Measuring the Air Quality and Transportation 

Impacts of Infill Development 
United States 

Environmental Protection Agency 

(1807-T) 

Washington, DC 20460 

Official Business 

Penalty for Private Use $300 

EPA 231-R-07-001 

November 2007 

www.epa.gov/smartgrowth 
Acknowledgements 

The U.S. Environmental Protection Agency (EPA) initiated this project to quantify the air quality emissions 

impacts from regional brownfield and infill development policies. Industrial Economics, Inc., in association with 

Cambridge Systematics, Inc., was selected by EPA to perform this work in collaboration with the Metropolitan 

Area Planning Council (Boston), Denver Regional Council of Governments, and the City of Charlotte. The report 

was reviewed by a panel of transportation planners: Ken Cervenka, North Central Texas Council of 

Governments; Gordon Garry, Sacramento Council of Governments; and Scott Lane, The Louis Berger Group. 

To request additional copies of this report, contact EPA’s National Center for Environmental Publications at 800-

490-9198 or by email at nscep@bps-lmit.com and ask for publication number EPA 231-R-07-001. 

Front Cover Photos: 

Jefferson North End, Dallas, Texas. This 540 unit residential complex, opened in 1998, was built on a site that had 

been vacant for 20 years. (Photo Courtesy of US EPA, Office of Brownfields and Land Revitalization) 

Back Cover Photos: 

Southside Neighborhood, Greensboro, North Carolina. This mixed use redevelopment is within a five to ten 

minute walk from the central business district. It includes 30 single-family homes, 10 two-family homes, 50 

townhouses, 10 restored historic homes, and 20 live/work units. (Photo Courtesy of City of Greensboro, 

Department of Housing and Community Development) 
i

Measuring the Air Quality and Transportation Impacts of 

Infill Development
ii

Executive Summary 

Infill Development as a Key Transportation Management Strategy 

Many regions are struggling to balance transportation needs with community 

revitalization and environmental protection. The potential for infill development to 

support all three goals is what sets it apart as a unique strategy. While the positive impact 

of redevelopment projects may be readily apparent at the community level, their regional 

transportation and air quality benefits can be harder to quantify. 

Fundamentally, well designed neighborhoods in more accessible places make walking, 

biking and transit more convenient options. Therefore, policies that increase the amount 

of urban and suburban infill development can help more people meet their everyday 

needs with less driving. In turn, this can reduce traffic and contribute to better regional 

air quality. 

The complicated nature of redevelopment often requires the public sector to act as a 

catalyst by providing financial subsidies, assembling land or upgrading infrastructure. 

The direct economic benefits justify many public investments, but more substantial 

commitments could be supported if the indirect transportation and air quality benefits 

could also be quantified. 

This study illustrates how regions can calculate these benefits. The basic approach relies 

upon standard transportation forecasting models currently used by Metropolitan 

Planning Organizations across the country. The results suggest that strong support for 

infill development can be one of the most effective transportation and emission reduction 

investments regions can pursue. 

The Report’s Purpose: Demonstration of Methods 

Although less vehicle travel and fewer emissions are reasonable outcomes to expect from 

infill development, quantifying such benefits has proved challenging. In most cases, the 

forecasting models used for regional transportation planning are not set up to capture the 

effect of innovative land use strategies. Therefore, they typically do not capture the 

changes in vehicle travel generated by increasing development in walkable communities 

with convenient access to transit. Quantifying benefits is also complicated by the need to 

establish baseline development trends. In other words, measuring the net benefit of a set 

of infill projects requires establishing where development would have otherwise gone. 

This report summarizes three case studies, each testing slightly different approaches to 

these analytical problems within traditional four-step travel-demand models. The 

analysis shows how standard forecasting tools can be modified to capture at least some of 

the transportation and air quality benefits of brownfield and infill development. 
iii

Some of the key modifications include: 

• Indices to reflect how mixed use development changes travel patterns 

• Adjustments to account for shifts to non-motorized travel 

• Smaller analysis zones in the models to capture the impact of neighborhood land 

use patterns 

• Emissions estimates based on the number of car trips as well as by distance 

traveled 

Key Findings 

Across the three case studies, redirecting jobs and households to brownfield and other 

infill sites reduces overall travel, congestion and emissions from cars. For example, if just 

8 percent of Denver’s jobs and households were shifted over time toward 10 regional 

centers, congestion would be reduced by over 6 percent and emissions would be reduced 

by about 4 percent. This would be equivalent to removing nearly half a million trips per 

day from the region’s roads, a significant share of the daily average (12.7 million miles). If 

the same amount of development was concentrated in 31 locations, the reduction in 

emissions would be somewhat smaller (3 percent). 

The Charlotte case study evaluated the impact of increased infill development in a single 

corridor. Although, a much smaller number of jobs and homes were relocated to infill 

sites, the analysis demonstrates the benefits of focused development around transit. 

While the new rail service alone did reduce congestion in the corridor, it had a minimal 

impact on the region’s emissions. However, when 16,000 households and 10,000 jobs are 

relocated near the South Corridor stations, the reduction in emissions was 10 times greater 

and transit ridership increased by more than 6,000 trips each day. 

In Boston, the analysis considered redevelopment in just 13 suburban towns along the I495 Corridor. Redirecting new development to brownfield sites in these towns reduced 

vehicle travel by 154,000 miles during the evening rush hour. Given the corridor’s 

average car trip of 15 miles, this reduction is equivalent to eliminating more than 10,000 

trips. In road capacity terms, this close to the additional trips accommodated by specific 

road-widening projects proposed for these towns. These projects are expected to cost $5 

to $17 million each. (Boston Metropolitan Planning Organization 2006) 

Compared with other policies adopted to meet regional air quality goals, these reductions 

are both significant and cost effective. For example, the reductions generated by projects 

funded under the Congestion Mitigation and Air Quality (CMAQ) program typically cost 

around $20,000 per year for each ton of pollution they eliminate. (FHWA 2006, Appendix 

4) With such a benchmark, Denver could spend roughly $17 million to facilitate the kind 

of infill development evaluated in this study and still be more cost effective.1

 

 1

 Based on the assumption that land use related reductions are maintained over a 30-year period, 

the rough lifetime of many commercial properties. Residential development tends to have a much 

longer lifespan and would likely produce VMT reduction benefits over a much longer period. 

However, a more conservative assumption was made for a basic cost-effectiveness illustration. 
iv

Intended Audience and Structure of the Report 

The report is intended for both land use and transportation planners, to act as a bridge 

between innovative redevelopment policy and transportation modeling. The first part of 

the report outlines the issues, methods, and findings of each case study in general terms. 

The goal is to help non-technical readers understand how the benefits of infill 

development might be measured using traditional transportation planning tools. 

Technical appendices cover the details of each regional analysis at the level of detail 

needed to design and launch a comparable study. However, as noted by the peer 

reviewers, the complexity of travel demand models and their unique evolution in each 

region mean that even the technical appendices are not exact instructions for replicating 

the analysis. Rather, they illustrate methodologies that must be customized to fit each 

region. On the other hand, they do describe specific concepts and techniques that can 

serve as a starting point for modifying most regional travel demand models. 
v

Table of Contents 

Executive Summary ii 

1.0 Introduction 1 

1.1 Brief Overview of the Case Studies 2 

1.1.1 Boston 2 

1.1.2 Denver 5 

1.1.3 Charlotte 7 

1.2 Key Findings 11 

2.0 Study Design: Lessons Learned 13 

 2.1 Limitations of Traditional Travel Demand Models 13 

 2.1.1 Aggregate nature of models 14 

 2.1.2 Non-motorized travel 14 

 2.1.3 Trip chaining and tour based modeling 14 

 2.2 Emissions Analysis Issues 15 

 2.3 Evaluation Measures 15 

 2.3.1 Measures of the Amount of Travel 15 

 2.3.2 Measures of Transportation Level of Service 16 

 2.3.3 Measures of Environmental Impacts 17 

3.0 Comparison of Results 17 

 3.1 Land Use and Vehicle Miles Traveled 17 

 3.2 Congestion and Speed 18 

 3.3 Emissions 18 

 3.4 Effects of Increased Development 19 

 3.5 Conclusions and Implications 22 

 3.5.1 Reductions in Vehicle Travel and Emissions 22 

 3.5.2 Use and Limitations of Travel Demand Models 22 

 3.5.3 Relative Contribution of Land Use Strategies 23 

 3.5.4 Future Research Needs 24 

Detailed Technical Appendices 

Appendix A – Boston 

 Background A-1 

 Detailed Results A-2 

 Transportation Analysis Methods A-4 

 Intrazonal VMT Adjustment A-5 

 Emissions Analysis Methods A-7 

Appendix B – Charlotte 

 Background B-1 

 Detailed Results B-2 

 Transportation Analysis Methods B-6 

 Emissions Analysis Methods B-9 
vi

Appendix C – Denver 

Background C-1 

 Detailed Results C-5 

 Transportation Analysis Methods C-6 

 Intrazonal Trip Changes C-7 

 Development and Calculation of Mixed Land Use Indices C-7 

 Calculation of Zonal MUIs for Year 2001 and Year 2025 C-11 

 Calculation of Intrazonal Trips C-15 

 Intrazonal Trip Adjustment Models C-15 

 Revision of Mixed Land Use Indices C-20 

 Model Results Discussion and Recommendations C-20 

 Trip Generation Revision C-20 

 Pedestrian or Bicycle Friendly Zones in Boulder County C-20 

 Trip Adjustment Factor Calculation C-21 

 Emissions Analysis Methods C-22 

Appendix D – Benefits of Using MOBILE6 D-1 
1

1.0 Introduction

Local governments across the country are paying more attention to infill development. Former 

industrial sites, declining suburban malls, vacant properties and other underutilized land all 

provide opportunities for redevelopment. Projects developed on such sites are often pursued 

for their economic development benefits. However, redeveloping underutilized land in cities 

and suburbs also has the potential to reduce vehicle travel and contribute to better air quality. 

In fact, the studies summarized in this report suggest that actively supporting infill 

development can be a highly effective regional transportation policy. If done well, 

redevelopment creates neighborhoods where residents can accomplish their daily activities 

with less driving. Previous site level studies suggest that shifting development to more 

accessible locations reduces vehicle travel per person by 30 to 60%2

. 

At a regional level, a significant number of well designed infill projects can go a long way 

toward helping meet air quality goals. Specifically, such changes can make important 

contributions to the emission reduction targets contained in State Implementation Plans. These 

SIP plans are critical because they determine if future transportation investments conflict with 

regional air quality goals defined under the Clean Air Act. 

This study quantifies the air quality benefits of regional growth scenarios that increase 

development on brownfield and other infill sites. To achieve this objective, three Metropolitan 

Planning Organizations (MPOs) incorporated new analytical components into their existing 

travel demand forecasting tools. Their models were enhanced to reflect techniques described in 

EPA’s Comparing Methodologies to Assess Transportation and Air Quality Impacts of Brownfield and 

Infill Development report. Various publications from the U.S. Department of Transportation’s 

Travel Model Improvement Program (TMIP) also served as a key resource in the process. The 

goal was to develop techniques for estimating emission reductions that would be both 

transferable and acceptable within a State Implementation Plan (SIP), conformity determination, 

or ozone flex plan. 

However, achieving this objective required overcoming a few major obstacles. First and 

foremost, the analytical framework3

 at the heart of nearly all regional transportation planning 

models has difficulty capturing interactions between land use and transportation systems. 

Even when MPOs have incorporated land use feedbacks into their travel demand models, they 

tend to be regional in nature and fail to capture the key neighborhood level characteristics. It is 

these smaller scale land use patterns that often contribute most to the reduced driving expected 

from well designed infill projects. Other common limitations include: only examining workrelated travel, not considering walking as a mode of travel, and including very little detail on 

land use characteristics between “travel analysis zones.” 

 2

 Ewing, R. and R. Cervero. 2001. Travel and the Built Environment: A Synthesis. Transportation 

Research Record 1780, 87-114. 

U.S. EPA, 1999, The Transportation and Environmental Impacts of Infill Versus Greenfield 

Development: A Comparative Case Study Analysis, EPA publication number 231-R-99-005. 

3

 Nearly all regional travel demand models follow the “four step” process. Typically this is a set of 

connected models that estimate the following- (1) trip generation, (2) trip distribution, (3) mode 

choice and (4) traffic assignment. 
2

Staff from the three participating transportation agencies performed the comprehensive 

analyses. They included the metropolitan planning organizations in Boston and Denver and the 

City of Charlotte. Each worked with EPA to test whether a specific set of infill projects in the 

region could reduce emissions relative to current development trends. They used their existing 

travel demand models, along with EPA’s MOBILE6 emissions software, to measure the benefits 

of compact infill development scenarios. The analysis also produced transportation outcome 

measures such as person trips, average trip lengths, vehicle miles traveled, vehicle hours 

traveled, transit mode share, average speed, and congestion. In each case, the analysis found 

that increased brownfield and infill development would result in substantially lower emissions 

of hydrocarbons (VOCs), nitrogen oxides (NOx), and carbon monoxide (CO). 

The case studies examined projects at differing regional scales. In Boston, infill development 

was examined in a small portion of the metropolitan area. It tested how changing growth 

patterns in one corridor could improve the air quality outlook of the region as a whole. Denver 

examined the entire region to see how focusing development in a few large urban and suburban 

centers would compare to current development trends. Charlotte focused on the impact of infill 

development concentrated around a new light-rail transit line. 

1.1 Brief Overview of the Case Studies 

1.1.1 Boston 

This regional case study evaluated the impact of reorienting development patterns in 13 towns 

along the I-495 corridor, roughly 20 miles west of downtown Boston. The Metropolitan Area 

Planning Council (MAPC) and the Central Transportation Planning Staff (CTPS) ran two 

scenarios: 

1) Current development trends continued 

2) Concentrated development in town centers and interchanges along I-495. 

Both scenarios assumed the same amount of employment and household growth. However, 

under the Concentrated Growth scenario, about 18,000 households (14 percent of the corridor’s 

total) and 102,000 jobs (22 percent of the total) would be redirected over time into more 

accessible locations. The specific parcels to be redeveloped in the scenario were Ashland Center 

and Hopkinton Center, two large brownfield sites, and a number of smaller infill sites near the 

town centers and freeway interchanges in the other 11 towns. 

The MAPCs travel demand model found that more accessible development would produce 

significant transportation benefits. The analysis forecast less driving and traffic congestion as 

well as more trips made by transit or walking. Overall, people who live and work in the 

corridor would drive 239,000 fewer miles each day during the evening rush hour. These 

changes would result in significant air quality benefits for each town and the region as a whole. 

