Potential Changes in Emissions
Due to Improvements in Travel
Efficiency - Final Report
SEPA
United States
Environmental Protection
Agency
Office of Transportation and Air Quality
EPA-420-R-11-003
March 2011
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Potential Changes in Emissions Due to
Improvements in Travel Efficiency
Final Report
Transportation and Regional Programs 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
&EPA
United States
Environmental Protection
Agency
EPA-420-R-11-003
March 2011
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The EPA Office of Transportation and Air Quality would like to acknowledge the following
individuals for their input and support on this project: Keith Bartholomew, Caroline J. Rodier,
Sisinnio Concas, Nigel H. M. Wilson, and Phillip L. Winters.
Also acknowledged are the following metropolitan planning organizations and supporting
agencies which provided data and information that provided significant assistance to the project.
Chittenden County MPO (South Burlington, Vermont)
Council of Fresno County Government (Fresno, California)
Denver Regional COG (Denver, Colorado)
Genesee Transportation Council (Rochester, New York)
Institute of Transportation Research and Education (Raleigh, North Carolina)
Knoxville Urban Area MPO (Knoxville, Tennessee)
Memphis MPO (Memphis, Tennessee)
Metro (Portland, Oregon)
Metropolitan Transportation Commission (San Francisco, California)
Metropolitan Washington COG (District of Columbia)
North Carolina Department of Transportation (Raleigh, North Carolina)
Puget Sound Regional Council (Seattle, Washington)
Sacramento Area COG (Sacramento, California)
San Diego AOG (San Diego, California)
Wasatch Front Regional Council (Salt Lake City, Utah)
Wilmington Urban Area MPO (Wilmington, North Carolina)
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As transportation and air quality officials confront the contribution of the transportation sector to
climate change, there is a growing interest in understanding the role travel efficiency strategies
can have on reducing the impacts of travel on greenhouse gas (GHG) levels in the atmosphere.
The impact of travel activity on total GHG emissions in the United States cannot be overlooked.
Based on GHG emissions reporting for 2008, the transportation sector accounted for around 27
percent of the total U.S. GHG emissions. This represents the second largest source of GHG
emissions, exceeded only by electricity generation. Since 1990 transportation has been one of
the fastest-growing sources of GHG in the country, representing 41 percent of the total increase
in GHG (EPA 201 Oa).
The Environmental Protection Agency (EPA), as well as state and local air quality and
transportation agencies, has a strong interest in supporting efforts to reduce criteria pollutants
as well as GHG emissions. Many criteria pollutants and their precursor emissions also impact
climate, presenting "win-win" scenarios for climate and air quality when they are reduced
(Shindell et al., 2008). The Transportation and Regional Programs Division (TRPD) of EPA's
Office of Transportation and Air Quality (OTAQ) provides analysis, guidance and technical
assistance on transportation policy and program effects on mobile source emissions and air
quality to federal, state, and local agencies. These stakeholders are increasingly interested in
understanding the effectiveness of the Transportation Control Measures (TCM) listed in the
Clean Air Act (CAA) and other measures, such as road pricing and smart growth, to reduce
emissions and vehicle miles traveled (VMT). The term TCM is used broadly in this report to
include those travel efficiency measures listed in the CAA and other approaches for reducing
VMT.
Strategies to reduce emissions also include operational measures that affect network speeds
such as the application of intelligent transportation systems, speed limit controls, and the
practice of eco-driving. These measures were not analyzed in this study because the
methodology used in this analysis could not account for speed changes, upon which these
strategies depend. There is also interest in understanding the various co-benefits resulting from
these measures in addition to reducing emissions such as reduction in fossil fuel consumption,
congestion, and accidents, which EPA will be exploring in a subsequent analysis.
The purposes of this report are to establish a reliable and useful source of information on the
effectiveness of TCMs for changing travel activity and to quantify the potential national
emissions reductions that could result from those changes using EPA's MOVES2010 emission
model. This study is intended to support a national policy-level assessment of TCMs by using
actual metropolitan planning organization (MPO) travel demand modeling results and examining
their effectiveness at a national scale. The study focus is on light-duty vehicles and as such only
considers gas and diesel fueled passenger cars and light duty trucks. The results therefore
represent the reduction in urban VMT and emissions nationwide with rural travel assumed to
remain unaffected. Although the analysis is based on actual travel data and characteristics of
real metropolitan areas, the predicted changes to travel activity and resulting emissions from
this analysis are not intended to represent the effectiveness of the TCMs for any particular area.
The strategies selected for analysis were: travel demand management (TDM), land use
policies, transit-related strategies, and parking and road pricing. The strategies were further
combined into future scenarios building from combinations of the most widely applied strategies
to more aggressive approaches like transportation pricing. A sketch-planning tool developed at
the University of South Florida, called Trip Reduction Impacts for Mobility Management
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Strategies (TRIMMS), and the data from representative metropolitan regions were used to
estimate the national potential for reductions in VMT under a variety of scenarios. Emissions
Factors obtained from EPA's MOVES2010 (Motor Vehicle Emissions Simulator) model were
then used to convert VMT reductions into emissions reductions. Recognizing that not all areas
are currently willing to incorporate the complete range of TCMs in their transportation system,
the application of the more aggressive TCMs, such as mileage fees, was limited to areas above
a population threshold (>1.5 million). Key aspects of the study include:
« A review of recent studies to determine the range of effectiveness (in terms of elasticities and
reported impacts) of various TCMs in addressing travel demand
« Development of an assessment methodology (Travel Efficiency Assessment Method, or
TEAM), with input from a panel of subject matter experts
« Defining a set of future scenarios that incorporate various strategies expected to reduce travel
activity and emissions
« Sketch-planning analysis of actual metropolitan areas representing a range of populations
and transportation characteristics using available local data from regional planning
organizations
« MOVES 2010 emissions modeling using results from the sketch-planning analysis of the
surrogate metropolitan areas
In order to support the regional analysis using TRIMMS a number of decisions were required to
account for incomplete or unavailable data. The decisions were guided primarily by current
research and best practice observed in the metropolitan regions surveyed. Collectively these
assumptions may result in an overall conservative result.
The time period for analysis and forecasting begins in 2010 with current year policies and
develops through 2050. MOVES2010 was used to generate national-level, fleet-wide emission
factors for this analysis reflecting emissions from start, refueling, and urban driving activities for
years 2010, 2020, 2030, 2040, and 2050. These factors account for all changes incorporated in
the model's default assumptions regarding vehicle technology and fuel characteristics. No
additional strategies, including alternative vehicles and fuels or special use of retrofit
technologies, were included.
The consistency of the results of this study with other similar studies, suggests that at the
national level, understanding the potential for VMT reductions may be moving toward
consensus. Although many factors contribute to the ability to reduce VMT and emissions, the
interactions of the different strategies in different regional types suggest that it will be
challenging to identify a single strategy or scenario that performs consistently across all
metropolitan regions. The attractiveness of TCM strategies across all regions is that many of
the technical and financial hurdles have been addressed; however, public opinion remains a
challenge for some strategies, like pricing. The real determination of what works best in an
individual region will be based on the willingness of the public and policy makers to support
change. The present interest in addressing GHG and other aspects of climate change is
supportive of these strategies. This study is intended to inform that interest.
As expected, the greatest benefit in emissions reduction results from a combination of effective
strategies. The table below provides an overview of the potential reductions for each scenario
VI
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evaluated for selected pollutants. The detailed results for reduction in VMT and emissions from
this analysis are captured in Chapter 4 of the report and in the appendices.
Resulting Emissions Reductions for Selected Pollutants
Scenario
1- Region-wide TDM
2 - TDM + land use changes
3 - TDM + land use changes +
transit fare reduction
4 - TDM + land use changes +
transit fare reduction +
transit service
improvements
5 - TDM + land use changes +
transit fare reduction +
transit service
improvements + parking
fees
6 - TDM + land use changes +
transit fare reduction +
transit service
improvements + mileage
fees
7 - TDM + land use changes +
transit fare reduction +
transit service
improvements + parking
fees + mileage fees
Percent Emissions Reduction
2030
C02
equivalent*
0.10%
1.01%
1.40%
1.44%
2.92%
1.94%
3.42%
PM2.5
0.10%
1.01%
1.40%
1.44%
2.92%
1.93%
3.42%
NOx
0.10%
1.00%
1.39%
1.43%
2.91%
1.92%
3.40%
voc
0.09%
0.98%
1.36%
1.41%
2.90%
1.87%
3.35%
2050
C02
equivalent*
0.26%
2.97%
4.19%
4.30%
6.98%
6.28%
8.83%
PM2.5
0.26%
2.96%
4.18%
4.29%
6.94%
6.25%
8.78%
NOx
0.26%
2.93%
4.16%
4.28%
6.87%
6.17%
8.65%
VOC
0.25%
2.86%
4.08%
4.23%
6.68%
5.95%
8.29%
* CO2 equivalent = [CO2 + 2r(CH4) + 310*(N2)]
Although this research is intended to illustrate the collective national impact of the different
scenarios, the analysis may also be useful at the regional level in several ways. The most basic
way for any region to use the study results is to compare travel characteristics from the input
data and assumptions to existing regional model data in order to find a best-fit cluster for their
area. The cluster-level results may prove informative on which strategies may offer the most
potential to a real-world area. A second approach is to use specific model data from the region
in TRIMMS to compare regional results to the study's cluster-level impacts. Those regions that
are guided by the results may further validate the applicability to their specific region by using
their travel demand model to test one or more scenarios. The input data and assumptions
along with information collected from literature on elasticities will be helpful in establishing the
modeling parameters.
VII
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VIM
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS iii
EXECUTIVE SUMMARY v
CHAPTER 1: INTRODUCTION 1
CHAPTER 2: CONSIDERATION OF EXISTING RESEARCH 5
2.1 Recent Changes in Transportation Control Measures 5
2.2 Efforts to Analyze Emissions Reductions 6
2.3 Impacts of TCMs and Elasticity Estimates from Literature 8
2.4 Tools Available forTCM Analysis 9
2.5 Subject Matter Expertise 10
CHAPTER 3: ANALYSIS METHODOLOGY 11
3.1 Representative Metropolitan Region Selection and Data Use 11
3.2 Strategies Analyzed 14
3.3 Scenario Development 16
3.4 Scenario Analysis 17
3.5 Data Limitations and Assumptions 23
CHAPTER 4: RESULTS AND CONCLUSIONS 26
4.1 National Level Results 26
4.2 Scenario Comparisons 29
4.3 Conclusion 34
REFERENCES 35
APPENDIX A A-1
APPENDIX B B-1
LIST OF TABLES
Table 1. Transportation Greenhouse Gas Emissions by Mode, 1990 and 2008 1
Table 2. U.S. Metropoligan Regions in Clusters 12
Table 3. Cluster Definitions and Representative Areas 12
Table 4. TCM Strategies Analyzed 14
Table 5. Scenarios 17
Table 6. Scenario Assumptions and Modeling Approach forTCM Strategies 18
Table 7. Methodology for Scaling to the National Level 22
Table 8. National Percent Reductions 28
LIST OF FIGURES
Figure 1. Analysis Steps 18
Figure 2. National VMT Reductions from Baseline 28
Figure 3. Cluster Response to Scenarios in 2050 31
IX
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Chapter 1: Introduction
Since 1990 transportation has been one of the fastest-growing sources of greenhouse gas (GHG)
emissions in the country; representing 41 percent of the total increase in GHG emissions. Based
on GHG emissions reporting for 2008, the transportation sector accounted for approximately 27
percent of the total U.S. GHG emissions. This represents the second largest source of GHG
emissions, exceeded only by electricity generation (EPA 201 Oa). The largest share of carbon
dioxide emissions nationwide can be attributed to transportation (U.S. Department of Energy
2009). As Table 1 indicates, carbon dioxide emissions continue to grow significantly from 1990
well into the 21st century, despite the potential impacts of GHG emissions being widely
acknowledged. While the percent growth is highest with respect to medium and heavy trucks and
buses, light duty vehicles still contribute more total carbon dioxide emissions. The largest
sources of transportation GHGs in 2008 were passenger cars (33%) and light duty trucks, which
include sport utility vehicles, pickup trucks, and minivans (29%). Together with motorcycles,
these light-duty vehicles made up about 63% of transportation GHG emissions (EPA 201 Oa).
Table 1. Transportation Greenhouse Gas Emissions by Mode, 1990 and 2008
Carbon Dioxide
Methane
Nitrous Oxide
Percent change 1990-2008
Highway Total
Cars, light trucks, motorcycles
Medium & heavy trucks and buses
27.2%
17.0%
67.9%
-61.9%
-62.5%
-50.0%
-44.8%
-46.0%
12.5%
Environmental Protection Agency, Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2008, Tables 3-12,3-13,
3-14, April 2010.
The large-scale impacts of the transportation sector's emissions on global climate change have
been gaining attention in recent years, as evidenced by initiatives from all levels of government to
reduce trips and vehicle miles traveled (VMT). In 2000, the Federal Workforce Transportation
Executive Order was passed, providing all federal employees in the National Capital Region a
benefit equal to their commuting costs, not to exceed $65 per month (Executive Order 13150,
2000). In 2007, the Energy Independence and Security Act (P.L 110-140, H.R. 6) was passed as
an omnibus energy policy law designed to increase energy efficiency and the availability of
renewable energy. In it, section 1131 increases the federal share for Congestion Mitigation and
Air Quality Improvement Program (CMAQ) projects from a minimum of 80% of the project cost, to
100% of the cost. Section 1133 also states that while constructing new roadways or rehabilitating
existing facilities, state and local governments should employ policies designed to accommodate
all users, including motorists, pedestrians, cyclists, transit riders, and people of all ages and
abilities. These initiatives, while focused on criteria pollutants, indicate that the federal
government supports the numerous benefits accompanying more efficient travel, including
improved air quality, corresponding health benefits and reduced congestion on the nation's
highways. The U.S. Department of Transportation's (DOT) report to Congress in 2010,
"Transportation's Role in Reducing Greenhouse Gas Emissions," provides the most recent
overview of how transportation-related efforts may help reduce this impact.
At the state level California has taken the unprecedented step of establishing targets to reduce
GHGs to 1990 levels by 2020. California's SB 375 directs the Air Resources Board (ARE) to set
GHG reduction targets for regions of the state and to work with the metropolitan planning
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organizations (MPOs) to incorporate GHG reduction efforts into transportation, housing, and land
use plans. The Assembly Bill 32 Scoping Plan adopted in 2008 contains regulations, incentives,
voluntary actions and funding to reduce GHGs that contribute to climate change (AB 32, 2010).
In 2007, the Oregon Legislature passed HB 3543, which set goals to reduce greenhouse gas
emission to 75 percent below 1990 levels by 2050. Washington and Maryland have also passed
similar laws to address climate change issues.
The term Transportation Control Measures (TCMs) refers primarily to the sixteen broad
categories of strategies listed in Section 108(f)(1)(A) of the Clean Air Act (CAA) that are mostly
aimed at reducing VMT and, therefore, emissions from travel activity. TCMs encompass both
transportation systems management and travel demand management. The use of the term TCM
in this report includes the travel efficiency related measures listed in the CAA and other VMT-
reducing strategies, such as road pricing, not listed in the CAA. TCM are often considered for
inclusion in State Implementation Plans (SIPs) for air quality and Transportation Improvement
Programs (TIPs) for transportation conformity purposes and therefore have been widely used
since the 1990s. The recognition of the contribution of the transportation sector to national GHG
emissions has increased the level of attention on TCMs to reduce these emissions and on the
techniques available to estimate and evaluate their effectiveness. As described, many states
have begun to commit to targets for GHG emissions reduction through regional and state climate
action plans and other initiatives. As a result, urban areas have increased their efforts to analyze
the potential effectiveness of various TCMs.
As the number of areas attempting to
address GHG emissions from the
transportation sector increases, there is a
greater need to understand the effectiveness
of TCMs. The purpose of this analysis is to
quantify the effectiveness of VMT-reducing
strategies in metropolitan regions in order to
determine the potential for reducing VMT and
emissions at the national-level.
Vehicle miles traveled represents a primary
measure used by transportation
professionals in identifying changes in travel
behavior at any scale. It is also a required
input into current emissions modeling and
therefore has a defined relationship with
emissions measurement. The Trip
Reduction Impacts for Mobility Management
Strategies (TRIMMS, version 2.0) model
developed by the University of South Florida
was selected for the analysis of TCMs in this
study, after comparing several models that
are available for this purpose (see Tables A-
1 and A-2 in Appendix A for a comparison).
