Four Case Studies in
Applying TEAM in Regional
Sketch Planning:
PUGET SOUND, WASHINGTON
CHAMPAIGN, ILLINOIS
LAKE CHARLES, LOUISIANA
STATE OF CONNECTICUT
United States
Environmental Protection
^1	Agency
Office of Transportation and Air Quality
EPA-420-R-18-018
November 2018

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Applying TEAM in Regional
Sketch Planning:
Four Case Studies in:
PUGET SOUND, WASHINGTON
CHAMPAIGN, ILLINOIS
LAKE CHARLES, LOUISIANA
STATE OF CONNECTICUT
Transportation and Climate Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
&EPA
United States
Environmental Protection
Agency
EPA-420-R-18-018
November 2018

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Applying TEAM in Regional Sketch Planning: Four Case Studies
Acknowledgments
The U.S. EPA Office of Transportation and Air Quality thanks the following organizations for their
partnership and support in providing the critical data and thoughtful input required for the successful
completion of this project and report.
•	Champaign-Urbana Urban Area Transportation Study
•	Imperial Calcasieu Regional Planning and Development Commission
•	Northeast States for Coordinated Air Use Management
•	Puget Sound Clean Air Agency
ICF International provided technical support to the U.S. Environmental Protection Agency in the
development of the methodologies and analysis employed to develop the emission inventories and
other tasks related to this assessment.
Acronyms and Abbreviations
BAU	business as usual
C02	carbon dioxide
C02e	carbon dioxide equivalent
CTDEEP	Connecticut Department of Energy and Environmental Protection
CTDOT	Connecticut Department of Transportation
CTR	Commute Trip Reduction
CUUATS	Champaign Urbana Urbanized Area Transportation Study
EPA	U.S. Environmental Protection Agency
GHG	greenhouse gas
IMCAL	Imperial Calcasieu Regional Planning and Development Commission
LCMPO	Lake Charles Metropolitan Planning Organization
LRTP	Long Range Transportation Plan
MOVES	Motor Vehicle Emission Simulator (EPA's motor vehicle emissions model)
MPO	Metropolitan Planning Organization
NESCAUM	Northeast States for Coordinated Air Use Management
NOx	nitrogen oxides
PM	particulate matter
PSCAA	Puget Sound Clean Air Agency
PSRC	Puget Sound Regional Council
TAZ	traffic analysis zone
TDM	Transportation Demand Management
TEAM	Travel Efficiency Assessment Method
TE	travel efficiency
TRIMMS	Trip Reduction Impacts of Mobility Management Strategies
VMT	vehicle miles traveled
VOC	volatile organic compound
U.S. Environmental Protection Agency

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Applying TEAM in Regional Sketch Planning: Four Case Studies
Table of Contents
Executive Summary	i
1.	Background on Travel Efficiency Assessment Methodology Analysis	1
1.1.	Introduction	1
1.2.	Analysis Tools	2
1.3.	Strategy Selection, Scenario Development, and Baselines	3
1.3.1.	Strategy Selection	3
1.3.2.	Strategy Scope: Geographies and Subpopulations	4
1.4.	Data Collection and Validation	5
1.5.	Analysis	6
1.5.1.	VMT Analysis	6
1.5.2.	MOVES Analysis and Emission Rates	9
1.5.3.	Transit VMT and Emissions	10
1.5.4.	Comparison with Previous Results	11
2.	Champaign-Urbana, Illinois - Champaign-Urbana Urbanized Area Transportation Study	12
2.1.	Background	12
2.2.	Scenario Development	13
2.2.1.	Scenario 1 - Local Transit Hubs and Bus Improvements + Bicycle and Pedestrian
Improvements	13
2.2.2.	Scenario 2 - Scenario 1 + Parking Pricing at the University	14
2.2.3.	Scenario 3 - Scenario 2 + Smart Growth Land Use	15
2.2.4.	Scenario 4 - Scenario 3 + High Speed Rail to Chicago	16
2.3.	Scenario Summary	16
2.4.	Emissions Analysis	18
2.5.	CUUATS Scenario Results	19
2.6.	Transit VMT and Emissions	21
3.	Lake Charles, Louisiana - Imperial Calcasieu Regional Planning and Development Commission	23
3.1.	Background	23
3.2.	Scenario Development	23
3.2.1.	Scenario 1 - TDM Program for Petrochemical Employees	23
3.2.2.	Scenario 2 - Scenario 1 + Transit Improvement in North Lake Charles	24
3.2.3.	Scenario 3 - Scenario 2 + Parking Pricing in Downtown Lake Charles	25
3.2.4.	Scenario 4 - Scenario 3 + Smart Growth Land Use	26
3.3.	Scenario Summary	26
3.4.	Emissions Analysis	28
3.5.	IMCAL Scenario Results	28
3.6.	Transit VMT and Emissions	31
4.	State of Connecticut - Northeast States for Coordinated Air Use Management	32
4.1.	Background	32
4.2.	Scenario Development	33
4.2.1.	Scenario 1 - Commuter Rail Improvements	33
4.2.2.	Scenario 2 - Scenario 1 + Local Bus Improvements	34
4.2.3.	Scenario 3 - Scenario 2 + Smart Growth Land Use	34
4.3.	Scenario Summary	35
4.4.	Emissions Analysis	37
4.5.	Scenario Results	39
4.6.	Transit VMT and Emissions	40
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Applying TEAM in Regional Sketch Planning: Four Case Studies
4.7. Additional Transit Analysis Details	41
5.	Puget Sound, Washington - Puget Sound Clean Air Agency	43
5.1.	Background	43
5.2.	Scenario Development	43
5.2.1.	Scenario 1 - Expand Commute Trip Reduction Program	44
5.2.2.	Scenario 2 - Scenario 1 + Expand Access to Free Transit within Environmental
Justice/Low-Income Populations	44
5.2.3.	Scenario 3 - Scenario 2 + VMT Pricing	45
5.2.4.	Scenario 4 - Scenario 3 + Smart growth land use	46
5.3.	Scenario Summary	46
5.4.	Emissions Analysis	47
5.5.	PSCAA Scenario Results	48
5.6.	Transit VMT and Emissions	50
6.	Results and Observations	51
6.1.	Regional VMT and Emission Results	51
6.2.	Observations and Lessons Learned	52
6.2.1.	Accessibility to Different Organizations	52
6.2.2.	Flexibility of Strategy and Scenario Evaluation	52
6.2.3.	Scalability of Affected Population and Geographic Area of Analysis	53
6.3.	Conclusion	53
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Applying TEAM in Regional Sketch Planning: Four Case Studies
List of Tables and Figures
Figure ES-1 TEAM Case Study Sites	ii
Table ES-1. Summary of Travel Efficiency Strategies Selected	iv
Table ES-2. Percent Regional VMT and Emissions Changes	v
Table 1-1. TEAM Analysis Options	4
Table 1-2. Example of National Datasets for Use in TEAM	6
Table 1 3. Ewing and Cervero (2010) Elasticity Values for the Multivariate Land Use Analysis	8
Table 2-1 CUUATS Access and Travel Time by Scenario	13
Table 2-2. CUUATS TRIMMS Input: Access and Travel Time (minutes)	14
Table 2-3. CUUATS TRIMMS Input: Financial and Pricing Strategies (costs per person)	15
Table 2-4. CUUATS Scenario Input Details	17
Table 2-5. CUUATS Emission Rates	19
Table 2-6. CUUATS Percent Change in VMT and Emissions for 2040 BAU Compared to 2040 Scenario ... 19
Table 2-7. CUUATS VMT and Emission Changes by Scenario Relative to 2040 BAU	20
Table 2-8. CUAATS VMT and Emission Changes by Scenario Relative to 2010 Baseline	20
Table 2-9. CUUATS Comparison of VMT Reductions with Previous Case Studies	21
Table 2 10. CUUATS Transit Vehicle Percent VMT and Emissions Increases from BAU	22
Table 3-1. IMCAL TRIMMS Scenario 2 Input: Access and Travel Time Improvements (minutes)	25
Table 3-2. IMCAL TRIMMS Scenario 3 Input: Financial and Pricing Strategies (costs per person)	25
Table 3-3. IMCAL Scenario Details	26
Table 3-4. IMCAL Emission Rates	28
Table 3-5. IMCAL Percent Change in VMT and Emissions for 2040 BAU Compared to 2040 Scenario	28
Table 3 6. IMCAL VMT and Emission Changes by Scenario Relative to 2040 BAU	29
Table 3 7. IMCAL VMT and Emission Changes by Scenario Relative to 2015 Baseline	29
Table 3-8. IMCAL Comparison of VMT Reductions Strategies and Previous Case Studies	30
Table 3 8. IMCAL Transit Vehicle Percent VMT and Emissions Increases from BAU	31
Table 4-1. NESCAUM TRIMMS Scenario 1 Input: Access and Travel Time Improvements (minutes)	34
Table 4-2. NESCAUM TRIMMS Scenario 2 Input: Access and Travel Time Improvements (minutes)	34
Table 4-3. NESCAUM TRIMMS Input: Financial and Pricing Strategies (costs per person)	35
Table 4-4. NESCAUM Scenario Details	36
Table 4-5. NESCAUM New York-New Haven Corridor Emission Rates	38
Table 4-6. NESCAUM Connecticut Statewide Emission Rates	38
Table 4-7. NESCAUM Percent Change in VMT and Emissions for 2040 BAU Compared to 2040 Scenario 39
Table 4-8. NESCAUM VMT and Emission Changes by Scenario Relative to 2040 BAU	39
Table 4 9. NESCAUM Travel and Emission Changes by Scenario Relative to 2015 Baseline	39
Table 4-10. NESCAUM Comparison of Strategy VMT Reductions with Previous Case Studies	40
Table 4-10. NESCAUM Transit Vehicle Percent VMT and Emissions Increases from BAU	41
Table 5-1. PSCAA TRIMMS Input: Financial and Pricing Strategies for 1st Run (costs per person)	44
Table 5-2. PSCAA TRIMMS Input: Financial and Pricing Strategies for 2nd Run (costs per person)	45
Table 5-3. PSCAA TRIMMS Input: Financial and Pricing Strategies (costs per person)	45
Table 5-4. PSCAA Scenario Details	46
Table 5-5. PSCAA Emission Rates	48
Table 5-6. PSCAA Percent Change in VMT and Emissions for 2040 BAU Compared to 2040 Scenario	48
Table 5 7. PSCAA Travel and Emission Changes by Scenario Relative to 2040 BAU	49
Table 5 8. PSCAA Travel and Emission Changes by Scenario Relative to 2014 Baseline	49
Table 5-8. PSCAA Comparison of VMT Reductions Strategies and Previous Case Studies	49
Table 6-1. Percent Regional VMT and Emission Changes from the Case Study Areas	51
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Applying TEAM in Regional Sketch Planning: Four Case Studies
Executive Summary
Despite significant improvements in vehicle technologies and fuels, the transportation sector continues
to be one of the largest sources of criteria pollutants and GHG emissions in the country.1 While
emissions per mile traveled have decreased, growth in travel activity has partially offset those gains, and
presents a challenge to achieving and maintaining healthy air quality in many areas. For air quality and
transportation planners that are interested in reducing transportation emissions in their regions, the
ability to estimate the emission reduction potential of a given strategy aimed at reducing travel activity
is critical to long range planning and programmatic investment. Over the past several years, the U.S.
Environmental Protection Agency (EPA) has supported air quality and transportation planning activities
by developing methods to quantify the potential emission reductions from travel efficiency strategies,
and has worked with various state and local agencies to apply these methods in a series of case studies.
The term "travel efficiency" (TE) strategies refers to a broad range of strategies designed to reduce
travel activity, especially single-occupancy travel. TE strategies build on the traditional Transportation
Control Measures (TCMs), such as employer-based transportation management programs and transit
improvements, listed in Section 108(f)(1)(A) of the Clean Air Act by adding smart growth and related
land use strategies, road and parking pricing, and other strategies aimed at reducing mobile source
emissions by reducing vehicle travel activity.
EPA has developed the Travel Efficiency Assessment Method (TEAM), an approach to quantify the
potential emission benefits of travel efficiency strategies. TEAM uses available travel data and a
transportation sketch model analysis to quantify the change in VMT resulting from TE strategies. In a
TEAM analysis, a future analysis year is chosen. VMT and emissions are estimated in the future
"Business as Usual" (BAU) case that does not include the TE strategies. Then VMT and emissions
estimated in future TE strategy scenarios are compared against the BAU case. Emission factors are
developed using the current version of EPA's MOVES (Motor Vehicle Emission Simulator). TEAM allows
for the analysis of potential travel efficiency strategies to reduce emissions without having to run an
area's travel demand model, saving time and resources.
The case studies in this report are EPA's latest in this field of study. With this latest round of case
studies, EPA has worked with ten areas to assess the impact of travel efficiency strategies using the
TEAM approach. In 2010, EPA conducted a national scale TEAM assessment of potential emission
reductions that could be achieved if travel efficiency strategies were adopted in all the urban areas of
the country.2 EPA furthered this work in 2014 through a series of case studies featuring Tucson, AZ,
Kansas City, MO-KS, and Boston, MA.3 In 2016, EPA completed a second round of case studies,
highlighting St. Louis, MO-IL, Atlanta, GA, and Orlando, FL, which further refined TEAM to include two
1	EPA, Our Nation's Air: Status and Trends Through 2016, available at https://www.epa.gov/air-trends. This
interactive report includes a table showing the contribution of various source sectors to air pollution at:
https://gispub.epa.gOv/air/trendsreport/2017/#sources.
2	EPA, Potential Changes in Emissions Due to Improvements in Travel Efficiency, EPA-420-R-11-003, March 2011,
available on the web at: https://nepis,epa,gov/Exe/ZyPdf,cgi/P100AGMT,pdf?Dockey=P100AGMT,pdf
3	EPA, Estimating Emission Reductions from Travel Efficiency Strategies: Three Sketch Modeling Case Studies, EPA-
420-R014-003a, June 2014, available on the web at:
https://nepis.epa.gov/Exe/Zy PDF. cgi/P100JWK8.PDF?Dockey=P100JWK8. PDF.
U.S. Environmental Protection Agency
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Applying TEAM in Regional Sketch Planning: Four Case Studies
alternative approaches for estimating effects of land use strategies, and began accounting for additional
VMT and transit emissions when calculating the overall emission reductions.4
As with the previous case studies, for this latest round, EPA solicited letters of interest from agencies
interested in applying TEAM in their local context and evaluating their selected strategies. Four agencies
were selected from the submitted letters:
•	Champaign-Urbana Urban Area Transportation Study (CUUATS), the MPO for the
Champaign-Urbana area in Illinois;
•	Imperial Calcasieu Regional Planning and Development Commission (IMCAL), the MPO for
the Lake Charles area in Louisiana;
•	Northeast States for Coordinated Air Use Management (NESCAUM), an association of air
quality agencies in eight Northeast states: CT, ME, MA, NH, Rl, VT, NJ, and NY; and
•	Puget Sound Clean Air Agency (PSCAA), which covers the Seattle, Washington region.
Figure ES-1 shows the locations of the case studies completed to date, including those discussed in this
report.
Figure ES-1 TEAM Case Study Sites
Puget Sound, W<
Cha
Co
cut
Kansas City, MO
Tucson, AZ
Atlanta, GA
2014 Case Study
*2016 Case Study
*2018 Case Study
In addition to the two MPO participants, this round of TEAM case studies involved agencies that are
specifically responsible for air quality decision making rather than transportation planning. NESCAUM's
purpose is to provide scientific, technical, analytical, and policy support to the air quality programs of
4 EPA, Applying TEAM in Regional Sketch Planning: Three Case Studies in Atlanta, Orlando, St. Louis, EPA-420-R-16-
009, July 2016, available on the web at: https://www.epa.gov/state-and-local-transportation/applying-team-
regional-sketch-planning-three-case-studies-atlanta.
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Applying TEAM in Regional Sketch Planning: Four Case Studies
the states it represents. NESCAUM, in coordination with Connecticut Department of Transportation
(CTDOT) and Connecticut Department of Energy and Environmental Protection (CTDEEP), used
Connecticut as the basis for this analysis, with an interest in applying to other states in the consortium in
the future. Puget Sound represents the first TEAM partnership with a local agency responsible for air
quality planning. Participants also vary considerably in the complexity of their travel demand models
and resources available. Some areas had all the data necessary to conduct an analysis, while others
needed to seek data from other sources.
In this round of case studies, the analysis year used was 2040. In each case study, four different
scenarios of travel efficiency strategies were compared to the BAU case. For these scenarios, the
partner agencies chose the combination of travel efficiency strategies of most interest to them. The
results of each case study are included in Sections 2 through 5 in this report.
Analysis
This report is specific to the analysis task of the study, and covers data collection, VMT analysis, and
emissions analysis. Each case study is reported independently with a focus on the technical attributes of
the analysis and the lessons learned.
Analysis Tools
This round of TEAM case studies used the Trip Reduction Impacts of Mobility Management Strategies
(TRIMMS) sketch model developed by the Center for Urban Transportation Research (CUTR) at the
University of South Florida as part of the TEAM approach.5 TRIMMS offers a variety of features making
it suitable for this type of analysis. However, the land use component of TRIMMS was not employed in
this study as described later in this document. The regional analysis was conducted with TRIMMS 3.0,
the latest version of this sketch model available at the time the case studies were undertaken.
MOVES2014a was used to determine appropriate, regional average emission rates for all regions.
MOVES was run in Inventory mode based on regionally provided inputs to produce activity-weighted
average emission rates for the four primary pollutants considered in this analysis: C02-Equivalent (C02e),
NOx, PM2.5, and VOC.
Land Use Analysis
Land use strategies are one of the most important, and most complex, means by which regions can
reduce VMT. Land use patterns affect how people travel, and therefore an area's geographic size and
density have an impact on emissions. Areas that are more compact will have shorter average trip
lengths and fewer vehicle trips. Supportive land use policies can provide for the commercial and
residential densities to enable transit to be viable and cost effective. Land use strategies in these case
studies were analyzed using the Multivariate Approach developed in previous TEAM case studies.
5 The TRIMMS model can be found at http://trimms.com/.
U.S. Environmental Protection Agency

