3
Impacts of Land Use Patterns on Vehicle Miles Traveled Evidence from the Literature

The congressional request for this study asks for consideration of “the correlation, if any, between land development patterns and increases in vehicle miles traveled (VMT),” implying that sprawl induces more travel. This chapter summarizes what is known from the literature about the effect of changes in the built environment—in particular, more compact, mixed-use development—on VMT. It starts with a brief discussion of the built environment–VMT connection. It then examines issues related to research design and data that help explain the variability in study results. Drawing on a paper commissioned by the committee (Brownstone 2008) and earlier reviews of the literature, the main section of the chapter summarizes the results of the most methodologically sound studies that examine the relationship between household travel and the built environment while controlling for socioeconomic variables and other factors (e.g., attitudes, preferences) that influence travel behavior. Few of these studies, however, consider the potential effects on VMT of a package of policies that combine increased density with higher employment concentrations, improved access to a mix of diverse destinations, a good transit network, and parking charges. The potential synergies of these policies for VMT reduction are discussed next through two case studies that demonstrate what can be accomplished but also underscore the associated challenges and costs. The final section presents a series of findings. Additional detail on the two case studies is provided in Annex 3-1.



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3 | Impacts of Land Use Patterns on Vehicle Miles Traveled Evidence from the Literature The congressional request for this study asks for consideration of “the correlation, if any, between land development patterns and increases in vehicle miles traveled (VMT),” implying that sprawl induces more travel. This chapter summarizes what is known from the literature about the effect of changes in the built environment—in particular, more compact, mixed-use development—on VMT. It starts with a brief discussion of the built environment–VMT connection. It then examines issues related to research design and data that help explain the variability in study results. Drawing on a paper commissioned by the committee (Brownstone 2008) and earlier reviews of the literature, the main section of the chapter summarizes the results of the most methodologically sound studies that examine the relationship between household travel and the built environment while controlling for socioeconomic variables and other factors (e.g., attitudes, preferences) that influence travel behavior. Few of these studies, however, consider the potential effects on VMT of a package of policies that combine increased density with higher employment concentrations, improved access to a mix of diverse destinations, a good transit network, and parking charges. The potential synergies of these policies for VMT reduction are discussed next through two case studies that demonstrate what can be accomplished but also underscore the associated challenges and costs. The final section presents a series of findings. Additional detail on the two case studies is provided in Annex 3-1. 50

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51 Impacts of Land Use Patterns on Vehicle Miles Traveled the built environment–vmt connection Chapters 1 and 2 describe the dimensions of the built environment (land use) and transportation networks that are believed to affect VMT. The built environment dimensions include density, mix or diversity of land uses, concentration of development into centers, spatial arrange- ment of land uses, and design. The transportation network dimensions include the spatial patterns of the transportation system (whether the networks are sparse or dense, gridlike or hierarchical). Together, the land use and transportation network measures interact to affect destination accessibility (ease of travel between trip origins and desired destinations) and distance between development and transit. These dimensions are referred to in the literature as “the D’s” (see Box 3-1). A final set of characteristics—travel demand—can complement the first two, particularly through pricing. Density is probably the most studied land use dimension, in part because it is readily measured. However, the effect of higher densities on VMT is not entirely straightforward, making it difficult to determine the net reduction in automobile use from increased densities. For example, trip frequencies may increase if desired destinations are closer and easier to access. Shifts to other modes, such as transit, require that transit services be available and that density thresholds be sufficient to support adequate and reliable service. VMT itself is a composite measure—the product of trip length, trip frequency, and mode choice (Ewing and Cervero 2001). Moreover, increasing density alone may not be sufficient to lower VMT by a significant amount. A diversity of land uses that results in locating desired destinations, such as jobs and shopping, near housing (preferably in centers) and improved accessibility to these destina- tions from either home or work are also necessary. Development designs and street networks that provide good connectivity between locations and accommodate nonvehicular travel are important. Finally, demand management policies that complement efforts to lower VMT,

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52 Driving and the Built Environment Box 3-1 the five d’s Land development patterns that describe the built environment, particularly in the context of those features that encourage more compact development, have come to be characterized in the litera- ture by the shorthand of “the D’s.” The initial three D’s, first used by Cervero and Kockelman (1997), have now been expanded to five: • Density: Population and employment by geographic unit (e.g., per square mile, per developed acre). • Diversity: Mix of land uses, typically residential and commercial development, and the degree to which they are balanced in an area (e.g., jobs–housing balance). • Design: Neighborhood layout and street characteristics, partic- ularly connectivity, presence of sidewalks, and other design features (e.g., shade, scenery, presence of attractive homes and stores) that enhance the pedestrian- and bicycle-friendliness of an area. • Destination accessibility: Ease or convenience of trip destinations from point of origin, often measured at the zonal level in terms of distance from the central business district or other major centers. • Distance to transit: Ease of access to transit from home or work (e.g., bus or rail stop within ¼ to ½ mile of trip origin) such as establishing maximum rather than minimum parking require- ments and introducing market-based parking fees, are also needed. As will be shown, however, few studies include many or all of these dimensions. Even if it can be demonstrated that more compact, mixed-use devel- opment is associated with lower VMT, encourages mode shifts, and

