Potential Changes in Emissions 

Due to Improvements in Travel 

Efficiency - Final Report 

Office of Transportation and Air Quality 

EPA-420-R-11-003 

March 2011 
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-420-R-11-003 

March 2011 
 

Acknowledgements 

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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Executive Summary 

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 2010a). 

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 

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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 Percent Emissions Reduction 

2030 2050 

CO2 

equivalent* PM2.5 NOx VOC 

CO2 

equivalent* PM2.5 NOx VOC 

1- Region-wide TDM 0.10% 0.10% 0.10% 0.09% 0.26% 0.26% 0.26% 0.25% 

2 - TDM + land use changes 1.01% 1.01% 1.00% 0.98% 2.97% 2.96% 2.93% 2.86% 

3 - TDM + land use changes + 

transit fare reduction 1.40% 1.40% 1.39% 1.36% 4.19% 4.18% 4.16% 4.08% 

4 - TDM + land use changes + 

transit fare reduction + 

transit service 

improvements 

1.44% 1.44% 1.43% 1.41% 4.30% 4.29% 4.28% 4.23% 

5 - TDM + land use changes + 

transit fare reduction + 

transit service 

improvements + parking 

fees 

2.92% 2.92% 2.91% 2.90% 6.98% 6.94% 6.87% 6.68% 

6 - TDM + land use changes + 

transit fare reduction + 

transit service 

improvements + mileage 

fees 

1.94% 1.93% 1.92% 1.87% 6.28% 6.25% 6.17% 5.95% 

7 - TDM + land use changes + 

transit fare reduction + 

transit service 

improvements + parking 

fees + mileage fees 

3.42% 3.42% 3.40% 3.35% 8.83% 8.78% 8.65% 8.29% 

* CO2 equivalent = [CO2 + 21*(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. 

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viii 
 

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 for TCM 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 for TCM 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 

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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 2010a). 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 2010a). 

Table 1. Transportation Greenhouse Gas Emissions by Mode, 1990 and 2008 

Carbon Dioxide Methane Nitrous Oxide 

Percent change 1990–2008 

Highway Total 27.2% -61.9% -44.8% 

Cars, light trucks, motorcycles 17.0% -62.5% -46.0% 

Medium & heavy trucks and buses 67.9% -50.0% 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 (ARB) to set 

GHG reduction targets for regions of the state and to work with the metropolitan planning 

1 
 

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 VMTreducing 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 A1 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 can be combined with emission factors from 

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 

2 
 

 

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 

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, ecodriving 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. 

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. 

 Emissions factors from MOVES2010 were used to determine the potential emissions reduction 

based on the estimated VMT reduction in each cluster under each scenario. 

3 
 

 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. 

4 
 

Chapter 2: Consideration of Existing Research 

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 Cooler (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 

5 
 

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. 

2.2 Efforts To Analyze Emissions Reductions 

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 ARB 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 

6 
 

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 metaanalysis 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 

7 
 

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. 

2.3 Impacts of TCMs and Elasticity Estimates from Literature 

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; Rodier 2008; 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 

8 
 

 

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. Sketchplanning 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 sketchplanning 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 

9 
 

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
 
 

 

Chapter 3: Analysis Methodology 

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 regionwide 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
 
 

 

 

 

 

 

 

 

Table 2. U.S. Metropolitan Regions in Clusters 

Cluster Definition Total Daily VMT 

Share of National 

Daily Urban VMT 

Average Share 

of Transit 

Number of U.S. 

Cities Represented 

Cluster 1 [pop > 2.9 mil; 

transit share > 9%] 846,523,000 17% 15.6% 6 

Cluster 2 [pop > 2.9 mil; 

transit share 9% or less] 1,084,936,000 22% 3.9% 9 

Cluster 3 [pop 1.5 - 2.9 

mil; transit share > 4%] 314,828,000 6% 6.4% 7 

Cluster 4 [pop 1.5 - 2.9 

mil; transit share 4% or 

less] 

368,438,000 7% 2.5% 8 

Cluster 5 [pop 750,000 -

1,499,999] 585,546,000 12% 3.5% 21 

Cluster 6 [pop 250 -

749,999] 914,805,000 18% 1.8% 87 

Cluster 7 [pop < 250,000] 832,103,000 17% 1.6% 313 

SUM TOTAL 4,947,179,000 100% 2.0% 451 

Table 3. Cluster Definitions and Representative Areas 

Cluster Definition Representative Areas 

1 Population ≥2.9 million 

High Transit Share (>9%) 

San Francisco, CA 

Washington, DC 

2 Population ≥2.9 million 

Low Transit Share (9% or less) 

San Diego, CA 

Seattle, WA 

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

High Transit Share (>4%) 

Portland, OR 

Denver, CO 

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

Low Transit Share (4% or less) 

Sacramento, CO 

Salt Lake City, UT 

5 Population 750,000-1,499,999 Memphis, TN 

Raleigh-Durham, NC 

6 Population 250,000-749,999 

Fresno, CA 

Knoxville, TN 

Rochester, NY 

7 Population < 250,000 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
 
 

 

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. 

13
 
 

 

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 TCMs Included in the Analysis 

Travel Demand Management (TDM) Rideshare Programs 

Employer-based Programs 

Public Outreach/ Education 

Land Use / Smart Growth TOD: Improved Transit Access 

Mixed Land Use 

Promotion of Higher Density 

Transit Increased Transit Frequency 

Lower Fares or Transit Subsidies 

Pricing Parking Pricing 

Mileage Fees 

 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. 

14
 
 

 

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
 
 

 

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 subareas, 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. 

16
 
 

 

 

 

 

 

 

Table 5. Scenarios 

Scenario 

Strategy Combinations 

Region-wide 

TDM 

Land 

Use/Smart 

Growth 

Transit 

Fare 

Reduction 

Transit 

Service 

Improvements 

Pricing Mileage 

Fees 

Pricing Parking 

Fees 

Baseline Current conditions without any of the above strategies 

Scenario 1 3

Scenario 2 3 3

Scenario 3 3 3 3

Scenario 4 3 3 3 3

Scenario 5 3 3 3 3 3

Scenario 6 3 3 3 3 3

Scenario 7 3 3 3 3 3 3

One purpose of the study is to analyze prospective TCMs that are not widely used today, and 

where few estimates of the benefits currently exist. Some of these measures such as mileage 

fees, smart growth, and parking pricing are relatively new. They have been receiving significant 

attention in recent years and are of substantial interest to state and local policy makers. 

Wherever possible, the results of the analysis of these strategies were validated using existing 

studies. 

The use of scenarios combined with the modeling approach, is unique with respect to other 

national-level studies that attempt to quantify potential future reductions in emissions resulting 

from these strategies. Recent studies have used individually established baselines along with 

current research findings to develop meta-analyses which evaluate strategies independent of one 

another. In contrast, this research approach relies on available data from metropolitan regions 

that act as representatives of similar regions. The scenarios take into account natural synergies 

between strategies to demonstrate the effect that can be built over time as strategies are 

incrementally added. 

3.4 Scenario Analysis 

The foundation of the analysis is detailed travel data submitted by participating metropolitan 

regions. These data provide specific inputs to the TRIMMS modeling and support an 

understanding of how individual TCMs are currently included in regional transportation planning. 

The analysis was further informed and supplemented by consideration of recent research on TCM 

impacts and elasticities. 

Each step in the analysis moves progressively toward estimating the national potential for 

reductions in VMT and corresponding emissions reductions. The first step was to model the 

application of scenarios in order to estimate VMT reductions for each of the representative areas. 