Compared to current development patterns, shifting growth to the more accessible sites would 

result in 5 to 8 percent lower emissions of VOVs, NOx and CO by (see Figure 1.1). 

The significance of these reductions is best understood when compared to typical transportation 

demand management and emission reduction policies. Effective regional strategies are 

generally comprised of many actions that together add up to a significant reduction of regional 
3

-10%

-5%

0%

5%

10%

15%

20%

25%

Households Jobs Vehicle

Miles

Congestion VOC CO NOx

Percent Change 

Share of 13 Town Total

Share of Regional Total

Shift of Development 

toward Infill Areas...

Results in the following changes in 

travel and emissions

emissions. It is unusual for any single action to reduce regional emission by more than 1 

percent, but 10 to 20 actions together can achieve the air quality targets established under the 

Clean Air Act. Similarly, individual demand management policies cannot solve traffic 

problems, but a comprehensive set of policies do have an impact. The reductions in driving and 

emissions that would result from putting more homes and businesses in the corridor’s town 

centers exceeds many of the demand management actions outlined in the MAPC’s Regional 

Transportation Plan. This is particularly significant in light of the more than 80 jurisdictions for 

which no land use changes were considered. 

Figure 1.1 –Reductions from Focused Redevelopment in the I-495 Corridor
4

Figure 1.2 Towns in the Boston Case Study
5

Specific modifications to the region’s travel demand model were required to examine the 

potential benefits. First, the model’s traffic analysis zones were redrawn to significantly 

increase the level of detail (from 54 zones to 137 zones). This change was critical to capturing 

the effect of moving development to town center locations. The previous zones often grouped 

these suburban centers and their surrounding town areas into the same zone. The staff also 

developed a mixed-use index indicator to better capture the travel demand impact of co-locating 

houses, jobs, shopping, and entertainment. This variable was intended to reflect how modest 

changes in the convenience of daily activities can substantially increase the share of trips made 

on foot or transit rather than by car. However, the changes in vehicle travel primarily reflect a 

shift toward shorter car trips rather than substantial increases in walking or transit use. This is 

not surprising given that the infill projects in the corridor were still surrounded by 

predominantly low density communities with limited transit services. 

1.1.2 Denver 

Analysts at the Denver Regional Council of Governments (DRCOG) examined three land use 

scenarios. The scenarios evaluated differences in driving, congestion, and emissions over a 30 

year timeframe. The first extended current development trends, with a majority of new homes 

and employment widely dispersed across suburban locations. The second focused 

development into 31 mixed-use centers. The third focused development into 10 higher-density 

mixed-use centers (Figure 1.3). It is important to note that both alternative development 

scenarios assumed substantial infill in suburban locations. Together these two alternative 

scenarios would shift about 7 percent of all households and 9 percent of all jobs into more 

compact areas. 

Total Employment

Figure 1.3
6

-8%

-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

Households Jobs Vehicle

Miles

Congestion VOC CO NOx

Percent Change in Regional Total

31 Regional Centers

10 Regional Centers

Shift of Development 

toward Infill Areas...

Results in the following changes in 

travel and emissions

When the travel demand model was run, they showed focusing development could 

substantially reduce travel at a regional scale. The 31-Center approach could reduce driving by 

more than 2.7 million miles per day, about 3 percent of the regional total. Focusing new growth 

into a smaller number of high-density centers – the 10-Center scenario - would reduce the daily 

vehicle travel in the region by 3.6 percent. In both cases, more focused development lowered 

congestion and raised average travel speeds compared to the trend scenario. Both scenarios 

would also increase the share of transit trips – by more than 11 percent in the 10-Center scenario. 

The changes in vehicle travel from the scenarios were also applied to a regional emissions 

model that estimated reductions in VOC, NOx, and CO emissions by 3 percent to 4 percent 

(Figure 1.4). 

Figure 1.4 –Reductions from Focused Redevelopment in Regional Mixed Use Centers
7

The Denver analysis also relied upon modifications to the existing regional travel demand 

model. First, better estimation of non-work trips was achieved by more accurately 

representing the attractors of such travel, such as retail centers and major institutions. Second, 

the original model limited mode choice to car or transit and did not incorporate walking to bike 

trips. Since a greater share of trips made on foot or by bike is a primary way that infill 

development reduces driving, this modification of the model was critical. Adjustments to the 

mode choice component of the travel demand model were tested in a portion of the DRCOG 

modeling area - Boulder County. Staff divided the county into three zones – from least to most 

friendly for bicycling or walking. Travel records for these areas were examined in detail. In 

Zone 1, people walked or bicycled for 40 percent of commuting trips and about 44 percent of 

errands run from home. At the other end of the spectrum, in Zone 3, just 4 percent of 

commuting trips and 10 percent of errand trips were via foot or bicycle (see Figure 1.5). Based 

on the relationship between key land use characteristics in these three zones and rates of 

walking and biking, the team calculated adjustment factors for the rest of the region’s zones. 

1.1.3 Charlotte 

A third case study was conducted by the city of Charlotte, which also maintains a regional 

travel demand model. While Denver and Boston each assumed the transportation 

infrastructure would essentially stay the same, Charlotte wanted to examine the transportation 

impacts of infill development in relation to their proposed light-rail system. The city conducted 

its analysis while the South Corridor was still in the planning stages. The project has since 

moved forward and the rail line is scheduled to open in the Fall of 2007. 

Impacts of Land Use on 

Non-Motorized Travel in Boulder, CO

0% 10% 20% 30% 40% 50%

zone 1

zone 2

zone 3

Percent trips via bike, walk

errands

commute

Figure 1.5 - Impact of Land Use on Non-Motorized Travel in Boulder, CO 
8

South LRT

Figure 1.6 - Charlotte Transit Corridors with Study Area 
9

 

Charlotte looked at four scenarios. The Baseline assumed no rail line and a continuation of 

current development trends. The Transit scenario assumed the light-rail line would be built, but 

with no change in current development patterns. The Transit-Station Infill scenario projected 

building light rail and concentrating more development around the 12 station areas in the South 

Corridor. Finally, the Transit-Station Infill-Road Improvement scenario also added improvements 

to the street network around the station areas. The two infill scenarios assumed that about 4 

percent of households in the region would eventually be shifted into the South Corridor, 

tripling the number of households in the areas surrounding the new light-rail stations. Under 

these scenarios, a much smaller share of regional employment would be redirected to station 

areas, but amount of business activity near the stations would still increase by about 50 percent. 

Evaluating four scenarios provided an opportunity to isolate the impact of each on vehicle 

travel, congestion and emissions in the region. The analysis separately measured the impact of: 

1) simply adding transit, 2) adding transit and changing land use patterns, and 3) making street 

improvements near the station areas to accommodate the new development. The model also 

measured the impact at three different geographic scales: the immediate vicinity of the stations, 

the entire South Corridor, and throughout Mecklenburg County. 

Organizing the scenarios in this manner highlighted important results. For example, adding the 

rail line without making land use changes would increase transit use in the corridor (1,000 trips 

per day), but only have modest impact on vehicle travel at the county level. However, when 

combined with infill development, transit ridership jumped by more than 7,000 trips per day 

and reduced overall travel in the county by 2 percent (Figure 1.7). On the other hand, moving a 

significant number of homes and jobs to the station areas did increase vehicle travel and 

congestion at the neighborhood level (10 to 15 percent). Therefore, the fourth scenario 

examined whether making road improvements in the station areas mitigated these impacts. In 

short, the road improvements further increased vehicle travel, but mitigated the localized 

congestion that accompanied the infill development. This produced slightly lower emissions 

relative to station area development without local road improvements. 

The changes in emissions followed a similar pattern. The biggest emission reductions came 

from supporting infill development around the rail stations. While the two infill scenarios 

slightly increased emissions within the corridor, they significantly reduced emissions at the 

regional level (See Figure 1.8). Since the precursor emissions responsible for ozone pollution 

(VOC and NOx) are generally not harmful until they combine in the atmosphere, the county 

emissions total is outcome measure with the most direct implications for public health. Further, 

making road improvements along with the station area development substantially offsets the 

local increase in congestion and emissions. 
10

Projected Emission and Travel Reductions from 

Charlotte South Corridor Alternatives

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

VMT (000’s) VOC (kg) CO (kg) NOx (kg)

Reduction Relative to Trend 

Trasnit Only

Station Area Redevelopment 

Station Redevelopment with

Road Improvements

-5.0%

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

Households Jobs Vehicle

Miles

Congestion VOC CO NOx

Percent Change in Regional Total

Mecklenberg Coounty

South Corridor

Shift of Development 

toward Infill Areas...

Results in the following changes in 

travel and emissions

Figure 1.7 Emission Reductions in Charlotte 

Figure 1.8 – More Travel and Emissions in Corridor Offset by Regional Benefits 
11

1.2 Key Findings 

The three case studies demonstrate - across a range of scenarios and regional contexts - that 

redirecting development to more walkable, transit accessible areas reduces driving and 

emissions. Shifting 5 to 10 percent of a region’s homes and jobs to infill locations was estimated 

to produce 2 to 5 percent less vehicle travel and a 3 to 8 percent reduction in emissions (Figure 

1.9). 

The majority of the reductions were due to shorter vehicle trips. While this is a key finding 

consistent with previous studies, it also reflects a weakness shared by the three analyses. In 

spite of modifications, the travel demand models still did not have the ability to examine how 

good site location and design might increase rates of walking and biking. For example, the 

Charlotte model was only able to consider personal vehicles and transit as travel modes. 

Although a mixed-use and mode choice analysis was conducted in Boston, it was not included 

in the final results. These are important analytical shortcomings since research4

 shows that 

community design significantly influences the amount of bicycling and walking. Therefore, the 

 

4

 Land Use and Site Design: Traveler Response to Transportation System Changes, TCRP Report 95 Chapter 15 

Previous Studies and Guidance - The Atlantic Station Project (Atlanta, GA) 

EPA first examined the potential air quality benefits of infill development for the 

proposed 135-acre Atlantic Station project in downtown Atlanta. A bridge was 

needed to connect the development with a nearby MARTA rail station. However, 

due to air quality constraints, the regional transportation agency could not add 

the bridge to its capital construction plan. Before the project could move forward, 

the transportation agency had to demonstrate the net gains in air quality it would 

produce. 

EPA’s analysis compared the emissions impacts of the new development to the 

same number of homes and jobs located on typical sites in suburban jurisdictions. 

The analysis found that developing the abandoned Atlantic Steel site would 

substantially reduce driving relative to alternative sites on previously 

undeveloped land. Shorter car trips alone were estimated to reduce overall 

vehicle travel by 14 to 50 percent. Additionally, if the new community were 

designed to encourage bicycling, walking, and transit, driving would be reduced 

another 5 percent. These changes were then coupled with a regional emissions 

model to quantify the reductions in key pollutants. As a result of this analysis, 

the development was designated a Transportation Control Measure in the 

region’s air quality plan. EPA has since issued the following guidance on 

methodologies communities can use to document the air quality benefits of infill 

development: 

Comparing Methodologies to Assess Transportation and Air Quality Impacts of 

Brownfield and Infill Development, EPA-231-R-01-001

Granting Air Quality Credit for Land Use Measures: 

Policy Options, EPA SR99-09-01
12

-8%

-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

Households Jobs Vehicle

Miles

Congestion VOC CO NOx

Percent Change in Regional Total

Denver (10 Regional Centers)

Charlotte (Single Rail Corridor)

Boston (13 Suburban Towns)

Shift of Development 

toward Infill Areas...

Results in the following changes in 

travel and emissions

VMT reductions and emissions benefits documented in this project are most likely 

underestimated. 

On the other hand, the project did demonstrate how regions can use existing tools to measure 

the relationship between redevelopment and air quality. The travel demand models remain 

limited in their ability to account for small scale changes to the built environment. However, 

modifications to the models can help mitigate some of these problems. Communities interested 

in using existing models should consider: 

• Smaller zone sizes for analysis in order to capture intrazonal trips at a finer land 

use scale 

• Adjusting mode choice models to capture for increased walking and cycling 

• Using indices to represent the degree of mixed use and its impact on travel patterns 

• Estimating emissions by number of trips as well as by distance traveled 

This report shows that directing new growth into reclaimed brownfield and infill sites can help 

meet their need for growth while addressing regional air quality issues. While still limited, 

existing transportation planning tools can help quantify the potential impact of these changes. 

This is an important step in allowing decision makers to choose the best course for the future of 

their region. 

 

 

 Figure 1.9 – The Effect on Travel and Emissions from Shifting Development to Infill Sites
13

2.0 Study Design: Lessons Learned 

While the MPOs in this study had a variety of objectives, they shared a common purpose - to 

better capture the benefits of brownfield and infill development. Each had sketched out plans 

for quantifying the impacts of alternative land use patterns. However, they differed in the scale 

and context for their analysis. 

• Boston - Examined a small part of the metropolitan area to test the impact of local 

redevelopment on the region as a whole 

• Denver – Modeled region-wide shifts in development to see how redirecting 

growth toward key regional mixed use centers compared with the current pattern 

of unfocused growth 

• Charlotte - Examined the impact of infill development around the station areas of a 

proposed light rail corridor 

They also made different assumptions about their regional transportation systems. Boston and 

Denver assumed no changes to the system, even though shifting land use patterns often create 

pressure for road capacity expansion. Based on these simplified assumptions, neither study 

was able to estimate the secondary effect of changes in transportation supply associated with 

alternative development patterns. In contrast, the Charlotte analyses examined the effects of 

changes in both the transit and street networks associated with the proposed land use scenarios. 

2.1 Limitations of Traditional Travel Demand Models 

All three of the partner communities use traditional four-step models, but have incorporated 

some features not typically found in other regions. For example, each considers the feedbacks 

between the transportation network and regional land use patterns. They also take some steps 

to consider the impact of land use patterns on travel behavior. Even with such modifications, 

the four-step approach still has some important limitations when evaluating the travel demand 

impacts of infill development. 

The follow sections discuss these specific limitations. However, before discussing the critiques 

in detail, it is important to first describe the basic components that make up a traditional travel 

demand forecasting models: 

1. Trip generation – The number of person trips by purpose generated in each traffic 

analysis zone. Estimated based on the amount of activity in each zone, defined by 

population, households, and employment. 

2. Trip distribution – The trips generated from each zone in step 1 are distributed to 

create person trips from origins to destinations. They are often segmented by travel 

purpose (e.g. work vs. non-work trips). 

3. Mode choice – The trip tables estimated in step 2 are split into different travel modes 

based on the characteristics of the trip (purpose, distance, etc.). 

