Analyzing Emission Reductions
         from Travel Efficiency

         A Guide to the TEAM Approach
United States
Environmental Protection
Office of Transportation and Air Quality
          September 2011

           Analyzing Emission
                  from Travel Efficiency

                A Guide to the TEAM Approach
                        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
United States
Environmental Protection
September 2011

TEAM User Guide

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

TEAM User Guide
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TEAM User Guide
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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TEAM User Guide

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 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

    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.

    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

    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:
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TEAM User Guide

       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 and times away from congested
       facilities and times of day.

    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.

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 policy-
level analysis of the emission and VMT reduction benefits of smart growth compared to current growth

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.
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)(l) of the Clean Air Act3 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
2Note 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.
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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TEAM User Guide

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: 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.

 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

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 co-
benefits 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
!      Identify
    Strategies of
     Select the
  V        W
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.
 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:
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TEAM User Guide
2.1.   Identifying Strategies of Interest
                                                                                   •^	X
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.

   1    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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TEAM User Guide
       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 These values indicate the
       degree to which transit ridership can be expected to increase when transit travel times are

       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.

       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 TRIMMSC Model. Final Report No. BD 549 WO 52 prepared by National Center for Transit
  Research for Florida Department of Transportation. Available at:
7 Litman, Todd, (2010), Transportation Elasticities: How Prices and Other Factors Affect Travel Behavior, (Victoria,
  BC: Victoria Transport Policy Institute); available at:; 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 sub-
      areas 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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TEAM User Guide

      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-l 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
Strategies of
 Select the
1 Bartholomew and Ewing (2009); Ewing and Cervero (2010); Ewing et al. (2008); Rodier (2008)
1 Ewing and Cervero (2001, 2010)
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TEAM User Guide

       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

    "   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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TEAM User Guide
Table 1. Input Requirements and Output Capabilities for Travel Efficiency Strategy Analysis Tools and Models

Spreadsheet-Based Tools/Methods
model TRIMM!
Per capita income
New vehicle sales
Mode shares (no. of trips)
Average vehicle
occupancies by mode
Travel times by mode (in-
vehicle and out-of-vehicle)
Average trip costs by mode
(including parking, fees,
tolls, fuel costs, transit fares)
Includes non-motorized trips
Average trip lengths
Baseline regional VMT
Trip tables
Baseline vehicle speeds
Vehicle fleet mix
Fuel price per gallon
Average fuel economy
Emissions factors





























Change in mode shares (no.
of trips by mode)
Change in travel time
Change in VMT
Change in emissions
Change in speeds
Fuel demand
Benefits and costs




















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.
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TEAM User Guide

2.2.1.  The TRIMMS Model
The Trip Reduction Impacts of Mobility Management Strategies (TRIMMS) model10 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 sub-
area 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.

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.

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
v^	S
  The TRIMMS model and related documentation are available at:
                                                                                     Page | 13

TEAM User Guide

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.
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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TEAM User Guide
Table 2. Regional Data Inputs for TEAM Methodology
Base Case Inputs
Modal information (light-duty vehicles, non-
commercial trips) for:
• Auto-drive alone
• Auto-rideshare
• Vanpool
• Public transit
• Cycling
• Walking
• (Other modes)
Peak and off-peak trips
Total population
Total working population (16 and over)
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
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)
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 Cross-
elasticities 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
  Litman (2010)
                                                                                      Page | 15

TEAM User Guide
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
          Transit elasticities, for peak times, off-peak
          times, and on average
          Travel time elasticities, for peak times, off-
          peak times, and on average
By trip purpose:
  •   Commuting
  •   Business
  •   Education
  •   Other
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
For 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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TEAM User Guide
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 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.

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.

    1  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.

    1  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:

                                                                                     Page | 17

TEAM User Guide
       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 2010.1S

       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 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
13 NHTS data for 2009 available at:
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:  2005-2007/ctpp profiles.html
15 Schrank, D. and T. Lomax, eds. (2011), The 2010 Urban Mobility Report, (College Station: Texas Transportation
16 Colliers International (2011), North American Central Business District Parking Rate Survey 2011, available at: The EPA analysis
  used the 2008 data.

