Office of Transportation and Air Quality

EPA-420-R-11-025

September 2011

Analyzing Emission Reductions 

from Travel Efficiency 

Strategies:

A Guide to the TEAM Approach
EPA-420-R-11-014

September 2011

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-06-094

Work Assignment No. 4-09

Analyzing Emission Reductions 

from Travel Efficiency 

Strategies:

A Guide to the TEAM Approach
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Table of Contents

1. Introduction ...............................................................................................................................1

1.1. Background ................................................................................................................................... 3

1.2. The TEAM Approach ..................................................................................................................... 4

2. Applying TEAM...........................................................................................................................5

2.1. Identifying Strategies of Interest .................................................................................................. 6

2.2. Selecting the Sketch-Planning Tool............................................................................................... 9

2.2.1. The TRIMMS Model.............................................................................................................13

2.3. Collecting the Data......................................................................................................................13

2.3.1. Data required in TRIMMS....................................................................................................14

2.3.2. Alternate data sources for missing inputs ..........................................................................17

2.4. Completing the VMT Analysis.....................................................................................................20

2.4.1. Inputs required to define strategies....................................................................................20

2.4.2. Limitations of the analysis ..................................................................................................23

2.5. Conducting the MOVES Analysis................................................................................................. 24

2.5.1. Setting the MOVES Parameters and Obtaining Results......................................................25

3. Considering Strategies and Potential Emissions Reductions .......................................................27

Appendices......................................................................................................................................29

A. List of Acronyms and Abbreviations ...............................................................................................29

B. References ......................................................................................................................................31

C. Data from Literature and Model Information.................................................................................33

D. Regional Results from EPA National Analysis .................................................................................41
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1. Introduction

Transportation is one of the largest and fastest-growing sources of greenhouse gas (GHG) in the country. 

Transportation and environmental agencies at all levels are looking for ways to reduce GHG emissions, 

and lessen the health and environmental impacts associated with transportation-related emissions. 

Urban areas provide the greatest opportunity for reducing GHG as well as other air pollutants through 

the adoption of specific policies and strategies to improve travel efficiency by reducing congestion and 

growth in vehicle miles traveled (VMT). The result is more efficient access to goods and services along 

with improved health and overall quality of life.

Travel efficiency strategies such as commuter programs, land use changes, transit improvements, 

increased parking charges, road pricing, and others have been shown to reduce VMT and travel in 

congested conditions, and correspondingly reduce air pollutant emissions. As states and regions look 

for additional ways to reduce emissions, travel efficiency strategies are becoming increasingly attractive 

because they are often less costly to implement, can have both short and long term impacts, and can 

create more sustainable and livable communities when compared to the construction of additional miles 

of new roadway. Although many areas have embraced such strategies for a variety of reasons, there is 

increasing interest in considering whether a comprehensive combination of these strategies can 

substantially contribute to reductions in transportation-related emissions. 

The Travel Efficiency Assessment Method (TEAM) is intended to assist professionals in assessing the 

potential role travel efficiency strategies can play in reducing criteria and GHG emissions. TEAM 

supports a preliminary exploration of how specific transportation and land use changes may result in air 

quality improvements, whether air quality is the primary reason for adopting such changes or an 

associated co-benefit. The travel efficiency strategies tested using TEAM are based on existing and 

anticipated local conditions with data drawn from a traditional travel demand model or other sources. 

Because it relies on a simple spreadsheet analysis, the TEAM approach provides a quicker assessment 

than an approach that uses a transportation model. This relationship with more detailed analysis means 

that TEAM augments and supports the existing analysis rather than competing with it. TEAM provides 

useful information for a planner or decision-maker to evaluate the potential impacts of certain policies. 

Practitioners can be assured that further detailed analysis will refine and enhance the TEAM results 

rather than produce conflicting information. In this way TEAM can save time and resources for the user. 

TEAM relies on EPA’s Motor Vehicle Emissions Simulator (MOVES) model to estimate the potential 

emission reductions from changes in travel activity. Because TEAM is scalable from the level of a single 

site, zone, or region up to a multi-county region, there are many applications for its use in planning 

efforts as a screening tool for initial decision-making. 

Air Quality Planning: Several areas that do not meet the National Ambient Air Quality Standards 

(NAAQS) must work with State authorities to develop and implement State Implementation Plans (SIPs) 

to improve air quality. In addition, the transportation conformity requirements ensure long range 

transportation plans (LRTP) and transportation improvement programs (TIP) prepared by metropolitan 

planning organizations (MPOs) are consistent with transportation emissions limits established by the 
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SIP. TEAM does not replace the procedures and methodologies used to support air quality planning, and 

should not be used for calculating emission reductions for SIP development or conformity 

determinations. 1

o SIP Development and Transportation Conformity Analysis: Travel efficiency strategies can be 

included in LRTPs and TIPs where emission reductions are needed to meet transportation 

conformity requirements. TEAM can then be used to compare and shortlist travel efficiency 

options for further consideration and analysis. 

Instead it provides a means to compare potential strategies and groups of strategies 

to help quickly screen options and identify promising alternatives and their potential emission 

reductions. The following bullets provide additional details on potential uses of the TEAM 

methodology.

o Congestion Mitigation and Air Quality Improvement (CMAQ) Program: CMAQ project 

eligibility requires that project and programs selected for funding result in emission reductions. 

TEAM can be used to evaluate individual projects as well as regional programs where data is 

available at the appropriate geographic scale.

o Greenhouse Gas Analysis (GHG): Many states and urban areas that have an interest in reducing 

GHGs lack appropriate tools and techniques to support this analysis. TEAM uses the latest 

vehicle emissions information available through the MOVES model to allow analysis of potential 

GHG reductions.

Transportation Planning: The decision making process that supports transportation planning in urban 

areas is defined by law and regulation. This process is supported by detailed analysis at various levels of 

sophistication across the country. TEAM does not alter the existing requirements for supporting analysis 

but rather allows a preliminary consideration of options using an “off-model” approach.

o Long Range Transportation Planning: Decision makers need an understanding of how different 

strategies might help achieve regional goals such as reduction in emissions or VMT. TEAM can 

be used to screen options in order to inform decisions as well as focus limited technical 

resources on those strategies which appear most effective. 

o Travel Demand Management (TDM): Commuter programs include incentives for ridesharing, 

walking, cycling, or using transit and vanpools, opportunities for telecommuting, flexible work 

hours, and so on. These strategies can be analyzed at the level of an individual site or employer 

or a regionwide level using data appropriate for the scale of analysis. These strategies can 

reduce emissions by reducing total VMT and reducing peak period travel. TEAM provides a way 

to compare effectiveness of TDM strategies based on the estimated level of support within the 

region.

o Transportation Pricing Analysis: Strategies such as parking pricing, tolling, VMT fees, and other 

road pricing strategies that change the user costs of driving, as well as strategies affecting transit 

 

1 For SIP and conformity purposes, state and local agencies should contact their EPA Regional Office and review 

relevant SIP and conformity guidance documents at: www.epa.gov/otaq/stateresources/index.htm. 
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fares, are incentives/disincentives with respect to travel behavior. These strategies also have an 

impact on emissions by altering travelers’ choices towards modes like transit, ridesharing, 

walking, and cycling, and altering their choices of travel routes2

o Multi-modal Considerations: Travel time improvements include improvements in transit service 

and frequency. They can also include reduction in access time that may occur due to land use 

strategies such as transit-oriented development, increased density and mixed use developments 

as part of smart growth plans. These strategies, along with supporting strategies such as better 

amenities for transit, walking and cycling can potentially impact transportation emissions by 

making modes other than automobiles more attractive to the traveler due to a reduction in 

overall travel time. Better amenities for transit, walking, and cycling can result in a shift to these 

modes, thus reducing VMT and emissions from automobile travel.

and times away from congested 

facilities and times of day.

Land Use Planning: The federal Partnership for Sustainable Communities, consisting of the U.S. 

Department of Housing and Urban Development (HUD), the U.S. Department of Transportation (DOT), 

and the U.S. Environmental Protection Agency (EPA) calls for the integrated consideration of housing, 

transportation, and the environment in planning and development decisions. TEAM supports policylevel analysis of the emission and VMT reduction benefits of smart growth compared to current growth 

patterns.

The TEAM approach provides a flexible and adaptable means of considering options to reduce 

transportation emissions. This guide is intended to help practitioners through the steps to conduct an 

analysis that is sufficiently rigorous to support comparison of strategies in order to make policy-level 

decisions as well as support more detailed analysis of promising strategies.

1.1. Background

In 2010, EPA developed an analysis approach to quantify the potential emissions reductions from “travel 

efficiency strategies” at the national level. EPA calls this approach the Travel Efficiency Assessment 

Method (TEAM). The term “travel efficiency” is used to refer to those strategies defined in Section 

108(f)(1) of the Clean Air Act3

 

2

Note that the use of an alternative route may increase VMT and corresponding emissions, although this may be 

offset by the reduction in emissions resulting from travel in less congested conditions.

such as employer-based transportation management programs, transit 

improvements, smart growth and related land use strategies, as well as road and parking pricing, and 

other strategies aimed at reducing mobile source emissions by reducing vehicle travel activity. TEAM 

uses available travel data and a sketch model analysis to quantify the change in VMT, combined with 

MOVES emission factors to calculate the emission reductions that can reasonably be expected by 

3 The Clean Air Act lists 16 “transportation control measures” (Title 42, Chapter 85, Subchapter 1, Part A, Section 

7408(f)) as “methods to reduce or control pollutants in transportation; reduction of mobile source related 

pollutants; reduction of impact on public health”. There are additional strategies to those listed in the Clean Air 

Act such as road pricing and parking charges that result in similar benefits. Collectively these strategies are 

broadly useful to address all regulated pollutant emissions as well as GHG emissions. They also have other 

associated benefits such as reducing demand for foreign oil and decreasing fuel consumption costs.
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applying these strategies. As described above, the results can be used to support many ongoing 

planning activities and analyses within state and local air quality, transportation and land-use agencies.

