Land Use Impacts on Transport

How Land Use Patterns Affect Travel Behavior

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TDM Encyclopedia

Victoria Transport Policy Institute

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Updated 6 September 2019

This chapter describes how land use factors affect travel behavior. These factors include density, mix, roadway connectivity, parking facility management, and site design. This information is useful for evaluating how land use management strategies such as Smart Growth, New Urbanism and Access Management can help achieve transport planning objectives. For more information see the report “Land Use Impacts On Travel Behavior” at www.vtpi.org/landtravel.pdf.

 

Introduction. 1

Land Use Factors That Affect Travel 2

Density and Clustering. 2

Regional Accessibility. 4

Centeredness. 5

Land Use Mix. 5

Connectivity. 6

Roadway Design. 6

Parking Management 7

Transit-Oriented Development 7

Walking and Cycling Conditions. 8

Site Design and Building Orientation. 9

Transportation Demand Management 9

Cumulative Impacts. 9

Modeling Land Use Impacts on Travel Behavior 12

General Conclusions. 13

Related Chapters and Resources. 14

References And Resources For More Information. 14

 

 

Introduction

Land Use (also called Land Development, Spatial Development, Community Design, Urban Design, Cityscape or The Built Environment) refers to various land use factors, such as density, mix, connectivity and the quality of the pedestrian environment, as summarized in Table 1.

 

Table 1            Land Use Factors (CARB 2010-2011; Litman 2007; Sadek et al. 2011)

Factor

Definition

Density

People or jobs per hectare.

Regional Accessibility

A site’s location relative to the regional urban center, and the number of jobs and public services available within a given travel time

 

Centeredness

Degree to which commercial and other public activities are located in downtowns and other activity centers.

Land Use Mix

Degree to which residential, commercial and institutional land uses are located close together.

Connectivity

Degree to which roads and paths are connected and allow direct travel between destinations.

Roadway Design

Scale and design of streets, and how various uses are managed. Traffic calming refers to street design features intended to reduce traffic speeds and volumes.

Walking & Cycling Conditions

Quality of walking and cycling transport conditions. (Active transport is a general term for walking, cycling, and their variants). 

Transit Accessibility

Degree to which destinations are accessible by quality public transit.

Parking Management

Number of parking spaces per building unit or hectare. Parking management includes pricing and regulations

Site Design

 

 

Transportation Demand Management

Various strategies and programs that encourage more efficient travel patterns, often implemented as an alternative to road and parking facility expansion, and in conjunction with land use policy reforms.

This table describes various land use factors that can affect travel behavior and population health.

 

 

These factors affect travel behavior by affecting the distances that need to be traveled between destinations, and the relative efficiency of different modes. Some TDM strategies change land use patterns directly (Smart Growth, Access Management, Transit-Oriented Development, Location-Efficient Development, Road Space Reallocation, Parking Management, Downtowns, Roadway Connectivity), and most TDM strategies affect land use indirectly through impacts on travel behavior. This chapter examines how land use factors affect travel behavior and therefore the effectiveness of land use planning strategies to achieve TDM objectives.

 

 

Land Use Factors That Affect Travel

This section describes how different land use factors affect travel patterns.

 

Density and Clustering

Density refers to the number of people or jobs in a given area (Campoli and MacLean, 2002). Clustering refers to related activities located close together, often in Commercial Centers. Density and clustering can be measured at various scales: regional, county level, municipal jurisdiction, neighborhood, census tract, city block or individual campuses and buildings. Density and clustering affect travel patterns through the following mechanisms:

 

·         Land Use Accessibility. The number of potential destinations located within a geographic area tends to increase with population and employment density, reducing travel distances and the need for automobile travel. For example, in low-density areas a school may serve hundreds of square miles, requiring most students to travel by motor vehicle. In higher density areas, schools may serve just a few square miles, reducing average travel distances and allowing more students to walk or cycle. Similarly, average travel distances for errands, commuting and business-to-business transactions can decline with density.

 

·         Transportation Diversity. Increased density tends to increase the number of transportation options available in an area due to economies of scale. Higher density areas tend to have better sidewalks, bicycle facilities and transit service because increased demand makes them more cost effective.

 

·         Reduced Automobile Accessibility. Increased density tends to reduce traffic speeds, increase traffic congestion and reduce parking supply, making driving relatively less attractive than alternative modes.

 

 

As a result of these factors, increased density and clustering tend to reduce per capita automobile ownership and use, and increase use of alternative modes (Ewing, Pendall and Chen, 2002; Kuzmyak and Pratt, 2003; TRL, 2004; Turcotte, 2008; TRB 2009). Bento, et al (2004) conclude that residents reduce their automobile travel by about 25% if they shift from a dispersed, automobile-dependent city such as Atlanta to a more centralized city, multi-modal city such as Boston, holding other economic and demographic factors constant. Lui (2003) finds that higher density infill development can reduce per capita vehicle travel by up to 27% compared with conventional residential development.

 

Figure 1          Annual VMT Per Household (Holtzclaw 1994)

This figure illustrates how density and transit accessibility affect household vehicle mileage. The Transit Accessibility Index (TAI) indicates daily transit service nearby.

 

 

Holtzclaw (1994) and Holtzclaw, et al (2002) find that average vehicle ownership, vehicle travel, and vehicle expenditure per household decline with increasing residential densities and proximity to public transit, holding constant other demographic factors such as household size and income. The This View of Density Calculator (www.sflcv.org/density) uses this model to predict the effects of different land use patterns on travel behavior. For example, a reduction from 20 to 5 dwelling units per acre (i.e., urban to suburban densities) increases average vehicle travel and automobile expenditures by about 40%.

 

Density at both origins and destinations affect travel behavior. One study found that increasing urban residential population density to 40 people per acre increased transit use from about 2% to 7%, while increasing densities in Commercial Centers to 100 employees per acre resulted in an additional 4% increase in transit use, to an 11% total mode share (Frank and Pivo, 1995). Both work trips and shopping trips are affected by population and employment densities.

 

Some of the differences in travel behavior between higher and lower density land use patterns may result from demographic sorting (also called self-selection). People who cannot drive are more likely to choose homes in older, higher-density urban neighborhoods, and some of these neighborhoods have low average household incomes, which also tends to reduce per capita vehicle travel. However, studies that account for demographic factors find that virtually all groups that live in higher density areas reduce their average annual vehicle mileage (Cambridge Systematics, 1994; Holtzclaw, 1994; Cao, Mokhtarian and Handy 2008; Cervero 2007).

 

 

Regional Accessibility

Regional accessibility refers to an individual site’s location relative to the regional urban center (either a central city or a central business district [CBD]), or other major employment centers, and the portion of residents, employment and activities located close to that center (Kuzmyak and Pratt, 2003; Ewing, 1995).