4. Trip assignment – The vehicle and transit trips resulting from the mode choice model 

are loaded onto the highway and transit networks to produce volumes on roadways 

and transit ridership estimates. Walking and bicycle trips are seldom modeled 

directly. 
14

2.1.1 Aggregate nature of models 

Because the four-step process was conceived at a time when computers were much less 

powerful, it was necessary to simplify the way in which urban areas were modeled. One 

simplification was the aggregation of regions into traffic analysis zones (TAZ). With a few 

exceptions, all estimated travel is based on the average characteristics for each TAZ. Trips may 

begin or end at different points within a zone, but are tied to the same average land use and 

household characteristics. This introduces an aggregation error into the model. Over the years, 

as computing capabilities increased, models were able to have a much greater number of zones, 

diminishing—although not eliminating—the effect of aggregation error. Currently, a typical 

modeling region may consist of several hundred zones or several thousand zones in a larger 

metro area. 

The treatment of trips that stay within a single zone is one key issue where using TAZs as the 

basis for analysis becomes a critical limitation. Since intra-zonal trips cannot be assigned to a 

traditional model’s transportation network, the system is calibrated so travel within a TAZ 

matches observations in the baseline year. In scenarios where land use patterns become 

significantly more concentrated, this calibration will probably underestimate intrazonal travel. 

The necessary aggregation of trip end locations into zones also has implications for the type of 

analysis required for this project. Any concentrated development scenario must be represented 

by the same zone system as in the baseline scenario. Therefore, if new development were 

concentrated in zone near a transit station, the model results would still be based on the 

assumption that growth was spread evenly throughout the zone. However, in the Boston 

analysis, MAPC and CTPS recognized the constraints of the zone system used in their model 

and developed a more fine grain set of zones for the 13 towns. This reduced the aggregation 

error associated with the analysis. 

2.1.2 Non-motorized travel 

Most travel models in the U.S. do not consider travel by non-motorized modes such as walking 

and bicycling. The major outputs of models typically include traffic volume and speeds along 

major roads. Many also include ridership on transit systems. Non-motorized travel historically 

has played a much smaller role if considered at all. This is due in large part to the difficulty of 

obtaining empirical information on the amount of non-motorized travel. 

Recently there has been more interest in modeling non-motorized travel. This is particularly 

important for this analysis since concentrated infill development is likely to produce more 

frequent and shorter trips were walking or bicycling is more feasible. However, among the 

three models in this analysis, only Boston’s currently considers non-motorized trips. 

2.1.3 Trip chaining and tour based modeling 

One major criticism of the trip-based model is that many trips are actually part of a larger 

journey. Essentially, stops are treated as independent decisions rather than linked to the overall 

purpose of the tour. Additionally, while non-home based trips are generated by four step 

models, their frequency is based on household characteristics. Overall these structural features 

make it difficult to examine how more accessible land use patterns might reduce vehicle travel. 

Such changes may lead to more efficient trip-chaining by car or greater use of transit because of 

the ability to accomplish tasks on the way to or from the station. 
15

In recent years a few urban areas have developed tour based models, which explicitly model the 

formation of tours and can be responsive to the effects of land use patterns on trip chaining. 

However, none of three partner communities has a tour based model. 

2.2 Emissions Analysis Issues 

Two issues are particularly important to appropriately estimating emission impacts. As 

discussed above, the degree to which travel demand analysis is sensitive to the characteristics 

that define good infill development is critical for emissions analysis. Additionally, how the 

emissions analysis takes advantage of the additional capabilities incorporated into MOBILE6 

model is important. While MOBILE65

 was used in all three of the analyses, there were 

differences in its application. The details are described under each case in the technical 

appendix. A section specifically addressing the benefits of using MOBILE6 rather than 

MOBILE5 is also included. Staff from Cambridge Systematics and Industrial Economics 

worked with the participating organizations to describe potential approaches. In particular, 

they discussed how current standard procedures use in the region could be enhanced. The 

approaches described in the following subsections reflect the results of this technical assistance 

and coordination. 

2.3 Evaluation Measures 

The scenarios analyzed and the analytical procedures used by the three partner communities all 

involve the use of regional travel demand models and EPA’s MOBILE6 emissions estimation 

software. A variety of measures can be obtained from these procedures that are relevant to the 

estimation of the effects of brownfield development. These include measures of the amount of 

travel, the quality of transportation level of service, and the environmental impacts related to 

emissions. 

This section describes the evaluation measures used in each partner community. The general 

measures are the same for all three communities, but in some cases the agency was unable to 

provide some details. In all three cases the measures were computed for the whole region. 

Where the scenarios target specific subregions (in Boston and Charlotte), the measures are also 

presented for these subareas. 

2.3.1 Measures of the Amount of Travel 

Person trips – All of the scenarios examined in the three partner communities preserved the 

amount of development regionally; any growth moved to a specific area of concentration was 

“moved” or reallocated from somewhere else in the future trend scenario. However, under 

different development patterns, the total number of person trips could change. In addition, the 

amount of development and, therefore, the number of person trips in sub-areas may change. 

This occurred in Scenarios 3 and 4 in Charlotte, where some development was assumed to occur 

in station areas rather than outlying areas. 

 5

 The initially released version of MOBILE6, now referred to as MOBILE6.0, incorporated 

capabilities only for CO, VOC, and NOX emissions. EPA subsequently released draft MOBILE 

version 6.2 that incorporates particulate matter and air toxic emissions, and also a draft approach 

for estimating carbon dioxide, CO2, emissions. The original MOBILE6.0 CO, VOC, and NOX 

capabilities remain unchanged in draft MOBILE6.2, and so these results are identical with both 

versions. 
16

Average trip lengths – By definition, brownfields are located in areas that are already 

developed, and therefore developments in such areas will more likely be closer to potential 

travelers. Such locations include residences of employees, customers, and visitors to the 

developments. Comparing the average person trip lengths among alternative scenarios 

provides a means of quantifying the reduction in travel distance associated with developments 

that are located in such areas. 

Vehicle-miles traveled – The total number of vehicle-miles traveled (VMT) is a standard 

measure of the amount of automobile and commercial vehicle travel consumed. Previous work 

has shown that infill redevelopment could result in lower VMT on aggregate and per capita 

than for a similar amount of development located away from current populations. This is due 

not only to the shorter average trip lengths associated with such scenarios, but also to the fact 

that brownfield sites are also more likely to be located near public transportation, which means 

that travelers to the development are more likely to have choices for travel. It should be noted 

that even with regionally lower trip lengths and automobile mode shares, there still may be 

increases in VMT in subareas where there is a greater amount of development under 

brownfields development scenarios. 

Vehicle-hours traveled – The total number of vehicle-hours traveled (VHT) is another standard 

measure of the amount of vehicle travel. Brownfield/ infill redevelopment could result in 

lower VHT than for a similar amount of greenfields development. This is due not only to the 

lower VMT associated with such scenarios, as described above, but also potentially to the 

higher average speeds, which are discussed below. It should be noted that even if VMT 

declines and speeds increase in the region, there still may be increases in VHT in subareas 

where there is a greater amount of development under brownfields development scenarios.

Transit mode shares – The percentage of trips made by transit may change due to changes in 

land use patterns or changes in the transit or highway level of service. Comparing transit mode 

shares among alternative scenarios provides a way of measuring how well the scenario 

provides additional opportunities for persons to travel by means other than private auto. 

Walk mode shares – The percentage of trips made by walking (and bicycling) may change due 

to changes in land use patterns or changes in the transit or highway level of service. Comparing 

walk mode shares among alternative scenarios provides a way of measuring how well the 

scenario provides additional opportunities for persons to travel by means other than private 

auto. Travel by walking and bicycling is not considered, and therefore cannot be estimated, by 

the travel models in Charlotte and Denver. 

2.3.2 Measures of Transportation Level of Service 

Average speed – If brownfield/infill development reduces VMT, the subsequent reductions in 

congestion could increase in vehicle speeds. The average speeds for each scenario are 

compared, where available, by roadway functional classification. It should be noted that in subareas where VMT increases under brownfield/infill development scenarios, there could be 

associated decreases in speeds. For this project, average speeds for a region or sub-area were 

computed as the VMT divided by the VHT. 

Congestion – The amount of congestion is measured as the difference in the vehicle-hours 

traveled (VHT) under free-flow conditions and the VHT under congested conditions. One of 

the benefits of decreased VMT could be a reduction in levels of congestion. 
17

2.3.3 Measures of Environmental Impacts 

Emissions – The pollutants evaluated in each study are the three major categories computed by 

EPA’s MOBILE6 model. Travel speeds and the vehicle fleet in a particular region both impact 

the emissions per mile of travel. MOBILE6 takes inputs from travel demand models and 

produces regional emissions. Outputs are produced for three pollutants: 

• Volatile organic compounds (VOC); 

• Carbon monoxide (CO); and 

• Nitrogen oxides (NOx). 

3.0 Comparison of Results

This section compares the results from the three case studies in terms of changes in the amount 

of travel, congestion and speeds, and emissions. The localized effects of additional 

development in specific areas and the potential for mitigation are also examined. 

3.1 Land Use and Vehicle Miles Traveled 

Since all three case studies dealt with the effects of concentrating growth in specific areas, the 

effects of the land use changes on the amount of travel in the three studies can be compared. 

Table 3.1 compares the amount of land use change in each case study. The Boston and 

Charlotte case studies each have one alternative pattern, while the Denver case study has two. 

The overall amount of development relocated in each Denver scenario is approximately the 

same, however, the degree of concentration differs (31 versus 10 regional centers) and alters the 

travel demand outcomes of the two alternate scenarios. 

Table 3.1 – Magnitude of Land Use Change Under Each Scenario 

Boston Charlotte Denver 

13 

Towns

Entire 

Region 

 

Percentage of Development Moved in Alternative Scenarios

 

Households 14% 1% 4% 7% 

Employment 22% 4% 1% 9% 

Average change 17% 2% 3% 10% 

The land use changes in Table 3.1 are expressed as the percentages of total households and 

employment that are reallocated from the trend scenario under each land use alternative. 

Because all of the land use changes in the Boston case study occurred within the 13 towns, it 

makes sense to present the Boston results in the context of the “13 Towns” area only. In 

Charlotte, development under the South Corridor alternative was shifted from areas growing 

under the trend scenario, and it makes sense to present the results as percentages of the entire 

region. 
18

Table 3.1 summarizes the percentage of each region’s jobs and homes relocated in the land use 

scenarios. The row labeled “average change” represents a weighted average of the growth rates 

for households and employment. The weights represent the relative contributions of 

households and employment centers to trip making in the trip generation models. 

3.2 Congestion and Speed 

Table 3.2 shows the estimated changes in the levels of congestion and average speeds for the 

three case studies. The increases in average speeds are modest for all of the case studies. This is 

dependent on both the differences among the regions transportation systems and the specific 

land use alternatives evaluated. The Denver results indicate that, despite large VMT reductions 

in the 10 regional center scenario, congestion is higher and speeds are a bit lower. Although the 

31 center scenario does less to reduce overall travel, spreading development out across more 

infill locations reduces congestion at the sites. In Charlotte, congestion at the regional level is 

significantly reduced by concentrating more development around the rail stations. However, in 

the corridor itself, congestion levels do not change much as the increased share of trips made by 

transit is offset by a greater share of the region’s trips being shifted to the area. In the Boston 

analysis, overall travel delays are reduced substantially in the 13 towns, but increase somewhat 

at a regional level. 

3.3 Emissions 

Table 3.2 shows the average emissions rates from the model runs. It is important to recognize 

that per mile rate will vary among the communities due to differences in average speeds, 

climatic conditions, vehicle fleet mixes and other factors that affect emissions. The rates in 

Table 3.3 represent the average rates per mile for the baseline condition; the rates for the other 

scenarios are similar to those for the baseline scenario. 

Table 3.2 also shows that the emissions rates vary in several ways. The VOC rate in Boston is 

about 20 percent lower than in Charlotte and Denver, while Boston’s NOx rate is about one 

third lower than those in Charlotte and Denver. Charlotte’s rate for CO emissions is about half 

of the rate in Denver and two thirds of the rate in Boston. 

Table 3.3 presents the estimated changes in emissions for the land use scenarios for the three 

case studies. 
19

Table 3.2 Average Emissions Rates for Each Community (Base Case) 

Boston* Charlotte Denver 

VMT (000’s) 3,313 23,078 92,308 

Emissions (kg)

VOC 783 6,849 28,200 

CO 30,615 136,263 1,018,200 

NOx 661 6,614 28,200 

Average Emissions Rate (g/mile)

VOC 0.236 0.297 0.370 

CO 9.241 5.904 10.922 

NOx 0.200 0.287 0.322 

* p.m. peak period and “13 Towns” area only 

Table 3.3 Estimated Changes in Emissions for Each Community 

Boston* Charlotte Denver 

31 Centers

 Denver 

10 Centers

Percentage of Development Moved 17% 3% 10% 10% 

Change in VOC emissions -5.5% -1.4% -3.2% -4.0% 

Change in CO emissions -4.8% -1.2% -2.8% -3.5% 

Change in NOx emissions -8.1% -1.1% -2.7% -3.6% 

* “13 Towns” area only 

3.4 Effects of Increased Development in Specific Areas 

All three of the case studies examined alternative land use patterns where growth would be 

concentrated in specific areas. These changes were compared to a baseline consistent with most 

recent trends. In effect, this implies that some parts of each region will have more development 

in the alternative land use scenarios than in the baseline. The result would be more trips to and 

from these areas, and possibly more congestion. The extent to which the increased 

development – and the associated trips – lead to more vehicle travel, congestion and emissions, 

will depend on the length of car trips and use of alternative modes. It is worthwhile to examine 

the types of localized impacts that might be introduced by the alternative land use patterns. 
20

Although this type of analysis could be done using the results from any of the three partner 

communities, Charlotte is the best suited to illustrating the key issues: 

• All of areas with more development in the alternative scenarios are located within a 

single corridor. 

• As part of their analysis, Charlotte summarized the results for the corridor and 

station areas as well as the region. 

• One of the scenarios tested included street network improvements designed to 

mitigate these localized effects. This was not done in the other two case studies. 

All of the redirected development was focused on infill sites close to three stations in the South 

Corridor. (More detailed presentation of the analyses are presented in technical appendix C.) 

The 16,500 households reallocated in the alternative land use scenario nearly triples the number 

of people living near the station areas. However, the 10,500 jobs relocated near the rail stations 

increases total employment in the corridor more modestly, by about 50 percent. 