The TEAM approach was used to conduct a national-level analysis to estimate the potential emissions

reductions that could result from the implementation of seven different travel efficiency scenarios 

comprising multiple strategies. A description of the analysis and results are documented in Potential 

Changes in Emissions Due to Improvements in Travel Efficiency – Final Report (available at: 

http://www.epa.gov/otaq/stateresources/policy/420r11003.pdf and referenced here as EPA Final 

Report). Because the approach was based on analysis of 15 different urban areas using data from the 

respective MPOs’ travel demand models, the lessons learned in collecting data and modeling strategies 

can be relevant to other areas. Specific information from the national-level analysis is included 

throughout this guide as examples for users.

1.2. The TEAM Approach

Regional planning organizations as well as state and local transportation and air quality agencies can 

benefit from lower cost and less data-intensive sketch tools and methodologies to assess the air quality 

impacts of travel efficiency strategies. These sketch planning approaches, when appropriately applied, 

can provide useful information for decision makers. This level of analysis is conducted using existing 

outputs from the regional travel demand model without direct use of the model. It is therefore less 

data-intensive and less costly to run or implement. This approach can help planners effectively screen a 

broader range of alternatives or scenarios in order to reduce or eliminate the time and effort spent on 

modeling and maximize the time spent analyzing promising alternatives. The basic approach of using 

outputs from the travel demand model or other regional sources as inputs to a sketch planning analysis 

offers an efficient and defensible way in which to consider travel changes and related emissions 

benefits. 

This guide describes the TEAM approach to estimating the emission reductions from travel efficiencies 

at the regional level using information that is typically readily available from a travel demand model. 

The analysis can also be conducted for local areas to the extent that local jurisdictions are covered by a 

travel demand model and the data for these jurisdictions 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. The analysis can be 

done for a single year, which may be the current year, or any future year for which transportation and 

demographic data are available. 

The methodology presented here is not a substitute for travel demand modeling or emissions modeling 

required for SIP and/or conformity purposes. It is most useful to provide a starting point to evaluate 

promising strategies for further in-depth analysis. 
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2. Applying TEAM 

TEAM begins with estimating the potential travel activity changes (measured in terms of trips and VMT) 

of selected travel efficiency strategies. The estimated change in trips and VMT can then be used to 

estimate the corresponding emission changes. These values can also be used to estimate other cobenefits associated with the reduction in VMT, such as fuel consumed and savings in vehicle operating 

costs, if desired. The following information describes in detail the steps that are the framework for the 

TEAM approach, illustrated in Figure 1 below. In instances where alternatives may exist, these are noted 

to provide choices about the data and analysis methods that best suit individual situations.

Figure 1. TEAM Approach Steps

The methodology that supports TEAM has been tested and peer reviewed using a regional analysis 

approach to develop a national-level understanding of potential benefits from the use of travel 

efficiency strategies and documented in the EPA Final Report. That analysis was conducted using data 

from 15 metropolitan regions of varying population and transit mode share and provides the basis for 

the information in this guide. References are made to that analysis using the Trip Reduction Impacts of 

Mobility Management Strategies (TRIMMS©4

) model5 along with detailed supporting information in 

the appropriate sections. Although TRIMMS was the sketch planning model used for this analysis, the 

TEAM approach is flexible with regard to the choice of sketch model. A minimum requirement is that 

the model selected allows the user to enter valid data inputs obtained from the regional travel demand 

model or other reliable local source, apply assumptions for the strategies of interest, and estimate 

reductions in trips and/or VMT. Several models are available for this purpose. A more complete

discussion of sketch model choice follows in Section 2.2. Throughout this guide the descriptions will 

reference the use of the TRIMMS model.

 

4 The TRIMMS© model is under copyright; the symbol is being used in this first reference to the model but will not 

be repeated in the text hereafter.

5 At the time of publication, the newest version of TRIMMS was being prepared for release. The TRIMMS model 

and related documentation (Center for Urban Transportation Research (CUTR) (2009) Quantifying the Net Social 

Benefits of Vehicle Trip Reductions: Guidance for Customizing the TRIMMS© Model, prepared for Florida DOT, 

Tampa, FL: CUTR at the University of South Florida) are available at: www.trimms.com
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2.1. Identifying Strategies of Interest

The first step in using 

TEAM is to develop 

an initial list of 

strategies to 

consider. In addition 

to the travel 

strategies listed in the 

CAA, there are 

numerous sources of information about travel efficiency strategies available to guide the development 

of an initial list. The existing travel characteristics of the area, forecasted growth, availability of data, and 

the willingness of decision makers to support new approaches to reducing emissions will further help 

refine the strategy list. In selecting the strategies that will be modeled, regions can consider results 

from previous studies for individual strategies or strategies that have been analyzed in other regions 

with similar characteristics. 

The TEAM approach was applied to the following four categories of travel efficiency strategies in the 

national-level analysis (EPA Final Report). The examples describe considerations for developing a 

strategy for testing along with the required data for analysis. The descriptions below include reference 

to the TRIMMS model and the way the strategies can be analyzed using this particular tool because the 

TEAM approach was based on this. However, any sketch planning tool capable of analyzing these 

strategies can be used with this conceptual understanding.

 Regional Transportation Demand Management: 

Transportation demand management (TDM) includes a range of strategies that encourage 

travelers to use the transportation system in a way that contributes less to congestion and

improves air quality. TDM covers many aspects of trip-making, including whether to make a trip, 

when to make the trip, what transportation mode to use, and what route to choose. TDM 

program choices available in TRIMMS include employer-based strategies such as flexible work 

hours, telecommuting, guaranteed ride home programs, transit subsidies, and incentives for 

carpooling, walking, and biking. TDM programs implemented at a regional scale may be analyzed

in TRIMMS, based on the percentage of employees or working population in the region that are 

assumed to participate in these programs. Note that the TDM strategy applies only to 

work/commute trips; therefore, the population and other input data should pertain to these trips 

only. Although the TDM strategies do not have a direct impact on the cost of driving or the value 

of travel time in the model, they exert an indirect effect on the choice of alternative modes. 

Individual models will account for this indirect effect in different ways.

The TRIMMS model considers TDM to be a “soft program” where employer-initiated strategies

lead to voluntary changes in travel behavior. The term “soft program” refers to those 

program/strategies usually defined as support programs, which have an impact on travel 

behavior without necessarily having a direct impact on travel time or costs. For example, 
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opportunities and incentives for employees to work flexible hours or to telecommute would fall 

under this category. The term “hard programs” refers to those strategies directly affecting the 

generalized cost of travel, such as transit frequency improvements, transit subsidies or parking 

surcharges. TRIMMS can analyze both types of programs either independently or jointly in a 

combined scenario. Soft programs rely on education or on internalizing some of the costs of 

driving to encourage travel behavior changes; programs include travel planning, advertising, and 

guaranteed ride home programs. Hard programs include both incentives and disincentives, such 

as parking pricing, modal subsidies, and land use strategies that affect transportation access and 

travel times. TRIMMS models the impacts of site-specific and region-wide TDM strategies using a 

set of previously estimated parameters based on an econometric analysis of the relationship 

between hard programs and soft programs like TDM.

6

 Transit strategies: 

The national analysis (EPA Final Report) included the modeling of two transit-related strategies: 

(1) increased frequency of transit services and (2) lower transit fares through discounts, 

subsidies, free transfers or other policies. 

Transit service improvements in the form of improved service frequency and reduced time 

between buses or trains, can lead to a reduction in wait time and overall travel time for transit 

passengers. To model these improvements, assumptions must be made for the expected 

reduction in transit travel time and provided as inputs to the model. The results would then 

represent the VMT reduction possible from any of several strategies to improve transit service 

and operations. TRIMMS can be used to analyze these transit strategies with the application of 

transit travel time elasticity values documented in existing research.

7

Another transit-related strategy that may be modeled is fare reduction, reflecting employer 

subsidies for transit use or commuter discounts offered by transit agencies. To analyze the 

impacts of transit fare discounts and subsidies, the TRIMMS model applies transit fare elasticities. 

The elasticities reflect the sensitivity of transit mode share to a change in the cost of commuting 

by transit, and the default values in TRIMMS have been obtained from a survey of the literature. 

 These values indicate the 

degree to which transit ridership can be expected to increase when transit travel times are 

reduced. 

Note that the impacts of improving qualitative aspects such as the level of comfort or quality of 

transit service cannot be captured in such an analysis.

 Pricing strategies: 

Pricing strategies such as peak hour tolls, variable pricing with charges varying by the time of day 

or level of congestion on new and existing lanes, and conversion of High Occupancy Vehicle 

 

6 Concas, S. & Winters, P.L. (2009). Quantifying the Net Social Benefits of Vehicle Trip Reductions: Guidance for 

Customizing the TRIMMS© Model. Final Report No. BD 549 WO 52 prepared by National Center for Transit 

Research for Florida Department of Transportation. Available at: http://www.nctr.usf.edu/pdf/77704.pdf. 7 Litman, Todd, (2010), Transportation Elasticities: How Prices and Other Factors Affect Travel Behavior, (Victoria, 

BC: Victoria Transport Policy Institute); available at: http://www.vtpi.org/elasticities.pdf; CUTR (2009)
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(HOV) lanes to High Occupancy Toll (HOT) lanes have been implemented in some regions of the 

U.S. in recent years. 

The TEAM approach can be used to estimate the VMT and trip reduction impacts of pricing 

strategies that affect the operating costs of vehicles. These include higher parking charges, 

regional peak period tolls, and mileage fees. Peak period pricing charges can be modeled in 

TRIMMS since it breaks down results and inputs based on time of day. TRIMMS can also model 

mileage fees and facility tolls. Parking charges can be modeled if average baseline parking costs 

for the region are known or estimated from a regional travel demand model. 

Other types of pricing strategies such as corridor-level tolls and cordon-based or area-specific

pricing cannot be modeled using this method since these require detailed disaggregated 

information for sub-areas, such as mode shares and travel costs on particular corridors or in subareas of a region. This information can be effectively analyzed by the regional travel demand 

models or sub-area models. 