 

Although regional accessibility tends to have little effect on total trip generation (the total number of trips people make), it tends to have a major effect on trip length and therefore per capita vehicle travel. People who live and work several miles from a city tend to drive significantly more annual miles than if located in the same type of development closer to the urban center. Kockelman (1997) found that accessibility (measured as the number of jobs within a 30-minute travel distance) was one of the strongest predictors of household vehicle travel, stronger than land use density.

 

Travel time maps use isochrones (lines of constant time) to indicate the time needed to travel from a particular origin to other areas (Lightfoot and Steinberg, 2006). For example, areas within one hour may be colored a dark red, within two hours a lighter red, within three hours a dark orange, and within four hours a light orange. Maps can indicate and compare travel times by different modes. For example, one set of maps could show travel times for automobile travel and another for public transit travel. Travel time maps are an indication of accessibility.

 

 

Centeredness

Centeredness refers to the portion of employment, commercial, entertainment, and other major activities concentrated in multi-modal Centers, such as central business districts (CBDs), downtowns and large industrial parks. Such centers reduce the amount of travel required between destinations and are more amenable to alternative modes, particularly public transit. People who work in major multi-modal activity centers tend to commute by transit significantly more than those who work in more dispersed locations, and they tend to drive less for errands. Centeredness affects overall regional travel, not just the trips made to the center. For example, Los Angeles is one of the densest cities in North America, but it lacks strong centers, and so is relatively automobile dependent, with higher rates of vehicle ownership and use than cities such as Chicago, which have similar density but stronger centers (Ewing, Pendall and Chen, 2002).

 

 

Land Use Mix

Land Use Mix refers to locating different types of land uses (residential, commercial, institutional, recreational, etc.) close together. This can occur at various scales, including mixing within a building (such as ground-floor retail, with offices and residential above), along a street, and within a neighborhood. It can also include mixing housing types, so an area contains a variety of demographic and income classes. Such mixing is normal in cities and is a key feature of New Urbanism.

 

Increased land use mix tends to reduce the distances that residents must travel for errands and allows more use of walking and cycling for such trips. It can reduce commute distances (some residents may obtain jobs in nearby businesses), and employees who work in a mixed-use commercial area are more likely to commute by alternative modes (Modarres, 1993; Kuzmyak and Pratt, 2003). Certain combinations of land use are particularly effective at reducing travel, such as incorporating schools, stores, parks and other commonly-used services within residential neighborhoods and employment centers. This creates urban villages, which are walkable centers and small neighborhoods that contain the services and activities people most often need. The table below summarizes the results of one study concerning how various land use features affected drive-alone commute rates. Important amenities include bank machines, cafes, on-site childcare, fitness facilities, and postal services.

 

Table 2            Drive Alone Share At Worksites Based on Land Use Characteristics (Cambridge Systematics, 1994, Table 3.12)

Land Use Characteristics

Without

With

Difference

Mix of Land Uses

71.7

70.8

-0.9

Accessibility to Services

72.1

70.5

-1.6

Preponderance of Convenient Services

72.4

69.6

-2.8

Perception of Safety

73.2

70.6

-2.6

Aesthetic Urban Setting

72.3

66.6

-5.7

 

 

Jobs/Housing Balance refers to the ratio of residents and jobs in an area. Research indicates that a jobs/housing balance of about 1.0 tends to reduce average commute distance and per capita vehicle travel (Weitz, 2003; Kuzmyak and Pratt, 2003). In some situations, suburban dispersion of employment can reduce average commute distance, although it tends to increase total per-capita vehicle travel. Crane and Chatman (2003) find that a five percent increase in the amount of employment in a metropolitan area’s outlying counties will lead to a 1.5 percent reduction in the average commute distance, with significant differences by industry. The suburbanization of construction, wholesale, and service employment is associated with shorter commutes, while manufacturing and finance deconcentration (weakly) explain longer commutes. However, this may be offset by increased non-work vehicle mileage.

 

 

Connectivity

Connectivity refers to the degree to which a road or path system is connected, and therefore the directness of travel between destinations (“Connectivity,” VTPI, 2005). A hierarchical road network with many dead-end streets that connect to a few major arterials provides less accessibility than a well-connected network. Increased connectivity reduces vehicle travel by reducing travel distances between destinations and by improving walking and cycling access, particularly where paths provide shortcuts, so walking and cycling are relatively direct.

 

Connectivity can be evaluated using various indices (Handy, Paterson and Butler, 2004; Dill, 2005). This can be measured separately for pedestrian, bicycle and motor vehicle travel, taking into account shortcuts for nonmotorized modes. The Smart Growth Index (USEPA, 2002) describes a methodology for calculating the effects of increased roadway connectivity on vehicle trips and mileage.

 

 

Roadway Design

Roadway design can affect travel behavior in several ways. A Connected road network provides better Accessibility than a conventional hierarchical road network with a large portion of dead-end streets (Handy, Paterson and Butler, 2004). Increased connectivity can reduce vehicle travel by reducing travel distances between destinations and by improving Walking and Cycling conditions, particularly where paths provide shortcuts, so walking and cycling are relatively direct (Dill, 2005).

 

A USEPA study (2004) found that regardless of population density, transportation system design features such as greater street connectivity, a more pedestrian-friendly environment, shorter route options, and more extensive transit service have a positive impact on urban transportation system performance, (per-capita vehicle travel, congestion delays, traffic accidents and pollution emissions), while roadway supply (lane-miles per capita) had no measurable effect. The Smart Growth Index (USEPA, 2002) describes a methodology for calculating the effects of increased roadway connectivity on vehicle trips and vehicle travel.

 

Traffic Calming, Streetscaping and Walking and Cycling Improvements can also affect travel behavior. Cervero and Kockelman (1997) find that residents of neighborhoods with connected street networks and limited commercial parking rely more on alternative modes for non-work trips and drive significantly less than residents of conventional suburban neighborhoods. Residents in a pedestrian friendly community walked, bicycled, or rode transit for 49% of work trips and 15% of their non-work trips, 18- and 11-percentage points more than residents of a comparable automobile oriented community (Cervero and Radisch 1995). Another study found that walking is three times more common in a community with pedestrian friendly streets than in otherwise comparable communities that are less conducive to foot travel (Moudon, et al, 1996).

 

 

Parking Management

Parking Management refers to the supply, price and regulation of parking facilities. How parking is managed can significantly affect travel behavior. As parking becomes more abundant and cheaper, automobile ownership and use increase and destinations become more dispersed, reducing land use Accessibility. Parking supply and pricing have a significant impact on commute mode split (Morrall and Bolger, 1996; Shoup, 1997).