Table 3.4 summarizes the model results for both the South Corridor and the Region. The tables 

reveal that while travel and emissions do increase in the Corridor, they are substantially less at 

a regional level. This result does reveal a tension between the local and regional impacts that 

such scenarios can produce. When substantially more development is focused around station 

areas there will also be an increase in vehicle travel, congestion and emissions in the local area. 

While road improvements 

Table 3.5 demonstrates that at least some of the impacts of the increased development in the 

Station Areas can be mitigated to a certain extent. While the street network improvements 

implemented as part of Scenario 4 would increase VMT in the Station Areas even more than the 

increases due to the land use changes (probably by diverting additional through traffic), the 

percentage increases in emissions are lower than the percentage increases in VMT. The level of 

congestion in the corridor would drop to only six percent higher than the baseline condition, 

and average speeds would actually be higher than in the baseline scenario. 
21

Table 3.4 Summary of Charlotte Results (Regional Total vs. Station Areas) 

Measure 

Scenario 1 

Baseline 

Scenario 2 

Transit 

Scenario 3 

Development 

Around 

Transit 

Scenario 4 

Station Area 

Road 

Improvements 

VMT (000’s) 

 Mecklenberg County 23,078 23,044 22,604 22,609 

 Station Areas 665 658 728 760 

VOC (kg) 

 Mecklenberg County 6,849 6,837 6,742 6,737 

 Station Areas 242 240 257 261 

CO (kg) 

 Mecklenberg County 136,263 136,069 134,451 134,402 

 Station Areas 4,496 4,452 4,762 4,932 

NOx (kg) 

 Mecklenberg County 6,614 6,605 6,532 6,529 

 Station Areas 207 205 217 225 

Congestion (000s of VHT) 

 Mecklenberg County 114 113 109 109 

 Station Areas 3.40 3.35 3.93 3.60 

Average Speed (mph) 

 Mecklenberg County 29.7 29.8 29.8 29.9 

 Station Areas 25.2 25.2 25.0 26.5 

Transit Trips (000’s) 

 Mecklenberg County 139 145 150 150 

 Station Areas 2.9 3.9 10.0 10.0 

Person Trips (000’s) 

 Mecklenberg County 4,156 4,156 4,156 4,156 

 Station Areas 89 89 185 185 

Transit Share 

 Mecklenberg County 3.4% 3.5% 3.6% 3.6% 

 Station Areas 3.3% 4.4% 5.4% 5.4% 

Table 3.5 Summary of Differences between Baseline and Alternative Scenarios 

(Mecklenberg County) 

Measure Transit 

Only 

Transit Plus 

Infill 

Infill with 

Road Improv. 

VMT -0.1% -2.1% -2.0% 

VOC -0.2% -1.6% -1.6% 

CO -0.1% -1.3% -1.4% 

NOx -0.1% -1.2% -1.3% 

Congestion -0.9% -4.4% -4.4% 

Average Speed 0.3% 0.3% 0.7% 

Transit Trips 4.3% 7.9% 7.9% 

Transit Share 2.9% 5.9% 5.9% 
22

3.5 Conclusions and Implications 

The results of this project demonstrate that land use patterns involving infill and the 

redevelopment of brownfield areas provide regional transportation and environmental benefits, 

specifically reductions in vehicular distances traveled and congestion as well as emissions from 

vehicles. This section summarizes the conclusions and discusses the implications of these 

conclusions in the context of implementation. 

3.5.1 Reductions in Vehicle Travel and Emissions 

All three case studies indicated that the amount of regional VMT (aggregate and per capita) can 

be lowered by concentrating future development in infill areas, closer to trip destinations and 

public transit, as compared to conventional suburban development patterns. Although it is not 

possible to make general statements about these reductions based on three specific case studies, 

it is clear that for these three regions, concentration of future development into specific areas 

results in substantial reductions in regional vehicle-miles traveled (VMT), and that greater 

amounts of travel reduction occur if more development is concentrated in these areas. In these 

case studies the reductions in VMT within the areas within which development was moved 

were in the two to five percent range. Importantly, these reductions in VMT result exclusively 

from reductions in trip lengths, not from reductions in trip-making. In other words, travelers 

are satisfying their mobility goals with fewer motorized miles. 

The reductions in VMT lead to several related benefits. Both vehicle emissions and congestion 

are reduced. Emissions reductions in these case studies ranged from three to eight percent 

regionally (somewhat less in Charlotte where the land use changes were concentrated in a 

single corridor of the region). Overall, congestion decreased by five to seven percent in these 

case studies. 

The Charlotte case study shows that the amount of vehicle distances traveled (and therefore 

congestion and emissions) can be decreased further by coordinating the land use concentration 

with good transit service. Because all of the development relative to the base scenario was 

moved to light rail station areas in the Charlotte case study, additional opportunities to reduce 

vehicle travel existed. 

It is important to recognize that while the types of policies analyzed in this project show clear 

benefits regionally, those benefits are not uniformly distributed across the region. Areas where 

development is concentrated can experience increased total (although lower per capita) 

vehicular travel, congestion, and/or emissions, presenting a challenge that may require 

additional transportation improvements to mitigate these local impacts. Based on the results of 

the Charlotte case study, it was possible to at least partially mitigate these increases through 

targeted street network improvements in the areas where development would be concentrated. 

3.5.2 Use and Limitations of Travel Demand Models 

The state-of-the-practice travel demand models used in the three case study analyses 

demonstrate that conventional models are useful in analyzing the effects of brownfield and 

infill development on vehicle travel and emissions. This is particularly true when the models 

are used to examine alternative land use scenarios; for the most part, transportation planning 
23

studies in urban areas assume only a single fixed land use scenario. However, there are several 

limitations with conventional modeling. These include the following: 

• The necessity of using aggregate analysis zones, which do not allow the analysis of 

different land use patterns within a zone, and limit the ability to analyze intrazonal 

trips. 

• The lack of specific variables to examine the effects of higher density mixed use 

developments. 

• The lack of ability to analyze non-motorized travel (except in the Boston analysis), 

which would likely increase under scenarios with more concentrated land use 

patterns. 

• The lack of ability to model trip chaining, which might increase under scenarios with 

more concentrated land use patterns. 

The effect of these limitations, though, can be minimized through the implementation of one or 

more enhancements, as demonstrated in each of the three pilot communities. These include the 

use of smaller zone sizes, consideration of non-motorized travel, use of intrazonal VMT 

adjustment factors, and calculation of mixed land use indices. In addition, the use of a tripbased emissions estimation procedure, rather than one based simply on VMT changes, captures 

emission impacts associated with the changes in trip length that occur with brownfield and 

infill development strategies. 

While even enhanced conventional state-of-the-practice model systems may not be inherently as 

good as newer and more specialized alternatives, they nonetheless represent the capabilities 

that currently are used by nearly all MPOs and state DOTs. Consequently, they represent a 

good presently available approach for evaluating the impacts of a mix of land use and 

transportation strategies. Based on the experience of working with these three pilot 

communities, the conclusion is that application of these methodologies sufficiently captures the 

impacts of regional brownfield and infill policies to permit the results to be incorporated into 

transportation and air quality decision-making. 

3.5.3 Relative Contribution of Land Use Strategies 

Compared to other air pollution control and transportation strategies, land use policies appear 

to offer a way to reduce emissions. For example, in Boston the estimated 239,000 mile reduction 

in vehicle travel associated with the concentrated development scenario is greater than the 

reductions estimated for most of the transit projects outlined in the Regional Transportation 

Plan.6

 While this represents less than one percent of the mobile source VOC, NOx, and CO 

emissions budgets for the entire region, it reflects only land use changes in 13 of the 101 cities 

and towns in the region. 

 6 Regional Transportation Plan 2004-2005 of the Boston Region MPO, Prepared by the Central 

Transportation Planning Staff for the Boston Metropolitan Planning Organization, September 11, 

2003. 
24

3.5.4 Future Research Needs 

As discussed throughout this report, the currently available models make several 

simplifications about changes in travel behavior. Additional research is needed to expand the 

application of four-step travel models to evaluate urban infill development. Specifically, 

enhancements are needed to improve methodologies for estimating non-motorized trips and 

accounting for changes in the number and average trip length as a function of changes in the 

mix of development and density. Other areas of future research that would be useful in 

validating the results of these case studies include trip generation rate changes, changes in trip 

balancing procedures, and trip table factoring. Furthermore, development of a mixed-use index 

based on empirical research would also be valuable. 
25

Sources Cited 

Boston Metropolitan Planning Organization, 2007, The Recommended Transportation Plan, Ch 

13 in Journey to 2030, Central Transportation Planning Staff. 

http://www.bostonmpo.org/bostonmpo/resources/plan/2030plan-13-15.pdf

Ewing, R. and R. Cervero. 2001. Travel and the Built Environment: A Synthesis. Transportation 

Research Record 1780, 87-114. 

Federal Highway Administration, 2004, Emissions Benefits of Land Use Planning Strategies

FHWA TOPR 29, prepared by The Louis Berger Group, Inc.. 

http://www.fhwa.dot.gov/environment/conformity/benefits/index.htm

Federal Highway Administration, 2006, Comparative Cost Effectiveness of Potential CMAQ 

Projects, Appendix 4 in Interim Program Guidance: The Congestion Mitigation and Air Quality 

(CMAQ) Improvement Program. 

http://www.fhwa.dot.gov/environment/cmaqpgs/06guide.htm#Appendix4

Transit Cooperative Research Program Land Use and Site Design: Traveler Response to 

Transportation System Changes, Report 95 Chapter 15, Transportation Research Board, 

Washington DC, 2003. 

Transportation Research Board, 2005, Does the Build Environment Influence Physical 

Activity? Examining the Evidence, Committee on Physical Activity, Health, 

Transportation and Land Use. TRB Special Report 282, Washington, D.C. 

U.S. Department of Transportation, Travel Model Improvement Program (TMIP), 

http://tmip.fhwa.dot.gov/

U.S. EPA, Comparing Methodologies to Assess Transportation and Air Quality Impacts of 

Brownfield and Infill Development, EPA-231-R-01-001. 

http://www.epa.gov/smartgrowth/pdf/comparing_methodologies.pdf

U.S. EPA, Granting Air Quality Credit for Land Use Measures: Policy Options, EPA SR99-09-

01. http://www.epa.gov/otaq/stateresources/policy/transp/landuse/lupol.pdf

U.S. EPA, The Transportation and Environmental Impacts of Infill Versus Greenfield 

Development: A Comparative Case Study Analysis, EPA publication number 231-R-99-005. 
A-1 

Detailed Technical Appendices 

Appendix A - Boston 

Background 

The Metropolitan Area Planning Council (MAPC) and the staff of the Boston MPO tested the 

impact of different development patterns in 13 communities in an arc along the I-495 corridor. 

Some of the questions the community wanted to answer include the following: 

• Should development be in community centers or along I-495? 

• What is the impact of mixed used development on future transportation 

infrastructure? 

The project ran two scenarios - one based on current development patterns, the other based on 

concentrating development in town centers or I-495 interchanges. Both scenarios projected the 

same amount of employment and household growth, with the concentrated growth scenario 

moving about 14 percent of households and 22 percent of employees into more compact 

developments. 

The traditional four-step model was modified by disaggregating the existing 54 zones into 137 

zones, and also used the MOBILE6 model to calculate emissions estimates. It focused on peak 

travel and assumed the same transportation and highway improvements for each scenario. 

The transportation and air quality impacts of current development patterns were compared to 

those that could result from an alternative land use scenario. The analysis quantified 

congestion impacts, vehicle miles traveled (VMT), vehicle delays, and commuter rail use. 

The 13 communities in the study area were chosen according to three criteria: 

• They are in the I-495 corridor, which has the highest growth rate and largest amount 

of developable land in the region. 

• They represent the three subregions in the corridor. 

• They include the area considering formation of a new transit authority. 

The 13 towns are projected to grow to a total population of 334,681 over the next 25 years. This 

represents a projected 127,174 households. The forecasted employment for 2025 is 465,792. 

There was no attempt by MAPC and CTPS to balance the growth in households and 

employment. It turns out that the trend scenario assumed a higher growth rate for employment 

(about 35 percent) than for households (about 15 percent). This resulted in the population 

estimate being close to the 2025 MAPC forecast, but the employment being higher than the 

forecast. The imbalance was not reconciled, and the transportation model had to account for 

the imbalance by assuming a higher rate of trips produced outside the MAPC region attracted 

to communities within the region, including the 13 communities in this study. 

The concept of trend was introduced by MAPC to determine the effects of the maximum 

amount of development allowable under current regulations. While trend is not necessarily 

associated with any particular forecast year, the analyses performed for this project was 

generally assumed the towns would develop available parcels by 2025. Buildout analyses have 
A-2 

been done for most of the MAPC communities. This pilot study was intended to determine the 

effects of alternative development patterns on transportation. Although an additional scenario 

using projections based on historical development patterns would have been useful, resource 

constraints did not allow for this analysis. 

Two scenarios were analyzed. Thee trend scenario, representing the maximum allowable 

development under current zoning. Scenario 2 concentrated development in existing town 

centers or near I-495 interchanges. MAPC staff developed this scenario manually by selecting 

available infill sites that could accommodate the redirected growth. Examples of the alternative 

sites in scenario 2 included the Ashland Center brownfield site, infill development in 

Hopkinton Center, and additional development in the western section of Hudson. Scenario 2 

also included some redevelopment of sites with a current active land use. The amount of 

employment and households by type was held constant between the two scenarios for each 

community. Under Scenario 2 about 18,000 (14 percent) of the households and about 102,000 

(22 percent) employees in the 13 towns were moved relative to Scenario 1. 

Detailed Results 

Tables A.1 and A.2 present the results for two geographic areas. The first area is the entire 

metropolitan Boston region as modeled by the Metropolitan Area Planning Council (MAPC) 

and the Central Transportation Planning Staff (CTPS). The second area is the “13 Towns” area, 

which consists of those towns where development was redistributed under Scenario 2. When 

reviewing these results, it should be noted that while growth is redistributed, the total amount 

of development within the 13 Towns area is held constant for both scenarios. The analysis was 

performed only for the p.m. peak period. Information on person, transit, and walk trips and on 

average trip lengths was provided only for the 13 Towns area. 

According to the results shown in Table A.2 for the 13 towns, the land use pattern under 

Scenario 2 would result in significant reductions in VMT, emissions, and congestion, on the 

order of five to 10 percent, while speeds would increase. Regionally, the reductions in VMT, 

emissions, and congestion would be on the order of 0.5 to 1.6 percent. This is quite large, given 

that the 13 Towns area accounts for only 10 percent of regional travel. 

