Office of Transportation and Air Quality

EPA-420-R-14-003

January 2014

Estimating Emission Reductions 

from Travel Efficiency Strategies:

Three Sketch Modeling Case Studies
EPA-420-R-14-003

January 2014

Transportation and Climate Division

Office of Transportation and Air Quality

U.S. Environmental Protection Agency

Prepared for EPA by

ICF International

EPA Contract No. EP-C-12-011

Work Assignment No. 1-08

Estimating Emission Reductions 

from Travel Efficiency Strategies:

Three Sketch Modeling Case Studies
Estimating Emission Reductions from Travel Efficiency Strategies: Three Sketch Modeling Case Studies

ICF International U.S. Environmental Protection Agency 10-000

Acknowledgements

The U.S. EPA Office of Transportation and Air Quality would like to thank the following individuals and organizations 

for their partnership and support. Along with their supporting agencies, they provided the critical data, information, 

and thoughtful input required for the successful completion of this project.

Amanda Graor – Mid-America Regional Council (Kansas City Metropolitan Area)

Catherine Cagle – Massachusetts Department of Transportation (Boston Metropolitan Area)

Susanne T. Cotty – Pima Association of Governments (Tucson Metropolitan Area)

Sisinnio Concas – Center for Urban Transportation Research, University of South Florida

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Table of Contents

Executive Summary............................................................................................................ES-1

Background.....................................................................................................................ES-1

Analysis and Results.......................................................................................................ES-2

Results and Lessons Learned .........................................................................................ES-4

1. Introduction and Background ........................................................................................... 1

1.1. Introduction to TEAM ................................................................................................. 1

1.2. 2010 National Study................................................................................................... 1

1.3. Case Study Participants............................................................................................. 2

2. Methodology and Key Findings ........................................................................................ 4

2.1. Scenario Selection and Baselines.............................................................................. 4

2.2. Data Collection and Validation ................................................................................... 5

2.3. TRIMMS Analysis ...................................................................................................... 6

2.4. MOVES Analysis ..................................................................................................... 11

3. Case Study Results...........................................................................................................15

3.1. Pima Association of Governments (PAG) – Tucson, Arizona................................... 16

3.2. Massachusetts Department of Transportation (MassDOT) – Boston Region............ 26

3.3. Mid-America Regional Council (MARC) – Greater Kansas City ............................... 37

4. Conclusions and Recommendations...............................................................................45

4.1. TEAM Data Requirements ....................................................................................... 45

4.2. TRIMMS Support of TEAM ...................................................................................... 45

4.3. MOVES Support of TEAM........................................................................................ 48

4.4. Regional Realities and Implications for Using TEAM................................................ 50

5. Appendix A: Strategies Identified in the 2010 Potential Changes in Emissions Due to 

Improvements in Travel Efficiency Report.................................................................... A-1

6. Appendix B: Emission Change Quantities and Additional Technical Details for the 

MOVES Analysis ............................................................................................................. B-1

List of Tables and Figures 

Table 1. Regional Scenario Descriptions...............................................................................ES-3

Table 2. Percent VMT and Emissions Changes ....................................................................ES-6

Table 3. TRIMMS Analysis Options............................................................................................ 9

Table 4. Data Inputs for MOVES Runs......................................................................................12

Table 5. PAG Scenario Details..................................................................................................20

Table 6. Resulting Emissions Changes for Selected Pollutants.................................................21

Table 7. PAG Data Sources......................................................................................................22

Table 8. Emissions Factors for PAG .........................................................................................23

Table 9. PAG Regionwide Percent Emissions Changes............................................................24

Table 10. MassDOT Scenario Details .......................................................................................31
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Table 11. MassDOT Data Sources............................................................................................32

Table 12. Emission Factors for MassDOT.................................................................................34

Table 13. MassDOT Regionwide Percent Emissions Changes .................................................35

Table 14. MARC Scenario Details.............................................................................................41

Table 15. MARC Data Sources .................................................................................................42

Table 16. Emission factors for MARC .......................................................................................43

Table 17. MARC Regionwide Percent Emissions Changes.......................................................44

Table 18. Travel and Emission Changes by Region and Scenario .......................................... B-1

Table 19. Full Pollutant List..................................................................................................... B-3

Table 20. Difference in Activity Values: Hourly-to-Annual Aggregation.................................... B-4

Table 21. Difference in Emission Values: Hourly-to-Annual Aggregation................................. B-4

Table 22. MassDOT VMT allocation factors ............................................................................ B-7

Figure 1: TRIMMS Employer Demand Management Inputs ....................................................... 7

Figure 2: TRIMMS Financial, Pricing, Access, and Travel Times Inputs..................................... 7

Figure 3: TRIMMS Land Use Inputs........................................................................................... 8
Executive Summary

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Executive Summary

Background

The purpose of this report is to document how the “Travel Efficiency Assessment Method” 

previously used for a national assessment could be applied to specific regions to estimate the 

reductions in vehicle miles traveled (VMT), greenhouse gas (GHG) emissions, and criteria 

pollutant emissions. The U.S. Environmental Protection Agency (EPA) collaborated with state 

and local government officials in three regions to complete these case studies: Pima County 

Association of Governments (PAG) for the Tucson, AZ region, Massachusetts Department of 

Transportation (MassDOT) for the Boston, MA region, and Mid-America Regional Council 

(MARC), for the Kansas City, MO Region. EPA offered technical assistance and the 

collaborating agencies offered their time, expertise, and local data to assess reductions in 

greenhouse gas (GHG) and criteria emissions from a set of travel efficiency strategies selected 

by and tailored to each particular region. 

Travel efficiency strategies represent the broad range of strategies designed to reduce travel 

activity, especially single-occupancy travel. The term “travel efficiency strategies” builds on the 

traditional Transportation Control Measures (TCMs) listed in Section 108(f)(1)(A) of the Clean 

Air Act (CAA) such as provision of transit, high-occupancy vehicle (HOV) lanes, and park and 

ride lots, and includes other strategies such as transportation pricing such as parking pricing 

and per-mile pricing and smart growth, such as transit-oriented development. 

These case studies build on other EPA research to quantify the potential reductions in

transportation-related emissions resulting from travel efficiency strategies that reduce VMT. 

EPA developed a methodology to estimate VMT and emissions reduced from travel efficiency 

strategies called the Travel Efficiency Assessment Method (TEAM). TEAM uses available travel 

data and a sketch model analysis to quantify the change in VMT, combined with the EPA 

MOVES2010 (Motor Vehicle Emission Simulator) model’s emission factors to estimate the 

emission reductions that can reasonably be expected. The method allows evaluation and 

comparison of scenarios, and thus provides a useful tool for state and local planners who want 

to assess impacts of possible strategies before adopting them. This method was applied to 

urban areas nationwide to estimate the potential impact that adopting travel efficiency strategies 

could have, and the results were documented in EPA’s recent study, Potential Changes in 

Emissions Due to Improvements in Travel Efficiency1 (2010 national study). 

EPA had also provided a user guidance document that describes the methodology in detail, so 

that others interested in such an analysis could follow the method established. In 2011, EPA 

issued the guidebook describing the TEAM approach titled, Analyzing Emission Reductions 

from Travel Efficiency Strategies: A Guide to the TEAM Approach.

2

 The guide describes the 

TEAM approach to estimating the emission reductions from travel efficiencies at the regional 

 

1 2010 national study, www.epa.gov/otaq/stateresources/policy/420r11003.pdf

2 User Guidance for TEAM, http://www.epa.gov/otaq/stateresources/policy/420r11025.pdf
Estimating Emission Reductions from Travel Efficiency Strategies: Three Sketch Modeling Case Studies

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level using information that is typically available from a travel demand model. The analysis can 

also be conducted for smaller geographical areas to the extent that the areas are covered by a 

travel demand model and the data for these areas can be extracted from the model. The guide

describes the information and data required for analysis, step-by-step procedures for performing 

the analysis, considerations for making assumptions about the strategies of interest, and 

considerations for interpreting the results. In addition, it identifies default values, alternative 

sources of information, and data that can be used when local data and information is incomplete 

or absent.

EPA began these case studies in 2012, by requesting letters from agencies interested in 

applying TEAM in their local region to evaluate their own selected travel efficiency strategies.

From those that applied with a letter of interest, three agencies were selected as case studies 

for testing the TEAM approach at a regional level: Pima County Association of Governments 

(PAG) for the Tucson, AZ region, Massachusetts Department of Transportation (MassDOT) for 

the Boston, MA region, and Mid-America Regional Council (MARC), for the Kansas City, MO

Region. The selection of these participants to apply TEAM in a regional analysis provides a 

collaborative opportunity to determine its applicability across a range of geographic, 

developmental and travel activity contexts. These lead agencies identified stakeholder groups to 

support their strategy selection and data collection. For purposes of analysis, each region’s

selected strategies were grouped into four scenarios. Both the strategies and their underlying 

assumptions represent a broad range of potential scenarios for evaluation of corresponding 

emissions reductions.

The results of these regional studies were compared with the results of the 2010 national study 

to see if they were of similar magnitude. The aforementioned Guide to the TEAM Approach 

recommended this validation approach, and the current study provides an opportunity for 

comparison. In most cases, the comparison validated that individual agency results were 

reasonable. This 2013 regional study estimates fewer potential emissions reduction in response 

to the scenarios selected by the regions, especially in regard to land-use strategies. Because of 

this, the 2013 regional study highlights some important lessons learned that can inform future 

applications of the TEAM approach. 

Analysis and Results

Selected Strategies

The three regions selected travel efficiency strategies for modeling that fall into four categories: 

Travel Demand Management (TDM) or Employer Incentives, Transit, Land Use, and Pricing.

PAG defined its scenarios as four separate strategies with no overlap, while the other two case 

study regions applied their strategies to achieve a progressive increase in VMT reduction across 

scenarios. Table 1 provides a brief description of each region’s selected strategies.
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Table 1. Regional Scenario Descriptions

Scenario Description

PAG

Scenario 1: SunTran All Access 

Pass

Provides an unlimited-ride transit pass for almost 90,000 faculty, staff, and students at the 

University of Arizona and Pima Community College

Scenario 2: Expanded Employerbased Incentives

Increases subsidies for those currently eligible for commute subsidies

Scenario 3: Bus RapidTransit 

(BRT) on 2 Corridors

BRT on two major corridors in the region that bring traffic into and out of downtown

Scenario 4: Parking Pricing in 

Downtown

Doubles parking prices in a specific downtown-university area along with expanding the 

share of parking that is priced

EPA Scenario: Land Use 

changes with all other scenarios

Concentrates population growth in existing urban centers

MassDOT

Scenario 1: Expanded Healthy 

Modes Program

Increase the number of employees with access to MassRIDES by 25% and expand access 

to employer-paid monetary subsidies to all employees with access to MassRIDES

Scenario 2: Scenario 1 + Land 

Use

Adds Smart Growth Land Use to Scenario 1 with an increased emphasis on growth in 

existing urban centers

Scenario 3: Scenario 2 + HOV 

Lanes

Adds HOV to Scenario 2 with a decrease in rideshare travel time for the entire region based 

on a network of HOV lanes

Scenario 4: Scenario 3 + 

Expanded Transit

Adds transit network expansion and improvement to Scenario 3 to reduce both transit trip 

times and access times (wait times) for the regional population

MARC

Scenario 1: Expanded TDM Expands the group with access to telework and flexwork programs and adds alternative 

mode subsidies for this expanded group

Scenario 2: Scenario 1 + 

Enhanced Transit

Adds transit improvement and promotion to Scenario 1 by reducing transit trip times, 

reducing walking distance to transit, and expanding the University’s successful transit pass 

program

Scenario 3: Scenario 2 + Land 

Use

Adds Smart Growth Land Use to Scenario 2 to increase average residential density and 

mixed land uses for the entire regional population

Scenario 4: Scenario 3 + Pricing Adds transportation pricing to Scenario 3 as an increase in the average cost of auto trips 

and increase parking costs in the Downtown Area

An important element of this study was to evaluate how changes in land use, particularly the 

implementation of smart growth principles, support emissions reductions. Given the long lead 

times needed for cities to implement smart growth development plans, EPA chose a thirty year 

time horizon to model the results of the different scenarios. 

Both MassDOT and MARC included a land use strategy; however, PAG chose not to test the 

effectiveness of a land use strategy. To provide a balanced comparison across regions and a 

point of comparison with the previous national study, a reasonable land use strategy was 

independently developed by EPA for the PAG region. The results are included as an additional 

scenario that uses land use as a foundation for the other strategies requested by PAG.
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After conducting a review of various sketch planning models in EPA’s 2010 national study, the 

TRIMMS model developed by the Center for Urban Transportation Research (CUTR)3 was 

selected to demonstrate the TEAM approach. Several factors recommend using TRIMMS for 

this type of analysis, including the type and format of inputs required, the geographic scale of 

analysis, and the capability of modeling a variety of TCMs, making it highly useful for this type of 

analysis. 

MOVES2010b was used to determine appropriate emission factors for all regions. MOVES was 

run in inventory mode to produce activity-weighted average emission factors. Four primary 

pollutants were considered in this analysis: CO2-Equivalent, NOx, PM2.5, and VOC.

Results and Lessons Learned

While estimates of VMT and emission reductions from each case study region are generally 

within the range of results for similar regions in the 2010 national study, they are not directly 

comparable to the 2010 national study, or to each other. The differences observed can be 

attributed to the underlying assumptions and the affected population for each region and the 

scenarios selected to be modeled. For example, in one of the cases, a strategy was applied to 

a limited set of corridors rather than to all roads; in a couple of the cases, strategies were 

applied to a subset of the population rather than to the entire population. The scenarios were 

tailored to each region and based on the agencies’ individual goals for the case study. The 

regions were diverse in their selection of strategies, as well as availability, type and 

completeness of data for the models. This limits their comparability to each other, and to the 

2010 national study. However, focusing on specific regions in this way was the one of the 

reasons for pursuing case studies. In addition, the differences in the regions provided more 

insight to the strengths and challenges of the analytical tools used.

To expand on that point, each of the regions had a different future “business as usual” (BAU) 

scenario that was used as the benchmark to compare that region’s scenarios of travel efficiency 

strategies. The BAU scenario was unique to each region and reflected what is already planned

in that area for the future. Each region made a decision for what to include in their BAU. In 

Boston, the MPO developed two land use forecasts, one the continues recent historical 

development and one that concentrates future growth in core areas served by transit. The BAU 

in the Boston case study included the latter. With this in mind, the differences between the BAU 

and the other scenarios modeled in the Boston region is smaller than it would have been if the 

BAU scenario reflected the more dispersed land use patterns of the recent past. In the Kansas 

City region, the MPO created three land use forecasts: one that continues current trends, one 

that focuses growth in core areas, and a compromise between these two that was adopted by 

the MPO board. In contrast to Boston, in the Kansas City case study the BAU scenario included 

 

3 Concas, S. and P.L. Winters; Economics of Transportation Demand Management (TDM), 2007, “Estimating Costs and Benefits 

of Emission Reduction Strategies for Transit by Extending the TRIMMS model.” 2012, and Quantifying the Net Social Benefits 

of Vehicle Trip Reductions: Guidance for Customizing the TRIMMS Model, 2009;National Center for Transit Research at the 

Center for Urban Transportation Research: Tampa
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the current trends forecast rather than the focused growth forecast or the adopted compromise. 

The fact that each region had a unique BAU scenario is one reason why results from one region 

are not directly comparable to another. The results illustrate the flexibility of the method, with 

this particular version of the sketch model used. 

Agencies wanting to model more aggressive policies, applied to broader populations, showed 

larger emission reductions. As in the national-level analysis, pricing strategies outperform the 

others when applied to a significant share of the population. The largest reductions result from a 

combination of mutually supportive strategies modeled together, compared to the sum of

individual strategies modeled separately. For example, land use, transit and TDM improvements

can be mutually supportive and produce better results than when considered individually. The 

scale of implementation of a strategy can have important implications for the results as well. 

When the population or geography is reduced to a subset of the region, the benefits may be 

large to the affected population, but quite small for the region as a whole. 

VMT and emission reductions for each of the future year scenarios, expressed as a percent 

change, as compared to the business as usual baseline, are provided in Table 2. 
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Table 2. Percent VMT and Emissions Changes

Percent Emissions Changes for Selected Pollutants

2040 BAU compared to 2040 Scenario

Scenario Light-Duty 

VMT

GHGs 

(CO2 

equivalent)

PM2.5 NOx VOC

PAG

Scenario 1: SunTran All Access Pass -0.99% -0.97% -0.94% -0.86% -0.77%

Scenario 2: Expanded Employer-based Incentives -0.43% -0.43% -0.42% -0.40% -0.44%

Scenario 3: BRT on 2 Corridors -0.02% -0.02% -0.02% -0.02% -0.02%

Scenario 4: Parking Pricing in Downtown -0.26% -0.25% -0.25% -0.24% -0.26%

EPA Scenario: Land Use changes with all other 

scenarios

-1.95% -1.87% -1.73% -1.43% -0.71%

MassDOT

Scenario 1: Expanded Healthy Modes Program -2.80% -2.80% -2.80% -2.79% -2.77%

Scenario 2: Scenario 1 + Land Use -3.88% -3.89% -3.88% -3.88% -3.84%

Scenario 3: Scenario 2 + HOV Lanes -4.06% -4.06% -4.06% -4.05% -4.02%

Scenario 4: Scenario 3 + Expanded Transit -4.40% -4.41% -4.40% -4.39% -4.36%

MARC

Scenario 1: Expanded TDM -0.93% -0.93% -0.93% -0.92% -0.92%

Scenario 2: Scenario 1 + Enhanced Transit -2.35% -2.35% -2.35% -2.35% -2.34%

Scenario 3: Scenario 2 + Land Use -2.49% -2.49% -2.49% -2.48% -2.48%

Scenario 4: Scenario 3 + Pricing -12.05% -12.05% -12.05% -12.03% -12.02%

Data

Regions that undertake a TEAM analysis should allow for a significant amount of preparation

time to identify data requirements, collect or identify substitute data elements and validate the 

appropriateness of the data for this type of analysis. TEAM was developed to use regional

planning data without requiring significant additional data collection or extensive re-evaluation of 

the information. Data validation to determine the appropriateness for analysis is an essential 

step in the TEAM approach. This study found that the reasonableness of data in common use 

shouldn’t be taken for granted. For instance, the distribution of VMT for transit vehicles among

road types is unlikely to be the same as for passenger cars. Road type has an impact on vehicle 

speeds, and thus can have a significant impact on emissions. A critical element for applying 

TEAM successfully is the ability to identify questionable data and develop substitutions when 

needed. 

Analysis Tools

The analysis demonstrated that TRIMMS can be an effective sketch modeling tool to estimate 

VMT reductions from a variety of travel efficiency strategies. Many of the model’s functions 

work by manipulation of travel times and travel costs, common factors used in travel demand 

modeling. While TRIMMS can provide a relatively rapid and low cost evaluation of travel 

efficiency strategies, more comprehensive land-use and transportation modeling should be used 
Executive Summary

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to confirm the analysis before making final policy decisions. The analysis also raised questions 

about the estimated impacts of higher density land use. TRIMMS land use analysis features are 

in an early stage of development, and the results appear to underestimate impacts on VMT and 

are inconsistent with current literature. The web-based version currently under development is 

intended to improve the land use capabilities.

MOVES is EPA’s on-road mobile source emissions model, and is required to be used for 

regulatory purposes under the Clean Air Act. Specifically, MOVES is required to be used for 

estimating on-road emissions in State Implementation Plans (SIP) and transportation conformity 

determinations. This project has shown that some regions are becoming increasingly familiar 

with its use. Accordingly, many are developing their own local MOVES input data to make the 

analyses more sensitive to local transportation characteristics. If local inputs were available, 

they were employed for the TEAM analysis and where unavailable, MOVES default data was 

used. For this non-regulatory planning purpose, the MOVES default data inputs were sufficient 

to compare and contrast different scenarios. TEAM analyses and CAA regulatory analyses may 

rely on some common data, but TEAM cannot be used to perform the regulatory analyses 

required by the CAA. 