 

 

Transit-Oriented Development

Transit Oriented Development (TOD) refers to communities designed to provide convenient access to high-quality transit services. Several studies indicate that TOD can significantly reduce per capita automobile travel (Cervero, et al, 2004; Gard, 2007; Transform 2014). This occurs because some trips shift to transit, and because transit stations often serve as a catalyst for more accessible land use, creating higher density, mixed-use, walkable Centers. People who live or work in such areas tend to own fewer cars, drive less and use transit more than in other locations (Cambridge Systematics, 1994). As a result of these various factors, Transit Oriented Development tends to “leverage” much greater reductions in vehicle travel than what is directly shifted from automobile to transit (Litman, 2005). Cervero, et al. (2004) develop a model for predicting the effects of increased residential and commercial density, and improved walkability around a station on transit ridership. For example, increasing residential density near transit stations from 10 to 20 units per gross acre increases transit commute mode split from 20.4% to 24.1%, and up to 27.6% if implemented with pedestrian improvements.

 

The table below shows trip reduction predictions for travel impacts of development location and design factors used in Portland, Oregon. For example, if development has a FAR (Floor Area Ratio) of 1.0, and is located in a commercial area near an LRT station, vehicle trips are expected to be 5% less than the same development in a typical suburban area.

 

Table 3            Trip Reduction of Development Location, Design and Density (Portland, 1995)

Minimum

Floor Area Ratio

 

Mixed-Use

Commercial Near Bus

Commercial Near LRT Station

Mixed-Use Near Bus

Mixed-Use Near LRT

No minimum

-

1%

2.0%

-

-

0.5

1.9%

1.9%

2.9%

2.7%

3.9%

0.75

2.4%

2.4%

3.7%

3.4%

4.9%

1.0

3.0%

3.0%

5.0%

4.3%

6.7%

1.25

3.6%

3.6%

6.7%

5.1%

8.9%

1.5

4.2%

4.2%

8.9%

6.0%

11.9%

1.75

5.0%

5.0%

11.6%

7.1%

15.5%

2.0

7.0%

7.0%

15.0%

10.0%

20%

Mixed-Use means commercial, restaurants and light industry with 30% or more floor area devoted to residential. Near bus or LRT (Light Rail Transit) means location within ¼-mile of a bus corridor or LRT station. Floor Area Ratio (FAR) = ratio of floor space to land area.

 

 

Walking and Cycling Conditions

Walking and Cycling (also called nonmotorized or active transportation) conditions are affected by the quantity and quality of sidewalks, crosswalks and paths, path system connectivity, the security and attractiveness of pedestrian facilities, and support features such as bike racks and changing facilities. Improved walking and cycling conditions tend to increase nonmotorized travel, increase transit travel, and reduce automobile travel (Mackett and Brown 2011; “Nonmotorized Transport Planning,” VTPI, 2005).

 

Cervero and Radisch (1995) found that residents in a pedestrian friendly community walked, bicycled, or rode transit for 49% of work trips and 15% of their non-work trips, 18- and 11-percentage points more than residents of a comparable automobile oriented community. Another study found that walking is three times more common in a community with pedestrian friendly streets than in otherwise comparable communities that are less conducive to foot travel (Moudon, et al, 1996). Handy and Mokhtarian (2005) also found that people tend to walk more in more walkable communities, and that a portion of this walking substitutes for driving.

 

Research by Buehler, et al. (2011) using comparable travel surveys in Germany and the U.S. in 2001 and 2008 indicates that transport and land use policies can significantly affect walking and cycling activity. Between 2001 and 2008, the proportion of “any walking” was stable in the U.S. (18.5%) but increased in Germany from 36.5% to 42.3%. The proportion of “any cycling” in the U.S. remained at 1.8% but increased in Germany from 12.1% to 14.1%. In 2008, the proportion of “30 minutes of walking and cycling” in Germany was 21.2% and 7.8%, respectively, compared to 7.7% and 1.0% in the U.S. Virtually all demographic groups in Germany walk and cycle much more than their counterparts in the U.S.

 

 

Site Design and Building Orientation

Some research indicates that people walk more and drive less in areas with traditional pedestrian-oriented commercial districts where building entrances connect directly to the sidewalk than in areas with automobile-oriented commercial strips where buildings are set back and separated by large parking lots (PBQD, 1994).

 

 

Transportation Demand Management

Transportation Demand Management (also called Mobility Management) policies and programs, which encourage more efficient travel behavior, can be implemented as an alternative to road and parking facility capacity expansion. TDM affects land use indirectly, by reducing the need to increase road and parking facility capacity, providing incentives to businesses and consumers to favor more accessible, clustered, development with improved transport choices. Mobility management programs, such as Commute Trips Reduction programs, can often reduce affected automobile trips by 10-30% compared with what would otherwise occur. Smart Growth can be considered the land use component of TDM, and TDM can be considered the transportation component of Smart Growth.

 

 

Cumulative Impacts

The effects of individual land use factors tend to be cumulative. Areas that contain a combination of land use density, mix, connectivity and walkability tend to have significantly lower overall per capita vehicle ownership and use, and higher use of alternative modes than average (Ewing and Hamidi 2014). Allen and Benfield (2003) found that a suburban New Urbanist neighborhood in Tennessee, with modestly higher density, mix and connectivity, has 25% less per capita VMT than otherwise comparable nearby neighborhoods. Similarly, Khattak and Rodriguez (2005) found that residents of a New Urbanist neighborhood in North Carolina generate 22.1% fewer automobile trips and take three times as many walking trips than residents of an otherwise similar neighborhood, even when controlling for demographic factors and preferences. Daisa and Parker (2010) also find that automobile trip generation rates and mode shares are much lower (typically 25-75%) in urban areas than ITE publication recommendations for both residential and commercial buildings.

 

Ewing and Cervero (2002) calculate the elasticity of per capita vehicle trips and vehicle travel with respect to various land use factors, as summarized in Table 4. For example, this indicates that doubling neighborhood density reduces per capita automobile travel by 5%. Similarly, doubling land use mix or improving land use design to support alternative modes also reduces per capita automobile travel by 5%.

 

Table 4            Typical Elasticities of Travel With Respect to the Built Environment (Ewing and Cervero 2002)

Factor

Description

Trips

VMT

Local Density

Residents and employees divided by land area.

-0.05

-0.05

Local Diversity (Mix)

Jobs/residential population

-0.03

-0.05

Local Design

Sidewalk completeness/route directness and street network density.

-0.05

-0.03

Regional Accessibility

Distance to other activity centers in the region.

--

-0.20

This table shows the elasticity values of Vehicle Trips and Vehicle Miles Traveled (VMT) with respect to various land use factors.

