Faci l i tat ing Mode Shift : Investing in Bicycle-, Pedestrian- and Transit-ripe Communities

Tessa Greegor

Principal Planner

Cascade Bicycle Club

Funding made available through

the Bullitt Foundation

3

IntroductIon

Performance measures for transportation investments aren’t new. The predominant metric used to evaluate needs and benefits has principally been the volume of motor-vehicles and the presence of traffic congestion. This approach has often resulted in the dedication of funding and resources to transportation projects that ultimately induce sprawl and increase vehicle miles traveled (VMT). However, with the advent of the Transportation Investment Generating Economic Recovery (TIGER) Discretionary Grant Program, there is a growing awareness that expanding the scope of costs and benefits used to direct scarce transportation resources can deliver better projects with better outcomes. As communities seek improved health, local economies and more sustainable environments, transportation investments that are planned and prioritized around these public objectives have greater potential to achieve the desired outcomes.

At the time this paper was written in 2011, the United States awaited a new federal transportation bill. The Safe, Ac-countable, Flexible, Efficient Transportation Equity Act (SAFETEA-LU) expired on September 30, 2009, and has since been operating on a series of short-term extensions. Meanwhile, the need remains to establish federal transportation policies and funding that align with efforts to increase the use of healthy and environmentally sustainable modes of trans-portation (bicycling, walking and transit), while reducing VMT and greenhouse gas emissions within communities. Under current spending through SAFETEA-LU, despite increased spending (as compared to past federal transportation bills), congestion has nearly doubled.

In light of the budget deficits and resource constraints faced at both the federal and local level, the aim of this paper is to highlight policies and related investments that have the potential to be most cost-effective in facilitating bicycling, walking and public transit use. Land use, and its inherent connection to transportation, health and the environment, is at the forefront of this discussion. This paper synthesizes existing research identifying key land use characteristics that have proven successful in facilitating bicycling, walking and transit use. The research presented in this paper suggests that transportation investments in places that have the supporting land use framework can be a cost-effective strategy for reducing VMT, though further research is needed to demonstrate the cost-benefit of transportation investments as they relate to different land use patterns.

Communities with high levels of bicycling, walking and transit use typically reflect supportive land use characteristics, such as higher density, mixed land uses, connected street networks and access to regional employment centers. This paper looks at increasing densities, transportation systems, and travel habits of residents in selected census tracts within the central Puget Sound region. While increased density is an important factor in facilitating alternative modes of trans-portation, case studies of these cities suggest that density without other complementary land use characteristics, such as regional accessibility and mixed land uses, may not have as much influence on increasing bicycling, walking and transit use as would density in a neighborhood that does exhibit these characteristics.

With federal, state and local policies framing the allocation of today’s limited resources, the structure of transportation funding allocation plays a significant role in the formation of our cities and transportation systems, and moreover, the livability, health and economic vibrancy of our communities. This paper highlights specific policies, such as mode-neutral funding and direct aid to cities, which provide a framework to support outcome-oriented investments and efficient fund-ing allocation.

Transportation policies and investments that both reward and incentivize “bicycle-, walk- and transit-ripe communities”, can cost-effectively produce reductions in VMT and green house gas emissions, as the framework already exists to sup-port the use of these modes. The premise of this paper suggests that prioritizing transportation investments in places that are best able to realize a mode shift to bicycling, walking and transit use – like regional centers – can yield the great-est return on investment as it relates to increasing the use of these modes.

4

Land use, transportatIon, aIr QuaLIty and HeaLtH

The Land Use, Transportation, Air Quality and Health (LUTAQH) study, now HealthScape, provides a local study demon-strating the types of land use patterns that support reduced VMT and automobile dependency. The findings indicate that compact development, a wide variety of land uses close to home and work, and a connected street network with pedes-trian facilities can help reduce automobile dependency and increase transportation efficiency, while improving air quality and health. Results from the study showed that residents living in the most interconnected areas of King County travel 26 percent fewer vehicle miles per day than residents of sprawling areas within county. The study also found that increased residential density, street connectivity and land use mix near home and work are associated with significantly lower per capita vehicle emissions, and that mixed land uses matter most when it comes to transportation efficiency. While residen-tial density is important in supporting transit and commercial land uses, providing retail destinations near where people live and work is critical. LUTAQH also found that automobile dependency decreased in neighborhoods with higher residential density and better street connectivity.