TRIMMS is a sketch planning tool that relies
on current understanding of price and travel
time elasticities and synergistic effects of
various strategies for analysis in order to
estimate VMT changes resulting from
defined future scenarios. The modeling results
Trip Reduction Impacts for Mobility Management
Strategies (TRIMMS) model, version 2.0
Description: Spreadsheet-based sketch planning tool to
measure travel impacts of regional and employer-based TCMs
Developer: Center for Urban Transportation Research,
University of South Florida
Updated: 2009
Scale of analysis'. Site-level or regional
Inputs: Average mode shares, trip lengths and travel times by
mode, average vehicle occupancy, parking and trip costs by
mode, and details about TCMs
Outputs: Changes in mode shares, trips, VMT, and emissions;
also benefits and costs and benefit-cost ratio (emissions and
benefit-cost outputs not used in this analysis)
Methodology: TRIMMS applies values of travel time and price
elasticities for each mode based on a survey of empirical
literature to calculate the reductions in VMT and trips. A
baseline for VMT and trips is created from data input by the
user and the reductions are calculated for a single year.
Multiple strategies can be modeled simultaneously, capturing
the combined effects. The values of elasticities can be changed
by the user. TRIMMS also provides estimates of the reduction
in emissions and does a benefit-cost analysis of the strategies
that are applied. However, in this analysis, only the TRIMMS
outputs of trips and VMT reductions were used.
Guidance and model available on this link:
http://www.nctr.usf.edu/spreadsheet/TRIMMS2.zip
can be combined with emission factors from
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EPA's MOVES2010 emissions model to provide the
corresponding emissions for GHG and other pollutants.
The research approach followed in this study began with
a review of current findings from the literature. Several
important research studies in recent years have
attempted to quantify the potential of selected TCMs to
reduce GHG emissions. Most notable are Moving Cooler
(Cambridge Systematics 2009) and Driving and the Built
Environment (Transportation Research Board [TRB]
2009). While Driving and the Built Environment
addresses land development patterns and associated
strategies, Moving Cooler considers a long list of TCMs
and their potential effectiveness in reducing emissions.
Both studies consider TCMs included in the present
research, but were conducted using a significantly
different methodology. Efforts to quantify the emissions
reductions related to TCMs at the national level primarily
rely upon meta-analysis of the findings from the collective
body of research. In contrast, this study uses actual
regional travel data from metropolitan regions varying in
size and degree of transit use to analyze TCMs and their
synergies with one another. Although comparison
between the studies is limited by the difference in
approach and specific assumptions, the results are
similar and the review of previous studies provided an
understanding of impacts that are reasonable to expect
from different TCMs alone or in combination. In addition, several national experts were consulted
for recommendations on the analysis methodology and the reasonableness of the study results.
Real data from MPO regions were used to arrive at a national estimate of VMT and GHG
emission reductions using the following approach:
Transit use and population variables were used to define seven different groups of metropolitan
regions across the United States, called clusters.
All metropolitan regions across the United States were assigned to the appropriate cluster
based on their transit use and population. Two metropolitan regions from each cluster were
selected as representatives for modeling purposes. Actual data were collected from the MPOs
for each representative region.
Using the real data from the representative regions, the effects of the different scenarios on
VMT were modeled using TRIMMS. The model results from the two representative regions in
each cluster were averaged together, resulting in an estimated reduction in VMT for individual
regions in each cluster under each scenario.
Comparison of Present Analysis to
Moving Cooler
The Moving Cooler report developed a range
of scenarios (called "Bundles") to estimate the
potential GHG emission reductions from travel
efficiency strategies. The results from Bundle
6, which overlaps with many of the travel
efficiency strategies in this analysis, shows a
reduction in 2050 of 15% to 18% in GHG
emissions. There are a number of reasons
why this range is substantially greater than the
reductions estimated in this report. The
primary reason is that the Moving Cooler
Report includes speed limit reductions, eco-
driving and systems operation, multi-modal
freight strategies, and management strategies,
which were not modeled in the EPA analysis.
These four strategies accounted for
approximately 50 percent of the total Moving
Cooler reductions. The different
methodologies and assumptions for the
baseline used by the two reports could also
contribute to the differences. Excluding these
four strategies, the results of the EPA analysis
is generally consistent with the Moving Cooler
report.
Emissions factors from MOVES2010 were used to determine the potential emissions reduction
based on the estimated VMT reduction in each cluster under each scenario.
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« The cluster values for VMT and GHG emissions were extrapolated to represent the entire
cluster (the averaged values were multiplied by the number of regions in the cluster) and then
summed across clusters to reach a national estimate.
Additional information about the selection of representative regions, the models used and
definitions of the clusters is provided in Chapter 2. The following chapters provide a detailed
discussion of each step of the study approach, the results and understanding of the lessons
learned, and estimates of national emissions reductions under seven scenarios. Where
limitations of the data and methodology could be improved to support regional analysis, it is
identified throughout the report. The appendices provide the inputs and assumptions for the
analysis along with other supporting information on modeling options and individual pollutant and
GHG emission reduction.
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The study began with a review of current national research on the effects of TCMs, smart growth,
and other strategies for reducing GHG emissions. This included an evaluation of the available
analysis tools, informed the selection of potential representative urban areas, and considered
quantitative research on TCM efforts at the national, regional, and project level over the past
decade. To obtain a broad understanding of current practices, the literature review took into
account several major reports: Multi-pollutant Emissions Benefits of Transportation Strategies
(FHWA 2006), Growing Coo/er(Ewing et al. 2008a), Moving Cooler: An Analysis of
Transportation Strategies for Reducing Greenhouse Gas Emissions (Cambridge Systematics
2009). These reports were supplemented with evaluations of projects funded under the FHWA
Congestion Mitigation and Air Quality (CMAQ) grants (FHWA 2008b), reports from the
Transportation Research Board and the National Cooperative Highway Research Program,
journal articles, regional planning studies and numerous conversations with regional
representatives. A full list of resources is provided in the References section. A summary of the
information gathered from existing research and how it informed the current study is provided in
this chapter.
2.1 Recent Changes in Transportation Control Measures
As noted, a list of TCMs was included in the Clean Air Act. During the past decade new TCMs to
reduce VMT have emerged. Road pricing is one of those emerging TCMs. Research indicates
that the most common road pricing strategies implemented in the United States involve peak hour
tolls and variable pricing on new and existing lanes. The conversion of High Occupancy Vehicle
(HOV) lanes to High Occupancy Toll (HOT) lanes is one example. Other strategies, such as
downtown congestion pricing, distance-based pricing, regional variable pricing, and pricing for
heavy goods vehicles, have been implemented in countries around the world and are increasingly
being considered by states and regions in the United States. A few experimental and pilot
projects in the United States have introduced pay-as-you-drive insurance charges, mileage fees,
variable parking pricing, and strategies such as parking cash-out. For instance, trials for mileage
fees have been conducted in Oregon, the Puget Sound region, Minneapolis/St. Paul and Atlanta,
and are currently in progress in six regions around the country as part of ongoing research by the
University of Iowa. Parking cash-out programs are now authorized by state law in California.
Under the U.S. DOT'S Urban Partnerships Program and the Congestion Reduction Demonstration
initiative, San Francisco, Miami, Seattle, Minneapolis/St. Paul, Atlanta, and Los Angeles, San
Francisco and New York City are planning implementation of a variety of road pricing strategies
including variable tolls on existing capacity, conversion of existing HOV lanes to HOT lanes,
variable parking pricing and downtown congestion charging, combined in many cases with
improvements to transit capacity. Pay-as-you-drive insurance has been legalized in some states,
but has been implemented only on a small scale in the United States by a few private-sector
insurance companies, including G.M.A.C. Insurance by General Motors, MileMeter, and GEICO
Car Insurance. Massachusetts, Oregon, California, and Texas are some of the states promoting
these programs.
An important impetus to the increasing focus on pricing-oriented TCMs in the United States has
been the U.S. DOT'S Value Pricing Pilot Program (VPPP). The program has been established to
encourage states and local governments to test innovative pricing strategies, demonstrate their
potential, and assess their effectiveness. The U.S. DOT has also recently established the
National Strategy to Reduce Congestion on America's Transportation Network. Pricing strategies
are a key element of this strategy, and are supported through the Urban Partnership Agreements
in several metropolitan areas. Additionally, the need for alternative sources of revenues to build
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and maintain transportation infrastructure has created great interest in pricing programs around
the country. Unfortunately, the need for transportation infrastructure improvements is outstripping
the availability of funding from traditional revenue sources such as the Highway Trust Fund. A
result of this funding shortfall is the broad consideration of innovative revenue sources including
pricing strategies.
Another important recent change is that employer-initiated transportation demand management
(TDM) programs are now being implemented in a more widespread way. State and local
governments within Arizona, California, Maryland, New Jersey, Oregon, and Washington all have
programs in place that work with major employers to provide financial incentives to encourage the
use of alternative forms of commuting for employees, including transit benefits, parking cash-out
programs, ride-matching programs, and alternative work schedules. Seattle's transit agency has
developed one of the nation's first self-serve, public, internet-based rideshare matching services
(VTPI 2008a). The State of Oregon, working with area employers, has set a target of a 10%
commute reduction over three years and even has the ability to fine employers who fail to make a
good faith effort to encourage employees to reduce automobile commute trips (VTPI 2008b). One
Arizona MPO has adopted a mandatory travel reduction program for employers with 100 or more
employees at a single site, which has led to significant annual savings in VMT, gas, dollars, and
pounds of criteria pollutants (Pima 2007). The Philadelphia metropolitan area launched an
Employer Trip Reduction program, which requires employers to meet vehicle occupancy targets
for their employees by promoting trip reduction strategies. The variety of programs shows that
different governments are picking and choosing programs that are adaptable in their region.
.'..' ::' ,' v '.', .; -,; "/., . .:. / . ,
Several state and regional agencies, including state air quality, environmental, energy and
transportation agencies, as well as MPOs, are taking steps to quantify transportation GHG
emissions, despite the limitations of existing tools and uncertainties about how policy in this area
will evolve. The ability to accurately estimate the emissions generated by current and future
transportation systems, and the ability to estimate potential reductions in emissions from certain
strategies is limited in most areas. In the absence of standardized tools and approaches, officials
and planning agencies are relying on currently available methods to quantify GHG emissions or
have begun developing new methods and tools that cater to their specific circumstances and
needs.
California represents one of the most aggressive states in their efforts to address GHG emissions.
California's SB 375 directs their ARE to set GHG reduction targets for regions of the state and to
work with the MPOs to incorporate GHG reduction efforts into transportation, housing, and land
use plans. In 2006, California's Global Warming Solutions Act (AB 32) established the goal of
reducing GHG emissions to 1990 levels by the year 2020. Agencies that plan to conduct
analyses on GHG emissions, or are developing analysis tools to do so, include statewide
agencies and MPOs in California, Washington, Montana, and New York (FHWA 2008a). In 2004,
Oregon's Governor's Advisory Group on Global Warming wrote the "Oregon Strategy to Reduce
Greenhouse Gas Emissions" report, which recommended 84 specific actions that Oregon could
take to reduce its GHG emissions. In 2007, the Oregon Legislature passed HB 3543, which sets
goals to reduce greenhouse gas emissions to 75 percent below 1990 levels by 2050. It also
establishes a Global Warming Commission, which will make recommendations to meet the GHG
reduction targets. An advisory group followed up with the 2008 report "A Framework for
Addressing Rapid Climate Change," which notes which of the 84 recommendations have been
implemented, are currently in progress, and not yet implemented. State analysts estimate that
Oregon is likely to meet its 2010 goal of arresting emission growth. In 2009, Maryland's governor
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signed into law the Maryland Greenhouse Gas Emissions Reduction Act of 2009, requiring the
state to achieve 25 percent reduction in 2006 GHG emissions by 2020. Because of this law,
Maryland DOT is responsible for addressing GHG emissions reductions in transportation and land
use mitigation and policy options and will also work with other agencies on strategies such as
Pay-As-You-Drive insurance and transportation technology improvements.
Several MPOs are leading efforts to improve the analysis capabilities in order to quantify GHG
emissions as well as to improve modeling practices so that strategies can be more effectively
analyzed. The San Francisco Bay Area MPO is conducting a preliminary analysis on proposed
packages of investments to see if they will help reach their initial CO2 emissions and VMT per
capita reduction targets. Included in these packages are strategies such as rail transit,
comprehensive road-pricing policy, and land-use policies based on smart growth principles. The
Puget Sound Regional Council is using the U.S. EPA's Motor Vehicle Emission Simulator
(MOVES) model to do a regional level analysis of GHG emissions in its long range transportation
plan. Albany, NY's MPO has taken an innovative approach to the use of their travel demand
model and based the region's calculated GHG emissions impacts on the assumptions that a
range of policies and principles addressing transportation and sustainability will reduce trip
generation per household by 15%, that there will be no new major highway construction, and that
any widening will involve managed and HOV lanes. Sacramento Area Council of Governments
has developed SacSim, the first activity-based travel demand model to use individual land parcels
as the level of input data. This allows land use and transportation interactions to be more fully
captured than travel demand models that use data aggregated at the traffic analysis zone (TAZ)
level (SACOG Final Metropolitan Transportation Plan 2008).
From the literature review, it was determined that both pre-project estimates and demonstrated
impacts at the project level can be found for some of the most common strategies that have been
implemented to date. These include bicycle and pedestrian programs; ridesharing programs
(including park and ride facilities); HOV/HOT lanes; carpool/vanpool programs implemented by
individual employers or regional transportation management associations; improvements in transit
marketing, information, and amenities; and some types of land use strategies. Impacts of pricing
strategies such as parking pricing, parking cash out, conversion of HOV to HOT lanes, variably
priced lanes, congestion pricing, and distance-based pricing, have been compiled from projects
implemented under the federally-supported VPPP and from regional modeling studies.
The impacts of land use strategies on VMT documented in this report are based on a meta-
analysis of over fifty recent studies (Ewing and Cervero 2010) and on data available from the
Growing Cooler report (Ewing et al 2008a). These studies provide VMT reduction factors and
elasticities for different types of land use strategies that have a significant effect on VMT only in
the long term (TRB 2009; Cambridge Systematics 2009). These strategies are thus best
implemented along with others that would have an impact in the short term.
TCMs have an impact on GHG emissions through four variables: change in number of trips (trip
rates), change in vehicle miles traveled (trip lengths and overall VMT), change in highway mode
shares (private passenger vehicles and transit), and change in vehicle speeds. The TCMs that
have been implemented most frequently are at a local scale and lead to localized changes in the
number of trips and VMT. These often do not have a significant effect on regional trips, VMT,
mode shares, or speeds.
The TCMs that would result in a measurable regional reduction in automobile trips and VMT are
those which affect regional mode share and speeds, and would consequently have the highest
impact on regional emissions. These tend to be strategies involving regional transit expansion
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and service improvement, incentives for vanpools and carpools including construction of
HOV/HOT lanes, and regional pricing strategies. For strategies that are implemented at a
regional scale, impacts can be expected to be greater in larger regions where the absolute
number of trips and VMT is larger. Even a small percentage reduction in regional VMT and
automobile trips can potentially have a significant effect on regional emissions.
In order to narrow the list of TCMs for analysis, a range of quantitative estimates of the travel
activity impacts of TCMs were compiled from several studies. The estimates identified which
strategies have greater potential to reduce automobile trips and VMT, and would consequently
have a higher impact on regional emissions (Cambridge Systematics 2009; Evans 2004; Evan et
al. 2003; Ewing et al. 2008a; Ewing and Cervero 2010; FHWA 2008b; Johnston 2006; Litman
2010; Pew Center 2003; Rodier2008; Shoup 1997; Small and Winston 1999; Pratt et. al. 2000;
Vaca and Kuzmyak 2005). The ranges provided in Table A- 3 in Appendix A show estimates of
the change in automobile travel or transit ridership for a given change in user travel time or travel
cost. For some types of strategies, such as employer-based programs and vehicle restrictions,
specific estimated elasticities are not available in the literature but quantitative data on observed
impacts from implemented projects and modeling studies can be considered. Where specific
elasticities are not available, Table A-3 lists impacts in terms of percentage reductions in travel
demand (trips or VMT). The elasticities shown in Table A-3 are travel demand elasticities,
defined as the percentage change in travel (VMT or trips) caused by a one-percent change in
user travel costs or travel time. In this study, travel costs are equivalent to out-of-pocket
operating costs for auto drivers and passengers, and transit fares per trip for transit riders. For
example, an elasticity of -0.5 with respect to fuel prices means that each 1% increase in the price
of fuel results in a 0.5% reduction in vehicle mileage or trips. Similarly, transit service elasticity is
defined as the percentage change in transit ridership resulting from each 1% change in transit
service, measured in terms of headway or frequency. A negative sign indicates that the effect
operates in the opposite direction from the cause (an increase in price causes a reduction in
travel) (Litman 2010).