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53 Impacts of Land Use Patterns on Vehicle Miles Traveled lessens trip making by automobile, it is important to know the mag- nitude of these effects and whether they are of sufficient size to be relevant to policy. Researchers often use elasticities as a way of report- ing the size of effects.1 Thus, for a percentage increase in density—say, for example, a 100 percent increase in or a doubling of density (the independent variable)—they estimate the corresponding percentage reduction in VMT (the dependent variable). Relatively few of the studies reviewed in this chapter estimate elasticities, but they are reported when available. It should also be noted that changes in the built environment, such as increased density, do not directly “cause” reductions in VMT. Rather, the built environment, as represented by residential and employment density and neighborhood or employment center design, provides the context for behavioral decisions regarding location choice (e.g., residence and jobs), automobile ownership, and travel modes that are also strongly affected by income, age, household size, and other socioeconomic variables (Badoe and Miller 2000). Measuring and controlling for these effects empirically raises significant issues with respect to research methods and data, which are addressed in the following section. 1 A point elasticity is the ratio of a percentage change in the dependent variable to a 1 percent change in the independent variable. The elasticities reported in the literature are generally point elasticities. Strictly speaking, the percentage impact on the dependent variable of a very large percentage change in the independent variable, such as doubling (a 100 percent increase), constitutes an arc elasticity. Consistent with common practice, the present discussion assumes a proportional change in the point elasticity to represent the arc elasticity (for example, if the point elasticity is −0.05, meaning that a 1 percent increase in the independent variable leads to a 0.05 percent decrease in the dependent variable, it is assumed that a 100 percent increase in the independent variable leads to a 5 percent decrease in the dependent variable), but the reader should be cautioned that the larger the increase assumed, the less accurate the proportionality assumption can be. Point elasticities can range in magnitude from zero to infinity. Elasticities of less than 1.0 (in magnitude) are called ineslastic and reflect changes in the dependent variable that are, proportionately, smaller than the change in the independent variable. Elasticities greater than 1.0 (in magnitude) are called elastic, and reflect changes in the dependent variable that are, proportionately, larger than the change in the independent variable.

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54 Driving and the Built Environment issues related to research design and data This section reviews issues of aggregate versus disaggregate analyses, cross-sectional versus longitudinal studies, self-section and causality, measurement and scale, and generalizability that are important in understanding the variable results of studies of the relationship between more compact, mixed-use development and VMT. Aggregate Versus Disaggregate Analyses Worldwide attention was drawn to the relationship between urban form and automobile dependence through a series of books and articles by Newman and Kenworthy (1989, 1999, 2006). In their 1989 cross- national comparison of 32 cities,2 these authors showed that per capita gasoline consumption—a proxy for automobile use—is far higher in U.S. cities than abroad, a fact the authors attribute to lower metropolitan densities in the United States. A follow-on study of 37 cities in 1999 directly linked low-density cities, particularly in the United States and Australia, to higher per capita VMT. Notwithstanding the problems of attempting to translate experience from abroad to the United States because of substantial differences in public preferences, laws and regulations governing land development, fuel prices, income levels, and the supply of alternative modes of travel to the automobile, the Newman and Kenworthy studies illustrate the methodological problem of analyses that rely on aggregate data to draw simple cross-sectional correlations without controlling for other variables that affect VMT (see Gómez-Ibáñez 1991 and Brownstone 2008). Aggregate analyses such as Newman and Kenworthy’s mask real differences in densities within metropolitan areas, as well as in the travel behavior of subpopulations, that vary on the basis of socio- economic characteristics. For example, central cities may house dis- 2 The cities are metropolitan regions, not city centers. In the United States, the former are called standard metropolitan statistical areas.