In the second step, results of the representative area analysis were used to develop the potential 

VMT and emissions reductions for each of the seven clusters. Finally, the cluster results were 

applied to all metropolitan regions in the country based on their defining characteristics and 

aggregated to provide a national-level forecast of VMT and emission reductions. This 

methodology was followed to analyze each scenario, or group of strategies, and is described in 

detail below. The appendices contain the specific data inputs and assumptions as well as results 

17
 
 

at the cluster and national level. Figure 1 below illustrates the sequence of analysis steps 

followed. 

Figure 1. Analysis Steps 

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 Specific strategy Strategy information 2010 - 2030 2030 - 2050 

Employerbased TDM 

strategies 

 Flexible work hours 

 Incentives for carpooling 

 Guaranteed ride home 

programs 

 Ride sharing/ ride matching 

 TDM outreach/public 

outreach programs 

 Subsidies/discounts for 

transit, pedestrian and bike 

modes 

 Telecommuting 

Whether or not 

employer offers 

(TRIMMS asks for a 

yes/no answer) to take 

these programs into 

consideration 

30% of employers 

Region-wide offer these 

programs; includes all 

TDM strategies except 

walk and bike subsidies 

50% of employers 

Region-wide offer these 

programs; includes all 

TDM strategies 

18
 
 

Table 6. Scenario Assumptions and Modeling Approach for TCM Strategies 

TCM Strategy Specific strategy Strategy information 2010 - 2030 2030 - 2050 

Land use 

policies 

TOD, smart growth, increase 

in density, mixed use 

developments 

Change in travel times 

for all modes, change 

in average trip lengths 

3% reduction in all 

access times, 5% 

reduction in transit travel 

time and walk/bike times; 

5% increase in auto travel 

time due to density/ 

congestion effects 

Note: Access time taken

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. 

Transit 

projects and 

policies 

Transit service 

expansion/increase in 

frequency, improved access 

Improvement in transit 

travel time and access 

time 

5% reduction in transit 

travel time 

10% reduction in transit 

travel time 

Fare discounts, reduction, 

subsidies, or free transfers 

Change in transit fares 10% reduction in transit 

fares 

20% reduction in transit 

fares 

Pricing 

policies 

Parking charges Increase in auto 

parking costs 

$2 increase per day $5 increase per day 

VMT fees or congestion 

pricing 

Increase in peak hour 

driving costs 

$0.10 increase per mile $0.25 increase per mile 

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. 

19
 
 

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 shortterm 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. 

20
 
 

 

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. 

21
 
 

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 

22
 
 

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. 

23
 
 

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: MPO 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 

24
 
 

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. 

25
 
 

Chapter 4: Results and Conclusions 

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 

26
 
 

 

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. 

27
 
 

 

 

 

Table 8. National Percent Reductions 

Percent VMT Reduction 

Percent Emissions 

Reduction* 

Scenario 2030 2050 2030 2050 

1- Regionwide TDM 0.1% 0.3% 0.1% 0.2-0.3% 

2 - TDM + land use changes 1.0% 3.0% 1.0% 2.9 – 3.0% 

3 - TDM + land use changes + transit fare subsidies 1.4% 4.2% 1.4% 4.1 – 4.2% 

4 - TDM + land use changes + transit fare subsidies + transit 

service improvements 1.5% 4.4% 1.4% 4.2 – 4.3% 

5 - TDM + land use changes + transit fare subsidies + transit 

service improvements + parking fees 2.9% 7.0% 2.9% 6.7 – 7.0% 

6 - TDM + land use changes + transit fare subsidies + transit 

service improvements + mileage fees 2.0% 6.3% 1.9% 6.0 – 6.3% 

7 - TDM + land use changes + transit fare subsidies + transit 

service improvements + parking & mileage fees 3.4% 8.8% 3.3 – 3.4% 8.3 – 8.8% 

* Ranges reflect reductions in all pollutants considered. For example, Scenario 7 results in 8.3% reduction in volatile organic compounds 

(VOC), 8.6% reduction in NOx, and 8.8% reduction in each of PM2.5 and greenhouse gases (CO2 equivalent). 

Figure 2. National VMT Reductions from Baseline 

28
 
 

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 Pricing 

strategy 

included in 

Strategy 

Description 

Level of 

charge 

Percent VMT 

Reduction in 

2050 

Percent 

Emissions 

Reduction 

scenario in 2050* 

5A Parking 

charges 

Increase in daily 

auto parking 

charges 

$8 per day 

increase 

7.2% 6.8 – 7.2% 

6A VMT/mileage 

fees 

Increase in peak 

hour driving costs 

$0.35 

increase/mile 

6.7% 6.2 – 6.6% 

7A Parking 

charges and 

VMT fees 

Increase in 

average auto 

parking and peak 

hour driving costs 

$8 increase 

and $0.35 

increase/mile 

9.2% 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 offpeak 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 

0.00%2.00%4.00%6.00%8.00%

10.00%12.00%14.00%16.00%18.00%

% VMT Reduction

Scenarios

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Regionwide TDM

Land use 

changes + TDM

Land use changes +

Transit fare change +

TDM

Land use changes +

Transit fare change +

Transit service

improvements + TDM

Land use changes +

Transit fare change +

Transit service 

improvements + Parking 

Fees + TDM

Land use changes +

Transit fare change +

Transit service

improvements +

MileageFees + TDM

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
 
 

 

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. 

34
 
 

References 

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

Vehicle Travel and Land Consumption: A Meta-Analysis. Journal of the American 

Planning Association, 75 (1), 13-27. 

California Air Resources Board (2008), AB 32 Scoping Plan. Sacramento, CA: Californai Air 

Resources Board. Available at: http://www.arb.ca.gov/cc/scopingplan/scopingplan.htm. 

Cambridge Systematics (2009), Moving Cooler: An Analysis of Transportation Strategies for 

Reducing Greenhouse Gas Emissions, prepared for Urban land Institute, Washington, 

DC: Urban Land Institute. 

Center for Urban Transportation Research (CUTR). (2007). Economics of Travel Demand 

Management: Comparative Cost Effectiveness and Public Investment, prepared for 

Florida DOT, Tampa, FL: CUTR at the University of South Florida. Available at: 

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

Concas, Sisinnio and Philip L. Winters (2008), Estimating Societal Benefits and Costs of 

Transportation Demand Management, paper presented at Transportation Research 

Board Annual Conference, Washington, DC. 

Concas, Sisinnio and Philip L. Winters (2009), Quantifying the Net Social Benefits of Vehicle 

Trip Reductions: Guidance for Customizing the TRIMMS© Model, prepared for Florida 

DOT, Tampa, FL: CUTR at the University of South Florida. Available at: 

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

Cowart, Bill (2008), How Much Can We Slow VMT Growth? The Potential Savings of 

Implementing Best Practice Everywhere, presentation to the CCAP VMT and Climate 

Change Workshop. 

Denver Regional Council of Governments (2008), 2008 Amendment Cycle 2 DRCOG 

Conformity Determination (CO, PM10, and 1-hour Ozone) for the Amended Fiscally 

Constrained 2035 Regional Transportation Plan and the Amended 2008-2013 

Transportation Improvement Program Public Hearing Draft. Available at: 

http://www.drcog.org/documents/11­

08Public%20Hearing%20Draft%202008%20Conformity%20Cycle%202.pdf. 

Difiglio and Fulton (2000), How to Reduce US Automobile Greenhouse Gas Emissions, Energy, 

25, 657-673. 

Dowling, Richard (2005), Predicting Air Quality Effects of Traffic-Flow Improvements: Final 

Report and User’s Guide. NCHRP Report 535, Washington, DC: National Cooperative 

Highway Research Program, TRB. 

El Paso Metropolitan Planning Organization (2007), Transportation Conformity Report for the 

Gateway 2030 Metropolitan Transportation Plan and 2006-2010 Amended 

Transportation Improvement Program, El Paso, TX: El Paso MPO. Available at: 

http://www.elpasompo.org/Portals/0/Publications/Conformity/TCR.pdf. 