In TRIMMS, congestion charges can be modeled by applying the increased cost to a specific 

proportion of all trips (e.g., peak hour trips only). Ongoing studies show that regions considering 

mileage fees favor a congestion pricing component that allows the fee to vary by location and 

time of day in future years. The analysis takes this into account and applies the higher mileage 

fees during peak hours, using data on the proportion of regional trips occurring in peak hours 

provided at the input stage. The mileage fees are applied to a baseline level of average auto 

operating costs also input by the user. 

Although parking charges are best modeled at a disaggregate scale using zonal information, it is 

possible to do a sketch-level analysis at a regional scale using the TRIMMS model and data on 

baseline (existing) average daily parking charges. 

 Land use strategies:

Land use strategies are often modeled in terms of assumptions about one or more of the five “D” 

variables -- density, diversity of land uses, design (street network characteristics), destination 

accessibility, and distance to transit facilities. A common land use strategy is transit-oriented 

development (TOD), which calls for dense, mixed-use developments around transit stations that 

are designed to facilitate use of transit and walking and bicycling. Land use strategies can be 

assessed to a limited extent using sketch models and the TEAM approach; however, the results 

are subject to a number of uncertainties. The independent effects of land use strategies such as 

TOD, promotion of higher density, or incentives for mixed use development, are difficult to 

estimate in TRIMMS or any tool that does not analyze impacts at a relatively high resolution, 

typically at the level of traffic analysis zones, census tracts, or even land parcels. Since it is 

difficult to isolate the impacts of these strategies (or “D” variables) due to the interrelationships 

between them, these strategies are often combined together. 

Using the TEAM approach, a single land use scenario may be modeled that combines the effects 

of some common strategies including density increase, mixed use development, and TOD. In 
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doing so, assumptions can be made, considering existing research or local information, for each 

mode with respect to changes in travel time and trip lengths resulting from the land use 

strategies considered. Assumptions for expected changes in travel conditions can be drawn

from previous studies as was done for the national analysis (EPA Final Report).8

 For example, a 

doubling of housing density can be expected to result in a four to five percent reduction in VMT.9

To comprehensively define each strategy, the assumptions or parameters should be developed from a 

thorough review of strategies proposed regionally, a realistic judgment of what is appropriate to 

consider for the region being modeled, and professional and academic studies focusing on analysis of 

travel efficiency strategies. This thorough assessment will allow practitioners to identify the specific 

regional data needed for analysis as well as how to adjust data from sources outside the region, such as 

national default values so that they can be applied to the region. Strategies may be combined into 

scenarios and modeled simultaneously. This will limit model runs and help identify synergies or conflicts 

between the strategies. It is useful to consider how the strategies may be applied within a region in 

deciding whether to combine strategies or to analyze them individually. For example, TDM strategies 

are only applied to work trips, while land use strategies may be applied to all trips. This implies that 

these strategies are best modeled independently with different baseline input data, rather than 

modeled together in the same run. Table C-1 in the Appendix shows a range of strategies considered by 

regions around the country and quantitative estimates of the modeled or observed impacts. Not all of 

these strategies can be analyzed using the TRIMMS model (e.g., some types of pricing strategies as 

noted above and freight-related strategies), but these may be analyzed using other available tools.

 

2.2. Selecting the Sketch-Planning Tool 

There are many 

sketch-planning tools 

that rely on different 

types of data inputs 

and provide various 

outputs (see Table 1). 

In addition, some 

transportation and air 

quality agencies develop in-house tools that meet a variety of needs. When selecting or developing a 

tool for the TEAM approach, users should pay particular attention to the following aspects:

 Range of strategies that can be modeled: Some tools may not directly model certain strategies 

but can still capture the impacts of those strategies via secondary inputs such as changes in 

travel time, costs, or trip distances that are expected to result from implementing the strategy. 

For example, transit-oriented development (TOD) cannot be modeled directly in TRIMMS since 

the model does not allow sub-regional analysis. However, if estimates for the reduction in 

 

8 Bartholomew and Ewing (2009); Ewing and Cervero (2010); Ewing et al. (2008); Rodier (2008) 9 Ewing and Cervero (2001, 2010)
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transit access or travel time resulting from TOD strategies are available these may be used as 

parameters for modeling TOD in TRIMMS.

 Time periods of interest: Some tools allow analysis for a single year based on data inputs for 

that year, while other tools allow the user to estimate impacts for multiple years over which a 

strategy will be implemented or over the life of a project.

 Scale of analysis: Some tools are applicable only for site-level or sub-regional analysis, while 

others can be used at a broader scale for regional analysis of strategies. Site-level analyses 

typically require specific and detailed data that may need to be gathered from the parties 

involved, whereas regional analyses can often take advantage of data available from travel 

demand models and other public data sources.

 Data inputs: Some tools have more intensive data needs, while others require fewer inputs but 

draw more information from surveys and existing data sources. Data inputs can sometimes be 

substituted with default values, but to accurately reflect local conditions this should be limited 

to the extent possible. 

 Outputs: The outputs need to be in a format that can be converted into emissions changes. 

Tools that provide change in travel activity (VMT and trips) as an output are the most 

appropriate for use with the MOVES emissions model. The results from tools that convert 

changes in trip activity to emissions savings directly may still need to be manipulated if they are 

not consistent with EPA’s MOVES emissions model. 

 Flexibility of the tool: It is important that the selected tool allow users to alter the model 

parameters based on local data. For example, TRIMMS allows users to alter the travel time and 

cost elasticities based on information that may be available from studies or surveys done in the 

region, or from the travel demand model. This feature represents an additional value of the 

TRIMMS model and is not commonly found in sketch planning tools. Some models provide 

assumed values for vehicle occupancy. Since average occupancy varies by region and is often 

available at the regional scale, it is preferable if users are able to alter this parameter. Not only 

should the tool be transparent in its assumptions and data sources, but also flexible in 

permitting the user to alter common parameters based on updated data sources or more 

accurate local information that might be available. 

 Trip types to be analyzed: For any tool that is used, users must familiarize themselves 

thoroughly with the assumptions related to trip types, regional population, and other 

parameters. For instance, some tools are meant to model work trips only, not all trips. 

Therefore, if strategies applicable to all trips are to be modeled, the user must either find a tool 

that allows analysis of all trips, or adjust the results obtained from work trip modeling tools to 

estimate the impact of the strategies on all trips.

There are several currently available tools that meet the above criteria to varying degrees and may be 

considered for use with the TEAM approach. The models/methodologies are listed below along with the 
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year they were last updated. Table 1 summarizes the input requirements and output capabilities of 

each.

 Meta-analysis: this is not a tool but a methodology for estimating the impacts of a strategy 

based on modeled or observed impacts obtained from a meta-analysis of literature

 EPA's COMMUTER model, 2005

 Trip Reduction Impacts for Mobility Management Strategies (TRIMMS) model, 2009

 Center for Clean Air Policy (CCAP)’s Transportation Emissions Guidebook (TEG), 2006

 Transportation Control Measure (TCM) Tools, early 1990s

 TCM Analyst, 1994

 Travel Demand Management (TDM) Evaluation Model, 1993

 Surface Transportation Efficiency Analysis Model (STEAM), 2006

 MARKAL (Market Allocation)-MACRO

 National Energy Modeling System (NEMS): Transportation Sector Module (TRAN), 2006
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Table C-2 in the appendix provides more detail on each tool or model listed in Table 1. This list is not

exhaustive and new tools are expected to become available over time. Users may also choose to 

develop their own tool to fit their particular needs.

Table 1. Input Requirements and Output Capabilities for Travel Efficiency Strategy Analysis Tools and Models

Spreadsheet-Based Tools/Methods Models

Metaanalysis

EPA 

COMMUTER

model TRIMMS

CCAPTEGTCM 

Tools

TCM 

Analyst

TDM 

Evaluation

Model STEAM

MARKALMACRONEMS

TRAN

INPUTS

Population X X X

Per capita income X

New vehicle sales X

Mode shares (no. of trips) X X X X X X X X

Average vehicle 

occupancies by mode X X X X X 

Travel times by mode (invehicle and out-of-vehicle) X X X X 

Average trip costs by mode 

(including parking, fees, 

tolls, fuel costs, transit fares)

X X X X X X 

Includes non-motorized trips X X X X X

Average trip lengths X X X X X

Baseline regional VMT X X X X

Trip tables X X

Baseline vehicle speeds X X X X

Vehicle fleet mix X X X X

Fuel price per gallon X X X X

Average fuel economy X X X

Emissions factors X X

OUTPUTS

Change in mode shares (no. 

of trips by mode) X X X X X X 

Change in travel time X

Change in VMT X X X X X X X X X X

Change in emissions X X X X X X X X X

Change in speeds X X

Fuel demand X X X

Benefits and costs X X
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2.2.1. The TRIMMS Model

The Trip Reduction Impacts of Mobility Management Strategies (TRIMMS) model10

TRIMMS evaluates strategies that directly affect the cost of travel such as transit fare subsidies, parking 

pricing, pay-as-you-go pricing initiatives and other financial incentives. TRIMMS also evaluates the 

impact of strategies affecting access and travel times. The model allows the user to account for 

employer-based program support strategies, such as flexible working hours, teleworking, and 

guaranteed ride home programs. It allows the analyst to use local data or defaults from national 

research findings. The VMT impacts of either a single strategy or a given package of strategies are 

subsequently calculated. 

is used as the basis 

for this guidance because the TRIMMS model accepts regional inputs from travel demand models and 

allows alteration of assumed parameters such as travel time and travel cost elasticities. The TRIMMS 

model thus meets the needs of the TEAM approach well; however, other tools may be selected or 

developed to meet individual needs and available data following the TEAM framework. TRIMMS is a 

sketch planning tool that can be customized to analyze many types of strategies at a regional or subarea scale, which would normally be analyzed using a regional travel demand model. For example, 

TRIMMS can be used to analyze strategies involving construction of new infrastructure such as new 

HOV/HOT lanes, new transit lines, and new bicycle/pedestrian facilities. In the TRIMMS model, such 

strategies can be modeled using the change in travel times and travel costs that such strategies 

represent. The TRIMMS model does not use trip tables. It requires average regional mode shares, 

average trip lengths and travel time by mode, average vehicle occupancy, parking costs, and trip costs as 

inputs. The user can change the price and travel time elasticity values. The tool provides changes in 

mode shares, trips, and VMT as outputs. 