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55 Impacts of Land Use Patterns on Vehicle Miles Traveled proportionate shares of lower-income residents, who are less able to afford owning and operating an automobile, and younger people and older households without children whose travel is below average. On the other hand, suburban areas tend to include a disproportionate share of families, who are often in higher-income groups with higher levels of automobile ownership and travel demands for jobs, education, and extracurricular events. Another well-known study (Holtzclaw et al. 2002) analyzes auto- mobile ownership and use, controlling for socioeconomic variables, with results that corroborate the findings of Newman and Kenworthy. The authors use traffic zones3 within three metropolitan areas—Chicago, Los Angeles, and San Francisco—as the geographic unit of analysis, control for household size and income effects, and draw on odometer readings (as captured by legally mandated smog checks) rather than self-reported diaries to measure VMT.4 They find that both automobile ownership and use decline in a systematic and predictable pattern as a function of increasing residential density. These findings, however, are subject to many of the flaws of aggregate analyses. The travel analysis zones are large, with an average size of 7,000 residents per zone; limited socioeconomic variables are available at the zonal level; and key available control variables, such as income, are measured on a per capita basis. The result is to mask potentially important variability within zones, particularly with respect to household size and income differences, that could help explain automobile ownership and use patterns (Brownstone 2008). In addition, several of the independent variables are highly correlated (e.g., density measures, transit access, local shopping, center proximity, and pedestrian and bicycle friendliness), making it difficult to identify their separate effects (Holtzclaw et al. 2002). 3 Travel analysis zones are the unit of analysis used in metropolitan area travel demand modeling. Typically, such models do not need detailed data at the neighborhood or household level to analyze the travel impacts of various investment decisions. 4 Brownstone (2008) notes, however, that California exempts new vehicles from smog checks for the first 2 years, thus systematically biasing VMT downward for zones with large numbers of new vehicles in two of the three metropolitan areas studied.

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56 Driving and the Built Environment A more recent, widely circulated book, Growing Cooler (Ewing et al. 2007), includes an ambitious effort to model the effect of land use on VMT by using structural equations modeling. Two models are estimated—a cross-sectional model based on 84 urbanized areas in 2005 and a longitudinal model of the same urbanized areas for the two 10-year periods between 1985 and 2005. The data set, assembled by the Texas Transportation Institute, includes population density, high- way lane miles, transit revenue miles, and real fuel prices. The authors find that greater population density, among other variables, has a negative influence on VMT. They estimate elasticities of a 0.213 percent reduction in VMT from a 1 percent increase in population density on the basis of their cross-sectional model and a 0.152 percent reduction in VMT from a 1 percent increase in population density on the basis of their longitudinal model (Ewing et al. 2007, 123). However, the coarseness of the level of analysis (urbanized area), the quality of the data, and questions about their model specification limit the reliability of these results.5 To minimize or eliminate the aggregation issues that cloud the relation- ship between the built environment and travel behavior, many studies use disaggregate data—household-level travel data and neighborhood-, census tract–, or zip code–level data on the built environment—in regression models, controlling for a much richer combination of socio- economic variables available at the household level. However, these studies are also subject to research design and data issues discussed below, which may help explain the wide range of their results. 5 The data on urbanized areas and VMT that are the basis for Ewing et al.’s analysis come from state reports to the Federal Highway Administration as part of the Highway Performance Monitoring System. The states are not very rigorous in remaining consistent with census boundaries and population estimates for urbanized areas. Urban VMT data are also suspect because of inconsistent sampling (the states follow their own procedures). As noted, moreover, the authors’ model specification raises several questions, and structural equations models can be extremely sensitive to relatively small changes in a model specification. In the final models, for example, why is transit supply allowed to affect population density while road supply is not? Why is supply allowed to affect demand but not the converse?

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57 Impacts of Land Use Patterns on Vehicle Miles Traveled Cross-Sectional Versus Longitudinal Studies Most of the studies reviewed for this report are cross-sectional; that is, they examine the relationship between the built environment and VMT at a single point in time. Many of the studies use regression analysis to hold constant demographic and socioeconomic variables to isolate the variables of interest. Cross-sectional studies may find a statistically significant correlation between the built environment and VMT. Well-specified analyses that use disaggregate data from metro- politan areas and carefully control for socioeconomic variables and other factors that affect residential location and travel choices are valuable. Nevertheless, they cannot be used to determine the temporal relation between variables, and evidence of cause and effect cannot be assumed. Establishing causal relationships more reliably requires a longitu- dinal approach, typically collecting panel data and following households over time. This research is time-consuming and expensive—several decades of data may be needed to observe large enough changes in the built environment. It is also challenging as other factors are likely to change during that time period (i.e., household characteristics, such as household size, ages of its members, income, employment and marital status), thus affecting the results. For these reasons, with the few exceptions noted in the following section, most studies have not adopted a longitudinal approach. Self-Selection and Causality One of the main issues that confounds study results, particularly for studies of the effects of the built environment on travel at the neighbor- hood or other microscale level, is self-selection. Boarnet and Crane (2001), among others, note that the observed correlation between higher-density neighborhoods and less automobile travel may be due in part to the fact that some residents who dislike driving and prefer tran- sit or walking or bicycling may have self-selected into neighborhoods