For each of the regions, MOVES was used to estimate average emissions per mile and per 

start, and these rates were applied to the resulting change in VMT and trips to calculate the 

potential benefit of the strategies. These case studies illustrate the flexibility inherent in 

MOVES. EPA’s MOVES guidance documents describe different methods that can be used to 

estimate equivalent emissions in a given area, and in each of the regions, a difference approach 

was used.4

 In one case, MOVES was run for the one relevant county. In another, MOVES was 

run for a custom domain, using inputs from one “representative” county. For the third, several 

counties were modeled individually and the results summed. In addition, the three areas had a 

variety of locally available data and relied on the default data within MOVES to varying degrees. 

The MOVES results in terms of per-mile and per-start rates developed in each of the three 

areas can be found in Tables 8, 12, and 16. 

Conclusions

The foundation of the TEAM approach is the development of scenarios and translating those 

scenarios into reasonable input values that can be used by the TRIMMS and MOVES models. 

The scenarios can represent reasonably achievable goals or can be an acknowledged “reach” 

for a more significant change from the BAU case. A stakeholder group composed of wellinformed local transportation planners, experienced modelers and land use planners can 

develop the scenarios and associated model inputs for a region based on their professional 

knowledge and limited additional research. While scenarios can be reasonably achievable in the 

 

4 Using MOVES to Prepare Emission Inventories in State Implementation Plans and Transportation Conformity: Technical 

guidance for MOVES2010, 2010a and 2010b, EPA-420-B-12-028, April 2012,

Using MOVES for Estimating State and Local Inventories of On-Road Greenhouse Gas Emissions and Energy Consumption –

Final (PDF) (74 pp, 2.4M, EPA-420-B-12-068, November 2012) 
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long term, they can also represent aggressive regional goals, unconstrained by shorter term 

political, fiscal, or technological challenges. 

Although results for each region were compared for reasonableness to the similar 

representative regional clusters from the 2010 national study, in some cases it was more 

instructive to compare the results to the full range of results of the 2010 national study. 

Population size and transit mode share do not capture other regional characteristics that can 

affect the results, such as average trip lengths, travel costs, climate and geography. In addition, 

strategy assumptions, as translated into model input values, may have a significant effect on the 

results. Strategies can be specified in more or less aggressive inputs, and can be restricted to 

sub-populations and sub-geographies or a region. 

The TEAM approach, utilizing travel sketch modeling, regional travel data, information from the 

literature, and emissions estimates from the MOVES model, produces reasonable estimates of 

emissions reductions from travel efficiency strategies. These case studies tested the approach 

in a variety of regional contexts, and the results, while more conservative, fall within the range 

predicted by the 2010 national study. Differences appear more related to the appropriateness 

of the data used and the limitations of the travel sketch model than the approach itself. The 

regional analysis was conducted with TRIMMS 3.0, which includes some significant changes 

since the previous version used for the 2010 national analysis, and may have contributed to 

some differing results. Given more time, regions should be able to develop better data and

improve the assumptions in TRIMMS and MOVES, making TEAM a useful approach to support 

regional decision-making. 
Introduction and Background

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1. Introduction and Background

1.1. Introduction to TEAM

The U.S. Environmental Protection Agency (EPA) has a long history of supporting research to

quantify the potential reductions in transportation-related emissions resulting from travel 

efficiency strategies that reduce vehicle miles traveled (VMT). Travel efficiency strategies 

represent the broad range of strategies designed to reduce travel activity, especially singleoccupancy travel. The term “travel efficiency strategies” builds on the traditional Transportation 

Control Measures (TCMs) listed in Section 108(f)(1)(A) of the Clean Air Act (CAA) by adding 

other strategies such as transportation pricing and smart growth. 

This report documents EPA’s latest effort to contribute to the state of the practice for assessing

the impacts of travel efficiency strategies in reducing transportation emissions. The purpose of 

the effort was to develop a collaboration with state or local government officials to demonstrate 

a methodology developed by EPA and documented in the recent study, Potential Changes in 

Emissions Due to Improvements in Travel Efficiency5 (2010 national study), and to quantify 

potential emission reductions from travel efficiency strategies at the regional level. EPA 

identifies the methodology as the Travel Efficiency Assessment Method (TEAM), TEAM uses 

commonly available regional travel data and sketch modeling analysis to quantify changes in 

VMT, combined with emissions estimates from the EPA MOVES2010 (Motor Vehicle Emission 

Simulator) model’s emission factors to calculate the emission reductions that can reasonably be 

expected. In support of this effort, EPA provided two resources for demonstrating the 

methodology at the regional scale: (1) a user guidance document that describes the 

methodology in detail and (2) technical assistance to three regions interested in assessing 

reductions in greenhouse gas (GHG) and criteria emissions from these strategies. The process 

and results of this effort are discussed in this case study of the three selected regions. 

1.2. 2010 National Study

The 2010 national study was intended to establish a reliable and useful source of information on 

the effectiveness of selected travel efficiency strategies and to quantify the potential national 

emission reductions that could result from those strategies. The study focused on light-duty 

vehicles and as such only considered gas and diesel fueled passenger cars and light duty 

trucks. The results represent the reduction in urban VMT and emissions that could be achieved 

with selected travel efficiency strategies applied to urban areas nationwide, with rural travel 

assumed to remain unaffected. 

The study used data from 15 metropolitan regions to determine the potential for national-level 

emissions reductions. These regions were grouped into “clusters” based on the size of their 

population and the extent to which transit is used. Although the analysis is based on actual 

travel data and characteristics of real metropolitan areas, because of the aggregation and 

 

5 2010 National Study, www.epa.gov/otaq/stateresources/policy/420r11003.pdf
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averaging of data, the predicted changes to travel activity and resulting emissions from this 

analysis are not intended to represent the effectiveness of the strategies for any particular area. 

The strategies analyzed were: travel demand management (TDM), land use policies, transitrelated strategies, and parking and road pricing. The strategies were further combined into 

future scenarios building from combinations of the most widely applied strategies to more 

aggressive approaches like transportation pricing. A sketch-planning tool developed at the 

University of South Florida, named Trip Reduction Impacts for Mobility Management Strategies 

(TRIMMS, version 2.0), and the data from representative metropolitan regions were used to 

estimate the national potential for reductions in VMT under a variety of scenarios. Emission 

factors obtained from EPA’s MOVES2010 (Motor Vehicle Emissions Simulator) model were 

then used to convert VMT reductions into emissions reductions. Key aspects of the study 

included: 

• A review of recent studies to determine the range of effectiveness of various strategies in 

addressing travel demand

• Development of an assessment methodology (Travel Efficiency Assessment Method, or TEAM), 

with input from a panel of subject matter experts

• Defining a set of future scenarios that incorporate various strategies expected to reduce travel 

activity and emissions

• Sketch-planning analysis of actual metropolitan areas representing a range of populations and

transportation characteristics using available local data from regional planning organizations

• MOVES 2010 emissions modeling using results from the sketch-planning analysis of the 

surrogate metropolitan areas

MOVES2010 was used to generate national-level, fleet-wide emission factors for this analysis 

reflecting emissions from start, refueling, and urban driving activities for years 2010, 2020, 

2030, 2040, and 2050. These factors account for all changes incorporated in the model’s default 

assumptions regarding vehicle technology and fuel characteristics. No additional strategies, 

including alternative vehicles and fuels or special use of retrofit technologies, were included. 

Although not directly comparable due to differences in approach, the results of this study were 

similar to other studies estimating the potential reductions from travel efficiency strategies 

conducted in the recent past, such as Moving Cooler (Cambridge Systematics 2009). 

1.3. 2013 Case Study Participants

EPA recruited regional partners to collaborate with on this project by soliciting letters of interest 

from state and local transportation and air quality planning agencies. The received letters of 

interest were considered based on the following key factors: 

• A demonstrated interest in GHG planning and analysis

• Regional population above 200,000 in the MPO or Metropolitan Statistical Area (MSA)

• Availability of data and staff resources to support the analysis and collaborate with EPA
Introduction and Background

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EPA targeted technical support to areas that are interested in exploring tools to conduct an 

analysis of transportation-related emissions more quickly and efficiently than the methods they 

currently use, if any. TEAM is a sketch planning approach that allows for some strategies to be 

analyzed at a regional or sub-regional scale without requiring the use of a sophisticated regional 

travel demand model. The approach can therefore be used either in regions with sophisticated 

analysis capabilities to prioritize strategies for further analysis, or as the primary analytical 

method in regions that do not have any established methodologies. EPA was particularly 

interested in partnering with agencies that expressed an intent to collaborate with regional and 

local partners such as state agencies, regional air quality boards, and environmental or natural 

resource agencies on GHG planning and analysis. 

EPA received ten letters of interest from a variety of transportation agencies engaged in 

regional planning. Three agencies were selected to participate in the case studies, testing the 

TEAM approach at a regional level: Pima County Association of Governments (PAG), 

Massachusetts Department of Transportation (MassDOT), and Mid-America Regional Council 

(MARC). Each agency brought a unique perspective and interest to the study as identified 

below.

Pima Association of Governments – local commitment for GHG reduction through U.S. 

Mayor’s Climate Protection Agreement; presence of baseline GHG emissions inventory; 

interest in wider range of strategies, including transit and TDM with ability to provide detailed 

data (rideshare program currently in effect); experience with generalized sketch planning 

tools like Climate and Air Pollution Planning Assistant (CAPPA), where the TEAM approach 

and use of TRIMMS could add value. 

Massachusetts Department of Transportation – strong collaboration across the region 

and state; motivation due to state legislation and regional targets for GHG reduction; 

capacity to leverage knowledge gained through the case study across MPOs in the state as 

well as other Northeastern and Mid-Atlantic states through the Transportation and Climate 

Initiative; opportunity to compare results from TRIMMS against their own tools developed to 

test transit and non-motorized transportation strategies.

Mid-America Regional Council – availability of CarbonFIT model for land use and TDM 

analysis that can be used to compare results from TRIMMS; interest in a wide range of 

strategies including transit, land use, TDM; strong collaboration across multiple jurisdictions 

and two states and playing a leadership role on GHG reduction efforts in the region.

Each agency selected strategies of interest to their region and developed scenarios with these 

strategies based on individual goals for the case study. The regions were diverse in their 

selection of strategies, availability, type and completeness of data for the models, and this 

provided more insight to the strengths and challenges of the analytical tools used.
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2. Methodology and Key Findings

2.1. Scenario Selection and Baselines

Agencies were asked to consider strategies for their region as identified in the 2010 national 

study (see Appendix A for the referenced tables 4 and 6) for inclusion in their scenarios. 

Selected strategies must be able to be specified to fit the capabilities of TRIMMS in order to 

estimate VMT reductions. This requirement limited the full range of potential strategies under 

consideration because some strategies cannot easily be modeled in TRIMMS. For instance, 

high-occupancy vehicle (HOV) lanes must be translated into travel time savings for input to 

TRIMMS. In each region, thought was given to how the travel efficiency strategies chosen could 

be reflected in TRIMMS, as explained further in Section 2.3. 

Scenarios were comprised of groups of strategies, and assessed for their overall combined 

VMT reduction and emissions benefit. The 2010 national study combined strategies in

increasingly aggressive scenarios by starting with the most basic strategies of travel demand 

management (TDM) and land use, and progressing to the more controversial strategies for 

many regions to implement, such as road pricing. This combining of strategies (through 

sequential application and modeling) helped identify how benefits may incrementally increase 

with additional actions over time. Both MassDOT and MARC elected to use a similar approach. 

PAG was more interested in specific strategies that were likely to garner political and public 

support, and therefore selected single strategies for each scenario. The specific strategies 

selected to comprise each scenario, along with the data needed to model them are provided for 

each agency in Section 3 of this report.

The TEAM results provide a comparison of potential emissions reductions from selected 

strategies with the potential emissions from a business as usual (BAU) scenario. The results are 

presented as percent reductions based on this comparison. For this reason the selected BAU 

baseline is a critical component of using this approach. The BAU scenario represents likely 

emissions based on the existing regional plan for transportation infrastructure and regional 

growth. The development of a BAU can be challenging, so participating agencies provided a 

range of BAU scenarios for use in this study. In most cases the BAU represents the future year 

infrastructure and travel activity without additional travel efficiency strategies. This is typically 

the scenario in the adopted long range transportation plan and is well suited for use with TEAM. 

It is essential to ensure that the scenarios analyzed for comparison match the geographic 

boundaries, fleet characteristics, population, and other parameters of the BAU scenario.

EPA has a strong interest in how land use changes, particularly smart growth principles, 

influence emissions reductions. MassDOT and MARC included land use strategies in their 

scenarios; however, PAG decided not to explore this strategy. To provide a balanced 

comparison across regions, a reasonable land use strategy for PAG was developed by EPA 

independent from the region’s other strategies. The EPA land use strategy was then combined 

with the other strategies selected by the region to provide an additional scenario for PAG. The 

results of this analysis are discussed in Section 3 as a part of the PAG case study.
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2.2. Data Collection and Validation

Data collection and validation is the most important task within any analysis approach, and 

TEAM is no exception. One of the advantages of TEAM is that it relies primarily on data inputs 

and outputs from typical travel demand models used for regional planning, without the need to 

actually run the travel demand model for each scenario being considered. This can save 

valuable time and other costs associated with performing several travel demand model 

analyses. The intent of TEAM is to support early decision making by providing a comparison of 

different strategies under consistent future conditions. Strategies that appear most effective and 

have the necessary public and political support can then be more rigorously analyzed for 

precise impacts using the travel demand model and other tools available to the region. When 

the data being used is consistent across these levels of analysis, it is more likely that confidence

in the outcome of the scenario can be maintained. 

Data collection for the analysis involved obtaining local data from the agencies to include in 

MOVES, processing them as necessary, completing quality assurance reviews of provided 

inputs and revising as needed, and filling gaps in the local data with national default data from 

MOVES to create a complete dataset. Locally specific data is preferred to default data, and 

efforts were made to obtain local data wherever possible. All three regions provided some local 

data that generally was used by the agencies for other analytical or planning purposes. Only 

PAG performed data collection unique to this analysis.

The data required for the TEAM analysis is primarily from the regional travel demand model, but 

not entirely. Responsibility for the analysis that supports transportation planning can vary across 

states and regions. This variation in responsibility may require cooperation among local 

agencies and stakeholders to support the data collection and scenario development necessary 

to undertake the TEAM analysis. The EPA notice for letters of interest explicitly stated that “EPA 

would prefer to engage with agencies that collaborate with regional and local partners such as 

state agencies, regional air quality boards, and environmental or natural resource agencies on 

GHG planning and analysis.” 

Obtaining the data for this study included stakeholder participation in the PAG and MassDOT 

regions. The degree to which stakeholders were involved had a significant impact on the 

amount of effort required of the lead agency. MassDOT was the region with the broadest array 

of stakeholders, which allowed the agency to spread the data requirements and reduce the 

demand on lead agency staff. The region with the most independent process, MARC, was 

limited in what information they could provide and relied heavily of the use of default data, 

especially with respect to the MOVES inputs. The data input elements required for MOVES are 

shown in Table 4. In PAG’s case study, stakeholder working group meetings helped identify 

detailed data for sub-regional geographies to support their specific sub-regional strategies of 

interest. 

Initially, some questions arose about whether the TEAM results could be used for transportation 

conformity, because some of the data provided for use in the MOVES analysis was drawn from 

the data used in the area’s most recent regional transportation conformity analysis. Although 

both analyses rely on some of the same data, TEAM is not part of the transportation conformity 
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process and the TEAM approach does not meet conformity requirements. For example, in the 

TEAM approach, changes in VMT estimates are generated with TRIMMS rather than the 

region’s travel demand model. However, for conformity purposes, use of a travel demand 

model is required for certain areas, as well as in any areas where the MPO has a travel demand 

model.6

 Travel demand models provide vehicle activity estimates beyond the capability of 

TRIMMS. This detailed activity, such as volumes and speeds on each facility, is then used with 

MOVES to more precisely estimate emissions. The TEAM analysis is a less rigorous analysis 

than that required for transportation conformity, as it dispenses with detailed transportation 

facility classification and associated speed data as used in transportation conformity analyses 

where areas are required to use a travel demand model. While TEAM can inform an area’s 

overarching conformity discussion about the potential emission benefits of particular strategies, 

it does not provide results that can be directly used in a regional transportation conformity 

determination. 

2.3. TRIMMS Analysis

The use of the TRIMMS model to estimate VMT reduction has been a standard feature of the 

TEAM approach since the initial 2010 national study. Several factors recommend using 

TRIMMS for this type of analysis, including the type and format of inputs required, the 

geographic scale of analysis, and the capability of modeling a variety of TCMs, making it highly 

useful for this type of analysis. 

The previous study used TRIMMS 2.0; however, the analysis for the 2013 regional study was 

conducted with TRIMMS 3.0. The Center for Urban Transportation Research (CUTR) at the 

University of South Florida made some significant changes between these versions. The user 

interface was updated to reduce the number of steps required to conduct the analysis, and the 

outputs were expanded in detail. Most significantly, TRIMMS 3.0 added a new land use module 

that includes controls for increasing residential densities, land use mixing, transit station 

accessibility, and transit-oriented development (TOD). In the previous version of TRIMMS, land 

use strategies could be analyzed only by translating them into changes in travel times and travel 

distances. Further investigation into the TRIMMS 3.0 land use module are required in order to 

fully understand how this change may account for for the lower response to changes in land use 

compared to the 2010 study.

TRIMMS strategies are input into three main tables, as shown in the figures below: Employer 

Demand Management strategies (Figure 1); Financial, Pricing, Access, and Travel Times 

(Figure 2); and Land Use Controls (Figure 3). The employer demand management inputs work 

primarily through the use of “radio buttons” where the user selects either yes or no for the 

application of the strategy. Financial, pricing, access, and travel times are input as numerical 

values. The land use controls are a mixture of sliding scales, where the user specifies a 

percentage change from the baseline, and radio buttons.

 

6 The conformity rule at 40 CFR 93.122(b) requires the use of travel demand models for serious and above ozone nonattainment 

and maintenance areas with a population over 200,000; in addition, the rule at 40 CFR 93.122(d) requires MPOs that have a 

travel demand model to use that model for conformity purposes. 
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Figure 1: TRIMMS Employer Demand Management Inputs

Figure 2: TRIMMS Financial, Pricing, Access, and Travel Times Inputs

Yes No

Carpool Subsidies

Transit Subsidies

Vanpool Subsidies

Bike Subsidies

Walk Subsidies

Yes No

Carpool matching service offered?

Emergency ride home provided?

Vehicle for non-work trips?

Yes No

Flexible working hours offered?

Compressed work week offered?

Telework program offered?

Program Subsidies 

Guaranteed Ride Home and Ride Match 

Telework and Flexible Work Schedules

Accessibility Yes No

Bus or train station onsite or within 1/4 mile

Bike lanes onsite or within 1/4 mile

Dedicated sidewalk onsite 

Amenities

Shopping onsite or within 1/4 mile

Restaurant onsite or within 1/4 mile

Bank onsite or within 1/4 mile

Childcare onsite or within 1/4 mile

Parking

Parking charge for carpooling?

Parking charge for vanpooling?

Number of free onsite parking spaces 

Yes No

Internal snail-mail of promotional material?

Internal promotional email?