35
 
 

 

EPA (2010a), Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2008, 

Washington, DC: Environmental Protection Agency. Available at: 

http://www.epa.gov/climatechange/emissions/usgginv_archive.html 

EPA (2010b), DOT, EPA Set Aggressive National Standards for Fuel Economy and First Ever 

Greenhouse Gas Emission Levels for Passenger Cars and Light Trucks, Washington, 

DC: Environmental Protection Agency. Available at: 

http://yosemite.epa.gov/opa/admpress.nsf/8b770facf5edf6f185257359003fb69e/562b44f 

2588b871a852576f800544e01!OpenDocument. 

EPA (2006), Greenhouse Gas Emissions from the U.S. Transportation Sector: 1990-2003, 

Washington, DC: Environmental Protection Agency. 

EPA (1998), Technical Methods for Analyzing Pricing Measures to Reduce Transportation 

Emissions, Washington, DC: Environmental Protection Agency. 

Evans, John E. (2004), Transit Scheduling and Frequency: Traveler Response to Transportation 

System Changes, TCRP Report 95, Chapter 9, Washington, DC: TRB. Available at: 

http://gulliver.trb.org/publications/tcrp/tcrp_rpt_95c9.pdf. 

Evans, John E., Kiran Bhatt, and Katherine Turnbull (2003), Road Value Pricing: Traveler 

Response to Transport System Changes, TCRP Report 95, Chapter 14, Washington, 

DC: TRB. Available at http://trb.org/publications/tcrp/tcrp_rpt_95c14.pdf. 

Ewing, Reid and Robert Cervero (2010), Travel and the Built Environment, Journal of the 

American Planning Association, 76:3, 265 - 294 

Evans, John E., Kiran Bhatt, and Katherine Turnbull (2003), Road Value Pricing: Traveler 

Response to Transport System Changes, TCRP Report 95, Chapter 14; TRB; at 

http://trb.org/publications/tcrp/tcrp_rpt_95c14.pdf. 

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

Evidence on Urban Development and Climate Change, prepared for Urban land 

Institute, Washington, DC: Urban Land Institute. 

Ewing, Reid, Keith Bartholomew, Steve Winkelman, Jerry Walters, and Geoffrey Anderson 

(2008b), Urban development and climate change, Journal of Urbanism, 1 (3), 201–216. 

Executive Order 13150 (2000), U.S. General Services Administration. Available at: 

http://www.gsa.gov/Portal/gsa/ep/contentView.do?contentId=16569&contentType=GSA_ 

BASIC&noc=T. 

FHWA (2006), Multi-Pollutant Emissions Benefits of Transportation Strategies, prepared by ICF 

International for Federal Highway Administration, Washington, DC: Federal Highway 

Administration. Available at: 

http://www.fhwa.dot.gov/environment/conformity/mpe_benefits/. 

FHWA (2008a), Integrating Climate Change into the Transportation Planning Process. 

Washington, DC: Federal Highway Administration. Available at: 

http://www.fhwa.dot.gov/HEP/climatechange/chapter_five.htm. 

36
 
 

 

FHWA (2009), Highway Statistics, Table VM-1, Annual Vehicle Distance Traveled in Miles and 

Related Data - 2008 By Highway Category and Vehicle Type, Washington, DC: Federal 

Highway Administration. Available at: 

http://www.fhwa.dot.gov/policyinformation/statistics/2008/vm1.cfm 

FHWA (2008b), SAFETEA-LU 1808: Congestion Mitigation and Air Quality Improvement 

(CMAQ) Program Evaluation and Assessment: Phase I Final Report, Washington, DC: 

Federal Highway Administration. Available at: 

http://www.fhwa.dot.gov/environment/cmaqpgs/safetealu1808/index.htm. 

FHWA (2000), A Sampling of Emissions Analysis Techniques for Transportation Control 

Measures, Washington, DC: Federal Highway Administration. 

Keep Oregon Cool (2009) “Goals & Getting There.” KeepOregonCool.com. Available at: 

http://www.keeporegoncool.org/content/goals-getting-there. 

Oregon Department of Energy, “House Bill 3543: Global Warming Actions.” Oregon.gov. 

Available at: http://www.oregon.gov/ENERGY/GBLWRM/HB3543.shtml. 

Johnston, Robert A. (2006), Review of U.S. and European Regional Modeling Studies of 

Policies Intended to reduce Motorized Travel, Fuel use, and Emissions, Victoria, BC: 

Victoria Transport Policy Institute. Available at: www.vtpi.org/johnston.pdf. 

Johnston, Robert A. and Caroline J. Rodier (1994), Critique of Metropolitan Planning 

Organizations Capabilities for Modeling Transportation Control Measures in California, 

Berkeley, CA: University of California Transportation Center. Available at: 

http://www.uctc.net/papers/271.pdf. 

Knoxville Regional Transportation Planning Organization (2006), Air Quality Conformity 

Determination, Knoxville, TN: Knoxville Regional Transportation Planning Organization. 

Litman, Todd (2010), Transportation Elasticities: How Prices and Other Factors Affect Travel 

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

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

Memphis Metropolitan Planning Organization (2008), Conformity Demonstration with 1990 

Clean Air Act Amendments for Memphis Urban Area 2030 Long- Range Transportation 

Plan and 2008-2011 Transportation Improvement Program, Memphis, TN: Memphis 

MPO. Available at: http://www.dpdgov.com/. 

Cambridge Systematics (2001), Quantifying Air Quality and Other Benefits and Costs of 

Transportation Control Measures, NCHRP Report 462, Washington, DC: National 

Cooperative Highway Research Program, TRB. 

National Research Council (2003), Modeling Mobile Source Emissions, Washington, DC: 

National Academy Press. 

Pew Center on Global Climate Change (2003), Reducing Greenhouse Gas Emissions from 

U.S. Transportation. Arlington, VA: Pew Center on Global Climate Change. 

37
 
 

Pima Association of Governments (PAG) (2007). Travel Reduction Program: 2007 Annual 

Report, Pima, AZ: PAG. Available at: 

http://www.pagnet.org/documents/TRP/AnnualReports/AnnualReport2007-2008.pdf. 

Oregon Department of Environmental Quality, Employee Commute Options – ECO. Available 

at: http://www.deq.state.or.us/nwr/ECO/eco.htm. 

Pratt, Richard H. et. al. (2000), Traveler Response to Transportation System Changes: Interim 

Handbook, TCRP Web Document 12 (Project B-12): Contractor’s Interim Handbook, 

Washington, DC: Transportation Research Board. Available at: 

http://onlinepubs.trb.org/Onlinepubs/tcrp/tcrp_webdoc_12.pdf. 

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

and Auto Pricing Strategies to Reduce Vehicle Miles Traveled and Greenhouse Gas 

Emissions, Paper presented at the Transportation research board Annual Meeting 2009. 

SACOG (2008), Final Metropolitan Transportation Plan – MTP 2035, Appendix C8, Congestion 

Management Process, Sacramento, CA: Sacramento Area Council of Governments. 

Available at: http://www.sacog.org/mtp/2035/ 

Shoup, Donald (1997), Evaluating the Effects of California’s Parking Cash-out Law: Eight Case 

Studies, Transport Policy 4 (4), 201-216. 

Small, Kenneth and Clifford Winston (1999), The Demand for Transportation: Models and 

Applications, in Essays in Transportation Economics and Policy, Washington, DC: 

Brookings Institute. 

Thomas D. Schuster, Megan Schuster, Abhijit Banerjee, and Anisha Shankar (2004), 

Transportation Strategies to Improve Air Quality, Newark, DE: Center for Energy and 

Environmental Policy, University of Delaware. Available at: 

http://www.ceep.udel.edu/publications/sustainabledevelopment/reports/sd_transport_str 

ategies/2004_transport_strategies.pdf. 