Although the TRIMMS model can be used with regional inputs from sources like the American 

Community Survey and Census data, values obtained directly from a regional travel demand model will 

provide more accurate results. Although TRIMMS is primarily designed for the analysis of commute 

trips, the user can adapt the model for analysis of all trips by appropriately adjusting the inputs and the 

results obtained from the model.

2.3. Collecting the Data 

When using the 

TEAM approach, it is 

important to use data 

that closely 

approximates local 

conditions. Some 

models, like TRIMMS, 

contain a built-in 

database of nationally-available data for metropolitan statistical areas (MSAs) or urbanized areas (UAs) 

in the United States, allowing the user to select the relevant region for modeling. However, these data 

 

10 The TRIMMS model and related documentation are available at: http://www.nctr.usf.edu/2009/04/trimms2/ 
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may be drawn from surveys or databases for a particular year and may not reflect current local 

conditions, which can limit their accuracy. For instance, TRIMMS draws data for 85 MSAs from the 

American Community Survey for the years 2005-2007. Adjusting the model inputs and parameters to 

use more specific and current local data generates results that better reflect the expected impact of the 

strategies tested. For a strategy being applied and analyzed at the regional scale, the best input data will 

generally come from the local or regional travel demand model, which uses or provides most of the 

required data. 

The TEAM approach was developed using the TRIMMS model, and the specific data requirements 

discussed below will refer primarily to that tool’s requirements and capabilities. When using a different 

model, the specific details of data and parameters as well as the ability to customize will be based on 

that tool.

2.3.1. Data required in TRIMMS

The TEAM approach uses three sets of data inputs: 

• Inputs for the ‘base case’ that reflect conditions in the absence of any strategy being applied 

• The magnitude of change in travel time or cost that would result from the proposed strategy 

and that will be used to adjust the base case data

• Elasticities that measure the change in travel demand in response to changes in travel time or 

trip costs. 

The current year base case or baseline reflects existing conditions in the region. When the analysis is 

done for a future year, the base case reflects anticipated population growth and socio-economic 

changes but without additional strategies included. The data inputs needed to establish the base case 

are provided in Table 2. 
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Table 2. Regional Data Inputs for TEAM Methodology

Base Case Inputs

Population Total population

Total working population (16 and over)

Modal information (light-duty vehicles, noncommercial trips) for:

• Auto-drive alone

• Auto-rideshare

• Vanpool

• Public transit

• Cycling

• Walking

• (Other modes)

Mode share

Average trip length (miles)

Average vehicle occupancy (no. of persons)

For auto-rideshare, vanpool, and bus only

Trip costs (current $ per trip) – does not include parking 

costs or other costs such as tolls, feeds, and peak hour 

charges

Automobile parking costs (current $ per auto per day)

For auto-drive alone and auto-rideshare only

Other auto trip costs not included in parking costs, e.g., 

tolls, peak hour fees, etc. (current $ per trip)

For auto-drive alone and auto-rideshare only

Average trip travel time (minutes)

Detailed trip travel time – access time and travel time 

separately (minutes)

Peak and off-peak trips Percentage of total trips in peak hours (%)

Total trips in peak hours

Total trips in off-peak hours

The second set of inputs establishes the parameters or limits for the selected strategy. In TRIMMS, 

these are primarily in the form of the magnitude of change in travel time or cost variables that would 

result from the proposed strategy. For example, the user may assume a dollar increase in auto driving 

costs or parking charges in to reflect the application of congestion charges or parking charges 

respectively. Similarly, the user may assume a reduced travel time in minutes for transit modes to 

reflect reduced wait times for transit resulting from improvements in service. 

How the base case and strategy inputs will impact travel behavior is determined by the elasticity values. 

Elasticity is an economic concept that measures the change in demand or supply of a good (e.g. travel 

demand) in response to a change in some factor that influences that supply or demand (e.g. travel time). 

A negative sign indicates that the effect operates in the opposite direction from the cause (an increase 

in gas price causes a reduction in travel), while a positive sign indicates an effect in the same direction 

(e.g., an increase in transit frequency causes an increase in transit demand or mode share.11

 

11 Litman (2010) 

Crosselasticities indicate how a change in the attractiveness of one mode (in cost or time) will affect another 

mode, thus reflecting the degree to which travelers substitute one mode for another when conditions 

change. For example, a cross elasticity of 0.040 for transit with respect to auto parking costs would 
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indicate that a 10% increase in the cost of parking would cause a 0.4% increase in transit trips as some 

drivers stop driving and take transit instead. 

Travel time and price elasticities provide the fine adjustments within TRIMMS that influence the 

potential impacts of a strategy, making this information very important. These elasticity values are 

usually an important consideration in several other models used for this purpose; however, few models 

allow these values to be altered by the user. Table 3 shows typical regional elasticity data that may be 

needed as inputs to TRIMMS or other sketch planning models, if the option to alter elasticities is 

available. TRIMMS comes loaded with default elasticity values with the option of setting customized 

values, if available.

Table 3. Regional Elasticity Inputs (if available)

Elasticity with respect to parking/driving 

costs, for the following modes:

• Auto-drive alone

• Rideshare

• Public transit

• Other

By trip purpose:

• Commuting

• Business

• Education

• Other

Transit elasticities, for peak times, off-peak 

times, and on average

Transit ridership with respect to transit fare

Transit ridership with respect to transit service

Transit ridership with respect to auto operating costs

Auto travel with respect to transit costs

Travel time elasticities, for peak times, offpeak times, and on averageFor modes:

• Auto-drive alone

• Rideshare

• Public transit

The data needed for analysis using TRIMMS is described in Table 2 with optional data included in Table 3

above. Other models will require different data inputs. Please refer to Table C-2 in the Appendix for a 

more complete listing of data needs of some existing models. 

Average regional values may be used for all the required data; however, some inputs may be difficult to 

estimate at a regional scale. For example, parking charges typically vary by zone or sub-region and can 

further vary by time of day. To simplify the analysis, average daily parking charges per vehicle trip may 

be assumed from knowledge of average local hourly rates. 

Note that the TRIMMS model allows analysis of a strategy for one year at a time. Therefore, if a region 

must analyze strategies for multiple years, baseline values for the data inputs must also be available for 

each future year. In some cases, future year inputs for data such as transit fares or parking charges may 

not be available. These can be estimated using reasonable inflation rates or trends of growth seen in 

the region.
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2.3.2. Alternate data sources for missing inputs

Some regions will not have all the data that the model requires. The TRIMMS model includes default 

data and default travel behavior parameters tailored for 85 MSAs in the U.S., encompassing large and 

small urban areas.

12

Future year data can be difficult to obtain in some cases and so assumptions may need to be made to 

extrapolate data for future years based on available information. For data that cannot be obtained from 

the regional model, alternate sources may be used as described below along with guidance on 

assumptions for conducting analyses for future years. 

 This provides an immediate data source for those regions that may not have local 

input data for the base case. If the user’s region is not included in the MSAs represented in TRIMMS, the 

model user guidance recommends selecting the geographical area that most closely matches the region. 

The TRIMMS user guidance also provides measurement methods to assist agencies in customizing some 

of the default values to their areas. 

 Mode share: Given the importance of mode shares in the TRIMMS model, to the extent possible

regions should use values from the travel demand model. Walk and bike mode shares are often 

not available, but can be estimated using regional surveys. If such local data are not available, 

values for walk and bike mode shares may be assumed based on data from other regions with 

similar characteristics or from national estimates. If such assumptions are made, the other mode 

shares must be adjusted accordingly so that the total of all mode shares does not exceed one 

hundred percent. For baseline mode shares in the future year, the user can assume that past 

trends seen in the region will hold in the future if travel demand model data is not available.

 Trip length: Where unavailable at the regional level, the TRIMMS default values may be 

considered for applicability to the region. These values are drawn from the 2001 National 

Household Travel Survey (NHTS) and are as follows for each mode. Note that these trip lengths 

are for commute trips and the user may wish to input their own values if the analysis pertains to 

all trips. Average commute trip lengths are typically higher than average trip lengths when all 

trips are considered.

Auto drive alone 12.2 miles

Auto rideshare 12.2 miles

Vanpool 20.4 miles

Public transport 12.2 miles

Cycling 2.9 miles

Walking 0.9 miles

Other 12.2 miles

 

12 See user guidance for the TRIMMS model for model assumptions, guidelines on modifying input parameters, and 

list of 85 MSAs for which default data are included: CUTR (2009), Quantifying the Net Social Benefits of Vehicle 

Trip Reductions: Guidance for Customizing the TRIMMS© Model, prepared for Florida DOT, Tampa, FL: CUTR at 

the University of South Florida) are available at: http://www.nctr.usf.edu/2009/04/trimms2/
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The user may refer to the most recent NHTS data, available for 2009, for updated data.13 For 

future years, the user can assume that that trip lengths will be the same as in the base year or 

assume that past trends seen in the region will hold in the future.

 Travel times: Walk and bike travel times in particular may not be available from a regional travel 

model. Data from the American Community Survey, available by urban area, can be substituted 

instead.14 Since this source groups walk, bike, and other modes into the category “other,” no 

separate travel times are given for walk and bike and the travel times listed can be considered 

an average across these modes. For future years, changes in travel time can be estimated by 

using the Texas Transportation Institute’s estimates for the increase in the travel time index for 

urban areas in the country by the year 2030, relative to the year 2010.15

 Regional trip cost data by mode: Data on trip costs, particularly for non-transit modes are not 

easily available from all regions. Where automobile operating costs are not available, trip cost 

can be calculated based on average trip lengths, fuel price and other cost components, and 

mileage data for the base year. Regions often face a problem in modeling parking charges; 

aggregate regional analysis severely underestimates baseline parking charges. Parking charges 

are therefore best modeled at a sub-regional or zonal level. For this analysis, the user may 

estimate an average daily parking charge based on data on average local hourly rates. The 

national analysis (EPA Final Report) consulted an annual survey of parking rates conducted by 

Colliers International that provides average daily parking charges in the central business districts 

(CBD) of U.S. cities.