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Applying TEAM in Regional Sketch Planning: Four Case Studies
Selected Strategies
The selection of travel efficiency strategies makes the TEAM analysis unique to that region by
representing what is important to understand about how a strategy works and what can be defined by
regionally applicable data. The combination of strategies in this report represents a broad range of
strategies, and application to specific geographies and populations, including the statewide level. The
agencies, in consultation with EPA, chose the strategies they were interested in. The agencies explored
available data to support their strategy selection, and in the case of land use strategies, conducted
additional analysis to provide required data. Table ES-1 provides an overview of the selected strategies
and their individual geographical and population application.
Table ES-1. Summary of Travel Efficiency Strategies Selected
Case Study Area
Strategies
Geographic Area Covered
Applied to
Champa ign-
Urbana, Illinois
Restructure transit network to reduce
wait times and travel times. Expand
bicycle lanes and sidewalk coverage.
Urbanized area
161,000 residents of
urbanized area
Increase the cost that University
employees pay for parking
University of Illinois
24,300 employees
Increase densities, land use mixing,
and job accessibility
Champaign County
246,000 county
residents
Upgrade existing rail corridor to
Chicago to high speed rail
Champaign-Chicago corridor
775,000 daily riders
Lake Charles,
Louisiana
TDM program for petrochemical
employees
Petrochemical employment
cluster
7,500 employees
Transit improvement in North Lake
Charles
North Lake Charles
residential neighborhood
13,500 residents
Parking pricing in downtown area
Downtown Lake Charles
13,000 daily travelers
Smart growth land use
MPO area
260,000 residents
State of
Connecticut
Commuter rail improvements
New York-New Haven
corridor
1.35 million residents
Local bus improvements
New York-New Haven
corridor
1.35 million residents
Smart growth land use
New York-New Haven
corridor
1.35 million residents
VMT pricing
State of CT
4.01 million residents
Puget Sound,
Washington
Expand Commute Trip Reduction
(CTR) Program
Puget Sound region
156,000 additional
employees
Expand access to free transit within
EJ/low-income populations
Puget Sound region
169,000 EJ/low-income
residents
VMT pricing
Puget Sound region
4.85 million residents
Smart growth land use
Puget Sound region
4.85 million residents
U.S. Environmental Protection Agency
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Applying TEAM in Regional Sketch Planning: Four Case Studies
Results
Table ES-2 shows the percent VMT and emission reductions for the scenarios analyzed for each area.
The values represent the percent change from the BAU 2040 future case, and are cumulative (i.e.,
Scenario 2 results include the impacts of both Scenario 1 and 2, Scenario 3 includes the impacts of
Scenarios 1, 2, 3, etc.).
The range of estimated regional VMT and emission changes resulting from the various scenarios
analyzed reflect the variety of strategies and strength of implementation envisioned by the partner
agencies. As expected, the greatest reductions result from scenarios that represent a combination of
strategies that are mutually supportive and apply to a significant portion of the regional population.
Where strategies affect only a small subset of the regional population or only apply in a designated
subarea of the region, the impacts are limited.
It is important to note that the percent change in VMT and emissions shown in Table ES-2 are relative to
the future year BAU case. If a strong program of travel efficiency strategies is already included in the
LRTP for the region, the incremental addition or strengthening of strategies will result in modest
changes compared to the BAU. Where a scenario represents an aggressive departure from the BAU and
is applied broadly across the region, the reductions can be significant.
Table ES-2. Percent Regional VMT and Emissions Changes
Percent Regional Emissions Changes for Future Year Business as Usual compared to Future Year Scenario
Scenario
Light-Duty
VMT
C02e
PM2.5
NOx
voc
Champaign Urbana Urban Area Transportation Study
Scenario 1: Local Transit Hubs and Bus
Improvements + Bicycle and Pedestrian
Improvements
-2.96%
-3.39%
-4.35%
-5.59%
-7.48%
Scenario 2: Scenario 1 + Parking Pricing at the
University
-3.23%
-3.66%
-4.63%
-5.87%
-7.77%
Scenario 3: Scenario 2 + Smart Growth Land
Use
-7.87%
-8.18%
-8.86%
-9.74%
-11.09%
Scenario 4: Scenario 3 + High Speed Rail -8.09% -8.38% -9.04% -9.88% -11.16%
Imperial Calcasieu Regional Planning and Development Commission
Scenario 1: TDM Program for Petrochemical
Employees
-0.07%
-0.07%
-0.07%
-0.07%
-0.07%
Scenario 2: Scenario 1 + Transit Improvement
in North Lake Charles
-0.10%
-0.10%
-0.10%
-0.10%
-0.10%
Scenario 3: Scenario 2 + Parking Pricing in
Downtown Lake Charles
-0.24%
-0.24%
-0.24%
-0.23%
-0.22%
Scenario 4: Scenario 3 + Smart Growth Land
Use
-1.05%
-1.04%
-1.04%
-1.01%
-0.97%
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Applying TEAM in Regional Sketch Planning: Four Case Studies
Percent Regional Emissions Changes for Future Year Business as Usual compared to Future Year Scenario
Scenario
Light-Duty
VMT
C02e
PM2.5
NOx
voc
Northeast States for Coordinated Air Use Management
Scenario 1: Commuter Rail Improvements
-0.40%
-0.40%
-0.40%
-0.41%
-0.42%
Scenario 2: Scenario 1 + Local Bus
Improvements
-0.95%
-0.95%
-0.95%
-0.98%
-1.00%
Scenario 3: Scenario 2 + Smart Growth Land
Use
-1.18%
-1.18%
-1.17%
-1.16%
-1.14%
Scenario 4: Scenario 3 + VMT Pricing
-5.42%
-5.44%
-5.45%
-5.54%
-5.64%
Puget Sound Clean Air Agency
Scenario 1: Expand Commute Trip Reduction
Program
-0.09%
-0.10%
-0.10%
-0.10%
-0.10%
Scenario 2: Scenario 1 + Expand access to free
transit within EJ/low-income populations
-1.87%
-1.89%
-1.91%
-2.00%
-2.17%
Scenario 3: Scenario 2 + VMT Pricing
-5.11%
-5.16%
-5.21%
-5.44%
-5.86%
Scenario 4: Scenario 3 + Smart growth land
use
-11.91%
-11.82%
-11.71%
-11.28%
-10.49%
With this third round of case studies that are described in this report, EPA has worked with ten areas to
assess the impact of travel efficiency strategies using the TEAM approach. Throughout the rounds of
case studies, TEAM has proved to be accessible to a wide variety of organizations with varying degrees
of topical and technical expertise. TEAM is also unique in its flexibility to explore an array of different
travel efficiency strategies. For example, TEAM has been used to explore hypothetical "what-if"
exercises, evaluate program-level decisions, and been tested on numerous new strategy applications,
and has produced useful results in each case. TEAM has also proven to be scalable, as it has been used
successfully to evaluate the VMT and emission reduction benefits of strategies applied to a specific
corridor, city or county, or entire state. EPA believes that these case studies provide a valuable resource
to encourage agencies not only to conduct these analyses, but ultimately, to adopt effective travel
efficiency strategies to improve local air quality and reduce emissions.
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1. Background on Travel Efficiency Assessment Methodology
Analysis
Introduction
Over the past several years, the U.S. Environmental Protection Agency (EPA) has supported research on
the potential to lower emissions by reducing single-occupancy vehicle travel and, correspondingly,
vehicle miles traveled (VMT). This research led to an approach that used regional data to quantify the
potential emission reductions from several travel efficiency strategies. Travel efficiency strategies
represent the broad range of strategies designed to reduce travel activity, especially travel that involved
one individual per vehicle (i.e., single-occupancy travel). The term "travel efficiency strategies" builds on
the traditional Transportation Control Measures (TCMs) listed in Clean Air Act section 108(f)(1)(A) such
as employer-based transportation management programs and transit improvements, by adding smart
growth and related land use strategies, road and parking pricing, and other strategies aimed at reducing
mobile source emissions by reducing vehicle travel activity.
EPA has developed an approach to quantify the potential emission benefits of travel efficiency strategies
and this approach is the Travel Efficiency Assessment Method (TEAM). TEAM uses available travel data
and a transportation sketch model analysis to quantify the change in VMT resulting from the strategies.
In a TEAM analysis, a future analysis year is chosen, and the VMT in the future analysis year assuming
the travel efficiency strategy in place is compared to the VMT in same future year without it. In a TEAM
analysis, the future year without any additional travel efficiency strategies is known as the "Business as
Usual" (BAU) case. The difference in VMT and number of trips that results from each strategy compared
to the BAU are then combined with emission factors for the chosen analysis year from the current
version of EPA's MOVES (Motor Vehicle Emission Simulator) model to calculate reasonably expected
emission reductions. The TEAM approach allows for the analysis of potential travel efficiency strategies
to reduce emissions without having to run an area's travel demand model, saving time and resources.
The case studies in this report are EPA's latest in this field of study. In 2010, EPA conducted a national
scale TEAM assessment of potential emission reductions that could be achieved if travel efficiency
strategies were adopted in all the urban areas of the country.6 EPA furthered this work in 2014 through
a series of case studies featuring Tucson, AZ, Kansas City, MO-KS, and Boston, MA.7 In 2016, EPA
completed a second round of case studies, highlighting St. Louis, MO-IL, Atlanta, GA, and Orlando, FL,
which further refined TEAM to include two alternative approaches for estimating effects of land use
strategies, and began accounting for additional VMT and transit emissions when calculating the overall
emission reductions.8
6	EPA, Potential Changes in Emissions Due to Improvements in Travel Efficiency, EPA-420-R-11-003, March 2011,
available on the web at: https://nepis.epa.gov/Exe/ZyPdf.cgi/P100AGMT.pdf?Dockey=P100AGMT.pdf.
7	EPA, Estimating Emission Reductions from Travel Efficiency Strategies: Three Sketch Modeling Case Studies, EPA-
420-R014-003a, June 2014, available on the web at:
https://nepis.epa.gov/Exe/Zy PDF. cgi/P100JWK8.PDF?Dockey=P100JWK8. PDF
8	EPA, Applying TEAM in Regional Sketch Planning: Three Case Studies in Atlanta, Orlando, St. Louis, EPA-420-R-16-
009, July 2016, available on the web at: https://www.epa.gov/state-and-local-transportation/applying-team-
regional-sketch-planning-three-case-studies-atlanta.
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For this latest round of case studies, four agencies were selected:
•	Champaign-Urbana Urban Area Transportation Study (CUUATS), the metropolitan planning
organization (MPO) for the Champaign-Urbana area in Illinois;
•	Imperial Calcasieu Regional Planning and Development Commission (IMCAL), the MPO for
the Lake Charles area in Louisiana;
•	Northeast States for Coordinated Air Use Management (NESCAUM), an association of air
quality agencies in eight Northeast states: CT, ME, MA, NH, Rl, VT, NJ, and NY; and
•	Puget Sound Clean Air Agency (PSCAA), which covers the Seattle, Washington region.
In addition to the two MPO participants, this round of TEAM case studies involved agencies that are
specifically responsible for air quality decision making rather than transportation planning. NESCAUM's
purpose is to provide scientific, technical, analytical, and policy support to the air quality programs of
the states it represents. NESCAUM used Connecticut as the basis for this analysis, with an interest in
applying to other states in the consortium in the future. Puget Sound represents the first TEAM
partnership with a local agency responsible for air quality.
In this round of case studies, the analysis year used was 2040. In each case study, four different
scenarios of travel efficiency strategies were compared to the BAU case. For these scenarios, the
partner agencies chose the combination of travel efficiency strategies of most interest to them.
1.2. Analysis Tools
TEAM is based on a transportation sketch planning analysis which relies on spreadsheet tools and
calculations to determine the potential VMT reductions of various strategies. Although there are many
sketch planning models, they are often developed for specific uses with varying capabilities and data
requirements. Individual transportation agencies may also develop their own unique models for this
purpose.
The Trip Reduction Impacts of Mobility Management Strategies (TRIMMS) model, developed by the
Center for Urban Transportation Research at the University of South Florida, has been used in the TEAM
approach in identifying changes in VMT for several types of strategies.9 TRIMMS is a sketch planning
model that estimates mode shift, VMT, and trip reductions. The TRIMMS model offers a variety of
features and because it is spreadsheet based, once inputs are determined it is easy to use, making it
suitable for this type of analysis. The principal input factors for strategies in TRIMMS are changes in
travel time and travel cost. TRIMMS 3.0 was used for most of the regional VMT analysis, similar to the
previous TEAM case studies.
Several 'off-model' approaches for analyzing VMT have been developed to fill gaps in the capabilities of
TRIMMS for specific strategies:
•	Bicycle and pedestrian infrastructure investment
•	Smart growth land use
•	High-speed rail
9 The TRIMMS model can be found at http://trimms.com/. The latest version of TRIMMS is version 4.0. These case
studies used TRIMMS 3.0, the latest version available at the time the case studies were undertaken.
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First, the approach for bicycle and pedestrian infrastructure applies elasticities drawn from the literature
that explain the relationship between availability of infrastructure and bicycle and pedestrian mode
share. Next, for land use, two approaches have been developed in previous TEAM case studies: the
neighborhood approach and the multivariate approach. Both land use approaches derive from
literature on the relationship between land use and VMT and can be used interchangeably. When land
use strategies are selected, the case study area chooses a preferred approach based on the land use
data and any previous, available scenario work. Lastly, high-speed rail was introduced for the first time
in this group of TEAM case studies. The approach used was an off-model analysis based on expectations
within the Champaign-Urbana region. For more detail on this strategy, refer to Section 3.
Emissions estimates are performed in TEAM through a separate analysis step using EPA's Motor Vehicle
Emission Simulator (MOVES). MOVES is EPA's state-of-the-science emission modeling system that
estimates emissions for mobile sources at the national, county, and project scales for criteria pollutants,
GHGs, and air toxics under a wide range of user-defined conditions.10 Four pollutants are considered in
this TEAM analysis: C02 equivalent (C02e), NOx, PM2.5, and VOC.
MOVES2014a, the version of EPA's mobile source emissions model at the time of this analysis, was used
for all emissions analyses in this study. MOVES was run in Inventory mode based on the regionally
provided inputs. In TEAM, the MOVES analysis is focused on generating activity-weighted, regional-
average emission rates for each case study region. Details of the calculation method for these emission
rates are discussed in Section 1.5.2.
1.3. Strategy Selection, Scenai /elopment, and Baselines
A scenario in the TEAM analysis framework is a group of one or more travel efficiency strategies tested
for an overall combined benefit. The development of scenarios using TEAM has consistently combined
strategies, through sequential application and modeling, to help identify how benefits might increase
with additional actions over time. This report presents the specific strategies selected for each scenario
and the data needed to model them.
TEAM results provide a comparison of potential future year emission reductions from selected strategies
with the potential emissions from a future year business as usual (BAU) scenario. The BAU scenario
represents likely emissions based on the existing and planned transportation infrastructure with future-
year demographic changes. The results are presented as percent reduction based on this comparison.
For this reason, establishing the BAU baseline is a critical component of this approach.
1.3.1. Strategy Selection
Strategies of interest generally fall into the five categories. Table 1-1 provides information about the
strategies that are routinely analyzed in TEAM, including data needs for each. Additional strategies of
interest, such as high-speed rail, are analyzed off model (outside of a sketch model like TRIMMS), with
individual methodologies based on available data. The strategies that are chosen for analysis are
influenced by the capabilities of the model selected and the data available. For example, to model an
improvement to the transit system in TRIMMS, the user must have available and input the changes in
typical travel times for transit trips. For the off-model approaches, the data is used directly to estimate
changes in VMT.
10 User Guide for MOVES2014, EPA-420-B-14-055, July 2014.
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Table 1-1. TEAM Analysis Options
Strategy Categories
Data Needs
TRIMMS Options
TRIMMS Analysis Options
Transportation Demand
• Share of regional employees covered
• Financial and pricing strategy
Management or Employer
• Average subsidy offered to employees
entries: parking cost and trip cost
Incentives
(by mode)
• Program subsidy radio buttons

• Guaranteed ride home, ride match,
• Guaranteed ride home, ride

telework, and flexible work schedules
match, telework, and flexible work

program availability
schedules radio buttons
Transit
• Share of regional population affected
• Financial and pricing strategy

• Average decrease in transit trip cost
entries: access time and travel

Transit travel time and access time
time
Pricing
• Share of all parking (public and private)
• Financial and pricing strategy

that is priced
entries: parking cost and trip cost

• Average increase in parking cost per


trip


• Average increase in trip cost

Off Model (Non-TRIMMS) Analysis Options
Land Use
•	Share of regional population in affected
areas
•	Neighborhood approach:
•	Percent population by neighborhood
type
•	Multivariate approach:
•	Increase in weighted average
residential density (persons per square
mile)
•	Increase in job accessibility by car
•	Increase in job accessibility by transit
•	Average decrease in distance to transit
•	Average increase in land use mixing
Not Applicable
Bicycle and Pedestrian
• Increase in density of bicycle facilities
Not Applicable
Infrastructure
(facility miles/square mile)