                                                                                        Page |  18

TEAM User Guide
       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 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.

       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:

        (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.  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.

       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).
17 U.S. Department of Energy, Energy Information Administration (2011), Annual Energy Outlook,  available at:
18 Litman (2010): 14.

                                                                                     Page |  19

TEAM User Guide
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 trade-
offs 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.
                                                                                     Page | 20

TEAM User Guide
Table 4. Illustrative Assumptions Used for Modeling Strategies in the EPA National Analysis
based TDM
Land use
projects and
Specific strategy
• Flexible work hours
• Incentives for carpooling
• Guaranteed ride home
• Ride sharing/ ride matching
• TDM outreach/public
outreach programs
• Subsidies/discounts for
transit, pedestrian and bike
• Telecommuting
TOD, smart growth, increase
in density, mixed use
Transit service
expansion/increase in
frequency, improved access
Fare discounts, reduction,
subsidies, or free transfers
Parking charges
VMT fees or congestion
Strategy information
Whether or not
employer offers
yes/no answer) to take
these programs into
Change in travel times
for all modes, change
in average trip lengths
Improvement in transit
travel time and access
Change in transit fares
Increase in auto
parking costs
Increase in peak hour
driving costs
30% of employers region-
wide offer these
programs; includes all
TDM strategies except
walk and bike subsidies
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
Note: Access time take
5% reduction in transit
travel time
10% reduction in transit
$2 increase per day (may
be modeled as a
percentage increase)
$0.10 increase per mile
(may be modeled as a
percentage increase)
50% of employers region-
wide offer these programs;
includes all TDM strategies
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
i as proxy for trip length19.
10% reduction in transit
travel time
20% reduction in transit
$5 increase per day (may be
modeled as a percentage
$0.25 increase per mile
(may be modeled as a
percentage increase)
Refer to the TRIMMS model guidance
on exactly how the above strategy assumptions can be input into the
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).
  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.
 5 See user guidance for the TRIMMS model for model assumptions and guidelines on modifying input parameters,
  CUTR (2009).
                                                                                        Page | 21

    TEAM User Guide
                                      Figure 2. TRIMMS 2.0 Model Results
                                                              TRIMMS 2.0 MODEL RESULTS
                                                                  POLICY EVALUATED
AgeiCs' Name
Analysis Tide
Analysis fype
Agency 1
Pa- Jn; Charge f-ar

nil to $2, Mileage fee from 0.2 to 0.4

!ute "'15/2209
Segion 0
~a:al AFerteii Encp o^FKr:
3 15.000

Mode Share pi)
Auto-Drive Alone
•Valc Transit
j 00.00
?S Change
                                                                        S te Access iT-prD^f^en
                                                                        Trarsrt Se-vice Ifrp'm'e
                                                                        F lardal lrKeitk*es
                                                                        Pay-as-yoi.-eo Prcgrsrrs
                                                                        Parking Pricing
                                                                        Sjpaot P-oerams
Baseline Tripe
Firs ~ips
Trip PedLrtian
!t Trip Pedictan
Baseline V'MT
Firs VVT
=4 VMT Redjctior
    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 blue-
            collar 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
                                                                                           Page | 22

TEAM User Guide

       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.
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 The user may find it useful to consider the impacts of these limitations,
as discussed below.

    •   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.

    •  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)
                                                                                      Page | 23

TEAM User Guide
       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 sketch-
planning 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
 Note that MOVES is not used in California. The latest EMission FACtors (EMFAC) model is used in California.

                                                                                      Page | 24

TEAM User Guide

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.

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

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.   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.
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.

                                                                                      Page | 25

TEAM User Guide

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 national-
average 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
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
                                                                                       Page | 26

TEAM User Guide

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 overtime.