Within the 13 towns, 14 percent of the households and 25 percent of the employment were 

relocated under Scenario 2. This represented about half of the growth in households and about 

15 percent of the growth in employment from the base year to 2025. 
A-3 

Table A.1. Analysis Results for Boston (p.m. peak period) 

Measure Area Scenario 1 

Trend 

Scenario 2 

Focused Development 

Entire Model 30,577 30,338 VMT (000’s) 13 Towns 3,313 3,159 

Entire Model 1,315 1,300 VHT (000’s) 13 Towns 154 145 

Entire Model 6,329 6,295 VOC (kg) 13 Towns 783 740 

Entire Model 294,836 293,089 CO(kg) 13 Towns 30,615 29,149 

Entire Model 5,871 5,832 NOx (kg) 13 Towns 661 608 

Congestion (000s of Entire Model 565.41 556.59 

VHT) 13 Towns 77.93 72.29 

Entire Model 23.20 23.34 Average Speed (mph) 13 Towns 21.50 21.76 

Entire Model n/a n/a Person Trips (000’s) 13 Towns 654 654 

Entire Model n/a n/a Transit Share 13 Towns 0.22% 0.23% 

Entire Model n/a n/a Walk Share 13 Towns 6.21% 7.37% 

Entire Model n/a n/a Avg. Trip Length (miles) 13 Towns 14.95 14.30 

Scenario Definitions: 

1. Base - trend for all towns 

2. Concentrated redevelopment in 13 towns 

It can be concluded from the Boston analysis that the redistribution of development in 

concentrated areas has significant benefits in terms of reduced vehicular travel, decreases in 

emissions, and improved congestion levels. The reduced vehicular travel is a result of shorter 

trip lengths and mode shifts from auto to transit and walking. The benefits of having fewer and 

shorter trips are noticeable not only in the areas where the development is redistributed, but 

also regionally. Within the 13 Towns area the reductions in VMT, congestion, and emissions 

and the increase in transit share are all on the order of five to eight percent. Regionally, these 

effects are on the order of one to two percent although it should be noted that in most cases the 

benefits extend beyond the boundaries of the 13 towns. 
A-4 

Table A.2. Difference between Boston Scenarios 

Scenario 2 Minus Scenario 1 

Measure Area Change Due to Focused Development 

Value Percent 

Entire Model -239 -0.8% VMT (000’s) 13 Towns -154 -4.7% 

Entire Model -15 -1.2% VHT (000’s) 13 Towns -9 -6.2% 

Entire Model -34 -0.5% VOC (kg) 13 Towns -43 -5.5% 

Entire Model -1,747 -0.6% CO (kg) 13 Towns -1,466 -4.8% 

Entire Model -39 -0.7% NOx (kg) 13 Towns -53 -8.1% 

Entire Model -8.82 -1.6% Congestion (000s of VHT) 13 Towns -5.65 -7.2% 

Entire Model 0.14 0.6% Average Speed (mph) 13 Towns 0.26 1.2% 

Entire Model n/a n/a Person Trips 13 Towns -475 -0.1% 

Entire Model n/a n/a Transit Share 13 Towns 0.01% 4.2% 

Entire Model n/a n/a Walk Share 13 Towns 1.16% 18.8% 

Entire Model n/a n/a Avg. Trip Length (miles) 13 Towns -0.65 -4.3% 

Scenario Definitions: 

1. Base - trend for all towns 

2. Concentrated redevelopment in 13 towns 

Transportation Analysis Methods 

CTPS performed the transportation and emissions modeling for the two scenarios described 

below. The transportation modeling was performed using the regional travel model 

maintained by CTPS. This is a conventional four-step travel model based on person trips—

including both motorized and non-motorized trips. Trip generation, trip distribution, mode 

choice, and highway and transit assignment were performed using the EMME/2 modeling 

software. Emissions modeling was performed using the most recent version of EPA’s MOBILE 

program. 

To explain the differences between the two land use scenarios, a greater level of detail was 

needed within the 13 communities. The zone structure within these communities was 

disaggregated from 54 zones to 137 zones. The highway and transit networks were assumed to 

be the same for both scenarios, and the 2025 regional transportation plan networks were used. 

No new transit or highway improvements were assumed in the corridor under either scenario. 
A-5 

The model runs were performed for the two peak periods (a.m. and p.m.) only. Neither 

transportation nor emissions modeling for the entire day was performed. The time of day 

modeling was performed by factoring the daily trips after trip generation into peak and offpeak trips, and performing trip distribution separately for these two time periods, factoring the 

peak trips into a.m. and p.m. and the off-peak trips into mid-day and night, and then 

performing mode choice, and assignment separately for each of the four time periods. The 

results were prepared only for the p.m. peak period 

Intrazonal VMT Adjustment 

The mode choice model embedded in the CTPS regional travel model splits the intrazonal trips 

that are output from the trip distribution model into auto and transit person trips and nonmotorized (mostly walk) trips. 

One of the inputs required by the model to split the intrazonal trips is the intrazonal 

impedances by the walk and auto modes. The intrazonal impedance for either the auto or walk 

mode for any zone is estimated as 50% of the impedance by that mode to the nearest zone. 

These impedances are usually held constant among different scenarios. Therefore, any 

reduction in intrazonal impedance resulting from a “Smart Growth” strategy (more mixed use) 

is not reflected in the CTPS model. 

It has been hypothesized that the CTPS model may be underestimating the number of 

intrazonal trips in Scenario 2. It would make sense that the more “mixed-use” a development 

is, the higher the percentage of intrazonal trips.1

 One way to measure the mixed-use nature of a 

zone is to combine the different employment types plus housing. To address the potential 

under representation of intrazonal trips, CTPS developed a procedure that uses the concept of 

“mixed use index”2 and corrects for the presumed underestimation of intrazonal trips. In this 

procedure, when more intrazonal trips are added to a given zone, an equivalent number of 

interzonal trips to and from that zone are removed. 

The mixed use index for each zone is defined as follows: 

 BE E x BH H 

 MUI = ----------------- 

 BE E + BH H 

where: 

MUI = mixed use index for the zone 

E = employment in the zone 

BE = weight for employment 

H = number of households in the zone 

BH = weight for households 

 1

 For example, in Seattle, neighborhoods with mixed use development are almost four times as 

likely to be able to meet trip needs within a mile of the home, compared to surrounding areas. For 

more information, see http://onlinepubs.trb.org/onlinepubs/tcrp/tcrp_rpt_95c15.pdf. 

2

 Rossi, Thomas. “Potential Model Enhancements for EPA Project,” Memorandum to Erik Sabina, 

DRCOG, November 15, 2002. 
A-6 

This formula will produce higher values when either the amount of development increases or 

its mix of residential and commercial uses becomes more even. The weights are used to 

represent the relative importance of employment or households in trip making. 

The calculations embedded in the procedure are illustrated in the following steps. All 

calculations were performed for each zone in the study area. The procedure was implemented 

in an Excel spreadsheet. 

1. The weight for employment for each zone, BE, was determined by dividing the total 

employment into basic, retail and service types, applying different trip generation 

rates for each employment type (based on the CTPS trip attraction model), and 

estimating a weighted average trip generation rate. 

2. The weight for households for each zone, BH, was assumed to be 10, which is the 

typical trip production rate per household. 

3. Using the employment and household weight factors and the land use/ 

demographic data, the value of MUI for each zone was calculated for both 

scenarios. 

4. Next, for each zone, the Intrazonal Trip Adjustment Factor (ITAF) was computed 

as the ratio of the MUI for the Scenario 1 to the MUI for the Buildout Scenario. 

5. For each zone, the number of intrazonal trips obtained from the trip distribution 

model for Scenario 1 was multiplied by the ITAF for the zone. The resulting 

number is the adjusted intrazonal trips for Scenario 1. 

6. The difference between the model-estimated and adjusted intrazonal trips for each 

zone represents the change in intrazonal trips resulting from a better mixed-use 

type of development in the Smart Growth scenario. Since the total number of trips 

in the study area is largely unaltered (population and employment do not change), 

the increase in intrazonal trips should be accompanied by an equal decrease in 

interzonal trips. 

7. It was assumed that 20 percent of the new intrazonal trips would divert to the walk 

mode. The remaining 80 percent would be auto trips. CTPS based this assumption 

on experience in other parts of the region.3

8. The VMT resulting from the additional intrazonal auto trips was estimated by 

multiplying the number of new intrazonal auto trips by the average intrazonal trips 

length for the zone. 

9. The average trip length for all interzonal trips originating or destined to each zone 

was estimated from the trip tables. 

 3

 Although it is difficult to isolate the effect of mixed use development from density and other 

confounding factors, several studies have been conducted that support the substitution of walking 

for motorized transit as mixed uses increase. For example, traveler response studies in Houston 

and Seattle were within this range. See 

http://onlinepubs.trb.org/onlinepubs/tcrp/tcrp_rpt_95c15.pdf for more information. 
A-7 

10. For each zone, the average interzonal trip length estimated in Step 9 was multiplied 

by the decrease in interzonal trips to estimate the reduction in VMT from these 

trips. 

11. The net VMT reduction was calculated by adding the VMT increase from Step 8 

and VMT decrease from Step 10. 

Emissions Analysis Methods 

Subsequent to completion of the initial MOBILE5-based analysis for the I-495 corridor, the 

Massachusetts DEP completed development of 2007 MOBILE6 files for the SIP development 

and also developed generic files for use by others in conducting build/no-build and other 

similar emission analyses. The files are set up to produce emission factors by 1.0 mph 

increments for freeways and arterials, which then can be applied in a spreadsheet to link-level 

VMT by speed, similar to the MOBILE5 approach. 

Based on the availability of these new MOBILE6 results and the successful experience of the 

Massachusetts Highway Department in using these data, CTPS applied MOBILE6 for purposes 

of the I-495 corridor EPA brownfield and infill development analysis. 
B-1 

Appendix B - Charlotte 

Background 

The City of Charlotte modeled transportation impacts from alternative land use scenarios in a 

corridor proposed to be served by a new light rail line. VMT and emissions estimates for the 

alternative scenarios were compared to those for the 2025 Regional Transportation Plan, which 

assumes continuing trends in land use. The scenarios analyzed include land use shifts to 

proposed transit (including light rail) station locations. The regional transportation model 

maintained by the City was used to estimate the effects of these land use scenarios. The project 

also examined alternative transit and network capacity improvements. 

Charlotte’s analyses focus on the south corridor, where a light rail line has been proposed. A 1⁄2 

cent sales tax has been passed to fund the development of light rail. The build and baseline 

scenario for this light rail line have already been defined in support of the city’s application to 

the Federal Transit Administration (FTA) for New Starts funding. To conform to FTA 

requirements, the land use for both scenarios was assumed to be the same. This land use 

scenario was based on existing trends. Figure 2.2 shows the locations of the transit corridors in 

Charlotte’s plan, and Figure 2.3 shows the south corridor in greater detail. 

The City was interested in examining the effects of different land use patterns associated with 

transit stations. Station area plans were developed for seven stations. These plans included 

both a maximum and a minimum estimate of potential development. The latter was assumed 

for the scenarios due to the fact that it was felt to be more reasonable and was already the basis 

of previous transportation model runs. 

The following alternative scenarios were modeled by the City of Charlotte: 

• Scenario 1 – Transit No Build (as defined for FTA New Starts analysis) – with trendbased land use assumptions; 

• Scenario 2 – Transit Build Scenario (as defined for FTA New Starts analysis) – with 

trend-based land use assumptions; and 

• Scenario 3 – Transit Build Scenario with Revised Land Use – with land use 

assumptions reflecting development plans at stations; and 

• Scenario 4 – Transit Build Scenario with Revised Land Use and Highway 

Improvements – with land use assumptions reflecting development plans at stations, 

with street network improvements to address issues resulting from the revised land 

use. 

Scenario 1 is the baseline. Scenario 2 adds the proposed light rail line to the baseline. Therefore, 

the differences between Scenarios 1 and 2 can be considered to be due to the introduction of the 

new transit service. Scenario 3 introduces the alternative land use pattern, including the station 

area plans, to Scenario 2. This land use plan includes the redevelopment of some brownfield 

sites. The differences between Scenarios 2 and 3 are due to the land use changes in the presence 

of the improved transit. Scenario 4 adds the street network improvements to Scenario 3. The 
B-2 

differences between Scenarios 3 and 4 can be considered to be due to the highway 

improvements in the presence of the improved transit and revised land use. 

These scenarios comprise two different transit scenarios (scenarios 1 and 2), two different land 

use scenarios (2 and 3), and two different highway network scenarios (3 and 4), in each case 

with everything else held constant. 

Both land use scenarios assumed the same amount of growth and total development in the 

county, however the alternative land use scenarios attract growth that would have occurred in 

parts of the county outside the study area under the trend scenario. By the analysis year of 

2025, it is projected that the employment in Mecklenburg County, where Charlotte is located, 

will reach 764,862. The forecasted 2025 population is 944,649, representing 397,001 households. 

Under the alternative land use scenarios (3 and 4), a total of 16,500 households (about 4.2 

percent of the county total) and 10,500 employees (about 1.4 percent of the county total) are 

located in the south corridor that under the trend scenario would have been located elsewhere 

in the county. 

Detailed Results 

The City of Charlotte has produced a detailed documentation report for the analysis they 

performed for Charlotte4

. While it is impossible to incorporate all of the details provided in that 

report here, the report is referenced where appropriate. 

Table B.1 presents the analysis results for Charlotte. The emissions analysis was performed in 

two ways: 1) VMT-based only, and 2) including the VMT, vehicle trip, and vehicle based 

emissions. To be consistent with the analysis performed for the other two partner communities, 

the emissions shown B.1 are the VMT-based emissions. 

The results for Charlotte are presented for three geographic areas. The first area is the whole of 

Mecklenburg County. The second area is the “South Corridor,” which is wide enough to 

include the location of the transit right-of-way and an interstate or freeway also within the 

corridor. The third area, referred to as the “Station Areas,” is the most focused on the station 

areas. Figure B.1 shows the South Corridor (orange) and Station Areas (red). 