It is important to emphasize that although these ranges can provide an upper and lower bound on
the impacts of various TCMs, the actual impacts on travel activity will differ by the particular
characteristics of a region (density, size, transit availability), trip purpose, horizon year (long term
or short term), and the other measures that would be simultaneously implemented. The values
provided in Table A-3 represent the range of impacts of each TCM on travel activity. Where
elasticity values were available and could be compared, the travel time and travel cost elasticities
for each mode used in this study fall within the reported ranges shown in Table A-3. The actual
direct and cross-price elasticities used in the analysis are provided in Tables A-4, A-5, and A-6 of
Appendix A.
Note that Tables A-4, A-5, and A-6 provide values of direct elasticities and cross elasticities.
Direct elasticities reflect the percentage change in the demand for trips of any given mode
resulting from a change in that mode's price or other measurable service characteristics. Cross
elasticities refer to the percentage change in the demand for trips of any given mode caused by a
change in price or other measurable characteristics of other modes (Concas and Winters 2009,
pp. 43-46). For example, an increase in peak period travel time or travel costs (e.g. parking
prices) for autos causes a direct reduction in auto travel demand (negative direct elasticity) and
an increase in transit travel demand (positive elasticity). Cross elasticities recognize and measure
the potential degree of substitution or mode shift between transportation modes. The TRIMMS
model uses default parameters compiled from a survey of empirical literature. More information
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about these values can be found in the guidance document available with the TRIMMS model
(Concas and Winters 2009).
2.4 Tools Available for TCM Analysis
Methods for analyzing the effectiveness of TCMs have been developed with different capabilities
and limitations. This following information considers the current modeling techniques, tools, and
methods that were evaluated for use in this analysis. Table A-1 in Appendix A provides an
overview of the input requirements and output capabilities of each tool. It also provides a more
detailed assessment of each of the tools and discusses the key features, inputs, and outputs of
those tools.
As the primary long range transportation planning tool, regional travel demand forecasting models
are used for estimating the travel activity effects of infrastructure changes and land use plans at
the regional scale. The review of studies and modeling practices undertaken for this study
indicated that while some regions use a regional travel demand model for TCM analysis involving
sub-areas, TCMs are routinely analyzed off-model using sketch planning methods, EPA's
Commuter Model, or a broad range of spreadsheet-based tools because of their better sensitivity
to TCM strategies. In addition, nuances such as changes in travel patterns due to
pedestrian/bicycle facilities, transit-oriented development, and similar features are inherently
difficult to model with regional travel demand forecasting models. Of the off-model tools used at
the regional scale, spreadsheet-based sketch planning methods are most prevalent. Sketch-
planning is often used in transportation planning to make high-level or preliminary decisions prior
to detailed analysis or to narrow the range of options considered. For these reasons the sketch-
planning approach was chosen as a reasonable way to assess the effectiveness of TCMs for
changing travel activity.
Only two models that require detailed trip table inputs from regional organizations were identified:
the Federal Highway Administration (FHWA) TDM Evaluation Model and the Surface
Transportation Efficiency Analysis Model (STEAM), both developed by FHWA. The STEAM
model is essentially a benefit-cost analysis tool that can also be used to analyze travel activity
and emissions changes. In the 1990s, the FHWA TDM Evaluation model was used to evaluate
TCM strategies and was a robust tool for that purpose. Although some studies still use this tool, it
has not been updated since 1993. While the elasticities could be changed to reflect current
trends in transportation demand, travel time, and strategy participation rates, the model does not
take into account other important factors such as mode share for non-motorized transportation or
fuel prices. Further, anecdotal evidence from practitioners at the local level indicated that the
TDM Evaluation model is not widely used today. The STEAM model, like the TDM Evaluation
model it is based on, requires extensive inputs from regional agencies in the form of baseline and
improvement case trip tables for each type of TCM. Due to the intense data requirements of
STEAM, it was not selected for this study.
Of the spreadsheet-based analysis tools and models, the 2005 updated version of the EPA
Commuter model appeared to be most commonly used at the state level. A tool known as
TRIMMS (Trip Reduction Impacts for Mobility Management Strategies) has recently been
developed by the University of South Florida and is capable of modeling the synergistic effects
between strategies. Other sketch planning tools include TCM Analyst, developed by the Texas
Transportation Institute and the Transportation Emissions Guidebook (TEG), developed by the
Center for Clean Air Policy (CCAP). These sketch planning tools can be used to analyze a large
number of strategies across multiple regions. However, the TCM Analyst model is based on
relatively old data, having been developed in 1994-95, and the CCAP TEG model is a less
-------
precise tool than either the EPA Commuter model or TRIMMS because it is based on rule of
thumb guidance on TCM impacts obtained from literature and requires many more assumptions
(see Table A-2 in the Appendix for a comparison of several relevant models). All of these tools,
including the most recent version of TRIMMS, provide changes in travel activity along with the
corresponding reductions in emissions that are built into the model.
Two of the models identified in the research, the US DOE - EPA MARKAL (Market Allocation)-
MACRO model and the US DOE National Energy Modeling System (NEMS) model, only analyze
strategies affecting user costs applicable at a national scale, such as changes in fuel taxation and
distance-based pricing, but they cannot evaluate regional or urban strategies and were not
created for that purpose. For example, NEMS, the more detailed of the two models, can only
analyze strategies at the broad level of the nine Census regions, not at an urban or sub-region
level.
Smaller regions may not have the data needed for more sophisticated tools. On the other hand,
larger regions that have the required expertise and tools often rely on multiple staff to provide
data for modeling a single strategy, which requires much time and resources. It was found that
sketch planning tools are widely used to analyze TCMs in large and small regions because they
provide greater versatility to analyze a variety of strategies using a common, standardized
platform. Several potential tools were compared for their suitability in undertaking this analysis.
Tables A-1 and A-2 in Appendix A show the various features of all models that were explored.
Based on conclusions drawn from the research, the TRIMMS 2.0 model was used for the analysis
effort, an updated version of the original model developed in 2009 with funding from U.S. DOT
and Florida DOT. The TRIMMS 2.0 model was selected for its ability to handle synergies and
substitution effects among TCMs in a robust way, using values of cross-elasticity between modes
to calculate changes in mode shares. This assumes that the different mode choices are not
independent of each other, but rather are interactive. For example, when financial incentives like
fare subsidies are provided for the use of transit and higher parking fees or tolls are introduced for
autos, TRIMMS can capture the combined VMT effects of the resulting shift in mode shares. The
model also allows the user to capture the effects of TCMs in different timeframes by the use of
short term and long term elasticities, as well as to distinguish peak and off-peak impacts at the
regional scale. Additional information related to the TRIMMS model is included in Appendix A.
2.5 Subject Matter Expertise
Recognizing that changes in travel patterns, characteristics, and modes may change the
effectiveness of TCM strategies, a review of the national literature was used to identify experts in
TCM implementation, research on land use interactions, and emissions analysis techniques.
Subject matter experts selected from both the academic environment as well as knowledgeable
transportation professionals bring practical experience to apply to the considered research and
data. Five experts provided feedback on the methodology and the policy scenarios, provided
recent studies on the impacts of TCMs and data they had come across in their work, and
reviewed an interim report for the analysis. Their inputs were used to revise and enhance the
methodology, highlight caveats, and validate the results of the analysis.
10
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""".in <;,i"i" :; . i t\ "ii I'-'h-ii!.:! -l-'-jy
This chapter describes in detail the steps that were taken to conduct the analysis. These steps
are the framework for the Travel Efficiency Assessment Method (TEAM). The effort began by
dividing metropolitan regions into seven types or "clusters" and then selecting two representative
metropolitan regions for each cluster. Concurrently, TCM strategies were selected for analysis
and defined to meet the requirements of the TRIMMS model. Finally, individual strategies were
combined into seven scenarios. The scenarios begin with a single strategy, the use of region-
wide TDM, and add increasingly challenging strategies such that the final scenario represents the
combined impact of all strategies.
The resulting framework of clusters and scenarios allowed data collection and input for modeling.
A three-step analysis approach was used to determine: (1) potential VMT reduction in the
representative regions through the TRIMMS analysis, (2) anticipated cluster-level reductions in
both VMT and emissions by averaging the regional results, and (3) a national-level forecast of
VMT and corresponding emissions reductions from 2010 to 2050 in 10-year increments.
Emissions reductions were determined using factors from the MOVES2010 model applied both at
the cluster and national level.
3.1 Representative Metropolitan Region Selection and Data Use
As described in Chapter 1, nationwide VMT and GHG emissions reductions under different TCM
scenarios were estimated by extrapolating the modeling results based on real data from
representative regions to regions with similar characteristics in the same cluster, and then
summing the results across clusters.
To characterize the clusters, data were collected for all U.S. Census metropolitan statistical areas
(MSAs) around the following variables: population; area; proportion of people who use transit,
drive alone, or carpool to work; total daily VMT and road miles; and calculated population density,
daily VMT per capita, and road miles per capita. Formal statistical methods and graphical
estimation were used to identify the explanatory variables from the data, that is, the variables that
control the other data. Population and transit mode share were found to be explanatory variables
and were used to define the clusters. Using the population and transit mode share data collected
for the MSAs, all metropolitan areas across the country were placed into the seven clusters. The
identification of clusters as "high" or "low" transit use is based on the average value for the
regions within that cluster. Table 2 illustrates the breakdown of U.S. metropolitan areas into their
representative clusters.
The characteristics and representative metropolitan areas for each cluster are shown in Table 3.
The four clusters with the largest populations were defined by both population and transit mode
share. The three clusters with the smallest populations did not differ significantly in transit mode
share so were defined only by population. The representative metropolitan areas in each cluster
were chosen with consideration for geographic diversity, their approaches and strategies to
address climate change and greenhouse gas emissions, the ability for the metropolitan area to
represent areas with similar characteristics, data availability, and MPOs' interest in providing
useful data. Although metropolitan areas in California were used to represent four clusters, this
was considered advantageous because of the population density in California and the innovative
approaches historically used in California that can benefit other regions. Many of the states in the
center of the country are relatively low in population and are not currently designated as air quality
non-attainment areas, affecting the availability of data.
11
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Table 2. U.S. Metropolitan Regions in Clusters
Cluster Definition
Cluster 1 [pop > 2.9 mil;
transit share > 9%]
Cluster 2 [pop > 2. 9 mil;
transit share 9% or less]
Cluster 3 [pop 1.5 -2. 9
mil; transit share > 4%]
Cluster 4 [pop 1.5 -2. 9
mil; transit share 4% or
less]
Cluster 5 [pop 750,000 -
1,499,999]
Cluster 6 [pop 250 -
749,999]
Cluster 7 [pop < 250,000]
SUM TOTAL
Total Daily VMT
846,523,000
1,084,936,000
314,828,000
368,438,000
585,546,000
914,805,000
832,103,000
4,947,179,000
Share of National
Daily Urban VMT
17%
22%
6%
7%
12%
18%
17%
100%
Average Share
of Transit
15.6%
3.9%
6.4%
2.5%
3.5%
1.8%
1.6%
2.0%
Number of U.S.
Cities Represented
6
9
7
8
21
87
313
451
Table 3. Cluster Definitions and Representative Areas
Cluster
1
2
3
4
5
6
7
Definition
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
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
Actual regional travel data inputs and related information in the analysis were used to support a
national-level understanding of the results that is grounded in reality. However, specific regional
data and response to strategies creates the possibility that unique characteristics of a region
could bias the national results. In order to minimize this potential for bias, the study used data
from two participating regions with similar size and transit use to develop a range of possible
impacts. The average of the response of the representative regions to a scenario was considered
representative of other regions with similar characteristics.
Using this approach in a sketch planning analysis, the collected regional data quickly loses their
connection to the representative region and becomes a general characteristic for a segment of
12
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the national transportation demand. This is why the cluster-level results should not be considered
representative of the response expected for any specific region. However, there is value in
understanding response to strategies at a finer scale than the national perspective provides.
Because the analysis was conducted in increasingly aggregate levels, some conclusions can be
drawn at each level. The Results and Conclusions section of the report provides some of the
insights gained at the regional and cluster level in addition to providing national results.
The primary criteria used to identify potential MPO participants were population and transit share.
Secondary consideration was given to geographic diversity and current use of TCMs. Ultimately,
fifteen MPOs agreed to provide specific data from their travel demand model, regional long range
transportation plan, and individual special studies.
Because the individual MPO travel demand models varied considerably in complexity and detail, it
was important that the data analyzed in this study be consistent across the regions. A data
collection form (shown in Appendix B) that focused on model inputs and outputs was provided to
all participating MPOs. While the requested data were not uniformly available, the regions were
not asked to perform any additional analysis in order to generate the data for this study. However
to supplement the collected information, interviews were conducted as necessary with identified
modeling and planning staff for a full understanding of the information. As the analysis was
conducted and compared with the information compiled from the literature review, urban planners
from the individual regions were consulted when the outcomes appeared counter-intuitive or
strongly outside the normal range. This partnership with the metropolitan areas provided as
robust an understanding as possible for this level of analysis. The data collected from research
on expected TCM impacts and range of elasticities were used to address gaps in data or to
support detailed assumptions for the analysis.
Regional travel demand modeling specifies a base year and a horizon year for the current long
range transportation plan. Although some areas had identified intermediate years in their
planning analysis, these data were used only as supporting information. In general, the base year
data were assumed to represent 2010 conditions and the horizon year data to represent 2030.
This assumption provides the basis for many subsequent assumptions and allows the data to be
forecast to 2050.
Additional validation of this approach and use of data was provided by expert review. Each
expert provided detailed knowledge of the state of the practice with regard to individual strategies.
These experts were engaged from the outset and helped shape the methodology through their
knowledge of what could be anticipated and where significant meaning could be gained or
missed. Their input was used to adjust the methodology within the limitation of the sketch
planning analysis and the intended purpose to provide national-level results and understanding.
A significant treatment of the data used in the analysis was the combining of trip purposes for
scenario evaluation with the exception of using only work trips in the TDM scenario. A concern
shared by several of the experts was that mode splits and elasticities are very different for work
and non-work travel. Using average mode split and elasticities across all trip purposes was
considered more appropriate based on the desire to apply the results to all metropolitan areas
represented by a cluster. Alternatively, by selecting a specific mode split or using multiple trip
purposes, the results would become less broadly applicable. Although this averaging is
considered appropriate for a national-level analysis, subsequent efforts to apply the methodology
and/or results at a regional level should consider this issue and adjust appropriately.
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3.2 Strategies Analyzed
There are many TCMs that can reduce VMT, but their degree of effectiveness varies. A
comprehensive list of TCMs is provided in Table A-3 (see Appendix A). In selecting TCMs for
analysis, consideration was given to: (1) measures under consideration by the widest range of
MPOs in recent years as indicated by the survey response and (2) measures that offer the
greatest potential to reduce automobile trips and VMT from the literature review. These individual
measures were then grouped for analysis into four general strategy categories, to take advantage
of natural synergies and to draw conclusions that are meaningful at the national scale.
The strategy categories for reducing vehicle travel demand selected for analysis are shown in
Table 4. They include: (1) travel demand management incentives provided by employers,
(2) land use strategies including transit-oriented development and promotion of higher densities,
(3) changes in public transit travel times or fares, and (4) pricing of auto travel including parking
charges. The analysis did not specifically address vehicle technologies and alternative fuels
within the strategies. It is anticipated that changes will occur in these areas in the future, and this
is represented in the emissions analysis using MOVES2010. In addition, the national-level
baseline also includes assumptions related to fuel technologies, fuel economy, and fuel prices for
light duty vehicles in addition to macroeconomic variables and is drawn from the U.S. Energy
Information Administration's Annual Energy Outlook (AEO) 2009. For more information, see the
national-level analysis section (3.3).
The description of strategies provided here is supplemented with more detailed information in
Table 6, highlighting the assumptions used in the analysis of scenarios.
Table 4. TCM Strategies Analyzed
Strategy Categories
Travel Demand Management (TDM)
Land Use / Smart Growth
Transit
Pricing
TCMs Included in the Analysis
Rideshare Programs
Employer-based Programs
Public Outreach/ Education
TOD: Improved Transit Access
Mixed Land Use
Promotion of Higher Density
Increased Transit Frequency
Lower Fares or Transit Subsidies
Parking Pricing
Mileage Fees
Strategy 1: Region-wide Travel Demand Management (TDM)
TDM policies were evaluated using the percentage of employees or working population in the
region that are assumed to be affected by flexible work hours, telecommuting, guaranteed ride
home programs, modal subsidies, and incentives for carpooling, walking, and biking. As noted
above, the TDM strategy applies only to work trips while all other strategies apply to total trips
across all types.
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The TRIMMS model considers TDM strategies to be supporting strategies or "soft programs."
Because these are typically voluntary programs initiated by employers, they are included in
packages of measures along with other strategies aimed at altering travel behavior. Thus the
TRIMMS model assumes that voluntary travel behavior initiatives lead to changes in travel
behavior only in the presence of other strategies related to transit, land use, and pricing, often
referred to as "hard programs." Although these 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. TRIMMS models the impacts of region-wide TDM strategies on
travel behavior using a set of previously estimated parameters based on an econometric
analysis of the relationship between hard programs and soft programs like TDM (CUTR 2007).