Specific strategies for resource allocation were identified based on the findings from the LUTAQH study, including pri-oritizing new transit investments in areas best able to support transit use and allocating more transportation funding to transit and nonmotorized improvements (Frank L. , 2005).

LIterature revIew

The following literature summary will further illustrate the connection between land use variables, such as density, mixed land uses and proximity to major employment centers, with reducing vehicle miles traveled and automobile depen-dence. The literature provides evidence in support of the overarching premise of this paper, which is that public policies should prioritize investments in areas and communities with better development patterns, principally regional centers.

Land use patterns, supportive of bicycling, walking and transit use and lower vMt

Extensive literature and research characterizes the relationship between specific urban form and land use variables and VMT and travel behavior. Variables that have been found to support lower VMT and automobile use include: density (population and employment), mixed land uses, street design and connectivity and regional accessibility.

It is evident from the research that neighborhood design and land use patterns can facilitate greater choice in travel mode and ultimately reduce the need for people to drive alone (Washington, 2009). Over the past several decades, sprawling development patterns have required people to travel longer distances to access services and employment centers. When land uses are dispersed, more trips tend to be made by automobile as bicycling, walking and taking transit become less convenient. Data from the Texas Transportation Institute and the U.S. Department of Transportation indicated that between 1983 and 1990, 69 percent of the growth in driving was due to three factors: 1) longer average trips; 2) less carpooling; and 3) a switch from biking, walking or transit to driving (Partnership, 1999). It was concluded that each of these factors was in part related to changing development patterns (Travel Behavior Issues in the 90’s, 1992). In another study, An Analysis of Relationships Between Urban Form and Travel Behavior, findings indicated that higher levels of employment density, population density and mixed land uses were associated with lower single-occupancy vehicle (SOV) use and higher rates of transit use and walking (Frank L. D., 1994).

The literature also suggests that the effects of individual land use factors (those identified in the previous paragraph) on travel behavior are often cumulative. For example, increasing residential density in an area without proximity to a major employment center may be less influential on reducing VMT than if the two variables were combined. Concluded in Measuring Sprawl and Its Impact, areas that support the combination of land use density, mix, connectivity and walk-ability tend to have lower per capita vehicle ownership and use and higher use of alternative modes (Ewing, Pendall, &

5

Chen, Measuring Sprawl and Its Impacts, 2002). Case studies of New Urbanist neighborhoods – where density, mix and connectivity are generally high – have shown significantly lower per capita VMT and fewer automobile trips (22-25 per-cent) compared to other neighborhoods, without the same land use characteristics (Allen & Benfield, 2003), (Rodriguez & Khattak, 2005).

street connectivity, design and accessibility

The design and layout of streets have also been shown to influence travel patterns. Traditional grid-like street patterns with high connectivity support shorter trips and provide greater choice of routes for all modes – making bicycling, walk-ing and taking transit more attractive and convenient than in more suburban, cul-de-sac development, where con-nectivity and accessibility through the street network is low. One study, conducted by the United States Environmental Protection Agency (US EPA) found that regardless of residential density, street design features, such as connected street networks and pedestrian-friendly streets, positively influenced per-capita vehicle travel and congestion delays (among other transportation system variables) (Victoria Transport Policy Institute, 2010).

In addition to street connectivity, street design such as traffic calming and bicycle and pedestrian infrastructure, can play a role in travel behavior. One study concluded that residents of a pedestrian-friendly community walked, bicycled and took transit for 49 percent of work trips, as compared to 31 percent in automobile-oriented communities (Cervero & Radisch, 1995).

density and vehicle miles traveled

There is general consensus among the literature that higher density land use is associated with lower VMT and higher use of non-SOV modes. Compact, higher density development patterns tend to shorten the distances people must travel to access destinations. One study found that by doubling a neighborhood’s compactness, resident vehicle trips reduced by 20 to 30 percent (Holtzclaw, 1994). Additionally, higher residential densities can provide more transportation choices for residents – at densities of 7 units per acre, some transit service can be supported (30-minute headways) and at 15 units per acre, frequent transit center can be supported (10-minute headways). Between, high-capacity transit becomes and option (Ewing, 1996).