Transportation Research Board (2009), Driving and the Built Environment: The Effects of 

Compact Development on Motorized Travel, Energy Use, and CO2 Emissions, Special 

Report 298, Washington, DC: Transportation Research Board. 

Transportation Research Board (2002), The Congestion Mitigation and Air Quality Improvement 

Program, Assessing 10 Years of Experience, Special Report 264, Washington, DC: 

Transportation Research Board. 

U.S. Department of Energy (2009), Emissions of Greenhouse Gases in the United States, 2008, 

Washington, DC: Energy Information Administration. Available at: http://wwwcta.ornl.gov/data/chapter11.shtml. 

U.S. Energy Information Administration (2009). Assumptions to the Annual Energy Outlook 

2009, Washington, DC: Energy Information Administration. Available at: 

http://www.eia.doe.gov/oiaf/archive/aeo09/assumption/transportation.html. 

U.S. Department of Transportation (2010), Transportation’s Role in Reducing Greenhouse Gas 

Emissions, Volume 1: Synthesis Report, report to Congress. Available at: 

38
 
 

http://ntl.bts.gov/lib/32000/32700/32779/DOT_Climate_Change_Report_-_April_2010_­

_Volume_1_and_2.pdf 

Vaca, Erin and J. Richard Kuzmyak (2005), Parking Pricing and Fees: Traveler Response to 

Transport System Changes, Chapter 13, TCRP Report 95, Washington, DC: 

Transportation Research Board. Available at: 

www.trb.org/publications/tcrp/tcrp_rpt_95c13.pdf. 

VTPI (2008a), Ridesharing, TDM Encyclopedia, Victoria, BC: Victoria Transport Policy Institute. 

Available at: http://www.vtpi.org/tdm/tdm34.htm. 

VTPI (2008b), Commute Trip Reduction (CTR), TDM Encyclopedia, Victoria, BC: Victoria 

Transport Policy Institute. Available at: 

http://www.vtpi.org/tdm/tdm11.htm#_Toc161022574. 

Zegras, Chris (2010), Transportation and the Built Environment. MITWorld, Cambridge, MA: 

MITWorld, April 6, 2010. Available at: http://mitworld.mit.edu/video/786. 

39
 
 

blank 

page 

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Appendix A 

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 

Spreadsheet-Based Tools/Methods Models 

Metaanalysis 

EPA 

Commuter 

model TRIMMS 

CCAPTEG TCM 

Tools 

TCM 

Analyst 

TDM 

Evaluation 

Model STEAM 

MARKALMACRO NEMS 

INPUTS 

Population X X X 

Per capita income X 

New vehicle sales X 

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

Average vehicle 

occupancies by mode X X X X X 

Travel times by mode (invehicle and out-ofvehicle) 

X X X X 

Average trip costs by 

mode (including parking, 

fees, tolls, fuel costs, 

transit fares) 

X X X X X X 

Includes non-motorized 

trips X X X X X 

Average trip lengths X X X X X 

Baseline regional VMT X X X X 

Trip tables X X 

Baseline vehicle speeds X X X X 

Vehicle fleet mix X X X X 

Fuel price per gallon X X X X 

Average fuel economy X X X 

Emissions factors X X 

OUTPUTS 

Change in mode shares 

(no. of trips by mode) X X X X X X 

Change in travel time X 

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

Change in emissions X X X X X X X X 

Change in speeds X X 

Fuel demand X X 

Benefits and costs X X 

A-2 
Table A-2. Assessment of Methods for Analyzing Travel Impacts of Transportation Control Measures (TCMs) 

Methodologies / 

Models Developer 

Last 

Update 

Inputs Required Outputs 

Scale of Analysis 

(sub-area, regional, 

national) 

TCMs Modeling 

Capability 

1 Travel Demand 

Management (TDM) Evaluation Model 

COMSIS and R.H. Pratt Consultants for FHWA 

1993 Base case trip tables, 

vehicle occupancy, model coefficients (invehicle time, out-of vehicle time, transit time, transit fare, parking cost, HOV time saved), mode shares, and information about TCMs 

Change in VMT and trips 

Sub-area and with limited capability, regional 

Following TCMs canbe modeled:  Land use strategi Incentives for bicy

use and pedestria

 Travel time chang

(alternative work hours or peak perpricing). 

 Some pricing 

strategies, e.g., distance-based pricing and fuel prchanges. 

2 EPA's Commuter 

model 

Sierra Research; updated by Cambridge Systematics 

2005 Population, mode 

shares, trip lengths, occupancy levels, baseline VMT, baseline speeds, mode choice time and cost coefficients, fleet mix, and details about the TCMs 

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

Sub-area and regional, with some adjustment 

Cannot model:  Regional land use 

strategies and any TCMs that will change regional travel patterns. 

 TCMs that affect 

vehicle speeds. 

 Location-specific 

strategies such as area-wide pricing higher parking charges in certain areas. 

A-3 
Table A-2. Assessment of Methods for Analyzing Travel Impacts of Transportation Control Measures (TCMs) 

Methodologies /

Models Developer 

Last 

Update 

Inputs Required Outputs 

Scale of Analysis 

(sub-area, regional, 

national) 

TCMs Modeling

Capability 

3 Trip Reduction 

Impacts for Mobility Management Strategies (TRIMMS) model 

Center for Urban Transportation Research, University of South Florida 

2009 No trip tables. Needs 

average regional mode shares, average trip length and travel time by mode, average vehicle occupancy, parking and trip costs, and details about the TCMs 

Changes in mode shares, trips, VMT, and emissions 

Sub-area and regional, with some adjustment Practitioner-oriented sketch planning tool to measure travel impacts of regional and employer-based TCMs. 

 Can model any 

strategy that affecthe cost of using existing modes or travel times. 

 Can model packa

of strategies. The user can change price and travel timelasticity values. 

4 Surface 

Transportation Efficiency Analysis Model (STEAM) 

Cambridge Systematics 

2006 Base case and 

improvement case trip tables, vehicle occupancy, model coefficients (trip time and cost), mode shares, and TCM characteristics (in terms of change in trip costs or travel time). 

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

Regional and subarea/corridor Most TCMs can be modeled. 

5 Transportation 

Emissions Guidebook (TEG) 

Center for Clean Air Policy (CCAP) 

2006 (?) Number of trips by 

mode, mode split, trip lengths 

VMT and Emissions 

Regional and Subarea Spreadsheets provid

rule of thumb guidanon impacts of TCMs based on literature; most TCMs can be modeled. 

6 TCM Tools Sierra Research Early 

1990s 

Has separate Transportation and Emissions modules – 

Changes in mode share, vehicletrips, VMT, travel 

More applicable at regional scale; some sub-area policies can 

Wide range of stratecan be modeled, including land use 

A-4 
Table A-2. Assessment of Methods for Analyzing Travel Impacts of Transportation Control Measures (TCMs) 

Methodologies /

Models Developer 

Last 

Update 

Inputs Required Outputs 

Scale of Analysis 

(sub-area, regional, 

national) 

TCMs Modeling 

Capability 

trips, VMT, speed speeds, and 

emissions 

be modeled strategies, but canno

model scenarios wel

7 TCM Analyst Texas 

Transportation Institute 

1994; will hear about updates from TTI on 12/5/08 

Trips, distances, speeds, emissions factors, TCM details 

Changes in trips, VMT, average travel speeds, and emissions 

Regional or sub-area Pricing strategies ca

be modeled 

8 MARKAL (Market 

Allocation)-MACRO 

US DOE and EPA 

Used internati onally and currently in use 

Baseline VMT by vehicle type, fuel costs 

VMT, emissions, and fuel demand 

National TCMs relevant at su

area, urban, or state level cannot be mode

9 National Energy 

Modeling System (NEMS): Transportation Sector Module (TRAN) 