Strategy 2: Land use strategies
The analysis of land use strategies at the regional scale is subject to a number of uncertainties.
Land use strategies are often modeled in terms of assumptions about one or more of five "D"
variables - density, diversity, design, destination accessibility, and distance to transit facilities
as part of transit-oriented development. The individual effects of land use strategies such as
transit-oriented development (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
or below the level of sub-areas such as traffic analysis zones. Although studies that attempt to
do this are available, it is difficult to isolate the impacts of these strategies (or "D" variables)
from each other since they are closely related. Therefore, these strategies are often combined
together in scenarios representing smart growth, as for instance, in the Moving Cooler study.
Similarly, in this research a single land use scenario was modeled that combined the effects of
some common strategies including density increase, mixed use development, and TOD. In
doing so, assumptions were made, consistent with the literature, for each mode with respect to
changes in travel time and trip lengths resulting from the land use strategies considered. In
this approach the land use scenario is based on expected changes in travel conditions from
previous studies (Bartholomew and Ewing 2009; Ewing and Cervero 2010, Ewing et al. 2008a,
Rodier 2008). Congestion effects arising from increased density were also considered for the
automobile mode.
The literature review was also relied upon to inform the selection of parameters used in the
TRIMMS analysis. To model the land use strategies, the TRIMMS model was used to
calculate the change in VMT using elasticity values for travel time. These values for expected
changes in travel time (access time and in-vehicle time) and trip lengths resulting from land use
measures were based on a review of the above mentioned studies and values included in
EPA's Smart Growth Index (SGI) model. This method allowed the modeling of scenarios that
combine land use with other strategies in TRIMMS in order to allow comparison with other
strategies. The most accurate method of modeling land use impacts is with a disaggregated
model that can capture land uses at a zonal or sub-area level.
Strategy 3: Transit Fare Changes and Service Improvements
In this analysis, transit service improvements refer to an improvement in transit travel time
through improved service frequency. Because the analysis used improvement in transit
access and/or travel time as inputs, the results represent the VMT reduction possible from any
of several strategies to improve transit service and operations. TRIMMS was used to analyze
strategies in this category with the application of documented transit travel time elasticity
values (Litman, 2010). Another transit-related strategy modeled is fare reduction, reflecting
employer subsidies for transit use or commuter discounts offered by transit agencies. For
15
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instance, the Denver MPO has considered highly subsidized or free transit in a scenario that
includes congestion pricing for automobiles.
To analyze the impacts of transit fare discounts and subsidies, price elasticities of transit travel
demand were used in the TRIMMS model. The elasticities reflect the sensitivity of transit mode
share to a change in the cost of commuting by transit, and as mentioned above, were obtained
from a survey of the literature (Concas and Winters 2009). Note that the impacts of improving
qualitative aspects such as the quality of transit service cannot be captured in this analysis.
The category of transit-related strategies modeled the effects of: (1) higher transit frequency
and (2) lower transit fares through discounts, subsidies, free transfers or other policies.
Strategy 4: Pricing policies with Mileage Fees and Parking Charges
In this category, the VMT impacts of pricing strategies that affect the operating costs of
vehicles including higher parking charges, mileage fees and/or congestion charges were
modeled using TRIMMS. However, corridor-level tolls and cordon-based or area-wide pricing
policies cannot be modeled since these require detailed disaggregated information for sub-
areas, such as mode shares and travel costs on particular corridors or groups of TAZs in a
region. This information can be effectively analyzed only by the regional travel demand
models.
Since complete information to model congestion charges was not available from all regions,
only mileage fees were modeled in this category. 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 in the peak hours, using data on
the proportion of trips occurring in peak hours provided by each MPO. The mileage fees are
applied to a baseline level of auto operating costs provided by each MPO. 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. In this analysis, parking charges were considered as
a separate auto pricing strategy, using information received from the regions on existing
average daily parking charges and policies under consideration for increasing parking prices at
a region-wide scale.
3.3 Scenario Development
In order to develop individual scenarios for analysis, the strategies were combined to form the
seven scenarios shown in Table 5. The combinations were based on natural synergies, building
cumulatively from the most basic strategies to the most aggressive. This approach and the
resulting combinations are representative of the general order in which regions typically consider
implementing TCMs.
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Tables. Scenarios
Scenario
Baseline
Scenario 1
Scenario 2
Scenario 3
Scenario 4
Scenario 5
Scenario 6
Scenario 7
Strategy Combinations
Region-wide
TDM
Land
Use/Smart
Growth
Transit
Fare
Reduction
Transit
Service
Improvements
Pricing Mileage
Fees
Pricing Parking
Fees
Current conditions without any of the above strategies
s
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at the cluster and national level. Figure 1 below illustrates the sequence of analysis steps
followed.
Figure 1. Analysis Steps
Regional
Travel Data
Regional-
level VMT
Analysis
, 6luster-!evei\
VMT and
Emissions
Analysis
National-
level
Forecast
Regional 'j
Perspective I
National
Results
A
Step 1: Regional VMT Analysis
Analysis at the regional level provides
the necessary connection to real-world
metropolitan transportation systems
through the use of existing and
forecasted data from the participating
representative areas. Estimating the
regional VMT effects of individual
strategies and scenarios is the first
step in the analysis.
The individual features of the scenarios modeled were based on programs and policies that have
either already been included in MPO plans or are currently being considered in different regions.
Baseline data on the number of trips, trip lengths, trip times, vehicle occupancies, and trip costs
for each mode were obtained from metropolitan regions representative of all seven clusters. For
each scenario, the strategy assumptions or parameters have been drawn from a thorough
literature review of strategies proposed regionally and nationally, information received from MPO
surveys about their modeling assumptions, and professional and academic studies focusing on
scenario analysis of TCMs. These references are listed at the end of the report. The assumed
features of each strategy are in Table 6.
Table 6. Scenario Assumptions and Modeling Approach for TCM Strategies
TCM Strategy
Employer-
based TDM
strategies
Specific strategy
Flexible work hours
Incentives for carpooling
Guaranteed ride home
programs
Ride sharing/ ride matching
TDM outreach/public
outreach programs
Subsidies/discounts for
transit, pedestrian and bike
modes
Telecommuting
Strategy information
Whether or not
employer offers
(TRIM MS asks for a
yes/no answer) to take
these programs into
consideration
2010-2030
30% of employers
Region-wide offer these
programs; includes all
TDM strategies except
walk and bike subsidies
2030 - 2050
50% of employers
Region-wide offer these
programs; includes all
TDM strategies
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Table 6. Scenario Assumptions and Modeling Approach for TCM Strategies
TCM Strategy
Land use
policies
Transit
projects and
policies
Pricing
policies
Specific strategy
TOD, smart growth, increase
in density, mixed use
developments
Transit service
expansion/increase in
frequency, improved access
Fare discounts, reduction,
subsidies, or free transfers
Parking charges
VMT fees or congestion
pricing
Strategy information
Change in travel times
for all modes, change
in average trip lengths
Improvement in transit
travel time and access
time
Change in transit fares
Increase in auto
parking costs
Increase in peak hour
driving costs
2010-2030
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 taker
5% reduction in transit
travel time
10% reduction in transit
fares
$2 increase per day
$0.10 increase per mile
2030 - 2050
6% reduction in all
access times, 10%
reduction in transit travel
time and walk/bike times;
10% increase in auto
travel time due to density/
congestion effects
as proxy for trip length.
10% reduction in transit
travel time
20% reduction in transit
fares
$5 increase per day
$0.25 increase per mile
Using the base year and horizon year plan data available from each of the MPOs, each scenario
was modeled in two timeframes - (1) in 2010 with impacts expected to occur by 2030, and (2) in
2030 with impacts expected to occur by 2050, although the effects of these scenarios will occur
over different time frames. This was because the input data consistently available from all MPOs
was for their base year (2005-2010 timeframe) or future year (2030-2040 timeframe). The intent
has been to simulate the gradual application of strategies in every decade while accommodating
a natural growth in regional VMT by applying two implementation phases for each scenario. With
population and economic growth, VMT is expected to grow in future years and the effectiveness
or adoption of strategies would also most likely increase over time. Both these factors were taken
into account in projecting the VMT reduction for each scenario. The growth rate assumed for the
growth in baseline national VMT was 1.47%, the average growth rate per annum in 2030
assumed in the Annual Energy Outlook 2009 (U.S. Energy Information Administration 2009).
The reduction in VMT for a given year was measured from the baseline VMT for that year in the
absence of any strategy. This was estimated by the TRIMMS model using the population, trip
length, and mode share data provided by the regions as well as regional trip rates. In designing
the scenarios, the assumptions for the base and future years were varied to simulate the increase
in the expected rate of adoption or effectiveness of a strategy over time. Future year scenarios
were, therefore made more aggressive than base year scenarios. Also, larger values of travel
time and travel cost elasticities were used to estimate impacts in future years. This reflects the
greater long-term impact of strategies owing to greater adoption and effectiveness over time. A
primary assumption in the analysis is that the strategies would take effect over approximately a
20-year period. This assumption is based on the inclusion of land use strategies in all scenarios.
Land use changes take effect over the longest period of time, either passively or through active
policy intervention.
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As laid out in the scenarios, strategies were combined and modeled together and the VMT
reduction reflects the cumulative reduction for all strategies from the business-as-usual baseline
out to 2050. The long-term outlook was incorporated in the analysis in two ways: (1) As shown in
Table 6, a more aggressive scenario was assumed in the future year, and (2) the travel time and
cost elasticity values were increased in the future year analysis, based on a survey of long-term
and short-term elasticities (Concas and Winters 2009). For example, if 15% of employers are
assumed to initiate TDM measures in the base year, this figure may be increased to 30% in the
future year analysis. Transit subsidies in future years may also be expected to increase. To
incorporate the need to consider the differing relevance and effectiveness with which strategies
are applied in different regions, one of the strategies, application of VMT-fees, was not considered
in cities with population lower than 1.5 million. VMT-fees, also known as mileage fees or
distance-based pricing, is considered a fairly aggressive strategy in the regions surveyed and has
not been implemented for light duty vehicles, except in the form of small-scale pilot programs in
large metropolitan areas like Seattle and Portland. Congestion needs to be at a significantly high
enough level for a region to consider this strategy, so it may not be warranted for small and
medium metropolitan areas.
The current year data provided by the regions are indicative of the present situation, while the
future year data indicate how the region expects to grow over the next 20 years. Although the
attempt was made to model the future year with none of the policies implemented, some regions
provided future year data that accounted for infrastructure investments in transit or other facilities
and land development. This occurred in regions that have already been implementing actions
towards more sustainable transportation, and specific infrastructure changes cannot be
separately accounted for in a sketch level analysis. For instance, Portland was one of the few
regions where trip lengths are expected to decrease in the future and the resulting effect is seen
in the VMT analysis.
The 2030 baseline data provide the best estimate of a future year base case available. Baseline
data for intermediate years were not largely available. Therefore, to simulate the effect of
applying a strategy progressively in each decade, the effect of applying the strategy at two levels
was calculated. First, a milder version of the strategy was applied in the current year with
complete effects occurring over the following 15-20 years. Second, a more aggressive version of
the strategy was applied in the future year (2030) with complete effects expected to occur by
2050.
Once impacts were calculated to 2030 and 2050, straight line averages were used to provide the
expected VMT reduction in intermediate years. Elasticity values considered for the future year
were assumed to be long-term elasticities found in the literature that are higher than the short-
term elasticities used for the current year analysis. This captures the effect of increased adoption
or compliance rates in future years, once a policy has been in effect for some decades.
In addition to the assumption about when impacts can be expected to occur, the application of
strategies across all trip purposes may not be representative of individual regions. Mode splits
and elasticities are very different for work and non-work travel, and individual regions have
identified specific trip purposes as a basis for their travel demand modeling. While comparison of
strategy effects across trip purposes will be meaningful for individual regions, using one trip
purpose is more appropriate in this analysis because the impacts at the regional level are
ultimately aggregated to the national level. As stated previously, the exception is for TDM
strategies, which are applied to work trips only and are combined with other strategies in every
scenario.
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Step 2: Cluster-level Analysis
The second step of analysis moves
beyond the analysis of individual
regions to estimating the response of
any region with similar population and
transit use. In this step the regional
response in VMT of two representative
areas is averaged for each scenario.
The resulting data are then used to calculate the associated emissions. This provides the VMT
and corresponding emissions that may be reduced by travel behavior changes in each
representative cluster.
The Motor Vehicle Emissions Simulator (MOVES2010) emission model was designed as the
replacement for EPA's previous mobile source emission factor model, MOBILE 6.2. MOVES2010
represents the most advanced state-of-the-practice in estimating on-road mobile source
emissions. At its core MOVES2010 is a database based on analysis of millions of emission test
results and considerable advances in the understanding of vehicle emissions. It incorporates
several changes to the EPA's approach to mobile source emission modeling based upon
recommendations made by the National Research Council (NRC 2000) Given the improvements
in the current MOVES model over its predecessor for several of the pollutants of interest, MOVES
(EPA http://www.epa.gov/otaq/models/moves/index.htm) is the best model to use in this analysis.
At the time this report was being written, EPA was anticipating the release of the next version of
the model, MOVES2010.
On-road driving emission factors for urban roads as well as off-network emissions, including start
and refueling emissions, were derived with MOVES2010 employing national default fleet
characteristics. The study focus is on light-duty vehicles and as such only considered gas and
diesel fueled passenger cars and trucks. The emissions analysis was conducted for each
representative cluster. All factors were derived from an emission inventory approach, normalizing
total emissions to total activity rather than directly exporting emission factors from the model.
Starts were linked to trips assuming one start for each vehicle trip.
Emissions factors were determined for criteria pollutants (NOX, PM, and VOC), and the three
principal GHG pollutants (CO2, CH4, N2O). All exhaust species consider tailpipe and crankcase
emissions. Particulate matter (PM) species also include brake and tire wear. Volatile Organic
Compounds (VOC) emissions include exhaust and refueling emissions, although evaporative
emissions were not included. Emissions were determined for the current year (2010) and a future
year (2030) for each representative cluster. Total national emissions reductions were
subsequently determined by multiplying the representative cluster results by the number of
metropolitan areas the cluster represents.
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and
.i^lii .lif-,
/
Notional
Rfraultb
Step 3: VMT and Emissions
Impacts at the National Level
From a national perspective, the
response of each cluster to the
analysis represents the anticipated
response of all regions which match
the associated characteristics. For
example, Cluster 1 represents all metropolitan areas that have a population above 2.9 million and
a transit share greater than 9%. Based on the U.S Census definition of urbanized areas in 2007
there are six metropolitan areas that meet this description. Therefore the results of the Cluster 1
analysis are applied to six metropolitan areas in the nation, with the expectation that they are
likely to respond similarly. Combining the results of the six areas provides the total potential
contribution of Cluster 1 areas to the national reduction in VMT and corresponding emissions.
The resulting VMT and emissions reductions for each cluster are then combined to estimate the
national response to each scenario in 2010 and forecasted for each subsequent decade up to
2050. The specific methodology is described below and in Table 7.
The baseline used for light duty VMT for the years 2010-2030 in this study is from the U.S.
Energy Information Administration's Annual Energy Outlook (AEO) 2009. The AEO projections
were available to 2030 and were extrapolated to 2050. The AEO uses assumptions related to
fuel technologies, fuel economy, and fuel prices for light duty vehicles to estimate the VMT in
future years, in addition to macroeconomic variables like gross domestic product, disposable
personal income, industrial output, new car and light truck sales, and population. These
assumptions are based on current policies such as state and federal government mandates for
minimum sales volumes of alternative-fueled vehicles (see U.S. Energy Information
Administration's AEO 2009 for a complete set of assumptions).
Table 7. Methodology for Scaling to the National Level
Data Available for Analysis*
Methodology Applied
A. VMT reduction - number of miles by scenario by
cluster and by decade
B. Emission reductions - pollutant by scenario by
cluster and by decade (includes start, refueling,
and urban driving emissions)
C. Number of regions nationwide that belong in
each cluster (based on FHWA 2007 urbanized
area populations and 2000 Census
Transportation Planning Package transit shares)
D. Share of national VMT attributed from each
cluster (FHWA 2007 VMT data)
E. EPA Forecasted National VMT (2005 - 2030;
extrapolated for 2040 and 2050): 68% of the
national VMT are assumed to be urban, based on
several data sources
* Note: letters indicate variables used on right side
1. National Cluster VMT Reduction = Clusters(i) (Ai x Ci) per
scenario and decade
2. National Cluster Emissions Reduction = Clusters(i) (Bi x Ci) per
scenario, decade and pollutant
3. National VMT Reduction = Sum of Clusters(i) 1-7 (Ai x Ci) per
scenario and decade
4. National Emissions Reduction = Sum of Clusters(i) 1-7 (Bi x Ci)
per scenario, decade and pollutant
5. Percent National VMT Reduction = (E - National VMT Reduction)
/E
6. Percent Cluster VMT Reduction = Sum of all cities with Cluster(i)
(E*D - National Cluster VMT Reduction) / E*D per scenario and
decade
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In order to scale up the regional VMT reductions to the national level, the VMT shares for each
metropolitan cluster were determined using the FHWA Highway Statistics Database for 2008
(FHWA 2009), which provides daily VMT for each urbanized area (population >50,000) in the
United States. Using this database, the number of urbanized areas that fall within each of the
clusters was also determined. Using the national VMT projections from the AEO and a factor of
68% derived from the same database to estimate the proportion of the national VMT that is urban,
the contribution of each cluster to reducing overall national VMT was estimated. The results
therefore represent the reduction in urban VMT and emissions nationwide because the TCM
scenarios are primarily applicable in urban areas and the data modeled were for urban areas.