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58 Driving and the Built Environment where these travel options are available. To the extent that this is true, the causal link between density and reduced automobile travel may in reality be weaker than it appears. The question of what difference it makes whether the effect is directly one of the built environment or of people choosing to live in certain environments is often raised. Either way, the built envi- ronment clearly has an influence. The reason the distinction matters is the need to predict with some degree of accuracy the impact of substantial changes in the built environment on travel behavior. If future policies encourage a dramatic increase in the number of people living in compact, mixed-use areas but the increase is due primarily to policy incentives or to a limited supply of compact developments rather than to an intrinsic desire to live in such areas, the VMT reduc- tions for those responding to such policies will probably not be as great as for those actively preferring to live in such areas. Thus, if one does not account for self-selection, the impacts of an aggressive land use policy could be overestimated, and the opportunity costs of such an outcome could be high. It is true that, over time, the built environment (e.g., living in more compact, mixed-use developments) and travel behavior (e.g., taking transit because it is convenient) could influence attitudes to be more consonant with such an environment, which in turn could reinforce the travel behavior most suited to that environment. However, it is also possible for dissonance between one’s environment and preferences to increase over time and eventually prompt a move to a residential location more consonant with one’s predispositions. The fact that researchers do not have a good sense of which of these two outcomes dominates, and under what circumstances, points to the need for addi- tional longitudinal research into changes in the relationship among attitudes, the built environment, and travel behavior (as well as socio- demographic characteristics) over time. To solve the self-selection problem, researchers ideally would ran- domly assign households to treatment and control groups to observe

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59 Impacts of Land Use Patterns on Vehicle Miles Traveled their behavior—a method used in the medical profession in clinical trials for drug testing. Of course, assigning households to neighbor- hoods with different characteristics and observing their travel behavior is not feasible, so researchers have adopted numerous other methods for controlling for self-selection. Boarnet and Sarmiento (1998), for example, use instrumental variables6 to control for choice of residential location in studying how what they term “neotraditional neighborhoods” affect nonwork automobile trip generation. They find a statistically significant negative association between retail employment density (measured at the zip code level) and nonwork automobile trips after controlling for residential location choices. This finding is replicated in a subsequent study (Boarnet and Greenwald 2000) using Portland, Oregon, data. Applying a similar approach, a more recent German study (Vance and Hedel 2007) finds statistically significant effects of commercial density, road density, and walking time to public transit on daily weekday travel, perhaps reflecting the higher densities and better access to transit of German cities (Brownstone 2008). Brownstone and Golob (2009) use a simultaneous equations model7 to control for self-selection and a broad set of socioeconomic variables and find a statistically signifi- 6 In technical terms, the self-selection issue is a manifestation of “endogeneity bias.” Ordinary least-squares regression analysis requires that observed explanatory variables be deterministic (not random) and uncorrelated with any unobserved explanatory variables (captured by the error term of the equation). When that requirement is violated, as it is when an explanatory variable itself is a nondeterministic function of other variables in the model, the resulting coefficient estimates are biased. In the present case, the explanatory variable residential location is apt to be determined partly by such variables as attitudes toward travel—variables that are also likely to be observed or unobserved influences on travel behavior itself. Thus, residential location is endogenous. The instrumental variables technique treats this problem by purging the endogenous variable (residential location) of its correlation with other variables in the equation for travel behavior. It does so by first estimating residential location as a function of variables not expected to be associated with travel behavior. The estimated value of residential location then meets the requirements for unbiased ordinary least-squares estimation of the equation for travel behavior. 7 A structural or simultaneous equations model recognizes that causal influences may work in more than one direction; therefore, multiple equations reflecting these causal linkages are simultaneously modeled (hence using a “structural model” rather than a single equation).

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60 Driving and the Built Environment cant but small remaining effect of the built environment on VMT and fuel use. Still other studies deal with the self-selection issue by attempt- ing to measure preferences through attitude surveys in addition to controlling for residential location type. Bagley and Mokhtarian (2002) find little remaining effect of neighborhood type on VMT after controlling for attitudes, lifestyle preferences, and sociodemographic variables. In contrast, using a survey of neighborhood preferences and attitudes in Atlanta, Frank et al. (2007) find, after controlling for demographic variables, that survey participants who lived in walkable neighborhoods drove less than those living in automobile-oriented neighborhoods, regardless of whether they preferred this neighbor- hood type.8 A final approach attempts to control for self-selection by looking at households that move, comparing their travel behavior before and after moving to a more compact neighborhood. Using data from the Puget Sound Transportation Panel, Krizek (2003) examines the travel behavior of a sample of households that moved to neighborhoods with higher local accessibility during 1989–1997. He finds that, all else being equal, the movers significantly reduced vehicle and person miles traveled, although they took more trip tours.9 Krizek estimates a decrease of about 5 VMT per day per household that moved to a neighborhood with better accessibility, not as large as the estimate of Frank et al. 8 Respondents who preferred automobile-oriented neighborhoods but lived in high-walkability neighborhoods drove about 26 miles per day as compared with their counterparts in automobile- oriented neighborhoods, who drove 43 miles per day (Frank et al. 2007, Table 9, 1911). Respondents who preferred high-walkability neighborhoods but lived in automobile-oriented neighborhoods drove 37 miles per day, more than the 26 miles per day of their counterparts in high-walkability neighborhoods but less than the 43 miles per day of those who preferred automobile-oriented neighborhoods. 9 The study controlled for changes in life cycle and regional and workplace accessibility to focus primarily on neighborhood travel.