Energy Information Administration, US DOE 

2006 Vehicle fleet (includes 

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

VMT, emissions, and fuel demand 

Census region and national 

Cannot model:  TCMs relevant at 

sub-area, urban, ostate level 

 TCMs involving m

switching 

 Includes useful 

feedback effects, can be used to validate national 

A-5 
Table A-2. Assessment of Methods for Analyzing Travel Impacts of Transportation Control Measures (TCMs) 

Methodologies /

Models Developer 

Last 

Update 

Inputs Required Outputs 

Scale of Analysis 

(sub-area, regional, 

national) 

TCMs Modeling

Capability 

estimates 

10 Spreadsheet 

analysis with elasticity factors from literature 

-- Mode shares, trip costs 

by mode, average VMT 

VMT change – followed by emissions analysis 

Regional Without trip tables, la

use strategies are bemodeled this way 

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 Regional implementation: 0.1 to 0.5% reduction in VMT 

High-Occupancy Vehicle (HOV) lanes Long run (LR) travel time elasticity, regional: -1.0, urban: -0.6, rural: -1.3 

0.2 to 1.4% VMT reduction 

Rideshare matching programs 0.1 to 2.0% VMT reduction 

Carpool/vanpool incentives 0.2 to 3.3% VMT reduction 

Car-sharing Limited quantitative data 

Bicycle and Pedestrian Facilities and Programs 

Bike paths / lanes / routes <0.1% VMT reduction 

Bike/ped facilities to support transit Limited quantitative data 

Transit Projects and Policies 

Transit service expansion /increase in 

frequency 

-0.6 to -1.0; for buses 

-0.5 (time between buses) for service frequency alone 

Improved transit travel times and operations 

(busways, BRT, signal prioritization for transit 

vehicles, heavy and light rail, managed lanes) 

-0.4 (travel time elasticity with respect to ridership) 

Improved transit access through shuttle and 

feeder bus services, paratransit 

Relates to improving travel time above, not measured separately 

Transit service integration and intermodal 

transfer centers 

Relates to improving travel time above 

Fare integration for easy transfers Relates to improving travel time above 

Improved transit marketing, information, 

amenities 

Limited quantitative data 

Commuter discounts/fare reductions -0.3 to -0.4 (fare elasticity with respect to ridership) 

Peak/off-peak transit fares -0.1 to -0.3 (peak fares) and -0.1 to -0.7 (off-peak fares, depending on trip 

purpose; lower for work trips) 

Transit improvement policies, overall Studies estimate 0 to 2.6% VMT reduction 

Parking Management and Incentives 

Parking cash-out Elasticities are not available; although some quantitative data on percentage 

reduction in regional VMT are available from specific projects and studies. Preferential parking for carpools and vanpools 

Parking duration restrictions 

Employer-based Programs (effects depend on level of adoption) 

Flexible work schedules Elasticities are not available; although some quantitative data on percentage 

reduction in regional VMT are available from specific projects and studies. Telecommuting 

Compressed work weeks 

Employer-provided transit passes 

Guaranteed ride home programs 

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 -0.1 to -0.4 (urban road pricing); 10-25% reduction in central city VMT with 

cordon pricing; 0.2 to 5.7% regional VMT reduction 

Distance-based pricing or mileage fees LR: -0.1 to -0.8 (price elasticity). Conservative LR estimate for the U.S. would 

be -0.3 

Peak period pricing/ variably priced lanes -0.03 to -0.4 (depending on time of day) 

Parking pricing/fees Overall LR elasticity: -0.1 to -0.5 

LR regional: -0.3; at sites: -0.1 to -0.2 

LR (non-commute): -0.2 to -0.4 

Studies show 0.5-4% reduction in work-related VMT; 3.1 to 4.2% reduction in 

non-work VMT 

High Occupancy Toll (HOT) lanes/toll 

increases 

-0.1 to -0.4; data from specific projects are available 

Pay-as-you-drive Insurance -0.3 

Fuel taxes LR: -0.1 to -0.3, tending towards the lower end 

Freight vehicle pricing -0.25 to -0.35 (price elasticity); -0.3 to -0.7 (travel time elasticity) 

Integrated Land Use and Transportation Strategies 

Transit-oriented development and incentives 

(Design and transit access) 

-0.05 (vehicle trips) and -0.03 to -0.08 (VMT) 

Smart growth and mixed use development 

(Diversity) 

-0.03 (vehicle trips) and -0.05 (VMT) 

Land use controls for compact, dense urban 

development (Density) 

-0.05 (vehicle trips) and -0.05 to -0.12 (VMT) 

Improved regional accessibility due to 

combined measures 

-0.18 to -0.22 (VMT); studies estimate regional VMT reduction by 2-20% in 20 

years with doubling of results in 40 years. 

Land use measures, overall Regional VMT reduction of 0 to 5.2% 

Vehicle Restrictions by Geographic Area or in Peak Periods 

Freight vehicle controls Elasticities are not available; although some quantitative data on percentage 

reduction in regional VMT are available from specific projects and studies. 

No-drive days 

Urban non-motorized zones 

Public Education and Outreach Programs 

TDM outreach programs by employers These measures are typically implemented as part of other measures. Difficult 

to estimate impacts separately as it could lead to double-counting. Episodic programs (e.g. ozone action days) 

Public communication about the impacts of 

travel decisions 

A-8 
 

 

 

 

 

Table A-4. Fare and Price Elasticities used in TRIMMS 

Mode 

Elasticity 

Short run Long run Source Notes

Auto - Drive Alone 

Direct -0.11 -0.22 Litman (2010) 

Table 22, pp.27 (TRIMMS 

default); long run auto drive alone 

elasticity assumed double of short 

run elasticity 

Cross-Price: Transit 0.05 0.05 Litman (2010) 

TRIMMS default uses the lower 

ranges; long run elasticity 

assumed same as short run since 

no better information available 

Auto - Rideshare 

Direct n/a n/a Assumed zero since no 

information is available 

Cross-Price: Transit 0.05 0.05 Litman (2010) Same long run elasticity as autodrive alone assumed 

Vanpool 

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

about short run vs. long run 

vanpool elasticities, so assumed 

same 

Direct: Off-peak -0.32 -0.32 

Cross-Price: Transit 0.05 0.05 TRIMMS default 

Transit 

Direct: Peak -0.10 -0.10 

TRIMMS default; no information 

about short run vs. long run 

transit elasticities, so assumed 

same 

Direct: Off-Peak -0.30 -0.30 TRIMMS default 

Cross-Price: Auto Drive Alone 0.15 0.15 Litman (2010) TRIMMS default uses the lower 

ranges 

Cross-Price: Auto Rideshare 0 0.15 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 
 

Table A-5. Travel Time Elasticities 

Mode 

Elasticity 

Peak Off peak Notes

Auto - Drive Alone 

TRIMMS default 

assumptions 

Direct -0.225 -0.170 

Cross: Auto -Rideshare 0.037 0.001 

Cross: Transit 0.036 0.001 

Auto - Rideshare 

Direct -0.303 -0.189 

Cross: Auto -Drive Alone 0.030 0.000 

Cross: Transit 0.030 0.000 

Vanpool 

Direct -0.60 n/a 

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

Cross: Transit 0.032 0.000 

Transit 

Direct -0.129 -0.074 

Cross: Auto -Drive Alone 0.010 0.000 

Cross: Auto -Rideshare 0.032 0.000 

Source: Litman (2010)Table 31, pp. 35 

Table A-6. Parking Pricing Elasticities 

Parking Elasticities 

Trip Purpose 

Auto – Drive 

Alone 

Auto -

Rideshare Transit Slow Mode 

Commuting -0.08 -0.02 -0.02 -0.02 

Source: Litman (2010), Table 13, pp. 17 

A-10 
Table A-7. Summary of Populations, Calculations & Correction Factors 

Summary of Populations, Calculations & Correcti

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluste

Pop > 2.9mil ; Trns > 9% Pop > 2.9mil ; Trns < 9% Pop > 1.5mil ; Trns > 4% Pop > 1.5mil ; Trns > 4% Pop > 7