Improvements
• Increase in sidewalk coverage

1.3.2. Strategy Scope: Geographies and Subpopulations
A travel efficiency strategy can target different geographies and subpopulations. Often, TEAM case
study agencies want to apply different strategies to different sub-regions or sub-populations. For
analysis purposes, a sub-region is a division of the larger geographical region, and a subpopulation is a
subset of the larger regional population. For example, a region might want to assess the following
combination of strategies:
1.	Provide transit subsidies to an additional 50,000 workers who currently do not have them;
2.	Improve transit travel times on three primary transit corridors; and
3.	Price parking only in the downtown area.
Such analyses can be done with TEAM. However, a sketch model such as TRIMMS cannot account for
multiple geographies and populations in a single run. Therefore, for these strategies, the analysis must:
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•	Define and gather data for each strategy geography or subpopulation separately. For
example, a strategy that applies a VMT charge to the entire region can use regional averages
for inputs, but a strategy that applies to only the employed population should use inputs
specific to employed persons.
•	Consider where strategy geographies and subpopulations overlap. For example, Strategy A
may apply to the entire region, while Strategy B applies to employed persons and Strategy C
applies to persons living in a certain neighborhood. In this case, inputs are needed for 4
different subpopulations:
o	The population subject to Strategy A only;
o	The population subject to Strategies A and B only;
o	The population subject to Strategies A and C only;
o	The population subject to Strategies A, B, and C.
•	Run the sketch model once for each analysis geography and sum VMT reductions across
each sketch model run. The analysis would produce reductions in daily VMT for each
subpopulation. Summing these results would produce the total VMT reduction for the
region.
TEAM is easiest to use when all strategies apply equally to the same geography and the same
population. For example, if the region of interest has a population of 1 million and transit subsidies and
parking pricing strategies apply to the entire region, then 1 million people would have equal access to
transit subsidies and every parking space in the region would be priced equally.
1.4. Data Collection and Validation
TEAM requires two types of data inputs:
•	Regional base-year and future-year BAU inputs - These are used to establish an
understanding of regional travel patterns in the base year and future year (BAU) and are
required, regardless of the strategies selected. They include total population, total jobs, and
trip characteristics for VMT reductions as well as MOVES required inputs.
•	Strategy-specific inputs - These vary depending on the type and scope of the strategies
selected. Strategy-specific inputs are discussed further in each Scenario Development
section.
One advantage of TEAM is its use of readily available data inputs. Many of the regional base-year and
future-year BAU inputs are data that MPOs already have on-hand as inputs to the regional travel
demand model or as outputs from pre-existing runs of the travel demand model. As the primary
analysis tool at the regional scale, travel demand model inputs and outputs are the best source of
information to maintain consistency with other regional planning analysis. However, regions vary
considerably in the complexity of their travel demand models and resources available. Some regions
will have all the data necessary to conduct an analysis, while others will need to seek data from other
sources.
When a region does not have readily available local data, they can often use surrogates or data points
from publicly available national datasets. Because a TEAM analysis compares emissions with and
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without specific strategies to estimate percentage differences, the input data applies to both cases. This
limits any concern about whether, for example, national average data adequately represents local
conditions. Furthermore, since a TEAM analysis cannot be used to meet regulatory requirements, using
data surrogates instead of local data is an acceptable approach here. This series and previous TEAM
case studies have used national datasets such as highlighted in Table 1-2.
Table 1-2. Example of National Datasets for Use in TEAM
Data set
Provider
Example Data Points Available
American Community Survey (ACS)
U.S. Census Bureau
Trip length
Trip time
National Household Travel Survey (NHTS)
Federal Highway
Administration (FHWA)
Vehicle occupancy
Mode split
Vehicle miles traveled
Average trip length
Trip purpose
Your Driving Costs
American Automobile
Association (AAA)
Trip cost
National Transit Database (NTD)
Federal Transit
Administration (FTA)
Transit vehicle miles traveled (VMT)
The MOVES portion of the TEAM analysis uses regional data to determine regional average emission
rates. One advantage of TEAM is the ability to use MOVES data developed for other purposes, such as
Clean Air Act requirements, if available. The emissions factors are applied to the VMT estimates from
the BAU and the travel efficiency scenarios to estimate potential emission reductions.
Regardless of the data sources, the TEAM process always includes a step of validating, and sometimes
refining, data inputs collected. Local data sources are preferred when available, but national datasets
and default inputs available in some sketch models can help fill gaps in local data.
1.5. Analysis
This section describes the analytical tools and processes used for all TEAM case studies. Sections 2
through 5 discuss the specific analytical choices and processes that apply to individual case studies.
1.5.1. VMT Analysis
To calculate emissions resulting from travel efficiency scenarios, VMT and vehicle trips must first be
calculated for each scenario. Several methods can be used based on the types of strategies selected for
analysis and the data available.
1.5.1.1. TRIMMS Analysis
For the strategies analyzed in TRIMMS, each strategy generally represents an individual TRIMMS model
run. The population input to TRIMMS is the population uniformly affected by that strategy. If a single
strategy affects different parts of the population in different ways, the population would be split and
analyzed in separate TRIMMS runs. For example, a strategy might both increase the dollar value of an
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existing transit subsidy and expand the number of people who have access to the subsidy. The analysis
would treat people who already had access to the subsidy separately from people who did not.
Reduction of VMT and trips output from the TRIMMS model are used to represent the impact of the
strategy in subsequent calculations in most cases. However, there are cases where adjustments need to
be made, as discussed below and in Sections 2 through 5, where they apply.
One such case is when a TRIMMS analysis indicates an increase in VMT in response to a travel efficiency
strategy. For example, TRIMMS might estimate a strategy that increases carpooling also increases total
VMT because it does not account for a corresponding reduction in drive-alone trips. TRIMMS provides
no mechanism to account for interactions or tradeoffs across strategy categories (further discussed in
Section 1.5.1.4.). To ensure that the total number of passenger trips remains consistent, the analysis
must consider whether TRIMMS is appropriately estimating the change in the total number of passenger
trips, and subtract the appropriate number of drive-alone trips.
Another case where output should be considered is where a strategy would result in additional transit
vehicle trips. TRIMMS may not accurately account for change in transit vehicle travel. Because it
assumes the average passenger load on transit vehicles is unchanged, TRIMMS projects an increase in
transit vehicle mileage proportional to the increase in transit ridership. This assumption would not be
true in cases where additional ridership can be accommodated by the existing transit vehicles and
routes. To predict the impacts of increased transit service more accurately, an alternative approach is
used, based on data from the National Transit Database, and described in the Section 1.5.3
TRIMMS underestimates light-duty VMT due to the intrinsic assumption that each person makes just
two trips per day: one trip from home to work, and a second trip from work to home. To correct for
this assumption, a scaling factor is applied during post-processing. The scaling factor is the ratio of VMT
from the regional travel demand model to TRIMMS-modeled VMT, for the same future year. The scaling
factor is applied to the trip and VMT results for all TRIMMS runs conducted for that future year. For
example, with a TRIMMS-modeled daily VMT of 2 million and an agency-provided daily VMT of 3 million
for the year 2030, a scaling factor of 3/2 = 1.5 would be applied to all the results in 2030.
1.5.1.2. Land Use Analysis
To analyze smart growth land use, an approach based on elasticities found in academic research was
used.11 This approach, called the "multivariate approach," calculates the change in VMT from land use
strategies by comparing the following variables for the BAU and scenarios assessed:
•	Household/population density (sourced from inputs to the travel demand model)
•	Job access by auto (an output from some travel demand models)
•	Job access by transit (an output from some travel demand models)
•	Distance to nearest transit stop (sourced from inputs to the travel demand model)
•	Land use diversity (calculated from inputs to the travel demand model)
11 Ewing and Cervero, "Travel and the Built Environment: A Meta-Analysis," Journal of American Planning
Association, 2010. For more detail on how this information is used in TEAM, see
www.epa.gov/sites/production/files/2016-07/documents/420rl6009.pdf
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This approach is based on research that relates changes in each of these variables to changes in VMT
using elasticities, which quantify the percent change in VMT associated with a 1% change in each
variable. To apply this approach, the percent change in each variable between the BAU and strategy is
calculated at the level of the traffic analysis zone (TAZ) and then at the regional level. Elasticities are
applied to the percent change in each variable at the regional level to determine the corresponding
percent change in VMT which is then summed to determine a total percent change in VMT.
The elasticity values from the Ewing and Cervero study are calculated as a weighted average of results
from more than 50 studies, including both national and regional studies. This approach fits well with the
TEAM approach, which is intended to be applicable to all U.S. regions. The weighted averages provided
allow for immediate application to all regions. These values are presented in Table 1-3.
Table 1 3. Ewing and Cervero (2010) Elasticity Values for the Multivariate Land Use Analysis12
D" Category	Variable	Elasticity Value
Density
Household/ population density
-0.04
Job density
0
Diversity
Land use mix (entropy)
-0.09
Jobs-housing balance
-0.02
Design
Intersection/ street density
-0.12
% 4-way intersections
-0.12
Destinations
Job access by auto
-0.2
Job access by transit
-0.05
Distance to downtown
-0.22
Distance to Transit
Distance to nearest transit stop
0.05
1.5.1.3. Bicycle and Pedestrian Analysis
TEAM has also been used to anticipate the potential for VMT reduction based on expanded bicycle and
pedestrian infrastructure. The analysis approach is based on a method used by the San Diego
Association of Governments (SANDAG) for their 2050 Regional Transportation Plan that has been
recognized as an acceptable analysis method for SANDAG's Sustainable Communities Strategy as
required under California law.13 SANDAG's approach assumes:
• A 1% increase in bicycle mode share for every additional mile of bicycle facilities per square
mile of land area, based on academic research by Dill and Carr.14
12	Ibid
13	Technical Appendix 15: SANDAG Travel Demand Model Documentation." 2050 Regional Transportation Plan, San
Diego Association of Governments (SANDAG),
www.sandag.org/index.asp?projectid=360&fuseaction=projects. detail
14	Bicycle Commuting and Facilities in Major U.S. Cities: If You Build Them, Commuters Will Use Them - Another
Look. Dill, J., T. Carr. 2003. Transportation Research Board 1828, National Academy of Sciences, Washington, D.C.
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•	The increase in the walk mode share is equal to the percent increase in sidewalk miles after
applying an elasticity value of 0.27 (from Ewing et al. 2009).15
1.5.1.4. Combining Strategies into Scenarios
After obtaining the VMT reductions from each strategy separately, the strategies are combined to
estimate VMT reductions from scenarios. This step is performed in a post-processing spreadsheet. VMT
from a set of strategies cannot just be summed together because it is likely that this would double-count
reductions. Therefore, this part of the analysis process involves:
•	Calculating the percent VMT reduction for each strategy at the regional level
•	Isolating the strategies that make up a given scenario
•	Calculating the percent VMT reduction for each scenario as the cumulative reduction from
its component strategies; for example, combining Strategy 1 (with a 2% regional VMT
reduction) and Strategy 2 (with a 5% regional VMT reduction) would yield a scenario VMT
reduction of 1 - (1 - 0.02) * (1 - 0.05) = 6.9%
1.5.2. MOVES Analysis and Emission Rates
The TEAM approach uses regionally specific current and future emission rates from MOVES applied to
the VMT and trips from the sketch model (e.g., TRIMMS) to calculate emission reductions outside of
these models. For TEAM, MOVES is run in "Inventory" mode to obtain total emissions, using regional
data when available and default data when necessary as appropriate. The resulting emissions are
divided by activity to produce activity-weighted, regional average gram per start and gram per mile
emission rates for the specified year. These starting and driving emission rates then are applied to the
changes in the number of starts (representing trips) and VMT from the sketch model to estimate the
change in emissions. This method is an efficient approach to determining impacts of the strategies with
the given resolution of the input data.
Emissions analyses were conducted with MOVES2014a, which was the most recent MOVES version as of
the date of the analyses. The choices made for the run specification file are consistent with EPA
guidance.16 Run specifications included the following selections:
•	The geographic scale selected for MOVES modeling was the county scale. For case study
areas made up of several counties, MOVES was run for each individual county and the
results from all of them used to create activity-weighted emissions factors.
•	All MOVES runs for TEAM obtained annual emissions and were conducted without pre-
aggregation, (i.e., emissions estimated on an hourly basis).
•	All available MOVES vehicle types were included in the MOVES runs, and all possible fuel
types for a given vehicle type were included.
•	All road types were included.
15	Ewing, R., Greenwald, M. J., Zhang, M., Walters, J., Feldman, M., Cervero, R., Thomas, J. (2009). Measuring the
impact of urban form and transit access on mixed use site trip generation rates—Portland pilot study. Washington,
DC: U.S. Environmental Protection Agency.
16	Analysis for scale and other parameters adhered to EPA's current guidance for estimating on-road greenhouse
gas emissions: Using MOVES for Estimating State and Local Inventories of On-Road Greenhouse Gas Emissions and
Energy Consumption - Final (EPA-420-B-16-059, June 2016).
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•	Four primary pollutants were considered in this analysis: C02-equivalent (C02e), NOx, PM25,
and VOC.17
•	All the starting and running emission processes were chosen (start exhaust, crankcase start
exhaust, running exhaust, crankcase running exhaust, brake wear, and tire wear).
For input databases, local information was included in the MOVES runs whenever available. Details
about the collection, processing, sources, and quality assurance of these data items appear in the
following sections.
MOVES output was post-processed into average emission rates. While all vehicles types were included,
TEAM focuses on strategies affecting light-duty and transit vehicles. To be consistent with TRIMMS,
emission rates were produced by combining MOVES vehicle and fuel types to match the TRIMMS
composite definitions. For example, in TRIMMS, "auto drive alone" and "auto rideshare" represent trips
with any light-duty vehicles, therefore the emission rates included the MOVES motorcycle, passenger
car, and passenger truck vehicle types. Similarly, the TRIMMS "vanpool mode" corresponds to MOVES
passenger truck and light commercial truck vehicle types. These composite emission rate calculations
are made off-model. The resulting emission rates are listed in a simple table in each of the case studies,
Sections 2 through 5.
Finally, total emissions were calculated by multiplying the emission rates by activity. Emission rates for
starts were multiplied by the number of trips reduced, and emission rates for driving by the VMT
reduced.
For future year emission rates, MOVES incorporates new emissions and fuel economy standards
consistent with EPA regulations, thus emission rates for the future years analyzed in this study decrease.
For criteria pollutants (PM25, NOx, and VOC), region-specific future-year emission rates for each agency
were developed using the MOVES model using the same method described above. For GHGs, future-
year emission rates were adjusted using outputs from the MOVES model. MOVES can be used to
estimate current-year and future-year C02 emission rates. These two rates were used to calculate a
reduction factor, and this reduction factor was multiplied by the base-year C02 emission rate to
estimate a future-year rate. These future-year emission rates may not account for possible significant
emissions improvements in vehicle technology (e.g., shift to electric hybrid or fully electric options).
1.5.3. Transit VMT and Emissions
The preceding discussion is focused on the data and procedures used to estimate the impact of travel
efficiency strategies on light-duty passenger vehicle trips, VMT and emissions. When a transit
improvement strategy causes a shift in travel from light-duty passenger vehicle to transit, the potential
for an increase in transit trips, VMT and emissions should be considered. Where the increase in transit
travel can be accommodated on the existing routes and service area, it can be assumed that no
additional transit trips, VMT or emissions occur. However, if additional transit trips or VMT would be
required, further analysis on the impacts of the additional transit travel activity is needed to provide a
more complete understanding of the impact of the strategy. The analysis is discussed below and
additional region-specific details are presented in Sections 2 through 5.
17 Other pollutants necessary for the model to compute these four were also included. In MOVES, some pollutants
require the selection of other pollutants as prerequisites
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The transit improvement strategies in these case studies (for both buses and commuter rail) were
specified through reduced headways and trip times, and thus assumed to increase the number of transit
vehicles and route miles traveled. For example, a reduction in headways from 20 minutes to 10 minutes
implies that twice as many transit vehicles would be required and that total route miles would be
doubled. The area-specific increase in transit VMT is based on baseline data from the 2016 National
Transit Database. Using this approach, transit VMT can be estimated for the BAU and for the transit
strategy envisioned for the scenario.
Transit emission rates were derived from two different sources:
1.	For criteria pollutants (PM2.5, NOx, and VOC), region-specific base-year and future year
emission rates were developed using the EPA MOVES model.
2.	For GHGs, C02 emission rates were estimated using the fuel consumption data collected
from the National Transit Database or sourced directly from the relevant transit agency, and
fuel emission rates from EPA's Mandatory Reporting of Greenhouse Gases Final Rule
documentation.18 Total fuel consumed by fuel type was multiplied by the emission rate for
that fuel type to estimate the total CO2 emitted consuming that fuel. The total C02
emissions were divided by the transit VMT to estimate an average base-year C02 emission
rate (kg C02/mile). Standard fuel emission rates (kg C02e per unit of fuel consumed) were
then applied to the miles-per-gallon figures to estimate grams of C02e per mile.
1.5.4. Comparison with Previous Results
To put the individual results in context, results were compared to those from previous TEAM case
studies of six other regions. Previous case studies have analyzed strategies similar to those selected by
the participating agencies. Key factors that affect the range of results for each strategy include the
percentage of the regional population to which it was applied and the aggressiveness of the policy
implementation:
1.	in the case of TDM programs, the dollar amount of any subsidy;
2.	for transit improvements, the improvement in travel times;
3.	for land use, the percent increase in the 'D' variables; and
4.	for parking pricing, the dollar value of the charge.
See individual case study comparisons to the range of VMT reductions in Sections 2 through 5.
18 Mandatory Reporting of Greenhouse Gases; Final Rule, 74 FR 56259, October 30, 2009, Tables C-l and C-2. Table
of Final 2013 Revisions to the Greenhouse Gas LNG sourced from: EPA (2008) Climate Leaders Greenhouse Gas
Inventory Protocol Core Module Guidance - Direct Emissions from Mobile Combustion Sources, Table B-5.
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2. Champaign-Urbana, Illinois - Champaign-Urbana Urbanized
Area Transportation Study
ickground
The Champaign-Urbana Urbanized Area is located in east-central Illinois and has a population of
approximately 148,000 based on 2016 information. The urbanized area includes the University of Illinois
primary campus with approximately 44,000 students, located between the cities of Champaign and
Urbana. The Champaign Urbana Urbanized Area Transportation Study (CUUATS) is the metropolitan
planning organization (MPO) for the Champaign Urbana Urbanized Area. The members of CUUATS are
the University of Illinois, City of Champaign, City of Urbana, Village of Savoy, Champaign County,
Champaign Urbana Mass Transit District, Champaign County Regional Planning Commission, and Illinois
Department of Transportation. CUUATS submitted a letter of interest seeking to use TEAM to consider
multiple future scenarios in the next Long Range Transportation Plan (LRTP) update.
CUUATS is incorporating sustainability into the planning and programming activities of the MPO. The
LRTP, Sustainable Choices 2040, was approved in December 2014. The plan places sustainability at the
core of the transportation planning activities in the urbanized area. For the development of the LRTP,
CUUATS evaluated two alternative scenarios: Traditional Development 2040 and Sustainable Choices
2040. A set of interconnected models was used to analyze potential impacts of future planning
decisions on the community through 2040 for the two scenarios. The MPO would like these future
conditions and strategies to inform the public and stakeholders during public outreach and to support
more detailed analysis for the next LRTP update.
At the local level, the cities of Champaign and Urbana and the University of Illinois have initiated
aggressive plans to address GHG emissions. These plans provide a basis for some of the strategies that
CUUATS proposed for analysis using TEAM, and the CUUATS staff used this opportunity to explore travel
efficiency strategies that could be included in the next LRTP. CUUATS staff view the TEAM analysis as a
way to build evidence for strategies that is consistent with the goals and expectations of their partner
agencies. Agency staff supported all data needs for the analysis from internal resources or through
partner input.
For several years, CUUATS has closely considered regional mobile source emissions and their potential
impacts on the health, safety, and welfare of local populations and the environment and on the local
economies. The Champaign-Urbana area is experiencing growth in population, total number of
households, and VMT, and expects continued growth in the future.
The MPO is proactive in considering emissions in their transportation planning processes. For this case
study, CUUATS staff developed a localized database of inputs for MOVES to generate existing emissions
inventories and emission rates to conduct community-wide analyses for the 2040 LRTP in 2014, with
2010 as the base year. These existing condition inventories included 2010 hourly meteorological data
obtained from the National Climate Data Center, 2010 vehicle registration data obtained from the
Illinois Secretary of State, road type distribution aggregated from the Highway Performance Monitoring
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Applying TEAM in Regional Sketch Planning: Four Case Studies
System, MOVES default fuel table, and VMT outputs from the travel demand model.19 These inputs are
the basis of all emissions modeling conducted for this analysis.
2.2. Scenario Development
CUUATS was interested in evaluating many strategies and narrowed their four selections to:
•	Local Transit Hubs, Bus Improvements and Bicycle and Pedestrian Improvements
•	Parking Pricing at the University of Illinois
•	Smart Growth Land Use; and
•	High Speed Rail to Chicago
The selected strategies were combined to develop the following scenarios. This approach allows the
agency to observe the cumulative effect of the individual strategies over time. The details of each
strategy within the scenarios are provided below.
2.2.1. Scenario 1 - Local Transit Hubs and Bus Improvements + Bicycle and Pedestrian
Improvements
The transit, bicycle, and pedestrian improvement policies analyzed in this scenario represent potential
changes at both the county and MPO scales. First, local transit hubs and bus improvements envisioned
in this scenario would involve an extensive restructuring of bus routes within the Champaign Urbana
Urbanized Area. By creating neighborhood-based bus hubs with efficient transfers between routes and
relying less on individual routes to cross the urban area, CUUATS and Champaign-Urbana Mass Transit
District predict they could reduce overall trip times.
To establish the BAU and strategy scenario, CUUATS estimated a reduction in average wait time for
buses from 9.9 minutes in the 2010 base year to 9.4 minutes in the 2040 BAU scenario. However,
worsening traffic congestion threatens to double the average bus passenger in-vehicle travel time from
22.9 minutes per trip in 2010 to 52 minutes per trip in 2040. This transit hub/bus improvement policy
would slightly reduce average bus in-vehicle travel time from 2010 to 2040 to just 20.4 minutes. In
addition, this policy would reduce average bus wait times from 9.4 minutes in the BAU scenario to 7.4
minutes in the future strategy scenario. This information is summarized in Table 2-1.
Table 2-1 CUUATS Access and Travel Time by Scenario
Scenario
Access Time (min)
In Vehicle Travel Time (min)
2010 (Base Year)
9.90
22.90
2040 BAU Forecast
9.40
52.00
2040 Transit Hub Strategy
7.40
20.40
The decreased wait times were estimated to require a 26.3% increase in bus VMT from the BAU
scenario. This is discussed further in Section 2.6 below. Table 2-2 shows how these improvements were
input to TRIMMS. For the individual TRIMMS runs, CUUATS provided separate trip characteristics and
population figures.
19 EPA recommends using the default fuel information in its MOVES guidance. The MOVES default fuel information
represents EPA's best information about the fuels used in every county in the United States.
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Table 2-2. CUUATS TRIMMS Input: Access and Travel Time (minutes)
Mode
BAU Access Time
Strategy Access Time
BAU Travel Time
Strategy Travel Time
Public Transport (2040)
9.40
7.40
52.00
20.40
Next, the bicycle and pedestrian improvements strategy would increase miles of bicycle lanes in
Champaign County from roughly 230 under the BAU to roughly 410, and would also provide sidewalks
on both sides of all streets, except interstate highways and expressways.
As previously stated, a literature-based method was used to model the impact of the bicycle and
pedestrian improvements on travel patterns. The increase in bicycle facilities in the Champaign Urbana
Urbanized Area was calculated as 4.24 lane miles of additional bike facilities per square mile resulting in
a 4.24 % bicycle mode share increase (2.95% to 7.19%) within the Champaign Urbana Urbanized Area.
The new cycling trips are assumed to replace driving trips equal in length to the average cycling trip.
For pedestrian facilities, an elasticity of walk trips with respect to sidewalk coverage of 0.27 is used.20
CUUATS provided an estimated sidewalk coverage of 58.9% in the BAU scenario. An increase to 100%
coverage (sidewalks on both sides of all streets except highways and expressways) for this scenario
would represent a 69.8% increase in sidewalk coverage. Walking mode share is thus projected to
increase from 26.69% to 31.7% (26.69% x (1 + 0.27 x 0.698)) under this scenario. The new walking trips
are assumed to replace driving trips equal in length to the average walking trip.
2.2.2. Scenario 2 - Scenario 1 + Parking Pricing at the University
This scenario would increase parking fees for employees at the University of Illinois by 50%.
Approximately 24,000 employees are eligible to purchase a parking pass at the University. CUUATS staff
estimated the BAU driving costs per person based on information from:
•	the Commute Cost Calculator developed by the Washington State Department of
Transportation,
•	an assumed 30% increase in the price of gasoline as projected by the U.S. Energy
Information Administration to 2040, and
•	a monthly parking fee at the University of Illinois of $55.
The parking pricing strategy under this scenario would increase the monthly parking fee by 50% to
$82.50 per month. This resulting increase in parking fees would increase this population's driving trip
costs (including gas, basic vehicle maintenance, and parking) roughly 20%.
The methodology for this scenario compared the differences in trip cost between individuals driving and
parking alone versus those using rideshare options. Daily parking costs per vehicle in the BAU scenario
were assumed to be equal to $55 per month / 21 working days per month = $2.62 per day. The strategy
parking cost is then assumed to increase by 50% (+$1.31 per day) resulting in $3.93 daily cost per
vehicle. For auto-rideshare, costs are halved, assuming 2 people split the cost of each vehicle trip. Table
2-3 shows how the values were input to TRIMMS. The values shown are the sum of gasoline and parking
costs.
20 Ewing et a I, (2009), "Measuring the impact of urban form and transit access on mixed use site trip generation
rates—Portland pilot study". Washington, DC: U.S. Environmental Protection Agency.
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Table 2-3. CUUATS TRIMMS Input: Financial and Pricing Strategies (costs per person)
Mode
BAU Trip Cost
Strategy Trip Cost
Auto-Drive Alone
$6.32
$7.63
Auto-Rideshare
$3.16
$3.81
2.2.3. Scenario 3 - Scenario 2 + Smart Growth Land Use
Scenario 3 adds a policy that would increase densities and land use mixing in Champaign County.
CUUATS provided the following data for the roughly 300 traffic analysis zones (TAZ) in the county:
•	Land area
•	Population under 2040 BAU and scenario
•	Jobs accessible within 30 minutes by auto under 2040 BAU and scenario
•	Jobs accessible within 30 minutes by transit under 2040 BAU and scenario
•	Average distance to nearest transit for residents (current transit network)
•	Employment by retail, office, and other under 2040 BAU and scenario
These inputs were used to calculate TAZ-level values for each of 4 'D' variables:
•	Density (of population)
•	Diversity (of land uses)
•	Destinations via auto (accessibility of jobs)
•	Destinations via transit (accessibility of jobs)
Note: A fifth 'D' variable, Distance to transit, was not used in this analysis because the future
locations of bus stops could not be projected with confidence.
County-wide averages for each "D" variable were calculated according to the following steps (using
Density as a representative example):
•	Calculate Density of each TAZ as population divided by land area
•	Calculate the % of regional population resident in each TAZ
•	Assign the % of regional population as the weighting factor for each TAZ. The sum of all
weighting factors should equal 1.
•	Multiply the Density value for each TAZ by its weighting factor
•	Sum the results of Step 3 across all TAZs to calculate population-weighted average Density—
a single value for the County
Repeating Steps 1-5 above for all 4 'D' variables for both the BAU and Scenario 3 allows for the
calculation of % changes in each 'D' variable at the county level, and application of the following
elasticities of VMT with respect to each of the variables:
1.	Density (Household/ population density): -0.04
2.	Diversity (Land use mix):-0.09
3.	Destinations (Job access by auto): -0.2
4.	Destinations (Job access by transit): -0.05
Note: See Section 1.5.1.2 for more information about elasticities for 'D' variables.
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2.2.4. Scenario 4 - Scenario 3 + High Speed Rail to Chicago
The CUUATS final scenario adds high speed rail on the Amtrak corridor from Champaign to Chicago. This
strategy is based on a feasibility study sponsored by the Illinois Department of Transportation.21 EPA's
analysis assumed that high speed rail on the Champaign-Chicago corridor would be implemented as
described in the feasibility study. For example, this scenario involves the introduction of a 220-mph
train running every 30 minutes during peak periods from Champaign's Union Station to Downtown
Chicago and the O'Hare airport. The upgraded facility would cut travel time from Champaign to Chicago
by nearly 75%. Although high speed rail would affect travel patterns and emissions along the entire
corridor between Champaign and Chicago, this analysis considers only VMT and emission reductions
within Champaign County.
To estimate the travel impacts of this strategy, increased ridership on the corridor was calculated based
on forecasts provided by CUUATS (and based on forecasts by Amtrak). The increase is the difference
between the forecast high speed rail ridership (775,000 riders per year) and the forecast Amtrak
ridership on the same corridor, without high speed rail (365,500). Of the additional 409,500 annual rail
riders, a standard assumption is that roughly half would be driving in the absence of high speed rail.22
Therefore, this strategy was assumed to eliminate 204,750 annual car trips between Champaign and
Chicago. Approximately 22 miles of each of those driving trips would occur within the study boundary of
Champaign County. Therefore, under this scenario, VMT would be reduced annually by 4,504,400
vehicle miles.
2.3. Scenario Summary
Input parameters were provided in Table 2-4 for current conditions in the 2010 baseline year, a 2040
BAU future year, and the four scenarios selected by CUUATS. Specific input values were provided for
the scenarios.
21	"220 MPH High Speed Rail Preliminary Feasibility Study - Executive Report." University of Illinois at Urbana-
Champaign and University of Illinois at Chicago, 24 Sept. 2013,
www.midwesthsr.org/sites/default/files/studies/IDOT_HSR_220_Executive_Report.pdf. Accessed 30 Apr. 2018.
22	American Public Transportation Association, Recommended Practice for Quantifying Greenhouse Gas Emissions
from Transit, 2009. www.apta.com/resources/hottopics/sustainability/Documents/Quantifying-Greenhouse-Gas-
Emissions-APTA-Recommended-Practices.pdf. See Figure 16.
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Table 2-4. CUUATS Scenario Input Details
Scenario
Description
Data Inputs
Current Conditions
Existing conditions
Region profile:

across all strategies in
• population - 201,688

2010
• jobs-101,597


Mode shares:


• auto, drive alone-47.7%


• auto, rideshare - 23.3%


• transit-4.2%


• bike-2.6%


• walk-22.2%


Average vehicle occupancy, auto rideshare - 2.38


Average vehicle trip lengths, one-way (miles):


• auto, drive alone-4.80


• auto, rideshare-4.83


• transit - 1.94


• bike-2.55


• walk - 1.24


Bike/pedestrian facilities:


• bike lanes (Urbanized Area) - 60 lane miles


• sidewalk coverage (Urbanized Area) - 47% of streets
Business as Usual
2040 conditions with
Region profile:
(BAU)
current levels of transit,
• population - 245,827

parking pricing, land
• jobs-149,177

use, and regional rail
Land use:


• population density (pop/sq. mi) - 7,484


• job access by auto - 140,608


• job access by transit - 7,091


• land use mix-0.37


Mode shares:


• auto, drive alone - 50.3%


• auto, rideshare - 24.6%


• transit-3.4%


• bike-2.4%


• walk - 19.4%


Average vehicle occupancy: auto, rideshare - 2.38


Average vehicle trip lengths, one-way (miles):


• auto, drive alone - 5.03


• auto, rideshare - 5.08


• transit-2.00


• bike-2.76


• walk - 1.26


Trip Time (min)


• transit, in-vehicle - 50


• transit, wait time - 9


• rail (to Chicago) - 165


Bike/pedestrian facilities:


• bike lanes (Urbanized Area) - 227 lane miles
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Scenario
Description
Data Inputs


•	sidewalk coverage (Urbanized Area) - 59% of streets
Trip Cost
•	Average auto trip cost (University employees) - $6.32
Rail (to Chicago) 366,000 daily riders
Scenario 1: Local
Transit Hubs and Bus
Improvements +
Bicycle and Pedestrian
Improvements
Restructure transit
network to reduce wait
times and travel times.
Expand bicycle lanes
and sidewalk coverage.
Trip Time (min)
•	transit, in-vehicle - 20
•	transit, wait time - 7
Bike/pedestrian facilities:
•	bike lanes (Urbanized Area) - 410 lane miles
•	sidewalk coverage (Urbanized Area) -100% of streets
Scenario 2: Scenario 1
+ Parking Pricing at
the University
Increase the cost that
University employees
pay for parking
• Average car trip cost for University employees (gas,
maintenance, parking) of $7.63
Scenario 3: Scenario 2
+ Smart Growth Land
Use
Increase densities, land
use mixing, and job
accessibility
Land use (Weighted average values for all TAZs from 2040
BAU):
•	population density (pop/sq. mi) - +4.5%
•	job access by auto - +0.3%
•	job access by transit-+70.4%
•	land use mix -+12.3%
Scenario 4: Scenario 3
+ High Speed Rail to
Chicago
Upgrade existing rail
corridor to Chicago to
achieve travel speeds
of 220 mph
Trip Time (min)
•	45 minutes by rail to Chicago
Ridership (daily)
•	775,000
2.4. Emissions Analysis
In TEAM, the MOVES analysis is focused on generating activity-weighted, regional average emission
rates that represent the general parameters of the study region. All data used for this analysis was
provided by CUUATS. MOVES inputs were originally generated for the entire county for the most recent
LRTP analysis for use with MOVES2010b model. The input files for this analysis were converted for use
in MOVES2014a.23 Transit fuel consumption and VMT data were compiled from two databases in the
National Transit Database, the 2016 Fuel and Energy Database, and 2016 Service Database, respectively,
for bus service provided by the Champaign-Urbana Mass Transit District. The resulting emission rates
are shown in Table 2-5.
23 The MOVES2014a model has built-in tools that can be used to convert input databases created with
MOVES2010b into a form that can be used within MOVES2014a.
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Table 2-5. CUUATS Emission Rates
Emissions
g/mi
g/start
Base Year (2010)
Future Year (2040)
Base Year (2010)
Future Year (2040)
Auto (Motorcycles + Passenger Cars + Passenger Trucks)
CC>2e
367.66
196.97
164.29
102.75
NOx
1.10
0.05
2.11
0.33
PM2.5
0.02
0.01
0.07
0.01
VOC
0.26
0.01
3.69
0.61
Transit Vehicles (buses)
CC>2e
1342.12
1208.31
141.72
153.47
NOx
14.74
1.19
0.05
0.01
PM2.5
0.33
0.17
0.03
0.02
VOC
1.09
0.05
0.61
0.27
Vanpool (Passenger Trucks + Light-Duty Trucks)
C02e
482.65
254.26
210.93
122.19
NOx
1.80
0.07
2.98
0.34
PM2.5
0.03
0.01
0.09
0.02
VOC
0.43
0.01
5.01
0.60
2.5. CUUATS Scenario Results
Table 2-6 provides the regionwide cumulative percent VMT and emission changes from the BAU for
light-duty vehicles.
Table 2-6. CUUATS Percent Change in VMT and Emissions for 2040 BAU Compared to 2040 Scenario
Scenario
Light Duty
VMT
CChe
PM2.5
NOx
VOC
Scenario 1: Local Transit Hubs and Bus
Improvements + Bicycle and Pedestrian
Improvements
-2.96%
-3.39%
-4.35%
-5.59%
-7.48%
Scenario 2: Scenario 1 + University
Parking Pricing
-3.23%
-3.66%
-4.63%
-5.87%
-7.77%
Scenario 3: Scenario 2 + Smart Growth
Land Use
-7.87%
-8.18%
-8.86%
-9.74%
-11.09%
Scenario 4: Scenario 3 + High Speed Rail
-8.09%
-8.38%
-9.04%
-9.88%
-11.16%
Tables 2-7 and 2-8 provide the regionwide cumulative reduction in light-duty passenger VMT and
emissions from the 2040 BAU and 2010 Baseline.
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Table 2-7. CUUATS VMT and Emission Changes by Scenario Relative to 2040 BAU