Density tends to be characteristic of the types land uses that influence travel, such as mixed land uses, transit access, quality pedestrian environments and proximity to regional employment. Because there is typically a direct correlation between these variables, there is less evidence concluding that residential density, independent of all other variables, reduces vehicle miles traveled. The studies that have evaluated the influence of residential density on VMT independent of other land use variables have found that other variables such as regional access to jobs have a greater influence (Boar-net & Handy, 2010). With this understanding, increasing residential density in more central locations may have a greater impact on reducing VMT than increasing residential density in places further from the central core. Given the existing research, it is generally believed that policy attention should focus on a holistic set of land use characteristics, rather than density alone (Boarnet & Handy, 2010) (Chatman, 2008). From the DRAFT Policy Brief on the Impacts of Residential Den-sity Based on a Review of the Empirical Literature:

“Increases in density should be considered as part of coordinated land use plans, rather than in isolation. There are many possible co-benefits from land use policies that encourage higher residential densities, concentrations of em-ployment, shopping, and service destinations, and infrastructure and urban design that make non-motorized travel modes (e.g., walking and bicycling) more attractive options (Boarnet & Handy, 2010).”

It is evident from the research that the degree of influence that residential density has on reducing VMT and converting auto-trips to other modes depends on the prevalence of other variables.

Location is an important variable as well: a study from Minneapolis/St. Paul found that people who live on the edges of

6

metropolitan areas have longer commutes and travel times than those living in more central locations, regardless of the density at the place of residence (Barnes & Davis, 2001). The further people live from destinations, other modes of trans-portation generally becomes less convenient and accessible.

As suggested throughout this paper, residential density by itself may not produce the desired benefits in terms of reduc-ing VMT and increasing bicycling, walking and transit use. However when coupled with other variables, often referred to as the “4D Indices” – density, diversity, design and destinations – the percentage change in vehicle miles or trips has the potential to be much greater. One example shows that by doubling residential density, per-household vehicle trips would reduce by 4 percent, whereas doubling all four of the variables (residential or job density, jobs/housing diversity, neigh-borhood design, destinations), vehicle trips would reduce by 15 percent (Smart Growth Planning).

Other calculations have been made evaluating the influence of land use factors on vehicle travel, as illustrated in the Table 1 below. These calculations suggest that neighborhood design factors can reduce per capita automobile travel between 10 and 20 percent, whereas the proximity of the neighborhood to urban centers can reduce travel by 20 to 40 percent. One example from Davis, California, shows that people who live in a central location typically drive 20 to 40 percent less and walk, bicycle and use public transit two to four times more than they would if in an urban-fringe loca-tion (Litman, 2011). The US EPA has incorporated these calculations into their Smart Growth Index Model as a metric for predicting the impact of land use strategies on transportation outcomes (Criterion Planners/Engineers Inc., 2002).

Table 1: Typical Elasticities of Travel With Respect to the Built Environment (Ewing & Cervero, Travel and the Built Envi-ronment: A Synthesis, 2002)

Factor Description Trips VMT

Local Density Residents and employees divided by land area -5% -5%

Local Diversity (Mix) Jobs/residential population -3% -5%

Local Design Sidewalk completeness/route directness and street network density

-5% -3%

Regional Accessibility Distance to other activity centers in the region -- -20%

To summarize the literature, the following general conclusions have been drawn through Todd Litman’s synthesis of The Effects of Land Use Patterns on Travel Behavior:

Per capita automobile travel tends to decline:

With increasing population and employment density•

With increased land use mix•

In areas with connected street networks•

In areas with bicycle and pedestrian friendly streets and traffic calmed streets•

With the presence of a strong transit system and good pedestrian access•

In areas of larger and higher-density commercial centers•

Literature conclusions

The relevance of the literature to the premise of this paper is to demonstrate that development and land use patterns have a significant influence on travel behavior. While residential density is an important factor, in order to reduce VMT and SOV trips to the degree supportive of environmental and smart growth objectives, density should be located in areas that provide access to major employment centers, support efficient transit service, sustain mixed land uses and exhibit connected street networks.