San 

Francisco

Washington 

DC

San Diego

Seattle

Denver

Portland

Sacramento

Salt Lake 

City

Memphis

POPULATIONS & TRANSIT MODE SHARES 

2000 Census Pop 2,995,291 3,932,927 2,674,996 2,712,338 1,984,585 1,582,863 1,394,615 887,916 971,282 

2007 FHWA UA Pop 3,162,000 4,332,000 2,951,000 3,108,000 2,184,000 1,805,000 1,858,000 970,000 1,035,000 

MPO Provided Values1 

Modeled Total BY Pop 7,159,379 6,808,844 3,089,035 3,695,516 2,685,000 1,961,153 1,936,006 1,933,000 1,103,539 

Modeled Working BY Pop 3,282,403 3,547,408 2,399,837 1,934,713 2,120,000 1,032,246 1,407,816 1,029,000 533,378 

Modeled Total FY Pop 9,031,498 8,282,368 3,984,753 4,988,135 4,388,529 3,097,402 3,349,000 2,820,000 1,499,124 

Modeled Working FY Pop 5,016,501 4,315,114 3,098,248 2,789,293 3,322,116 1,799,152 1,546,000 1,575,000 745,973 

TRIMMS Modeled Values2 

TRIMMS BY Pop Input 3,282,403 3,547,408 2,399,837 1,934,713 2,120,000 1,961,153 1,407,816 1,029,000 533,378 

TRIMMS FY Pop Input 5,015,512 4,315,114 3,098,248 2,789,293 3,322,116 3,097,402 1,546,000 1,575,000 745,973 

TRIMMS BY TDM Pop Input 984,721 1,064,222 719,951 580,414 636,000 588,346 422,345 308,700 160,013 

TRIMMS FY TDM Pop Input 2,507,756 2,157,557 1,549,124 1,394,647 1,661,058 1,548,701 773,000 787,500 372,987 

Transit Mode Shares3 

CTPP 2000 (Urbanized Area) 16.0% 13.0% 4.0% 8.0% 5.0% 7.0% 3.0% 4.0% 1.95% 

MPO Provided Values 5.3% 4.5% 1.5% 2.9% 2.0% 3.3% 3.3% 1.3% 1.0% 

VMT Shares4 

Share of FHWA estimated national VMT based on FHWA 2007 Pop and CTPP 2000 transit share 

17% 22% 6% 7% 12% 

CALCULATIONS & CORRECTION FACTORS 

Trip Rate Calculations5 2007 Daily VMT (FHWA)7 68,939,000 97,860,000 68,442,000 71,358,000 52,735,000 35,211,000 34,838,000 22,317,000 26,900,000 

MPO Provided Auto Trip Length 11.8 10.8 7.12 12.8 11.4 6.6 12.5 7.1 11.04 

Calculated Daily # of Trips 5,842,288 9,061,111 9,612,640 5,574,844 4,625,877 5,335,000 2,787,040 3,143,239 2,436,594 

Calculated Daily Trip Rate 1.8 2.1 3.3 1.8 2.1 3.0 1.5 3.2 2.4 

Researched Trip Rate 3.0 3.8 3.1 4.1 

Correction Factors6 

Modeling Correction Factor-BY 2.18 1.92 1.29 1.91 1.27 1.00 1.38 1.88 2.07 

Modeling Correction Factor-FY 1.80 1.92 1.29 1.79 1.32 1.00 2.17 1.79 2.01 

Trips Correction Factor 1.48 1.05 1.63 0.90 1.90 1.57 2.04 1.62 1.18 

Population Correction Factor 1.84 1.57 2.00 2.06 1.94 1.94 1.86 2.13 2.14 

FINAL Correction Factor-BY 5.95 3.15 4.19 3.53 4.67 3.04 5.22 6.48 5.21 

FINAL Correction Factor-FY 4.91 3.15 4.19 3.30 4.87 3.04 8.22 6.18 5.06 

FINAL TDM Correction Factor 1.84 1.57 2.00 2.06 1.94 1.94 1.86 2.13 2.14 

(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 w(1) These are the population figures provided by the MPOs. (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 ruCORRECTION 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 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 knan 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 ra

(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. Tstudies 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. (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 (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. (2) These are the values that were used for the TRIMMS model runs. 

A-11 
Table A-8. Data from MPOs and Other Sources 

MPO Provided Values - All TRIMMS Model Input

Cluster 1 Cluster 2 Cluster 3 

Pop > 2.9mil ; Trns > 9% Pop > 2.9mil ; Trns < 9% Pop > 1.5mil ; Trns > 4% 

San

Francisco

Washington

DC

San Diego

Seattle

Denver

Portland 

BY FY BY FY BY FY BY FY BY FY BY 

Model Years 2006 2035 2010 2030 2006 2030 2006 2040 2008 2040 2005 

Peak Hour Trips (%) 54 52 42 42 50 30 50 50 50 50 50 

Vehicle Occupany 1.34 1.31 1.25 1.26 1.00 1.00 1.00 1.00 1.33 1.32 2.30 

Populations Modeled Total Pop 7,159,379 9,031,498 6,808,844 8,282,368 3,089,035 3,984,753 3,695,516 4,988,135 2,685,000 4,388,529 1,961,153 3,0

Modeled Working Pop 3,282,403 5,015,512 3,547,408 4,315,114 2,399,837 3,098,248 1,934,713 2,789,293 2,120,000 3,322,116 1,032,246 1,7

Change in Total Pop 26% 1,872,119 22% 1,473,524 29% 895,718 35% 1,292,619 63% 1,703,529 58% 1,

Change in Working Pop 53% 1,733,109 22% 767,706 29% 698,411 44% 854,580 57% 1,202,116 74% 7

Mode Shares Auto-drive alone 61.54 62.30 75.50 75.20 52.00 51.50 43.40 43.70 51.00 53.00 45.60 Auto-rideshare 21.28 20.20 20.00 19.60 42.90 43.70 43.30 40.00 44.00 42.00 40.70 

Public transit 5.29 6.10 4.50 5.20 1.50 1.60 2.90 4.30 2.00 2.00 3.30 

Cycling 1.70 1.50 - - 0.20 0.20 5.20 6.00 - - 1.00 

Walking 10.19 9.90 - - 2.60 2.40 5.20 6.00 - - 6.50 

Other - - - - 0.80 0.60 - - 3.00 3.00 3.00 

Trip Lengths (miles) Auto-drive alone 11.8 11.9 10.8 10.9 7.1 7.0 12.8 12.8 11.4 13.3 6.6 

Auto-rideshare 11.8 11.9 10.8 10.9 6.0 6.5 12.8 12.8 11.4 13.3 6.6 

Public transit 1 11.8 11.9 10.3 10.4 6.9 7.5 12.8 12.8 11.4 13.3 6.9 

Cycling 2 - - - - 1.7 1.7 - - - - 2.5 

Walking 3 - - - - 0.9 0.9 - - - - 1.4 Other 4 - - - - 7.5 7.2 - - - - 2.7 

Travel Times (minutes) Auto-drive alone 26.9 32.5 30.6 33.5 24.3 29.3 22.3 28.9 22.5 28.9 21.1 

Auto-rideshare 26.9 32.5 30.6 33.5 27.1 32.7 0.0 32.1 26.6 34.2 24.5 

Public transit 36.8 36.8 46.1 45.5 50.5 50.5 35.1 35.1 38.1 49.0 38.2 

Cycling - - - - - - - - - - - 

Walking - - - - - - - - - - - Other 5 18.3 18.3 - 16.4 17.1 17.1 - 18.0 17.0 21.9 19.7 