 

16

 

13 NHTS data for 2009 available at: 

 Note that the national analysis did not use the regional average of parking 

charges as a baseline, but rather used the CBD-area charges for this purpose. This was 

considered acceptable because parking pricing is most likely to be implemented in locations that 

have high demand for parking, where parking prices are already at a premium, and where 

congestion levels are high enough to warrant creating a disincentive to driving by introducing 

parking pricing. These are typically the CBD areas within cities. For transit trip costs, 

information on average fares per trip can be collected from the transit authorities where it is not 

available at the regional level. 

There is no consistent methodology across regions to estimate future year trip costs. Most 

regions follow the practice of assuming constant auto operating costs in future years because of 

the uncertainty in how vehicle fuel efficiency and fuel prices will change in the future. This 

approach is valid for the TEAM analysis. The assumption can be considered acceptable because 

http://nhts.ornl.gov/ 14 For the 2005-2007 period, see American Association of State Highway and Transportation Officials (AASHTO), 

(2010) Census Transportation Planning Package (CTPP) Profile Sheets, available at: 

http://download.ctpp.transportation.org/profiles_2005-2007/ctpp_profiles.html 15 Schrank, D. and T. Lomax, eds. (2011), The 2010 Urban Mobility Report, (College Station: Texas Transportation 

Institute). 16 Colliers International (2011), North American Central Business District Parking Rate Survey 2011, available at: 

http://www.colliers.com/Country/UnitedStates/content/colliersparkingratesurvey2011.pdf. The EPA analysis 

used the 2008 data. 
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even though fuel prices may be expected to increase, higher vehicle fuel efficiency is likely to 

help offset any increase in operating costs. In the absence of local data, analysts may use the 

Energy Information Administration’s Annual Energy Outlook for 201117

 Elasticities: Since travel time and cost elasticities are often not directly available from a regional 

travel demand model, they can be estimated at the regional level in one of three ways: 

for fuel price projections. 

Fuel efficiency projections can be based on the national default light duty vehicle fleet mix

available in the MOVES model as in the TEAM approach (see Section 2.5 for details). Future year 

transit fares and parking costs can be assumed to follow past regional trends. Alternately, it 

may be assumed that transit fares rise with inflation (at about three percent per year), and 

future year parking costs increase slightly less (at about two percent per year), based on model 

assumptions used in some regions.

(1) Using results from surveys providing information on expected or demonstrated changes in 

travel behavior from changes in travel costs or time

(2) Using modeling results in a travel demand model for changes in trips resulting from a 

specific change in trips costs or travel time. For example, the user can quantify the price 

elasticity of auto travel demand using the change in the number of regional trips by auto 

estimated by the travel demand model in response to a 10% increase in the average cost 

of driving. The travel time elasticities can be similarly estimated.

(3) Using default values obtained from a survey of the literature

The first two methods are preferred because adjusting the elasticity values based on local data 

will enhance the accuracy of the analysis in predicting local impacts. Elasticities are typically 

divided between short term and long term estimates. For longer term estimates (10 years and 

longer), the elasticities are typically larger than for short term estimates (2-5 years) Long-term 

elasticities are recognized to be roughly two to three times short-term elasticities18

TRIMMS has a default set of cross-elasticities but also allows users to specify their own elasticity 

values. Some of the default transit fare and price elasticities, parking price elasticities, and 

cross-elasticities used in TRIMMS appear lower (more conservative) than the values obtained 

from current literature. The default values are provided in Tables C-3 to C-5 in the Appendix 

with notes describing changes made for the national analysis (EPA Final Report). 

. This 

reflects greater long-term impact owing to greater adoption and effectiveness of strategies over 

time and adaptation of travel behavior. For example, land use changes take effect over the 

longest period of time, either passively or through active policy intervention. 

 

17 U.S. Department of Energy, Energy Information Administration (2011), Annual Energy Outlook, available at:

http://www.eia.gov/forecasts/aeo/. 18 Litman (2010): 14. 
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2.4. Completing the VMT Analysis 

After the user has 

defined the strategies 

of interest, selected 

the tool and collected 

all data inputs that 

will be used, the next 

step is to conduct the 

VMT analysis. To 

begin the user must first assume parameters for modeling each strategy. In TRIMMS, these parameters 

are expressed in terms of changes in travel times by the different modes and/or changes in travel costs

(examples shown in Table 4). Other sketch planning tools may require additional assumptions to

completely define each strategy that will be modeled. The reasonableness of the assumptions should 

be checked by consulting the relevant literature. Users may also consult the regional information

provided in the Appendix of the national analysis (EPA Final Report) to select values based on data from 

similar regions. 

As mentioned in section 2.1, strategies can either be analyzed individually or combined into scenarios of 

complementary strategies, depending on how the strategies are likely to be applied in a region. 

Modeling multiple strategies in a single scenario has the advantage of capturing the synergies or tradeoffs between them and will also help limit the number of times the model is run. However, a 

disadvantage is that isolated impacts of strategies will not be available. 

2.4.1. Inputs required to define strategies

Table 4 illustrates how to take a general strategy and define it sufficiently for analysis purposes. This

table provides two alternatives for parameters that were used to model the strategies of interest in the 

national analysis (EPA Final Report), the second alternative being more aggressive than the first. 
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Table 4. Illustrative Assumptions Used for Modeling Strategies in the EPA National Analysis

Strategy Specific strategy Strategy information ALTERNATIVE 1 ALTERNATIVE 2

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 regionwide offer these 

programs; includes all 

TDM strategies except 

walk and bike subsidies 

50% of employers regionwide offer these programs; 

includes all TDM strategies 

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 length19.

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 (may 

be modeled as a 

percentage increase)

$5 increase per day (may be 

modeled as a percentage 

increase)

VMT fees or congestion 

pricing

Increase in peak hour

driving costs 

$0.10 increase per mile

(may be modeled as a 

percentage increase)

$0.25 increase per mile

(may be modeled as a 

percentage increase)

Refer to the TRIMMS model guidance20 on exactly how the above strategy assumptions can be input into the 

model.

Figure 2 illustrates how the TRIMMS model provides the results after modeling a strategy or a 

combination of strategies. Once the user has completed the modeling, the results from TRIMMS can be 

validated against results obtained from modeling done using other tools, the regional model, or even 

results in other regions having a similar size and mode share profile. Results from representative

regions are available in Appendix D. These results were developed as part of the EPA national analysis

(EPA Final Report). 

 

19 TRIMMS does not allow detailed modeling of land use strategies such as smart growth. Therefore, the reduction 

in trip lengths and improved accessibility expected to result from these strategies were modeled in TRIMMS as a 

reduction in travel time and access time, factors correlated with trip length and accessibility. 20 See user guidance for the TRIMMS model for model assumptions and guidelines on modifying input parameters, 

CUTR (2009).
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Figure 2. TRIMMS 2.0 Model Results

Some of the assumptions of the TRIMMS model may not apply in all cases. Approaches to work around

inapplicable assumptions are described below.

1. The TRIMMS model is based on working population instead of total population, since it was 

originally meant to model strategies affecting work trips. As in the national analysis, if the user 

wishes to analyze some other sub-set of all trips or analyze all trips instead of work trips, an 

adjustment factor may be used to scale the results. In analyzing strategies that are likely to 

affect all travelers, not just commuters, the user should use the total regional population as an 

input in TRIMMS. Regardless of whether the model is run using either the total or the working 

population, the relationship of working population to total population may be used to adjust the 

final VMT and trip change estimates obtained at the end. One such adjustment that users may 

commonly need to make is described in the next point (2), below.

2. Since TRIMMS focuses on employee travel behavior, it always assumes a trip rate of two trips 

per person per day (i.e., one round trip, assuming a worker goes from his home to the employer 

site and back home). To cover all trip purposes, it is necessary to adjust the trip rate to the 

region’s actual trip rate for all trips. This can be done by multiplying the TRIMMS model outputs 

by the best known trip rate for the urban area and dividing by 2.

3. TRIMMS also makes an assumption that only a subset of the population will be affected by 

these TDMs– i.e., only white collar employees (managerial and professional staff) and not bluecollar employees. This is based on the assumption that higher-level employees are more likely 

to have the flexibility to alter their travel behavior. Consequently, there is a difference between 
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the “total population” data input by the user on the first screen and the “population affected” 

by the strategy shown by TRIMMS on the results screen. This is based on data within TRIMMS 

on the proportion of workers of each type in each sector in each of the 85 MSAs, one of which 

the user selects at the start of the analysis. Since the elasticity values account for the 

proportion of people that will change travel behavior, no additional assumptions were 

considered in the EPA national analysis (EPA Final Report). To change or remove this 

assumption, the TRIMMS model outputs can be multiplied by the ratio of total regional 

population (from the MPO usually) to TRIMMS’ affected population (“total affected 

employment” in the output). 

It must be noted that these kinds of adjustments are not limited to the TRIMMS model but may be 

necessary for other sketch planning models as well, such as the COMMUTER model developed by EPA. 

Careful evaluation of the tool of choice can help avoid unintentional errors caused by inconsistency 

between model assumptions and available data.

2.4.2. Limitations of the analysis

As with any other sketch-planning tool, TRIMMS has limitations that come from the need to aggregate 

data and the assumptions related to estimating baseline trips and VMT. The limitations result from the 

need to strike a balance between the complexity and intensive data needs of traditional transportation 

analysis tools (like regional travel forecasting models) and the substantial time and cost savings of 

sketch-planning applications.