The scenarios considered in this analysis are not expected to affect VMT in rural areas.
The same procedure was followed for the emissions reduction estimates derived for each cluster
and for the nation as a whole. However, unlike VMT, a national baseline for emissions was not
available. Future projections for both trips and VMT are required to create such a baseline
because the number of trips helps determine the magnitude of emissions associated with vehicle
starts. Therefore, to estimate the reductions in national emissions, a baseline was derived using
regional data for trips scaled up to the national level, as described above, and the AEO 2009
VMT. It is important to recognize that over time some rural areas will convert to urban areas.
One study reports that while about two-thirds of national VMT is urban today, by the year 2050,
about four-fifths of all national VMT will be urban (Ewing et al. 2008b). This change in relative
proportions of rural to urban VMT was not considered in the analysis, as only one data source
was available to validate this premise. The cumulative reductions in VMT and emissions from the
business-as-usual baseline for all decades out to 2050 are presented in the next chapter. This
baseline reflects the growth in VMT in the absence of any strategy being applied. The results can
be found in Section 4.1 (Table 8) and in the appendices. This baseline reflects the growth in VMT
in the absence of any strategy being applied. Specific results can be found in the appendices.
3.5 Data Limitations and Assumptions
The methodology and assumptions made in this report may lead to results that are more
conservative when compared to other national studies. In order to support the regional analysis
using TRIMMS, a number of decisions were required to account for incomplete or unavailable
data. The decisions were guided primarily by current research and best practice observed in the
metropolitan regions surveyed. Collectively these assumptions may result in an overall
conservative result. However, this informs the full range of potential outcomes when considered
within the context of other recent national studies. The following information describes the impact
of the assumptions in the study and should be considered carefully when performing analysis of
individual regions in order to more closely reflect their specific travel related characteristics.
1. 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 were not provided, trip
cost was calculated based on average trip lengths that were provided, fuel price and other
cost components, and mileage data for the base year. Regions particularly face a problem in
modeling parking charges; aggregate regional analysis severely underestimates parking
charges, therefore parking charges are best modeled at a sub-regional or zonal level. To
resolve this issue, the analysis considered the highest parking charge available for the region
as the baseline, under the assumption that strategies to reduce driving by increasing parking
charges are most commonly implemented in locations where they are already high due to high
land costs, congestion, and traffic volumes (e.g. in downtown areas). For transit trip costs,
information was obtained directly from the regions or collected from the websites of the transit
authorities.
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There is no consistent methodology across regions to estimate future year trip costs;
therefore, for the future year analysis, auto operating costs were kept constant. 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 would change in the future. This was
the approach followed in the analysis. This assumption was considered acceptable because
even though fuel prices may be expected to increase, higher vehicle fuel efficiency would be
likely to help offset any increase in operating costs. Future year transit fares were assumed to
rise with inflation at three percent per year, while future year parking costs were assumed to
increase by two percent per year, following guidance used by some MPOs.
2. Regional elasticity values by mode: As expected, data in this category were difficult to obtain.
Elasticity values were obtained from only two of the fifteen metropolitan areas that provided
data. These elasticity values were compared with national values that were obtained from the
literature review and found to be broadly within the same range. Some values in TRIMMS 2.0
are lower (more conservative) than the values obtained from the literature review, but the
model allows users to specify their own elasticity values. Given that the majority of
metropolitan areas were not able to provide regional elasticities, the same ranges of values for
all metropolitan areas, derived from the literature review, were used. This is acceptable since
it reflects the current state of the practice and information that is available from the literature.
Despite similar elasticity values, the expectation was that there would be sufficient variation in
strategy impacts between regions based on the differences in transportation data such as
mode shares, trip lengths, trip costs, and travel times by mode.
3. Uncertainty in future year trip length: M PO estimates for future year trip lengths are highly
uncertain since they depend on the implementation of several land development,
transportation, and other planning strategies whose impacts are uncertain over the long term.
Where available, the analysis used trip length trends (typically in the range of ± 2% compared
to the base year) that were provided by the region through their future year planning efforts.
For regions that did not provide this information, this analysis used the same trip lengths for
the future year as in the base year. The assumption used here is that if regions had provided
a trip length estimate for the future year from their models, they are likely planning for policies
(land use and other strategies) or expected growth that will affect trip lengths. If regions were
unable to provide horizon year trip lengths, it is likely that they do not expect trip lengths to
change or are not considering measures that will alter trip lengths. Assuming the same trip
length as in the baseline year implies a conservative estimate for this analysis.
4. 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
selected tool for this study is not adequate for this analysis; the impact of reducing change in
vehicle speeds on national emissions cannot be meaningfully considered within this study.
5. Pricing strategies: For the application of mileage fees, the use of average trip length as an
input in TRIMMS provides an estimate of aggregate average impacts. Such a policy can be
expected to offer the greatest VMT reduction for longer trips and trips made during peak
hours, while potentially moving some auto drive-alone trips to off-peak hours and other
modes. This shift may cause some of the reduction in auto drive-alone VMT to be offset by
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6. 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, our input data were obtained as regional averages for mode shares, trip
lengths, and trip costs, across trip purposes. Since the results are ultimately aggregated up to
the national level, using average data inputs for a region are not expected to significantly
affect the analysis, but it may slightly underestimate the impacts of these strategies.
7. Limited application of pricing policies: VMT-fees or mileage fees were not modeled for cities in
Cluster 5, 6 and 7 primarily because the MPOs were unable to provide estimates of current
vehicle operating costs which were required to calculate impacts. Although the data were not
available, this was considered an acceptable limitation because currently no small cities are
exploring VMT fees as a potential strategy. Clusters 5, 6, and 7 represent a large share of the
national population, and therefore broader acceptance of pricing strategies may greatly affect
the national perspective.
8. National-level baseline: At the national level, the light duty vehicle VMT baseline for the years
2010-2050 was available from the Annual Energy Outlook (AEO) 2009. However, no similar
baseline was available for national trips, which was required to estimate emissions associated
with vehicle starts. To estimate the reduction in national emissions, an emissions baseline
was derived using regional data for trips scaled up to the national level reflecting trip start
emissions and the AEO 2009 VMT reflecting driving emissions.
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The purpose of this analysis is to provide a source of information on TCM strategy effectiveness
to inform the discussion among federal, state, and local planning agencies interested in reducing
mobile source GHG emissions. U.S. EPA and U.S.DOT as well as many state and local agencies
support policies and programs in this effort. Estimates of the national potential for reducing VMT
and GHG emissions may assist planning agencies in this regard. Although many of the results
may appear intuitive and therefore less dramatic, the methodology and analysis provides
quantifiable results rather than anecdotal evidence. It also allows comparison with other studies
to inform the national conversation as well as the growing body of research.
While scenario modeling is based on actual travel data and characteristics of real metropolitan
areas, the predicted changes to travel activity and resulting emissions are not intended to
represent the effectiveness of the strategies for any particular area. The intent is to illustrate the
potential effectiveness of strategies relative to one another, in combination, and with respect to
travel behavior characteristics. However, the analysis provides additional information which may
assist metropolitan regions in their efforts to address transportation-related emissions. By
comparing their regional travel characteristics to the data used in the analysis, planner and policy
makers may support regional discussions on the potential effectiveness of individual or groups of
strategies. Through the allocation of staff resources and using local data and information in a
detailed analysis, the region may more accurately estimate potential strategy effectiveness. This
will help focus on those strategies that can be supported by policy makers and which appear to be
the most promising for an individual region.
4.1 National Level Results
In general, greater opportunity for change results in a greater impact of strategies. Those regions
with long trip length, high population growth, and limited strategies currently in place will show the
most significant response to the scenarios. This response highlights the potential reductions that
can be achieved from behavioral changes in the small and medium-sized regions that are
experiencing high growth. In contrast, those regions that have initiated TCMs in a substantive
way may need to consider more aggressive strategies. While a challenge for policy makers in the
absence of public support, these areas could more effectively reduce emissions through
incorporation of pricing strategies.
It would be a mistake to judge the response of TDM and land use strategies as demonstrating
limited effectiveness as compared to the other strategies examined here. The TDM strategy was
applied only to working population and work trips, and only a fraction of the total working
population was assumed to be covered by employer-initiated TDM programs, therefore its
impacts appear lower when compared to the other strategies. Within this research the
assumption of 30% of employers participating in TDM programs in the current year and 50% in
the future year may represent a conservative assessment based on recent gas prices and other
external factors. As indicated previously, the importance of land use in supporting behavioral
changes cannot be over-stated. In this study land use policy provides the basis for all strategies
except TDM and is assumed to represent a slowly evolving change. We know that certain land
use changes may be reasonably quick to implement in localized areas; however, studies show
that the regional effect of localized change may appear relatively small initially and greater over
the long term. This land use impact points to the importance of implementation in the near term in
order to gain full effectiveness over the longer timeframe considered in this study. This paradigm
is the basis for several assumptions in the analysis, and it impacts the level of total reduction
identified in the period of study. Because TDM addresses behavior changes in work trips, and
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land use can be expected to have a stronger impact on non-work travel, using the two strategies
in combination makes the most sense and can be implemented in any region as a starting point
for more aggressive strategies.
The transit strategies provide an interesting comparison between fare changes and service
changes. The analysis demonstrates a responsiveness to fare changes much greater than to
service improvements. This implies that a policy decision on fare adjustments can be
implemented quickly for an immediate response. Although reductions in fare have an impact on
available funding, this should be much less than the cost of additional routes with higher operating
and capital costs. Regions can be encouraged to target transit service to the greatest area of
need rather than extend their reach to take in a larger area.
It seems very likely that as strategies are added, the actual results will be greater than the sum of
the parts, capturing expected synergies between the strategies. For example, as the pricing
element kicks in, it is very likely to increase the effects of land use strategies as people make
residential and work location decisions to reduce their exposure to the effect of the pricing
policies. The increasing range of response illustrated in Figure 2 supports this perspective. As
the number of strategies applied increases, the percent reduction grows significantly.
There will be changes in socio-demographic factors between the present and 2050, such as the
impact of an aging population on VMT, which cannot be accounted for in this analysis. The
growing interest in "livable communities" is increasingly supported with grants and programs that
may cause a dramatic long term effect if the policy is sustained. These more qualitative
assessments can be used to adjust the input data in order to reflect expectations about future
scenarios at both the regional and national scale. The range of VMT reductions across all
clusters out to 2050 is broad because each city's growth projection is so varied. The national
reductions in Figure 2 reflect the percentage contribution of each cluster to national urban VMT.
For graphical descriptions of all scenarios and clusters refer to the charts and tables in
Appendix A.
Table 8 shows the national reduction in urban VMT and emissions for light-duty vehicles
estimated for 2030 and 2050 from the business-as-usual baseline. The reduction in 2050
represents the cumulative reduction expected over the four decades. As more strategies are
added to the scenarios, the reduction in VMT increases. Emissions reduction percentages
closely follow the VMT reduction across all pollutants. The impact of strategies on individual
pollutants can be found in Table A-9 in Appendix A.
Figure 2 illustrates scenario response over time. The significant increase in effectiveness
between Scenario 4 and 5 is notable. The addition of parking charges in Scenario 5 results in a
dramatic reduction in VMT even though parking charges varied greatly across regions: from $2 to
$11 per day in the base year. As previously mentioned, Scenario 6 was not applied to Clusters 5,
6, and 7 based on the lack of input data from representative regions and the lower relevance of
mileage fees in these smaller regions. Consequently, Scenario 5 is the same as Scenario 7 for
these regions. However, the addition of pricing strategies shows a strong increase even with
fewer urban areas included. This may point to the synergistic effect of strategy combinations
mentioned previously.
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Table 8. National Percent Reductions
Scenario
1- Regionwide TDM
2 - TDM + land use changes
3 - TDM + land use changes + transit fare subsidies
4 - TDM + land use changes + transit fare subsidies + transit
service improvements
5 - TDM + land use changes + transit fare subsidies + transit
service improvements + parking fees
6 - TDM + land use changes + transit fare subsidies + transit
service improvements + mileage fees
7 - TDM + land use changes + transit fare subsidies + transit
service improvements + parking & mileage fees
Percent VMT Reduction
2030
0.1%
1.0%
1.4%
1.5%
2.9%
2.0%
3.4%
2050
0.3%
3.0%
4.2%
4.4%
7.0%
6.3%
8.8%
Percent Emissions
Reduction*
2030
0.1%
1.0%
1.4%
1.4%
2.9%
1.9%
3.3-3.4%
2050
0.2-0.3%
2.9 - 3.0%
4.1-4.2%
4.2 - 4.3%
6.7 - 7.0%
6.0 - 6.3%
8.3-8.8%
* Ranges reflect reductions in all pollutants considered. For example, Scenario 7 results in
(VOC), 8.6% reduction in NOX, and 8.8% reduction in each of PWh.5 and greenhouse gases
8.3% reduction in volatile organic compounds
(C02 equivalent).
Figure 2. National VMT Reductions from Baseline
10.0%
9.0%
8.0%
1.0%
0.0%
2010
20M
-Scenario 1
-Scenario 2
Scenario 3
Scenario r>
-Scenario Ci
-Scenano 7
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Effectiveness of Alternative Aggressive Pricing Strategies
Because pricing scenarios result in the largest emissions and VMT reductions, additional
analyses were conducted for pricing scenarios that exceed those being considered by the
transportation planning agencies interviewed for this study. These more aggressive
pricing strategies may be of interest to policy makers and planners. These scenarios
were applied only to the larger cities in the first four clusters and only in the 2030 to 2050
timeframe. For parking charges, an average daily increase of $8 was used instead of $5,
and for mileage fees, a peak hour fee of $0.35 was used instead of $0.25. The results of
three new scenarios, which relate to Scenarios 5, 6, and 7 in Table 8, are provided below.
National Percent Reductions with Aggressive Alternatives for Pricing Strategies
Scenario
5A
6A
7A
Pricing
strategy
included in
scenario
Parking
charges
VMT/mileage
fees
Parking
charges and
VMT fees
Strategy
Description
Increase in daily
auto parking
charges
Increase in peak
hour driving costs
Increase in
average auto
parking and peak
hour driving costs
Level of
charge
$8 per day
increase
$0.35
increase/mile
$8 increase
and $0.35
increase/mile
Percent VMT
Reduction in
2050
7.2%
6.7%
9.2%
Percent
Emissions
Reduction
in 2050*
6.8-7.2%
6.2-6.6%
8.6-9.2%
* Ranges reflect reductions in all pollutants considered.
Compared with the original pricing scenarios shown in Table 8, the alternative pricing
levels shown above increase emission reductions only by a small amount nationally. The
primary reason is that the effect of higher charges (applied only to the first four clusters)
is diminished when averaged across all clusters. Clusters 5 though 7 retain the same
charge levels in the 2030-2050 timeframe as in the earlier analysis. This reduces the full
impact of the aggressive strategies when all clusters are combined for the national
impact. In addition, once lower value trips are reduced in the earlier scenarios, these
incrementally higher charges tend to shift the remaining higher value trips to off-peak
periods and to other modes like rideshare rather than reduce them. While a shift to off-
peak trips helps to reduce congestion, it does not reduce VMT. Similarly, a shift to
rideshare reduces only some of the auto VMT.
4.2 Scenario Comparisons
Figure 3 is useful in illustrating how the results may be used at the regional level. This graph
shows how each cluster responds individually to the scenarios rather than collectively. In order to
effectively use this information, the region must first recall the analysis methodology. The VMT
reduction for two representative regions (three regions for Cluster 6) was averaged to provide the
cluster response. This combines regional characteristics such as trip length, mode share, and
other factors to describe an average region as the cluster surrogate. Therefore, to interpret the
figure correctly, the user must consider the input and assumptions data in Appendix A. There are
many possible interpretations for the differences observed. In order to make any strong
29
-------
conclusions, more detailed study is needed at the regional level. The following analysis provides
an example of how to use this figure.