 4

 City of Charlotte. “Air Quality Benefits of Brownfields Development: Methodology Report and 

Summary Results” (Draft). June 21, 2003. 
B-3 

Figure B.1. Location of Charlotte South Corridor and Station Areas
B-4 

Table B.1. Analysis Results for Charlotte (weekday) 

Measure Area Scenario 1 

Baseline 

Scenario 2 

Transit 

Scenario 3 

Development 

Around 

Transit 

Scenario 4 

Station Area 

Road 

Improvements 

Mecklenburg Co. 23,078 23,044 22,604 22,609 

VMT (000’s) South Corridor 2,911 2,892 2,968 2,979 

Station Areas 665 658 728 760 

Mecklenburg Co. 776 774 758 756 

VHT (000’s) South Corridor 113 112 116 114 

Station Areas 26 26 29 29 

Mecklenburg Co. 6,849 6,837 6,742 6,737 

VOC (kg) South Corridor 1,392 1,384 1,400 1,397 

Station Areas 242 240 257 261 

Mecklenburg Co. 136,263 136,069 134,451 134,402 

CO (kg) South Corridor 27,199 27,050 27,348 27,376 

Station Areas 4,496 4,452 4,762 4,932 

Mecklenburg Co. 6,614 6,605 6,532 6,529 

NOx (kg) South Corridor 1,364 1,357 1,369 1,370 

Station Areas 207 205 217 225 

Mecklenburg Co. 114 113 109 109 

South Corridor 17 17 17 17 Congestion 

(000s of VHT) 

Station Areas 3 3 4 4 

Mecklenburg Co. 29.7 29.8 29.8 29.9 

South Corridor 25.7 25.7 25.6 26.0 Average Speed 

(mph) 

Station Areas 25.2 25.2 25.0 26.5 

Mecklenburg Co. 139 145 150 150 

South Corridor 9 12 18 18 Transit Trips* 

(000’s) 

Station Areas 3 4 10 10 

Mecklenburg Co. 4,156 4,156 4,156 4,156 

South Corridor 327 327 423 423 Person Trips* 

(000’s) 

Station Areas 89 89 185 185 

Mecklenburg Co. 3.4% 3.5% 3.6% 3.6% 

Transit Share* South Corridor 2.8% 3.6% 4.3% 4.3% 

Station Areas 3.3% 4.4% 5.4% 5.4% 

Avg. Trip 

Length (mi) Mecklenburg Co. 8.00 8.00 7.93 7.93 

* Trips originating in specified area. 

Scenario Definitions: 

1. Transit no build scenario 

2. Transit build scenario with trend-based land use assumptions 

3. Transit build scenario with more focused redevelopment around stations 

4. Transit build scenario with revised land use assumptions and station area road improvements 
B-5 

In examining the results, it is important to recognize that while the total amount of 

development is held constant on a regional basis, the amount of development in the South 

Corridor and Station Areas increases under the revised land use (Scenarios 3 and 4). For 

comparison purposes, the number of person trips generated in each scenario is presented as a 

measure of the amount of the increased development in these areas under the revised land use 

scenario. The number of person trips increases by 29 percent in the South Corridor area and by 

108 percent in the Station Areas under the revised land use scenario. 

The number of households reallocated in Scenarios 3 and 4 is 16,500, which is about four 

percent of the regional total households. This household reallocation nearly triples the number 

of households in the Station Areas and increases the number within the larger South Corridor 

area by about 60 percent. The total employment reallocated in Scenarios 3 and 4 is 10,500, 

which is about 1.4 percent of the regional total employment. This reallocation increases the 

total employment in the Station Areas by about 50 percent and increases the number within the 

larger South Corridor area by about 10 percent. 

Examination of the differences between Scenarios 2 and 1 shows that the introduction of the 

light rail line would result in decreases of about one percent in VMT, emissions of VOC, CO, 

and NOx, and congestion in the South Corridor and Station Areas. Speeds would increase 

slightly while the transit share would increase by approximately 30 percent. Regionally, there 

would be small reductions in VMT, emissions, and congestion along with a 4.3 percent increase 

in transit share. Examination of the differences between Scenarios 3 and 2 shows that in the 

South Corridor, the revised land use would result in increases in VMT (about three percent), 

emissions (one percent), and congestion (four percent) and decreases in speed (0.3 percent) that 

more than offset the benefits resulting from the introduction of the improved transit. These 

changes are due to the significant increase in development in the area. This indicates that 

although the new development would be concentrated in the areas of the new light rail stations, 

many of the trips to and from these developments would be made by auto. 

However, the percentage increases in VMT, emissions, and congestion are much lower than the 

increases in the number of person trips. This reflects the fact that the VMT and emissions 

associated with trips through the corridor, which are included in the summaries, would not 

change significantly since they would not travel to or from the new development. This also 

reflects that the new station area development would generate relatively fewer auto trips, as 

indicated by the increases in transit shares in the area beyond the increases associated with the 

introduction of the transit improvements alone, as well as shorter trips, due to the more 

compact nature of the development in the station areas. 

Despite the increases in VMT, emissions, and congestion in the South Corridor, it must be noted 

that regionally there would be decreases in these measures. Regionally, VMT and emissions 

would decrease by nearly two percent while congestion would decline by nearly four percent. 

The highway improvements associated with Scenario 4 would increase VMT in the Station 

Areas by 4.3 percent. This is presumably due to traffic that might be diverted to the expanded 

roadways since regional VMT (and emissions) would be essentially unchanged. However, the 

highway improvements would reduce the level of congestion, even in the Station Areas where 

the VMT would increase. The level of congestion would improve in the South Corridor area to 

about the same level as in the baseline scenario (0.2 percent higher) even with 29 percent more 
B-6 

development. Regionally, congestion levels would be nearly five percent better than in the 

baseline scenario. 

It can be concluded from the analysis that the combination of increased transit service and 

corresponding supportive changes in land use patterns can result in significant benefits in terms 

of reduced vehicular travel, increased transit use, decreased emissions, and improved 

congestion levels. These benefits are regional in nature as developments occur in the transit 

corridor rather than in areas where people are more likely to make more and longer vehicle 

trips. While vehicle travel and emissions are likely to increase in the areas in the transit corridor 

where development is concentrated, these effects can be at least partially mitigated through 

selective highway improvements. 

Transportation Analysis Methods 

The regional land use and travel models maintained by the City of Charlotte were used to 

perform the analyses. These models are documented by the City in a separate report5

. 

The City has a land use allocation model. It assumes regional employment and housing totals 

are held constant. The model allows specific developments and proposed projects to be added 

manually. There is a subregional component to this model. This model was used to produce 

the land use scenario for Scenarios 1 and 2 (the same land use assumptions were used for these 

two scenarios). The land use scenario used for Scenarios 3 and 4 was based on this land use 

scenario and manually revised according to the station area plans. 

The regional transportation model is a conventional four-step model based on motorized trips 

(auto and transit). Trip generation, trip distribution, mode choice, and highway and transit 

assignment were performed using the TransCAD software. There is also an auto ownership 

model. The highway assignment is done for four time periods, two peak and two off-peak 

periods. 

 5

 “Air Quality Benefits of Brownfields Development – Charlotte, NC: Methodology Report and 

Summary Results, June 21, 2003. 
B-7 

Figure B.2 Charlotte Transit Corridors 
B-8 

Figure B.3 Charlotte South Corridor 
B-9 

Emissions Analysis Methods 

The Charlotte analysis was performed using MOBILE6 input files developed in January 

2002 by the North Carolina Department of Transportation. Output emission factors are for 

calendar year 2025. The input files include the following locality-specific inputs for 

Mecklenburg County: 

• Vehicle age distributions; 

• VMT mix (percent of VMT by vehicle type); 

• Inspection and maintenance (I/M) program; 

• Soak distributions; 

• Temperature; 

• Fuel RVP; and 

• Vehicle speed. 

For each land use/transit scenario, 18 MOBILE6 scenarios were run, each with an average speed 

for six different functional classes and three time periods. Speed was estimated from travel 

model output as VMT divided by vehicle-hours traveled (VHT). The time periods included 

a.m. peak and p.m. peak (two hours each) and off-peak (remainder of the 24-hour period). The 

six different functional classes, along with their corresponding MOBILE6 facility type(s), are 

shown in Table 3.1. 

To account for changes in vehicle trips versus average trip lengths, grams/start emission factors 

were applied separately to the total number of vehicle trips in the study area under each 

scenario. (Grams/start were calculated as daily start emissions divided by the default number 

of daily starts assumed in MOBILE6). Grams/mile emission factors, net of start emissions, were 

then applied to total VMT under each scenario. 

EPA has not published guidance on the proper use of MOBILE6 to model trip-based versus 

VMT-based emissions. The methodology is straightforward for CO and NOx, for which 

MOBILE6 reports separate “start” and “running” emission factors. For VOC, however, there 

are eight different components that must be allocated to trip ends, VMT, or simply the existence 

of the vehicle. Discussions with EPA staff suggested that the following allocation approach was 

reasonable: 
B-10 

Table B.1 Functional Classes in the Charlotte Emissions Analysis 

Functional Class MOBILE Facility Type Other Comments 

Urban Interstate Freeway 

Ramp (% from assignment) 

No Cold Starts 

Urban Freeway Freeway 

Ramp (3%) 

Urban Principal Arterial Arterial 

Urban Minor Arterial Arterial 

Urban Collector Local 

Urban Local Street Local 

1. Running -- VMT 

2. Start -- Trip-End 

3. Hot Soak Loss -- Trip-End 

4. Diurnal Loss -- Vehicle 

5. Resting Loss -- Vehicle 

6. Running Loss – VMT 

7. Crankcase Loss – VMT 

8. Refueling Loss – VMT 

Diurnal and resting emissions depend primarily on whether the vehicle exists, not how 

much it is used. Although there is evidence that proximity to transit is associated with 

lower automobile ownership rates, this relationship is complex and is not captured in the 

model. Some of these eight components could be affected in other ways by the number of 

starts and/or VMT per day, but some effects will be negative rather than positive. For 

example, reducing VMT per vehicle may slightly increase diurnal emissions because of the 

longer time not running and fewer interrupted diurnals. 

Because the emissions analysis performed by the other two partner communities included 

only the VMT-based emissions from MOBILE6, the emissions results for Charlotte were 

reported in two ways: 1) VMT-based only, and 2) including the VMT, vehicle trip, and 

vehicle based emissions. 

Table 3.2 shows the percentage of emissions allocated to the VMT, vehicle trip, and vehicle 

for a Charlotte arterial at an average speed of 30 mph in 2025. 
B-11 

Table B.2. Sample Allocation of Emissions to VMT, Trip-Ends, and Vehicles 

 VOC CO NOx

 LDGV LDGT LDGV LDGT LDGV LDGT 

VMT Portion 58% 55% 61% 61% 87% 82% 

Trip-End Portion 34% 37% 39% 39% 13% 17% 

Vehicle Portion 8% 7% 0% 0% 0% 0% 

Percentages may not add to 100% due to rounding. 
C-1 

Appendix C - Denver 

Background 

The Denver Regional Council of Governments (DRCOG), which both provides regional 

planning services and acts as the MPO for the Denver region, is in the process of extending 

their Metro Vision 2020 Plan to a new horizon year, 2030. One of the core elements of the 

Metro Vision Plan is the establishment of urban centers around the region, as a means of 

focusing development, reducing the prevalence of dispersed development, and improving 

transit accessibility. As part of the evaluation of the effectiveness of such a measure, it is 

important to forecast how different scenarios of infill and brownfield development affect 

both transportation and air quality. To identify those impacts, DRCOG carried out the 

following activities: 

• Defined three alternative development scenarios that reflect varying urban densities 

and rates of infill, and forecast households and employment by traffic analysis zone. 

• With the assistance of the City and County of Denver’s Department of Health and 

Hospitals, identified brownfield sites (consistent with EPA’s brownfield definition). 

Highlight and compared the growth predicted in each of the scenarios between 2002 

and 2030 for these brownfield sites. 

• Used the land development information from the three scenarios as demographic 

inputs for the transportation model. 

• Used this information in the air quality model to predict emissions for major 

pollutants such as carbon monoxide and nitrogen oxides. 

The 2030 population forecast for the DRCOG region is about 3.5 million, which represents 

about 1.4 million households. The employment forecast is nearly 2 million. 

The types of land use scenarios were defined by DRCOG in cooperation with EPA and the 

consultant team. The following is DRCOG’s summary descriptions of the three scenarios: 

Scenario 1 – Limited Brownfield Redevelopment (Baseline): Under this scenario, the 

added growth is spread outside the existing developed urban area. Household growth 

followed an increased suburbanization pattern, with new employment located in 

suburban employment centers. These employment centers tend to locate at major 

interchanges of the freeway system in a metropolitan area. The land use pattern can vary 

from high density, office towers (as is often seen around airports) to campus-style office 

clusters. The residential patterns tend to follow the suburban development patterns, with 

large tracts of residential areas supported by neighborhood retail. This scenario was 

developed to provide a generalized base case, replicating development patterns of the last 

twenty to forty years. The development pattern for this scenario is shown in Figure C.1 

Scenario 2 – Multiple Brownfield Centers: This scenario focuses growth into 31 specified 

urban density centers. These centers are spread throughout the metropolitan area, but 

tend to concentrate in the urban core of the metro area. Both housing and employment 

development is focused into these areas, allowing for increased multi-modal usage (transit, 
C-2 

pedestrian and biking alternatives). Many of the brownfield development projects 

propose a mixed-use emphasis, relying both on connections within the development along 

with connections to existing community areas. In many cases, the infrastructure is already 

in place (water, power, and transportation) allowing for some costs savings for this type of 

development (to offset land prices and clean-up costs). This scenario provides for many, 

smaller scale redevelopment projects, which may be a means to overcome costly 

infrastructure expenses (matching closer to existing scale) and traditional concerns. The 

development pattern for this scenario is shown in Figure C.2 

Scenario 3 – Concentrated Brownfield Centers: The third scenario focuses development 

into 10 specified redevelopment centers. The scale of the brownfield development is much 

higher, in part to represent the scale that may be associated with higher land and clean-up 

costs. While the associated development has much higher densities, the projects may 

serve as catalyst for further redevelopment in the surrounding community. In the Denver 

region, examples of this type of project include the Central Platte Valley, Gates Rubber, 

Stapleton, and Fitzsimons sites. These developments have varied shares of residential and 

employment components, and still rely on existing infrastructure. However in every case, 

the development will likely cause some additional infrastructure improvement to 

adequately serve the scale of development proposed. The development pattern for this 

scenario is shown in Figure C.3. 

The amount of development reallocated into centers was similar under both Scenarios 2 

and 3, measured relative to Scenario 1. Under each scenario, about 100,000 households 

(about seven percent of the total) and about 180,000 to 190,000 employees (about 14 

percent of the total) shifted locations. 