21

• Using regional averages as inputs: The impacts of some of the strategies such as TDM strategies 

and land use strategies will vary by trip purpose. For example, land use strategies are likely to 

have a higher impact on non-work travel than work travel and vice versa for TDM strategies. 

However, the TRIMMS model uses regional averages for mode shares, trip lengths, and trip

costs across trip purposes and this may underestimate the impacts of these strategies in 

particular locations.

 The user may find it useful to consider the impacts of these limitations, 

as discussed below.

• Modeling pricing strategies using aggregate average inputs: For the application of mileage fees, 

average trip length can be used as an input in TRIMMS to obtain an estimate of aggregate 

average impacts. However, such a policy will not affect all trips similarly. When applied to all 

VMT, longer trips are likely to be reduced. When applied to congested VMT, peak hour trips and 

VMT are likely to be reduced, with some trips being shifted to off-peak times, possibly increasing 

off-peak VMT. A shift to other modes may also lead to a slight increase in rideshare/vanpool 

VMT. For other pricing strategies, such as parking charges or tolls, the relative impact on trip 

cost is higher for shorter trips than longer trips; therefore, VMT reductions from shorter trips 

will be greater than from longer trips. Since the TEAM approach uses a sketch planning tool, the 

varying impacts on short and long trips or on trips made for different purposes cannot be 

captured. Apart from VMT reduction, congestion pricing and HOT lanes trigger other choices 

 

21 CUTR (2009)
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such as route changes and elimination of trips that cannot be captured with a sketch planning 

tool like TRIMMS.

• Impact of vehicle speed on emissions: Although both speed and VMT are used in emissions 

analysis, speed represents a response to congestion rather than a change in travel behavior as 

indicated by a reduction in VMT. It is true that congestion can have an impact on emissions; 

however, these impacts tend to be smaller and more localized than the impacts of VMT 

reduction. In addition, the consideration of speed requires data at a greater level of detail and is 

best accomplished using the regional travel demand model. Because congestion impacts are 

very context-specific, the data required to analyze them are significant, and the TRIMMS model

is not adequate for this analysis. 

Reduction in VMT and/or trips is the first result of the TEAM analysis and provides an indication 

of which strategies may be most effective in the individual region to increase travel efficiency. 

This information can then be used to consider benefits in reduced emissions for pollutants that 

are of particular interest to the region including GHGs. 

2.5. Conducting the MOVES Analysis 

In this step, the 

reductions in VMT 

and trips obtained 

from TRIMMS or 

another sketchplanning tool are 

combined with 

emissions factors to 

estimate potential emission reductions. Emission factors from various vehicle activities (e.g., starting, 

idling, refueling, and running) and processes (e.g., evaporative, exhaust, and physical wear) should be 

obtained from the MOVES model (or other emissions model, such as the EMission FACtors (EMFAC) 

model in California). The recommended approach for areas outside of California is to use the emissions 

factors from the MOVES model, EPA’s current, state-of-the-science tool for estimating emissions from 

on-road motor vehicles. 22

Air quality analysis is conducted by different groups depending on current practice within individual 

states. The guidance presented here applies emissions factors for on-road driving and vehicle starts 

from the MOVES model to the regional reduction in VMT for a given strategy. As far as possible, the 

analysis should include the existing assumptions used for air quality analysis within the region in order 

to reflect emissions reductions that are consistent with other regional analyses.

 

MOVES2010, originally released in December 2009 and updated to MOVES2010a in September 2010, 

can be used to analyze emissions and potential emission reductions from various strategies for urban (or 

 

22Note that MOVES is not used in California. The latest EMission FACtors (EMFAC) model is used in California. 
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rural) road activities, including TDM and land use approaches for activity and emission reductions. The 

model is based on analysis of millions of emission test results and recent advances in understanding of 

vehicle emission processes. Under development since 2000, the MOVES emission model was designed 

as the replacement for MOBILE, which began development in 1978. Compared to MOBILE, MOVES 

enables more precise analysis of vehicle emissions, including GHGs. The update to MOVES2010a 

accounted primarily for newly adopted passenger vehicle energy and GHG standards affecting future 

year predictions. It is this version that was used in the national analysis for the EPA Final Report. Unless 

EPA notes otherwise, this guide is applicable to current and future versions of the MOVES model.

2.5.1. Setting the MOVES Parameters and Obtaining Results

MOVES offers strong support for the TEAM approach in that it provides a means of estimating emissions 

accurately at the regional or local scale through the input of data specific to the area. As stated 

previously, TEAM was initially used to provide national-level potential emissions reductions; however,

the methodology followed in the EPA Final Report for emissions analysis is applicable at a regional scale 

as well. Although the previous analysis necessarily relied on national default fleet characteristics, the 

use of default values within MOVES is not recommended at the regional level. If regional or local data 

are only partially available, MOVES can be run at the county scale to employ county level default values 

that should be closer to actual regional values. In order to identify the specific data and decisions 

needed to use MOVES in the TEAM approach, the national-level analysis is explained in detail below. 

Tailoring MOVES for a regional or local analysis using the appropriate inputs is described later in this 

section.

While the MOVES model can directly produce emission factors for an area of interest, the approach 

described here involves the use of national-default values for all vehicle parameters. 23

 

23 More information is available in U.S. EPA, (2009), MOVES User’s Guide: Motor Vehicle Emission Simulator 

(MOVES) 2010 User Guide, EPA-420-B-09-041, December. 

 This approach is 

generally faster and simpler while providing more generalized results. For the analysis reported in the 

EPA Final Report, the MOVES model was run using an emission inventory approach. This approach was 

useful to quickly quantify and compare future year emission reductions from a range of strategies. The 

resulting total “running emissions” (emissions due to on-road driving and brake and tire wear) from the 

MOVES outputs were then divided by the model’s total VMT output to calculate emission factors in 

grams per mile for all pollutants and GHGs. These emission factors were multiplied by the VMT 

reductions to calculate emission reductions. To further refine the potential emissions benefits of the 

strategies, emissions associated with vehicle starts and refueling, were developed with MOVES and are 

termed as “off-network emissions.” These emissions were developed using the same national default 

fleet characteristics and emissions inventory approach. Note that the unit for the off-network emission 

factors is grams per vehicle, which is different from the grams per mile unit for running emissions. 

Assuming one vehicle per trip and one start for each vehicle trip, these off-network emissions factors 

can be calculated in the same way as the running emission factors, but dividing by total trips instead of 

total VMT. Note that this is a simplifying assumption for this analysis that is not appropriate for any SIP 

or conformity analysis. Also note that MOVES offers the option of directly producing emission rate 

output with a much higher level of detail than is possible when using the approach described here.
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The national analysis (EPA Final Report) determined emissions factors for criteria pollutants (CO, NOx, 

SO2, PM, VOC), and the three principal GHG pollutants (CO2, CH4, N2O, or, equivalently, CO2e).Tailpipe 

and crankcase emissions were considered for all pollutant types. PM emissions also include brake and 

tire wear. VOC results included exhaust and refueling emissions, but not evaporative emissions. Total 

emissions reductions were determined by applying the national emission factors derived from MOVES to 

the regional reductions in VMT and trips as described above. If the user is interested in estimating the 

reduction in several pollutants an off-model spreadsheet analysis will be useful to collect and analyze 

the data from MOVES. 

MOVES can be used to calculate emission factors for any year in the future out to 2050, based on 

assumptions about future vehicle standards and fleet distribution built into the model and the 

approaches discussed above. Employing regional activity forecasts, including VMT, for future years 

allows estimation of future year emissions. Comparison of baseline to forecast emissions allows a 

characterization of changes in emissions in future years. This approach is recommended to estimate 

future year emission reductions. 

A national default approach was appropriate in the test analysis given the goal of estimating nationalaverage emission changes. However, more localized scale approaches may be preferred for other 

analyses where a national scale approach would not adequately consider key factors that may differ 

between areas of the country, such as vehicle age distributions. If the analyst has the required local 

data to conduct a MOVES analysis for a specific area, the local data should be used instead of the 

national default data as inputs to MOVES to develop the appropriate emission factors. Use of local fleet 

activity and fuel information, if available, is encouraged to allow MOVES predictions to be more locally 

specific. The advantage of using as much locally representative data as possible is that resulting emission 

factors would be more representative of the local fleet. A model run for a specific county or group of 

counties using the national defaults may not provide an accurate portrayal of specific emission 

differences that are due to fuel, activity, and/or fleet characteristics, such as vehicle age distributions. 

In order to use local data, the user will need to perform calculations at the county scale, where the 

model replaces national default allocations with user-supplied data for the specific county24

 

. Local data 

are entered in MOVES through the County Data Manager (see Section 2.3.3 of the MOVES 2010 User’s 

Guide). 

 

24 The use of appropriate local data is described in more detail in EPA’s “Technical Guidance on the use of 

MOVES2010 for Emission Inventory Preparation in State Implementation Plans and Transportation Conformity” at 

www.epa.gov/otaq/models/moves/420b10023.pdf . 
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3. Considering Strategies and Potential Emissions Reductions 

The TEAM approach 

provides the ability to 

make comparisons 

between strategies 

and draw conclusions 

about individual 

strategy effectiveness

in a particular region. 

Using TEAM with region-specific data and assumptions can provide a preliminary assessment of the 

effectiveness of individual and grouped strategies. The results can help narrow the focus of more 

detailed and costly analysis as well as assist areas as they consider GHG emission reduction targets. The 

methodology is most applicable to support policy discussions at the regional or subarea level. 

The national analysis (EPA Final Report) illustrated that regional differences play a significant role in the 

effectiveness of individual strategies. In all regions land use changes provide a strong foundation for 

other strategies. This is particularly important with respect to transit strategies such as TOD. 

Combinations of strategies were analyzed in this study which proved to be most effective in reducing 

emissions over time. 

The impact of the modeled strategies will depend on several factors including rates of population 

growth, shares of other modes relative to autos, average trip lengths, and average travel costs. Regions 

that experience slow population growth may see a higher impact of certain strategies if the auto mode 

shares and vehicle trip lengths are higher. Regions that have relatively higher levels of ridesharing, 

transit, bicycling, and walking, compared to regions with low levels of ridesharing, shorter trip lengths, 

and lower population growth, show a lower impact. This means that areas already advanced in the use 

of travel efficiency strategies may need more aggressive strategies such as pricing to see a significant

impact.

