Land Use and Traffic Congestion - SSTI

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EXECUTIVESUMMARY The purpose of this project was to analyze and interpret the relationship between higherdensity development and traffic congestion. Governments have expressed increased interest in the possible benefits of compact, mixed land use—referred to in many circles as smart growth—to reduce auto dependency and use. If true, this finding could be of significance in planning solutions to a host of transportation system investment, performance, and impact issues. Before considering any type of formal policy position in relation to land use, the Arizona Department of Transportation (ADOT) is expanding its understanding of the relationships between land use development patterns and transportation. Among ADOT’s key questions are: Does higher-density development reduce auto use, to what extent, and in response to what factors? Does higher-density development also generate higher levels of traffic congestion simply due to the higher concentration of activity? Do Arizonans know about smart growth, and what are their perceptions of its impacts and desirability? The research study that is summarized in this report was commissioned to address these specific issues. It involved a national-scale review of research and evidence on transportation and land use relationships; detailed local analysis of these relationships using data from metropolitan Phoenix; and a survey of officials in Arizona’s metropolitan areas about their perceptions of land use/transportation, how higher-density development is viewed, and whether there would be receptiveness for compact, mixed-use approaches regionally and in their own area. The findings of this study confirm the benefits of better land use. In its assessment of a prodigious volume of research on this topic, the project’s literature review was able to highlight the following findings: Density and Vehicle Miles Traveled Using residential density as a primary indicator of concentrated land use, a variety of studies have shown that households in higher-density (i.e., more urban) settings tend to own fewer vehicles, drive less, walk and take transit more often, and generate one-half to one-third of the daily vehicle miles traveled (VMT) of their suburban counterparts. Beyond Density: Research has found that the effects of land use on travel behavior are rooted in factors beyond simple density. Also important are related factors such as mix of uses, auto- vs. pedestrian-oriented design, and regional accessibility enhanced by multiple travel choices (especially transit). These characteristics of density, diversity, design, and destinations are commonly referred to as the 4Ds. Travel Purpose: Work travel, which is associated with peak period congestion, generally garners most of the attention in transportation planning and policy deliberations. Indeed, where compact land use is focused around high-quality 1 141 of 226
egional transit—at both the origin and destination of a journey—commuters will use transit in large numbers because of its convenience. However, the travel market that may be most influenced by compact mixed-land use is nonwork travel, which accounts for as much as 80 percent of routine household travel and has been the fastest-growing segment since the 1980s. This relationship/trend can be directly linked to land use, recognizing that in conventional suburban areas almost all household needs—shopping, transporting children, personal business, social, and recreation—require private vehicle travel. Areas where residents live in older, mixed-use communities with nearby services and restaurants show a much greater concentration of travel to local destinations—including walking, biking, or short car trips—despite a daily commute that may well be a long distance solo-driver trip. Market Forces: Critics of smart growth approaches to land use maintain that it is a planner’s notion that does not reflect market realities. However, real estate industry experts assert that the reason more compact, mixed-use development has not occurred has to do with restrictive local zoning codes or traffic level of service standards, and not because of market demand, which is gauged as twice as high as current build rates. This is borne out by visual preference surveys that show a general preference for older (pre-World War II) suburban development patterns, which are more compact and walkable, and foster more social interaction between residents. To ascertain the validity of these research findings in the Arizona environment, researchers performed a number of detailed studies using local data and both existing planning tools as well as some new ones developed specially for analysis of the role of land use. These analyses were focused on the Phoenix metropolitan area and were performed using data, modeling tools, and staff support of the Maricopa Association of Governments (MAG). Conventional four-step transportation planning models are unable to account for important differences in land use as represented by the 4Ds. The influence of land use is most relevant at the level of the individual traveler and what they can walk to within ¼ to ½ mile at either origin or destination. Four-step models operate at a traffic analysis zone (TAZ) level of aggregation, which is generally much too coarse for discerning land use differences. For this reason, this genre of models also does not deal directly with nonmotorized trips such as biking or walking, which are a critical element in compact mixed use designs. Using data from MAG’s 2001 regional household travel survey supplemented with information from its travel model and geographic information systems (GIS) databases, researchers developed a set of regression models to quantify the relationships between travel behavior, traveler demographic characteristics, and measures of the 4Ds. These models show the effect of the 4Ds of land use on both household vehicle ownership and on household VMT. Residential density, land use mix, walk opportunities, and regional transit accessibility to jobs were the variables used to represent the 4Ds. Negative signs on the coefficients for these variables indicate that as each of the land use variables 2 142 of 226
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