Table C.1 

Scenario 1: 

Limited Brownfield 

Scenario 2: 

Multiple Centers 

Scenario 3: 

Concentrated Centers 

Urban Density Variable Medium High 

Forecast of 

Employment 

Suburban employment 

centers focused at 

major freeway 

interchanges 

Focused on 31 

areas in urban core 

Focused in 10 

redevelopment centers 

Forecast of 

Households 

Suburban residential 

areas near employment 

centers 

Focused on 31 

areas in urban core 

Focused in 10 

redevelopment centers 

Infrastructure 

Requirements 

Requires new 

infrastructure 

Generally 

preexisting 

Preexisting, but 

improvements likely 

needed 
C-3 

Figure C.1 Denver Baseline Development Scenario 
C-4 

Figure C.2 Denver 31 Regional Center Development Scenario 
C-5 

Figure C.3 Denver 10 Regional Center Development Scenario 

Detailed Results 

Three types of land use scenarios were defined by DRCOG in cooperation with EPA and the 

consultant team. Scenario 1 represents baseline conditions, where household growth followed 

an increased suburbanization pattern and employment growth was focused in suburban 

employment centers. Scenario 2 focuses growth into 31 specified urban density centers spread 

throughout the metropolitan area. Scenario 3 focuses development into 10 specified 

redevelopment sites. About seven percent of all households and 14 percent of all employment 

were relocated in both the second and third scenarios, compared to the baseline scenario. 

Tables C.2 and C.3 present the analysis results for Denver. Because the alternative land use 

patterns were defined for the entire Denver region, it did not make sense to define subareas for 

presentation of the results. They are therefore presented only for the entire region. The results 

reflect the trip generation adjustment to the DRCOG travel model described below. 

According to the model, concentration of the growth in the 31 centers would result in about a 

three percent reduction in regional travel, as measured by VMT. Speeds would, on average, 

increase by 0.7 percent, and congestion would decrease by six percent. Emissions of VOC, CO, 

and NOx would decline by about three percent. 
C-6 

The results when growth is concentrated around 10 centers are similar to those for the more 

dispersed 31 center scenario. There would be larger decreases in VMT and emissions, but a 

smaller decline in congestion and a smaller increase in speed. As with Boston, it can be 

concluded from the Denver analysis that the redistribution of development in concentrated 

areas has significant benefits in terms of reduced vehicular travel, decreases in emissions, and 

improved congestion levels. 

Table C.2 Analysis Results for Denver (weekday) 

Measure Baseline 

Scenario 31 Centers 10 Centers 

VMT (000’s) 92,308 89,637 88,966 

VHT (000’s) 

VOC (kg) 34,500 33,400 33,100 

CO (kg) 1,018,200 989,900 982,100 

NOx (kg) 30,000 29,200 28,900 

Congestion (000s of VHT) 667 625 628 

Average Speed (mph) 31.85 32.07 31.98 

Transit Share 2.4% 2.6% 2.7% 

Avg. Trip Length 6.84 6.81 6.78 

Person Trips (000’s) 12,695 12,316 12,203 

Table C.3 Differences between Denver Scenarios 

Change with 31 Centers Change with 10 Centers Measure 

Value Percent Value Percent 

VMT (000’s) -2,761 -2.9% -3,342 -3.6% 

VHT (000’s) 

VOC (kg) -1,100 -3.2% -1,400 -4.0% 

CO (kg) -28,300 -2.8% -36,100 -3.5% 

NOx (kg) -800 -2.7% -1,100 -3.6% 

Congestion (000s of 

VHT) -42.27 -6.3% -39.34 -5.9% 

Average Speed (mph) 0.22 0.7% 0.13 0.4% 

Transit Share 0.2% 9.0% 0.3% 11.5% 

Avg. Trip Length -0.03 -0.4% -0.06 -0.8% 

Transportation Analysis Methods 

The regional transportation model maintained by DRCOG is a conventional four-step model. 

Trip generation, trip distribution, mode choice, and highway and transit assignment were 

performed using the MinUTP software. The model does have time of day analysis but does not 
C-7 

include non-motorized travel. The model’s parameters are currently being updated, but this 

analysis does not reflect the updates. 

The current model is not sensitive to detailed microscale-level land use assumptions, mainly 

due to its large zone size relative to the scale of typical transit oriented developments. Area 

type is an important input into several model components, including (motorized) trip 

generation and estimation of roadway capacity. There are currently five area types in the 

model: CBD, fringe, urban, suburban, and rural. The same highway and transit networks were 

used for all three scenarios. 

Intrazonal Trip Changes 

Mixed land use development could generate a higher percentage of intrazonal trips than typical 

land use patterns. Closer proximity between households and employment sites would facilitate 

walking and biking, and reduce dependence on automobile travel, which is more attractive for 

longer commute trips. It would also help to lower vehicle miles traveled and improve air 

quality. However, the modeled percentage of intrazonal trips estimated from DRCOG’s travel 

demand model for the Gates-Cherokee development, a mixed use development planned for a 

brownfield site in Denver, was lower than expected. To improve the model results, Cambridge 

Systematics developed mixed land use indices to measure land use mix, which could be used to 

adjust the intrazonal trips estimated from the gravity trip distribution model. 

Based on the analyses described below, the available data did not support the use of an 

adjustment factor to intrazonal trips based on any of the mixed use indices studied. It may be 

that the lack of correlation between MUI and intrazonal trip rates was due at least in part to the 

effects of zone size. In addition, the DRCOG model currently considers only motorized trips, 

and a large portion of intrazonal trips are made by walking or bicycling. This might explain the 

underestimation of intrazonal trip rates. Therefore, the intrazonal adjustment to the DRCOG 

model was not used. 

However, to improve the model’s sensitivity to changes in land use pattern, DRCOG did alter 

its traditional method of model execution in one important respect, balancing its trip generation 

model results to attractions rather than to productions. Conventional trip generation models 

estimate productions based on trip generation rates and households—in Denver’s case, the 

number of households as well as income classification and household size. Such models also 

estimate trip attractions based on attraction rates and employment—in Denver’s case, the model 

includes different attraction rates for different area types (CBD, urban, suburban, etc.) 

Balancing to productions is often considered the conservative approach as it is generally 

believed that household production rates are better known than attraction rates (since typical 

travel surveys focus on household travel diaries). However, since the Denver production model 

does not include sensitivity to area type, balancing to productions eliminates an important 

element of sensitivity to changes in land use pattern. For the model runs conducted for this 

project for all scenarios, the trip generation model therefore balanced the trip generation results 

to attractions. 

Development and Calculation of Mixed Land Use Indices 

Cambridge Systematics developed two mixed land uses indices for this project. Each of these 

indices requires weights to be computed for each of three land use types: residential, retail, and 
C-8 

other. Table C.4 summarizes the average number of trip productions and attractions generated 

by trip purpose for each of these land use types. 

MUI1

A proposed land use mix index was defined by a two-dimensional mixture between industrial 

and residential land uses. Suppose that there is a three-dimensional land use mixture: 

residential (X1), retail (X2), and other (X3), where the variables Xi represent the percentages of 

each land use, weighted by the relative contributions of each land use to trip making. The 

weights are computed by each land use type using the trip production and attraction rates in 

the existing DRCOG model, as discussed above. 

The mixed use ratio (Yij) is computed for each pair of land uses in the same way as in the twodimensional mixture: 

If X1=X2=0, then Y12=0; Else 1 2

1 2

12 X X

X X Y

+

× =

If X2=X3=0, then Y23=0; Else 2 3

2 3

23 X X

X X Y

+

× =

If X1=X3=0, then Y13=0; Else 1 3

1 3

13 X X

X X Y

+

× =

Table C.4 Average Productions and Attractions by Land Use Type 

 Trips Generated per 

 Household 

Retail 

Employee 

Non-Retail 

Employee (1) 

Productions (all purposes) 8.714 (2) - - 

Home based work attractions 0.035 1.389 1.389 

Home based non-work 

attractions 0.540 6.477 1.685 

Non-home based attractions 0.486 4.205 1.114 

Total Trips 9.775 12.071 4.188 

Weight 0.375 0.464 0.161 

Notes: 

1. Non-retail employment in the DRCOG model is separated into service and other employment. The 

figures in Table 3.1 reflect weighted averages of the contributions of these two employment types. 

2. Trip production rate per household weighted over the cross-classifications of income level and 

household size used in the DRCOG model 
C-9 

The combined ratio of mixed household, retail, and other land uses can be calculated using 

the following equation: 

MUI1 = (Y12+Y23+Y13) × 2 

where: 

 X1 = Ratio of zonal residential land use 

 X2 = Ratio of zonal retail land use 

 X3 = Ratio of zonal other industrial land use 

 Y12 = Mixed use index between residential use and retail industrial use 

 Y23 = Mixed use index between retail land use and other industrial use 

 Y13 = Mixed use index between residential use and other industrial use 

 MUI1 = Mixed land use ratio 1 for residential, retail industrial and all other use 

Assuming residential, retail, and other use are the only three types of land use in a zone, 

the proportion of each land use types Xi (i=1, 2, 3) has to fall within the following 

constraints: 

X1, X2, X3 >=0 

X1+X2+X3 = 1 

MUI2

Cambridge Systematics developed an alternative mixed use index. This index is based on 

the idea of square deviation about the mean, which is defined as the difference between 

the mean and the ratio of an individual land use type. If residential, retail and other land 

uses are evenly mixed in a region, the mean ratio of land uses X

w

will be 1/3. The square 

deviation of each land use ratio can be calculated as follows: 

2

1

2

1 1 )

3

1 Z = (X − X ) = (X − w

2

2

2

2 2 )

3

1 Z = (X − X ) = (X − w

2

3

2

3 3 )

3

1 Z = (X − X ) = (X − w
C-10 

The overall mixed land use index is calculated by adjusting the sum of the squared 

deviation about the mean: 

∑=

= − + + = − −

3

1

2 1 2 3 ( ) 2

3 ( ) 1

2

3 1

i

MUI Z Z Z Xi X

where: 

 X1 = Ratio of zonal residential land use 

 X2 = Ratio of zonal retail land use 

 X3 = Ratio of zonal other land use 

 Z1 = Mixed use index between residential use and retail use 

 Z2 = Mixed use index between retail land use and other use 

 Z3 = Mixed use index between residential use and other use 

 MUI2 = Mixed land use ratio 2 for residential, retail, and all other use. 

The calculation of MUI2 also assumes residential, retail, and other use as the only three 

types of land use in a zone, therefore the proportions land use types Xi (i=1, 2, 3) have to 

fall within the same constraints as in MUI1: 

X1, X2, X3 >=0 

X1+X2+X3 = 1 

Range of the MUIs 

Both MUI1 and MUI2 range between 0 and 1, with 0 meaning the least mixed development 

and 1 meaning a completely even mixture. Both MUI calculations give a higher number 

for zones that are more mixed-use, as well as those with more trip making. A lower mixed 

use index indicates that a dominant land use exists. This can be illustrated through the use 

of the following three examples, which represent three different levels of land use mixture 

using both of the MUIs: 

Case 1: When all three types of land use are evenly mixed (X1=X2=X3=1/3), we will have 

the maximum MUIs: 

1 6

1 Y12 = Y23 = Y13 = and MUI1 =

Z1 = Z2 = Z3 = 0 and MUI 2 = 1

Case 2: When there is one and only one type of land use in a zone (for example, 

X1 = X2 = 0 and X3 =1, i.e., a zone with only non-retail land uses), we will have the 

minimum MUIs: 

Y12 = Y23 = Y13 = 0 and MUI1 = 0
C-11 

0 9

4

9

1 Z1 = Z2 = and Z3 = and MUI 2 =

Case 3: When residential area accounts for 1⁄2 of the land, retail and other industrial land 

use each takes up 1⁄4 of the area (X1=1⁄2 and X2=X3=1⁄4), we will have the following MUIs: 

0.9167 12

11

8

1

6

1 Y12 = Y13 = and Y23 = and MUI1 = =

0.9375 144

1

36

1 Z1 = and Z2 = Z3 = and MUI 2 =

Of these three hypothetical cases, Case 1 is the most mixed land use, Case 2 is the least 

mixed land uses, and Case 3 is in between. The values of MUI1 and MUI2 reflect the degree 

of mixture. 

Calculation of Zonal MUIs for Year 2001 and Year 2025 

Each version of the mixed land use index was computed for all zones for the 2000 and 2025 

scenarios based on land use data provided by DRCOG. The overall results from both 

MUIs are very similar. Figures B.1 and B.2 illustrate the aggregated MUIs by county for 

2000 and 2025, respectively. These figures indicate that all counties in the region are 

expected to have a more mixed land use pattern over time. Figures C.6 and C.7, 

respectively, show maps of MUI1 at the zonal level for 2000 and 2025. Figures C.8 and C.9, 

respectively, show maps of MUI2 at the zonal level for 2000 and 2025. 
C-12 

Figure C.4 MUI1 by County for Year 2000 and Year 2025 

Figure C.5 MUI2 by County for Year 2000 and Year 2025 

Mixed Land Use Index (1) in Year 2001 and Year 2025

0.00

0.10

0.20

0.30

0.40

0.50

0.60

 Adams Arapahoe Boulder Denver Douglas Jefferson Weld N/A All

Counties Year 2001 Year 2025

Mixed Land Use Index (2) in Year 2001 and Year 2025

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

 Adams Arapahoe Boulder Denver Douglas Jefferson Weld N/A All

Counties Year 2001 Year 2025
C-13 

Figure C.6 Distribution of Zonal MUI1 for Year 2000 

Figure C.7 Distribution of Zonal MUI1 for Year 2025 
C-14 

Figure C.8 Distribution of Zonal MUI2 for Year 2001 

Figure C.9 Distribution of Zonal MUI2 for Year 2025 
C-15 

Calculation of Intrazonal Trips from Household Travel Survey and Gravity Model 

The next step was to calculate the percentage of intrazonal trips from the household travel 

survey and the DRCOG gravity model. 

First, the percentage of intrazonal trips for each zone from the household travel survey was 

calculated. The household survey is a survey of persons in the households within the region on 

the characteristics of the household, characteristics of each person residing in the households 

and any out-of-region visitors staying at the households, and the characteristics of travel made 

by the person living at the household on the travel survey day. Based on the household travel 

survey database, the percentages of intrazonal trips for the three trip purposes in the DRCOG 

model—home based work (HBW), home based non-work (HBNW) and non-home based 

(NHB)—were calculated. Next, the intrazonal trip percentages based on DRCOG’s trip 

distribution model were calculated. 

Figures C.10 through C.13 compare the county-level intrazonal trip percentages calculated from 

the household travel survey with the intrazonal trip percentages estimated from the gravity 

model. It is evident from these figures that the gravity models underestimated intrazonal trip 

rates for all trip purposes. 

Intrazonal Trip Adjustment Models 

The calculated MUIs can be used to adjust the intrazonal shares coming out of the gravity 

model, so as to overcome the underestimation of intrazonal trip rates. The hypothesis is that 

intrazonal trip rates should be higher in areas with higher mixture of land use. 