Do you hold promotional events

Program management and promotion (hrs./week) 8

Program Marketing

0

Worksite Characteristics

Mode

Current 

Parking 

Cost

New 

Parking 

Cost 

Current 

Trip Cost

New Trip 

Cost

Auto-Drive Alone

Auto-Rideshare

Vanpool

Public Transport

Cycling

Walking

Other

Mode

Current 

Access Time

New 

Access 

Time

Current 

Travel 

Time

New Travel 

Time

Auto-Drive Alone

Auto-Rideshare

Vanpool

Public Transport

Cycling

Walking

Other

% Workforce Affected 85.0%

Financial and Pricing Strategies ($)

Access and Travel Time Improvements (minutes)
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Figure 3: TRIMMS Land Use Inputs

Strategy Selection

The three regions selected individual strategies of interest that fit within the TRIMMS analytical 

capabilities. In general, these fall into the four strategy categories identified in Table 3. Many of 

the TRIMMS functions work by manipulation of travel times and travel costs, common factors in 

travel demand modeling. To model an expansion of HOV lanes in TRIMMS, for example, the 

user must input changes in typical travel times for carpool trips (and possibly single occupancy 

vehicle (SOV) trips). In order to use TRIMMS effectively, users must consider how to translate 

their strategy interest into the input options within the model. Table 3 provides information about 

the data needed and the analysis options within TRIMMS that were used to analyze the 

strategies selected.

 

Encouraging higher densities in residential areas Current New

Gross Population Density (persons/sq. mile) 835

Increase (%) 0.0

Encouraging mixed land-use Current New

Retail Establishment Density (number/sq. mile) 3

Increase (%) 0.0

Increasing station accessibility Current New

Walking distance to nearest station (miles) 0.68

Decrease (%) 0.0

Implementing TOD stations Yes No

Presence of TOD stop 

% Workforce Affected 75.0%

Land Use Controls
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Table 3. TRIMMS Analysis Options

Strategy Categories Data Needs TRIMMS Options

TDM or Employer Incentives • share of regional employees covered

• average subsidy offered to employees (by 

mode)

• are guaranteed ride home, ride match, telework, 

and flexible work schedules offered?

• financial and pricing strategy entries: 

parking cost and trip cost

• program subsidy radio buttons

• guaranteed ride home, ride match, 

telework, and flexible work schedules

radio buttons

Transit • share of regional population affected 

• average decrease in transit trip cost

• transit travel time and access time

• financial and pricing strategy entries: 

access time and travel time

Land Use • share of regional population in affected areas

• increase in weighted average residential density 

(persons per square mile)

• increase in weighted average retail 

establishment density (number per square mile)

• average decrease in walking distance to transit

• land use controls: residential density, 

retail establishment density, and 

walking distance to nearest station

Pricing • share of all parking (public and private) that is 

priced

• average increase in parking cost per trip

• average increase in trip cost

• financial and pricing strategy entries: 

parking cost and trip cost

Customizing TRIMMS

Elasticities

TRIMMS contains default elasticities that measure the relationship of travel costs and access 

times to travel patterns. Direct elasticities describe relationships between travel by one mode 

and the cost and time characteristics of that mode. For example, a direct elasticity of drive alone 

travel with respect to trip cost of -0.5 means that a 1% increase in drive alone trip cost is 

associated with a 0.5% decrease in drive alone travel. Cross elasticities describe relationships 

between different modes of travel. A cross elasticity of drive alone travel with respect to transit 

access times of 1.0 indicates that a 1% decrease in transit access times is associated with a 1% 

decrease in drive alone travel.

TRIMMS does not provide defaults for all elasticities. For example, TRIMMS 3.0 does not have 

a default elasticity to represent the shift in drive alone travel relative to the cost of carpool or 

vanpool travel. TRIMMS supplies zero values for these elasticities, whereas their true values 

are most likely non-zero. TRIMMS allows the substitution of elasticities for its default values. 

The national-level research for EPA identified elasticities that are well-supported in the 

literature. The research team relied upon these values to supplement and support what was 

available in TRIMMS. The TEAM user guidance identifies the desired elasticities for a regional 

analysis as well as provides a list of elasticities in its Appendix C.
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Still, there are several instances in which elasticities not supplied by either TRIMMS or the 

literature, and which therefore default to zero, can produce unreasonable analysis results. If the 

model is run with these values set to zero, the TRIMMS model may predict increases in trips for 

one mode of travel without corresponding decreases for another mode. TRIMMS automatically 

rebalances total trip numbers when evaluating employer-based strategies, but not for other 

strategy types. The user can correct for this by adjusting the outputs of TRIMMS to rebalance 

total trip numbers (the approach taken in this study, as described below) or by supplying a more 

complete set of elasticity values.

Alternative Populations and Geographies

In the 2010 national study, TEAM was applied to each region’s entire population for all 

strategies with the exception of TDM, which considered only the working population. However, 

in the 2013 regional study PAG requested modeling strategies that clearly applied to only a 

subset of the population and a limited geography within the region. Using TEAM for subgeographies and sub-populations is more complicated. Sub-geographies and populations 

sometimes require different baseline assumptions (e.g. mode shares and trip lengths for 

downtown vs. region), and sub-populations can be difficult to isolate (e.g. traveler population for 

a specific corridor). Combining strategies that apply to different sub-population or subgeographies requires that the effects of the strategies be summed together outside of TRIMMS 

as a post-processing step. In practice this is more likely to overestimate rather than 

underestimate impacts. When VMT reduction strategies are applied cumulatively within a single 

model run, the second strategy applies to a smaller baseline VMT than the first strategy, and 

therefore produces smaller absolute VMT reductions than if applied on its own. 

The value of the results using sub-geographies and sub-populations is sometimes limited. The 

corresponding emissions reductions are quite small when applied to the entire region and may 

be insignificant even for the limited geography. This type of analysis is more suited to use of a 

sub-regional model to capture changes in VMT. Regionwide averages and regional scale 

strategies are more appropriate for TRIMMS and more consistent with the intent and value of 

TEAM.

However, in some cases modeling for sub-populations and sub-geographies is unavoidable, 

such as when modeling employer-based strategies. These will naturally apply only to the 

employed population. In these cases post-processing is essential. For conducting TRIMMS 

model runs, each scenario was divided into the constituent populations and one model run was 

conducted for each population. For example, to combine an employer-based strategy with a 

pricing strategy we conducted two model runs: one run for the employed population, to which 

both TDM strategies and pricing apply; and one run for the rest of the population, to which only 

pricing applies. Absolute VMT reductions for each model run were summed together to produce 

total VMT reductions for the region.
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Reasonableness Check

For each case study region, a comparison was made to the results for similar representative 

regions (based on regional population size and transit mode share) in the 2010 national study. 

Results for each strategy type for the case study regions were expected to be similar to the 

results for the comparison representative regions. The aforementioned Guide to the TEAM 

Approach recommended this approach, and these case studies provide an opportunity to test 

the usefulness of this comparison. Where results for case study regions were dissimilar to those 

of the comparison region in the 2010 national study, it is noted in the discussion. In some cases, 

the comparison helped to identify necessary adjustments to the TRIMMS inputs.

2.4. MOVES Analysis

Modeling and post-processing proceeded in stages. First, the actual MOVES runs were 

conducted, including collection and assembly of all input data and runspec control files, quality 

assurance and correction of any data issues as necessary and extracting inventory results from 

the output database. Next, the results were post-processed into a form useable in this analysis. 

This consisted of converting the inventory values of emissions and activity into regional 

average, activity-weighted emission factors for each pollutant, vehicle type, and year for the 

same composite vehicle types used in the TRIMMS analysis. Each of these steps is described 

more specifically in the sections for each region. 

MOVES is EPA’s most current and capable emissions model, and yet is still somewhat 

unfamiliar to some MPOs. Accordingly, there was varying degree of experience from the three 

regions on coordinating and collecting the necessary data. The PAG region, in particular, 

expressed interest in shadowing our analysis, examining all aspects of data collection and 

processing to further their understanding of the model and its use. Of the three regions, the 

Boston region MPO showed the most expertise and comfort with the model, and had the most 

complete input data set. However, their inputs were based on data from a more geographically 

narrow area than that desired for the TEAM analysis. Accordingly, their data required some 

modifications for this analysis. MARC was the most straightforward case because little local

data was available, necessitating that the analysis be based largely on model defaults. Each of 

these is discussed more fully in Section 3. 
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Data Inputs

MOVES allows the user to select the scale and the geographic boundaries for the analysis.

When the county scale is chosen, the user can input data specific to the county of interest, or 

can use the default information within MOVES. Since a TEAM analysis is non-regulatory, there 

are no restrictions against using default data. However, at the county scale, no default data is 

available for vehicle type population or for VMT; the user must enter the appropriate local data. 

Default data is available for other data fields. The MOVES default database includes 

information that varies by county for fuel and I/M program type, based on survey data at the 

time the model was developed. It also contains meteorological data for each county and fuel 

supply and formulation information for each county. For the remaining data fields, the default 

data are national average data that are then used for the county as-is. For example, using the 

default age distribution will apply the U.S. average age distribution to the particular county, 

which may over or underestimate the real age distribution of the county. 

Table 4. Data Inputs for MOVES Runs

Data Type Description Data Elements

Modeling decision elements that are 

selected in the MOVES run 

specification file

Domain/Scale Geographic Bounds

Calculation Type Vehicle Type

Time Aggregation Road Type

Calendar Year Pollutants and Processes

Evaluation Month Strategies

Type of Day Activity

Evaluation Hour Emissions Detail

Fields for which county-specific data 

must be entered when using the 

county scale

Source (Vehicle) Type Population

Vehicle Type VMT

Fields for which MOVES includes 

default county-specific data

Meteorological Data

Fuel Supply/Formulation

I/M (Inspection and Maintenance) Program

Fields for which MOVES includes a 

national default that can be used for 

TEAM when county data is 

unavailable

Age Distribution

Ramp Fraction

Month, Day, Hour VMT Fractions

Average Speed Distribution

Alternative Vehicle and Fuel Technology
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The scale selected for MOVES modeling was the county scale, to make use of more precise 

local data and consistent with EPA Guidance,7 but the approach varied across the regions. The 

PAG analysis was performed for the entirety of Pima County. The greater Boston area was 

modeled as a custom domain encompassing several counties. The seven counties in the MARC 

region were modeled individually and the results aggregated outside the model. Data collection 

was focused on each element, as identified in Table 4, for analysis as chosen by each region. 

Details about the collection, processing, sources, and quality assurance of each of these data 

items appear in the regional discussions in Section 3. 

Deriving Emissions Factors

MOVES was run in inventory mode for each of the regions, and the resulting totals of emissions 

and activities (VMT and number of starts) were ratioed to produce activity-weighted average 

emission factors for the region. Four primary pollutants were considered in this analysis: CO2-

Equivalent, NOx, PM2.5, and VOC. Other pollutants were also included in the analysis as done in 

the initial study. These are provided in Appendix B. 

As noted above, TRIMMS uses composite vehicle types. Emission factors from MOVES were 

derived by combining MOVES vehicle types to represent the TRIMMS composite vehicle 

category definitions.8 For the TRIMMS auto drive alone and auto rideshare vehicle categories, 

composite emission factors representing motorcycles, passenger cars, and passenger trucks 

were combined from the MOVES model. For the TRIMMS vanpool vehicle category, composite 

emission factors representing MOVES passenger truck and light commercial truck vehicle types

were used. 

The MOVES analysis included all road types. MOVES emission process types included Start 

Exhaust, Crankcase Running Exhaust, Crankcase Start Exhaust, Running Exhaust, Brakewear, 

and Tirewear. All MOVES runs for this project were run without pre-aggregation of the data. 

While pre-aggregation saves modeling time, it can reduce precision. As recommended by EPA 

for most purposes, an hourly analysis was performed for all hours, days, months of the year. 

Emission factors were then calculated as total running emissions (in grams per year) divided by

total running activity (in miles per year); a similar analysis was made for starting emissions. 

Emission factors, in grams of pollutant per average mile driven and grams of pollutant per 

average start were produced. 

From this, a single emission factor was derived for each vehicle type, year, pollutant, and 

process type. This represents an overall average for that year and was used for every scenario

in that year. The current year emission factors were paired with baseline activity in the current 

year for total baseline emissions, and future year emission factors paired with activity for the 

 

7 Analysis for scale and other parameters adhered to EPA’s current guidance for estimating on-road greenhouse gas emissions: 

Using MOVES for Estimating State and Local Inventories of On-Road Greenhouse Gas Emissions and Energy Consumption – 

Final (EPA-420-B-12-068, November 2012). 

8 Other vehicle parameters are also combined within the model. These include fuel type and model year, which are based on the 

locally specific inputs collected from the regions. 
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BAU and all scenario alternatives to generate total future year emissions from the BAU and 

scenarios. Both starting emissions – based on TRIMMS-calculated number of trips – and 

running emissions – based on TRIMMS-calculated VMT were included in computing the total 

emissions and emission changes. The calculation of total emissions was done in a simple, offmodel spreadsheet calculation that assembled outputs from both the emissions and VMT 

modeling results. Regionally specific details and results are presented in the Section 3. 
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3. Case Study Results

Three agencies were selected as case studies for testing the TEAM approach at a regional 

level: Pima County Association of Governments (PAG), Massachusetts Department of 

Transportation (MassDOT), and Mid-America Regional Council (MARC). The lead agencies 

identified stakeholder groups to support their strategy selection and data collection. The 

selection of strategies was grouped into four scenarios for each region based on their individual 

interests and data availability. PAG defined its scenarios as four separate strategies with no 

overlap, in contrast to the other two case study regions, which incrementally applied strategies 

to achieve a progressive increase in VMT reduction across scenarios. The strategies and their 

underlying assumptions represent a broad range of potential futures to be evaluated for 

corresponding changes in travel activity and emissions reductions. Each of the regional 

analyses are examined in detail in the following sections. 
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3.1. Pima Association of Governments (PAG) – Tucson, Arizona

Background

PAG is a metropolitan planning organization (MPO) with regional planning responsibilities for 

transportation, air quality, and water. The organization is governed by a regional council 

composed of representatives of its member jurisdictions: Pima County, Tucson and South 

Tucson, the towns of Marana, Sahuarita, Oro Valley, and the Tohono O’odham Nation and 

Pascua Yaqui Tribe. PAG is responsible for development of the area’s long-range regional 

transportation plan. 

PAG has an active travel reduction program which includes voluntary computer based ridematching for residents and mandatory ride-matching for employers with greater than 100

employees. There is an interest in transit fare and TDM programs. The region’s GHG inventory 

is done every two years, and both county and city have adopted mandates to reduce GHG 

emissions. 

Interest in Participation

When submitting its letter of interest, Pima County was in compliance with the EPA’s air quality 

standards with an ozone level at 90% of the ozone standard, but was concerned that its status 

could change with a future, more stringent ozone standard. Gathering more accurate 

information on the emission benefits from travel efficiency strategies can potentially help the 

region lower VOC and NOx emissions and ultimately ozone concentrations.

Along with the concern about ozone, the region must comply with the requirements in the 

carbon monoxide Limited Maintenance Plan (CO LMP) due to CO violations in the late 1970’s 

and early 1980’s. One of these requirements is to implement a Travel Reduction Program (TRP) 

for the region to encourage alternate modes of transportation to improve regional air quality. 

The TRP is administered by PAG staff and has been in effect since 1988, with over 290 

companies and organizations participating. The program includes employers in unincorporated 

Pima County, Tucson, South Tucson, Marana, Oro Valley and Sahuarita. The benefits of the 

TRP program are quantified by estimated VMT reductions and gasoline savings. PAG is 

interested in improving staff ability to more precisely estimate the TRP benefits and tailor future 

planning to promote programs with the most significant pollution benefits. In addition to the TRP, 

both Tucson and Oro Valley have signed the U.S. Mayor’s Climate Protection Agreement

committing to reduce GHG emissions to seven percent below 1990 levels by 2012. Both entities 

have initiated aggressive plans to address this reduction goal.

PAG’s initial letter of interest indicated that using the TEAM approach to evaluate travel 

efficiency strategies could: 

• Provide assistance in meeting regional policies and actions such as TRP ordinances and the 

Mayor’s Climate Protection Agreement

• Help to refine actions already underway through the TRP program
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• Refine regional transportation-related emissions using the more complex MOVES model, 

resulting in data that are more closely tracking local conditions and GHG emissions

• Provide an improvement over the Clean Air and Climate Protection model, which does not 

incorporate speed, local meteorological or travel patterns or vehicle registration data

• Assist PAG’s jurisdictions with resource and program planning to select the travel reduction 

components that allow the greatest air quality benefits

PAG had a specific interest in the use of MOVES for this analysis, and requested permission to 

“shadow” the analysis effort as a training exercise. This level of interface resulted in more 

coordination between PAG and EPA to answer questions and resolve issues; however, it also 

produced a stronger understanding of how the MOVES model can be used for this type of 

analysis.

Prior Experience with Analysis of GHG Emissions

PAG staff conducts GHG modeling and analyses to develop a Regional GHG Inventory for 

eastern Pima County and the City of Tucson communities and their respective government 

operations. For the analysis, the most current edition of the inventory was released in 2011 and 

covers 1990 through 2008. The preliminary results from the 2011 inventory indicate that 

regional transportation contributed 32 percent to the 2010 Pima County’s GHG total. From 1990 

to 2010, regional, private/commercial VMT increased by 63 percent with an accompanying 34 

percent increase in GHG emissions. In 2010, PAG staff conducted GHG modeling and analyses 

and developed GHG emission inventories for PAG’s outlying jurisdictions (Marana, Oro Valley, 

and Sahuarita) spanning the 2000-2008 timeframe. PAG staff continues to provide these 

jurisdictions with GHG emissions and transportation data as requested.

PAG’s GHG inventories include emissions from private and commercial vehicles, public transit, 

government fleet travel and employee commuting, as well as energy use and waste disposal 

emissions. Private/commercial vehicle emissions are responsible for 99 percent of the region’s 

transportation emissions. Private and commercial VMT data used in the inventories was 

developed by PAG’s transportation planners using their travel demand model. Inventory 

commuter data are obtained from PAG’s annual employee survey. Public transit VMT by fuel 

type is supplied by the various transit providers. To develop GHG inventories and ongoing GHG 

analyses, PAG staff uses the Clean Air and Climate Protection (CACP) model and the Climate 

and Air Pollution Planning Assistant (CAPPA) software developed by the International Council 

for Local Environmental Initiatives (ICLEI) and others.

The City of Tucson formed the Citizen’s Climate Change Advisory Committee in response to the 

adoption of the U.S. Mayor’s Climate Protection Agreement by Tucson’s mayor in 2006. The 

committee developed a report, Action Plan Tucson, Phase One Climate Mitigation, passed by 

the Tucson’s Mayor and Council in December 2011. 

(http://cms3.tucsonaz.gov/sites/default/files/ocsd/act_phase1report_final_6dec11.pdf) 

PAG staff conducted the GHG emissions modeling and analyses that were the basis for this 

report and were instrumental in the development of the technical support document, Community 
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Economic Security and Climate Action Analysis, compiled by Westmorland. Both documents 

include numerous strategies to reduce emissions through transportation efficiency and land use 

strategies. Similarly, when the Pima County Board of Supervisors adopted a resolution to 

develop the Five-year Action Plan, implementing wide-ranging strategies aimed at reducing 

government GHG emissions to 2007 levels by 2020, PAG’s GHG inventory data served as the 

baseline for County government emissions. PAG staff continues to supply GHG emission data 

and analyses for assessing the plan’s progress.

Scenario Development

As the lead agency in the case study, PAG initially expressed an interest in understanding the 

impact of a variety of strategies on vehicle miles travelled, GHG emissions, and NOx and VOCs 

in PAG’s planning area, including parking cost increases (in the downtown University of Arizona 

corridor), bike travel increases, bus efficiency improvements, provision of an all-access bus 

pass for the populations at two academic institutions, incentives for rideshare, and streetcar 

enhancements.