Consider the impacts that a scenario with parking pricing would have on regions in relation to their 

projected VMT growth and current parking price levels. In the national analysis, regions that project 

lower levels of VMT growth generally showed lower VMT reductions than regions that predict higher 

rates of VMT growth in response to travel efficiency strategy scenarios. At the same time, regions with 

lower parking costs currently saw more impact from increasing the costs of driving than did regions that 

already have higher parking costs. Combining these two factors, a region with high VMT growth and low 

parking costs would be expected to have greater VMT reduction in response to travel efficiency 

strategies than would regions with lower VMT growth and/or higher existing parking costs.

While it is unlikely that any one strategy will be effective in all regions, all strategies showed a reduction 

in VMT and emissions to some degree. The attractiveness of travel efficiency strategies is that they are 

the most easily implemented and any degree of behavioral change is valuable, especially in light of the 
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supporting role or synergistic effects when combined with other strategies. See the EPA Final Report for 

additional discussion of these issues.

What works best in an individual region will be subject to the willingness of the public and policy makers 

to support change. There is today a broad interest in the effectiveness of transportation and related 

strategies for addressing GHG that not been seen on this scale previously. This methodology can help to 

inform that interest. 
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Appendices 

A. List of Acronyms and Abbreviations

CAA: Clean Air Act

CBD: Central business district

CH4: Methane

CMAQ: Congestion Mitigation and Air Quality Improvement program

CO: Carbon monoxide

CO2: Carbon dioxide

CO2e: Carbon dioxide equivalents

EPA: Environmental Protection Agency

GHG: Greenhouse gas

HOT: High occupancy toll

HOV: High occupancy vehicle

LRTP: Long range transportation plan

MOVES: Motor Vehicle Emission Simulator

MPO: Metropolitan planning organization

MSA: Metropolitan statistical area

N2O: Nitrous oxide

NAAQS: National Ambient Air Quality Standards

NHTS: National Household Travel Survey

NOx: Oxides of nitrogen

PM: Particulate matter

SIP: State Implementation Plan

SO2: Sulfur dioxide

TCM: Transportation control measures

TDM: Travel demand management
TEAM User Guide

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TEAM: Travel Efficiency Assessment Method

TIP: Transportation Improvement Program

TOD: Transit oriented development

TRIMMS: Trip Reduction Impacts of Mobility Management Strategies

UA: Urbanized area

VMT: Vehicle miles traveled

VOC: Volatile organic compounds
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B. References

American Association of State Highway and Transportation Officials (AASHTO). (2010). Census 

Transportation Planning Package (CTPP) Profile Sheets. Available at:

http://download.ctpp.transportation.org/profiles_2005-2007/ctpp_profiles.html. 

Bartholomew, Keith and R. Ewing. (2009). Land Use-Transportation Scenarios and Future Vehicle Travel 

and Land Consumption: A Meta-Analysis. Journal of the American Planning Association, 75 (1), 

13-27.

Colliers International. (2011). North American Central Business District Parking Rate Survey 2011. 

Available at: 

http://www.colliers.com/Country/UnitedStates/content/colliersparkingratesurvey2011.pdf

Concas, S. & Winters, P.L. (2007). Economics of Travel Demand Management: Comparative Cost 

Effectiveness and Public Investment. Final Report No. BD 549-26 prepared by National Center 

for Transit Research for Florida Department of Transportation. Available at: 

http://www.nctr.usf.edu/pdf/77704.pdf. 

Concas, S. & Winters, P.L. (2009). Quantifying the Net Social Benefits of Vehicle Trip Reductions: 

Guidance for Customizing the TRIMMS© Model. Final Report No. BD 549 WO 52 prepared by 

National Center for Transit Research for Florida Department of Transportation. Available at: 

http://www.nctr.usf.edu/pdf/77805.pdf. 

Ewing, Reid and R. Cervero. (2001). Travel and the built environment. Transportation Research Record, 

1780, 87–114.

Ewing, Reid and R. Cervero. (2010). Travel and the Built Environment. Journal of the American Planning 

Association, 76:3, 265-294 

Ewing, Reid, K. Bartholomew, S. Winkelman, J. Walters, and D. Chen. (2008). Growing Cooler: Evidence 

on Urban Development and Climate Change, prepared for Urban Land Institute, Washington, DC: 

Urban Land Institute. 

Litman, Todd. (2011). Transportation Elasticities: How Prices and Other Factors Affect Travel Behavior. 

Victoria, BC: Victoria Transport Policy Institute. Available at: 

http://www.vtpi.org/elasticities.pdf

Rodier, Caroline J. (2008). A Review of the International Modeling Literature: Transit, Land Use, and Auto 

Pricing Strategies to Reduce Vehicle Miles Traveled and Greenhouse Gas Emissions, Paper 

presented at the Transportation research board Annual Meeting 2009.

Schrank, D. and T. Lomax, eds. (2011). The 2010 Urban Mobility Report. College Station: Texas 

Transportation Institute.
TEAM User Guide

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U.S. Department of Energy, Energy Information Administration. (2011). Annual Energy Outlook. 

Available at: http://www.eia.gov/forecasts/aeo/. 

U.S. Department of Transportation. (2009). National Household Travel Survey. Available at: 

http://nhts.ornl.gov/. 

U.S. Environmental Protection Agency (EPA). (2009). MOVES User’s Guide: Motor Vehicle Emission 

Simulator (MOVES) 2010 User Guide. EPA-420-B-09-041. December.

U.S. EPA. (2011). Potential Changes in Emissions Due to Improvements in Travel Efficiency – Final Report. 

EPA-420-R-11-003. March. Available at: 

http://www.epa.gov/oms/stateresources/policy/420r11003.pdf
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C. Data from Literature and Model Information

Table C-1. Quantitative Estimates of Travel Activity Impacts of Travel Efficiency Strategies 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

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
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Table C-1. Quantitative Estimates of Travel Activity Impacts of Travel Efficiency Strategies 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
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Table C-2. Assessment of Methods for Analyzing Travel Impacts of Travel Efficiency Strategies*

Methodologies / 

Models Developer

Last 

Update

Inputs Required Outputs

Scale of Analysis 

(sub-area, regional, 

national)

Travel Efficiency 

Strategy Modeling 

Capability Limitations

1 Travel Demand 

Management 

(TDM) Evaluation 

Model

COMSIS and 

R.H. Pratt 

Consultants for 

Federal 

Highway 

Administration

1993 Base case trip tables, 

vehicle occupancy, 

model coefficients (invehicle time, out-of 

vehicle time, transit 

time, transit fare, 

parking cost, HOV time 

saved), mode shares, 

and information about 

travel efficiency 

strategies

Change in VMT 

and trips

Sub-area and with 

limited capability, 

regional

Following travel 

efficiency strategies 

cannot be modeled:

 Land use strategies

 Incentives for bicycle 

use and pedestrians

 Travel time changes 

(alternative work 

hours or peak period 

pricing)

 Some pricing 

strategies, e.g., 

distance-based 

pricing and fuel price 

changes

 Has not been 

updated, although 

user can input new 

model coefficients 

 Does not account for 

non-motorized trips

 Only evaluates 

home-based work 

trips for large 

regions

 Cannot model 

distance-based 

strategies 

 Does not appear to 

have been used 

recently

2 EPA's COMMUTER

model

Sierra 

Research; 

updated by 

Cambridge 

Systematics 

2005 Population, mode 

shares, trip lengths, 

occupancy levels, 

baseline VMT, baseline 

speeds, mode choice 

time and cost 

coefficients, fleet mix, 

and details about the 

travel efficiency 

strategies

Change in mode 

shares, trips and 

VMT, and 

emissions impacts 

(based on 

emissions factors 

in EPA’s MOBILE 

6.2 model)

Sub-area and 

regional, with some 

adjustment

Cannot model:

 Regional land use 

strategies and any 

travel efficiency 

strategies that will 

change regional 

travel patterns

 Travel efficiency 

strategies that affect 

vehicle speeds

 Location-specific 

strategies such as 

area-wide pricing and 

higher parking 

charges in certain 

areas

 In order to analyze 

strategies in a large 

region, separate 

geographic areas 

must be defined that 

have somewhat 

homogenous travel 

characteristics such 

as mode shares and 

travel distances. 

Embedded emission 

factors from 

MOBILE should be 

replace by MOVES 

emission factors for 

accurate results 
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Table C-2. Assessment of Methods for Analyzing Travel Impacts of Travel Efficiency Strategies*

Methodologies / 

Models Developer

Last 

Update

Inputs Required Outputs

Scale of Analysis 

(sub-area, regional, 

national)

Travel Efficiency 

Strategy Modeling 

Capability Limitations

3 Trip Reduction 

Impacts for Mobility 

Management 

Strategies 

(TRIMMS) model

Center for 

Urban 

Transportation 

Research, 

University of 

South Florida

2009 No trip tables

Needs average regional 

mode shares, average 

trip length and travel 

time by mode, average 

vehicle occupancy, 

parking and trip costs, 

and details about the 

travel efficiency 

strategies

Changes in mode 

shares, trips, VMT, 

and emissions

Sub-area and 

regional, with some 

adjustment 

Practitioner-oriented 

sketch planning tool 

to measure travel 

impacts of regional 

and employer-based 

travel efficiency 

strategies 

 Can model any 

strategy that affects 

the cost of using 

existing modes or 

travel times 

 Can model packages 

of strategies. 