Cluster 2 has a mild response across all scenarios because the growth in VMT
projected for the future year in that cluster is much lower than other clusters
(33% growth in VMT from current year, as compared to most other clusters that
project a growth of 70-80%). Like Cluster 2, Cluster 6 cities also project a growth
in VMT lower than the other clusters (35%), however because cities in Cluster 6
have a very high auto drive alone mode share, their response to most of the
TCMs is greater than Cluster 2. Cluster 7 has the highest auto drive alone mode
share of all clusters, and parking costs that are at the lower end of a wide range.
For this reason Cluster 7 shows a high response when parking charges are
added in Scenario 5.
Although this interpretation of the results provides a reasonable explanation, there may be other
reasons that these clusters respond as illustrated. In a collective way the clusters provide an
anticipated response that compares with other national-level studies; however, when
disaggregated in this way, the factors combine and work with each other to create more
information for consideration. Because the results of this study were focused on the national
perspective, there has been no additional analysis to validate or otherwise compare these
differences.
The analysis provides the ability to make comparisons between scenarios in order to identify
lessons learned. The following information may assist in identifying how strategies may be used
most effectively in individual situations.
30
-------
Figure 3. Cluster Response to Scenarios in 2050
18.00%
16.00%
31
-------
1. Region-wide TDM Programs
For this study, the assumptions regarding the application of TDM strategies were generally
conservative. However, some experts have argued that a higher percentage of employer
participation is reasonable to assume. Region-wide TDM programs were modeled separately
because they were assumed to apply only to work trips and to a subset of the population -
employed persons - while all other strategies applied to all trips made by the total population of
the region. The TDM programs considered included flexible work hours, incentives for carpooling,
ridematching programs, guaranteed ride home programs, subsidies for transit, pedestrian and
bicycle modes, and telecommuting programs. The impact of TDM strategies appears to depend
on several factors including population growth, shares of other modes relative to autos, and trip
lengths. All of these factors trade off against each other in determining the impacts. For
example, regions that experience slow population growth may see a higher impact of certain
strategies than regions with higher population growth if the auto mode shares and vehicle trip
lengths of the slow-growing regions are higher. Regions that on average already have relatively
higher mode shares for auto rideshare, transit, bicycling, and walking, compared to auto drive
alone mode shares, shorter trip lengths, and lesser growth expected in working population show a
lower impact. These include Clusters 2-5. Average trip lengths may also play a role. Regions
that project a high growth in population and have high auto drive alone mode shares are at the
higher end of the range, such as those in Clusters 6 and 7. It must be noted that the impacts of
TDM are influenced by the mode shares of vanpool, transit, non-motorized transport, and other
modes because the TDM strategies included those that incentivize these modes. However, in
many regions, mode shares for vanpool, walking and bicycling are not included in the travel
demand models and were not provided. This is a limitation in the results for this scenario. Since
the impact of this scenario on national VMT is very small, this limitation is not expected to affect
the results in any significant way.
2. Land Use Changes and Region-wide TDM Programs
Greater VMT reduction from TDM programs and land use changes is expected in larger regions,
especially those that have relatively longer trip lengths and project significant population growth.
These factors differ in the different regions and trade off against each other to determine impacts.
For example, Cluster 2 and Cluster 3 regions have similar average auto mode shares and similar
average trip lengths; however, the impacts of land use strategies appear to be greater in Cluster 3
regions due to the higher expected growth in population. Higher growth implies greater potential
reductions that may be expected from strategies involving densification and trip reduction through
mixing of land uses. In Cluster 1 cities, while population growth is expected to be slow, the longer
than average trip lengths imply a greater impact from land use and TDM strategies,
3. Land Use Changes, Transit Fare Reduction Policies and Region-wide TDM
Programs
Greater reduction in VMT from the combined application of land use policies, transit fare
reductions and TDM programs is expected in regions where the transit mode share is currently
very low relative to drive alone auto mode share, where transit fares are higher than average, and
where transit trip lengths are longer than average. One or more of these factors may play a role
in determining the impacts of fare reduction through the application of subsidies. A much shorter
transit trip length appears to lessen the impact of auto mode shares and transit fares in reducing
VMT. The cluster 2 regions we selected have higher transit fares but relatively lower transit mode
share and population growth rate than the other clusters. Even with fare subsidies, the travel
32
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costs do not reduce significantly in comparison to auto operating costs to make transit a more
attractive mode for travelers; therefore, the reduction in VMT in response to transit fare reduction
is lower in Cluster 2 than the other clusters. In Cluster 7 on the other hand, the combination of an
already low transit fare with the lowest (almost negligible) transit mode share and large growth
rate implies that a strategy that makes transit more attractive by further lowering fares would have
a larger impact compared to the other clusters. Overall, existing transit mode share, fares, and
population growth rates seem to determine impacts in this scenario, given that average transit trip
length is similar across clusters.
4. Land Use Changes, Transit Fare Reduction Policies, Transit Service Improvements
and Region-wide TDM Programs
In the presence of transit fare policies, the addition of transit service improvements (increase in
frequency and/or reduction in travel times) into the scenario did not show a significant additional
reduction in VMT across regions. The VMT reduction in this scenario was very similar to the
results for Scenario 3. The factors responsible are similar to those discussed above, most
importantly the relative mode shares of auto and transit modes. Where data on future year transit
travel times were not provided, the assumption of the same baseline travel time as the current
year was used. In general, the difference between base and future year travel times was
insignificant in regions that provided the data.
5. Land Use Changes, Transit Fare Reduction Policies, Transit Service Improvements,
Parking Charges and Region-wide TDM Programs
The introduction of parking charges into the scenario leads to a relatively broad range of expected
VMT reductions, reflecting key differences between clusters. Higher reduction in VMT is seen in
regions that have relatively higher auto mode shares and lower parking costs. Cluster 7 regions
have the lowest daily parking costs on average. These regions are expected to experience the
highest VMT reductions (in percentage) from a fixed increase in daily parking charges due to their
high auto-drive alone mode share. Cluster 2 regions show the lowest reduction in VMT because
of already high average daily parking charges and a relatively low auto drive-alone mode share.
The same is true for Cluster 5 regions, although the impact is tempered by a higher population
growth rate in these regions than in Cluster 2. Comparing results in Clusters 1 and 4, we find that
despite similar average auto mode shares, a higher growth rate and lower existing parking
charges in Cluster 4 result in a greater effect of this scenario in Cluster 4 regions.
6. Land Use Changes, Transit Fare Reduction Policies, Transit Service Improvements,
Mileage Fees and Region-wide TDM Programs
At the lowest end of the range of VMT reduction for this scenario are the Cluster 2 regions, where
auto drive-alone mode shares and population growth rates are lower than average. Despite
similar average values for auto mode shares and trip lengths, Clusters 2 and 3 show a
dramatically different response in this scenario. Cluster 3 shows a much higher impact due to a
higher population growth rate (almost double that of Cluster 2) and a lower existing auto trip cost.
Cluster 4 has a similar growth rate as Cluster 3, but higher auto drive-alone mode share and
lower trip costs, making the impact slightly higher.
33
-------
7. Land Use Changes, Transit Fare Reduction Policies, Transit Service Improvements,
Parking Fees, Mileage Fees and Region-wide TDM Programs
The inclusion of one or more automobile pricing strategies widens the range of impacts, thus
reflecting several regional differences. For the reasons discussed above, Cluster 4 regions show
the largest reduction in VMT, while Cluster 2 regions show the smallest reduction.
4.3 Conclusion
The intent of this study was to use reasonably comparable data in a consistent analysis
methodology to estimate the impacts of various transportation control measures. The results
indicate that there are many factors that contribute to the ability to reduce VMT and emissions.
The interactions of the different impacts in different regional types are strongly illustrated by the
graph of the individual cluster responses to each scenario. This implies that it will be difficult, if
not impossible, to identify a strategy or scenario that performs consistently across all metropolitan
regions. The attractiveness of TCM strategies is that they are most easily implemented and any
degree of behavioral change is valuable, especially in light of the supporting role or synergistic
effects when combined with other strategies. What works best in an individual region will be
subject to the willingness of the public and policy makers to support change. The broad interest
in the effectiveness of transportation and related strategies for addressing GHG represents an
important dynamic that has not been seen on this scale previously. The purpose of this study is
to inform that interest.
The results are reasonably compared to that of other national studies, although the study
methodologies were quite different. This suggests that at the national level, understanding the
potential for reductions may be moving toward consensus. At the regional level, where the
differences in characteristics are more important, a more detailed analysis will be more
informative. If the national level understanding represents a new baseline of what might be
achieved, the next logical step would be to conduct true regional analyses to compare to this
baseline and to measure localized reductions through corridor or subarea studies where the more
realistic impacts of land use, TDM, and transit changes can be seen.
While more detailed analysis at the regional level is desirable in order to gain a greater
understanding of how these strategies could play out for specific areas, using this methodology
with region-specific data and assumptions can provide an interim assessment of the effectiveness
of individual and grouped strategies. The results obtained 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 and results of this study are most applicable to support policy
discussions at both the regional and national level. As illustrated by the results, all strategies
have a contribution to make in efforts to reduce transportation-related emissions.
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The following are tables and figures illustrating the data and analyses for this study.
TRIMMS evaluates strategies that directly affect the cost of travel, like 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 a given mix of strategies are
subsequently calculated.
TRIMMS is a sketch planning tool that can be used to analyze many types of strategies at a
regional or sub-area scale. However, strategies involving construction of new infrastructure
such as new HOV/HOT lanes, new transit lines, and new bicycle/pedestrian facilities, can be
analyzed most effectively using a regional travel demand model. 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.
A-1
-------
Table A-1. Input Requirements and Output Capabilities for TCM Analysis Tools and Models
INPUTS
Population
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
OUTPUTS
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
Spreadsheet-Based Tools/Methods
Meta-
analysis
X
X
X
X
X
X
X
X
X
X
X
X
X
EPA
Commuter
model
X
X
X
X
X
X
X
X
X
X
X
X
X
TRIMMS
X
X
X
X
X
X
X
X
X
X
CCAP-
TEG
X
X
X
X
X
X
X
TCM
Tools
X
X
X
X
X
X
X
TCM
Analyst
X
X
X
X
X
X
X
X
Models
TDM
Evaluation
Model
X
X
X
X
X
X
X
STEAM
X
X
X
X
X
X
X
X
X
X
MARKAL-
MACRO
X
X
X
X
X
X
NEMS
X
X
X
X
X
X
X
X
X
X
X
A-2
-------
Table A-2. Assessment of Methods for Analyzing Travel Impacts of Transportation Control Measures (TCMs)
1
2
Methodologies /
Models
Travel Demand
Management
(TDM) Evaluation
Model
EPA's Commuter
model
Developer
COMSIS and
R.H. Pratt
Consultants for
FHWA
Sierra Research;
updated by
Cambridge
Systematics
Last
Update
1993
2005
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
TCMs
Population, mode
shares, trip lengths,
occupancy levels,
baseline VMT, baseline
speeds, mode choice
time and cost
coefficients, fleet mix,
and details about the
TCMs
Outputs
Change in VMT
and trips
Change in mode
shares, trips and
VMT, and
emissions impacts
(based on
emissions factors
in EPA's MOBILE
6.2 model)
Scale of Analysis
(sub-area, regional,
national)
Sub-area and with
limited capability,
regional
Sub-area and
regional, with some
adjustment
TCMs Modeling
Capability
Following TCMs cannot
be modeled:
« Land use strategies.
« Incentives for bicycle
use and pedestrians.
Travel time changes
(alternative work
hours or peak period
pricing).
Some pricing
strategies, e.g.,
distance-based
pricing and fuel price
changes.
Cannot model:
« Regional land use
strategies and any
TCMs that will
change regional
travel patterns.
TCMs that affect
vehicle speeds.
« Location-specific
strategies such as
area-wide pricing and
higher parking
charges in certain
areas.
Limitations
« Has not been
updated, although
user can input new
model coefficients.
« Does not account for
NMT trips.
« Only evaluates
home-based work
trips for large
regions.
Cannot model
distance-based
strategies.
Does not appear to
have been used
recently.
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.
A-3
-------
Table A-2. Assessment of Methods for Analyzing Travel Impacts of Transportation Control Measures (TCMs)
3
4
5
6
Methodologies /
Models
Trip Reduction
Impacts for Mobility
Management
Strategies
(TRIMMS) model
Surface
Transportation
Efficiency Analysis
Model (STEAM)
Transportation
Emissions
Guidebook (TEG)
TCM Tools
Developer
Center for Urban
Transportation
Research,
University of
South Florida
Cambridge
Systematics
Center for Clean
Air Policy
(CCAP)
Sierra Research
Last
Update
2009
2006
2006 (?)
Early
1990s
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
TCMs
Base case and
improvement case trip
tables, vehicle
occupancy, model
coefficients (trip time
and cost), mode shares,
and TCM characteristics
(in terms of change in
trip costs or travel time).
Number of trips by
mode, mode split, trip
lengths
Has separate
Transportation and
Emissions modules -
Outputs
Changes in mode
shares, trips, VMT,
and emissions
Change in VMT
and person miles
traveled, trips,
travel time, and
emissions
VMT and
Emissions
Changes in mode
share, vehicle-
trips, VMT, travel
Scale of Analysis
(sub-area, regional,
national)
Sub-area and
regional, with some
adjustment
Practitioner-oriented
sketch planning tool
to measure travel
impacts of regional
and employer-based
TCMs.
Regional and sub-
area/corridor
Regional and Sub-
area
More applicable at
regional scale; some
sub-area policies can
TCMs Modeling
Capability
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 TCMs can be
modeled.
Spreadsheets providing
rule of thumb guidance
on impacts of TCMs
based on literature;
most TCMs can be
modeled.
Wide range of strategies
can be modeled,
including land use
Limitations
Cannot model
regional land
use/smart growth
strategies
accurately. The
user will have to
make assumptions
about the effects of
land use strategies
on trip lengths or
travel times in order
to model these
strategies.
Much data and effort
required from
agencies to model
TCMs 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
assumptions.
Cannot estimate
mode shift or trip
reduction impacts.
Spreadsheet-based
sketch-planning tool.
A-4
-------
Table A-2. Assessment of Methods for Analyzing Travel Impacts of Transportation Control Measures (TCMs)
7
8
9
Methodologies /
Models
TCM Analyst
MARKAL (Market
Allocation)-MACRO
National Energy
Modeling System
(NEMS):
Transportation
Sector Module
(IRAN)
Developer
Texas
Transportation
Institute
US DOE and
EPA
Energy
Information
Administration,
US DOE
Last
Update
1994;
will hear
about
updates
from TTI
on
12/5/08
Used
internati
onally
and
currently
in use
2006
Inputs Required
trips, VMT, speed
Trips, distances,
speeds, emissions
factors, TCM details
Baseline VMT by
vehicle type, fuel costs
Vehicle fleet (includes
transit and freight), fuel
prices, fuel economy,
passenger miles,
change in user cost,
population, income, new
vehicles sales
Outputs
speeds, and
emissions
Changes in trips,
VMT, average
travel speeds, and
emissions
VMT, emissions,
and fuel demand
VMT, emissions,
and fuel demand
Scale of Analysis
(sub-area, regional,
national)
be modeled
Regional or sub-area
National
Census region and
national
TCMs Modeling
Capability
strategies, but cannot
model scenarios well
Pricing strategies cannot
be modeled
TCMs relevant at sub-
area, urban, or state
level cannot be modeled
Cannot model:
TCMs relevant at
sub-area, urban, or
state level
« TCMs involving mode
switching
« Includes useful
feedback effects, and
can be used to
validate national
Limitations
User must make
many assumptions
to calculate travel
impacts.
Emissions module
cumbersome to run.
« Elasticities and other
assumptions must
be defined by the
user.
« Land use and pricing
strategies cannot be
modeled
« Sketch planning tool.
« More
complicatedand not
as detailed as
NEMS.
Can only model
national level TCMs
such as fuel taxes,
emissions taxes.
Will model strategies
at the level of nine
Census regions, not
at urban or sub-
region level.
Can only models
TCMs that affect the
user cost of travel;
for others, some
meta-analysis is
A-5
-------
Table A-2. Assessment of Methods for Analyzing Travel Impacts of Transportation Control Measures (TCMs)
10
Methodologies /
Models
Spreadsheet
analysis with
elasticity factors
from literature
Developer
Last
Update
Inputs Required
Mode shares, trip costs
by mode, average VMT
Outputs
VMT change -
followed by
emissions analysis
Scale of Analysis
(sub-area, regional,
national)
Regional
TCMs Modeling
Capability
estimates
Without trip tables, land
use strategies are best
modeled this way
Limitations
required before
using NEMS.