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

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
                                                                                     Page |  27

TEAM User Guide

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.
                                                                                       Page | 28

TEAM User Guide
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
                                                                                   Page | 29

TEAM User Guide

TEAM: Travel Efficiency Assessment Method

TIP: Transportation Improvement Program

TOD: Transit oriented development

TRIMMS: Trip Reduction Impacts of Mobility Management Strategies

DA: Urbanized area

VMT: Vehicle miles traveled

VOC: Volatile organic compounds
                                                                                  Page | 30

TEAM User Guide

American Association of State Highway and Transportation Officials (AASHTO). (2010). Census
       Transportation Planning Package (CTPP) Profile Sheets. Available at: 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),

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

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:

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:

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:

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.
                                                                                      Page |  31

TEAM User Guide

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

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

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:
                                                                                      Page | 32

TEAM User Guide
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
High-Occupancy Vehicle (HOV) lanes
Rideshare matching programs
Carpool/vanpool incentives
Regional implementation: 0.1 to 0.5% reduction in VMT
Long run (LR) travel time elasticity, regional: -1.0, urban: -0.6, rural: -1.3
0.2 to 1.4% VMT reduction
0.1 to 2.0% VMT reduction
0.2 to 3.3% VMT reduction
Limited quantitative data
Bicycle and Pedestrian Facilities and Programs
Bike paths / lanes / routes
Bike/ped facilities to support transit
<0.1% VMT reduction
Limited quantitative data
Transit Projects and Policies
Transit service expansion /increase in
Improved transit travel times and operations
(busways, BRT, signal prioritization for transit
vehicles, heavy and light rail, managed lanes)
Improved transit access through shuttle and
feeder bus services, paratransit
Transit service integration and intermodal
transfer centers
Fare integration for easy transfers
Improved transit marketing, information,
Commuter discounts/fare reductions
Peak/off-peak transit fares
Transit improvement policies, overall
-0.6 to -1.0; for buses
-0.5 (time between buses) for service frequency alone
-0.4 (travel time elasticity with respect to ridership)
Relates to improving travel time above, not measured separately
Relates to improving travel time above
Relates to improving travel time above
Limited quantitative data
-0.3 to -0.4 (fare elasticity with respect to ridership)
-0.1 to -0.3 (peak fares) and -0.1 to -0.7 (off-peak fares, depending on trip
purpose; lower for work trips)
Studies estimate 0 to 2.6% VMT reduction
Parking Management and Incentives
Parking cash-out
Preferential parking for carpools and vanpools
Parking duration restrictions
Elasticities are not available; although some quantitative data on percentage
reduction in regional VMT are available from specific projects and studies.
Employer-based Programs (effects depend on level of adoption)
Flexible work schedules
Compressed work weeks
Employer-provided transit passes
Guaranteed ride home programs
Elasticities are not available; although some quantitative data on percentage
reduction in regional VMT are available from specific projects and studies.
                                                                                Page | 33

TEAM User Guide
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
Distance-based pricing or mileage fees
Peak period pricing/ variably priced lanes
Parking pricing/fees
High Occupancy Toll (HOT) lanes/toll
Pay-as-you-drive Insurance
Fuel taxes
Freight vehicle 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
LR: -0.1 to -0.8 (price elasticity). Conservative LR estimate for the U.S. would
be -0.3
-0.03 to -0.4 (depending on time of day)
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
-0.1 to -0.4; data from specific projects are available
LR: -0.1 to -0.3, tending towards the lower end
-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)
Smart growth and mixed use development
Land use controls for compact, dense urban
development (Density)
Improved regional accessibility due to
combined measures
Land use measures, overall
-0.05 (vehicle trips) and -0.03 to -0.08 (VMT)
-0.03 (vehicle trips) and -0.05 (VMT)
-0.05 (vehicle trips) and -0.05 to -0.12 (VMT)
-0.18 to -0.22 (VMT); studies estimate regional VMT reduction by 2-20% in 20
years with doubling of results in 40 years.
Regional VMT reduction of 0 to 5.2%
Vehicle Restrictions by Geographic Area or in Peak Periods
Freight vehicle controls
No-drive days
Urban non-motorized zones
Elasticities are not available; although some quantitative data on percentage
reduction in regional VMT are available from specific projects and studies.
Public Education and Outreach Programs
TDM outreach programs by employers
Episodic programs (e.g. ozone action days)
Public communication about the impacts of
travel decisions
These measures are typically implemented as part of other measures. Difficult
to estimate impacts separately as it could lead to double-counting.
                                                                                Page |  34