 

 

Vernez Moudon and Stewart (2013) reviewed research on how various land use factors affect travel activity, and the tools available for modeling these imapcts and related outcomes such as vehicle emissions and health co-benefits. Table 5 summarizes their findings.

 

Table 5            Typical Elasticities of Travel With Respect to the Built Environment (Vernez Moudon and Stewart 2013)

Category

Variable

VMT

Walking

Transit

Density

Household/population density

–0.04

0.07

0.07

 

Job density

0.00

0.04

0.01

 

Commercial Floor Area Ratio (FAR)

n/a

0.07

n/a

Diversity

Land use mix

-0.09

0.15

0.12

 

Jobs/housing balance

-0.02

0.19

n/a

 

Distance to a store

n/a

0.25

n/a

Design

Intersection/street density

-0.12

0.39

0.23

 

Percent 4-way intersections

-0.12

-0.06

0.29

Destination accessibility

Job accessibility by auto

-0.20

n/a

n/a

 

Job accessibility by transit

-0.05

n/a

n/a

 

Jobs within one mile

n/a

0.15

n/a

 

Distance to downtown

– 0.22

n/a

n/a

Distance to Transit

Distance to nearest transit stop

-0.05

0.15

0.29

An extensive body of literature examines how various land use factors affect travel activity.

 

 

This suggests that neighborhood design factors (density, diversity and design) can reduce per capita vehicle travel on the order of 10-20%, while regional accessibility factors (i.e., where a neighborhood is located with respect to the urban center) can reduce automobile travel by 20-40%. These values are incorporated into the US Environmental Protection Agency’s Smart Growth Index (SGI) Model, that can be used to predict how various types of land use management strategies can help achieve transportation management objectives (www.epa.gov/dced/topics/sgipilot.htm). Even greater reductions are possible if land use changes are reinforced by other TDM strategies.

 

Tomalty and Haider (2009) evaluated how community design factors (land use density and mix, street connectivity, sidewalk supply, street widths, block lengths, etc.) and a subjective walkability index rating (based on residents' evaluation of various factors) affect walking and biking activity, and health outcomes (hypertension and diabetes) in 16 diverse British Columbia neighborhoods. The analysis reveals a statistically significant association between improved walkability and more walking and cycling activity, lower body mass index (BMI), and lower hypertension. Regression analysis indicates that people living in more walkable neighbourhoods are more likely to walk for at least 10 daily minutes and are less likely to be obese than those living in less walkable areas, regardless of age, income or gender. The study also includes case studies which identified policy changes likely to improve health in specific communities.

 

Nelson/Nygaard (2005) use the results of various studies to develop a model which predicts the impacts of various Smart Growth and TDM on per capita vehicle trip generation and related emissions, including land use density, mix, transit service, walking and cycling conditions, affordable housing, parking management and pricing, transit service discounts, and other TDM programs. They indicate that significant reductions can be achieved relative to ITE trip generation estimates.

 

Table 6 summarizes these land use impacts on travel.

 

Table 6            Land Use Impacts on Travel (Litman 2006)

Factor

Definition

Travel Impacts

Density

People or jobs per unit of land area (acre or hectare).

Increased density tends to reduce per capita vehicle travel. Each 10% increase in urban densities typically reduces per capita VMT by 2-3%.

Mix

Degree that related land uses (housing, commercial, institutional) are mixed

Increased land use mix tends to reduce per capita vehicle travel, and increases use of alternative modes, particularly walking for errands. Neighborhoods with good land use mix typically have 5-15% lower vehicle-miles.

Regional Accessibility

Location of development relative to regional urban center.

Improved accessibility reduces per capita vehicle mileage. Residents of more central neighborhoods typically drive 10-30% fewer vehicle-miles than residents of more dispersed, urban fringe locations.

Centeredness

Portion of commercial, employment, and other activities in major activity centers.

Increased centeredness increases use of alternative commute modes. Typically 20-50% of commuters to major commercial centers drive alone, compared with 80-90% of commuters to dispersed locations.

Connectivity

Degree that walkways and roads are connected and allow direct travel between destinations.

Improved roadway connectivity can reduce vehicle mileage, and improved walkway connectivity tends to increase walking and cycling.

Roadway design and management

Scale, design and management of streets.

More multi-modal street design and management increases use of alternative modes. Traffic calming tends to reduce vehicle travel and increase walking and cycling.

Walking and Cycling conditions

Quantity and quality of sidewalks, crosswalks, paths and bike lanes, and the level of pedestrian security.

Improved walking and cycling conditions increases nonmotorized travel and can reduce automobile travel, particularly if implemented with land use mix, transit improvements, and incentives to reduce driving.

Transit quality and accessibility

Quality of transit service and degree to which destinations are transit accessible.

Improved transit service quality increases transit ridership and can reduce automobile trips, particularly for urban commuting.

Parking supply and management

Number of parking spaces per building unit or acre, and how parking is managed.

Reduced parking supply, increased parking pricing and increased application of other parking management strategies can significantly reduce per capita vehicle travel. Cost-recovery parking pricing (charging motorists directly for the cost of providing parking) typically reduces automobile trips by 10-30%.

Site design

The layout and design of buildings and parking facilities.

More multi-modal site design can reduce automobile trips, particularly if implemented with improved transit services.

Mobility Management

Various programs and strategies that encourage more efficient travel patterns.

Mobility management policies and programs can significantly reduce vehicle travel by affected trips. Vehicle travel reductions of 10-30% are common.

This table describes various land use factors that can affect travel behavior and population health.

 

 

Modeling Land Use Impacts on Travel Behavior

Several studies have examined the ability of transportation and land use models to predict the effects of land use management strategies on travel behavior (Cambridge Systematics, 1994; Frank and Pivo, 1995; Rosenbaum and Koenig, 1997; USEPA, 2001; Hunt and Brownlee, 2001; OTREC 2009). These studies indicate that land use factors can have significant impacts on travel patterns, but that current transportation models are not accurate at predicting their effects. For example, most travel surveys undercount nonmotorized trips (since they often ignore short trips, travel by children, and walking links of motorized trips), most models use analysis zones that are too large to capture small-scale design features (see discussion in Evaluating Nonmotorized Transport). As a result, the models are unable to predict the full travel impacts of land use management strategies such as Pedestrian and Cycling Improvements.

 

Current transportation models tend to incorporate relatively little information on many of the land use features that affect travel behavior, such as fine scale analysis of land use mix and pedestrian conditions. The following improvements are needed to allow existing models to evaluate land use management strategies (Rosenbaum and Koenig, 1997):

·         Analyze land use at finer spatial resolutions, such as census tracts or block level.