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95 Impacts of Land Use Patterns on Vehicle Miles Traveled initiated their Transit Station Area Planning Program, which included market studies, coordination with other regional planning efforts, and station area plans (including legally binding requirements for minimum densities, parking maximums, and design guidelines), and sought to identify, create, and promote opportunities for TODs along the planned LRT corridors. Since that time, the region has been pursuing a steady LRT, commuter rail, and streetcar expansion program, which has evolved as decision makers have gained experience with using rail investments to achieve broader community objectives (Cervero et al. 2004). Development along the 15-mile Eastside LRT line, opened in 1986, has been primarily infill, whereas the 18-mile Westside LRT, opened in 1998, was built largely into greenfields. The latter was one of the first efforts in the nation to combine extensive LRT expansion into the suburbs with deliberate TOD around the stations, connecting previously isolated communities to downtown and to each other and creating new mixed-use pockets of development in the middle of traditional suburbia (Cervero et al. 2004). In 2001, extension of a 5-mile segment to the airport provided the opportunity for a public– private partnership to finance the LRT construction and leverage the development of surplus airport property. In 2004, an inner-city 6-mile extension to the north provided a tool for revitalization in a low-income neighborhood. The newest extension, a 6.5-mile line to the south, is being built on a freeway right-of-way that was set aside for a transit corridor 30 years ago when the Interstate beltway was built (A. Cotugno, personal communication). Two of the most notable examples of TOD in the region, the Pearl District and Orenco Station, are discussed below. The Pearl District arose from a decision to use construction of the Portland streetcar line as a means to leverage large-scale redevelopment of a functionally obsolete warehouse and industrial zone in down- town Portland. The city entered into an innovative agreement with developers, requiring them to meet ambitious housing density levels

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96 Driving and the Built Environment to ensure a supply of affordable housing,55 donate land for parks and greenspace, and help pay for removal of a highway viaduct and construction of the streetcar line. The Pearl District has met all expectations for becoming a vibrant, desirable place to live. It currently contains approximately 5,500 housing units, along with 21,000 jobs and 1 million square feet of new commercial and retail space. As a result of its popularity, the district now has the most expensive housing in the Portland region as well as the highest density in the city, at approximately 120 housing units per acre. Orenco Station was designated one of a number of “town centers” along the Westside LRT line in the 2040 regional plan and is generally viewed as the most ambitious and successful such community to date. It contains 1,800 homes, mixed with office and retail spaces, in the town of Hillsboro, situated close to a large employment center in the metro- politan area’s high-tech corridor. In response to market surveys indicat- ing preferences for walkable streets and community-oriented spaces, the developers experimented with design elements such as communal greenspaces, narrow streets, houses located close to sidewalks, and garages placed behind homes. Free LRT passes are provided to all newcomers for their first year to encourage the use of transit. Orenco Station has won numerous national planning awards, and its housing units have com- manded as much as a 25 percent premium over larger suburban homes in the area (NRDC 2001). Metro’s TOD policies are thought to be one of the major factors in attracting people and businesses to the region. Over the decade of the 1990s, the number of college-educated 25- to 34-year-olds increased by 50 percent in the Portland metropolitan area—five times more rapidly 55 The development agreement provided that the developers had to build a certain amount of subsidized housing and some market-rate, lower-cost housing. The developers donated land for publicly subsidized buildings, which are permanently subsidized and managed by the housing agency. They also built some very small units on the lower floors of some of the high-rises so that while their rents will fluctuate over time, they will be more affordable than the larger units on the upper floors.

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97 Impacts of Land Use Patterns on Vehicle Miles Traveled than in the nation as a whole, with the fastest increase occurring in the city’s close-in neighborhoods (Cortright and Coletta 2004). At the same time, Portland’s streetcar line became an important catalyst for development at much higher densities than seen previously. More than half of all the central city development within the past decade has been within one block of the streetcar line. A wide array of studies has demonstrated the effect of these land use and transportation developments on travel behavior. While VMT per person has been increasing nationally, it has been declining in the Portland metropolitan area since about 1996 (see Annex 3-1 Figure 1 Annex 1). According to data from the U.S. and Oregon Departments of Trans- portation, Portland area residents traveled about 17 percent fewer miles per day than the national average for other urbanized areas in 2007, the most recent year for which national data are available. Portland is one of the few regions in the country where transit ridership is growing more rapidly than VMT, and bicycle use has also shown rapid growth.56 From 1993 to 2003, Portland’s population grew by 21 percent, its average VMT grew by 19 percent, while its transit ridership increased by 55 percent (Gustafson 2007). But the growth in transit ridership accounts for only a fraction of the reported reduction in VMT, which suggests that land use policies played a key role. Over the same period, according to Metro’s Data Resource Center, population density levels increased by 18 percent, from 3,136 to 3,721 persons per square mile, holding constant the urban growth area boundary.57 A large fraction of 56 Since 2000, daily bicycle trips have grown nearly threefold on Portland’s four main bicycle- friendly bridges across the Willamette River, from 6,015 trips to 16,711 trips (Portland Bicycle Counts Report 2008), while the bikeway network has grown by less than one-quarter, from 222.5 bikeway miles in 2000 to 274 bikeway miles in 2008. In 2008, bicycles represented 13 percent of the combined daily bicycle and automobile trips, up from only 4.6 percent of all combined trips in 2000. 57 In fact, the boundary increased by about 21,000 gross acres. If population density is calculated on the basis of the new UGB in 2003, population density is 3,411 persons per square mile, and the increase in density from 1993 falls to 8.8 percent. Downs (2004) notes that, as of the 2000 U.S. census, Portland ranked 24th among the 50 largest urbanized areas in population density increase from the 1990 census.