The existing or base year for the PAG analysis is 2010. The future year is 2040 as reflected in 

the adopted Regional Transportation Plan (RTP). The BAU scenario provided by PAG included 

transportation improvement projects included in the plan; it represents the preferred scenario in 

the RTP. Population and employment in 2040 are estimated to nearly double those of 2010;

resulting in substantial increases in travel activity and emissions of some pollutants. Average 

travel distances will increase under the plan, but walk share will increase too, with more on 

campus housing being built at the University of Arizona.

PAG was unique in its approach to scenario development. Rather than use the analysis in a 

cumulative way, building the impacts of one strategy on those of another, the agency wanted to 

see the specific impacts of each strategy as a stand-alone policy. The rationale for this 

approach was that the region would not likely adopt all of these measures at the same time, and 

it would be more meaningful for them to evaluate the benefits of individual strategies.

Much of the PAG data for the TRIMMS analysis was available from the agency’s travel demand 

model. An important stakeholder that was engaged early in the process was SunTran, the 

regional transit agency. Sun Tran’s involvement helped PAG provide the data needed for 

Scenarios 1 and 3, where transit was a major feature. 

Scenario 1 - Implement SunTran All Access Pass

This scenario considers how providing an unlimited ride transit pass for almost 90,000 faculty, 

staff, and students at the University of Arizona and Pima Community College would affect travel 

activity. Purchase of the pass would be mandatory, but would be bundled with student tuition 

and employment benefits, with costs possibly shared between the passholders and the 

academic institutions. However, the user experience is similar to a case of free transit, since the 

purchase is non-discretionary, the cost is largely not perceived by the user, and the marginal 

cost of each additional transit trip is zero.
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This strategy did not include an increase in transit service, so bus VMT does not change. This 

methodology is consistent with the 2010 national study, in which only light-duty vehicle travel is 

considered and bus VMT is excluded from the results. The TRIMMS analysis used baseline 

mode shares and trip lengths provided by PAG that are specific to the university populations. 

The transit pass was modeled as a reduction in transit cost to zero. 

Scenario 2 - Expand Employer-Based Incentives for Alternative Commute 

About one-third of employees in the region are currently eligible for commute subsidies. This 

strategy would increase subsidies as shown in Table 5 with all dollar amounts expressed in 

current year dollars throughout the analysis. 

The trip subsidy radio buttons in TRIMMS were used, providing a simple apply/do not apply 

input. Another option was to use baseline trip costs by mode and adjust these by the subsidy 

amounts. The research team initially tested this approach using baseline trip costs estimated by 

the research team, since PAG did not have baseline trip costs. The results of that analysis 

proved unreasonable, likely due to a combination of incomplete elasticities and underestimated 

baseline trip costs. In particular, the TRIMMS model lacks a default cross-elasticity for auto 

drive alone travel with respect to carpool/vanpool trip costs (See Section 2.3). 

Scenario 3 - Implement Bus Rapid Transit (BRT) on Two Corridors

PAG is exploring BRT on two major corridors in the region that bring traffic into and out of 

downtown Tucson: Oracle Road, a north-south corridor, and Broadway Boulevard, an east-west 

corridor. Although there was no specific data available on the change in transit level of service 

associated with the BRT proposals, SunTran estimated that the projects might reduce travel 

times and wait times between transit vehicles by 20% on the corridors. 

The PAG-supplied daily traffic counts on both corridors were assumed to be roughly equivalent 

to traveler population using the assumption of two trips per day (round trips) for this population. 

The base year traffic counts were then scaled up using projected regional population growth to 

provide the future year BAU estimate of the traveler population using the two corridors.

Scenario 4 - Expand Parking Pricing in the Downtown-University Corridor 

This scenario included a doubling of parking prices in a specific downtown-university area along 

with expanding the share of parking that is priced in the future year. PAG supplied an estimate 

of the share of priced parking in the corridor of interest, along with the average parking price per 

trip. The average price of all priced and unpriced parking in the downtown-university area was 

estimated as the product of average hourly rates and the proportion of priced parking. 

Input parameters are provided in Table 5 for current conditions, a BAU future, and the four 

scenarios selected by PAG. Specific input values are provided for the scenarios.
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Table 5. PAG Scenario Details

Scenario Description Geography Data Used

Current Conditions Existing conditions across 

all strategies in 2010

Regionwide and 

Subarea

• mode shares

• average vehicle occupancy

• average vehicle trip lengths

• regional population and employment

• TRIMMS default retail establishment density 

(0.31 per square mile)

• TRIMMS default distance to nearest transit 

stations (0.70 miles)

• parking pricing

• employer-based incentives for alternative 

commute modes

Business as Usual 2040 conditions with 

current levels of transit 

pass, employer-based 

incentives, BRT coverage, 

and parking pricing

Regionwide • mode shares

• average vehicle occupancy

• 2040 regional population and employment

• TRIMMS default retail establishment density 

(0.31 per square mile)

• TRIMMS default distance to nearest transit 

stations (0.70 miles)

• Parking cost per trip: $2.71/hr. x 80% 

population = $2.17/hr.

• current employer-based incentives for 

alternative commute modes

• average transit trip cost ($1.43)

• average transit travel time (72.46 minutes)

• average transit wait time (10 minutes)

Scenario 1: SunTran 

All Access Pass

Bundle ‘free’ transit pass 

with tuition for faculty, 

staff, and students at two 

local universities 

Regionwide • number of students, faculty, and staff who will 

use the pass (86,234)

• average transit trip cost ($0)

Scenario 2: 

Expanded Employerbased Incentives

Increase subsidies by 

$10-$50 per mode.

Regionwide • number of regional employees covered

(236,616)

Scenario 3: BRT on 2 

Corridors

BRT on Oracle Rd and 

Broadway Blvd. 

Subarea • traveling population affected (62,565)

• 20% travel time reduction (to 57.97 minutes)

• 20% headway (wait time) reduction (to 8 

minutes)

•

Scenario 4: Parking 

Pricing in DowntownUniversity Corridor

Double parking prices and 

expand number of priced 

spaces. 

Subarea • 95% share of all parking (public and private) is 

priced

• parking cost per trip: $5.41/hr. for priced 

parking x 95% of total parking that is priced= 

$5.14/hr for all priced and unpriced parking

Additional EPA Land Use Scenario for PAG Region

Unlike MARC and MassDOT, the PAG region did not select a land use strategy as part of its 

four scenarios. Given the evidence that land use strategies provide an important base for other 

travel efficiency strategies, a land use strategy was independently developed by EPA for the 
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PAG region. This land use strategy was analyzed in an additional scenario with the four 

strategies selected by PAG: a Transit All Access Pass, Expanded Employer-based Strategies, 

BRT on Two Corridors, and Downtown-University Corridor Parking Pricing.

The land use scenario was defined based on an existing vision for the region: the Imagine 

Greater Tucson (IGT) preferred land use scenario. IGT developed this scenario through a twoyear, community-based visioning process in which residents from across the greater Tucson 

region communicated their thoughts and opinions on how the region should develop and grow. 

Over 10,000 people participated in the process and the results show a strong regional desire for 

smarter growth options.

The regional population growth assumed in PAG’s RTP 2040 scenario is roughly equivalent to 

that assumed in IGT’s preferred scenario, and thus provides a consistent reference point for 

comparison. The IGT scenario concentrates population growth in existing urban centers.

PAG provided data on population densities for all traffic analysis zones (TAZs) in the modeling 

region for both the IGT scenario and PAG’s RTP 2040 scenario. The weighted average 

population density9 of the IGT scenario is roughly 50% higher than that of the RTP scenario. 

Therefore the land use scenario was modeled as a 50% increase in residential density for the 

region. Since higher residential densities would cluster development in central areas with better 

transit access, walking distance to the nearest station was reduced by a conservative 10%. 

Table 6 provides the results of the analysis of this additional scenario. Adding the land use 

option increases total region VMT reductions by only 0.25% on top of the other strategies. This 

result is very small compared to the results of previous analyses of land use strategies using 

TRIMMS 2.0, and to values estimated in other studies. CUTR has suggested that this result 

may be due to the relatively low level of transit service in the PAG region, as land use results in 

TRIMMS are driven by transit accessibility. See Section 4.2 for a further discussion of land use 

analyses with TRIMMS. 

Table 6. Resulting VMT and Emissions Changes for Selected Pollutants

Scenario

Percent Change - 2040 BAU compared to 2040 Scenario

Light-Duty VMT GHGs (CO2 

equivalent)

PM2.5 NOx VOC

Land use changes plus PAG scenarios 1-4 -1.95% -1.87% -1.69% -1.43% -0.71%

 

9 Weighted average population density summarizes regional land use densities in a way that is more representative of residents’ 

daily experience of land use patterns than average density. TAZs with higher populations, which tend to be denser areas, are 

weighted more heavily than TAZs with lower populations. In the weighted average density calculation, the density of each TAZ 

in the region is weighted by its proportion of total regional population. An increase in weighted average density does not 

indicate an increase in total regional population, but rather a shifting of population toward higher density centers.
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Emissions Analysis

MOVES2010b was used to determine appropriate emission factors for the PAG regional 

analysis. Based on information from PAG that 98% of the county’s population live within the 

MPO boundary, the domain was taken as the entirety of Pima County. Input data was provided 

by PAG and derived from several local, state, and national sources. Table 7 summarizes the 

MOVES inputs PAG provided and the data used in the final modeling. Table 7 also indicates

any modifications to the region-provided data before modeling. 

Table 7. PAG Data Sources

Inputs

Region Provided 

Data Data Used in Final Modeling

Base 

Year

Future

Year

Base

Year

Future

Year

Source Type (vehicle)

Population

x provided data Calculated data. Applied the 2010 

population/VMT ratio to the 2040 

population to get 2040 HPMS 

VMT totals.

Vehicle Type VMT x x provided data provided data

Road Type Distribution x x provided data provided data

Meteorological Data x provided data provided base year data (changed 

YEAR to 2040)

Age Distribution x provided data provided base year data (changed 

YEAR to 2040)

VMT 

Fractions

Hour MOVES defaults MOVES defaults

Day x x provided data provided data

Month x x provided data provided data

Average Speed 

Distribution

x x provided data provided data

Ramp Fraction MOVES defaults MOVES defaults

Fuel Supply x x provided data provided data

Formulation x x provided data provided data

I/M Program x x modified data: Changed USEIMYN 

field to N, so that I/M data were not 

used in modeling.

modified data: Changed 

USEIMYN field to N, so that I/M 

data were not used in modeling.

AVFT MOVES defaults MOVES defaults

PAG worked closely EPA to determine the locally representative data for the required MOVES 

inputs. Site-specific data for PAG were very complete, being derived from local, state, and 

federal modeling and datasets. This is a case of geographically specific strategies that require

data specific to that geographic sub-area.

For example, PAG used local data to develop inputs for inspection/maintenance, speed 

distribution, fuels, and meteorology. During review, concerns were raised about projections of 

these fields to future years and their effect on results. Ultimately inspection/maintenance data in 

the analysis was removed due to uncertainty of far-future program design and efficacy on future 

vehicles and the concern that uncertain I/M influences on emissions could mask other trends.

This approach was implemented in all three regional analyses. Baseline year meteorological 
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data provided were also used for the future year, as requested by PAG. Speed distribution 

values were extracted from the regional TDM modeling data. 

PAG also obtained current information from state agencies to develop model inputs. Current 

fuel information was obtained from the Arizona Department of Weights and Measures (model 

default fuels were used for future year analyses). Distributions of road type VMT and vehicle

ages came from the Arizona Department of Transportation (ADOT). Vehicle population data 

were developed from ADOT vehicle registrations for Pima County with methods based on EPA 

guidance10 for future year vehicle populations. In addition, appropriate local vehicle/road type 

VMT information and baseline year age distribution data was developed from PAG data, in

close coordination with EPA. PAG also coordinated with EPA in use of EPA’s MOVES converter 

tools to derive and/or reformat VMT allocation and speed distribution data, including future year 

vehicle populations, use of compressed natural gas (CNG) fueled transit buses, and day-type 

activity factors to create an accurate regional input dataset. 

As discovered during initial model runs, when the analysis year is a leap year and the MOVES 

inputs are selected to pre-aggregate the input data to annual resolution at the county scale, the 

analyses must be performed with hourly aggregation of inputs.

11 Appendix B discusses this 

further. No preaggregation was used for the analysis. 

Results

The hourly MOVES outputs (for each hour of each day type of each month and year, accounting 

for the number of hours in each) were manually post-processed to produce annual total 

emissions (g) and activity (starts or miles). The data were then aggregated to the TRIMMS 

vehicle types and emission factors were calculated as total emissions divided by total activity, 

as discussed previously. Table 8 shows the resulting emission factors.12 

Table 8. Emissions Factors for PAG

g/mi g/start

Base Year 

(2010)

Future Year 

(2040)

Base Year 

(2010)

Future Year 

(2040)

Auto (Motorcycles+Passenger Cars+Passenger Trucks)

GHGs (CO2-

equivalent)

377.22 309.10 117.93 74.88

NOx 0.84 0.14 1.47 0.35

PM2.5 0.02 0.01 0.03 0.01

VOCs 0.16 0.03 2.12 0.67

 

10 Using MOVES for Estimating State and Local Inventories of On-Road Greenhouse Gas Emissions and Energy Consumption – 

Final (EPA-420-B-12-068, November 2012). 

11 Note that this issue would not arise in MOVES runs done for official SIP or conformity purposes, as it is not acceptable to preaggregate over time for these purposes.

12 Note that final results omitted Transit emissions. Accordingly, the transit emission factors are not presented in Table 8. 
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Vanpool (Passenger Trucks+Light Duty Trucks)

GHGs (CO2-

equivalent)

512.47 383.41 162.37 87.48

NOx 1.80 0.36 2.36 0.44

PM2.5 0.04 0.01 0.04 0.01

VOCs 0.33 0.06 3.70 0.62

PAG Scenario Results

Table 9 provides the results of the analysis in terms of total regional impacts. It is important to 

note that regional reductions are often very modest when compared to the impact on the 

population targeted by the strategies. Additional explanation is provided on the following page

and numerical results are included in Appendix B. 

Table 9. PAG Regionwide Percent VMT and Emissions Changes

Percent Change - 2040 BAU compared to 2040 Scenario

Scenario Light-Duty VMT GHGs (CO2 

equivalent)

PM2.5 NOx VOC

Scenario 1: SunTran All Access Pass -0.99% -0.97% -0.94% -0.86% -0.77%

Scenario 2: Expanded Employer-based 

Incentives -0.43% -0.43% -0.42% -0.40% -0.44%

Scenario 3: BRT on 2 Corridors -0.02% -0.02% -0.02% -0.02% -0.02%

Scenario 4: Parking Pricing in DowntownUniversity Corridor -0.26% -0.25% -0.25% -0.24% -0.26%

• The SunTran pass extension has a large impact on the affected population with shifts to transit 

from SOV: close to a 16% VMT reduction for 90,000 people. More of the mode shift will happen 

at the University of Arizona, which offers higher transit accessibility. There is the potential to 

expand to other populations as well, such as residents of downtown and other transit accessible 

areas.

• Employer-based incentives show a more moderate impact on affected population (3% VMT 

reduction for approximately 240,000 people) with shifts from SOV to carpool, vanpool, transit 

and cycling. 

• BRT on two corridors that traverse the region, intersecting in downtown Tucson, shows a small 

impact on the affected population with only a 0.5% VMT reduction in shifts to transit. This result 

could be affected by more extensive transit improvements and land use changes.

• Parking pricing limited to a small downtown area indicates a moderate impact on the affected 

population (2% reduction for 400,000 people traveling downtown) with shifts from SOV to 

carpool, transit, cycling, and walking. 

Overall the PAG scenario analysis represents a pragmatic application of potential strategies in 

the region. Stakeholders were highly interested in feasible scenarios instead of making
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aggressive assumptions that have a remote chance of being enacted. PAG’s selected strategies 

were limited to specific sub-geographies and sub-populations, whereas other regions applied 

strategies more broadly and used more aggressive assumptions. PAG also chose not to 

analyze a land use strategy. Other regions included land use as a foundational strategy in 

several scenarios, reflecting an understanding of land use as a key strategy to maximize 

alternative mode options and minimize travel distances. As with any region, the aggressiveness 

of scenarios is an important decision for lead agencies to make when undertaking a “what if”

analysis of this type. The selection of sub-areas and limited affected populations, as well as the 

lack of a land use foundation for the transit and BRT scenarios contributed to the modest 

outcome. 

PAG has taken a strong interest in the details of this analysis, particularly with respect to the 

MOVES analysis. This level of involvement and data availability may encourage a repeat of the 

analysis with changes to the assumptions as an internal exercise in the future. This approach 

would allow PAG to test impacts before involving a larger group of stakeholders. 
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3.2. Massachusetts Department of Transportation (MassDOT) – Boston 

Region

Background

The lead agency for the second regional case study is MassDOT. MassDOT’s case study 

partners include the Metropolitan Area Planning Council (MAPC), the Central Transportation 

Planning Staff (CTPS, the Boston MPO (BMPO) staff), and the Georgetown Climate Center 

(GCC). This stakeholder group indicated that HOV lanes, carpool/vanpool incentives, rideshare 

matching programs, bicycle programs, transit expansion, and smart growth are strategies of 

priority for the case study. 

The BMPO region is the focus of this modeling study, covering 101 cities and towns and 

approximately 1,405 square miles in the greater Boston region. The region covers more than 

three million people and includes diverse communities from relatively rural to urban. The base 

year for the MassDOT regional analysis is 2009 with a future year of 2035. Population and 

employment data is from the community model domain, which is slightly larger and includes 164 

towns and cities in eastern Massachusetts. 

Interest in Participation

MassDOT expressed an interest in this study based on the state’s strong interest in climate 

change and GHG emissions reduction. MassDOT has an internal agency GHG emissions target 

of a 40% reduction from a 2002 baseline (in the GreenDOT Program).The Commonwealth has 

a general target for GHG reductions across the economy of 25% below 1990 levels, established 

pursuant to Massachusetts’ climate change legislation, the Global Warming Solutions Act. 

Under the general target, Massachusetts has also established a target for the transportation 

sector of a 7.1% reduction from a 1990 baseline by 2020.

MassDOT is a participant in the Transportation and Climate Initiative (TCI). The TCI is a 

regional collaboration of transportation, environment, and energy agencies in 11 Northeast and 

Mid-Atlantic states, as well as the District of Columbia, that seeks to develop the clean energy 

economy and reduce GHG emissions in the transportation sector. One of TCI’s stated goals is 

to promote sustainable communities. This project is intended to directly support TCI’s work in 

that area and facilitate the sharing of lessons learned from this effort with other states and 

metropolitan areas in the region.

Prior Experience with Analysis of GHG Emissions

MassDOT worked with other state agencies and consultants to develop the Massachusetts 

Clean Energy and Climate Plan 2020, which identified emission reduction potentials from 

specific strategies in the transportation sector, including from implementation of Sustainable

Development principles, implementation of MassDOT’s GreenDOT program, and 

implementation of a Smart Growth policy package. MassDOT has also recently released its 

GreenDOT implementation plan, identifying the actions MassDOT plans to take to achieve its 

sustainability goals, including GHG emissions reductions.
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The Central Transportation Planning Staff (CTPS) has been incorporating climate change into 

the planning and programming activities of the MPO through the LRTP and the TIP. The MPO 

uses its visions and policies established in the LRTP in the project selection process for both 

documents. Project evaluation criteria are used to identify projects that will help slow climate 

change through reduction in GHGs and projects that will help adapt to the effects of climate 

change by addressing flooding or improving evacuation routes. Several discussion papers 

developed by the MPO previous to this project have considered how projects that reduce GHG 

emissions can be supported through their programs.