·         Determine effects of special land use features, such as pedestrian-friendly environments, mixed-use development, and neighborhood attractiveness.

·         Determine relationships between mixed-use development and travel mode selection.

·         Improved methods for analyzing trip chaining.

·         Improve the way temporal choice (i.e., when people take trips) is incorporated into travel models.

 

 

General Conclusions

The following are general conclusions that can be made about the effects of land use patterns on travel behavior.

 

 

 

 

 

 

 

 

 

 

 

Wit and Humor

A son was sitting at his elderly father’s death bed.

“Where do you want to be buried,” asked the son, “Forest Lawn or Pleasant Acres?”

The old man looked at his son and replied, “Surprise me!”

 

 

Related Chapters and Resources

Smart Growth, New Urbanism, Access Management, Location Efficient Development, Clustering and Transit Oriented Development, Density, Road Space Reallocation, Parking Management, Downtowns, Roadway Connectivity, are specific land use management strategies. Land Use Evaluation describes how transportation decisions affect land use, and the economic, social and environmental impacts that can result. More detailed discussions of this subject is available in the comprehensive report Land Use Impacts On Travel Behavior available at www.vtpi.org/landtravel.pdf.

 

 

References And Resources For More Information

 

Eliot Allen and F. Kaid Benfield (2003), Environmental Characteristics of Smart-Growth Neighborhoods, National Resources Defense Council (www.nrdc.org/cities/smartGrowth/char/charnash.pdf).

 

Keith Bartholomew and Reid Ewing (2009), ‘Land Use-Transportation Scenarios and Future Vehicle Travel and Land Consumption: A Meta-Analysis,’ Journal of the American Planning Association, Vol. 75, No. 1, Winter 2009 (http://dx.doi.org/10.1080/01944360802508726); at http://tinyurl.com/osw9r4m.

 

Antonio M. Bento, et al. (2003), The Impact of Urban Spatial Structure on Travel Demand in the United States, World Bank Group Working Paper 2007, World Bank (http://econ.worldbank.org/files/24989_wps3007.pdf).

 

Robert Burchell, et al (1998), The Costs of Sprawl – Revisited, TCRP Report 39, Transportation Research Board (www.trb.org). This report includes a detailed review of literature on the effects of land use patterns on personal travel behavior.

 

Robert W. Burchell and Sahan Mukherji (2003), “Conventional Development Versus Managed Growth: The Costs of Sprawl,” American Journal of Public Health, Vol. 93, No. 9 (www.ajph.org), Sept., pp. 1534-1540; at www.ncbi.nlm.nih.gov/pmc/articles/PMC1448006.

 

Ralph Buehler, et al. (2011), “Active Travel in Germany and the U.S.: Contributions of Daily Walking and Cycling to Physical Activity,” American Journal of Preventive Medicine, Vol. 41/ 3, pp. 241–250; abstract at www.ajpmonline.org/article/S0749-3797(11)00325-4/abstract.

 

Calthorpe Associates (2010), The Role of Land Use in Reducing VMT and GHG Emissions: A Critique of TRB Special Report 298, Calthorpe Associates (www.calthorpe.com); at www.calthorpe.com/files/TRB-NAS%20Report%20298%20Critique_0.pdf.

Cambridge Systematics (1994), The Effects of Land Use and Travel Demand Management Strategies on Commuting Behavior, Travel Model Improvement Program, USDOT (www.bts.gov/tmip).

 

Julie Campoli and Alex MacLean (2002), Visualizing Density: A Catalog Illustrating the Density of Residential Neighborhoods, Lincoln Institute of Land Policy (www.lincolninst.edu).

 

Xinyu Cao, Susan L. Handy and Patricia L. Mokhtarian (2006), “The Influences Of The Built Environment And Residential Self-Selection On Pedestrian Behavior,” Transportation (www.springerlink.com), Vol. 33, No. 1, pp. 1 – 20.

 

Xinyu Cao, Patricia L. Mokhtarian and Susan L. Handy (2008), Examining The Impacts of Residential Self-Selection on Travel Behavior: Methodologies and Empirical Findings, Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-08-25; at http://pubs.its.ucdavis.edu/publication_detail.php?id=1194; also Report CTS 08-24, Center for Transportation Studies, University of Minnesota (www.cts.umn.edu); at www.cts.umn.edu/Publications/ResearchReports/reportdetail.html?id=1684.

 

CARB (2010-2015), Research on Impacts of Transportation and Land Use-Related Policies, California Air Resources Board (http://arb.ca.gov); at http://arb.ca.gov/cc/sb375/policies/policies.htm.

 

Robert Cervero and Carolyn Radisch (1995), Travel Choices in Pedestrian Versus Automobile Oriented Neighborhoods, UC Transportation Center, UCTC 281 (www.uctc.net).

 

Robert Cervero, et al (2004), Transit-Oriented Development in the United States: Experience, Challenges, and Prospects, Transit Cooperative Research Program, Transportation Research Board (http://gulliver.trb.org/publications/tcrp/tcrp_rpt_102.pdf).

 

Robert Cervero and Kara Kockelman (1997), “Travel Demand and the 3Ds: Density, Diversity, and Design,” Transportation Research D, Vol. 2, No. 3, Sept. 1997, pp. 199-219.

 

Community Impact Assessment Website (www.ciatrans.net), sponsored by the U.S. Federal Highway Administration, provides information on methods for evaluating the impacts of transportation projects and programs on communities.

 

Randall Crane and Daniel G. Chatman (2003), “Traffic and Sprawl: Evidence from U.S. Commuting, 1985 To 1997,” Planning and Markets, Volume 6, Issue 1 (www-pam.usc.edu), Sept. 2003.

 

James M. Daisa and Terry Parker (2010), “Trip Generation Rates for Urban Infill Uses In California,” ITE Journal (www.ite.org), Vol. 79, No. 6, June 2010, pp. 30-39.

 

DKS Associates (2007), Assessment of Local Models and Tools For Analyzing Smart Growth Strategies, California Department of Transportation (www.dot.ca.gov); at www.dot.ca.gov/newtech/researchreports/reports/2007/local_models_tools.pdf.

 

Jennifer Dill (2004), Travel Behavior and Attitudes: New Urbanist Vs. Traditional Suburban Neighborhoods, School of Urban Studies and Planning, Portland State University (http://web.pdx.edu/~jdill). 

 

Jennifer Dill (2005), Measuring Network Connectivity for Bicycling and Walking, School of Urban Studies and Planning, Portland State University (http://web.pdx.edu/~jdill/ALRbikes). 

 

John E. Evans and Richard H. Pratt (2007), Transit Oriented Development; Chapter 17, Travel Response To Transportation System Changes, TCRP Report 95, Transportation Research Board (www.trb.org); at www.trb.org/TRBNet/ProjectDisplay.asp?ProjectID=1034.