2040 Scenario to 2040 BAU
Scenario
Light Duty
VMT
CChe (kg)
PM2.5 (kg)
NOx (kg)
VOC (kg)
Scenario 1: Local transit hubs and bus
improvements + bicycle and pedestrian
improvements
-128,371
-27,867
-1
-15
-17
Scenario 2: Scenario 1 + University Parking
Pricing
-143,386
-31,131
-1
-17
-19
Scenario 3: Scenario 2 + Smart Growth
Land Use
-401,660
-85,235
-3
-42
-41
Scenario 4: Scenario 3 + High Speed Rail
-414,001
-87,723
-3
-43
-42
Table 2-8. CUAATS VMT and Emission Changes by Scenario Relative to 2010 Baseline

2040 Scenario to 2010 Baseline
Scenario
Light Duty
VMT
CChe (kg)
PM2.5 (kg)
NOx (kg)
VOC (kg)
Scenario 1: Local transit hubs and bus
improvements + bicycle and pedestrian
improvements
939,953
-621,101
-98
-6,082
-3,600
Scenario 2: Scenario 1 + University Parking
Pricing
924,938
-624,365
-98
-6,084
-3,602
Scenario 3: Scenario 2 + Smart Growth
Land Use
666,664
-678,468
-100
-6,108
-3,624
Scenario 4: Scenario 3 + High Speed Rail
654,323
-680,956
-100
-6,109
-3,625
As shown in Table 2-6, the most comprehensive scenario, Scenario 4, would reduce light-duty passenger
VMT and GHG emissions by 8-9%. Most of those reductions are attributed to the smart growth land use
strategy, which makes up the difference in performance between Scenarios 2 and 3.
Typically, in TEAM analyses, the percent reductions in VMT and each major pollutant are similar, and, for
simplicity, EPA typically focuses on the VMT result from case study analyses. CUUATS' results are an
exception to that approach, in that reductions of criteria pollutants are notably higher than reductions
of VMT and GHG emissions. The discrepancy is explained by the impacts of the bicycle and pedestrian
improvements strategy.
CUUATS' bicycle and pedestrian strategy reduces a larger share of regional driving trips than regional
VMT. This is because the trips that can be shifted to cycling and walking are typically less than 3 miles,
whereas the average length of all trips is closer to 5 miles. This is significant because emission rates are
generally greatest when a trip has just started and the engine is cold. Emissions of certain criteria
pollutants can be reduced by reducing vehicle starts. Therefore, eliminating short vehicle trips would
reduce these pollutants more effectively than by reducing overall VMT. Note that the percentage of
GHG emissions reduced is also greater than the percentage of VMT reduced, although not as much as
percentage of criteria pollutants reduced. This is for the same reason, i.e., the bicycle and pedestrian
strategy reduces more starts than VMT.
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To contextualize the results in Table 2-6, the percent VMT reduction results from previous TEAM case
studies of six other regions were examined for strategies like those selected by CUUATS. Table 2-9
presents the isolated effects of the individual strategies and compares the VMT reductions to the range
of VMT reductions estimated for similar strategies in previous TEAM analyses.
Table 2-9. CUUATS Comparison of VMT Reductions with Previous Case Studies

% Light Duty
Comparison
Strategy Category
Previous Results for Comparison Category
Strategy
VMT Reduction
CUUATS
Min.
Avg.
Max.
Local Transit Hubs and Bus
-2.96%
Transit
-0.02%
-0.41%
-1.42%
Improvements + Bicycle and

improvement



Pedestrian Improvements





Parking Pricing at the
-0.27%
Parking pricing
-0.26%
-1.13%
-1.99%
University of Illinois





Smart Growth Land Use
-4.64%
Land use
-0.16%
-2.70%
-6.43%
High Speed Rail from
-0.22%
Transit
-0.02%
-0.41%
-1.42%
Champaign to Chicago

improvement



For the combined transit and bicycle/pedestrian strategy, Transit Improvement was chosen as the
comparison category. No previous examples of TEAM analyses combine transit and bicycle/pedestrian
into a single strategy, so one of the two must be selected. Not surprisingly, the effect of CUUATS'
combined strategy is well above the observed range for transit improvement only. Both the transit
component and the bicycle/pedestrian component of this strategy are highly aggressive in comparison
to previous TEAM strategies.
Parking pricing at the University of Illinois, produces results within the comparison range, although at
the low end. The low impact of this strategy is attributed to the limited population to which it would
apply (University employees only).
The Smart growth land use strategy produces reductions near the high end of the comparison range.
Increases in population density, land use mixing, and job accessibility by auto all contribute to the VMT
reduction, but the increase in job accessibility produces the bulk of the change. The dramatic
improvement in the bus network under the transit strategy has the secondary effect of increasing the
number of jobs that the average resident can reach in a 30-minute commute by bus by 70%.
For high speed rail, the transit improvement category is used for comparison. The reduction of VMT due
to high speed rail is on the low end of the range and is commensurate with the volume of the county's
daily traffic to or from Chicago—a relatively small part of the county's total traffic. The benefits of high
speed rail would, of course, extend well beyond Champaign County, the area considered in this case
study.
2.6. Transit VMT and Emissions
As discussed in Section 1.5.3, shifting travel from light-duty vehicles to transit can increase transit VMT
and associated emissions. The results presented above in Table 2-5, Table 2-6, and Table 2-7 only
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Applying TEAM in Regional Sketch Planning: Four Case Studies
include reductions in trips, VMT, and emissions for light-duty vehicles to retain consistency with and to
allow comparison to previous TEAM analyses.
The bus improvements component of Scenario 1 is based on reduced wait and trip times. Headway and
trip time reductions were assumed to be achieved by increasing the number of buses and thus route
miles traveled along a given route. For example, halving the headway would require doubling the buses
running that route. CUUATS modeled a reduction in average wait time for buses from 9.9 minutes in the
base year to 9.4 minutes in the future BAU and 7.4 in the future scenario; CUUATS estimated that the
decreased scenario wait times would require an increase in bus miles of 26.3% over BAU miles. Using
this approach, future-year BAU and scenario VMT was estimated from base year VMT. Transit VMT
estimates and emission rates were used to calculate the total annual emissions related to CUUATS'
transit strategy in Scenario 1. The resultant increases in transit-related VMT and emissions are provided
in Table 2-10.
Table 2 10. CUUATS Transit Vehicle Percent VMT and Emissions Increases from BAU
Strategies
Transit VMT
C02e kg/day
PM2.5
(kg/day)
NOx
(kg/day)
voc
(kg/day)
Local transit hubs and bus improvements
26.3%
5,002
0.4
3
0.1
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3. Lake Charles, Louisiana - Imperial Calcasieu Regional
Planning and Development Commission
ickground
The Imperial Calcasieu Regional Planning and Development Commission (IMCAL) is a transportation
planning, safety, and economic development organization representing the five parish Southwest
Louisiana Lake Charles region. In addition, IMCAL staffs the Lake Charles Metropolitan Planning
Organization (LCMPO).
Since 2014, the IMCAL region has experienced an unprecedented industrial boom from the revival of the
country's liquefied natural gas sector. One hundred billion dollars' worth of new or proposed industrial
plants is projected in the region over the next several years. Currently, 20 petrochemical-related
facilities are under construction or pending. The new plants are projected to utilize 38,000 construction
workers and generate 18,000 permanent jobs. This increased activity impacts local transportation
demand and air quality. IMCAL wants to ensure the region continues to meet Clean Air Act
requirements associated with mobile source emissions.
The Lake Charles MPO urban boundary covers most of Calcasieu Parish with a regional population of
approximately 196,000 in 2015. Recent focus has been on a draft "Complete Streets" plan and
collecting geographic information system data for sidewalks. The existing travel demand model will be
updated within the next year, and therefore current model data is limited. The local transit agency is
currently working to improve data collection in general, and shared information generated by their
consultant to support data needs for this analysis. Downtown parking in Lake Charles is free and
abundant. Although the anticipated growth is significant, land use planning in the region has not
previously been explored as a potential solution.
3.2, Scenario Development
IMCAL was interested in many TE strategies and narrowed their four selections to:
•	Travel Demand Management (TDM) for petrochemical industry employees
•	Transit improvements in North Lake Charles
•	Public parking pricing in Downtown Lake Charles; and
•	Smart growth land use
As typical in the TEAM approach, selected strategies of interest are combined to develop the following
scenarios. This approach allows the agency to observe the cumulative effect of strategies over time.
The details of each strategy within the scenarios are provided below.
3.2.1.Scenario 1 - TDM Program for Petrochemical Employees
The TDM policy under this scenario would offer financial incentives for carpooling to workers in the
region's petrochemical industry: roughly 7,500 of 119,000 projected workers in 2040. The policy would
offer subsidies of $50 per month for ridesharing or vanpooling to each employee along with ridematch
programs and guaranteed/emergency ride home programs. The number of employees assumed to
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accept the subsidy would be less than 7,500, and is implicitly calculated by the TRIMMS model used in
the VMT part of this analysis.
The subsidy was modeled by estimating the change in the daily cost for rideshare trips when a savings of
up to $50 per month was applied. To estimate the BAU rideshare trip cost, the following data was used:
•	Average trip length - Current-year drive-alone trip length data were found in the 2009 National
Household Travel Survey.24 Average trip lengths for petrochemical employees in the Lake
Charles region were assumed to be 10.8 miles which is equal to the average light-duty vehicle
home-work trip length for individuals living in urbanized areas of fewer than 200,000
population. Using this trip length as the baseline, IMCAL estimated drive-alone trip length for
the BAU 2040 analysis year by applying a trip length growth factor of 6% derived from the
regional travel demand model. To estimate the 2040 rideshare trip length, the average length
for home-based rideshare work trips was divided by the drive-alone work trips as reported in
the 2009 National Household Travel Survey to calculate a rideshare trip length factor of 1.15.
This factor was multiplied by the BAU 2040 drive-alone trip length provided by IMCAL to
estimate rideshare trip length.
•	Variable driving cost - The per-mile driving cost data were the American Auto Association's
"2017 Your Driving Costs" publication, which provides a national average.25 The American Auto
Association annually publishes estimates for passenger vehicle ownership and variable costs.
For this analysis, the operating cost for medium sedans was used as a representative commute
vehicle.
•	Average vehicle occupancy - Average occupancy for rideshare trips was estimated using data
from the 2015 American Community Survey.26 Future-year occupancy was assumed not to
change without significant infrastructure or mobility changes.
The average roundtrip length was multiplied by the variable driving cost (i.e., cost per mile) and divided
by the average occupancy to estimate the daily commute cost for rideshare trips in the Lake Charles
area ($1.89). The daily trip cost was less than the daily $2.50 subsidy amount (assuming 21 work days
per month), therefore, the cost of rideshare trips changing from $1.89 to $0 in TRIMMS. In addition to
inputting the rideshare subsidy values described above, the ridematch and guaranteed ride home
programs were analyzed using the yes/no radio buttons provided in TRIMMS to model those programs.
3.2.2.Scenario 2 - Scenario 1 + Transit Improvement in North Lake Charles
This scenario would add improved bus service to the neighborhood of North Lake Charles, with a
population of roughly 13,500 people. The Lake Charles Transit System (LCTS) bus route #2 currently
serves this neighborhood and connects it with Downtown Lake Charles. The baseline transit miles for
LCTS bus route #2, which provides service between North Lake Charles and Downtown Lake Charles
were 54,126 miles. IMCAL, with input from LCTS, developed an estimate of the baseline average transit
travel time, in-vehicle time and out of vehicle time (wait and layover times), for typical bus trips to and
24	U.S. Department of Transportation, Federal Highway Administration, 2009 National Household Travel Survey.
https://nhts.ornl.gov.
25	Your Driving Costs, American Auto Association, https://exchange.aaa.com/automotive/driving-costs/
26	United Stats Census Bureau, www.census.gov/programs-surveys/acs/
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from North Lake Charles and the transit center. They estimated that, with additional resources, this
policy would improve the speed and frequency of service to reduce total average transit times (including
in-vehicle and out-of-vehicle time) for residents of the neighborhood by 17%. This reduction in travel
time via transit would require a 20% increase in bus service. Using this approach, future-year BAU and
scenario VMT was estimated from base-year VMT. LCTS also provided fuel consumption and fuel
efficiency data for their transit buses. LCTS reported that the average fuel efficiency for its bus fleet was
3.66 miles per gallon, and his information was used in the GHG calculations.
The transit improvement policy was analyzed by inputting the BAU trip time (66 minutes) and the
strategy trip time (55 minutes) in TRIMMS and running the model for Scenario 2. Table 3-1 shows how
these improvements were input to TRIMMS.
Table 3-1. IMCAL TRIMMS Scenario 2 Input: Access and Travel Time Improvements (minutes)
Mode
BAU Travel Time
Strategy Travel Time
Public Transport (2040)
66.00
55.40
3.2.3.Scenario 3 - Scenario 2 + Parking Pricing in Downtown Lake Charles
This scenario would add a charge for public parking used for non-work trips to Downtown Lake Charles,
where all parking is currently free. The charge modeled was $0.50 per hour.
Like the TDM policy introduced in Scenario 1, the parking charge policy was modeled in TRIMMS as a
change in travel cost. In this scenario, however, the change in cost was the additional cost of parking on
top of the baseline cost for a vehicle trip to Downtown Lake Charles. As with the TDM program, the
baseline trip cost was estimated by multiplying the average roundtrip distance by the variable driving
cost and dividing by the average occupancy. IMCAL assumed average parking time to be 1.6 hours per
trip based on their observations of travel patterns to downtown. With a parking cost of $0.50 per hour,
the new cost would add an average of $0.80 per downtown trip.
Table 3-2 shows how these improvements were input to TRIMMS for trips with one passenger (auto-
drive alone) versus trips with two people per vehicle (auto-rideshare). The values in the table represent
the sum of both variable (gas and maintenance) and fixed (parking) trip costs. Variable cost for the BAU
drive-alone was calculated as 17 cents per mile multiplied by the average one-way trip length of 13.04
miles * 2 = $4.43 per trip.27,28 Adding $0.80 in parking yields a total strategy trip cost of $5.23. The
auto-rideshare costs used were per person, assuming 2 people per vehicle.
Table 3-2. IMCAL TRIMMS Scenario 3 Input: Financial and Pricing Strategies (costs per person)
Mode
BAU Trip Cost
Strategy Trip Cost
Auto-Drive Alone
$4.43
$5.23
Auto-Rideshare
$2.21
$2.61
27	Your Driving Costs, "Operating a Medium Sedan". American Auto Association,
https://exchange.aaa.com/automotive/driving-costs/
28	A growth factor derived from IMCAL's travel demand model specifically for trips to downtown was used to
estimate the 2040 BAU trip length to downtown.
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3.2.4. Scenario 4 - Scenario 3 + Smart Growth Land Use
Scenario 4 adds a policy that would increase densities and land use mixing in the MPO area. IMCAL
provided the following data for the 845 traffic analysis zones (TAZ) in the MPO area:
•	Land area
•	Population under 2040 BAU and scenario
•	Jobs under 2040 BAU and scenario
A multivariate land use analysis was conducted outside of TRIMMS using these inputs.
The increase in the regionwide average that would result under the smart growth land use policy was
calculated for the two 'D' variables identified below:
•	Density (of population)
•	Diversity (of land uses)
To calculate the regionwide average, density and diversity values for each TAZ were weighted by the
population share in each TAZ. Using a weighted average allows calculation of a single value that
summarizes the change in each variable across the entire area that would result from this policy.
The other three 'D' variables typically included in an analysis of smart growth land use—job accessibility
by auto, job accessibility by transit, and distance to transit—were not calculated for this analysis
because the required data were not available. Therefore, the VMT reduction for smart growth land use
in this case study likely underestimates the potential reductions.
3.3. Scenario Summary
Table 3-3 provides input parameters for current conditions in the 2015 baseline year, a 2040 BAU future,
and the four scenarios IMCAL selected. Specific input values are provided for the scenarios.
Table 3-3. IMCAL Scenario Details
Scenario
Description
Data Inputs
Current Conditions
Existing conditions
Region profile:

across all strategies in
• population -195,882

2015
• jobs-84,103


Mode shares:


• auto, drive alone - 85.3%


• auto, rideshare - 9.4%


• transit-0.4%


• bike-0.3%


• walk - 1.4%


• Other-3.2%


Average vehicle occupancy, auto rideshare - 2.37


Average vehicle trip lengths, one-way (miles):


• auto, drive alone - 9.60


• auto, rideshare - 11.1
Business as Usual
2040 conditions with
Region profile:
(BAU)
current levels of transit,
• population - 259,698


• jobs-119,240
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Scenario
Description
Data Inputs

parking pricing, land use,
and regional rail
Land use:
•	population density (pop/sq. mi) - 2,512
•	land use mix-0.42
Mode shares:
•	auto, drive alone - 85.3%
•	auto, rideshare - 9.4%
•	transit-0.4%
•	bike-0.3%
•	walk - 1.4%
•	Other-3.2%
Average vehicle occupancy, auto rideshare - 2.37
Average vehicle trip lengths, one-way (miles):
•	auto, drive alone - 10.2
•	auto, rideshare - 11.8
Trip Time (min)
•	transit, total - 66
Scenario 1: TDM
Program for
Petrochemical
Employees
Provide subsidies for
carpooling, ridematch
and guaranteed ride
home programs to 7,500
employees
•	$50 per employee subsidy for carpooling
•	Ridematch and guaranteed ride home programs
Scenario 2: Scenario
1 + Transit
Improvement in
North Lake Charles
Reduce the average
transit trip time of
residents of North Lake
Charles
Trip Time (min):
• transit, total - 55 (reduced from 66 in 2040 BAU)
Scenario 3: Scenario
2 + Parking Pricing
in Downtown Lake
Charles
Price public parking used
for non-work trips to
Downtown Lake Charles
• 50 cents per hour parking charge
Scenario 4: Scenario
3 + Smart Growth
Land Use
Increase land use
densities and land use
mixing
Land use (From 2040 BAU):
•	Weighted average values for all TAZs in IMCAL MPO
increase from 2040 BAU as follows:
•	population density: 14.1%
•	land use mix: 2.7%
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3.4. Emissions Analysis
In TEAM, the MOVES analysis is focused on generating activity-weighted, regional average emission
rates from the model that represent the general parameters of the study region. IMCAL results
presented here rely on the local data provided by IMCAL and model default inputs, along with the
processing methodology described in Section 1.5.2. The resulting emission rates are shown in Table 3-4.
Table 3-4. IMCAL Emission Rates
Emissions
g/mi
g/start
Base Year (2015)
Future Year (2040)
Base Year (2016)
Future Year (2040)
Auto (Motorcycles + Passenger Cars + Passenger Trucks)
CC>2e
380.56
218.65
101.09
73.33
NOx
0.35
0.03
0.88
0.16
PM2.5
0.01
0.01
0.01
0.00
VOC
0.07
0.01
1.08
0.20
Transit Vehicles (buses)
CC>2e
1500.78
1391.65
123.18
127.66
NOx
10.15
1.22
0.03
0.01
PM2.5
0.29
0.04
0.02
0.00
VOC
0.76
0.06
0.21
0.10
Vanpool (Passenger Trucks + Light-duty Trucks)
C02e
463.22
270.72
121.39
86.36
NOx
0.56
0.04
1.22
0.18
PM2.5
0.01
0.01
0.01
0.01
VOC
0.11
0.01
1.44
0.20
3.5. IMCAL Scenario Results
Table 3-5 provides the cumulative percent VMT and emission changes from the BAU for light-duty
vehicles. Additional explanation is provided below.
Table 3-5. IMCAL Percent Change in VMT and Emissions for 2040 BAU Compared to 2040 Scenario
Scenario
Light Duty
VMT
C02e
PM2.5
NOx
VOC
Scenario 1: TDM Program for Petrochemical
Employees
-0.07%
-0.07%
-0.07%
-0.07%
-0.07%
Scenario 2: Scenario 1 + Transit
Improvement in North Lake Charles
-0.10%
-0.10%
-0.10%
-0.10%
-0.10%
Scenario 3: Scenario 2 + Parking Pricing in
Downtown Lake Charles
-0.24%
-0.24%
-0.24%
-0.23%
-0.22%
Scenario 4: Scenario 3 + Smart Growth Land
Use
-1.05%
-1.04%
-1.04%
-1.01%
-0.97%
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Tables 3-6 and 3-7 provide the regionwide cumulative reduction in VMT and emission from the 2015
Baseline and 2040 BAU.
Table 3 6. IMCAL VMT and Emission Changes by Scenario Relative to 2040 BAU

2040 BAU to 2040 Scenario
Scenario
Light Duty
VMT
CChe (kg)
PM2.5 (kg)
NOx (kg)
VOC (kg)
Scenario 1: TDM Program for
Petrochemical Employees
-4,913
-1,110
0.0
-0.2
-0.1
Scenario 2: Scenario 1 + Transit
Improvement in North Lake Charles
-6,953
-1,569
0.0
-0.3
-0.2
Scenario 3: Scenario 2 + Parking Pricing in
Downtown Lake Charles
-16,203
-3,643
-0.1
-0.8
-0.4
Scenario 4: Scenario 3 + Smart Growth
Land Use
-70,996
-15,962
-0.5
-3.4
-1.9
Table 3 7. IMCAL VMT and Emission Changes by Scenario Relative to 2015 Baseline

2015 Baseline to 2040 Scenario
Scenario
Light Duty
VMT
CChe (kg)
PM2.5 (kg)
NOx (kg)
VOC (kg)
Scenario 1: TDM Program for
Petrochemical Employees
1,954,245
-354,216
-8
-1,793
-686
Scenario 2: Scenario 1 + Transit
Improvement in North Lake Charles
1,952,206
-354,675
-8
-1,793
-686
Scenario 3: Scenario 2 + Parking Pricing in
Downtown Lake Charles
1,942,955
-356,750
-8
-1,794
-686
Scenario 4: Scenario 3 + Smart Growth
Land Use
1,888,162
-369,068
-8
-1,796
-688
As shown in Table 3-5, the most comprehensive scenario, Scenario 4, would reduce VMT and emissions
of each major pollutant by roughly 1%. Most of those reductions are attributed to the smart growth
land use strategy, which makes up the difference in performance between Scenarios 3 and 4.
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To contextualize the results in Table 3-5, the percent VMT reduction results from previous TEAM case
studies of six other regions were examined for strategies like those selected by CUUATS. Table 3-8
shows the effects of the individual strategies on VMT and compares them to VMT reductions from
IMCAL's individual strategies to the range of VMT reductions projected for similar strategies from
previous TEAM analyses in other areas
Table 3-8. IMCAL Comparison of VMT Reductions Strategies and Previous Case Studies
Strategy
% Light Duty VMT
Comparison
Previous Case Study Results for Category
Reduction IMCAL
Strategy Category
Min.
Avg.
Max.
TDM Program for
Petrochemical Employees
0.07%
Expanded TDM
0.43%
1.10%
2.80%
Transit Improvement in
North Lake Charles
0.03%
Transit
improvement
0.02%
0.41%
1.42%
Parking Pricing in
Downtown Lake Charles
0.14%
Parking pricing
0.26%
1.13%
1.99%
Smart Growth Land Use
0.81%
Land use
0.16%
2.70%
6.43%
Many of IMCAL's strategies would produce VMT reductions at the low end of, or even below, the range
of other case study results. This can be explained by the relatively small scope for applying the selected
strategies. Except for smart growth land use, each strategy targets a small subset (about 5%) of the
population or geographic region.
The TDM strategy produced results below the range of similar strategies assessed in previous case
studies. This outcome is most likely due to the scenario's incentive focused on carpooling only, while
typical TDM incentive programs analyzed in the TEAM framework generally encourage use of other
modes as well. In addition, the strategy focused on workers in only one industry, albeit a dominant
industry in the region.
The transit improvement strategy for North Lake Charles produced results within the range of previous
results. Results for IMCAL were on the low end, because the strategy was only applied to bus routes
that serve a single neighborhood, and the level of improvement in the bus route was modest.
The parking pricing strategy produced results below the range of previous case studies due to its
applicability to only a small share of regional trips—i.e., non-work trips to downtown.
The IMCAL land use change strategy was in the low-to-medium end of the VMT reduction spectrum of
other agencies' results. Although a 14% increase in the gross population density was projected, average
land use mix increased only moderately (3%). For this scenario, data was not available for changes in
job accessibility by auto and transit, or walking distance to the nearest transit stop; therefore, the land
use strategy results show a smaller overall VMT impact. In other case studies, the impact of job
accessibility is frequently the most powerful among the land use variables.
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3.6. Transit VMT and Emissions
As discussed in Section 1.5.3, shifting travel from light-duty vehicles to transit can increase transit VMT
and associated emissions. The results presented above in Table 3-5, Table 3-6, and Table 3-7 only
include reductions in trips, VMT, and emissions for light-duty vehicles to retain consistency with and to
allow comparison to previous TEAM analyses.
The transit improvement strategy in Scenario 2 was based on reduced wait and trip times. Headway and
trip time reductions were assumed to be achieved by increasing the number of buses and thus route
miles traveled along a given route. For example, halving the headway would require doubling the buses
running that route. IMCAL estimated that average trip times in the improvement area would be 66
minutes in both the base year and future BAU, but would be reduced to 55 minutes in the future
scenario; this reduction in travel time via transit would require a 20% increase in bus service. Using this
approach, future-year BAU and scenario VMT was estimated from base-year VMT. Transit VMT
estimates and emission rates were used to calculate the total annual emissions related to CUUATS'
transit strategy. The resultant increases in transit-related VMT and emission are provided in Table 3-8.
Table 3 8. IMCAL Transit Vehicle Percent VMT and Emissions Increases from BAU
Strategies
Transit
VMT
CChe (kg/day)
PM2.5 (kg/day)
NOx (kg/day)
VOC (kg/day)
Transit Improvement
20.0%
77
0.001
0.034
0.002
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4. State of Connecticut - Northeast States for Coordinated Air
Use Management
ickground
This analysis for the State of Connecticut was conducted in partnership with The Northeast States for
Coordinated Air Use Management (NESCAUM). NESCAUM is a 501(c)(3) non-profit association of the
state air quality agencies in Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York,
Rhode Island, and Vermont. The directors of these agencies serve as NESCAUM's Board of Directors.
NESCAUM provides scientific, technical, analytical, and policy support to the air quality and climate
programs of member states.
To evaluate TEAM for broader application to other NESCAUM states and the multistate region,
NESCAUM partnered with the State of Connecticut, specifically with the Connecticut Department of
Energy and Environmental Protection (CT DEEP) and the Connecticut Department of Transportation (CT
DOT), to evaluate the effect of travel efficiency strategies to reduce transportation-related greenhouse
gases (GHGs). Under Connecticut's Global Warming Solutions Act, CT DEEP is charged with analyzing
progress to date and additional actions needed to comply with the Act, including achieving an 80%
reduction in greenhouse gas (GHG) emissions by 2050.29 Additionally, CT DOT has an interest in TEAM
to support its long-term GHG mitigation planning efforts.
NESCAUM is helping CT DEEP develop GHG mitigation scenarios that would achieve the reduction
targets of the Global Warming Solutions Act, using the Long-Range Energy Alternative Planning
framework. The framework, however, is not designed to assess the GHG reduction potential of specific
VMT reduction measures.30 While transportation demand models are available, they can be resource
intensive and expensive to run; CT DOT was interested in a way to rapidly estimate the impact of specific
travel efficiency strategies. NESCAUM, CT DEEP, and CT DOT view TEAM as a flexible, cost-effective
planning tool to analyze potential VMT reduction measures that extend beyond current programs and
would help achieve Connecticut's aggressive 2050 GHG target.
In 2015, Connecticut launched "Let's Go CT!", the state's 30-year transportation vision to transform
Connecticut infrastructure into a premiere, integrated, multimodal system.31 Let's Go CT! calls for a
complete reevaluation of bus services in Connecticut, with the goal to increase bus service availability in
urbanized areas by 25%. Connecticut has been reevaluating the performance of the current bus
network and its ability to connect people with jobs, education, health care, and essential services, and
this case study represented an opportunity to support this work. Increased congestion levels, the need
for reverse commuting to suburban locations, federal mandates to reduce air pollution, and the state's
GHG mitigation requirements present a growing opportunity for bus transit, which in many cases is the
most cost-effective and flexible transit mode. The state's existing network has operated with only minor
29	Public Act No. 08-98 An Act Concerning Connecticut Global Warming Solutions; Sess. 2008. (C.T. 2008)
www.cga.ct.gov/2008/ACT/PA/2008PA-00098-R00HB-05600-PA.htm.
30	Heaps, C.G., 2016. Long-range Energy Alternatives Planning (LEAP) system. Stockholm Environment Institute.
Somerville, MA, USA. www.energycommunity.org.
31	"Let's Go CT: 30-Year Vision Document," Connecticut Department of Transportation, 17 Feb. 2015,
www.transformct.info/img/documents/CTDOT%2030%20YR%20Corrected_02.17.2015.pdf.
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adjustments for many years, often not responding to changing population and employment shifts
throughout the state such as increased development outside the central business districts.
Let's Go CT! also includes a "complete streets policy" for designing improved bike and pedestrian
conditions in community centers. Multimodal enhancements to the New Haven rail line to New York,
the nation's busiest commuter rail corridor, are another focus. The state intends to transform the New
Haven line from a "commuter railroad" to a "rapid transit" system with enhanced connectivity to bus
lines and upgraded branch lines to interior Connecticut communities.
The Let's Go CT! measures could reduce VMT in the state and contribute to meeting the state's GHG
reduction target in 2050. CT DOT is testing TEAM to help evaluate the GHG mitigation potential of these
measures and identify enhancements to improve the program's climate mitigation potential along with
improved transportation efficiency.
4,2, Scenario Development
This TEAM analysis examines travel activity within the state of Connecticut with emphasis on the
corridor connecting New York City to New Haven. Let's Go CT, the 30-year vision, was not considered
the BAU case for this analysis. Instead, the scenarios modeled for Connecticut reflect specific aspects of
the vision. Previous modeling conducted with CT DOT's statewide travel demand model provided
parameters for BAU mode shares and trip lengths at the state level. The model predicts a slight increase
in driving alone and in transit ridership and a slight decline in carpooling in 2040.
Mode shares and trip lengths for the New York-New Haven corridor could not be extracted readily from
the model. To estimate mode shares for the corridor, data from the 2015 American Community Survey
was used to modify the statewide averages. American Community Survey data showed that transit
mode share for work trips in Fairfield and New Haven counties (the two counties that comprise most of
the corridor) is roughly 50% higher than the statewide average. Accordingly, the statewide BAU transit
mode share was scaled up by 50% to estimate BAU transit mode share in the corridor. Mode shares for
drive alone and carpool were scaled back accordingly. Average trip lengths for all modes in the New
York-New Haven corridor were assumed the same as statewide average trip lengths.
Four strategies reflecting the Let's Go CT vision were selected for evaluation:
•	Commuter rail improvements in the New York - New Haven Corridor
•	Local bus improvements in the New York - New Haven Corridor
•	Smart growth land use in the New York - New Haven Corridor
•	Statewide VMT pricing
As typical in the TEAM approach, selected strategies of interest are combined to develop the following
scenarios. This approach allows the agency to observe the cumulative effect of strategies over time.
The details of each strategy within the scenarios are provided below.
4.2.1.Scenario 1 - Commuter Rail Improvements
This scenario evaluates potential improvements to the Metro North commuter trains that would reduce
average travel times on the corridor by 15% through faster trains and shorter wait times on trains that
serve the New York-New Haven corridor. The corridor geographic boundary is from Let's Go CT!.
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The forecasted 2040 corridor population is 1,347,169. This value was used to estimate the population
affected by this strategy. The corridor population was spread across both urban and rural areas in New
Haven and Fairfield Counties. Some of the population can access the rail line with a short walk, drive, or
bus trip to the rail station. Others live too far from stations for it to be a practical means of daily travel.
To avoid double-counting the impact of rail improvements (in this scenario) and bus improvements (in
other scenarios), the corridor population must be divided by the most common mode of transit. Mode
split data from the statewide travel demand model shows a transit ridership split of approximately
50/50 between bus and rail. Accordingly, it is estimated that 50% of the corridor population (673,585)
are primarily rail riders and the other 50% are primarily bus riders.
Table 4-1 shows the parameters as input to the TRIMMS model. CT DOT estimated 47 minutes for the
BAU trip time from Stamford, CT to Grand Central Station in New York City, NY for the 2040 BAU
scenario and 40 minutes with the improvements proposed by the strategy. CT DOT provided an
estimated BAU access time (average train transfer time at Stamford) of 5 minutes, decreasing to 3
minutes with the improvement strategy.
Table 4-1. NESCAUM TRIMMS Scenario 1 Input: Access and Travel Time Improvements (minutes)
Mode
BAU Access Time
Strategy Access
Time
BAU Travel Time
Strategy Travel
Time
Public Transport
5.00
3.00
47.00
40.00
4.2.2.Scenario 2 - Scenario 1 + Local Bus Improvements
This scenario would upgrade local bus service in all urban areas in the New York-New Haven corridor.
The policy, modeled in TRIMMS, would reduce average travel times by a third. This policy was assumed
to be consistent with Connecticut's goal to increase bus service availability in urbanized areas by 25%
(per Let's Go CT!). As explained in Scenario 1 above, the mode split between bus and rail was applied to
estimate the affected population of 673,585 for the bus improvements.
Table 4-2 shows the parameters as input to the TRIMMS model. CT DOT provided an estimated average
BAU trip time on local buses of 15 minutes in the 2040 BAU scenario, decreasing to 10 minutes with the
improvement strategy. CT DOT provided an estimated BAU access time (bus headway) of 5 minutes,
decreasing to 3 minutes with the improvement strategy.
Table 4-2. NESCAUM TRIMMS Scenario 2 Input: Access and Travel Time Improvements (minutes)
Mode
BAU Access Time
Strategy Access
Time
BAU Travel Time
Strategy Travel Time
Public Transport
5.00
3.00
15.00
10.00
4.2.3.Scenario 3 - Scenario 2 + Smart Growth Land Use
Scenario 3 added a policy that would increase densities and land use mix in the New York-New Haven
corridor. CT DOT provided the following data for the roughly 530 traffic analysis zones (TAZ) in the
corridor:
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•	Land area
•	Population under 2040 BAU and scenario
•	Average distance to nearest transit for residents (current transit network)
•	Employment by retail and non-retail under 2040 BAU and scenario
A multivariate land use analysis was conducted outside of TRIMMS using these inputs.
The increase in the regionwide average that would result under the smart growth land use policy was
calculated for the following three 'D' variables:
•	Density (of population)
•	Diversity (of land uses)
•	Distance to transit
To calculate the regionwide average, 'D' values for each TAZ were weighted by the share of the
population in each TAZ. Using a weighted average allowed calculation of a single value that summarizes
change in each variable across the entire area that would result from this policy.
The other two 'D' variables typically included in an analysis of smart growth land use—job accessibility
by auto and job accessibility by transit—were not calculated for this analysis because the required data
were not available from the statewide travel model. Therefore, the VMT reduction for smart growth
land use in this case study likely underestimates the potential reductions.
4.2.4. Scenario 4 - Scenario 3 + VMT Pricing
CT DOT's final scenario would add a statewide VMT fee of $0.05 per mile on all light-duty vehicle travel
throughout the state. The impact of that strategy was modeled in TRIMMS.
Table 4-3 shows the input parameters. CT DOT provided an average cost of gas per vehicle trip of $1.85,
assuming a cost of $0.23 per mile and an average trip length of 8.06 miles. Adding $0.05 per mile to that
figure would yield a new trip cost of $2.26. Those figures were used for trips of single individuals (auto
drive-alone). The cost per person was halved for auto-rideshare trips with an assumption of 2 people
per vehicle.
Table 4-3. NESCAUM TRIMMS Input: Financial and Pricing Strategies (costs per person)
Mode
BAU Trip Cost
Strategy Trip Cost
Auto-Drive Alone
$1.85
$2.26
Auto-Rideshare
$0.92
$1.13
4.3. Scenario Summary
Input parameters are provided in Table 4-4 for current conditions in the 2020 baseline year, a 2040 BAU
future year, and the four scenarios selected by CT DOT. Specific input values are provided for the
scenarios.
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Table 4-4. NESCAUM Scenario Details
Scenario
Description
Data Inputs
Current Conditions
Existing conditions across
Region profile (statewide):