7

Although not directly concluded from the Literature Review, the research suggests that transportation investments in places that can exhibit the land use characteristics mentioned in the previous paragraph and throughout the Literature Review (e.g. higher density, mixed land use, proximity to urban centers, connected street grid), would likely support greater shift in mode choice than in places that don’t. The phrase, “if you build it they will come” has been used to illus-trate the return on nonmotorized investments. Jennifer Dill and Theresa Carr’s research concluded that new bicycle lanes will be used by bicycle commuters; however, the other factors influencing travel behavior should too be recognized in determining the degree of influence that a nonmotorized investment will have on shifting trips. Public policies and fund-ing strategies that focus development and transportation investments in areas that can establish high levels of bicycling, walking and transit use will likely have the greatest influence in reducing VMT and SOV use.

LocaL case studIes

comparing commute Mode splits where residential population Has Increased (2000 – 2009)

The following “snapshots” represent a series of census tracts and noteworthy developments from the central Puget Sound region (King and Snohomish Counties). The locations were selected to represent different types of development patterns around the two counties, where residential populations have increased within the census tracts by at least 30 percent between 2000 and 2009. Because many of the census tracts represent large tracts of land (some developed, some undeveloped), person-density by census tract does not accurately convey (in this context) the development that has happened between the years. For example, the population of census tract 326.02 (comprised of Snoqualmie Ridge) increased from 2,587 to 8,468 between 2000 and 2009 however the density of the census tract increased by less than 1 person per acre (because the population is averaged over 7,546 acres). For this reason, population increase was used to select the examples shown below.

The examples are intended to highlight the changes, if any, that have occurred with respect to transportation commute modes. The findings are by no means conclusive, but rather suggestive of the impact of increasing residential density on travel mode, relative to the location of development within the regional context and other key factors discussed in the Literature Review. Additional research is necessary to conclusively determine the degree of influence of specific land use patterns on commute modes within these census tracts. The mode split data compares 2000 Census Journey to Work with 2005-2009 American Community Survey Journey to Work data.

8

Table 2: Comparing commute mode splits between 2000 and 2005 -2009 in selected census tracts where residential populations have increased

Neighborhood Description

Snoqualmie Ridge Black Diamond Redmond

RidgeLynnwood Parkview Vil-lage Condos

Bothell /Wood-inville Condos (Wisteria Sta-tion)

Seattle: Ballard

Seattle: South Lake Union/Den-ny Triangle

Seattle: 1st Hill/Central District

Census Tract 326.02 316.02 316.03 323.14 518.01 520.07 47.00 73.00 90.00

Acres 7546.09 5175.45 14,206.74 4491.82 991.22517.78

575.94 225.29 205.70

Inside UGA Yes Yes No No Yes Yes Yes Yes Yes

Distance from Seattle DT 27 32 32 20 19.2 22.2 5.5 1.5 1

Distance from Bellevue DT 21 27.5 27.5 12.8 19.4 17.5

Access to Transit Limited4 KC Metro Routes

4 KC Metro Routes

1 KC Metro Route Good Limited High (8

routes) High High

Population 2000 2,587 5,553 3,252 4,634 6,005 3,544 4,916 2,218 2,134

Population 2009 8,468 8,427 4,999 10,907 8,480 4,575 6,507 4,674 3,320

Percent Change: Population 227.33% 51.76% 53.72% 135.37% 41.22% 29.09% 32.36% 110.73% 55.58%

2000: SOV mode split 72.41% 77.63% 73.02% 78.03% 68.66% 71.43% 53.40% 32.65% 41.05%

2009: SOV mode split 74.18% 83.29% 77.47% 76.84% 75.43% 81.36% 50.18% 31.85% 33.73%