Travel Costs Parking - drive alone 9.40 15.40 11.00 16.35 4.00 5.94 10.91 19.76 5.30 9.60 5.71 

Parking - rideshare 7.00 11.80 8.79 13.00 4.00 5.94 10.91 19.76 4.00 7.30 2.48 

Trip Cost - drive alone 2.71 2.70 1.30 1.31 0.96 1.22 1.84 1.84 1.80 2.50 0.60 

Trip Cost - rideshare 2.02 2.01 1.02 1.01 0.81 1.14 1.84 1.84 0.91 1.90 0.26 

Transit Fare 2.00 4.20 2.30 4.20 2.50 4.52 2.50 6.10 0.75 1.82 1.21 

(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.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 aveNotes: Data not provided by MPOs are marked by “-“ (1) Used TRIMMS default public transit trip length of 12.2 where no data was provided by the MPO (2) Used TRIMMS default cycling trip length of 2.9 where no data was provided by the MPO (3) Used TRIMMS default walking trip length of 0.9 where no data was provided by the MPO (4) Used TRIMMS default 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 "othcities FY parking charges not available in most cases; assumed to increase by 2% based on guidance from Metropolitan Transportation Commission, San Francisco FY transit fares not available in many cases; assumed to increase with inflation at 3% per year Where data were not provided, 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 fares and parking charges were obtained from MPO websites when not provided or from Colliers International Parking Rate Survey, available at: Colliers International Parking Rate Survhttp://www.colliers.com/content/globalcolliersparkingratesurvey2009.pdf Default TRIMMS trip lengths used where trip length was not available by mode Where FY trip lengths and auto operating costs not provided, these were assumed same as BY BY: Base Year; FY: Future Year Total population was used to analyze all scenarios, except Scenario 1 (TDM), which used Working Population 

A-12 
Table A-8. Data from MPOs and Other Sources 

MPO Provided Values - All TRIMMS Model Input

Cluster 5 Cluster 6 

Pop > 750K Pop > 250K 

Memphis

RaleighDurham 

Fresno

Knoxville

Rochester

BY FY BY FY BY FY BY FY BY FY 

Model Years 2004 2030 2005 2035 2010 2035 2006 2034 2005 2031 

Peak Hour Trips (%) 51 51 47 46 50 50 47 47 15 16 Vehicle Occupany 2.21 2.13 1.29 1.29 1.00 1.00 1.70 1.70 1.24 1.25 

Populations Modeled Total Pop 1,103,539 1,499,124 1,312,000 2,647,000 992,997 1,402,217 862,903 1,306,460 823,147 868,076 1

Modeled Working Pop 533,378 745,973 683,000 1,332,000 415,840 609,437 429,939 872,930 422,942 446,027 1

Change in Total Pop 36% 395,585 102% 1,335,000 41% 409,220 51% 443,557 5% 44,929 

Change in Working Pop 40% 212,595 95% 649,000 47% 193,597 103% 442,991 5% 23,085 Mode Shares Auto-drive alone 45.00 47.00 54.00 54.00 96.90 97.20 72.00 72.00 92.27 92.47 

Auto-rideshare 46.00 46.00 37.40 37.40 - - 24.00 24.00 - - Public transit 1.00 0.50 1.20 1.20 1.20 1.10 1.00 1.00 0.82 0.80 

Cycling 2.50 2.00 - - 0.95 0.85 1.00 1.00 3.45 3.36 

Walking 2.50 2.00 - - 0.95 0.85 2.00 2.00 3.46 3.37 

Other 3.00 2.50 7.40 7.40 - - - - - - 

Trip Lengths (miles) Auto-drive alone 11.0 12.1 7.3 7.5 11.8 11.8 8.1 8.1 8.0 8.1 

Auto-rideshare 11.0 12.1 6.6 6.6 - - 7.3 7.3 - - 

Public transit 1 6.0 6.6 - - 11.8 11.8 - - 2.2 2.3 

Cycling 2 6.0 6.5 - - - - - - - - 

Walking 3 - - - - - - - - - - 

Other 4 9.4 11.7 7.3 7.5 - - - - 8.0 8.1 

Travel Times (minutes) Auto-drive alone 21.4 22.5 14.4 15.8 19.0 21.6 20.0 22.7 15.5 15.9 

Auto-rideshare 21.4 22.5 13.7 14.6 - - 19.0 21.5 - - 

Public transit 14.5 15.1 34.3 39.5 37.8 37.8 17.0 19.3 31.8 32.5 

Cycling 14.5 15.0 - - - - 45.0 51.0 - - 

Walking 14.5 15.1 - - - - 12.0 13.6 - - 

Other 5 20.5 22.9 18.6 21.4 - - - - - - 

Travel Costs Parking - drive alone 8.00 13.40 11.00 19.90 2.95 4.84 8.00 13.90 3.60 5.91 

Parking - rideshare 3.62 6.30 8.50 15.40 2.95 4.84 4.70 8.20 2.90 4.73 

Trip Cost - drive alone VMT fees not analyzed for Cluster 5 and below Trip Cost - rideshare Transit Fare 1.50 3.20 2.00 4.90 1.00 2.10 1.50 3.40 1.00 2.10 

Notes: 

(3) Used TRIMMS default walking trip length of 0.9 where no data was provided by the MPO (4) Used TRIMMS default 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 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 2007/ctpp_profiles.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 across these modes Default TRIMMS trip lengths used where trip length was not available by mode Where FY trip lengths and auto operating costs not provided, these were assumed same as BY Data not provided by MPOs are marked by “-“ (1) Used TRIMMS default public transit trip length of 12.2 where no data was provided by the MPO (2) Used TRIMMS default cycling trip length of 2.9 where no data was provided by the MPO 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 not available in most cases; assumed to increase by 2% based on guidance from Metropolitan Transportation Commission, San Francisco FY transit fares not available in many cases; assumed to increase with inflation at 3% per year Where data were not provided, FY travel times for drive alone and rideshare modes based on Texas Transportation Institute’s projections for change in Travel Time Index between 2BY transit fares and parking charges were obtained from MPO websites when not provided or from Colliers International Parking Rate Survey, available at: Colliers International Parkhttp://www.colliers.com/content/globalcolliersparkingratesurvey2009.pdf 

A-13 
blank 

page 

A-14 
 

 