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98 Driving and the Built Environment 28.0 Daily Vehicle Miles Traveled per Person 24.0 20.0 16.0 12.0 Portland Only 8.0 Portland–Vancouver U.S. National Average 4.0 0.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 ANNEX 3-1 FIGURE 1 Daily VMT per person by urbanized area, 1990–2007, Portland, Oregon, only; Portland–Vancouver, Oregon–Washington; and U.S. national average. [Before 2004, the 1990 census information was used to calculate the urbanized population for the Highway Performance Monitoring System (HPMS) submittal from which VMT is calculated. The only official population report for the urbanized area of Portland comes every 10 years from the U.S. census. The 2000 census data were reported in 2002, but because the urban boundary was not finalized in time, the HPMS report that was based on the 2000 census data was not included until the 2004 submittal. The method used to calculate the urbanized population each year is to apply the ratio of the total city population in 2000 to the urbanized population in 2000 to the total city population in 2004, 2005, 2006, etc., until an official new urbanized number is available from the 2010 census. The 2001–2003 population estimates were based on the 1990 ratio of city to urbanized areas. There was probably not a sudden jump in VMT for Portland and Portland–Vancouver from 2003 to 2004, but more likely a gradual increase that had been occurring over time and that had not been measured with the correct standard (the 2000 census data) until the 2004 data set was available. The break in the series from 2003 to 2004 denotes the break in trend.] Source: FHWA 2009, Table HM-72. the increase came from the construction of single-family housing on small lots.58 The relatively small size of the Portland urban area, due to the UGB, has also resulted in shorter average trip lengths. Several studies have examined the travel behavior of Portland residents before and after moving to housing located adjacent to an 58 According to the American Housing Survey, nearly three-fourths of the new lots constructed in the Portland metropolitan area between 1998 and 2002 were built on lots smaller than ¼ acre, and 65 percent of these were single-family dwellings.

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99 Impacts of Land Use Patterns on Vehicle Miles Traveled LRT station. In all such cases, residents reported that moving led to a significant increase in their use of rail transit and a concomitant decrease in automobile use (Podobnik 2002; Switzer 2002; Dill 2006; Evans et al. 2007). A related study examines travel behavior in two particular neighborhoods before and after the LRT system began running (in 1990 and 2000, respectively). In Orenco Station, residents’ automobile mode share dropped from 100 percent to 86 percent, and in Beaverton Central station, it dropped from 81 percent to 73 percent (Evans et al. 2007). None of these studies, however, controlled for self-selection. Results of a travel behavior survey of more than 7,500 households in four counties (Clackmas, Multnomah, and Washington Counties in Oregon and Clark County in Washington) clearly indicate that good transit service and mixed-use neighborhoods have had a significant influence on reducing automobile use and ownership (see Annex 3-1 Table 1). In a more recent survey of residents living near stations along the Westside LRT line, 23 to 33 percent reported using transit as their ANNEX 3-1 TABLE 1 Mode Share, VMT per Capita, and Automobile Ownership, Portland Region Transit Walking Mode Mode Automobile Automobile Share Share Mode Share VMT per Ownership Area (percent) (percent) (percent) Capita per Household Neighborhoods with mixed 11.5 27.0 58.1 9.80 0.93 use and good transit Neighborhoods with good 7.9 15.2 74.4 13.28 1.50 transit only Remainder of Multnomah 3.5 9.7 81.5 17.34 1.74 County Remainder of the region 1.2 6.1 87.3 21.79 1.93 Source: 1994 Metro Travel Behavior Survey for all trip types.

