Some other studies such as Cervero et al. (1997), Handy (1993), Bhat and Pozsgay (2002), and Bhat and Zhao (2002) have focused on a single measure of accessibility to study the effect of urban form on travel and related behavior. A handful of studies have considered multiple urban form measures jointly. These multiple measures have typically been one of the two composite urban form measures discussed earlier (density or accessibility) and two additional characteristics of urban form. For instance, Frank and Pivo (1994) consider density and land use mix, Holtzclaw (1994) and Kitamura et al. (2001) use density and an accessibility measure, Kockelman (1996) considers accessibility, land use mix, and land use balance, and Greenwald and Boarnet (2001) use a composite pedestrian environment factor, population and retail densities, and proportion of gridiron streets.
In this study, the focus is on capturing a multitude of urban form measures, some of which are composite indices (such as land use mix and accessibility) and others of which are direct, disaggregate measures of the built environment. Thus, for example, we consider not only the degree of mixing of different land uses, but also consider the actual kinds of land uses involved in the mixing. Hess et al. (2001) note that capturing the degree of mix may not suffice, and recommend including the actual kinds of land uses. Additionally, we examine the influence of the built environment, while controlling for the effects of sociodemographic and level-of-service variables on travel behavior.
1.3 Scale of Measurement and Level of Analysis
The studies of land use and travel behavior may use urban form measures based on spatially aggregate units (such as city-level or urban/suburban level) or on much more disaggregate spatial units (such as the neighborhood level). Similarly, the analysis may be conducted at the level of an aggregate group of individuals or at the individual level. Therefore, four combinations of geographic scale and level of analysis are possible: (a) aggregate spatial data (at the traffic analysis zone or zip code level) and aggregate sociodemographics (for example, see San Diego Association of Governments, 1993; Handy, 1993; Hotlzclaw, 1994; Parsons Brinckerhoff Quade Douglas, 1994; McNally and Kulkarni, 1997), (b) aggregate spatial data and disaggregate sociodemographics (at the individual tripmaker’s level) (for example, see Ewing, 1995; Schimek, 1996; Kockelman, 1996; Boarnet and Crane, 2001; Greenwald and Boarnet, 2000), (c) disaggregate spatial data and aggregate sociodemographics, and (d) disaggregate spatial data and disaggregate sociodemographics (for example, see Cervero, 1996; Kitamura et al., 1997; Handy and Clifton, 2001; and Reilly, 2002).
As should be obvious from above, there have been few studies that have employed urban form measures at a high level of spatial resolution and conducted the analysis at an individual level. In this paper, we use a GIS-based method to develop urban form measures at the neighborhood level of each household and conduct the analysis at an individual level.
1.4 Summary and Overview of Current Research
This paper examines the impact of the built environment on travel behavior, with specific focus in the relatively lesser-researched area of nonwork travel. In addition, the paper uses a multitude of urban form measures, including composite indices and direct disaggregate measures. The analysis in the paper is based on a high spatial resolution for developing measures of urban form and is at the level of the decision-making unit (i.e. the individual tripmaker) and. A discrete choice methodology is used to examine the effect of household and individual sociodemographics, level-of-service of travel modes, and urban form measures on nonwork travel mode choice. The primary data source used for this study is the 1995 Portland Metropolitan Area Activity Survey, which collected travel information from members of a sample of households over a two-weekday period.
The rest of this paper is organized as follows. The next section describes the data sources and the sample formation process. Section 3 discusses model specification issues. Section 4 presents the results of the empirical analysis. Finally, section 5 summarizes the significant findings from the research and identifies areas for future work.