MassDOT is developing performance measures for climate change through the weMove 

Massachusetts and GreenDOT processes, and the Boston MPO is developing performance 

measures to demonstrate the region’s success in managing their transportation network and the 

effectiveness of investments in moving towards its visions. The MassDOT and MPO 

performance measures will be coordinated as required in federal transportation law.

Scenario Development

MAPC developed two land use forecasts for its most recent regional transportation plan, a 

Current Trends forecast and the MetroFuture forecast. The Current Trends would continue 

recent historical patterns of land use dispersal in the Boston region, while MetroFuture would 

concentrate future growth in core areas well-served by transit. In 2008, MAPC adopted 

MetroFuture as the forecast for its 2030 regional land use plan, which includes supportive 

implementation strategies. The Boston Region MPO (BRMPO) then adopted the MetroFuture 

land use scenario in its most recent RTP (adopted in 2011).

All BAU inputs provided by the MassDOT stakeholder group represent a combination of the 

MetroFuture land use scenario and a no-build transportation scenario. Therefore the BAU 

scenario already represents a shift from current trends to more smart growth development 

patterns. The land use scenario incorporated in the BAU forecast clusters more development 

around transit while no new transit service is provided. Thus, the average transit trip length 

declines. In addition, mode shares tend to shift from driving to transit and walking given the 

assumption about new development. Trip lengths for driving trips increase slightly in the future 

due to some continued dispersal of land uses in the outer areas, despite the emphasis on 

densification. 

The BAU also includes the existing MassRIDES program, the statewide travel options program

that partners with employers to provide information about commuting by carpooling, bicycling, 

walking, public transportation, teleworking, and vanpooling. About 700,000 employees in the 

Boston region currently have access to MassRIDES programs, which include guaranteed ride 

home and ride match, telework and flex work programs, and program marketing. Approximately 

100,000 of the 700,000 have access to monetary subsidies for healthy modes. For the BAU 

scenario input, the number of covered employees was scaled by the natural rate of regional

employment growth. 
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Scenario 1 - Expand Programs and Incentives for Healthy Modes

The first MassDOT strategy is based on the existing MassRIDES program, and would increase 

the number of employees with access to MassRIDES by 25% – giving them access to 

guaranteed ride home and ride match, telework and flex work programs, and program 

marketing. The strategy would also expand access to employer-paid monetary subsidies to all 

employees with access to MassRIDES, who would each receive a subsidy of $70 a month.

This scenario was modeled as two separate strategies: 1) effect on new MassRides users 

(monetary subsidies and non-monetary benefits) and 2) effect on existing MassRides users 

(monetary subsidies only). 

For monetary subsidies, the baseline trip costs for all modes provided by MassDOT were used, 

and reduced according to the subsidy amounts, with some modifications. This is in contrast to 

the radio-button method used for a similar strategy for the PAG region. The actual dollar 

amounts were able to be used because MassDOT could provide reasonable baseline trip costs

for transit and vehicle trips, which included maintenance, tires, gas, and oil. Bicycling and 

walking subsidies were not explicitly modeled through this method; since both the baseline and 

final costs of these bicycling and walking trips is assumed to be zero.

For non-monetary benefits, the radio buttons were used for TDM programs and subsidies for 

Guaranteed Ride Home and Ride Match and Telework and Flexible Work Schedules. 

Scenario 2 - Expand Programs and Incentives for Healthy Modes with Smart Growth Land Use

Scenario 2 adds Smart Growth Land Use to the Scenario 1 strategy. This included an increased 

emphasis on growth in existing urban centers, in order to increase weighted average residential 

density, and an increase in land use mixing. 13 The Smart Growth Land Use Strategy 

represents an increase in weighted average density over that of the MetroFuture forecast, which 

is incorporated in the BAU. It is therefore not based on any prior detailed analysis or input 

process. For the strategy, MassDOT suggested doubling the increase in weighted average 

density between the MetroFuture (BAU) and base year. Although this represents a highly 

ambitious strategy, it is appropriate for a TEAM analysis of alternative “what if” scenarios. 

Population-weighted average density by TAZ was calculated for the entire region for the base 

year and the MetroFuture 2035 scenario. There is a projected 11% increase in density over this 

timeframe. Doubling this increased density relative to the base year results in a 22% increase. 

Population-weighted average density of retail establishments by TAZ was calculated for the 

entire region for the base year and the MetroFuture 2035 scenario, and the results showed

 

13 Weighted average population density summarizes regional land use densities in a way that is more representative of residents’

daily experience of land use patterns than average density. TAZs with higher populations, which tend to be denser areas, are 

weighted more heavily than TAZs with lower populations. In the weighted average density calculation, the density of each TAZ 

in the region is weighted by its proportion of total regional population. An increase in weighted average density does not 

indicate an increase in total regional population, but rather a shifting of population toward higher density centers.
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there is effectively no density increase projected. MassDOT instructed that the scenario should 

increase density relative to the base year by 10%.

MassDOT did not have an immediately available method to estimate the change in average 

walking distance to transit, so this was not modeled in TRIMMS. However, with the increased 

density envisioned in this scenario, this distance would decrease. Had it been modeled, it may 

have reduced VMT and emissions further. 

The TRIMMS model run for this scenario only includes land use changes, and the entire 

regional population is affected. The Healthy modes strategy was added in as a post-processing 

step. For residential population density, MassDOT provided the figures 10,205 people per 

square mile (BAU) and 11,230 people per square mile (scenario). TRIMMS default density 

figures are lower, because they are based on an unweighted density calculation, which gives 

equal weight to low and high density TAZs. Adjustments to the default baseline density in 

TRIMMS are not easily customizable; however, since the percentage increase drives the 

modeling results, the calculated increase can be applied to the TRIMMS default figure. 

Therefore an increase of 20% was applied to the TRIMMS default baseline. (The actual 

increase calculated was 22%, but TRIMMS only allows increases in 5% increments).

For retail establishment density, MassDOT provided estimates of 698 establishments per 

square mile (BAU) and 762 establishments per square mile (scenario). The TRIMMS default 

value was increased by 10% for the scenario. 

Scenario 3 - Expand Programs and Incentives for Healthy Modes with Smart Growth Land Use and

HOV Lanes 

The scenario adds HOV lanes to the previously analyzed strategies with a decrease in 

rideshare travel time by 10% for entire region through a network of HOV lanes. The HOV lanes 

strategy is not based on any specific proposal or plan. Parameters represent a hypothetical 

scenario, developed through internal conversations at MassDOT. The assumptions appear to 

be reasonable, given that they represent roughly a return to the 2009 base year travel time 

value.

The TRIMMS model run for this scenario includes HOV lanes and land use changes (Scenario 

2), since these both affect the entire regional population. As in Scenario 2, Healthy modes was 

added in as a post-processing step. The resulting reduction in SOV VMT reported was less than 

the increase in rideshare VMT; a counter-intuitive result. In order to capture the reduction in 

SOV VMT, rideshare VMT was held constant in post-processing. The response of TRIMMS to 

this analysis will be discussed more fully in the conclusions and recommendations.

Scenario 4 - Expand Programs and Incentives for Healthy Modes with Smart Growth Land Use, HOV 

Lanes and Transit Network Expansion and Improvement

The final MassDOT scenario adds transit network expansion and improvement to the previous 

analyses. This strategy is expected to reduce both transit trip times and access times (wait 

times) for regional population by 10%. The strategy is not based on any specific regional 

proposal or plan. Parameters represent a hypothetical scenario, developed through internal 
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conversations at MassDOT. MBTA has approximately 15 key bus routes, some or all of which 

could be upgraded to a BRT level of service, as part of the strategy. The model run includes 

transit network, land use changes (Scenario 2) and HOV lanes (Scenario 3). As in Scenarios 2 

and 3, Healthy modes were added in as a post-processing step. 

Input parameters are provided in Table 10 for current conditions, a BAU future, and the four 

scenarios selected by MassDOT. Specific input values are provided for the scenarios.
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Table 10. MassDOT Scenario Details

Scenario Description Geography Data Needs

Current Conditions Existing conditions across all 

strategies in 2009

Regionwide • mode shares

• average vehicle occupancy

• average vehicle trip lengths

• TRIMMS default vehicle ownership (1.72 

vehicles/household)

• regional population and employment

• employer-based incentives for alternative 

commute modes

Business as Usual 2035 conditions with current 

levels of employer program, 

land use, HOV lanes, and 

transit; future growth 

assumed to be focused in 

areas served by transit (the 

“Metro Futures” land use 

forecast) 

Regionwide • mode shares

• average vehicle occupancy

• average vehicle trip lengths

• 2035 regional population and employment

• current employer-based incentives for 

alternative commute modes 

• TRIMMS default vehicle ownership (1.72 

vehicles/household)

• current travel times (23.4 minutes for 

rideshare and 24.5 minutes for transit)

• current transit access times (10.5 minutes)

• current trip costs ($2.08 for rideshare, $4.00 

for vanpool, and $1.60 for transit)

• residential population density (10,205 persons 

per square mile)

• retail establishment density (698 per square 

mile)

Scenario 1: Expanded 

Healthy Modes 

Program

Expand the statewide travel 

options program that 

partners with employers to 

provide information about 

commuting by alternate 

modes of transportation. 

Regionwide • number of regional employees covered

(652,565 existing users, 152,265 new users)

• average monthly subsidy offered to 

employees ($70 for rideshare, vanpool, and 

transit)

• are guaranteed ride home, ride match, 

telework, and flexible work schedules offered?

Scenario 2: Scenario 1 

+ Land Use

Increase residential density 

and mixed use land uses in 

selected areas. 

Regionwide • 22% increase in population density (to 11,230 

persons per square mile)

• 10% increase in retail establishment density

(to 762 per square mile)

Scenario 3: Scenario 2 

+ HOV Lanes

Add HOV lanes. Regionwide • rideshare travel time reduction (10% reduction 

to 21.1 minutes)

Scenario 4: Scenario 3 

+ Expanded Transit

Expand transit network and 

improve transit infrastructure.

Regionwide • 10% transit travel time reduction (22.1 

minutes)

• 10% headway (wait time) reduction (to 9.5 

minutes
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Emissions Analysis

As previously described, MOVES2010b was used to determine appropriate emission factors for 

the MassDOT scenario analysis. This region was modeled as a single-zone, custom domain to 

capture the BMPO region of jurisdiction and best utilize the available input data:

14 

The BMPO regional boundary does not coincide well with county boundaries, including all or 

parts of seven counties. The MOVES analysis experience and capability in the region is well 

advanced, including previous modeling for several years and detailed inputs ready for use, but 

based on data for a single county, Middlesex. Their MOVES experience has focused on 

emission rates for this county for CAA purposes instead of an emission inventory approach as 

used in this study.

15 With this approach, emission rates from the representative county can be 

applied to activity from other counties, as long as they have the same fuels and I/M program as 

the representative county. That is the case for this region. Therefore most inputs provided were 

based on Middlesex County, except source population and total VMT (the two MOVES inputs 

that must be totals), which CTPS provided for the whole MPO rather than split into each of the 

seven counties.

Given the available data, it made sense to model the region as a “custom domain” in MOVES 

rather than model the counties individually. In the county scale, the custom domain option 

allows the user to model a multi-county area using one run. Executing a custom domain 

requires that some MOVES inputs be formatted slightly differently than county-scale inputs, but 

otherwise the run execution is similar to that of a county-scale run. Table 11 summarizes the 

MOVES inputs that MassDOT provided and the data used in the final modeling. Table 11 also 

indicates if any modifications to the region-provided data were made before modeling. 

Table 11. MassDOT Data Sources

Inputs

Region Provided 

Data Data Used in Final Modeling

Base 

Year

Future

Year Base Year Future Year

Source Type Population *x *x *provided data *provided data

Vehicle Type VMT *x *x *provided data with change: 

calculated VMT from provided data 

by annualizing, summing 

auto+truck, and allocating across 

source types using factors from 

region. 

*provided data with change: 

calculated VMTs from provided 

data by annualizing, summing 

auto+truck, and allocating across 

source types using factors from 

region.

Road Type Distribution x x provided data provided data

Meteorological Data x x provided data with change: 

ZONEID was changed to 1

provided data with change: 

ZONEID was changed to 1

 

14 http://www.ctps.org/Drupal/mpo. Other options include single county (as for PAG), or series of individual counties (as for 

MARC). 

15 Either MOVES calculation type, Inventory or Emission Rates, is acceptable for regulatory purposes; the emission factors within 

the model are the same regardless of calculation type chosen. 
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Inputs

Region Provided 

Data Data Used in Final Modeling

Base 

Year

Future

Year Base Year Future Year

Age Distribution x provided data with change: 

YEARID was changed to 2009

provided data with change: 

YEARID was changed to 2035

VMT 

Fractions

Hour x x provided data provided data

Day x x provided data provided data

Month x x provided data with change: 

ISLEAPYEAR was changed to N

provided data

Average Speed 

Distribution

x x provided data provided data

Ramp Fraction x x provided data provided data

Fuel Supply x x **provided data with change: 

FUELYEARID was changed to 

2009 and COUNTYID was 

changed to 99001

**provided data with change: 

COUNTYID was changed to 

99001

Formulation x x provided data provided data

I/M Program x x **provided data with change: 

YEARID was changed to 2009, 

STATEID was changed to 99, 

COUNTYID was changed to 

99001, and USEIMYN was 

changed to N.

**provided data with change: 

YEARID was changed to 2009, 

STATEID was changed to 99, 

COUNTYID was changed to 

99001, and USEIMYN was 

changed to N.

AVFT MOVES defaults MOVES defaults

**Zone All factors set to 1 because 

modeling used only one zone

All factors set to 1 because 

modeling used only one zone

**Zone Road Type All factors set to 1 because 

modeling used only one zone

All factors set to 1 because 

modeling used only one zone

* MassDOT provided inputs for Source Type Population and Vehicle Type VMT for the entire domain in aggregate (not subset by 

county). All other provided inputs were specific only to Middlesex County.

** Because the MassDOT MOVES modeling used a custom domain, a zone (with arbitrary ID ‘1’) was used in the modeling, 

requiring the ZONEID to be set to 1 in the meteorology data and arbitrary STATEIDs and COUNTYIDs of 99 and 99001, 

respectively, in inspection/maintenance and fuel supply inputs. Custom-domain runs also require the Zone and 

ZoneRoadType input sheets, which were not provided by MassDOT but contained zone allocation factors that were all set to 1 

because the domain included only one zone.

Lessons Learned

The MassDOT case study provides a good example of how a TEAM analysis can be done when 

the region of interest covers multiple counties but the available data is not provided for each

county. MassDOT provided most of the other MOVES inputs for a single county in the domain 

(Middlesex County), which represents the seven-county region in terms of the MOVES 

parameters such as fuels used, fleet age distribution, road type distribution, etc. Although much 

of the Middlesex County data was provided for year 2012, it was taken as representative of 

2009, so the value of the YEAR field was changed to 2009 where necessary. Age distribution 

data were only provided for the base year, so the same data were reused for future year by 

changing the YEAR field. Ultimately inspection/maintenance (I/M) was excluded from the model 

results by setting the USEIMYN field to ‘N’. MOVES default data were used for alternative fuels 
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and technology. This was done to remove uncertainty in future program design and efficacy of 

I/M for future vehicles, as well as the concern that uncertain I/M influences could mask other 

trends.

To capture the entire domain with input data provided for a single county, we ran the MOVES 

model, specifying inputs for a custom modeling domain that covers the entire region of the study 

(parts or whole of 7 counties: Middlesex, Suffolk, Norfolk, Plymouth, Essex, Worcester, Bristol) 

using provided inputs for Middlesex that are applicable to the entire region and provided 

regional totals for other fields (population and VMT) as appropriate. The setup for a customdomain run is nearly identical to that of a county-level run, and documented in the MOVES 

User’s Guide,

16 except for some minor reformats of some of the input sheets. It also uses a 

different Geographical Bounds specification and two additional input sheets (Zone and 

ZoneRoadType) with allocation factors that are all set to 1 for a single-zone custom domain

such as the application here. 

Summary of Results

The post-processing of hourly MOVES outputs to domain-wide, average emission factors for the 

MassDOT case study were calculated as described previously. Total running emissions from 

hourly outputs (in grams per year) were divided by total running activity (in miles per year) to 

produce regional and annual average, g/mile emission factors. A similar analysis was made for 

starting emissions. The resulting emission factors, in grams of pollutant per average mile driven 

or grams of pollutant per average start are shown in Table 12. 

Table 12. Emission Factors for MassDOT

g/mi g/start

Base Year 

(2009)

Future Year 

(2035)

Base Year 

(2009)

Future Year 

(2035)

Auto (Motorcycles+Passenger Cars+Passenger Trucks)

GHGs (CO2-equivalent) 418.16 316.97 160.18 106.41

NOx 0.85 0.17 1.77 0.46

PM2.5 0.02 0.02 0.04 0.02

VOCs 0.16 0.03 2.68 0.71

Vanpool (Passenger Trucks+Light Duty Trucks)

GHGs (CO2-equivalent) 510.61 373.36 191.76 118.59

NOx 1.28 0.31 2.46 0.52

PM2.5 0.03 0.02 0.04 0.02

VOCs 0.23 0.05 3.33 0.61

 

16 Motor Vehicle Emission Simulator (MOVES): User Guide for MOVES2010b, EPA-420-B-12-001b, June 2012. 
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MassDOT Scenario Results

Table 13 provides the regional results of the analysis. The MassDOT results are similar to the 

results of the 2010 national study for regions similar to Boston. Note that regional reductions are 

often modest when compared to the impact on the population targeted by the strategies where 

the impact is much greater. Additional explanation is provided on the following page and 

numerical results are included in Appendix B. 

Table 13. MassDOT Regionwide PercentVMT and Emissions Changes

Percent Change for 2035 BAU compared to 2035 Scenario

Scenario Light-Duty 

VMT

GHGs 

(CO2 

equivalent)

PM2.5 NOx VOC

Scenario 1: Expanded Healthy Modes 

Program -2.80% -2.80% -2.80% -2.79% -2.77%

Scenario 2: Scenario 1 + Land Use -3.89% -3.89% -3.88% -3.88% -3.84%

Scenario 3: Scenario 2 + HOV Lanes -4.07% -4.06% -4.06% -4.05% -4.02%

Scenario 4: Scenario 3 + Expanded Transit -4.41% -4.41% -4.40% -4.39% -4.36%

• The Healthy Modes scenario shows a large impact on the affected population with about 16% 

reduction in VMT for existing MassRides users (650,000 people) and 20% reduction in VMT for 

new users (150,000 people). The shifts occur from drive-alone to rideshare, cycling and walking. 

When the entire population of the region is considered rather than just the affected population, 

the results are more modest, as shown in Table 13. 

• For the land use strategy the VMT impacts are much greater for MassDOT than they were for 

PAG, and more consistent with current literature. Land use impacts may be more reasonable for 

MassDOT because Boston is a transit rich region, and the TRIMMS land use algorithms focus on 

transit ridership as the impact of land use densification. There is a moderate impact on regional 

population (1% VMT reduction) that results from a reduction of drive-alone trips. However, as 

noted earlier, the change in average walking distance to transit that resulted from the land use 

strategy was not modeled; had it been, greater reductions may have been seen. 

• HOV Lanes show small impacts with shifts from drive-alone to carpool. 

• Transit network improvements also show small impacts. While Boston has a robust transit 

system, transit still accounts for only 7-8% of trips. Therefore impacts of transit improvements 

are applied to a small baseline. 