 

Reid Ewing (1996), Best Development Practices, Planners Press (Chicago; www.planning.org); at www.epa.gov/dced/pdf/bestdevprimer.pdf.

 

Reid Ewing and Robert Cervero (2002), “Travel and the Built Environment – Synthesis,” Transportation Research Record 1780, TRB (www.trb.org).

 

Reid Ewing, Rolf Pendall and Don Chen (2002), Measuring Sprawl and Its Impacts, Smart Growth America (www.smartgrowthamerica.org).

 

Reid Ewing, et al (2003), “Relationship Between Urban Sprawl and Physical Activity, Obesity, and Morbidity,” American Journal of Health Promotion, Vol. 18, No. 1 (www.healthpromotionjournal.com), Sept/Oct. 2003, pp. 47-57, at www.smartgrowth.umd.edu/pdf/JournalArticle.pdf.

 

Reid Ewing, Christopher V. Forinash, and William Schroeer (2005), “Neighborhood Schools and Sidewalk Connections: What are the Impacts on Travel Mode Choice and Vehicle Emissions,” TR News, 237, Transportation Research Board (www.trb.org), March-April, 2005, pp. 4-10.

 

Reid Ewing, Keith Bartholomew, Steve Winkelman, Jerry Walters and Don Chen (2007), Growing Cooler: The Evidence on Urban Development and Climate Change, Urban Land Institute and Smart Growth America (www.smartgrowthamerica.org/gcindex.html).

 

Reid Ewing and Robert Cervero (2010), “Travel and the Built Environment: A Meta-Analysis,” Journal of the American Planning Association, Vol. 76, No. 3, Summer, pp. 265-294; at http://pdfserve.informaworld.com/287357__922131982.pdf.

 

Reid Ewing and Shima Hamidi (2014), Measuring Urban Sprawl and Validating Sprawl Measures, Metropolitan Research Center at the University of Utah for the National Cancer Institute, the Brookings Institution and Smart Growth America (www.smartgrowthamerica.org); at www.arch.utah.edu/cgi-bin/wordpress-metroresearch.

 

Lawrence Frank and Gary Pivo (1995), “Impacts of Mixed Use and Density on Utilization of Three Modes of Travel: SOV, Transit and Walking,” Transportation Research Record 1466, TRB (www.trb.org), pp. 44-55.

 

John Gard (2007), “Innovative Intermodal Solutions for Urban Transportation Paper Award: Quantifying Transit-Oriented Development's Ability To Change Travel Behavior,” ITE Journal (www.ite.org), Vol. 77, No. 11, November, pp. 42-46.

 

Jessica Y. Guo and Sasanka Gandavarapu (2010), “An Economic Evaluation Of Health-Promotive Built Environment Changes,” Preventive Medicine, Vol. 50, Supplement 1, January 2010, pp. S44-S49; at www.activelivingresearch.org/resourcesearch/journalspecialissues.

 

Susan Handy (1996), “Urban Form and Pedestrian Choices: Study of Austin Neighborhoods,” Transportation Research Record 1552, TRB (www.trb.org), pp. 135-144.

 

Susan Handy, Robert G. Paterson and Kent Butler (2004), Planning for Street Connectivity: Getting From Here to There, Planning Advisory Service Report 515, American Planning Association (www.planning.org).

 

Susan Handy and Patricia L. Mokhtarian (2005), “Which Comes First: The Neighborhood Or The Walking?,” ACCESS 26, University of California Transportation Center (www.uctc.net), Spring 2005, pp. 16-21.

 

Susan Hanson (1995), The Geography of Urban Transportation, Guilford Press (www.guilford.com).

 

John Holtzclaw (1994), Using Residential Patterns and Transit to Decrease Auto Dependence and Costs, National Resources Defense Council www.nrdc.org, funded by the California Home Energy Efficiency Rating Systems.

 

John Holtzclaw, Robert Clear, Hank Dittmar, David Goldstein and Peter Haas (2002), “Location Efficiency: Neighborhood and Socio-Economic Characteristics Determine Auto Ownership and Use?” Transportation Planning and Technology, Vol. 25, (www.tandf.co.uk/journals/online/0308-1060.html), March 2002, pp. 1-27.

 

Doug Hunt and Alan Brownlee (2001), Influences on the Quantity of Auto Use, Transportation Research Board Annual Meeting, Paper 01-3367 TRB (www.trb.org); at www.ucalgary.ca/~jabraham/Papers/qat4trbhunt/odyframe.htm.

 

ITE Smart Growth Task Force (2003), Smart Growth Transportation Guidelines, Institute of Transportation Engineers (www.ite.org).

 

Evan Jones (2003), “Walkable Towns: The Livable Neighborhood Strategy,” Sustainable Transport: Planning for Walking and Cycling In Urban Environments (Rodney Tolley Ed.), Woodhead Publishing (www.woodhead-publishing.com), pp. 314-325.

 

Eric Damian Kelly (1994), “The Transportation Land-Use Link,” Journal of Planning Literature, Vol. 9, No. 2, November 1994, p. 128-145.

 

Jeffrey R. Kenworthy and Felix B. Laube  (1999), An International Sourcebook of Automobile Dependence in Cities, 1960-1990, University Press of Colorado (www.upcolorado.com).

 

Asad J. Khattak and Daniel Rodriguez (2005), “Travel Behavior in Neo-Traditional Neighborhood Developments: A Case Study In USA,” Transportation Research A, Vol. 39, No. 6 (www.elsevier.com/locate/tra), July 2005, pp. 481-500.

 

Walter Kulash, Joe Anglin and David Marks (1990), “Traditional Neighborhood Development: Will the Traffic Work?” Development 21, July/August 1990, pp. 21-24.

 

Richard J. Kuzmyak and Richard H. Pratt (2003), Land Use and Site Design: Traveler Response to Transport System Changes, Chapter 15, Transit Cooperative Research Program Report 95, Transportation Research Board (www.trb.org).

 

Lawrence Frank and Company, Inc., Mark Bradley and Keith Lawton Associates (2005), Travel Behavior, Emissions, & Land Use Correlation Analysis In The Central Puget Sound, Washington State Transportation Commission, Department of Transportation, in cooperation with the U.S. Department of Transportation and the Federal Highway Administration; at www.wsdot.wa.gov/Research/Reports/600/625.1.htm.

 

Lawrence D. Frank, et al. (2010), “Carbonless Footprints: Promoting Health and Climate Stabilization through Active Transportation,” Preventive Medicine, Vol. 50, Supplement 1, pp. S99-S105; at www.activelivingresearch.org/resourcesearch/journalspecialissues.