SOV change (2000-2009) 1.77% 5.67% 4.44% -1.19% 6.77% 9.93% -3.22% -0.80% -7.32%

2000: Transit mode split 2.17% 1.10% 1.59% 3.20% 8.27% 1.80% 22.40% 21.94% 25.90%

2009: Transit mode split 2.47% 1.81% 2.30% 2.91% 9.55% 4.72% 25.85% 21.55% 39.42%

Transit Change (2000-2009) 0.29% 0.71% 0.70% -0.29% 1.28% 2.92% 3.45% -0.38% 13.52%

2000: Bicycle mode split 0.32% 0.64% 0 0.00% 0.22% 0.42% 2.09% 2.06% 3.17%

2009: Bicycle mode split 0 0.74% 0 0.26% 0.00% 0.00% 4.50% 2.54% 0.00%

Bicycle Change (2000-2009) -0.32% 0.11% 0 0.26% -0.22% -0.42% 2.40% 0.48% -3.17%

2000: Pedestrian mode split 2.09% 1.17% 3.00% 1.07% 2.45% 0.37% 9.48% 28.73%

1.79%

2009: Pedestrian mode split

0.88%0.00% 2.10% 0.00% 0.55% 0.75% 5.81% 34.23%

11.92%

Pedestrian Change (2000-2009)

-1.21% -1.17% -.90% -1.07% -1.89% 0.38% -3.67% 5.50% 10.13%

Data represents percentage of workers commuting by each mode of transportation2009 data represents 2005-2009 ACS data (5-year data summary)

9

Snoqualmie

Fall City

North Bend

LegendSnapshot Census Tract

Bus Stop

Paved Trail

Bus Route

Parcel

Inside UGA k

0 10.5Miles

Ravensdale

Maple Valley

BlackDiamond

Lake Morton-Berrydal

Hobart

Covington

Enumclaw

LegendSnapshot Census Tract

Bus Stop

Paved Trail

Bus Route

Parcel

Inside UGA k

0 10.5Miles

LegendSnapshot Census Tract

Bus Stop

Paved Trail

Bus Route

Parcel

Inside UGA k

0 0.30.15Miles

LegendSnapshot Census Tract

Bus Stop

Paved Trail

Bus Route

Parcel

Inside UGA k

0 0.50.25Miles

Lynnwood Martha Lake

MukilteoLegendSnapshot Census Tract

Bus Stop

Paved Trail

Bus Route

Parcel

Inside UGA k0 0.50.25

Miles

LegendSnapshot Census Tract

Bus Stop

Paved Trail

Bus Route

Parcel

Inside UGA k

0 0.30.15Miles

LegendSnapshot Census Tract

Bus Stop

Paved Trail

Bus Route

Parcel

Inside UGA k

0 0.250.125Miles

LegendSnapshot Census Tract

Bus Stop

Paved Trail

Bus Route

Parcel

Inside UGA k

0 0.30.15Miles

LegendSnapshot Census Tract

Bus Stop

Paved Trail

Bus Route

Parcel

Inside UGA k

0 0.30.15Miles

Maps illustrating characteristics of the census tract examples discussed in Table 2.

Snoqualmie Ridge: 326.02 Black Diamond: 316.02; 316.03 South Lake Union/Denny Triangle: 73.00

Redmond Ridge: 323.14 Lynnwood: 518.01 1st Hill/Central District: 90.00

Bothell/Woodinville: 520.07 Ballard: 47.00

10

case study summary

Without conducting a rigorous analysis of the variables involved in transportation behavior, the case studies used in this paper are not intended to provide conclusive evidence of the relationship between residential density and commute pat-terns. However, based on the examples used, there are specific trends that can be highlighted. General conclusions from the examples include:

In places closer to the urban core and major employment centers, transit access is better and transit ridership •is significantly higher.

Aside from Denny Triangle/South Lake Union – where transit use stayed fairly constant between the years – •in Seattle neighborhoods, transit mode split increased more than in the neighborhoods further away.

Aside from Redmond Ridge – where the SOV mode split decreased by 1.19 percent despite the increase in •residential densities – the SOV mode split increased significantly in places outside of Seattle and away from the urban core.