• The HOV strategy represents a reduction in rideshare travel times. The TRIMMS analysis showed 

a reduction in SOV VMT less than the increase in rideshare VMT; which is unexpected. This is a 

result of TRIMMS’ lack of controls on trip totals for analysis of pricing strategies (discussed 

further in Section 4). This was adjusted in post-processing by removing the increase in VMT due 

to rideshare. Making this adjustment produces a reduction in VMT that is consistent with the 

literature reviewed for the previous TEAM analysis. 
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Non-SOV travel subsidies can be effectively modeled using dollar values rather than the radio 

buttons if reasonable baseline trip costs and elasticities are supplied. The dollar value approach 

was used for MassDOT. This approach will tend to produce larger VMT reductions in TRIMMS

compared to the radio buttons, especially if combined with non-monetary employer-based 

programs. TRIMMS’ standard functions for employer-based strategies, which use on/off buttons 

for subsidies, use more conservative assumptions to model impacts. However, use of actual 

dollar amounts allows the impact to vary with the subsidy amount.
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3.3. Mid-America Regional Council (MARC) – Greater Kansas City

Background

MARC is a coordination entity and the metropolitan planning organization for the bistate region 

of Kansas City (Kansas and Missouri). The MARC region consists of 9 counties and 120 

municipalities, but the transportation planning boundary is slightly smaller and the air quality 

boundary is even smaller. The staff works with two different state DOTs and two different air 

quality offices. 

MARC is responsible for the long range regional transportation plan for the region, as well as 

many other planning and coordination initiatives. The LRTP, Transportation Outlook 2040, was 

adopted by the MARC Board of Directors and included, for the first time, a GHG goal as a 

fundamental element of the policy framework. During the same timeframe, the region’s 

voluntary Clean Air Action Plan underwent a comprehensive update and included co-benefit 

analysis of recommended measures including reduction of GHGs. MARC is in danger of 

violating the 2008 75ppb ozone standard after the completion of the 2012 ozone season and 

understands the importance of credible assessments of travel efficiency strategies to help attain 

the federal standards and estimate the additional benefits of GHG emission reductions.

Interest in Participation

For this study, MARC expressed an interest in investigating a combination of land use controls, 

transit-oriented development and smart growth; expansion of transit service; rideshare; and 

road pricing. The agency would like to be able to use the results from this study to inform the 

LRTP.

The MARC region is already active in operational improvements using strategies such as ramp 

metering, ITS infrastructure, signal prioritization and others. Most travel strategies for the region 

relate to congestion mitigation rather than VMT reduction. Although their goal in the past has 

been to build more lanes to add capacity, this study represents a shift in focus in order to get 

more support for VMT reduction. In addition, MARC anticipates the potential need to develop a

SIP, and this study could identify specific measures to include. 

Prior Experience with Analysis of GHG Emissions

Transportation Outlook 2040 included two alternative 2040 land-use scenarios that were 

subjected to several analyses, including trip travel time, roadway congestion, and cost of new 

infrastructure. Parsons Brinkerhoff evaluated the scenarios for their anticipated energy 

consumption and GHG emissions using the scenario-analysis tool called CarbonFIT, a model 

built on the platform of CommunityViz. Although the region has demonstrated an interest in 

GHG and climate change, MARC has not yet set specific goals in this regard.
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Scenario Development

The MARC base year is 2010 with a future year of 2040. MARC was able to provide only a 

limited amount of data, so TRIMMS defaults were used heavily. MARC produced three land use 

forecasts during the development of the long range plan. These are:

• Current Trends: continuing recent historical patterns of sprawling land uses

• Adaptive Scenario: focusing growth in the core areas

• Adopted Scenario: representing the compromise between Current Trends and Adaptive that 

was adopted by the MPO board

MARC provided BAU outputs from its travel demand model for year 2040. For land use, the 

BAU outputs use the Current Trends forecast rather than the Adopted Scenario. This approach 

is in contrast to the land use scenario Boston included in the BAU, and was chosen so that the 

all smart growth improvements could be analyzed. For transportation, the BAU outputs 

represent a build scenario, including all the projects identified in the most recent long range 

transportation plan, with a horizon year of 2040. The modeled mode shares are almost 

unchanged from 2010 to 2040, because the transportation investments in the plan supply 

additional roadway and transit facilities in proportion to the population growth, and consistent 

with existing travel patterns.

Scenario 1 - Expand Ridesharing and TDM programs

About 50,000 employees in the region have access to telework and flexwork programs, 

Guaranteed Ride Home and ridematching services. (A regional ridesharing program includes 

14,000 participants). The strategy expands the number of people covered by the program from 

50,000 to 300,000, an increase of 500%. It also adds alternative mode subsidies for the 

300,000 people covered from the current $25 to $50 dollars per month. Population figures were 

scaled based on the natural rate of job growth in the region.

This was modeled as two strategies in TRIMMS: 1) telework and flex work plus TDM programs 

(non-monetary only) and 2) subsidize work trips using alternate modes plus other programs 

(both monetary and non-monetary). For non-monetary strategies, the radio buttons for TDM 

programs and subsidies for Guaranteed Ride Home and Ride Match and Telework and Flexible 

Work Schedules were used. For monetary subsidies, MassDOT’s BAU trip costs were 

substituted as reasonable proxies for trip costs in the MARC region, which were not available 

from MARC. In reality trip costs are likely to be higher in the MassDOT region, where average 

trip lengths are longer and tolling is more prevalent. However, using higher baseline trip costs 

errs on the conservative side when modeling VMT reductions, since the subsidy amounts 

examined will be smaller in relative terms. 

Scenario 2 - Expand Ridesharing and TDM programs along with Transit Improvement and 

Promotion

This scenario adds transit improvement and promotion by reducing transit trip times by 20%, 

reducing walking distance to transit by 50% and expanding the successful university transit pass 

program (U-Pass) to 6% of total regional population. SmartMoves, the regional transit vision 
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updated in 2008, would support this strategy. Ridesharing and TDM programs were not included 

in the model run. Rather the impact of expanded ridesharing and TDM programs was summed 

with these TRIMMS model runs as a post-processing step. Because base year values were not 

available from MARC, substitute base year values from the 2010 national study for a similar 

metropolitan area (San Diego) were used. 

As in Scenario 1 this was modeled as two strategies in TRIMMS: 1) reduce transit trip times and 

walking distance only and 2) university transit pass program with reduced transit trips times and 

walking distance. TRIMMS does not allow specific adjustment of walking distance to transit. It 

uses percentage reductions from the provided default walking distance. The default value of 

1.26 miles was used and decreased by 50% for the scenario. For the second scenario, a 

baseline transit trip cost of $1.50 was used, based on fare information on the KCATA website.

17

In the scenario, the transit trip cost is $0, reflecting the marginal cost of each transit trip to the 

pass holder. This is not to say that the transit pass would be free, but in the scenario the transit 

pass costs are assumed to be bundled with tuition or employment benefits. 

Scenario 3 - Expand Ridesharing and TDM programs, Transit Improvement and Promotion with 

Smart Growth Land Use

Scenario 3 adds smart growth land use to the previous strategies. The smart growth land use 

strategy represents a shift from the region’s Current Trends scenario (incorporated in the BAU) 

to their Adaptive scenario. This increases weighted average residential density18 and mixed use 

land uses for the entire region. Thus, the entire regional population is affected.

MARC provided population densities by TAZ, from which population-weighted average density 

by TAZ was calculated. The TRIMMS default for retail establishment per square mile for the 

Kansas City region is 0.82. MARC provided retail employment/acre by TAZ. Weighted average 

density figures were calculated, and the percent change was applied to the TRIMMS default.

For residential population density, MARC provided the figures 2,655 people per square mile 

(BAU) and 3,693 people per square mile (scenario). After rounding these figures to units 

allowed by TRIMMS, the scenario population density was modeled as a 40% increase above 

the BAU population density. For retail establishment density, MARC provided the figures 0.45 

establishments per square mile (BAU) and 0.71 establishments per square mile (scenario). 

Similarly, after rounding, the BAU retail density was increased by 55% for the scenario. The 

VMT impacts for land use are considerably lower than expected. 

 

17 http://www.kcata.org/fares/

18 Weighted average population density summarizes regional land use densities in a way that is more representative of residents’

daily experience of land use patterns than average density. TAZs with higher populations, which tend to be denser areas, are 

weighted more heavily than TAZs with lower populations. In the weighted average density calculation, the density of each TAZ 

in the region is weighted by its proportion of total regional population. An increase in weighted average density does not 

indicate an increase in total regional population, but rather a shifting of population toward higher density centers.
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Scenario 4 - Expand Ridesharing and TDM programs, Transit Improvement and Promotion, Smart 

Growth Land Use and Transportation Pricing

The final scenario adds transportation pricing as an increase in the average cost of auto trips by 

100% and increase parking costs by 500% in the Downtown Area. Currently about 1% of 

regional parking is priced. This was expanded to 6% of total regional parking.

BAU trip costs represents the average fuel cost/mile for a sedan from AAA19 and the average 

trip length for Kansas City. Relative to fuel costs only, the scenario represents approximately a 

25 cent per mile charge. The rideshare trip cost was adjusted for vehicle occupancy. BAU 

parking cost is based on similar metropolitan areas from Colliers Parking Rate Survey.

20 The 

rideshare parking cost was assumed to be the same as auto-drive alone parking cost.

This was again modeled as two strategies: 1) parking pricing for 6% of the regional population 

and mileage pricing and 2) mileage pricing for the remaining 94% of the regional population. 

Initially, the entire scenario was applied regionwide by calculating average regionwide parking 

price as an intermediate step. However, this produced an unreasonable outcome because it 

results in an enormous relative increase in average parking cost. Instead, the parking strategy 

was modeled only for the sub-area to which it applies in order to reasonably limit the impact. 

Both strategies modeled include land use changes and transit improvement. TDM and university 

transit pass (part of transit improvement and promotion) were added in as a post-processing 

step

Input parameters are provided in Table 14 for current conditions, a business as usual future, 

and the four scenarios selected by MARC. Specific input values are provided for the scenarios.

 

19 http://fuelgaugereport.aaa.com/?redirectto=http://fuelgaugereport.opisnet.com/index.asp

20 http://www.colliers.com/~/media/files/marketresearch/unitedstates/colliers_2012_na_parking_survey.pdf
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Table 14. MARC Scenario Details

Scenario Description Geography Data Inputs

Current 

Conditions Existing conditions across 

all strategies in 2010

Regionwide • mode shares

• average vehicle occupancy

• average vehicle trip lengths

• regional population and employment

• TRIMMS default vehicle ownership (1.71 

vehicles/household)

• employer-based incentives for alternative commute 

modes

Business as 

Usual

2040 conditions with 

current levels of employer 

program, land use, HOV 

lanes, and transit

Regionwide • mode shares

• average vehicle occupancy

• TRIMMS default vehicle ownership (1.69 

vehicles/household)

• 2040 regional population and employment

• current employer-based incentives for alternative 

commute modes 

• current travel times (50 minutes for transit)

• walking distance to nearest transit station (1.26 miles)

• average parking costs ($10 for drive-alone and $4.26 

for rideshare)

• average trip costs ($2.41 for drive alone, $2.08 for 

rideshare, $4 for vanpool, and $1.50 for transit)

• residential population density (2,655 persons per 

square mile)

• retail establishment density (0.45 per square mile)

Scenario 1: 

Expanded TDM 

Expand access to telework 

and flexwork programs, 

Guaranteed Ride Home 

and ridematching services. 

Regionwide • share of regional employees covered (65,181 for nonmonetary subsidies only and 300,000 for all subsidies)

• average monthly subsidy offered to employees ($50 

for rideshare, vanpool, and transit)

• are guaranteed ride home, ride match, telework, and 

flexible work schedules offered?

Scenario 2: 

Scenario 1 + 

Enhanced Transit

Improve transit and expand 

transit pass program. 

Regionwide • 20% travel time reduction (to 40 minutes)

• 50% decrease in walking distance to nearest transit 

station (to 0.63 miles)

• Number of regional employees covered by university 

transit pass program (134,834)

• average transit trip cost for university transit pass ($0)

Scenario 3: 

Scenario 2 + 

Land Use

Increase residential density 

and mixed use land uses 

for entire regional 

population.

Regionwide • 39% increase in population density (to 3,693 persons 

per square mile)

• 57% increase in retail establishment density (to 0.71 

per square mile)

Scenario 4: 

Scenario 3 + 

Pricing

Implement mileage pricing 

and increase and expand 

coverage of parking costs. 

Regionwide • 6% of all parking (public and private) is priced

• 500% increase in average parking cost per trip (to $60 

for drive-alone and to $25.53 for rideshare)

• 100% increase in average cost per trip (to $4.82 for 

drive alone and to $4.16 for rideshare)
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Emissions Analysis

MARC provided a variety MOVES input data at county-level resolution for the seven counties in 

the MPO. However, this data only covered a small portion of the required inputs for MOVES, so 

a large amount of default data was included in the analysis. MOVES was run at the county scale 

for each county and year individually. The final calculated emission factors were then calculated 

for the domain as a whole by aggregating the MOVES results from each county. MOVES 

outputs for amounts of emissions and activity were summed across the seven counties, creating 

domain totals, and the ratio of emissions to activity created domain-wide emission factors. This 

approach required greater effort in data collection and manipulation, file management, model 

setup and runtime, and post-processing, but incorporated all the provided data to produce the 

regional average used in this approach. Table 15 summarizes the data MARC provided and 

used in the MOVES simulations, and any modifications made to the data before modeling. 

Table 15. MARC Data Sources

Inputs

Region Provided 

Data Data Used in Final Modeling

Base 

Year

Future 

Year Base Year Future Year

Source Type Population MOVES defaults* MOVES defaults*

Vehicle Type VMT MOVES defaults* MOVES defaults*

Road Type Distribution x x provided data provided data

Meteorological Data MOVES defaults MOVES defaults

Age Distribution x provided data provided baseline year data with 

change: YEARID was changed to 

2040

VMT 

Fractions

Hour x x provided data provided data

Day MOVES defaults MOVES defaults

Month MOVES defaults MOVES defaults

Average Speed 

Distribution

x x provided data provided data

Ramp Fraction MOVES defaults MOVES defaults

Fuel Supply MOVES defaults MOVES defaults

Formulation MOVES defaults MOVES defaults

I/M Program no data: these counties do not 

require emissions inspections

no data: these counties do not 

require emissions inspections

AVFT x 

(Kansas 

only)

provided data for Kansas counties. 

2008 values repeated for 2009 and 

2010; MOVES defaults for 

Missouri counties

MOVES defaults

*MOVES defaults for these inputscan be generated from a national scale run

Lessons Learned

The MARC case study is a good example of how emission factors can be estimated for a multicounty domain when at least some county-level data are available. MARC did not provide 

several of the most critical MOVES inputs, including vehicle populations and total VMT by 

vehicle type. MARC also did not provide data for meteorology, ramp fraction, monthly and daily 

VMT allocations, or fuel supply and formulation. Each of these were modeled with MOVES-
Case Study Results

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derived defaults. I/M is not required for these counties and was not included in the analysis. 

MARC did provide road type VMT and speed distribution data for all counties and years. For the 

remaining inputs—age distribution and alternative fuels and technologies—MARC provided data 

for some years and/or some counties, and otherwise defaults were used or both years shared 

the same values, depending on the input. 

In some cases, the year of provided data did not agree with the baseline year and had to be 

modified. In these cases, any local inputs provided were translated to the baseline year and any 

default values were re-extracted for the baseline year. Notably, the AVFT values were provided 

for the counties in Kansas but only extending to 2008. To update to the baseline 2010 year, the 

last year of provided data was repeated for 2009 and 2010. This approach was selected given 

the preference to rely on local data whenever possible, the reasonable doubt that there would 

be a significant change across the most recent three years, and the consistency maintained by 

using local data for all the years. 

Finally, no local traffic, vehicle registration, or other DOT information was available to derive 

source populations and VMT totals. Instead, national scale MOVES simulations were performed 

for each county of interest to extract defaults for these values, which were then re-imported to 

the county-scale runs. This is less ideal than using local data. The data for age distribution and 

alternative fuels and technologies could also be more complete, instead of a mix of local and 

default data. However, the case does serve as a demonstration of this method with very limited 

local input values. 

Summary of Results

The post-processing of hourly MOVES outputs to domain-average emission factors for the 

MARC case used the same methods as discussed for PAG, except that the emissions and 

activity were aggregated across all seven counties before domain-wide, activity-weighted, 

emission factors were calculated. The resulting emission factors are shown in Table 16. 

Table 16. Emission factors for MARC

g/mi g/start

Base Year 

(2010) 

Future Year 

(2040) 

Base Year 

(2010) 

Future Year 

(2040) 

Auto (Motorcycles+Passenger Cars+Passenger Trucks)

GHGs (CO2-equivalent) 398.53 291.07 153.99 97.94

NOx 0.90 0.14 1.76 0.40

PM2.5 0.03 0.01 0.04 0.02

VOCs 0.18 0.03 2.51 0.68

Vanpool (Passenger Trucks+Light Duty Trucks)

GHGs (CO2-equivalent) 509.56 361.64 181.22 110.55

NOx 1.50 0.30 2.34 0.49

PM2.5 0.05 0.02 0.05 0.02

VOCs 0.28 0.05 3.06 0.59
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MARC Scenario Results

Table 17 provides the regional results of the analysis. As noted earlier, regional reductions are 

often modest when compared to the impact on the population targeted by the strategies. 

Additional explanation is provided below and numerical results are included in Appendix B. 

Table 17. MARC Regionwide Percent VMT and Emissions Changes

Percent Change for 2040 BAU compared to 2040 Scenario

Scenario Light-Duty 

VMT

GHGs 

(CO2 

equivalent)

PM2.5 NOx VOC

MARC

Scenario 1: Expanded TDM -0.93% -0.93% -0.93% -0.92% -0.92%

Scenario 2: Scenario 1 + Enhanced Transit -2.35% -2.35% -2.35% -2.35% -2.34%

Scenario 3: Scenario 2 + Land Use -2.49% -2.49% -2.49% -2.48% -2.48%

Scenario 4: Scenario 3 + Pricing -12.06% -12.05% -12.05% -12.03% -12.02%

Ridesharing and TDM have a moderate impact on affected population with a 3.6% VMT

reduction and shifts from drive-alone to rideshare, cycling, walking and transit.

Transit improvements have a small impact on affected population (0.5% VMT reduction) for 

transit trip times and walking distance. However, there is a large impact on affected population 

for university transit pass (18.3% VMT reduction) with shifts from drive-alone and rideshare to 

transit. 

Land use also shows a small impact on affected population (0.14% VMT reduction) with shifts 

from drive-alone to transit. This is an unreasonably small impact, similar to that observed for 

PAG. CUTR has suggested that this result may be due to the relatively low level of transit 

service in the MARC region, as land use results in TRIMMS are driven by transit accessibility. 

See Section 4.2 for a further discussion of land use analyses with TRIMMS. 

As expected, transportation pricing has a large impact on the affected population (19.5% VMT 

reduction) for parking and mileage pricing. There is also a large impact on the affected 

population for mileage pricing only (9% VMT reduction) with shifts from drive-alone and 

rideshare to transit, vanpool, cycling and walking. 
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4. Conclusions and Recommendations

Although each region participated independently in the selection of scenarios and with respect 

to the available data, there were some common themes and overall comparisons that may be 

useful to other regions interested in applying TEAM. The options selected in TRIMMS and 

MOVES for the analysis of scenarios were similar across regions, except for adjustments made 

based on data availability. This standard analytical approach provides a basis of comparison

that is independent of individual strategy performance in each regional context. The lead 

agencies along with their stakeholders can apply the appropriate regional context for evaluation 

of the scenario outcomes. Their context and experience drawn from participating in the case 

studies will inform their view of how best to use the information gathered from this study.

4.1. TEAM Data Requirements

Regional data collection and validation was the most challenging aspect of the analysis. Many 

factors contributed to the extensive time and interaction that this task involved. MPOs have 

standard data elements that are used for various routine planning functions, and TEAM was 

developed to interface with this data without requiring additional data collection or extensive reevaluation of the information. Every MPO is unique in this regard. The availability of data 

depends on regional priorities and what data can be collected or borrowed. A strong 

understanding of both TRIMMS and MOVES allows adjustments to account for these regional 

data differences; however, some regions may find this challenging. 