« Change in modes
not easy to model
A-6
-------
Table A-3. Quantitative Estimates of Travel Activity Impacts of TCMs from Literature
Examples of Measures
Elasticity/ VMT Reduction %
Ridesharing Programs and Investments
Park-and-ride facilities
High-Occupancy Vehicle (HOV) lanes
Rideshare matching programs
CarpoolA/anpool incentives
Car-sharing
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
frequency
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,
amenities
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
Telecommuting
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.
A-7
-------
Table A-3. Quantitative Estimates of Travel Activity Impacts of TCMs from Literature
Examples of Measures
Elasticity/ VMT Reduction %
Pricing Policies
Area-wide road pricing/congestion pricing
Distance-based pricing or mileage fees
Peak period pricing/ variably priced lanes
Parking pricing/fees
High Occupancy Toll (HOT) lanes/toll
increases
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
-0.3
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
(Diversity)
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.
A-8
-------
Mode
Auto - Drive Alone
Direct
Cross-Price: Transit
Auto - Rideshare
Direct
Cross-Price: Transit
Vanpool
Direct: Peak
Direct: Off-peak
Cross-Price: Transit
Transit
Direct: Peak
Direct: Off-Peak
Cross-Price: Auto Drive Alone
Cross-Price: Auto Rideshare
Elasticity
Short run
-0.11
0.05
n/a
0.05
-0.16
-0.32
0.05
-0.10
-0.30
0.15
0
Long run
-0.22
0.05
n/a
0.05
-0.16
-0.32
0.05
-0.10
-0.30
0.15
0.15
Source
Litman (2010)
Litman (2010)
Litman (2010)
Litman (2010)
Notes
Table 22, pp.27 (TRIMMS
default); long run auto drive alone
elasticity assumed double of short
run elasticity
TRIMMS default uses the lower
ranges; long run elasticity
assumed same as short run since
no better information available
Assumed zero since no
information is available
Same long run elasticity as auto-
drive alone assumed
TRIMMS default; no information
about short run vs. long run
vanpool elasticities, so assumed
same
TRIMMS default
TRIMMS default; no information
about short run vs. long run
transit elasticities, so assumed
same
TRIMMS default
TRIMMS default uses the lower
ranges
Long run elasticity assumed same
as auto drive alone
Source; Adapted from Concas and Winters (2009) and from TRIMMS model version 2.0 received from CUTR on July 15, 2009 pp 44-46
A-9
-------
Mode
Auto - Drive Alone
Direct
Cross: Auto -Rideshare
Cross: Transit
Auto - Rideshare
Direct
Cross: Auto -Drive Alone
Cross: Transit
Vanpool
Direct
Cross-Price: Auto -Rideshare/Drive Alone
Cross: Transit
Transit
Direct
Cross: Auto -Drive Alone
Cross: Auto -Rideshare
Elasticity
Peak
-0.225
0.037
0.036
-0.303
0.030
0.030
-0.60
n/a
0.032
-0.129
0.010
0.032
Off peak
-0.170
0.001
0.001
-0.189
0.000
0.000
n/a
n/a
0.000
-0.074
0.000
0.000
Notes
TRIM MS default
assumptions
Source; Utman (2010)Table 31, pp. 35
Parking Elasticities
Trip Purpose
Commuting
Auto - Drive
Alone
-0.08
Auto-
Rideshare
-0.02
Transit
-0.02
Slow Mode
-0.02
Source: Litman (2010), Table 13, pp. 17
A-10
-------
Table A-7. Summary of Populations, Calculations & Correction Factors
Summary of Populations, Calculations & Correction Factors
Cluster!
Pop > 2.9mi
o
o
'o
ro 1
(/) 1 1
; Trns > 9%
o
-5 O
§ Q
Cluster 2
Pop > 2.9mil
o
D)
Q
ro
; Trns < 9%
Q)
(0
OT
Clusters
Pop > 1.5mil
Q)
Q)
Q
; Trns > 4%
Portland
Cluster 4
Pop > 1.5mil
o
"c
E
ro
o
ro
; Trns > 4%
"ro ±±
tt> O
Clusters
Pop >
Q.
E
750K
i!
250K
Fresno
X
o
(D
"oo
o
O
Cluster?
Pop < 250K
c
o
-t-«
D)
3
CO
Wilmington
POPULATIONS & TRANSIT MODE SHARES
2000 Census Pop
2007 FHWA UA Pop
MPO Provided Values1
Modeled Total BY Pop
Modeled Working BY Pop
Modeled Total FY Pop
Modeled Working FY Pop
TRIMMS Modeled Values2
TRIMMS BY Pop Input
TRIMMS FY Pop Input
TRIMMS BY TDM Pop Input
TRIMMS FY TDM Pop Input
Transit Mode Shares3
CTPP 2000 (Urbanized Area)
MPO Provided Values
VMT Shares4
Share of FHWA estimated national
VMT based on FHWA 2007 Pop and
CTPP 2000 transit share
2,995,291
3,162,000
3,932,927
4,332,000
7,159,379
3,282,403
9,031,498
5,016,501
6,808,844
3,547,408
8,282,368
4,315,114
3,282,403
5,015,512
984,721
2,507,756
3,547,408
4,315,114
1,064,222
2,157,557
16.0%
5.3%
13.0%
4.5%
17%
2,674,996
2,951,000
3,089,035
2,399,837
3,984,753
3,098,248
2,712,338
3,108,000
3,695,516
1,934,713
4,988,135
2,789,293
2,399,837
3,098,248
719,951
1,549,124
1,934,713
2,789,293
580,414
1,394,647
4.0%
1.5%
8.0%
2.9%
22%
1,984,585
2,184,000
2,685,000
2,120,000
4,388,529
3,322,116
1,582,863
1,805,000
1,961,153
1,032,246
3,097,402
1,799,152
2,120,000
3,322,116
636,000
1,661,058
1,961,153
3,097,402
588,346
1,548,701
5.0%
2.0%
7.0%
3.3%
6%
1,394,615
1,858,000
1,936,006
1,407,816
3,349,000
1,546,000
887,916
970,000
1,933,000
1,029,000
2,820,000
1,575,000
1,407,816
1,546,000
422,345
773,000
1,029,000
1,575,000
308,700
787,500
3.0%
3.3%
4.0%
1.3%
7%
971,282
1,035,000
828,683
949,000
1,103,539
533,378
1,499,124
745,973
1,312,000
683,000
2,647,000
1,332,000
533,378
745,973
160,013
372,987
683,000
1,332,000
204,900
666,000
1.95%
1.0%
2.69%
1.2%
12%
554,815
641,000
420,081
488,000
693,863
745,000
992,997
415,840
1,402,217
609,437
415,840
609,437
124,752
304,719
862,903
429,939
1,306,460
872,930
823,147
422,942
868,076
446,027
429,939
872,930
128,982
436,465
422,942
446,027
126,883
223,014
2.09%
1.2%
0.66%
1.0%
2.89%
0.8%
18%
105,573
135,000
161,079
194,000
147,000
116,000
212,000
174,000
226,961
112,845
430,154
221,344
116,000
174,000
34,800
87,000
112,845
221,344
33,854
110,672
2.0%
0.7%
1.0%
0.02%
17%
CALCULATIONS & CORRECTION FACTORS
Trip Rate Calculations5
2007 Daily VMT (FHWA)7
MPO Provided Auto Trip Length
Calculated Daily # of Trips
Calculated Daily Trip Rate
Researched Trip Rate
Correction Factors6
Modeling Correction Factor-BY
Modeling Correction Factor-FY
Trips Correction Factor
Population Correction Factor
FINAL Correction Factor-BY
FINAL Correction Factor-FY
FINAL TDM Correction Factor
68,939,000
11.8
5,842,288
1.8
3.0
97,860,000
10.8
9,061,111
2.1
2.18
1.80
1.48
1.84
5.95
4.91
1.84
1.92
1.92
1.05
1.57
3.15
3.15
1.57
68,442,000
7.12
9,612,640
3.3
71,358,000
12.8
5,574,844
1.8
1.29
1.29
1.63
2.00
4.19
4.19
2.00
1.91
1.79
0.90
2.06
3.53
3.30
2.06
52,735,000
11.4
4,625,877
2.1
3.8
35,211,000
6.6
5,335,000
3.0
3.1
1.27
1.32
1.90
1.94
4.67
4.87
1.94
1.00
1.00
1.57
1.94
3.04
3.04
1.94
34,838,000
12.5
2,787,040
1.5
4.1
22,317,000
7.1
3,143,239
3.2
1.38
2.17
2.04
1.86
5.22
8.22
1.86
1.88
1.79
1.62
2.13
6.48
6.18
2.13
26,900,000
11.04
2,436,594
2.4
35,108,000
7.26
4,835,813
5.1
4.2
2.07
2.01
1.18
2.14
5.21
5.06
2.14
1.92
1.99
2.10
1.77
7.12
7.37
1.77
11,967,000
11.8
1,014,153
1.6
2.0
16,050,000
8.1
1,981,481
4.1
3.5
16,742,000
8.1
2,066,914
2.8
2.39
2.30
1.00
2.23
5.33
5.13
2.23
2.01
1.50
1.73
2.04
7.06
5.27
2.04
1.95
1.95
1.39
1.86
5.02
5.02
1.86
3,211,000
11.8
272,119
2.0
5,273,000
3.42
1,541,813
7.9
4.3
1.27
1.22
1.01
1.92
2.45
2.36
1.92
2.01
1.94
2.13
2.02
8.63
8.34
2.02
(1) These are the population figures provided by the MPOs.
(2) These are the values that were used for the TRIMMS model runs.
(3) These are the transit shares available from the 2000 CTPP Urbanized Areas and the shares provided by the MPOs for their whole modeled areas.
(4) This is the share of national VMT (estimated by FHWA for 2007 VMT); dividing cities throughout the country into clusters based on their FHWA 2007 Urbanized Area population and 2000 CTPP transit shares.
(5) Trip rates were calculated using 2007 VMT estimated by FHWA, trip lengths provided by the MPOs and the 2007 Urbanized Area population estimated by FHWA. Trip rates were also researched for each MPO using various documents and
studies available on the MPOs' websites. Where a researched trip rate was found, it was used for the correction factors; otherwise the calculated trip rate was used.
(6) To adapt the results obtained from the TRIMMS model for our analysis, we applied the following three correction factors. (1) All TRIMMS model runs were originally run using working population instead of total population. The MODELING
CORRECTION FACTOR adjusts for this difference. (2) Since TRIMMS focuses on employee travel behavior, it always assumes a trip rate of two trips per person per day (assuming a worker goes from his home to the employer site and back
home). Our study covers all trip purposes and attempts to nullify this assumption by adjusting the trip rate (i.e. by multiplying the TRIMMS model outputs by the best known trip rate for each 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. To nullify this assumption, the TRIMMS model outputs were also multiplied by the ratio of Total MPO Provided Population to TRIMMS Affected Population.
(7) Data on daily VMT from FHWA's Highway Statistics (2007) does not include travel on interstates. These data were used to estimate daily trip rate where trip rates were not available and for calculating each cluster's share of national VMT.
A-11
-------
Table A-8. Data from MPOs and Other Sources
MPO Provided Values -All TRIM MS Model Inputs
Populations
Modeled Total Pop
Modeled Working Pop
Change in Total Pop
Change in Working Pop
7,159,379 9,031,498
3,282,403 5,015,512
26% 1,872,119
53% 1,733,109
6,808,844 8,282,368
3,547,408 4,315,114
22% 1,473,524
22% 767,706
3,089,035 3,984,753
2,399,837 3,098,248
29% 895,718
29% 698,411
3,695,516 4,988,135
1,934,713 2,789,293
35% 1,292,619
44% 854,580
2,685,000 4,388,529
2,120,000 3,322,116
63% 1,703,529
57% 1,202,116
1,961,153 3,097,402
1,032,246 1,799,152
58% 1,136,249
74% 766,906
1,936,006 3,349,000
1,407,816 1,546,000
73% 1,412,994
10% 138,184
1,933,000 2,820,000
1,029,000 1,575,000
46% 887,000
53% 546,000
Mode Shares
Auto-drive alone
Auto-rideshare
Public transit
Cycling
Walking
61.54
21.28
5.29
1.70
10.19
Other
62.30
20.20
6.10
1.50
9.90
75.50
20.00
4.50
75.20
19.60
5.20
52.00
42.90
1.50
0.20
2.60
0.80
51.50
43.70
1.60
0.20
2.40
0.60
43.40
43.30
2.90
5.20
5.20
43.70
40.00
4.30
6.00
6.00
51.00
44.00
2.00
3.00
53.00
42.00
2.00
3.00
45.60
40.70
3.30
1.00
6.50
3.00
44.50
40.20
4.20
1.10
7.20
2.90
91.90
3.30
2.40
2.40
88.80
3.60
3.80
3.80
42.46
47.26
1.29
4.49
4.50
41.75
47.50
2.45
4.15
4.15
Trip Lengths (miles)
Auto-drive alone
Auto-rideshare
Public transit
11.8
11.8
11.8
11.9
11.9
11.9
Cycling '
Walking
Other4
10.8
10.8
10.3
10.9
10.9
10.4
7.1
6.0
6.9
1.7
0.9
7.5
7.0
6.5
7.5
1.7
0.9
7.2
12.8
12.8
12.8
12.8
12.8
12.8
11.4
11.4
11.4
13.3
13.3
13.3
6.6
6.6
6.9
2.5
1.4
2.7
6.2
6.2
6.9
2.6
1.3
2.5
12.5 12.5
12.5 12.5
12.5 12.5
7.1
6.3
9.7
2.0
2.0
7.1
7.7
6.8
11.6
2.0
2.0
7.7
Travel Times (minutes)
Auto-drive alone
Auto-rideshare
Public transit
Cycling
Walking
Other -
26.9
26.9
36.8
18.3
32.5
32.5
36.8
18.3
30.6
30.6
46.1
33.5
33.5
45.5
16.4
24.3
29.3
27.1
50.5
17.1
32.7
50.5
17.1
22.3
0.0
35.1
28.9
32.1
35.1
18.0
22.5
26.6
38.1
17.0
28.9
34.2
49.0
21.9
21.1
24.5
38.2
19.7
26.9
31.3
38.2
19.7
22.8
26.1
41.5
28.8
33.0
41.5
15.4
14.0
13.0
50.0
17.7
17.7
16.0
15.0
49.0
17.7
17.7
Travel Costs
Parking - drive alone
Parking - rideshare
Trip Cost - drive alone
Trip Cost - rideshare
Transit Fare
9.40
7.00
2.71
2.02
2.00
15.40
11.80
2.70
2.01
4.20
11.00
8.79
1.30
1.02
2.30
16.35
13.00
1.31
1.01
4.20
4.00
4.00
5.94
5.94
0.96
0.81
2.50
1.22
1.14
4.52
10.91
10.91
1.84
1.84
2.50
19.76
19.76
1.84
1.84
6.10
5.30
4.00
1.80
0.91
0.75
9.60
7.30
2.50
1.90
1.82
5.71
2.48
0.60
0.26
1.21
12.93
9.44
0.81
0.59
2.94
4.50
3.40
1.90
1.40
2.25
7.38
5.70
2.34
1.80
4.71
10.00
4.00
0.92
0.33
1.35
16.40
6.60
1.25
0.50
2.80
Notes:
BY: Base Year; FY: Future Year
Total population was used to analyze all scenarios, except Scenario 1 (TDM), which used Working Population
FY parking charges notavailable in most cases; assumed to increase by 2% based on guidance from Metropolitan Transportation Commission, San Francisco
F Y transit feres not available in many cases; assumed to increase with inflation at 3% per year
Where data were notprovided, FY travel times for drive alone and rideshare modes based on Texas Transportation Institute's projections for change in Travel Time Index between 2003 and 2030
BY transit feres and parking charges were obtained from MPO websites when not provided or from Colliers International Parking Rate Survey, available at: Colliers International Parking Rate Survey instead:
http://www.colliers.com/content/globalcolliersparkingratesurvey2009.pdf
DefeultTRIMMStrip lengths used where trip length was not available by mode
Where FY trip lengths and auto operating costs notprovided, these were assumed same as BY
Data notprovided by MPOs are marked by"-"
(1) Used TRIMMS defeult public transit trip length of 12.2 where no data was provided by the MPO
(2) Used TRIMMS defeult cycling trip length of 2.9 where no data was provided by the MPO
(3) Used TRIMMS defeultwalking trip length ofO.9 where no data was provided by the MPO
(4) Used TRIMMS defeult Other trip length of 12.2 where no data was provided by the MPO; for Salt lake City, Rochester, Raleigh-Durham and Wilmington, used auto drive alone trip length for "other" mode since TRIMMS defeult appeared too high for these
cities
(5) In many cases, walk and bike travel times were not available; therefore, for consistency across cities, data from the American Community Survey 2005-2007 were used, available at: http://ctpp.fransporfetion.org/profles_2005-2007/ctpp_profles.html. 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 above under "other" can be considered average across these modes
A-12
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A-14
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Table A-9. National Emissions Reductions
1
NOX
VOC
PM25
CO2 equivalent
2
NOX
VOC
PM2.5
CO2 equivalent
3
NOX
VOC
PM25
CO2 equivalent
4
NOX
VOC
PM25
CO2 equivalent
5
NOX
VOC
PM25
CO2 equivalent
6
NOX
VOC
PM25
CO2 equivalent
7
NOX
VOC
PM25
CO2 equivalent
Actual (grams per day)
2030
2050
Percentage
2030
2050
Region-wide TDM
1,158,438
739,609
101,349
2,914,224,009
3,764,014
2,562,147
360,877
10,472,471,706
0.10%
0.09%
0.10%
0.10%
0.26%
0.25%
0.26%
0.26%
Land use changes + TDM
12,084,721
7,837,082
1,051,190
30,177,541,913
42,656,208
2,562,147
360,877
117,765,791,643
1 .00%
0.98%
1.01%
1.01%
2.93%
2.86%
2.96%
2.97%
Land use changes + Transit fare reduction + TDM
16,762,872
10,934,000
1,454,970
41,743,724,738
60,434,817
42,166,321
5,743,848
166,274,160,854
1 .39%
1 .36%
1 .40%
1 .40%
4.16%
4.08%
4.18%
4.19%
Land use changes + Transit fare reduction + Transit service
improvements + TDM
17,324,704
11,344,808
1,501,521
43,061,338,160
62,197,472
43,717,356
5,895,642
170,539,394,836
1 .43%
1.41%
1 .44%
1 .44%
4.28%
4.23%
4.29%
4.30%
Land use changes + Transit fare reduction + Transit service
improvements + Parking Fees + TDM
35,232,500
23,248,699
3,044,728
87,246,215,207
99,977,456
69,056,024
9,536,337
276,828,363,309
2.91%
2.90%
2.92%
2.92%
6.87%
6.68%
6.94%
6.98%
Land use changes + Transit fare reduction + Transit service
improvements + Mileage Fees + TDM
23,161,418
15,026,958
2,014,373
57,825,922,017
89,739,448
61,482,738
8,584,360
249,185,850,704
1 .92%
1 .87%
1 .93%
1 .94%
6.17%
5.95%
6.25%
6.28%
Land use changes + Transit fare reduction + Transit service
improvements + Parking Fees + Mileage Fees + TDM
41,063,105
26,869,067
3,559,935
102,101,807,189
125,781,137
85,645,173
12,058,051
350,512,043,690
3.40%
3.35%
3.42%
3.42%
8.65%
8.29%
8.78%
8.83%
CO2 equivalent = [CO2 + 21*(CH4) + 310*(N2)]
A-15
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Figure A-1. VMT Reduction for Each Scenario for Cluster 1 Regions
20,000,000
18,000,000 -\-
16,000,000
14,000,000
I 12,000,000
tJ
| 10.000.000
I 8,000,000
6,000,000
4,000,000
2,000,000
,-TL
4
Scenario
Scenarios:
1- Region-wide TDM
2 - TDM + land use changes
3 - TDM + land use changes + transit fare reduction
4 - TDM + land use changes + transit fare reduction + transit service improvements
5 - TDM + land use changes + transit fare reduction + transit service improvements + parking fees
6 - TDM + land use changes + transit fare reduction + transit service improvements + mileage fees
7 - TDM + land use changes + transit fare reduction + transit service improvements + parking fees + mileage fees
A-16
-------
Figure A-2. VMT Reduction for Each Scenario for Cluster 2 Regions
fi nnn nnn
E nnn nnn
§
s
H! ^ nnn nnn -
i_
5
*> nnn nnn
Innn nnn
DBase Year
D Future Year
1 1
i
1
n
i
2
)
i
S
<
fcer
f
iari*
>
c
I
(
5
i
7
Figure A-3. VMT Reduction for Each Scenario for Cluster 3 Regions
10,000,000
8,000,000
6.000.000
4,000,000
2.000.000
DBase Year
a Future Year
4
Scenario
A-17
-------
Figure A-4. VMT Reduction for Each Scenario for Cluster 4 Regions
6,000,000
4,000,000
2,000,000 -
Figure A-5. VMT Reduction for Each Scenario for Cluster 5 Regions
o nnn nnn
Icnn nnn
I
u
3
"2 t nnn nnn
K
cfifi nno -
-
QBose Year
D Fulure Year
1
F
I
Seer
!