In Seattle neighborhoods, SOV mode split declined between the 2000 and 2005-2009.•

In neighborhoods within Seattle, the SOV mode split is significantly lower than neighborhoods like Redmond •Ridge and Snoqualmie Ridge. In turn, bicycling, walking and transit mode splits are significantly higher in more central neighborhoods.

The case studies suggest that increasing densities in places that have the supporting infrastructure, efficient transit ser-vice, a connecting street-grid and offer proximity to major employment centers, may produce greater reduction in SOV use and ultimately VMT, and higher rates of bicycling, walking and transit use.

While not conclusively illustrated through the case studies, the literature suggests that rates of bicycling, walking and transit use are higher in communities with supportive land use characteristics (higher density, grid-connected, mixed land uses) and pedestrian and bicycle infrastructure. The case studies exhibiting the former of these characteristics (support-ive land use) show higher levels of bicycling, walking and transit use. Although further research is necessary to this end, it is logical that targeting transportation investments in areas with supportive land use patterns may have greater poten-tial to reduce VMT and increase the use of non-SOV modes. In communities that lack the supportive land use frame-work, it may require greater investment to produce the same results as in their counterparts.

poLIcy recoMMendatIons and concLusIon

Public policies that support both focused growth, and transportation investments in areas with supportive land use characteristics have the potential to substantially reduce VMT and SOV use. Federal transportation policies that support better development patterns and allocate resources accordingly are essential to producing the transportation outcomes necessary to reduce the country’s environmental footprint and dependence on fossil fuels, while improving the health and livability of local communities. Policy principles that align with these objectives include:

Support mode-neutral federal transportation funding

Mode-neutral transportation funding, a strategy that has been part of the federal transportation discussion in recent years presents a viable approach to investing in transportation outcomes. Mode-neutral transportation funding would allow money to be allocated based on a pre-determined set of criteria – criteria outlined to generate the greatest return on investment. Criteria could be based on various public objectives, including environmental outcomes, health out-

11

comes, or reductions in VMT. Current distribution of federal transportation dollars allocates approximately 80 percent to highways, while infrastructure for other modes of transportation competes for the remaining 20 percent (PolicyLink). Meanwhile, less than 2% of federal transportation dollars is currently spent to support bicycling and walking. To ensure that transportation investments are linked to desired outcomes, a “mode-neutral” funding structure would support ex-penditures based on the benefits and costs of a specific project. This approach would be performance-driven and would effectively consider the benefit of a specific project on people rather than specific modes of transportation.

support the Hud, dot and epa programs for sustainable communities

In June of 2009, the Partnership for Sustainable Communities was created by the Department of Housing and Urban Development (HUD), the US Department of Transportation (DOT) and the US EPA. Through this partnership, the three agencies have expressed a commitment to ensuring that housing and transportation goals are met while simultaneously protecting the environment, promoting equitable development and helping to address the challenges of climate change. The principles inherent to this partnership should be upheld through policy decisions regarding federal funding alloca-tion. These policies should place emphasis on providing more transportation choices and investing in existing communi-ties that are poised to become less dependent on the automobile.

promote compact land use development and infill

Promoting and funding transportation projects that support more efficient land use patterns has significant potential to reduce reliance on automobiles and VMT. As illustrated throughout this paper, poor land use can lead to higher SOV use and VMT.

provide direct aid to cities

With the majority of the United States population living in urbanized areas, and the potential that exists within urban-ized areas to convert automobile trips to more sustainable modes, federal policies supporting allocation of transporta-tion funds directly to cities rather than filtering through the states can be more resource and time efficient, with greater impact. This type of funding structure can also foster relationships between the US DOT and city transportation agencies. Cities with the expertise and capacity to implement projects should be eligible to apply for direct aid funding.