 

Lawrence Frank, Andrew Devlin, Shana Johnstone and Josh van Loon (2010), Neighbourhood Design, Travel, and Health in Metro Vancouver: Using a Walkability Index, Active Transportation Collaboratory, UBC (www.act-trans.ubc.ca); at www.act-trans.ubc.ca/documents/WalkReport_ExecSum_Oct2010_HighRes.pdf.

 

Keith T. Lawton (2001), The Urban Structure and Personal Travel: an Analysis of Portland, Oregon Data and Some National and International Data, E-Vision 2000 Conference (www.rand.org/scitech/stpi/Evision/Supplement/lawton.pdf).

 

David Levinson and Ajay Kumar (1997), “Density and the Journey to Work,” Growth and Change, Vol. 28, No. 2, pp. 147-72 (www.ce.umn.edu/~levinson/papers-pdf/doc-density.pdf).

 

Chris Lightfoot and Tom Steinberg (2006), Travel-time Maps and their Uses, My Society, funded by UK Department of Transport (www.mysociety.org/2006/travel-time-maps/index.php).

 

Todd Litman (1995), Evaluating Transportation Land Use Impacts, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/landuse.pdf.; originally published in World Transport Policy & Practice, Vol. 1, No. 4, pp. 9-16 (www.eco-logica.co.uk/worldtransport.html).

 

Todd Litman (2003), Evaluating Criticism of Smart Growth, VTPI (www.vtpi.org); at www.vtpi.org/sgcritics.pdf.

 

Todd Litman (2004), Pavement Busters Guide, VTPI (www.vtpi.org); at www.vtpi.org/pavbust.pdf.

 

Todd Litman (2005), Evaluating Public Transit Benefits and Costs, VTPI (www.vtpi.org); at www.vtpi.org/tranben.pdf.

 

Todd Litman (2005b), Understanding Smart Growth Savings: What We Know About Public Infrastructure and Service Cost Savings, And How They are Misrepresented By Critics, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/sg_save.pdf.

 

Todd Litman (2006), Smart Growth Policy Reforms, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/smart_growth_reforms.pdf.  

 

Todd Litman (2007), Land Use Impacts on Transport: How Land Use Factors Affect Travel Behavior, VTPI (www.vtpi.org); at www.vtpi.org/landtravel.pdf.

 

Todd Litman (2008), Evaluating Accessibility for Transportation Planning, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/access.pdf .

 

Todd Litman (2009), Where We Want To Be: Household Location Preferences And Their Implications For Smart Growth, VTPI (www.vtpi.org); at

www.vtpi.org/sgcp.pdf.

 

Todd Litman (2011), “Why and How to Reduce the Amount of Land Paved for Roads and Parking Facilities,” Environmental Practice, Vol. 13, No. 1, March, pp. 38-46 (http://journals.cambridge.org/action/displayJournal?jid=ENP); at www.vtpi.org/EP_Pav.pdf.

 

Todd Litman (2011b), “Can Smart Growth Policies Conserve Energy and Reduce Emissions?” Portland State University’s Center for Real Estate Quarterly  (www.pdx.edu/realestate/research_quarterly.html), Vol. 5, No. 2, Spring, pp. 21-30; at www.vtpi.org/REQJ.pdf.

 

Todd Litman (2011c), Critique of the National Association of Home Builders’ Research On Land Use Emission Reduction Impacts, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/NAHBcritique.pdf.

 

Todd Litman (2014), Analysis of Public Policies that Unintentionally Encourage and Subsidize Urban Sprawl, commissioned by LSE Cities (www.lsecities.net), for the Global Commission on the Economy and Climate (www.newclimateeconomy.net); at http://bit.ly/1EvGtIN.

 

Todd Litman (2015), Response to "Putting People First: An Alternative Perspective with an Evaluation of the NCE Cities 'Trillion Dollar' Report", Victoria Transport Policy Institute (www.vtpi.org); at http://www.vtpi.org/PPFR.pdf.

 

Feng Liu (2003), Quantifying Travel and Air Quality Benefits of Smart Growth in the State Implementation Plan, Transportation Research Board Annual Meeting, TRB (www.trb.org).

 

Roger L. Mackett and Belinda Brown (2011), Transport, Physical Activity and Health:  Present Knowledge and the Way Ahead, Centre for Transport Studies, University College London   (www.ucl.ac.uk/news/pdf/transportactivityhealth.pdf).

 

Orit Mindali, Adi Raveh and Ilan Salomon (2004), “Urban Density and Energy Consumption: A New Look At Old Statistics,” Transportation Research A, Vol. 38, No. 2 (www.elsevier.com/locate/tra), Feb. 2004, pp. 143-162.

 

Terry Moore and Paul Thorsnes (1994), The Transportation/Land Use Connection, Planning Advisory Service Report 448/449, American Planning Association (www.planning.org).

 

Anne Vernez Moudon, et al (1996), Effects of Site Design on Pedestrian Travel in Mixed Use, Medium-Density Environments, Washington State Transportation Center, Document WA-RD 432.1, (www.wsdot.wa.gov/ppsc/research/onepages/WA-RD4321.htm).

 

Nelson\Nygaard (2005), Crediting Low-Traffic Developments: Adjusting Site-Level Vehicle Trip Generation Using URBEMIS, Urban Emissions Model, California Air Districts  (www.urbemis.com).

 

Peter Newman and Jeffrey Kenworthy (1998), Sustainability and Cities; Overcoming Automobile Dependency, Island Press (www.islandpress.org).

 

NPTS (1995), National Personal Transportation Survey, USDOT (www-cta.ornl.gov/cgi/npts).

 

ODOT, Land Use and Transportation Modelling Program, Oregon DOT (www.odot.state.or.us/tdb/planning/modeling/modeling.html).

 

OEP (2012), Evaluating the Fiscal Impacts of Development, Part I - Final Report and User’s Manual, New Hampshire Office of Energy and Planning (www.nh.gov/oep); at www.costofsprawl.org/Evaluating-Fiscal-Impacts-of-Development-Part-I.pdf.

 

OTREC (2009), Co-Evolution of Transportation and Land Use: Modeling Historical Dependencies in Land Use and Transportation Decision Making, OTREC-RR-09-08, Oregon Transportation Research and Education Consortium (www.otrec.us); at www.otrec.us/project/68.

 

Portland (1995), Parking Ratio Rule Checklist; Self-Enforcing Strategies, City of Portland.

 

PBQD (1993), The Pedestrian Environment, 1000 Friends of Oregon (www.friends.org).

 

PBQD (1994), Building Orientation; Supplement to The Pedestrian Environment, 1000 Friends of Oregon (www.friends.org).