ario
1
t
i
A-18
-------
Figure A-6. VMT Reduction for Each Scenario for Cluster 6 Regions
1200,000
1,000,000
800,000
600,000
400,000
200,000
dBase Year
DFuture Year
3
Scenario
Figure A-7. VMT Reduction for Each Scenario for Cluster 7 Regions
450,000
400,000
A-19
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A-20
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Survey Data Collection Form for Analysis of VMT Impacts from
Transportation Control Measures
INTRODUCTION TO THE STUDY
ICF International is conducting a study for the US EPA Office of Transportation and Air Quality to
determine potential reduction in emissions from criteria, precursor, and Greenhouse Gas pollutants
based on a reduction in VMT. For the purposes of this study, reduction in VMT is in response to
the use of Transportation Control Measures (TCM) and other strategies that may be implemented in
order to reduce the use of single occupancy vehicles. Your region has been asked to participate in
this effort to serve as a representative of other regions of similar size and transit use. The analysis
completed within this project will allow an estimation of reduction in VMT at the regional level that
can be aggregated up to the national level for corresponding emissions reduction.
A sketch planning model will be used to develop estimated VMT reductions using data from the
regional travel demand model used to support long range transportation planning. Because your
region serves as a surrogate for many other similar regions, only general model data will be required.
The base case will be developed from the available existing condition data and the future year will
be represented by the most recently adopted plan. "Baseline information " refers to model data
which does not include any specific strategies beyond the addition of infrastructure. Any insights or
information that you can provide from regional analysis of TCM strategies will support a greater
understanding of the results of our analysis.
We greatly appreciate your willingness to assist in this effort. The survey has been developed
through interaction with MPO staff and others in order to most clearly convey the information
needed. Please respond to as many of the individual questions as possible; however, where the
requested information is not available, simply respond NA. If you need additional information or
clarification in order to respond, our staff is available via email or phone to provide support. We
hope that this participation will result in lessons learned that you will also find informative and
useful. At the end of the project, we will communicate the results to the entire group of participants.
SECTIONI.
Baseline MPO Data
Background Information
Names of cities/towns/entire counties included in region under MPO jurisdiction
Total population in modeled area
Total working population (16 and over) in modeled area
What is the base year for your travel demand model?
[The base year is the existing year for current planning in the modeled area].
What is the future/horizon year for your travel demand model?
B-21
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[The future year is the year of your adopted long range plan for the modeled area; e.g. 2035.].
Can you provide data for any intermediate years between your base year and future year? Intermediate years
may be any of those years considered in the air quality conformity process.
Please provide the following information about all trips made using light duty vehicles in your region for your
model base year, future/horizon year, and any intermediate years considered. Please do not include
commercial vehicle trips. If data are not available, please leave cells blank.
Note that the data we are looking for must be drawn from your travel model related to long range planning in your region. If you
have additional reports or sources for observed data through surveys, etc. please provide the references or web links separately.
These will help inform our analysis. Instructions for each individual subsection are provided below.
1) Current mode share (%)
In our analysis, mode share is defined as the percentage of population traveling by each mode, i.e. person-
trips rather than vehicle-trips. If mode shares are not available for all modes, please leave corresponding cells
blank. The total of all mode shares you provide must equal 100%. In the table below, please provide
baseline information for your future year, and an intermediate year for which you may have data. Base case
information for each year includes data on regional light duty trips assuming all existing and committed infrastructure
in place and your projections of population, jobs, and housing, but no future policy scenarios or strategies. Details
about scenarios or strategies that have been proposed and modeled in your region will be required in Section
II of this questionnaire.
If you are entering data for combined modes (e.g. Walking + Cycling, Auto-rideshare + Vanpool), please fill
in the value in a relevant row above and provide a brief explanation in Comments. Use the same definitions
of modes you enter here for the rest of the data items in this section.
Mode share (%)
Auto-drive alone
Auto-rideshare
Vanpool
Public transit
Cycling
Walking
Other
Base year
Future year ~ base case
Intermediate year ~ base case
Please specify the modes you have provided data for in the "Other" field
Comments:
B-22
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2) Average Trip Length (Miles)
In the table below, please provide baseline information for your future year, and an intermediate year for
which you may have data. If average trip lengths are not available for all modes, please leave corresponding
cells blank.
Average Trip
Length (miles)
Auto-drive alone
Auto-rideshare
Vanpool
Public transit
Cycling
Walking
Other
Base year
Future year ~ base case
Intermediate year ~ base case
3) Average Trip Travel Time (Minutes)
In the table below, please provide baseline information for your future year, and an intermediate year for
which you may have data. If peak and off-peak data are not available, please enter the average values for the
modes applicable. If average travel times are not available for some modes, leave corresponding cells blank.
Average Travel
Time (Minutes)
Auto-drive alone
Auto-rideshare
Vanpool
Public transit
Cycling
Walking
Other
Base year
Peak
Off-peak
Average
Future year
Peak
Off-peak
Average
Intermediate year
Peak
Off-peak
Average
4) Average Vehicle Occupancy (Number of persons)
In the table below, please provide baseline information for your model base year, horizon year, and any
intermediate years for which you have data. If peak and off-peak data are not available, please enter the
average values for the modes applicable. If vehicle occupancy numbers are not available for some modes,
leave corresponding cells blank.
B-23
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Avg. Vehicle
Occupancy
(persons)
Auto-rideshare
Vanpool
Bus
Base year
Peak
Off-peak
Average
Future year
Peak
Off-peak
Average
Intermediate year
Peak
Off-peak
Average
5) Peak and Off-peak Trips
Please state which hours you define as AM peak, PM peak, and off-peak
AM:
PM:
Off-peak:
Percentage of total trips
in peak hours (%)
Total trips in peak
hours
Total trips in off-peak
hours
Base year
Future year ~ base case
Intermediate year ~ base case
6) Trip and Parking Costs
Please enter the current passenger trip costs by mode in your region. Trip costs do not include parking costs
or other costs such as tolls, fees, and peak hour charges. If trip costs are not available, go to Section 7 to enter data
from which these can be estimated.
Trip Costs (Current $ per trip)
Average Trip
Costs
Auto-drive alone
Auto-rideshare
Vanpool costs
Public transit fare
Cycling
Other
Base year
Future year ~ base case
Intermediate year ~ base case
B-24
-------
Automobile Parking Costs (Current $ per auto per day)
Average Parking
Costs
Auto-drive alone
Auto-rideshare
Base year
Future year ~ base case
Intermediate year ~ base case
Other Auto Trip Costs not included in Parking Costs, e.g., tolls, peak hour fees, etc.
(Current $ per trip)
Average Parking
Costs
Auto-drive alone
Auto-rideshare
Base year
Future year ~ base case
Intermediate year ~ base case
Check the cost categories below that your model uses to calculate trip costs and briefly state the assumptions
used for future years
v'
Cost categories
Fuel
Insurance
Maintenance/repairs
Ownership costs, i.e. vehicle costs,
registration and licensing fees
Other; please specify
Assumptions for future years
B-25
-------
7) Vehicle Mileage data to Calculate Operating Costs
Average Mileage
(Miles Per Gallon)
Automobiles
Vans
Bus
Base year
Future year ~ base case
Intermediate year ~ base case
8) Detailed Trip Travel time Information
If access times and in-vehicle travel times are not available separately, please leave cells blanks. Average travel
times by mode will be assumed from the values you entered in part 3 above.
Trip Travel
Time (Minutes)
Auto-drive alone
Auto-rideshare
Vanpool
Public transit
Cycling
Walking
Other
Base year
Access time
travel time
Future year
Access time
travel time
Intermediate year
Access time
travel time
B-26
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SECTIONII.
Strategies Modeled by MPOs and Assumptions for Future Years
Assumptions for regional policies and investments: Check below which strategies have been included in
your base year, future year, and intermediate year. Add rows to add additional key scenarios comprising more
than one strategy
Policy Assumptions
Transit infrastructure or service
improvements [1]
Higher auto parking charges
[2]
Congestion fees, tolls or toll
increases for autos [3]
Land use strategies (TOD, mixed
use, higher density) [4]
Employer-initiated TDM policies
and incentives [5]
Other policies or investments
not mentioned
[specify, e.g. HOV lanes,
bike/ped. Incentives, roadway
improvements]
Package comprising more than
one of these strategies
[specify, e.g. 1+2, 2+3+4, etc.]
Base year
Future year
Intermediate year
B-27
-------
Please enter below the data results from your own modeling studies on the estimated impacts of the above
scenarios.
Mode Share Changes Resulting/mm Packages of Measures You Have Modeled in Your Region
Modeled packages of
strategies
Future year considered
Strategy assumptions
(e.g. reduction in transit
travel time of X%,
reduction in auto trip
lengths by Y%, increase
in auto driving costs by
Z%, etc.)
MODEL OUTCOMES
New Mode Shares (%)
Auto-drive alone
Auto-rideshare
Vanpool
Public transit
Cycling
Walking
Other
Package 1
Package 2
Package 3
Package 4
Package 5
If you have modeled the following strategies individually, and not in combined scenarios, please provide your
estimates of impacts in the tables below.
(1) Increase in Driving Costs
(2) Change in Transit Fares
(3) Change in Transit Access or Travel Time
(4) Change in Auto Travel Time
In the top section of each table, provide the values that you assumed or that were calculated through your
models, and the mode shares reflecting these changes. All results you record below should be relative to the
base case scenario for each year.
Note: if you did not model the strategies below individually but only as part of packages of strategies, please
enter the estimates of resulting mode share changes in the table above.
B-28
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Mode Share Changes Resulting/ram Increase in Driving Costs (higher tolls, par king, or congestion charges)
Modeled change in
pricing policy
% change in parking cost
% change in tolls,
congestion fee
% change in fuel price
assumed
MODEL OUTCOMES
New Mode Shares (%)
Auto-drive alone
Auto-rideshare
Vanpool
Public transit
Cycling
Walking
Other
Base year
Future year
Intermediate year
Mode Share Changes Resulting/ram Change in Transit Fares
Modeled change in
pricing policy
% change in transit fares
MODEL OUTCOMES
New Mode Shares (%)
Auto-drive alone
Auto-rideshare
Vanpool
Public transit
Cycling
Walking
Other
Base year
Future year
Intermediate year
B-29
-------
Mode Share Changes Resulting/mm Change in Transit Access or Travel Time
Modeled change in
pricing policy
% change in transit access
time
% change in transit travel
time
MODEL OUTCOMES
New Mode Shares (%)
Auto-drive alone
Auto-rideshare
Vanpool
Public transit
Cycling
Walking
Other
Base year
Future year
Intermediate year
Mode Share Changes Resulting/mm Change in Auto Travel Time
Modeled change in
pricing policy
% change in auto travel
time
MODEL OUTCOMES
New Mode Shares (%)
Auto-drive alone
Auto-rideshare
Vanpool
Public transit
Cycling
Walking
Other
Base year
Future year
Intermediate year
B-30
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If you would like to provide any comments about VMT and emissions reduction strategies/scenarios you
have considered in your region, please enter them here.
Please list the tool(s) you used to conduct your analyses along with a brief description:
B-31
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SECTIONIII
Elasticity Values Assumed for Sensitivity to Trip Costs and Travel Times
Demand elasticity is defined as the percentage change in the use of a particular transportation mode resulting
from a 1% change in an attribute such as price, travel time, or frequency of service offerings. Elasticity values
should be entered with a positive sign or a negative sign. Please enter transportation price elasticities by
mode, trip purpose and time of day below. If these values are not available by trip purpose or for peak/off-
peak hours, simply provide average elasticity values. Please fill in as much information as you can, even if it is
incomplete. If you have studies or surveys for your region that we can use to estimate these values, please email them to us with
your responses to this survey.
Elasticity with respect to Parking/Driving Costs
Trip Purpose
Commuting
Business
Education
Other
Modes
Auto
Rideshare
Public transit
Other
Transit Elasticities: In the table below, please provide elasticities of transit ridership
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
Peak
Off-Peak
Average
Travel Time Elasticities
Auto
Rideshare
Public transit
Peak
Off-Peak
Average
B-32
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Ifcross-elasticities across modes are available please provide these in the table below, otherwise move on to Section III.
Peak
Off-peak
Average
Auto
Rideshare
Public transit
Auto
Rideshare
Public transit
Auto
Rideshare
Public transit
Auto
Auto
Auto
Rideshare
Rideshare
Rideshare
Public transit
Public transit
Public transit
THANK YOU VERY MUCH FOR YOUR TIME.
YOUR PARTICIPA TIONIS GREA TLYAPPRECIA TED
B-33
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B-34
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