conclusion

Ensuring that the policy principles identified in the previous section are at the forefront of federal decision making, par-ticularly with respect to funding decisions, is paramount in seeking a more balanced and coordinated approach to invest-ments based on positive outcomes. There are several policy options that have the potential to direct transportation fund-ing in ways that will produce the types of outcomes desired in communities across the country. In places where the land use is compatible with bicycling, walking and taking transit, the evidence strongly suggests that focusing development and transportation investments in these areas will produce greater results with less investment than in places lacking the compatible land use characteristics. While all communities should have transportation options, federal policies should incentivize and support development patterns that enable bicycling, walking and taking transit where they will be most successful in reducing drive-alone trips and dependence on fossil fuels and increasing the health, vitality and livability of our communities.

policy resources

NACTO: National Association of City Transportation Officials: The Next U.S. Transportation Bill. September 2008.

HUD-DOT-EPA Partnership for Sustainable Communities: http://www.epa.gov/smartgrowth/partnership/

All Aboard: Making Equity and Inclusion Central to Federal Transportation Policy. PolicyLink.

12

works cIted

Allen, E., & Benfield, F. K. (2003). Environmental Characteristics of Smart-Growth Neighborhoods . National Resources Defense Council.

Barnes, G., & Davis, G. (2001). Land Use and Travel Choices in the Twin Cities, 1958–1990.

Boarnet, M., & Handy, S. (2010). DRAFT Policy Brief on the Impacts of Residential Density Based on a Review of the Em-pirical Literature.

Cervero, R., & Radisch, C. (1995). Travel Choices in Pedestrian Versus Automobile Oriented Neighborhoods. UC Trans-portation Center.

Chatman, D. (2008). Deconstructing development density: Quality, quantity and price effects on household non-work travel. Transportation Research Part A , 1008-1030 .

Criterion Planners/Engineers Inc. (2002). Smart Growth Index. U.S. EPA.

Ewing, R. (1996).

Ewing, R., & Cervero, R. (2002). Travel and the Built Environment: A Synthesis. Transportation Research Record 1780 , 87-114.

Ewing, R., Pendall, R., & Chen, D. (2002). Measuring Sprawl and Its Impacts. Smart Growth America .

Frank, L. D. (1994). An Analysis of Relationships Between Urban Form (Density, Mix, and Jobs: Housing Balance) and Travel Behavior (Mode Choice, Trip Generation, Trip Length, and Travel Time).

Frank, L. (2005). Travel Behavior, Environmental, and Health Impacts of Community Design and Transportation Invest-ment: A Study of Land Use, Transportation, Air Quality and Health in King County, Wa.

Holtzclaw, J. (1994). Using Residential Patterns and Transit to Decrease Auto Dependence and Costs. Natural Resources Defense Council.

Litman, T. (2011). Land Use Impacts on Transport: How Land Use Factors Affect Travel Behavior. VTPI.

Partnership, S. T. (1999). Why are the Roads so Congested: An Analysis of the Texas Transportation Institute’s Data on Metropolitan Congestion.

PolicyLink. All Aboard: Making Equity and Inclusion Central to Federal Transportation Policy.

Rodriguez, D., & Khattak, A. J. (2005). Travel Behavior in Neo-Traditional Neighborhood Developments: A Case Study In USA. Transportation Research A , 481-500.

Smart Growth Planning. (n.d.). Vehicle Miles Traveled. Retrieved from http://www.smartgrowthplanning.org/ForecastMea-sure.html#VehicleMilesTraveled

(1992). Travel Behavior Issues in the 90’s. Washington, DC: U.S. Department of Transportation. Federal Transit Administra-tion.

Victoria Transport Policy Institute. (2010, November). Land Use Impacts on Transport: How Land Use Patterns Affect Travel Behavior.

Washington, M. R. (2009, 09). Transportation Efficient Land Use: Planning and Land Use Strategies that Reduce the Need to Drive. Retrieved from http://www.mrsc.org/Subjects/Transpo/efficientlanduse.aspx

Made possible by funding from the Bullitt Foundation

Cascad

e Bicycle C

lub | Facilitating

Mod

e Shift: Investing in B

icycle-, Pedestrian- and

Transit-ripe C

omm

unities | June 2011

Report produced by:

Tessa GreegorPrincipal PlannerCascade Bicycle Club