all strategies in 2020
• Population - 3,728,635


• jobs -1,625,665


Region profile (New York-New Haven Corridor):


• Population -1,279,610


• jobs-563,438


Mode shares (statewide):


• auto, drive alone - 73.8%


• auto, rideshare - 24.6%


• transit - 1.6%


Mode shares (New York-New Haven Corridor):


• auto, drive alone - 73.0%


• auto, rideshare - 24.6%


• transit-2.4%


Average vehicle occupancy, auto rideshare - 2.35


Average vehicle trip lengths, one-way (miles):


• auto, drive alone - 8.12


• auto, rideshare - 8.12
Business as Usual
2040 conditions with
Region profile (statewide):
(BAU)
current levels of transit,
• Population-4,013,596

parking pricing, land use,
• jobs-1,751,623

and regional rail
Region profile (New York-New Haven Corridor):


• Population -1,347,169


• jobs-595,680


Mode shares (statewide):


• auto, drive alone - 75.7%


• auto, rideshare - 22.5%


• transit-1.8%


Mode shares (New York-New Haven Corridor):


• auto, drive alone - 74.8%


• auto, rideshare - 22.5%


• transit-2.7%


Average vehicle occupancy, auto rideshare - 2.35


Average vehicle trip lengths, one-way (miles):


• auto, drive alone - 8.06


• auto, rideshare - 8.06


Land use:


• population density (pop/sq. mi) - 4,831


• job access by auto - 140,608


• distance to transit (miles) - 1.48


• land use mix-0.64


Trip Cost ($/mile):


• auto-$0.25
Scenario 1:
Improve travel speeds and
Trip Time (min)
Commuter Rail
reduce headways on the
• rail, in-vehicle —15%
Improvements
Metro-North commuter
• rail, wait time--40%
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Scenario
Description
Data Inputs

rail service in the New
York-New Haven corridor
Affected population: 673,585
Scenario 2: Scenario
1 + Local Bus
Improvements
Improve travel speeds and
reduce headways for local
buses serving communities
in the New York-New
Haven corridor
Trip Time (min):
•	bus, in-vehicle--33%
•	bus, wait time--40%
Affected population - 673,585
Scenario 3: Scenario
2 + Smart Growth
Land Use
Increase densities and land
use mixing in communities
in the New York-New
Haven corridor
Land use (Corridor TAZs change from 2040 BAU):
•	population density (pop/sq. mi) - +3.8%
•	distance to transit--6%
•	land use mix-+0.1%
Scenario 4: Scenario
3 + VMT Pricing
Charge a fee for every mile
traveled by a light-duty
vehicle in Connecticut
Trip Cost ($/mile):
• auto-+0.05
4.4. Emissions Analysis
NESCAUM results presented here rely on the individual county inputs provided by CT DOT and the
processing methodology described previously.
CT DOT initially provided MOVES2014a run specification files and input databases for each of the eight
counties in the region. Input data was provided for 2018, and for use in the TEAM analysis, these were
used for 2020. For this update, local inputs describing the Low Emitting Vehicle program were removed
to avoid uncertainty in the implementation of the program, applicable to certain northeastern states in
future years. CT DOT also provided local meteorology, but the data were limited to July only. As TEAM
relies on annual average emission rates, default annual meteorology was used in all runs. Inspection
and maintenance program benefits were omitted, consistent with previous EPA case studies analyzed
using TEAM.
Emission rates were produced for two different geographies: one for the two-county "corridor" and one
for the entire state. In both cases, the resulting emission rates were determined by aggregating the
resulting emissions and activity values from the individual county MOVES simulations. The ratio of the
combined values was then determined to calculate activity-weighted emission rates, as described in
Section 1.5.2. Only the two counties along the commuter rail corridor, Fairfield and New Haven
Counties, were included in the average emission rate calculations for the corridor case. All eight
counties in Connecticut were included in the statewide average emission rate calculations.
The resulting emission rates for the corridor and statewide cases are shown in Table 4-5 and Table 4-6.
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Table 4-5. NESCAUM New York-New Haven Corridor Emission Rates
Emissions
g/mi
g/start
Base Year (2020)
Future Year (2040)
Base Year (2020)
Future Year (2040)
Auto (Motorcycles + Passenger Cars + Passenger Trucks)
C02e
345.00
221.20
127.89
100.41
NOx
0.17
0.03
0.67
0.24
PM2.5
0.01
0.01
0.02
0.01
VOCs
0.03
0.01
0.99
0.37
Transit Vehicles (buses)
C02e
1355.20
1314.89
151.09
148.52
NOx
2.43
1.20
0.01
0.01
PM2.5
0.06
0.03
0.01
0.00
VOCs
0.14
0.05
0.27
0.23
Vanpool (Passenger Trucks + Light-duty Trucks)
C02e
400.17
262.85
145.07
114.66
NOx
0.22
0.03
0.75
0.24
PM2.5
0.01
0.01
0.02
0.01
VOCs
0.04
0.01
1.02
0.35
Table 4-6. NESCAUM Connecticut Statewide Emission Rates
Emissions
g/mi
g/start
Base Year (2020)
Future Year (2040)
Base Year (2020)
Future Year (2040)
Auto (Motorcycles + Passenger Cars + Passenger Trucks)
C02e
338.86
216.53
129.32
101.59
NOx
0.17
0.03
0.67
0.24
PM2.5
0.01
0.01
0.02
0.01
VOC
0.03
0.01
1.01
0.38
Transit Vehicles (buses)
C02e
1334.11
1289.65
152.11
149.53
NOx
2.39
1.17
0.01
0.01
PM2.5
0.05
0.03
0.01
0.00
VOC
0.13
0.05
0.27
0.23
Vanpool (Passenger Trucks + Light-Duty Trucks)
C02e
392.63
256.91
146.62
115.92
NOx
0.22
0.03
0.75
0.25
PM2.5
0.01
0.01
0.02
0.01
VOC
0.04
0.01
1.03
0.36
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4.5. Scenario Results
Table 4-7 provides the cumulative percent VMT and emission changes from the BAU for light-duty
vehicles.
Table 4-7. NESCAUM Percent Change in VMT and Emissions for 2040 BAU Compared to 2040 Scenario
Scenario
Light Duty
VMT
CChe
PM2.5
NOx
voc
Scenario 1: Commuter Rail Improvements
-0.40%
-0.40%
-0.40%
-0.41%
-0.42%
Scenario 2: Scenario 1 + Local Bus
Improvements
-0.95%
-0.95%
-0.95%
-0.98%
-1.00%
Scenario 3: Scenario 2 + Smart Growth Land Use
-1.18%
-1.18%
-1.17%
-1.16%
-1.14%
Scenario 4: Scenario 3 + VMT Pricing
-5.42%
-5.44%
-5.45%
-5.54%
-5.64%
Table 4-8 and Table 4-9 provide the regionwide cumulative reduction in VMT and emissions from the
2015 Baseline and 2040 BAU.
Table 4-8. NESCAUM VMT and Emission Changes by Scenario Relative to 2040 BAU

2040 BAU to 2040 Scenario
Scenario
Light Duty
VMT
CChe (kg)

PM2.5 (kg)
NOx (kg)
VOC (kg)
Scenario 1: Commuter Rail Improvements
-361,742
-82,888
-3
-22
-20
Scenario 2: Scenario 1 + Local Bus
Improvements
-857,065
-196,385
-7
-51
-48
Scenario 3: Scenario 2 + Smart Growth Land Use
-1,068,241
-243,451
-8
-60
-55
Scenario 4: Scenario 3 + VMT Pricing
-4,919,398
-1,125,891
-38
-290
-269
Table 4 9. NESCAUM Travel and Emission Changes by Scenario Relative to 2015 Baseline

2015 Baseline to 2040 Scenario
Scenario
Light Duty
VMT
C02e (kg)
PM2.5 (kg)
NOx (kg)
VOC (kg)
Scenario 1: Commuter Rail Improvements
9,835,067
-7,841,461
-234
-14,424
-7,151
Scenario 2: Scenario 1 + Local Bus
Improvements
9,339,743
-7,954,957
-238
-14,453
-7,179
Scenario 3: Scenario 2 + Smart Growth Land Use
9,128,568
-8,002,023
-240
-14,463
-7,186
Scenario 4: Scenario 3 + VMT Pricing
5,277,411
-8,884,463
-270
-14,692
-7,400
The most comprehensive scenario, Scenario 4, would reduce VMT and emissions of each major pollutant
in Connecticut by 5-6%. Most of those reductions were attributed to the VMT pricing strategy, which
made up the difference in performance between Scenarios 3 and 4.
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These results were compared to results from previous TEAM case studies. Table 4-10 compares the
VMT reductions from NESCAUM's individual strategies to the range of VMT reductions projected for
other regions with similar strategies.
Table 4-10. NESCAUM Comparison of Strategy VMT Reductions with Previous Case Studies

% Light Duty
Comparison
Previous Results for Comparison Category
Strategy
VMT
NESCAUM
Strategy Category
Min.
Avg.
Max.
Commuter Rail
0.40%
Transit
0.02%
0.41%
1.42%
Improvements