Data validation is an essential step in the TEAM approach. It is not enough to ensure that the 

right type of data is used, but also necessary to consider the reasonableness of this data. For 

instance, the distribution of VMT for transit vehicles among road types is unlikely to be the same 

as for passenger cars. This study found that the reasonableness of data that may already be in 

use cannot be taken for granted. A critical element for applying TEAM successfully is the ability

to identify questionable data and develop substitutions when needed. In some cases this meant 

several revised data sets. In other cases it resulted in the extensive use of defaults. Knowledge 

about the underlying data and previous experience in their use is an advantage. 

Use of local data is the best way to ensure that the strategy effectiveness identified through 

TEAM is applicable to the region. Default data is available in both TRIMMS and MOVES to use 

when required, but care must be taken to ensure the default data is applicable to the region and 

to the strategies being evaluated. Regions that undertake a TEAM analysis should allow 

significant amount of preparation time to identify data requirements, collect or substitute data 

elements and validate the appropriateness of the data for this type of analysis.

4.2. TRIMMS Support of TEAM

TRIMMS 2.0, which was used for modeling the 2010 national study, was in the process of being 

updated at the time. TRIMMS 3.0 is the current version and was used for these case studies., A 

web-based version is now under development. To the extent possible, the new features and 

functionality in TRIMMS 3.0 were used in an effort to meet the strategy interests of the region. 

By putting TRIMMS to the test in this way, some potential shortcomings were identified. Many 
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of these issues have been discussed with the TRIMMS developers and in some cases, ways to 

work within the tool and through post-processing were identified. In a very small number of 

instances, TRIMMS just might not be the right tool for a selected strategy at this time. The 

information below is provides some understanding of the more significant issues.

Users of TEAM should be prepared to translate their travel efficiency strategies into TRIMMSready inputs. Many of TRIMMS’ functions work by manipulation of travel times and travel costs, 

common factors in travel demand modeling. To model an expansion of HOV lanes in TRIMMS, 

for example, the user must input changes in typical travel times for carpool trips (and possibly 

SOV trips). Precise changes would be difficult, if not impossible, to estimate without a detailed 

transportation network analysis. TRIMMS users can instead rely on assumptions derived from a 

literature review or expert opinions. For example, in the case studies conducted here, a travel 

demand modeler at the Boston MPO suggested that a 10% reduction in average carpool times 

could be reasonably achieved by an expansion of the HOV network in the region. Providing this 

kind of assumption makes the analysis a goal-based one. Determining what investments must 

be made to achieve that travel time reduction is not necessary to conduct an exploratory 

analysis of alternative scenarios. 

Land Use 

Land use is one of the most critical elements in the evaluation of travel efficiency strategies 

because it is so central to transportation planning and represents an almost universally 

identified approach to reducing travel. By shortening distances among travel destinations, 

increasing mixed use zoning, and concentrating growth around transit nodes, smart growth 

strategies make walking, cycling, and transit more viable modes of transportation and can even 

reduce the distances of some car trips. TRIMMS land use features are in an early stage of 

development, and the predicted impact of land use in these case studies is less than in other 

current literature. TRIMMS 3.0 includes a new function for estimating the impact of land use 

strategies, including increasing residential densities, land use mixing, transit station 

accessibility, and transit-oriented development (TOD). Although this capability was initially 

viewed as a positive attribute, the TRIMMS land use function raised concerns for two main 

reasons. First, it is based on a limited data sample. Second, it only considers the propensity of 

people living in denser areas to increase their use of transit. It does not consider the welldocumented effects of increased biking and walking, or shortened trips in private vehicles. As a 

result, the land use analyses conducted for this study produced smaller VMT reductions in all

regions than for the clusters in the 2010 national study used for comparison, where land use 

impacts were assessed outside of TRIMMS. Land use strategies produced slightly lower 

impacts than expected in the MassDOT region, and dramatically lower impacts than expected in 

the PAG and MARC regions. While the underestimation of land use impacts is believed to be a 

result of the model’s response to the availability of transit, (transit availability is much greater in 

the MassDOT region than in the PAG and MARC regions) more testing is required to determine 

the cause.

To use the land use functions in TRIMMS 3.0 for a regional analysis, using weighted average 

densities was determined to be most appropriate. Weighted average population density 
Conclusions and Recommendations

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summarizes regional land use densities in a way that is more representative of residents’ daily 

experience of land use patterns than average density. TAZs with higher populations and which 

tend to be denser areas, are weighted more heavily than TAZs with lower populations. This 

approach was used as a pre-processing step in scenario evaluation 

There are other approaches to analyzing land use strategies that we recommend for future 

efforts. The approach used in the 2010 national study with TRIMMS 2.0 (which did not have an 

explicit land use component) is still valid. In the 2010 study, the TRIMMS model was used to 

calculate the change in VMT using elasticity values for travel time. These values for expected 

changes in travel time (access time and in-vehicle time) and trip lengths resulting from land use 

measures were based on a review of the literature and values included in EPA’s Smart Growth 

Index (SGI) model. It is also possible to use the widely accepted density variable elasticities put 

forward by Ewing and Cervero.21 The web-based version of TRIMMS is expected to improve 

and expand the land use capabilities. 

Mode Shifts and Trip Lengths

The 2010 national study identified the importance of trip lengths in determining strategy 

effectiveness. TEAM is focused on the shift from automobile travel to other modes and to 

shorter trip lengths. TRIMMS 3.0 does not appear to ensure that trips of equal length are 

substituted for one another when travel mode shifts. For example, TRIMMS does not account 

for the fact that a 40 mile vanpool trip replaces a 40 mile car trip. It always applies the average 

trip length for each mode, which is typically shorter for a car trip than for a vanpool trip. Thus if 

car trips are replaced with a vanpool trip, the VMT benefit may be affected by the assumption of 

longer trips. 

CUTR has also confirmed that there are no internal controls in TRIMMS to ensure that the 

number of trips remains constant (or near constant) while trips are shifted between various 

modes, with the exception of the algorithms used to analyze employer-based TDM strategies. 

TRIMMS sometimes shows an increase in rideshare VMT without a commensurate decrease in 

drive-alone VMT when trip cost values are entered. For modeling HOV lanes (reduction in 

rideshare travel times), the reduction in SOV VMT is less than the increase in rideshare VMT. 

This result can be corrected within the TRIMMS model by adjusting elasticities or outside of the 

model by adjusting the changes in SOV and carpool trips to be more comparable.

In general, the total VMT results are more reliable than VMT by mode. This study presents 

results for light-duty vehicles only, and therefore does not include bus VMT. TRIMMS tends to 

over-estimate increases in transit VMT, because it does not allow for increases in transit vehicle 

occupancy.

There is some interest in strategy types that cannot be evaluated by TRIMMS at the regional 

scale in its current form. Bike strategies are one consistent example noted. Other interests 

 

21 Ewing, Reid and Cervero, Robert(2010) 'Travel and the Built Environment', Journal of the American Planning Association, First 

published on: 11 May 2010 (first)
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expressed were park-and-ride, transit fare integration, peak/off-peak fares, transit marketing, 

public education, and operational improvements such as intelligent transportation system (ITS). 

TRIMMS remains the most viable sketch planning tool to support TEAM, and these 

observations are not intended to detract from its usefulness. The developers at CUTR suggest 

that improvements to TRIMMS can be supported by users reporting their results and experience 

using the model. 

4.3. MOVES Support of TEAM

MOVES is the best model for estimating emissions for TEAM. It is EPA’s current mobile source 

emission inventory tool, and is required to be used for state and local regulatory analyses, such 

as for State Implementation Plans (SIP) and transportation conformity determinations.

22 These 

uses of the model are addressed in other EPA guidance and, although addressing model use 

for these purposes is beyond the scope of the present analysis, this project has shown that 

regions are becoming increasingly familiar with its use. Accordingly, many are developing their 

own, custom MOVES inputs. 

Although the inputs developed for those purposes may be used for TEAM, the use of the 

MOVES model for TEAM differs.23 Primarily, in a sketch analysis, detailed emission factors are 

not needed and the additional complexity in producing and using them is not warranted. Instead, 

as noted previously, overall regional, average emission factors representing the activityweighted mean of all starting and running activities in the region is produced and coupled with 

the TRIMMS outputs. These emission factors are calculated as total running emissions from 

hourly outputs (in grams per year) divided by total running activity (in miles per year) across the 

modeled region; a similar analysis is made for start emissions. Resulting emission factors are in

units of grams of pollutant per average mile driven or grams of pollutant per average start. This 

is explained further in the TEAM User’s Guide24 and in Section 2.4, above. 

A range of technical capability related to the use of MOVES was evident among the regions. 

There were also some policy questions about the analytical procedures for this study and the 

regulatory analysis conducted for CAA purposes. Although the same data may often be used, a 

TEAM analysis cannot be used for emission inventories, air quality demonstrations and 

transportation conformity determinations required by the CAA. Key topics on the use of MOVES 

for the case studies are discussed below. 

Selection and Use of Base and Future Years

Base and future years in MOVES should be selected to agree with those of the strategies being 

analyzed. A common issue encountered was that the baseline year selected for the scenario

 

22 E.g.,Using MOVES to Prepare Emission Inventories in State Implementation Plans and Transportation Conformity: Technical 

guidance for MOVES2010, 2010a and 2010b, EPA-420-B-12-028, April 2012

23 More explanation in the use of this approach is given in: Analyzing Emission Reductions from Travel Efficiency Strategies: A 

Guide to the TEAM Approach, EPA-420-R-11-025, September 2011. 

24 http://www.epa.gov/otaq/stateresources/policy/420r11025.pdf
Conclusions and Recommendations

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analysis was different than the year for which data had previously been prepared in the region, 

e.g., for a regional emissions analysis. In these cases, the emission inputs for the baseline year 

were required to be modified (or default values extracted) to agree with the scenario baseline 

year. The effort needed to adjust for differences in the baseline year should be reduced as more

regional data collection and preparation takes place with TEAM analysis in mind. 

Data Collection and Validation 

Of the three regions, MARC provided the least developed MOVES data. The data MARC 

provided had been prepared by a consultant for a previous analysis and MARC was not able to 

collect data beyond this. In some cases, MARC had data, or access to data, but it had not been 

formatted for or used in any MOVES applications. MARC’s data collection also was affected by 

its regional span across state boundaries, which complicated the efforts. 

The Boston region showed the most expertise with MOVES, but a small number of differences 

between their uses of the model, such as for transportation conformity analyses, and the 

methods of the TEAM approach had to be considered in this analysis. This is principally related 

to MassDOT’s use of “Emission Rates” calculation type instead of “Inventory” for a single, 

representative county, and how to translate that parameterization to the TEAM approach 

documented in Section 3.2. No unique data collection was needed for this TEAM analysis. 

Accordingly, CTPS indicated an interest in performing a comparison of the emission factors 

generated by the TEAM approach to those generated through their emission rate-based 

approach, since few regions have chosen this approach. Their preliminary comparison showed 

that the two sets of factors agreed well except for baseline year (2009) NOx running emissions, 

for which the TEAM values were greater than the curves of values generated by their previous 

analyses. Subsequent review showed no obvious errors in either analysis and this was noted as 

an interesting result that should be documented for potential future exploration. It is possible

that this was related to removing I/M in the TEAM approach, however this has not been 

demonstrated conclusively. 

The PAG region showed great interest in increasing staff understanding and mastering of 

MOVES. PAG collected extensive data for this effort and consistently updated their existing data 

and methods per discussions with EPA. This led to several revisions of input values, but 

produced one of the most locally specific analyses. In addition, PAG also independently 

produced their own emission factors. Comparisons of those with the factors generated in this 

study showed essentially identical results.

It is not clear that any data element was generally less available than others. Instead, the case 

studies illustrated that the experience level of each MPO and the availability of resources to 

collect and process data if it was not already in house are factors in the amount of time a TEAM 

analysis will take. Both of these factors should improve with increasing familiarity with the 

models and better coordination among various analytical planning efforts by the MPOs. 
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4.4. Regional Realities and Implications for Using TEAM

Making Assumptions

Using TRIMMS requires agencies to make assumptions about future conditions in terms of 

inputs that TRIMMS uses, such as trip costs and travel times. These assumptions, especially

about a distant future, are not easily made and may represent sensitive issues in the region. 

Agencies are faced with the decision of how aggressively strategies should be framed simply in 

the choice of inputs. Stakeholders are not always comfortable coming up with appropriate 

assumptions or dealing with the types of inputs that TRIMMS uses. For example, transit 

improvements need assumptions about impacts on access, headways and travel times. Dollar 

values must be applied to subsidies and pricing. Participating agencies dealt with this 

requirement differently. Some used it as a way to identify the value of much stronger policies. 

Others evaluated strategies that would be reasonably feasible in the region. This represents a 

broad range of approaches and will impact the results of the analysis.

The TEAM approach works best when the considered hypothetical scenario analysis is based 

on reasonable goals. A stakeholder group composed of well-informed local transportation 

planners, modelers and land use planners can draft a set of goals for a region based on their 

professional knowledge and limited additional research. While goals need to be reasonably 

achievable in the long term, they do not need to be constrained by shorter term political, fiscal, 

or engineering challenges. 

Comparisons and Validations

As discussed in Section 2.3, results for each region were validated against a comparison cluster 

from the 2010 national study. Clusters were defined by their population size and transit mode 

shares. These characteristics were used to select a comparison cluster for each of the three 

case study regions. Results for each strategy were expected to be similar to those from the 

comparison cluster.

This comparison was used to identify differences in model results that required further 

examination. Where the case study results were similar to those for the comparison cluster, the 

case study results were considered to be validated. In some cases it became clear that a 

selecting a single comparison cluster was not enough to validate results. For example, results 

for pricing strategies and employer TDM strategies varied broadly from the comparison cluster

selected for the MARC region. This difference highlighted that a single cluster may be too 

limited a comparison. In these cases it was more valuable to compare case study results to the 

full range of results across all urban clusters. Population size and transit mode share are useful 

factors to characterize urban regions, but they do not capture other nuances that can affect the 

results of strategies, such as average trip lengths, travel costs, and geography.

It is also important to consider the affect that the strategy assumptions have on results. 

Strategies can be specified in more or less aggressive terms, and can be restricted to subpopulations and sub-geographies. These types of variations must be considered when 

comparing results to those for the 2010 national study.
Appendix A: Strategies Identified in the 2010 Potential Changes in Emissions Due to Improvements in Travel Efficiency 

Report

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5. Appendix A: Strategies Identified in the 2010 Potential Changes 

in Emissions Due to Improvements in Travel Efficiency Report

The table below provides the strategy categories for reducing vehicle travel demand selected for 

analysis:

Table 4. TCM Strategies Analyzed

Strategy Categories TCMs Included in the Analysis

Travel Demand Management (TDM) Rideshare Programs

Employer-based Programs

Public Outreach/ Education

Land Use / Smart Growth TOD: Improved Transit Access

Mixed Land Use

Promotion of Higher Density

Transit Increased Transit Frequency

Lower Fares or Transit Subsidies

Pricing Parking Pricing

Mileage Fees

The table below provides detailed descriptions of strategies and the assumptions used in the 

analysis of scenarios.

Table 6. Scenario Assumptions and Modeling Approach for TCM Strategies

TCM Strategy Specific strategy Strategy information 2010 - 2030 2030 - 2050

Employerbased TDM 

strategies

 Flexible work hours

 Incentives for carpooling

 Guaranteed ride home 

programs

 Ride sharing/ ride matching

 TDM outreach/public 

outreach programs

 Subsidies/discounts for 

transit, pedestrian and bike 

modes

 Telecommuting

Whether or not 

employer offers 

(TRIMMS asks for a 

yes/no answer) to take 

these programs into 

consideration

30% of employers 

Region-wide offer these 

programs; includes all 

TDM strategies except 

walk and bike subsidies 

50% of employers 

Region-wide offer these 

programs; includes all 

TDM strategies 
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Table 6. Scenario Assumptions and Modeling Approach for TCM Strategies

TCM Strategy Specific strategy Strategy information 2010 - 2030 2030 - 2050

Land use 

policies

TOD, smart growth, increase 

in density, mixed use 

developments

Change in travel times 

for all modes, change 

in average trip lengths

3% reduction in all 

access times, 5% 

reduction in transit travel 

time and walk/bike times; 

5% increase in auto travel 

time due to density/

congestion effects

6% reduction in all 

access times, 10% 

reduction in transit travel 

time and walk/bike times; 

10% increase in auto 

travel time due to density/ 

congestion effects

Note: Access time taken as proxy for trip length.

Transit 

projects and 

policies

Transit service 

expansion/increase in 

frequency, improved access

Improvement in transit 

travel time and access 

time

5% reduction in transit 

travel time

10% reduction in transit 

travel time

Fare discounts, reduction, 

subsidies, or free transfers

Change in transit fares 10% reduction in transit 

fares 

20% reduction in transit 

fares

Pricing 

policies

Parking charges Increase in auto 

parking costs

$2 increase per day $5 increase per day 

VMT fees or congestion 

pricing

Increase in peak hour

driving costs

$0.10 increase per mile $0.25 increase per mile

The table below represents the range of impacts of each TCM on travel activity from the 

literature. Where elasticity values were available and could be compared, the travel time and 

travel cost elasticities for each mode used in the 2010 national study fall within the reported 

ranges shown in Table A-3. 

• The ranges provided show estimates of the change in automobile travel or transit ridership for a 

given change in user travel time or travel cost. 

• Where specific elasticities are not available, Table A-3 lists impacts in terms of percentage 

reductions in travel demand (trips or VMT). 

• The elasticities are travel demand elasticities, defined as the percentage change in travel (VMT 

or trips) caused by a one-percent change in user travel costs or travel time. In this study, travel 

costs are equivalent to out-of-pocket operating costs for auto drivers and passengers, and 

transit fares per trip for transit riders. 