An intrazonal trip adjustment model was estimated for each trip purpose by regressing the 

natural log transformation of intrazonal trips from the household survey against the natural log 

transformation of intrazonal trips from the gravity model as well as the mixed land use index. 

The regression models look like the following: 

Ln(intrazonal % from HH survey) = A*Ln(intrazonal % from gravity model) + B*Ln(MUI) + C 

where A, B and C are estimated parameters. Adjusting the currently modeled intrazonal 

percentages, rather than estimating new percentages based only on the MUI, would allow the 

use of other variables that are currently used in estimating intrazonal travel, such as distance to 

nearby zones. 

The intrazonal trip percentage from the household travel survey was plotted against the 

calculated MUIs. As seen in Figures C.14 and C.15, no strong correlation was found between 

the percentage of intrazonal trips and either of the mixed use indices. In Figure C.16, the 

observed intrazonal trip percentage was plotted against estimated intrazonal trip percentage 

from the gravity model. These correlations seem to be weak as well. 
C-16 

Figure C.10 Comparison of All Intrazonal Trips 

Figure C.11 Comparison of Home-Based Work Intrazonal Trips 

Intrazonal Trip Percentages (All Trips)

from Gravity Model and the Household Travel Survey 

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

 Adams Arapahoe Boulder Denver Douglas Jefferson Weld All

Counties HH Survey Gravity Model

Intrazonal Trip Percentages (HBW Trips)

from Gravity Model and the Household Travel Survey 

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

 Adams Arapahoe Boulder Denver Douglas Jefferson Weld All

Counties HH Survey Gravity Model
C-17 

Figure C.12 Comparison of Home-Based Non-Work Intrazonal Trips 

Figure C.13 Comparison of Non Home Based Intrazonal Trips 

Intrazonal Trip Percentages (HBNW Trips)

from Gravity Model and the Household Travel Survey 

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

 Adams Arapahoe Boulder Denver Douglas Jefferson Weld All

Counties HH Survey Gravity Model

Intrazonal Trip Percentages (NHB Trips)

from Gravity Model and the Household Travel Survey 

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

 Adams Arapahoe Boulder Denver Douglas Jefferson Weld All

Counties HH Survey Gravity Model
C-18 

Figure C.14 Plot of Observed Intrazonal Trip Percentage and Calculated MUI1

Mixed land use index 1, year 2001

-.2 0.0 .2 .4 .6 .8 1.0 1.2

Intrazonal trip % from household Travel Survey

80

60

40

20

0
C-19 

Figure C.15 Plot of Observed Intrazonal Trip Percentage and Calculated MUI2

Figure C.16 Plots of Observed and Estimated Intrazonal Trip Percentages 

-.2 0.0 .2 .4 .6 .8 1.0

Intrazonal trip % from household travel survey

80

60

40

20

0

Intrazonal trip % from gravity model, year 2001

-10 0 10 20 30 40 50

Intrazonal trip % from household travel survey

80

60

40

20

0
C-20 

Revision of Mixed Land Use Indices 

It was hypothesized that the lack of correlation between MUI and intrazonal trip rates was due 

at least in part to the effects of zone size. The original calculations of MUI1 and MUI2 do not 

account for zone size. More rural zones are generally large in area and tend to have mixed uses, 

even when those uses could be miles apart. Therefore, the calculation of mixed land use indices 

was revised to take zone size into account. The idea was that larger zones provided more 

opportunities for intrazonal travel. For example, in both of the original measures, a zone with 

10 households and 10 retail employees would have the same MUI value as a zone with 100 

households and 100 retail employees. But the larger zone could in reality provide more 

opportunities for intrazonal travel. 

To account for zone size, the MUI calculations were revised by using the number of households, 

retail employment, and other employment instead of percentages of these land use types. The 

question is, would zones that qualify as mixed use more because of their size than because they 

represent mixed use development be likely to have higher percentages of intrazonal trips than 

non-mixed use zones. This may be the case, in part because large zones would be more likely to 

have intrazonal trips. A trip to the supermarket may be a few miles long but still be intrazonal. 

Model Results Discussion and Recommendations 

Unlike the CTPS model in Boston, the DRCOG model currently considers only motorized trips, 

and a large portion of intrazonal trips are made by walking or bicycling. This might explain the 

underestimation of intrazonal trip rates. Regardless of the reason, it is clear that the available 

data did not support the use of an adjustment factor to intrazonal trips based on any of the 

mixed use indices studied. Therefore, the intrazonal adjustment to the DRCOG model was not 

used. 

Trip Generation Revision 

The second model enhancement analyzed for Denver is to adjust non-motorized trip rates. The 

idea behind this enhancement is to consider the hypothesis that pedestrian/bicycle friendly 

areas should have more non-motorized trips and, therefore, fewer motorized trips. Since the 

DRCOG model considers only motorized trips, the implication is that (motorized) trip 

generation rates should be lower in pedestrian or bicycle friendly zones. In model application, 

these lower rates are applied to zones identified as pedestrian/bicycle friendly. 

To implement this model enhancement, Boulder County was used as a test area. The trip rates 

from the household survey in Boulder County for households in pedestrian/bicycle friendly 

zones were compared to the rates for Boulder County households in other zones. Because trip 

rates for attractions and non-home based productions in the DRCOG model already were based 

on area type, only the home based work and home based non-work production rates were 

adjusted. 

Trip production rates in the DRCOG model are cross-classified by income level and household 

size. Separate adjustments, however, could not be computed for each cell in the crossclassification due to insufficient data in the household survey data set. 

Pedestrian or Bicycle Friendly Zones in Boulder County 

DRCOG staff identified zones in Boulder County that are considered pedestrian or bicycle 

friendly. Three levels of pedestrian or bicycle friendliness were identified as in the following: 
C-21 

• Level 1, the most pedestrian-friendly zones (eight zones) 

• Level 2, zones that are somewhat pedestrian-friendly (nine zones) 

• Level 3, all other zones in Boulder County 

To validate the assumption that these three levels of zones have different levels of 

pedestrian/bicycle friendliness, the home based work and home based non-work trip records 

person trip records in the household travel survey that correspond to the Boulder County zones 

were identified. Households with students at Colorado University were excluded. From these 

records, the average motorized and non-motorized trip rates by purpose for each zone type 

were computed. 

Table C.5 shows the ratios of non-motorized trips by purpose for zones with different levels of 

biking/pedestrian friendliness. This table indicates that non-motorized trips in the pedestrian 

or bicycle friendly zones are higher than average for all trip purposes. For the Level 1 zones, 

non-motorized trips account for 40 percent of all home-based work trips, 44 percent of homebased non-work trips, and 21 percent of non-home based trips. In contrast, the average regional 

non-motorized trip ratio is only 6 percent for all home-based work trips, 12 percent for homebased non-work trips, and 13 percent for non-home based trips. 

Trip Adjustment Factor Calculation 

The formula for calculating the trip rate adjustment factor for each trip purpose is: 

∑

∑

+ ÷ + =

i

ij ij

i

ij

ij ij

ij

ij MOTOR NONMOTOR

MOTOR

MOTOR NONMOTOR

MOTOR

A

( ) ( )

where: 

 i – Zone types (i = 1, 2, or 3) 

 j – Trip purposes (j=1 for HBW, j=2 for HBNW and j=3 for NHB) 

 Aij – Motorized trip adjustment factors for zone type i and trip purpose j 

 MOTORij – Motorized trip number for zone type i and trip purpose j 

 NONMOTORij – Non-Motorized trip number for zone type i and trip purpose j 

∑

i

MOTORij - Total Motorized trips for all zones with trip purpose j 

∑ ( + ) i

MOTOR ij NONMOTOR ij

 - Total trips (motorized and non-motorized) for all 

zones with trip purpose j 

This formula represents the ratio of the motorized mode share for the zone relative to the 

motorized mode share for all zones. Table C.6 presents the calculated adjustment factors 

for all three types of zones by trip purpose. 

It is interesting to note the counterintuitive result for home based non-work trips, where 

the adjustment for Level 2 zones is more significant than for Level 1 zones. To address this 
C-22 

concern, factors were computed for a two-tiered zone system, where Levels 1 and 2 were 

combined. The resultant adjustment factors are presented in Table C.7. 

Because of the inconsistent result for home based non-work trips, DRCOG and Cambridge 

Systematics decided to use the two-tiered zone classification system and the adjustment 

factors shown in Table C.7. The DRCOG trip generation program was revised to 

incorporate this revision. 

Table C.5 Percentage of Trips in Boulder County That Are Non-Motorized 

Zone Type HBW HBNW NHB 

Level 1 40% 44% 21% 

Level 2 21% 34% 28% 

Level 3 4% 10% 12% 

All Levels 6% 12% 13% 

Table C.6 Calculated Adjustment Factors for the Three-Tiered Zone System 

Zone Type Home Based Work Home Based Non-Work 

Level 1 0.653 0.789 

Level 2 0.835 0.726 

Level 3 1.016 1.012 

Table C.7 Calculated Adjustment Factors for the Two-Tiered Zone System 

Zone Type Home Based Work Home Based Non-Work 

Levels 1/2 0.779 0.739 

Level 3 1.016 1.012 

Emissions Analysis Methods 

The Denver emissions analysis was performed by the Colorado Department of Public Health, 

which undertakes emissions modeling for the Denver Regional Council of Governments 

(DRCOG) and other transportation agencies in Colorado. The analysis was performed using 

MOBILE6, with updated Denver-specific inputs for vehicle registration distribution (based on 

year 2000 State of Colorado data), VMT mix by road type and time of day, I/M program, and 

fuel characteristics. Average speeds by facility type were used for the ten time periods modeled 

by DRCOG. Start-based emissions were not modeled separately from VMT-based emissions. 
D-1 

Appendix D – Benefits of Using MOBILE6 

EPA’s MOBILE6 emissions factor model incorporates numerous enhancements to the previous 

MOBILE5 model, many of which are helpful in examining the emission impacts of a regional 

policy of promoting brownfield and infill development. Table A.1 summarizes the ability of 

MOBILE6 and MOBILE5 to capture the travel effects typically associated with brownfield and 

infill development. 

MOBILE6 produces different emission factors due to differences in emissions modeling 

assumptions. A number of states and urban areas have found MOBILE6 to produce higher 

emission factors in the short term and lower emission factors in the long term compared to 

MOBILE5. As a result, the absolute emission benefits of a buildout (long-term) analysis could 

be overstated by the use of MOBILE5. However, it is not likely that percentage differences 

between scenarios would differ significantly. 

In summary, MOBILE6 provides the following specific benefits over MOBILE5 for analyzing 

the emissions impacts of brownfield and infill developments: 

• Allows the use of start-based emission factors as opposed to those based on vehiclemiles traveled (VMT). This is important if infill developments reduce vehicle trips in 

different proportion to VMT (i.e., if vehicle trip-lengths are shorter but there are just as 

many vehicle trips or, conversely, if vehicle trip lengths remain the same but there are 

fewer vehicle trips.) MOBILE5 does not provide separate emission factors for trip ends 

versus vehicle-miles of travel. As a result, the use of MOBILE5 could potentially 

underestimate the benefits of strategies that reduce vehicle trips that are shorter than 

average (shorter trips would have higher per-mile emissions, because of the contribution 

of start emissions). 

• MOBILE6 produces facility-specific speed-based emission factors. MOBILE6 also 

contains updated/improved speed correction factors (SCFs) that vary by facility type. 

Thus, MOBILE6 provides more reliable estimates of the effects of changes in average 

vehicle speeds on emissions and also allows shifts in traffic among road types (freeways, 

arterials, local) to be assessed. These enhancements could lead to different estimates of 

the benefits of strategies that affect vehicle speeds and also of strategies that shift trips 

from one facility type to another. The impact of changes in speeds is likely to be smaller 

when estimated using MOBILE6 than for MOBILE5. Without looking at results in detail, 

however, it is difficult to say whether the speed and facility type changes alone lead to 

increases or decreases in emissions between scenarios. 

• MOBILE6 allows changes in the distribution of trip lengths to be assessed (although this 

would require additional processing of travel analysis outputs). 
D-2 

Table D.1 Benefits of Using MOBILE6 versus MOBILE5 for Analyzing Infill/ 

Brownfields Vehicle Emissions Impacts 

Effect on Travel Patterns Do common travel analysis 

methods give us this 

information? 

Can MOBILE5 

Measure? 

Can MOBILE6 

Measure? 

1. Shorter trip lengths due to: Yes (VMT-based 

emission factors) 

Yes/improved 

(effects of changes in 

distribution of trip 

lengths) 

a. Regional context/ 

accessibility 

Yes (interzonal trips are 

modeled) 

 

b. Local street connectivity No (unless intrazonal trip 

lengths are adjusted) 

 

2. Fewer vehicle trips due to: No Yes (Start-based 

emission factors) 

a. Mixed-use development 

and pedestrian access 

Possibly (adjustments such 

as pedestrian environment 

factors must be applied) 

 

b. Transit accessibility 

(regional) 

Yes 

3. Different traffic/driving 

patterns 

 

a. Slower average trip speeds 

due to urban setting 

Yes (within limitations of 

speed output of 4-step 

models) 

Yes (speed 

correction 

factors) 

Yes/improved 

(updated speed 

correction factors) 

b. Different mix of travel by 

roadway type (e.g., more 

local, less freeway) 

Yes (for freeways & 

arterials – but some 

limitations for local roads) 

No Yes (speed 

correction factors by 

roadway type) 

c. Lower acceleration and 

deceleration rates due to 

lower speeds, urban setting 

No No (except as 

embodied in 

SCF) 

No (except as 

embodied in SCF by 

roadway type) 

The benefits of applying MOBILE6 instead of MOBILE5 depend on having reliable data on 

travel pattern impacts, including effects of differences in development patterns and urban 

design on vehicle trip-making, vehicle trip lengths, and vehicle travel speeds by facility. The 

ability of existing analysis methods to assess these parameters varies. 
D-3 

Brownfield and infill developments also may have travel impacts that are not easily and 

routinely analyzed by common travel demand analysis methods, such as: 

• Changes in vehicle activity patterns (e.g., fewer peak-hour vehicle trips, fewer “coldstart” trips, different soak times.) 

• Differences in commercial vehicle trip rates or trip patterns. 

• Changing patterns of vehicle ownership and use (e.g., fewer miles per year per car). 

If data are available on these travel impacts, MOBILE6 can be used to assess the resulting 

emissions impacts (generally, more effectively than MOBILE5), but these data are not available 

in the travel modeling done for any of the three participating partner communities.