TEAM User Guide
Table C-2. Assessment of Methods for Analyzing Travel Impacts of Travel Efficiency Strategies*



Methodologies /
Travel Demand
(TDM) Evaluation


R.H. Pratt
Consultants for

updated by



Inputs Required

Base case trip tables,
vehicle occupancy,
model coefficients (in-
vehicle time, out-of
vehicle time, transit
time, transit fare,
parking cost, HOV time
saved), mode shares,
and information about
travel efficiency

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


Change in VMT
and trips

Change in mode
shares, trips and
VMT, and
emissions impacts
(based on
emissions factors
6.2 model)

Scale of Analysis
(sub-area, regional,
Sub-area and with
limited capability,

Sub-area and
regional, with some

Travel Efficiency
Strategy Modeling
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
• Some pricing
strategies, e.g.,
pricing and fuel price

Can not 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

• 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
• Cannot model
• Does not appear to
have been used
• 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
                                                                                                                  Page | 35

TEAM User Guide
Table C-2. Assessment of Methods for Analyzing Travel Impacts of Travel Efficiency Strategies*




Methodologies /
Trip Reduction
Impacts for Mobility
(TRIMMS) model

Efficiency Analysis
Model (STEAM)

Guidebook (TEG)

Center for
University of
South Florida


Center for Clean
Air Policy



2006 (?)

Inputs Required

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

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)

Number of trips by
mode, mode split, trip


Changes in mode
shares, trips, VMT,
and emissions

Change in VMT
and person miles
traveled, trips,
travel time, and

VMT and

Scale of Analysis
(sub-area, regional,
Sub-area and
regional, with some

sketch planning tool
to measure travel
impacts of regional
and employer-based
travel efficiency

Regional and sub-

Regional and Sub-

Travel Efficiency
Strategy Modeling
• 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

Most travel efficiency
strategies can be

Spreadsheets providing
rule of thumb guidance
on impacts of travel
efficiency strategies
based on literature;
most travel efficiency
strategies can be

• 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
• 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
• The user has to
make several
• Cannot estimate
mode shift or trip
reduction impacts

                                                                                                                 Page | 36

TEAM User Guide
Table C-2. Assessment of Methods for Analyzing Travel Impacts of Travel Efficiency Strategies*

Methodologies /
TCM Tools
TCM Analyst
MARKAL (Market
Allocation)- MACRO
Sierra Research
US Department
of Energy and
in use
Inputs Required
Has separate
Transportation and
Emissions modules -
trips, VMT, speed
Trips, distances,
speeds, emissions
factors, travel efficiency
strategies details
Baseline VMT by
vehicle type, fuel costs
Changes in mode
share, vehicle-
trips, VMT, travel
speeds, and
Changes in trips,
VMT, average
travel speeds, and
VMT, emissions,
and fuel demand
Scale of Analysis
(sub-area, regional,
More applicable at
regional scale; some
sub-area policies can
be modeled
Regional or sub-area
Travel Efficiency
Strategy Modeling
Wide range of strategies
can be modeled,
including land use
strategies, but cannot
model scenarios well
Pricing strategies cannot
be modeled
Travel efficiency
strategies relevant at
sub-area, urban, or state
level cannot be modeled
• Spreadsheet-based
sketch-planning tool
• User must make
many assumptions
to calculate travel
• Emissions module
cumbersome to run
• Elasticities and other
assumptions must
be defined by the
• Land use and pricing
strategies cannot be
• Sketch planning tool
• More complicated
and not as detailed
as NEMS (see
• Can only model
national level travel
efficiency strategies
such as fuel taxes,
emissions taxes
                                                                                                                 Page | 37