 The user can change 

price and travel time 

elasticity values

 Cannot model 

regional land 

use/smart growth 

strategies accurately

 The user will have to 

make assumptions 

about the effects of 

land use strategies 

on trip lengths or 

travel times in order 

to model these 

strategies

4 Surface 

Transportation 

Efficiency Analysis 

Model (STEAM)

Cambridge 

Systematics 

2006 Base case and 

improvement case trip 

tables, vehicle 

occupancy, model 

coefficients (trip time 

and cost), mode shares, 

and travel efficiency 

strategy characteristics 

(in terms of change in 

trip costs or travel time)

Change in VMT 

and person miles 

traveled, trips, 

travel time, and 

emissions

Regional and subarea/corridorMost travel efficiency 

strategies can be 

modeled

 Much data and effort 

required from 

agencies to model 

travel efficiency 

strategies using 

demand models

 Only a few test cities 

can be considered 

because extensive 

data inputs are 

required for STEAM

5 Transportation 

Emissions 

Guidebook (TEG)

Center for Clean 

Air Policy 

(CCAP)

2006 (?) Number of trips by 

mode, mode split, trip 

lengths

VMT and 

Emissions

Regional and SubareaSpreadsheets providing 

rule of thumb guidance 

on impacts of travel 

efficiency strategies

based on literature; 

most travel efficiency 

strategies can be 

modeled

 The user has to 

make several 

assumptions

 Cannot estimate 

mode shift or trip 

reduction impacts
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Table C-2. Assessment of Methods for Analyzing Travel Impacts of Travel Efficiency Strategies*

Methodologies / 

Models Developer

Last 

Update

Inputs Required Outputs

Scale of Analysis 

(sub-area, regional, 

national)

Travel Efficiency 

Strategy Modeling 

Capability Limitations

6 TCM Tools Sierra Research Early 

1990s

Has separate 

Transportation and 

Emissions modules – 

trips, VMT, speed

Changes in mode 

share, vehicletrips, VMT, travel 

speeds, and 

emissions

More applicable at 

regional scale; some 

sub-area policies can 

be modeled

Wide range of strategies 

can be modeled, 

including land use 

strategies, but cannot 

model scenarios well

 Spreadsheet-based 

sketch-planning tool

 User must make 

many assumptions 

to calculate travel 

impacts

 Emissions module 

cumbersome to run

7 TCM Analyst Texas 

Transportation 

Institute 

1994 Trips, distances, 

speeds, emissions 

factors, travel efficiency 

strategies details

Changes in trips, 

VMT, average 

travel speeds, and 

emissions

Regional or sub-area Pricing strategies cannot 

be modeled

 Elasticities and other 

assumptions must 

be defined by the 

user

 Land use and pricing 

strategies cannot be 

modeled

 Sketch planning tool

8 MARKAL (Market 

Allocation)-MACRO

US Department 

of Energy and 

EPA

Used 

internationally 

and 

currently 

in use

Baseline VMT by 

vehicle type, fuel costs

VMT, emissions, 

and fuel demand

National Travel efficiency 

strategies relevant at 

sub-area, urban, or state 

level cannot be modeled

 More complicated

and not as detailed 

as NEMS (see 

below)

 Can only model 

national level travel 

efficiency strategies

such as fuel taxes, 

emissions taxes
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Table C-2. Assessment of Methods for Analyzing Travel Impacts of Travel Efficiency Strategies*

Methodologies / 

Models Developer

Last 

Update

Inputs Required Outputs

Scale of Analysis 

(sub-area, regional, 

national)

Travel Efficiency 

Strategy Modeling 

Capability Limitations

9 National Energy 

Modeling System 

(NEMS): 

Transportation 

Sector Module 

(TRAN)

Energy 

Information 

Administration, 

US Department 

of Energy

2006 Vehicle fleet (includes 

transit and freight), fuel 

prices, fuel economy, 

passenger miles, 

change in user cost, 

population, income, new 

vehicles sales

VMT, emissions, 

and fuel demand

Census region and 

national

Cannot model:

 Travel efficiency 

strategies relevant at 

sub-area, urban, or 

state level 

 Travel efficiency 

strategies involving 

mode switching 

 Includes useful 

feedback effects, and 

can be used to 

validate national 

estimates

 Will model strategies 

at the level of nine 

Census regions, not 

at urban or subregion level

 Can only models 

travel efficiency 

strategies that affect 

the user cost of 

travel; for others, 

some meta-analysis 

is required before 

using NEMS

 Change in modes 

not easy to model

10 Spreadsheet 

analysis with 

elasticity factors 

from literature

-- Mode shares, trip costs 

by mode, average VMT

VMT change –

followed by 

emissions analysis 

Regional Without trip tables, land 

use strategies are best 

modeled this way

*The above list is current as of 2009
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Table C-3. Fare and Price Elasticities used in TRIMMS

Mode

Elasticity

Near term Long term Source Notes

Auto - Drive Alone 

Direct -0.11 -0.22 Litman (2011) 

Table 22, pp.27 (TRIMMS 

default); long term auto drive 

alone elasticity may be assumed 

double of short run elasticity

Cross-Price: Transit 0.05 0.05 Litman (2011) 

TRIMMS default uses the lower 

ranges; long term elasticity may 

be assumed same as near term if 

no better information available

Auto - Rideshare 

Direct 

n/a n/a May be assumed same as auto 

drive alone if no information is 

available

Cross-Price: Transit 0.05 0.05 Litman (2011) Same long term elasticity as autodrive alone may be assumed

Vanpool 

Direct: Peak -0.16 -0.16 TRIMMS default; if no information 

about near term vs. long term

vanpool elasticities is available, 

the same value may be assumed

Direct: Off-peak -0.32 -0.32

Cross-Price: Transit 0.05 0.05 TRIMMS default

Transit 

Direct: Peak -0.10 -0.10

TRIMMS default; if no information 

about near term vs. long term

transit elasticities is available, the 

same value may be assumed

Direct: Off-Peak -0.30 -0.30 TRIMMS default

Cross-Price: Auto Drive Alone 0.15 0.15 Litman (2011) TRIMMS default uses the lower 

ranges 

Cross-Price: Auto Rideshare 0 0.15

Long run elasticity may be 

assumed same as auto drive 

alone

Source: Adapted from CUTR (2009) and from TRIMMS model version 2.0 received from CUTR on July 15, 2009 pp 44-46
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Table C-4. Travel Time Elasticities

Mode

Elasticity

Peak Off peak Notes

Auto - Drive Alone 

TRIMMS default 

assumptions

Direct -0.225 -0.170 

Cross: Auto -Rideshare 0.037 0.001

Cross: Transit 0.036 0.001

Auto - Rideshare 

Direct -0.303 -0.189 

Cross: Auto -Drive Alone 0.030 0.000 

Cross: Transit 0.030 0.000 

Vanpool 

Direct -0.60 n/a 

Cross-Price: Auto -Rideshare/Drive Alone n/a n/a 

Cross: Transit 0.032 0.000 

Transit 

Direct -0.129 -0.074 

Cross: Auto -Drive Alone 0.010 0.000 

Cross: Auto -Rideshare 0.032 0.000 

Source: Litman (2011)Table 31, pp. 35

Table C-5. Parking Pricing Elasticities

Parking Elasticities

Trip Purpose

Auto – Drive 

Alone

Auto -

Rideshare Transit Slow Mode

Commuting -0.08 -0.02 -0.02 -0.02

Source: Litman (2011), Table 13, pp. 17
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D. Regional Results from EPA National Analysis

In the EPA national analysis (EPA Final Report), national-level impacts of travel efficiency strategies were 

estimated by extrapolating modeled regional results for representative regions to regions with similar 

characteristics in the same ‘cluster,’ and then summing the results across clusters. All metropolitan 

areas across the country were placed into seven clusters, which were characterized by population and 

transit mode share. The representative metropolitan areas in each cluster were chosen with 

consideration for geographic diversity, their approaches and strategies to address climate change and 

GHG emissions, the ability for the metropolitan area to represent areas with similar characteristics, data 

availability, and MPOs’ interest in providing useful data. The characteristics and representative 

metropolitan areas for each cluster are shown in Table D-1. 

Table D-1. Cluster Definitions and Representative Areas

Cluster Definition Number of U.S. 

Regions Represented

Share of National 

Daily Urban VMT Representative Areas

1 Population ≥2.9 million

High Transit Share (>9%) 6 17% San Francisco, CAWashington, DC

2 Population ≥2.9 million

Low Transit Share (9% or less) 9 22% San Diego, CASeattle, WA

3 Population 1,500,000-2,899,999

High Transit Share (>4%) 7 6% Portland, ORDenver, CO

4 Population 1,500,000-2,899,999

Low Transit Share (4% or less) 8 7% Sacramento, COSalt Lake City, UT

5 Population 750,000-1,499,999 21 12% Memphis, TN

Raleigh-Durham, NC

6 Population 250,000-749,999 87 18%

Fresno, CA

Knoxville, TN

Rochester, NY

7 Population < 250,000 313 17% Burlington, VT

Wilmington, NC

Figure D-1 below shows the cluster-level VMT reductions in 2050 under the seven scenarios modeled for 

the national analysis. Scenario strategies are briefly described along the horizontal axis and are related 

to the scenario examples described in Table 4 in this user guide. Note that the mileage fees were not 

modeled for clusters 5, 6, and 7. To interpret this figure correctly it is necessary to consider the input 

and assumption data behind these numbers. For example, Cluster 2 has a mild response across all 

scenarios because the forecasted growth in VMT for future years in that cluster is much lower than 

other clusters. Although a reasonable explanation, there may be additional reasons for the response 

illustrated. Users should keep this in mind and when comparing their results to this chart should consult 

the information on the assumptions and inputs behind this figure in the EPA Final Report. 
TEAM User Guide

Page | 42 

Figure D-1. Cluster Response to Scenarios in 2050

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

18.00%

% VMT Reduction

Scenarios

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Regionwide TDM Land use changes + TDM Land use changes + Transit fare change + TDM Land use changes + Transit fare change + Transit service improvements + TDM Land use changes + Transit fare change + 

Transit service 

improvements + Parking 

Fees + TDM

Land use changes + 

Transit fare change + 

Transit service 

improvements + 

MileageFees + TDM

Land use changes + Transit 

fare change + Transit service 

improvements + Parking 

Fees + Mileage Fees + TDM