Table A-9. National Emissions Reductions 

Actual (grams per day) Percentage 

2030 2050 2030 2050 

1 Region-wide TDM 

NOx 1,158,438 3,764,014 0.10% 0.26% 

VOC 739,609 2,562,147 0.09% 0.25% 

PM2.5 101,349 360,877 0.10% 0.26% 

CO2 equivalent 2,914,224,009 10,472,471,706 0.10% 0.26% 

2 Land use changes + TDM 

NOx 12,084,721 42,656,208 1.00% 2.93% 

VOC 7,837,082 2,562,147 0.98% 2.86% 

PM2.5 1,051,190 360,877 1.01% 2.96% 

CO2 equivalent 30,177,541,913 117,765,791,643 1.01% 2.97% 

3 Land use changes + Transit fare reduction + TDM 

NOx 16,762,872 60,434,817 1.39% 4.16% 

VOC 10,934,000 42,166,321 1.36% 4.08% 

PM2.5 1,454,970 5,743,848 1.40% 4.18% 

CO2 equivalent 41,743,724,738 166,274,160,854 1.40% 4.19% 

4 Land use changes + Transit fare reduction + Transit service 

improvements + TDM 

NOx 17,324,704 62,197,472 1.43% 4.28% 

VOC 11,344,808 43,717,356 1.41% 4.23% 

PM2.5 1,501,521 5,895,642 1.44% 4.29% 

CO2 equivalent 43,061,338,160 170,539,394,836 1.44% 4.30% 

5 Land use changes + Transit fare reduction + Transit service 

improvements + Parking Fees + TDM 

NOx 35,232,500 99,977,456 2.91% 6.87% 

VOC 23,248,699 69,056,024 2.90% 6.68% 

PM2.5 3,044,728 9,536,337 2.92% 6.94% 

CO2 equivalent 87,246,215,207 276,828,363,309 2.92% 6.98% 

6 Land use changes + Transit fare reduction + Transit service 

improvements + Mileage Fees + TDM 

NOx 23,161,418 89,739,448 1.92% 6.17% 

VOC 15,026,958 61,482,738 1.87% 5.95% 

PM2.5 2,014,373 8,584,360 1.93% 6.25% 

CO2 equivalent 57,825,922,017 249,185,850,704 1.94% 6.28% 

7 Land use changes + Transit fare reduction + Transit service 

improvements + Parking Fees + Mileage Fees + TDM 

NOx 41,063,105 125,781,137 3.40% 8.65% 

VOC 26,869,067 85,645,173 3.35% 8.29% 

PM2.5 3,559,935 12,058,051 3.42% 8.78% 

CO2 equivalent 102,101,807,189 350,512,043,690 3.42% 8.83% 

CO2 equivalent = [CO2 + 21*(CH4) + 310*(N2)] 

A-15 
 

 

Figure A-1. VMT Reduction for Each Scenario for Cluster 1 Regions 

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 

Figure A-3. VMT Reduction for Each Scenario for Cluster 3 Regions 

A-17 
 

Figure A-4. VMT Reduction for Each Scenario for Cluster 4 Regions 

Figure A-5. VMT Reduction for Each Scenario for Cluster 5 Regions 

A-18 
 

Figure A-6. VMT Reduction for Each Scenario for Cluster 6 Regions 

Figure A-7. VMT Reduction for Each Scenario for Cluster 7 Regions 

A-19 
 

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A-20 
 

Appendix B 

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. 

SECTION I. 

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 
 

 

 

 

 

 

 

____________________________________________________________________________________ 

[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. persontrips 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 (%) Base year Future year -- base case Intermediate year -- base case 

Auto-drive alone 

Auto-rideshare 

Vanpool 

Public transit 

Cycling 

Walking 

Other 

Please specify the modes you have provided data for in the “Other” field __________________________ 

Comments: __________________________________________________________________________
 

B-22 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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) Base year Future year -- base case Intermediate year -- base case 

Auto-drive alone 

Auto-rideshare 

Vanpool 

Public transit 

Cycling 

Walking 

Other 

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) 

Base year Future year Intermediate year 

Peak Off-peak Average Peak Off-peak Average Peak Off-peak Average 

Auto-drive alone 

Auto-rideshare 

Vanpool 

Public transit 

Cycling 

Walking 

Other 

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 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

Avg. Vehicle 

Occupancy 

(persons) 

Base year Future year Intermediate year 

Peak Off-peak Average Peak Off-peak Average Peak Off-peak Average 

Auto-rideshare 

Vanpool 

Bus 

5) Peak and Off-peak Trips 

Please state which hours you define as AM peak, PM peak, and off-peak 

AM: -----------------------------------

PM: -----------------------------------

Off-peak: ----------------------------

Base year Future year -- base case Intermediate year -- base case 

Percentage of total trips 

in peak hours (%) 

Total trips in peak 

hours 

Total trips in off-peak 

hours 

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 Base year Future year -- base case Intermediate year -- base case 

Auto-drive alone 

Auto-rideshare 

Vanpool costs 

Public transit fare 

Cycling 

Other 

B-24 
 

 

 

 

Automobile Parking Costs (Current $ per auto per day) 

Average Parking 

Costs Base year Future year -- base case Intermediate year -- base case 

Auto-drive alone 

Auto-rideshare 

Other Auto Trip Costs not included in Parking Costs, e.g., tolls, peak hour fees, etc. 

(Current $ per trip) 

Average Parking 

Costs Base year Future year -- base case Intermediate year -- base case 

Auto-drive alone 

Auto-rideshare 

Check the cost categories below that your model uses to calculate trip costs and briefly state the assumptions 

used for future years 

9 Cost categories Assumptions for future years 

Fuel 

Insurance 

Maintenance/repairs 

Ownership costs, i.e. vehicle costs, 

registration and licensing fees 

Other; please specify 

B-25 
 

 

 

 

 

 

 

 

 

 

 

7) Vehicle Mileage data to Calculate Operating Costs 

Average Mileage 

(Miles Per Gallon) Base year Future year -- base case Intermediate year -- base case 

Automobiles 

Vans 

Bus 

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) 

Base year Future year Intermediate year 

Access time Travel time Access time Travel time Access time Travel time 

Auto-drive alone 

Auto-rideshare 

Vanpool 

Public transit 

Cycling 

Walking 

Other 

B-26 
 

 

 

 

 

 

 

 

 

 

 

SECTION II. 

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 Base year Future year Intermediate year 

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.] 

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 from Packages of Measures You Have Modeled in Your Region 

Modeled packages of 

strategies 

Package 1 Package 2 Package 3 Package 4 Package 5 

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 

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 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Mode Share Changes Resulting from Increase in Driving Costs (higher tolls, parking, or congestion charges) 

Modeled change in 

pricing policy Base year Future year Intermediate year 

% 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 

Mode Share Changes Resulting from Change in Transit Fares 

Modeled change in 

pricing policy Base year Future year Intermediate year 

% change in transit fares 

MODEL OUTCOMES 

New Mode Shares (%) 

Auto-drive alone 

Auto-rideshare 

Vanpool 

Public transit 

Cycling 

Walking 

Other 

B-29 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Mode Share Changes Resulting from Change in Transit Access or Travel Time 

Modeled change in 

pricing policy Base year Future year Intermediate year 

% 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 

Mode Share Changes Resulting from Change in Auto Travel Time 

Modeled change in 

pricing policy 

Base year Future year Intermediate year 

% change in auto travel 

time 

MODEL OUTCOMES 

New Mode Shares (%) 

Auto-drive alone 

Auto-rideshare 

Vanpool 

Public transit 

Cycling 

Walking 

Other 

B-30 
 

_____________________________________________________________________________________ 

___________________________________________________________________________________ 

____________________________________________________________________________________ 

_____________________________________________________________________________________ 

___________________________________________________________________________________ 

____________________________________________________________________________________ 

_____________________________________________________________________________________ 

___________________________________________________________________________________ 

____________________________________________________________________________________ 

_____________________________________________________________________________________ 

_____________________________________________________________________________________ 

__________________________________________________________________________________ 

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 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

SECTION III 

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/offpeak 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 

Modes 

Auto Rideshare Public transit Other 

Commuting 

Business 

Education 

Other 

Transit Elasticities: In the table below, please provide elasticities of transit ridership 

Peak Off-Peak Average 

Transit Ridership with respect to Transit Fare 

Transit Ridership with respect to Transit Service 

Transit Ridership with respect to Auto Operating Costs 

Auto travel with respect to Transit Costs 

Travel Time Elasticities 

Peak Off-Peak Average 

Auto 

Rideshare 

Public transit 

B-32 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

If cross-elasticities across modes are available please provide these in the table below, otherwise move on to Section III. 

Auto Rideshare Public transit 

Peak Auto 

Rideshare 

Public transit 

Auto Rideshare Public transit 

Off-peak Auto 

Rideshare 

Public transit 

Auto Rideshare Public transit 

Average Auto 

Rideshare 

Public transit 

THANK YOU VERY MUCH FOR YOUR TIME. 

YOUR PARTICIPATION IS GREATLY APPRECIATED
 

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