Table A-3. Quantitative Estimates of Travel Activity Impacts of TCMs from Literature

Examples of Measures Elasticity/ VMT Reduction %

Ridesharing Programs and Investments

Park-and-ride facilities Regional implementation: 0.1 to 0.5% reduction in VMT

High-Occupancy Vehicle (HOV) lanes Long run (LR) travel time elasticity, regional: -1.0, urban: -0.6, rural: -1.3

0.2 to 1.4% VMT reduction 

Rideshare matching programs 0.1 to 2.0% VMT reduction
Appendix A: Strategies Identified in the 2010 Potential Changes in Emissions Due to Improvements in Travel Efficiency 

Report

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Table A-3. Quantitative Estimates of Travel Activity Impacts of TCMs from Literature

Examples of Measures Elasticity/ VMT Reduction %

Carpool/vanpool incentives 0.2 to 3.3% VMT reduction

Car-sharing Limited quantitative data

Bicycle and Pedestrian Facilities and Programs

Bike paths / lanes / routes <0.1% VMT reduction 

Bike/ped facilities to support transit Limited quantitative data

Transit Projects and Policies

Transit service expansion /increase in 

frequency

-0.6 to -1.0; for buses 

-0.5 (time between buses) for service frequency alone

Improved transit travel times and operations 

(busways, BRT, signal prioritization for transit 

vehicles, heavy and light rail, managed lanes)

-0.4 (travel time elasticity with respect to ridership)

Improved transit access through shuttle and 

feeder bus services, paratransit

Relates to improving travel time above, not measured separately

Transit service integration and intermodal 

transfer centers

Relates to improving travel time above

Fare integration for easy transfers Relates to improving travel time above

Improved transit marketing, information, 

amenities

Limited quantitative data

Commuter discounts/fare reductions -0.3 to -0.4 (fare elasticity with respect to ridership)

Peak/off-peak transit fares -0.1 to -0.3 (peak fares) and -0.1 to -0.7 (off-peak fares, depending on trip 

purpose; lower for work trips)

Transit improvement policies, overall Studies estimate 0 to 2.6% VMT reduction 

Parking Management and Incentives

Parking cash-out Elasticities are not available; although some quantitative data on percentage 

reduction in regional VMT are available from specific projects and studies. Preferential parking for carpools and vanpools

Parking duration restrictions

Employer-based Programs (effects depend on level of adoption)

Flexible work schedules Elasticities are not available; although some quantitative data on percentage 

reduction in regional VMT are available from specific projects and studies. Telecommuting

Compressed work weeks

Employer-provided transit passes

Guaranteed ride home programs
Estimating Emission Reductions from Travel Efficiency Strategies: Three Sketch Modeling Case Studies

ICF International A-4 U.S. Environmental Protection Agency 10-000

Table A-3. Quantitative Estimates of Travel Activity Impacts of TCMs from Literature

Examples of Measures Elasticity/ VMT Reduction %

Pricing Policies

Area-wide road pricing/congestion pricing -0.1 to -0.4 (urban road pricing); 10-25% reduction in central city VMT with 

cordon pricing; 0.2 to 5.7% regional VMT reduction 

Distance-based pricing or mileage fees LR: -0.1 to -0.8 (price elasticity). Conservative LR estimate for the U.S. would 

be -0.3

Peak period pricing/ variably priced lanes -0.03 to -0.4 (depending on time of day)

Parking pricing/fees Overall LR elasticity: -0.1 to -0.5

LR regional: -0.3; at sites: -0.1 to -0.2 

LR (non-commute): -0.2 to -0.4

Studies show 0.5-4% reduction in work-related VMT; 3.1 to 4.2% reduction in 

non-work VMT

High Occupancy Toll (HOT) lanes/toll 

increases

-0.1 to -0.4; data from specific projects are available

Pay-as-you-drive Insurance -0.3

Fuel taxes LR: -0.1 to -0.3, tending towards the lower end

Freight vehicle pricing -0.25 to -0.35 (price elasticity); -0.3 to -0.7 (travel time elasticity)

Integrated Land Use and Transportation Strategies

Transit-oriented development and incentives 

(Design and transit access)

-0.05 (vehicle trips) and -0.03 to -0.08 (VMT)

Smart growth and mixed use development 

(Diversity)

-0.03 (vehicle trips) and -0.05 (VMT)

Land use controls for compact, dense urban 

development (Density)

-0.05 (vehicle trips) and -0.05 to -0.12 (VMT) 

Improved regional accessibility due to 

combined measures

-0.18 to -0.22 (VMT); studies estimate regional VMT reduction by 2-20% in 20 

years with doubling of results in 40 years.

Land use measures, overall Regional VMT reduction of 0 to 5.2%

Vehicle Restrictions by Geographic Area or in Peak Periods

Freight vehicle controls Elasticities are not available; although some quantitative data on percentage 

reduction in regional VMT are available from specific projects and studies.

No-drive days

Urban non-motorized zones

Public Education and Outreach Programs

TDM outreach programs by employers These measures are typically implemented as part of other measures. Difficult 

to estimate impacts separately as it could lead to double-counting. Episodic programs (e.g. ozone action days)

Public communication about the impacts of 

travel decisions
Appendix B: Emission Change Quantities and Additional Technical Details for the MOVES Analysis

ICF International B-1 U.S. Environmental Protection Agency 10-000 

6. Appendix B: Emission Change Quantities and Additional 

Technical Details for the MOVES Analysis

As discussed previously, MOVES was run in inventory mode for each of the regions to produce 

activity-weighted, average emission factors each the region. These emission factors were 

coupled with the TRIMMS-predicted changes in activity to produce net emissions changes by 

scenario for each region. The corresponding relative reductions are presented for each region in 

the body of the report. Table 18 shows the calculated total emission and travel changes for each 

of the main pollutants. 

Table 18. VMT and Emission Changes by Region and Scenario

Resulting VMT and Emissions Changes for Selected Pollutants (kg), relative to Baseline or BAU Level, by Scenario

PAG

Scenario 

 

VMT and Emissions Changes - 2040 BAU to 2040 

Scenario

VMT and Emissions Changes - 2010 Baseline to 2040 

Scenario

Light-Duty VMT

GHGs 

(CO2 

equivalent)

PM2.5 NOx VOC Light-Duty 

VMT

GHGs 

(CO2 

equivalent)

PM2.5 NOx VOC 

Scenario 1: 

Implement 

SunTran All 

Access Pass

-40,970,861 -127,117 -5 -71 -38 21,645,838 5,542,039 81 -12,456 -3,821

Scenario 2: 

Expand 

EmployerBased 

Incentives for 

Alternative 

Commute 

Modes

-40,744,298 -55,843 -2 -33 -21 21,872,402 5,613,313 83 -12,418 -3,804

Scenario 3: 

Implement 

BRT on Two 

Corridors

-40,575,312 -2,148 0 -1 -1 22,041,387 5,667,007 85 -12,387 -3,784

Scenario 4: 

Expand 

Parking 

Pricing in the 

DowntownUniversity 

Corridor

-40,672,387 -33,130 -1 -20 -13 21,944,312 5,636,026 84 -12,405 -3,796

MassDOT

Scenario 

 

VMT and Emissions Changes - 2035 BAU to 2040 

Scenario

VMT and Emissions Changes - 2009 Baseline to 2035 

Scenario

Light-Duty VMT

GHGs 

(CO2 

equivalent)

PM2.5 NOx VOC Light-Duty 

VMT

GHGs 

(CO2 

equivalent)

PM2.5 NOx VOC 

Scenario 1: 

Expand 

Programs and 

Incentives for 

Healthy Modes

-103,148,176 -920,323 -48 -598 -283 4,465,026 -8,459,568 -573 -74,147 -29,627
Estimating Emission Reductions from Travel Efficiency Strategies: Three Sketch Modeling Case Studies

ICF International B-2 U.S. Environmental Protection Agency 10-000

Scenario 2: 

Expand 

Programs and 

Incentives for 

Healthy Modes 

+ Smart 

Growth Land 

Use

-104,237,520 -1,276,754 -66 -830 -393 3,375,683 -8,815,999 -592 -74,378 -29,737

Scenario 3: 

Expand 

Programs and 

Incentives for 

Healthy Modes 

+ Smart 

Growth Land 

Use + HOV 

Lanes

-104,415,406 -1,334,958 -70 -867 -411 3,197,796 -8,874,203 -595 -74,416 -29,755

Scenario 4: 

Expand 

Programs and 

Incentives for 

Healthy Modes 

+ Smart 

Growth Land 

Use + HOV 

Lanes + 

Transit 

Network 

Expansion 

and 

Improvement

-104,757,958 -1,447,040 -75 -940 -445 2,855,245 -8,986,285 -600 -74,489 -29,789

MARC

Scenario 

 

VMT and Emissions Changes - 2040 BAU to 2040 

Scenario

VMT and Emissions Changes - 2010 Baseline to 2040 

Scenario

Light-Duty VMT

GHGs 

(CO2 

equivalent)

PM2.5 NOx VOC Light-Duty 

VMT

GHGs 

(CO2 

equivalent)

PM2.5 NOx VOC 

Scenario 1: 

Expand 

Ridesharing 

and TDM 

programs

-51,104,975 -144,155 -8 -92 -63 12,821,202 -409,306 -410 -33,971 -14,180

Scenario 2: 

Expand 

Ridesharing 

and TDM 

programs + 

Transit 

Improvement 

and Promotion

-51,824,957 -364,530 -20 -234 -159 12,101,220 -629,681 -422 -34,113 -14,276

Scenario 3: 

Expand 

Ridesharing 

and TDM 

programs + 

Transit 

Improvement 

and Promotion 

+ Smart 

Growth Land 

Use

-51,895,054 -385,975 -21 -248 -169 12,031,123 -651,126 -423 -34,126 -14,286
Appendix B: Emission Change Quantities and Additional Technical Details for the MOVES Analysis

ICF International B-3 U.S. Environmental Protection Agency 10-000 

Scenario 4: 

Expand 

Ridesharing 

and TDM 

programs + 

Transit 

Improvement 

and Promotion 

+ Smart 

Growth Land 

Use + 

Transportation 

Pricing

-56,738,678 -1,868,248 -100 -1,200 -820 7,187,499 -2,133,399 -503 -35,079 -14,937

In addition to the four primary pollutants presented here, numerous other pollutants were also 

included in the analysis as done in the initial study. Table 19 lists the full set of pollutants 

modeled. 

Table 19. Full Pollutant List

Pollutants

Ammonia (NH3) Nitrous Acid (HONO) Primary PM10 - Brakewear 

Particulate 

Primary PM2.5 - Organic Carbon 

Atmospheric CO2 Nitrous Oxide (N2O) Primary PM10 - Elemental Carbon Primary PM2.5 - Sulfate 

Particulate 

CO2 Equivalent Non-Methane 

Hydrocarbons 

Primary PM10 - Organic Carbon Primary PM2.5 - Tirewear 

Particulate 

Carbon Monoxide 

(CO) 

Non-Methane Organic 

Gases 

Primary PM10 - Sulfate Particulate Sulfur Dioxide (SO2) 

Methane (CH4) Oxides of Nitrogen (NOx) Primary PM10 - Tirewear 

Particulate 

Total Energy Consumption 

Nitrogen Dioxide 

(NO2) 

Primary Exhaust PM10 -

Total 

Primary PM2.5 - Brakewear 

Particulate 

Total Gaseous Hydrocarbons 

Nitrogen Oxide 

(NO) 

Primary Exhaust PM2.5 -

Total 

Primary PM2.5 - Elemental Carbon Total Organic Gases 

Volatile Organic Compounds 

A variety of MOVES vehicle and fuel types were included in the analysis to characterize the 

vehicle types used in the TRIMMS model. Those were: 

• Diesel Passenger Car

• Diesel Passenger Truck

• Diesel Transit Bus

• Gasoline Light Commercial Truck

• Gasoline Motorcycle

• Gasoline Passenger Car

• Gasoline Passenger Truck

• Gasoline Transit Bus
Estimating Emission Reductions from Travel Efficiency Strategies: Three Sketch Modeling Case Studies

ICF International B-4 U.S. Environmental Protection Agency 10-000

For the majority of strategies considered here, additional VMT from increased transit ridership is 

minimal, so transit emissions were excluded. For the PAG region, the influence of preaggregation of input data on predictions was briefly considered. Our original approach was 

designed to use annual aggregation for speed, but EPA cautioned against this because it would 

compromise accuracy. The comparisons made in Table 20 and Table 21 indicate that the 

differences in emissions can be significant, although activity differences are very small. Cases 

where the data uses annual aggregation tend to underestimate annual emissions somewhat 

relative to hourly inputs (without aggregation). Aggregation has minimal influence on activity. 

The hourly activity results are within 5% of the yearly results – usually <1% difference.

Emissions vary more widely. Hourly results are usually greater than annual results – by an 

average of 7%, although the range reached 75-85% for buses, especially for off-network 

activities (starting). In a small number of cases hourly results were seen to be smaller than 

annual results, by up to 5%.

Table 20. Difference in Activity Values: Hourly-to-Annual Aggregation

Distance Traveled Starts

Vehicle Rural Restricted 

Access

Rural Unrestricted 

Access

Urban Restricted 

Access

Urban Unrestricted 

Access

OffNetwork

Light Commercial 

Truck

0% 0% 0% 0% 0%

Motorcycle 0% 0% 0% 0% -3%

Passenger Car 0% 0% 0% 0% 0%

Passenger Truck 0% 0% 0% 0% 1%

Transit Bus 0% 0% 0% 0% -4%

Table 21. Difference in Emission Values: Hourly-to-Annual Aggregation

Vehicle Pollutant Off-Network

Rural 

Restricted 

Access

Rural 

Unrestricted 

Access

Urban 

Restricted 

Access

Urban 

Unrestricted 

Access

Light Commercial 

Truck

Atmos CO2 -10% -1% -1% -1% -4%

CO -11% -4% -4% -4% -6%

CO2 Equivalent -7% -1% -1% -1% -4%

Methane (CH4) -12% -1% -1% -1% -5%

N2O 0% 0% 0% 0% -6%

NMHC -8% -1% -1% -1% -5%

NOx -4% -1% -2% -1% -7%

PM10 Brakewear 0% 0% -1% -6%

PM10 Elem C -24% -1% -1% -1% -3%

PM10 Organic C -24% -17% -13% -16% -13%

PM10 Sulfate -11% -1% -1% -1% -4%

PM10 Tirewear 0% 0% 0% -2%

PM2.5 Brakewear 0% 0% -1% -6%
Appendix B: Emission Change Quantities and Additional Technical Details for the MOVES Analysis

ICF International B-5 U.S. Environmental Protection Agency 10-000 

Vehicle Pollutant Off-Network

Rural 

Restricted 

Access

Rural 

Unrestricted 

Access

Urban 

Restricted 

Access

Urban 

Unrestricted 

Access

PM2.5 Elem C -24% -1% -1% -1% -3%

PM2.5 Organic C -24% -17% -12% -16% -13%

PM2.5 Sulfate -11% -1% -1% -1% -4%

PM2.5 Tirewear 0% 0% 0% -2%

PM2.5 Total Exh -24% -7% -5% -7% -8%

Total Energy -10% -1% -1% -1% -4%

Total Gas HC -8% -1% -1% -1% -5%

VOC -8% -1% -1% -1% -5%

Motor-cycle Atmos CO2 -6% 0% 0% 0% -1%

CO -48% 0% 0% 0% -1%

CO2 Equivalent -6% 0% 0% 0% -1%

Methane (CH4) -24% 0% 0% 0% -4%

N2O 3% 0% 0% 0% -6%

NMHC -15% 0% 0% 0% -4%

NOx -30% 0% 0% 0% 1%

PM10 Brakewear 0% 0% -1% -7%

PM10 Elem C -29% -21% -21% -21% -22%

PM10 Organic C -29% -21% -21% -21% -22%

PM10 Sulfate -9% 0% 0% 0% -1%

PM10 Tirewear 0% 0% 0% -2%

PM2.5 Brakewear 0% 0% -1% -7%

PM2.5 Elem C -29% -21% -21% -21% -22%

PM2.5 Organic C -29% -21% -21% -21% -22%

PM2.5 Sulfate -9% 0% 0% 0% -1%

PM2.5 Tirewear 0% 0% 0% -2%

PM2.5 Total Exh -29% -21% -21% -21% -22%

Total Energy -6% 0% 0% 0% -1%

Total Gas HC -17% 0% 0% 0% -4%

VOC -15% 0% 0% 0% -4%

Passenger Car Atmos CO2 -10% -1% -1% -1% -4%

CO -29% -5% -4% -4% -6%

CO2 Equivalent -8% -1% -1% -1% -4%

Methane (CH4) -15% -1% -1% -1% -4%

N2O 0% 0% 0% 0% -6%

NMHC -13% -1% -1% -1% -5%

NOx -4% -2% -2% -2% -6%

PM10 Brakewear 0% 0% -1% -6%

PM10 Elem C -31% -19% -20% -20% -22%
Estimating Emission Reductions from Travel Efficiency Strategies: Three Sketch Modeling Case Studies

ICF International B-6 U.S. Environmental Protection Agency 10-000

Vehicle Pollutant Off-Network

Rural 

Restricted 

Access

Rural 

Unrestricted 

Access

Urban 

Restricted 

Access

Urban 

Unrestricted 

Access

PM10 Organic C -31% -21% -21% -21% -23%

PM10 Sulfate -13% -1% -1% -1% -4%

PM10 Tirewear 0% 0% 0% -2%

PM2.5 Brakewear 0% 0% -1% -6%

PM2.5 Elem C -31% -19% -19% -20% -22%

PM2.5 Organic C -31% -21% -21% -21% -23%

PM2.5 Sulfate -13% -1% -1% -1% -4%

PM2.5 Tirewear 0% 0% 0% -2%

PM2.5 Total Exh -31% -20% -20% -21% -23%

Total Energy -10% -1% -1% -1% -4%

Total Gas HC -13% -1% -1% -1% -5%

VOC -13% -1% -1% -1% -5%

Passenger Truck Atmos CO2 -11% -1% -1% -1% -4%

CO -13% -4% -4% -4% -6%

CO2 Equivalent -8% -1% -1% -1% -4%

Methane (CH4) -12% -1% -1% -1% -5%

N2O -1% 0% 0% 0% -6%

NMHC -8% -1% -1% -1% -5%

NOx -3% -1% -2% -1% -5%

PM10 Brakewear 0% 0% -1% -6%

PM10 Elem C -32% -6% -4% -6% -7%

PM10 Organic C -32% -20% -19% -20% -20%

PM10 Sulfate -13% -1% -1% -1% -3%

PM10 Tirewear 0% 0% 0% -2%

PM2.5 Brakewear 0% 0% -1% -6%

PM2.5 Elem C -32% -5% -4% -6% -6%

PM2.5 Organic C -32% -20% -19% -20% -20%

PM2.5 Sulfate -13% -1% -1% -1% -3%

PM2.5 Tirewear 0% 0% 0% -2%

PM2.5 Total Exh -32% -17% -15% -17% -17%

Total Energy -11% -1% -1% -1% -4%

Total Gas HC -8% -1% -1% -1% -5%

VOC -8% -1% -1% -1% -5%

Transit Bus Atmos CO2 -7% -1% -1% -1% -3%

CO 3% 0% 0% 0% -3%

CO2 Equivalent -7% -1% -1% -1% -3%

Methane (CH4) -85% 0% 0% 0% -4%
Appendix B: Emission Change Quantities and Additional Technical Details for the MOVES Analysis

ICF International B-7 U.S. Environmental Protection Agency 10-000 

Vehicle Pollutant Off-Network

Rural 

Restricted 

Access

Rural 

Unrestricted 

Access

Urban 

Restricted 

Access

Urban 

Unrestricted 

Access

N2O 5% 0% 0% 0% -6%

NMHC -74% 0% 0% 0% -5%

NOx -85% 0% 0% 0% -3%

PM10 Brakewear 0% 0% -1% -6%

PM10 Elem C 0% 0% 0% 0% -1%

PM10 Organic C 0% 0% 0% -1% -5%

PM10 Sulfate -7% -1% -1% -1% -3%

PM10 Tirewear 0% 0% 0% -2%

PM2.5 Brakewear 0% 0% -1% -6%

PM2.5 Elem C 1% 0% 0% 0% -1%

PM2.5 Organic C 0% 0% 0% -1% -5%

PM2.5 Sulfate -7% -1% -1% -1% -3%

PM2.5 Tirewear 0% 0% 0% -2%

PM2.5 Total Exh 0% 0% 0% 0% -2%

Total Energy -7% -1% -1% -1% -3%

Total Gas HC -75% 0% 0% 0% -5%

VOC -74% 0% 0% 0% -5%

Based on these issues, all MOVES runs for this project were run without pre-aggregation, and 

hourly outputs from MOVES were manually aggregated to annual.

Another special case involved disaggregating MassDOT provided data. The source populations 

and total VMTs from MassDOT were cumulative across the seven-county domain. The total 

VMTs were not allocated to the HPMS vehicle types required by MOVES, so VMT by vehicle 

type was estimated from MassDOT-provided factors by multiplying the total VMT by the 

MassDOT-provided allocation factors shown in Table 22. 

Table 22. MassDOT VMT allocation factors

HPMS Vehicle Type Allocation

Factor

Motorcycle 0.50%

Passenger Car 51.40%

Passenger Truck/Light Commercial Truck 45.20%

Intercity Bus, Transit Bus, School Bus 0.10%

Refuse Truck, Short-Haul Single Unit, Long-Haul Single Unit, Motorhomes 0.70%

Short-Haul Combination, Long-Haul Combination 2.00%