 

PBQD (1996), An Evaluation of the Relationships Between Transit and Urban Form, Transit Cooperative Research Program, National Academy of Science  (www.trb.org), 1996; at www4.nationalacademies.org/trb/crp.nsf/All+Projects/TCRP+H-01.

 

Arlene S. Rosenbaum and Brett E. Koenig (1997), Evaluation of Modeling Tools for Assessing Land Use Policies and Strategies, Office of Mobile Sources, USEPA (www.epa.gov/oms/transp/publicat/pub_sust.htm).

 

Adel W. Sadek, et al. (2011), Reducing Vehicle Miles Traveled through Smart Land-use Design, New York State Energy Research And Development Authority And New York Department Of Transportation (www.dot.ny.gov); at www.dot.ny.gov/divisions/engineering/technical-services/trans-r-and-d-repository/C-08-29%20Final%20Report_December%202011%20%282%29.pdf.

 

Marc Schlossberg, Nathaniel Brown, Earl G. Bossard and David Roemer (2004), Using Spatial Indicators for Pre- and Post-Development Analysis of TOD Areas: A Case Study of Portland and the Silicon Valley, Mineta Transportation Institute (www.transweb.sjsu.edu/publications/schlossberg/SchlossbergBook.pdf).

 

Robert J. Schneider, Susan L. Handy and Kevan Shafizadeh (2014), “Trip Generation for Smart Growth Projects,” ACCESS 45, pp. 10-15; at http://tinyurl.com/oye8aqj. Also see the Smart Growth Trip-Generation Adjustment Tool (http://ultrans.its.ucdavis.edu/projects/smart-growth-trip-generation).

 

SFLCV (2003), This View of Density Calculator, San Francisco League of Conservation Voters (www.sflcv.org/density). This website illustrates various land use patterns, predicts their effects on travel behavior, and discusses various issues related to New Urbanist development.

 

Donald Shoup (1997), “The High Cost of Free Parking,” Access No. 10 (www.uctc.net), Spring 1997.

 

Smart Growth E-Learning Portal (www.moodleserv.com/smartgrowthca), is an educational program describing various smart growth concepts and implementation strategies, developed by the Smart Growth Canada Network, sponsored by Natural Resources Canada.

 

SmartGAP Software (www.trb.org/main/blurbs/168761.aspx), is an analysis tool for predicting how smart growth scenarios will affect travel activity.

 

Sprawl and Health (http://cascadiascorecard.typepad.com/sprawl_and_health) is an ongoing literature review by researchers at Northwest Environment Watch on the intersection of sprawl and health.

 

Ray Tomalty and Murtaza Haider (2009), Walkability and Health; BC Sprawl Report 2009, Smart Growth BC (www.smartgrowth.bc.ca); at www.smartgrowth.bc.ca/Portals/0/Downloads/sgbc-sprawlreport-2009.pdf.

 

TransForm (2014), Why Creating and Preserving Affordable Homes Near Transit is a Highly Effective Climate Protection Strategy, TransForm (www.TransFormCA.org) and the California Housing Partnership Corporation (www.chpc.net); at http://tinyurl.com/pnf7u86.

 

TRANSPLUS Website (www.transplus.net), provides information on research on transport planning, land use and sustainability, sponsored by the European Commission.

 

Transport Geography Research Group Website (www.abdn.ac.uk/tgrg) promotes the dissemination of information on transport geography among academics and practioners.

 

TRB (2009), Driving and the Built Environment: The Effects of Compact Development on Motorized Travel, Energy Use, and CO2 Emissions, Special Report 298, Transportation Research Board (www.trb.org); at http://onlinepubs.trb.org/Onlinepubs/sr/sr298prepub.pdf.

 

TRL (2004), The Demand for Public Transit: A Practical Guide, Transportation Research Laboratory, Report TRL 593 (www.trl.co.uk). This 240-page document is a detailed analysis of factors that affect transit demand, including demographic and geographic factors.

 

Martin Turcotte (2008), “Dependence on Cars in Urban Neighbourhoods: Life in Metropolitan Areas,” Canadian Social Trends, Statistics Canada (www.statcan.ca); at www.statcan.ca/english/freepub/11-008-XIE/2008001/article/10503-en.htm.

 

ULI (2010), Land Use and Driving: The Role Compact Development Can Play in Reducing Greenhouse Gas Emissions, Urban Land Institute (www.uli.org); at www.uli.org/ResearchAndPublications/PolicyPracticePriorityAreas/Infrastructure.aspx.

 

USEPA (1997), Evaluation of Modeling Tools for Assessing Land Use Policies and Strategies, USEPA, (www.epa.gov); at www.epa.gov/otaq/stateresources/policy/transp/landuse/lum-rpt.pdf.  

 

USEPA (2001), Our Built and Natural Environments: A Technical Review of the Interactions Between Land Use, Transportation and Environmental Quality, US Environmental Protection Agency (www.epa.gov); at www.smartgrowth.org/library/built.html.

 

USEPA (2001), Improving Air Quality Through Land Use Activities - EPA Guidance, EPA420-R-01-001, Office of Transportation and Air Quality, USEPA (www.epa.gov); at www.epa.gov/OMS/stateresources/policy/transp/landuse/r01001.pdf.

 

USEPA (2002), Smart Growth Index (SGI) Model, U.S. Environmental Protection Agency (www.epa.gov); at www.epa.gov/livablecommunities/topics/sg_index.htm. For technical information see, Criterion (2002), Smart Growth Index Indicator Dictionary, U.S. Environmental Protection Agency (www.epa.gov/smartgrowth/pdf/4_Indicator_Dictionary_026.pdf).

 

USEPA (2007), Measuring the Air Quality and Transportation Impacts of Infill Development, USEPA (www.epa.gov); at www.epa.gov/dced/pdf/transp_impacts_infill.pdf.

 

Anne Vernez Moudon and Orion Stewart (2013), Tools for Estimating VMT Reductions from Built Environment Changes, WA-RD 806.3, Washington State Department of Transportation (www.wsdot.wa.gov); at www.wsdot.wa.gov/research/reports/fullreports/806.3.pdf.

 

Jerry Weitz (2003), Jobs-Housing Balance, PAS 516, American Planning Advisory Service, American Planning Association (www.planning.org).

 

WSP–Parsons Brinckerhoff, et al. (2016), Linking Transit Agencies and Land Use Decision Making Guidebook for Transit Agencies, Report 182, Transportation Research Cooperative Program, TRB (www.trb.org); at www.trb.org/main/blurbs/173473.aspx. 

This Encyclopedia is produced by the Victoria Transport Policy Institute to help improve understanding of Transportation Demand Management. It is an ongoing project. Please send us your comments and suggestions for improvement.

 

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