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100 Driving and the Built Environment primary commute mode, compared with less than 10 percent of workers in the neighboring suburbs of Hillsboro and Beaverton and 15 percent of Portland workers overall (Dill 2006). However, not all aspects of the Portland region’s planning efforts have gone smoothly. Some TOD projects (such as the Round and Center Commons) have faced significant financial struggles, and many would not have succeeded without significant public subsidies, including a 10-year tax abatement offered for new developments within walking distance of a rail station. Critics charge that the dense development policies have led to rapidly increasing congestion, unaffordable hous- ing prices, and destruction of urban open spaces. And there have been recurring attempts by some civic and business interests over the past couple of decades to weaken or repeal key aspects of the growth management system. Despite these struggles, however, the Portland region is still highly regarded for the scale and extent of sustained commitment to TOD and innovative planning regulations. The region offers some important lessons for how to create well-designed mixed-use communities that are nodes along successful regional corridors of compact development and not just isolated islands of development. The Portland metropolitan area’s success is due to a host of political, regulatory, and economic factors, some of which are unique to the region but all of which may still offer useful lessons for other parts of the country: • Early leadership from a visionary governor and a supportive state legislature willing to pass strong state planning laws, including urban growth boundaries; • Strong public support for LRT investments and advocacy from citizens groups (in particular, the 1000 Friends of Oregon) capable of litigating when relevant authorities were not following planning requirements; • Unique powers of Metro to influence planning and investments for regional transportation and land use;

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101 Impacts of Land Use Patterns on Vehicle Miles Traveled • Strong congressional representation (e.g., as an aid for obtaining federal Transit New Start program funds); and • Local and regional policy makers willing to go beyond just channel- ing growth around transit by pressing developers to increase density, quality of design, and mix of uses in TOD zones, and the persistent use of transit infrastructure investments as a means to enhance community revitalization. arlington county, virginia, tod corridors The Washington, D.C., area’s 103-mile, 86-station Metrorail system is arguably the nation’s best example of a modern rapid transit system built specifically to incorporate a goal of shaping regional growth. The system, which opened in 1976, is overseen by the Washington Metropolitan Area Transit Authority (WMATA), an independent regional transportation authority involving coordination among the District of Columbia, Maryland, and Virginia. TOD leadership was exercised early on by Metrorail’s leaders and county planners, who realized in the 1970s that deteriorating corridors and large swaths of underutilized real estate in the region were ripe for redevelopment and provided an opportunity for revitalization through transit investment. Long before the rail system became operational, WMATA’s leaders adopted policies to create a public–private program for promoting development adjacent to Metrorail stations, creating a real estate development department that was given the resources to build a portfolio of holdings and encouraged to pursue joint development opportunities. By 2003, 52 joint development projects had been created around dozens of Metrorail stations. While successful TOD zones can be found throughout the region (particularly within downtown Washington, D.C., and in Montgomery County, Maryland), Arlington County, Virginia, in particular, is widely hailed as one of the nation’s best TOD success stories. When the Metrorail

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102 Driving and the Built Environment lines were being planned initially, a key decision was made to reorient the planned rail line from running along the county’s major high- way corridor, Interstate 66, to follow the Rosslyn–Ballston Metrorail corridor of five closely spaced stations that each could be developed into high-density, mixed-use town centers. A second Metrorail corridor along Fairfax Drive—the Jefferson Davis corridor—included stations at Pentagon City and Crystal City. As these plans have been implemented, Arlington County has expe- rienced major growth and renewal and is now among the most densely populated jurisdictions in the country (estimated at 8,062 persons per square mile in 2008). Since 1980, county office space has nearly doubled to about 44 million square feet, with almost 80 percent located within the two Metrorail corridors (Arlington County Planning Department 2008). Housing growth in the corridors has occurred two to three times more rapidly than the growth of the regional population, with the result that in 2003 there were 1.06 jobs for every employed county resident (Cervero et al. 2004). These trends are attributable in part to the growth of the region in general and the attraction of Arlington as a desirable location close to downtown Washington, but they also reflect the role of the Metrorail corridors as powerful magnets for development. The Arlington County Department of Public Works, for example, estimates that the presence of Metrorail stations attracted nearly $3 billion in real estate development between 1973 and 1990. More than 60 percent of the remaining office development capacity and almost 70 percent of the remaining residential development capacity are forecast to occur within the Metrorail corridors. Transit ridership has paralleled the growth in development at major stations. Today, Arlington County has one of the highest percentages of transit use in the nation. Of those living along the Metrorail corridors, approximately 39 percent use transit to commute, and 10 percent walk or bike (Cervero et al. 2004). Outside the corridors, only 17 percent commute by transit and 5 percent walk or bike—but these are high transit ridership and walking percentages for most counties.