TEAM User Guide
Table C-2. Assessment of Methods for Analyzing Travel Impacts of Travel Efficiency Strategies*



Methodologies /
National Energy
Modeling System
Sector Module

analysis with
elasticity factors
from literature

US Department
of Energy



Inputs Required

Vehicle fleet (includes
transit and freight), fuel
prices, fuel economy,
passenger miles,
change in user cost,
population, income, new
vehicles sales

Mode shares, trip costs
by mode, average VMT


VMT, emissions,
and fuel demand

VMT change -
followed by
emissions analysis

Scale of Analysis
(sub-area, regional,
Census region and


Travel Efficiency
Strategy Modeling
Can not 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

Without trip tables, land
use strategies are best
modeled this way

• Will model strategies
at the level of nine
Census regions, not
at urban or sub-
region 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

The above list is current as of 2009
                                                                                                                      Page | 38

TEAM User Guide
Table C-3. Fare and Price Elasticities used in TRIMMS
Auto - Drive Alone
Cross-Price: Transit
Auto - Rideshare
Cross-Price: Transit
Direct: Peak
Direct: Off-peak
Cross-Price: Transit
Direct: Peak
Direct: Off-Peak
Cross-Price: Auto Drive Alone
Cross-Price: Auto Rideshare
Near term




Long term









Table 22, pp.27 (TRIMMS
default); long term auto drive
alone elasticity may be assumed
double of short run elasticity
TRIMMS default uses the lower
ranges; long term elasticity may
be assumed same as near term if
no better information available

May be assumed same as auto
drive alone if no information is
Same long term elasticity as auto-
drive alone may be assumed

TRIMMS default; if no information
about near term vs. long term
vanpool elasticities is available,
the same value may be assumed
TRIMMS default

TRIMMS default; if no information
about near term vs. long term
transit elasticities is available, the
same value may be assumed
TRIMMS default
TRIMMS default uses the lower
Long run elasticity may be
assumed same as auto drive
Source; Adapted from CUTR (2009) and from TRIMMS model version 2.0 received from CUTR on July 15, 2009 pp 44-46
                                                                                                   Page  | 39

TEAM User Guide
Table C-4. Travel Time Elasticities
Auto - Drive Alone
Cross: Auto -Rideshare
Cross: Transit
Auto - Rideshare
Cross: Auto -Drive Alone
Cross: Transit
Cross-Price: Auto -Rideshare/Drive Alone
Cross: Transit
Cross: Auto -Drive Alone
Cross: Auto -Rideshare




Off peak




TRIMMS default
Source; Utman (2011)Table 31, pp. 35
Table C-5. Parking Pricing Elasticities
Parking Elasticities
Trip Purpose
Auto - Drive
Slow Mode
          Source; LJtman (2011), Table 13, pp. 17
                                                                                                  Page | 40

TEAM User Guide
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-l.
Table D-1. Cluster Definitions and Representative Areas
Population >2.9 million
High Transit Share (>9%)
Population >2.9 million
Low Transit Share (9% or less)
Population 1,500,000-2,899,999
High Transit Share (>4%)
Population 1,500,000-2,899,999
Low Transit Share (4% or less)
Population 750,000-1,499,999
Population 250,000-749,999
Population < 250,000
Number of U.S.
Regions Represented
Share of National
Daily Urban VMT
Representative Areas
San Francisco, CA
Washington, DC
San Diego, CA
Seattle, WA
Portland, OR
Denver, CO
Sacramento, CO
Salt Lake City, UT
Memphis, TN
Raleigh-Durham, NC
Fresno, CA
Knoxville, TN
Rochester, NY
Burlington, VT
Wilmington, NC
Figure D-l 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.
                                                                                      Page | 41

TEAM User Guide
                                                 Figure D-1. Cluster Response to Scenarios in 2050
                                                                                                                                    Page | 42