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103 Impacts of Land Use Patterns on Vehicle Miles Traveled Of course, the region faces ongoing challenges. These include a lack of affordable housing and some inconsistencies between land use and transportation planning efforts (for instance, some roads near Metrorail stations are more accommodating of high-speed traffic than of pedestrians). The Arlington corridor’s Metrorail lines increasingly struggle with serious overcrowding because there are not enough cars and tracks to meet the booming ridership demand. This shortfall stems in part from inherent design problems but also from more general budget problems. The Washington Metrorail system is virtually the only major transit system in the nation that receives no dedicated stream of revenue for capital or operating costs; rather, it is dependent on operating subsidies from its member jurisdictions, having to compete for the same pool of state and local government general fund revenues that subsidize public safety, education, parks, and many other needs. This situation leaves the system continually vulnerable to the vagaries of local budgeting, often scrambling to fill revenue gaps and unable to address system maintenance and upgrading needs. Despite these challenges, most planners look to the Washington Metrorail system in general, and Arlington County in particular, as a model of TOD, which can provide important lessons for other regions of the country. Some of Arlington County’s success may be attributable to unique local factors such as strong, stable support among the county board, manager, and other key local officials; a large base of locally rooted jobs in federal government agencies and related contracting organizations; and a manageable physical size (approximately 26 square miles) that made it possible for planners and officials to have a good grasp of the territory and communicate effectively with the community. The primary key to Arlington’s success, however, has been adherence to textbook planning principles. This has included the careful preparation of a general land use plan that set the broad policy framework for all development decisions along targeted growth axes, together with sector plans for orchestrating development activities (including land use and zoning ordinances, urban design, transportation planning, and open-space

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104 Driving and the Built Environment guidelines) within quarter-mile “bulls-eyes” of each Metrorail station. These plans have been instrumental in communicating to investors and residents about the types of developments planned and creating a sense of integrity with respect to plans and policies. Ongoing review and revision of the original plans have ensured that developments evolve in response to changing community goals and market conditions. Related keys to success have included the following: • A variety of strategies to attract private investments around stations, such as targeted infrastructure improvements and incentive-based, permissive zoning measures; • Rezoning of land adjacent to stations to high density while main- taining relatively low density and protecting greenspace in surrounding neighborhoods; • Dedication to continually pressing for top-quality design for housing and office developments, with a strong focus on creating attractive, walkable spaces; and • Proactive public outreach and community involvement, with business alliances, neighborhood groups, and individual residents frequently being invited to express their opinions on the design and scale of new developments through neighborhood meetings, workshops, and inter- active websites. references Abbreviations FHWA Federal Highway Administration NRDC National Resources Defense Council Arlington County Planning Department. 2008. Profile 2008: Summer Update. www. co.arlington.va.us/Departments/CPHD/planning/data_maps/pdf/page65081.pdf. Accessed Oct. 23, 2008. Cervero, R., S. Murphy, C. Ferrell, N. Goguts, Y.-H. Tsai, G. B. Arrington, J. Boroski, J. Smith-Heimer, R. Golem, P. Peninger, E. Nakajima, E. Chui, R. Dunphy, M. Myers, S. McKay, and N. Witenstein. 2004. TCRP Report 102: Transit-Oriented Development

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105 Impacts of Land Use Patterns on Vehicle Miles Traveled in the United States: Experiences, Challenges, and Prospects. Transportation Research Board of the National Academies, Washington, D.C. Cortright, J., and C. Coletta. 2004. The Young and the Restless: How Portland Competes for Talent. Impresa, Inc. Cotugno, A., and R. Benner. Forthcoming. Regional Planning Comes of Age. Rutgers University Press, Piscataway, N.J. Dill, J. 2006. Travel and Transit Use at Portland Area Transit-Oriented Developments. Portland State University, Portland, Ore. www.transnow.org/publication/Reports/ TNW2006-03.pdf. Accessed April 22, 2008. Downs, T. 2004. Still Stuck in Traffic: Coping with Peak-Hour Traffic Congestion. Brookings Institution, Washington, D.C. Evans, J. J., IV, R. H. Pratt, A. Stryker, and J. R. Kuzmyak. 2007. TCRP Report 95: Traveler Response to Transportation System Changes: Chapter 17—Transit- Oriented Development. Transportation Research Board of the National Academies, Washington, D.C. FHWA. 2009. Highway Statistics 2007. U.S. Department of Transportation, Washington, D.C. www.fhwa.dot.gov/policyinformation/statistics/2007. Accessed April 1, 2009. Gustafson, R. 2007. Streetcar Economics: The Trip Not Taken. www.portlandstreetcar. org. Accessed April 22, 2008. NRDC. 2001. Solving Sprawl. www.nrdc.org/cities/smartgrowth/solve/solveinx.asp. Accessed April 22, 2008. Podobnik, B. 2002. The Social and Environmental Achievements of New Urbanism: Evidence from Orenco Station. Department of Sociology, Lewis and Clark College, Portland, Ore. www.lclark.edu/∼podobnik.orenco02.pdf. Accessed April 8, 2008. Portland Bicycle Counts Report. 2008. www.portlandonline.com/TRANSPORTATION/ index.cfm?c=44671&a=217489. Accessed July 2, 2009. Switzer, C. R. 2002. The Center Commons Transit Oriented Development: A Case Study. Portland State University, Portland, Ore.