improvements



Local Bus Improvements
0.55%
Transit
improvements
0.02%
0.41%
1.42%
Smart Growth Land Use
0.23%
Land use
0.16%
2.70%
6.43%
VMT Pricing
4.25%
Road pricing
3.83%
6.70%
9.56%
NESCAUM's four strategies all produce results within the range previously observed.
Both the transit strategies, Commuter Rail Improvements and Locus Bus Improvements, had results near
the average previously observed for transit improvements. This is an indication of strategies that
provide meaningful improvements to transit for a significant part of the state's population, even though
most residents of the state live outside the affected areas.
The land use strategy NESCAUM selected resulted in reductions near the low end of the range.
Reductions were low due to a relatively small (4%) increase in gross population density and no increase
in land use mixing. Importantly, analyzing an increase in job accessibility was not possible because of
data limitations. If job accessibility had been included in the analysis, the resulting VMT reduction
would have been higher. The land use strategy also was not applied to the entire case study area, as
land use strategies have generally been in the past.
NESCAUM's VMT pricing strategy would increase driving costs by $0.05 per mile, a typical value found in
previous case studies. Accordingly, the results here are well within the previously observed range.
4.6. Transit VMT and Emissions
As discussed in Section 1.5.3, shifting travel from light-duty vehicles to transit can increase transit VMT
and associated emissions. The results presented above in Table 4-7, Table 4-8, and Table 4-9 only
include reductions in trips, VMT, and emissions for light-duty vehicles to retain consistency with and to
allow comparison to previous TEAM analyses.
The commuter train and bus improvements components of Scenario 1 and 2, respectively, were based
on reduced wait and travel times. Headway and trip time reductions were assumed to be achieved by
increasing the number of transit vehicles, and thus route miles traveled along a given route. For
example, a reduction in headways from 20 minutes to 10 minutes implies that twice as many transit
vehicles are running the route each hour. Therefore, we would assume that route miles are doubled.
The reduced headways in the scenarios would require an increase of 67% in transit route miles over the
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BAU for both buses and rail. Using this approach, future-year BAU and scenario VMT was estimated
from base-year VMT. Bus and rail VMT estimates and emission rates were used to calculate the total
annual emissions related to these transit strategies. The resultant increases in transit-related VMT and
emissions are provided in Table 4-10.
Table 4-10. NESCAUM Transit Vehicle Percent VMT and Emissions Increases from BAU
Strategies
Transit VMT
CChe (kg/day)
PM2.s(kg/day)
NOx (kg/day)
VOC (kg/day)
Commuter Train + Local Bus
Improvements
66.7%
163,149
1
50
2
Additional details on the methodology for calculations in Table 4-10 are provided in Section 4.7.
4.7. Additional Transit Analysis Details
Estimating the VMT and emission impacts of the transit strategies presented a challenge due to the
number of transit agencies and differing transit vehicles and fuels operating in the New York - New
Haven transit corridor. The following agencies and associated transit modes operate in the corridor:
•	Metro-North Commuter Railroad Company: commuter rail
•	Connecticut Department of Transportation - CT Transit New Haven Division: bus
•	Connecticut Department of Transportation - CT Transit Stamford Division: bus
•	The Greater New Haven Transit District: bus
•	Norwalk Transit District: bus
•	The Greater New Haven Transit District: bus
•	Connecticut Department of Transportation - CT Transit Waterbury: bus
•	Greater Bridgeport Transit Authority: bus
•	Housatonic Area Regional Transit: bus
•	Milford Transit District: bus
For transit VMT, vehicle mileage data was collected from the Service Database in 2016 National Transit
Database for the transit agencies and modes listed above, except for the Metro-North commuter rail
miles. To estimate annual rail vehicle miles traveled, weekday and weekend Metro-North timetables for
the New Haven Line were reviewed. The total number of trips and distance between stops were used to
calculate weekly train miles within Connecticut (up to the Greenwich rail stop). Weekly miles were
multiplied by 52 to estimate total annual Metro-North-New Haven Line train miles.
For criteria pollutants (PM25, NOx, and VOC), region-specific base-year emission rates for each agency
were developed using MOVES2014a. For GHGs, C02 emission rates were estimated using the fuel
consumption data collected from the transit agency. Total fuel consumed by fuel type was multiplied by
the emission factor for that fuel type to estimate the total C02 emitted when that fuel is consumed.
Fuel emission factors (kg C02 per unit of fuel consumed) were compiled from EPA's Mandatory
Reporting of Greenhouse Gases Final Rule documentation.32 Total C02 emissions were divided by the
32 Mandatory Reporting of Greenhouse Gases; Final Rule, 74 FR 56259, October 30, 2009, Tables C-l and C-2. Table
of Final 2013 Revisions to the Greenhouse Gas LNG sourced from: EPA (2008) Climate Leaders Greenhouse Gas
Inventory Protocol Core Module Guidance - Direct Emissions from Mobile Combustion Sources, Table B-5.
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transit VMT to estimate an average base-year C02 emission rate (kg C02/mile). This approach was used
for all liquid or gaseous fuels consumed in transit vehicles.
However, Metro-North commuter trains use some electricity for vehicle propulsion. To estimate the
average C02 emission rate for these trains, the regional electricity emission rate (in lbs. C02-equivalent
per megawatt hour) was obtained from the 2014 EPA eGRID (Emissions & Generation Resource
Integrated Database); the electricity emission rate for the Northeast Power Coordinating Council New
England region (1,072.6 lbs. C02-e/MWh) was used for NESCAUM estimates.33 This emission rate was
then multiplied by the total electricity consumed for Metro-North commuter rail propulsion reported in
the National Transit Database to estimate electricity-related C02 emissions. This emissions value was
combined with the C02 emissions from commuter rail diesel and divided by the total train VMT reported
in the National Transit Database to estimate the average emission rate.
33 E. H. Pechan & Associates., Inc. The Emissions & Generation Resource Integrated Database (eGRID) for 2016
Technical Support Document. U.S. Environmental Protection Agency, Washington, D.C., 2010. Available at:
https://www.epa.gov/energy/emissions-generation-resource-integrated-database-egrid
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5. Puget Sound, Washington - Puget Sound Clean Air Agency
ickground
The Puget Sound Clean Air Agency (PSCAA) is a local air agency based in Seattle, Washington with a
jurisdiction that includes King, Kitsap, Pierce, and Snohomish Counties and all the cities and
unincorporated areas therein. The agency represents roughly four million residents, approximately half
the state's population. In 2014, the PSCAA Board of Directors adopted a strategic plan with the
challenge to become the most climate-friendly region in the United States.34 The strategies and targets
within this plan were the basis of the Agency's letter of interest to be a TEAM case study area.
The initiatives in the 2014 to 2020 Strategic Plan necessitate significant actions by the Agency to meet
climate targets. Roughly half the region's GHG emissions are from transportation sources, so PSCAA has
chosen to focus on this sector. The health impacts of near-road pollution and exposures and existing
congestion issues, coupled with likely dramatic growth in upcoming decades, further support this focus.
The PSCAA Board of Directors and other partners are committed to discussing and prioritizing potential
transportation strategies, to achieve 2030 interim targets and strengthen 2050 targets. Agency staff
wanted to develop an in-house, simple modeling approach for use in rapid evaluation of proposed
scenarios and the associated uncertainties. A common area of concern regarding GHG emission
inventories is consistent methodology and assumptions. TEAM is potentially a useful resource in
supporting these interests.
PSCAA routinely works closely with regional partners, many of whom serve on the Agency's Advisory
Council and Board of Directors. Some of these partners helped develop GHG reduction strategies and
provided data for the TEAM analysis. The region's travel demand model, developed and maintained by
the Puget Sound Regional Council (PSRC), covers the entirety of the four counties. Both PSRC and the
Washington State Department of Ecology provided strategy analysis support with travel activity,
population, and MOVES data.
5,2, Scenario Development
Four strategies supportive of the PSCAA Strategic Plan were selected for evaluation in the four-county
Puget Sound Region:
•	Expand the regional Commuter Trip Reduction Program
•	Expand eligibility for transit subsidies and free transit for target populations
•	Use Regionwide VMT pricing
•	Use Smart growth land use
As in other case study areas, the TEAM approach combined the selected strategies of interest to develop
several scenarios. This approach allowed the agency to observe the cumulative effect of strategies over
time. The details of each strategy within the scenarios are provided below.
34 "Strategic Plan 2014 - 2020.", Puget Sound Clean Air Agency, 17 Feb. 2015,
www.pscleanair.org/DocumentCenter/View/445/2014-to-2020-Strategic-Plan-PDF.
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5.2.1. Scenario 1 - Expand Commute Trip Reduction Program
This scenario would expand the region's successful Commute Trip Reduction (CTR) program to cover
additional employees. In the Puget Sound region, approximately 25% of employees work for employers
having 100 or more employees and therefore are covered under the CTR program. The CTR program
provides financial incentives and support for the employees in using modes of transportation to work
other than driving alone.
This policy would expand the applicability of the CTR program to employers that have 50 or more
employees. As a result, it brings an additional 5% of regional employees (projected at 156,385 in 2040)
under the CTR program.
This policy was analyzed in TRIMMS by inputting the number of new employees that would qualify for
the CTR program (156,385) and selecting the following Transportation Demand Management (TDM)
program options in the model, all of which are part of the CTR program:
•	Program subsidies for carpool, transit, vanpool, bike, and walk
•	Carpool matching service
•	Emergency ride home
•	Flexible work arrangements
•	Telecommute work (telework) arrangements
5.2.2.Scenario 2 - Scenario 1 + Expand Access to Free Transit within Environmental
Justice/Low-Income Populations
This scenario would add a strategy that expands on the existing One Regional Card for All (ORCA) Lift
transit pass, which provides reduced fare transit in the Puget Sound region to approximately 5% of the
region's population. This strategy would bolster that program in two ways:
•	Make an additional 3.5% of the region's population eligible (169,000 people in 2040) for a total
of 390,000 people
•	Make transit free (instead of reduced fare) for the eligible populations
This strategy was analyzed in TRIMMS using two separate model runs for:
1.	The 5% of the population who are already eligible for ORCA Lift and will see the fares reduced to
zero
2.	The 3.5% of the population who will be newly eligible for ORCA Lift
PSCAA provided all population and cost information for developing this scenario. For the first model
run, a population of 220,868 (approximately 5% of the regional population) was input. Table 5-1 shows
the pricing parameters as input to TRIMMS—a current trip cost on transit of $1.50 and a new trip cost of
zero.
Table 5-1. PSCAA TRIMMS Input: Financial and Pricing Strategies for 1st Run (costs per person)
Mode
BAU Trip Cost
Strategy Parking Cost
BAU Trip Cost
Strategy Trip Cost
Public Transport
-
-
$1.50
$0.00
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For the second model run, a population of 168,726 was input. Table 5-2 below shows the pricing
parameters as input to TRIMMS—a current trip cost on public transit of $2.97 per trip and a new trip
cost of zero.
Table 5-2. PSCAA TRIMMS Input: Financial and Pricing Strategies for 2nd Run (costs per person)
Mode
BAU Trip Cost
Strategy Parking Cost
BAU Trip Cost
Strategy Trip Cost
Public Transport
-
-
$2.97
$0.00
5.2.3.Scenario 3 - Scenario 2 + VMT Pricing
The Puget Sound region is unusual in that the MPO is already exploring VMT pricing in the update of its
LRTP. An approximate charge of $0.10 per mile is included in the peak hour assumptions for the 2040
BAU in the MPO's Draft Regional Transportation Plan 2018.35
This scenario would increase VMT pricing $0.05 per mile for all light-duty vehicle travel in the region,
from $0.10 per mile envisioned in the LRTP to $0.15 per mile. This policy was modeled in TRIMMS by
entering the average driving trip cost with a $0.10 charge to represent the BAU, and the average driving
trip cost with a $0.15 charge to represent the strategy. To estimate the average driving trip cost, the
following input data were used:
1.	Average trip length - PSCAA supplied average trip lengths for drive-alone, rideshare, and
vanpool trips, which were provided by PSRC using the regional travel demand model.
2.	Operating driving cost - The per-mile driving cost data were collected from the American
Automobile Association's 2017 Your Driving Costs publication.36 The American Automobile
Association annually publishes estimates for passenger vehicle ownership and operating costs.
For this analysis, the operating cost for medium sedans was used as a representative commute
vehicle.
3.	VMT price - The VMT prices were added to the variable cost to estimate the total variable cost
of driving in the BAU and strategy analyses ($0.10 for BAU and $0.15 for the strategy).
4.	Average vehicle occupancy - Average occupancy for driving trips was provided by PSRC using
their regional travel demand model.
The average roundtrip length was multiplied by the total variable driving cost (including the BAU VMT
price and strategy VMT price) and divided by the average occupancy to estimate the daily trip cost
under the BAU and strategy scenarios. These costs were input into TRIMMS to model the impact of the
higher pricing strategy. Table 5-3 shows the per-trip costs as input into TRIMMS.
Table 5-3. PSCAA TRIMMS Input: Financial and Pricing Strategies (costs per person)
Mode
BAU Parking Cost
Strategy Parking Cost
BAU Trip Cost
Strategy Trip Cost
Auto-Drive Alone
-
-
$3.88
$4.60
Auto-Rideshare
-
-
$1.29
$1.53
35	On May 31, 2018, this draft plan was adopted by the MPO. Puget Sound Regional Council, Regional
Transportation Plan, 2018. The final plan also includes the $0.10 per mile peak user fee.
36	Your Driving Costs, American Auto Association, https://exchange.aaa.com/automotive/driving-costs/
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5.2.4.Scenario 4 - Scenario 3 + Smart growth land use
Scenario 4 adds a policy that would increase densities and land use mixing in the Puget Sound region.
PSCAA did not have access to detailed land use data by traffic analysis zone (TAZ) in the region's travel
demand model. So, unlike other case study regions, this analysis did not vary BAU and strategy inputs
by TAZ. Typically, the TEAM land use analysis involves calculating weighted regional averages of land
use variables based on TAZ-level data. In this case, PSCAA simply provided input in the form of a
percentage increase in each variable at the regional level.
PSCAA provided the specific input for percent change for all the following "D" variables:
•	Population density: +50%
•	Job accessibility by auto (within 30 minutes): 3%
•	Job accessibility by transit (within 30 minutes): 60%
•	Distance to nearest transit stop: -15%
•	Land use mixing: +5%
Note that PSCAA's inputs are interpreted by the agency as an intensification of the BAU land use
scenario in the PSRC LRTP and were provided without quantifying the BAU scenario in terms of the D
variables. An important note is that the feasibility of these assumptions is unknown because PSCAA's
regional land use scenario is not constructed from land use changes in individual TAZs. In addition, the
percentage changes specified are regional averages, and some individual TAZs would experience greater
or lesser changes than those averages.
5.3. Scenario Summary
Input parameters are provided in Table 5-4 for current conditions in the 2014 baseline year, a 2040 BAU
future, and the four scenarios selected by PSCAA. Specific input values are provided for the scenarios.
Table 5-4. PSCAA Scenario Details
Scenario
Description
Data Inputs
Current Conditions
Existing conditions across
Region profile:

all strategies in 2014
• population - 3,745,413


• jobs-1,945,129


Mode shares:


• auto, drive alone - 39.4%


• auto, rideshare - 38.2%


• transit-3.1%


• bike -1.7%


• walk - 15.4%


• Other-2.2%


Average vehicle occupancy, auto rideshare - 2.5


Average vehicle trip lengths, one-way (miles):


• auto, drive alone - 7.70


• auto, rideshare - 6.20


Trip Cost ($/mi)


• auto, drive alone - 1.61


• auto, rideshare-0.57
Business as Usual (BAU)
2040 conditions with
Region profile:
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Scenario
Description
Data Inputs

current levels of
Transportation Demand
Management, reduced
transit fare programs, and
no VMT pricing
•	population-4,853,061
•	jobs-2,981,034
Mode shares:
•	auto, drive alone - 37.3%
•	auto, rideshare - 35.5%
•	transit-4.4%
•	bike-2.0%
•	walk - 18.8%
•	Other-2.0%
Average vehicle occupancy, auto rideshare - 2.5
Average vehicle trip lengths, one-way (miles):
•	auto, drive alone - 7.20
•	auto, rideshare - 6.20
Trip Cost ($/mi)
•	auto, drive alone - 2.64
•	auto, rideshare-0.95
Scenario 1: Expand
Commute Trip
Reduction (CTR)
Program
Expand the existing CTR
program to cover
employers with 50 or more
employees
Mode shares for current CTR-covered employers
Scenario 2: Scenario 1 +
Expand access to free
transit within EJ/low-
income populations
Make reduced fare transit
free transit and expand
eligibility among
environmental justice/low-
income populations
Affected population - 390,000 people eligible in 2040
Scenario 3: Scenario 2 +
VMT Pricing
Add 5 cents per mile to the
potential 10 cents per mile
charge being explored
currently by regional
stakeholders
Trip Cost ($/mi):
• auto-+0.15
Scenario 4: Scenario 3 +
Smart growth land use
Increase population
densities, land use mixing,
job accessibility, and
reduce distances to transit
Land use (Corridor TAZs change from 2040 BAU):
•	population density-+50%
•	jobs access by auto - +3%
•	Jobs access by transit - +60%
•	Distance to transit—15%
•	Land use mix - +5%
5.4. Emissions Analysis
In TEAM, the MOVES analysis focuses on generating activity-weighted, regional average emission rates
from the model that represent the general parameters of the study region. PSRC provided individual
county- and year-specific run specification files and input databases that were used for the TEAM
MOVES analysis. This data, created for the LRTP update, represents the most recent modeling data for
the PSCAA region. PSRC's travel demand model provided parameters for BAU mode shares, trip lengths,
and trip costs by mode. The model predicts a decrease in driving mode share from 2014 to 2040. The
model also predicts a slight decrease in average driving distances. Data were available for the emissions
analysis, consistent with the region's conformity analysis for the LRTP update.
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PSRC also uses default values for population and VMT as inputs to the model and uses the same values
for both existing and future years. This approach is adequate for the emissions rate approach in MOVES
simulations, which does not depend on the population and VMT input values. Therefore, PSRC did not
provide the county- and vehicle type-resolved VMT and population values typically used for the TEAM
approach. Accordingly, all TEAM simulations for PSCAA were made using the VMT and population data
provided by PSRC without change. The inspection and maintenance program was not included in the
modeling, consistent with previous TEAM case studies, to enable comparisons of the results. The
resulting emission rates are shown in Table 5-5.
Table 5-5. PSCAA Emission Rates

g/mi
g/start
Emissions
Base Year (2014)
Future Year
(2040)
Base Year (2014)
Future Year
(2040)
Auto (Motorcycles + Passenger Cars + Passenger Trucks)
GHGs (CC>2-equivalent)
409.52
227.16
149.60
101.84
NOx
0.72
0.06
1.54
0.27
PM2.5
0.02
0.01
0.03
0.01
VOCs
0.16
0.01
2.13
0.38
Transit Vehicles (buses)
GHGs (CC>2-equivalent)
1434.16
1312.69
164.08
163.90
NOx
6.54
1.29
0.02
0.01
PM2.5
0.14
0.03
0.01
0.00
VOCs
0.54
0.07
0.35
0.21
Vanpool (Passenger Trucks + Light-duty Trucks)
GHGs (C02-equivalent)
484.31
270.14
176.78
115.80
NOx
1.07
0.07
2.04
0.29
PM2.5
0.02
0.01
0.04
0.01
VOCs
0.24
0.01
2.85
0.39
5.5. PSCAA Scenario Results
Table 5-6 provides the cumulative percent change for VMT and emission from the BAU for light-duty
vehicles.
Table 5-6. PSCAA Percent Change in VMT and Emissions for 2040 BAU Compared to 2040 Scenario
Scenario
Light Duty
VMT
CChe
PM2.5
NOx
VOC
Scenario 1: Expand CTR Program
-0.09%
-0.10%
-0.10%
-0.10%
-0.10%
Scenario 2: Scenario 1 + Expand access to free
transit within EJ/low-income populations
-1.87%
-1.89%
-1.91%
-2.00%
-2.17%
Scenario 3: Scenario 2 + VMT Pricing
-5.11%
-5.16%
-5.21%
-5.44%
-5.86%
Scenario 4: Scenario 3 + Smart growth land use
-11.91%
-11.82%
-11.71%
-11.28%
-10.49%
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Table 5-7 and Table 5-8 provide the regionwide cumulative reduction in VMT and emissions from the
2040 BAU and 2014 Baseline, respectively.
Table 5 7. PSCAA Travel and Emission Changes by Scenario Relative to 2040 BAU

2040 BAU to 2040 Scenario
Scenario
Light
Duty VMT
C02e
PM2.5
NOx
voc
Scenario 1: Expand CTR Program
-92,586
-22,284
-0.7
-8.8
-5.7
Scenario 2: Scenario 1 + Expand access to free
transit within EJ/low-income populations
-1,830,816
-442,901
-14.8
-179.4
-121.4
Scenario 3: Scenario 2 + VMT Pricing
-4,991,474
-1,206,800
-40.3
-487.3
-328.2
Scenario 4: Scenario 3 + Smart growth land use
-11,635,384
-2,765,817
-90.6
-1,010.2
-588.1
Table 5 8. PSCAA Travel and Emission Changes by Scenario Relative to 2014 Baseline

2014 Baseline to 2040 Scenario
Scenario
Light
Duty VMT
C02e
PM2.5
NOx
VOC
Scenario 1: Expand CTR Program
16,935,817
-11,036,988
-718
-63,684
-26,699
Scenario 2: Scenario 1 + Expand access to free
transit within EJ/low-income populations
15,197,588
-11,457,605
-732
-63,854
-26,815
Scenario 3: Scenario 2 + VMT Pricing
12,036,930
-12,221,504
-758
-64,162
-27,022
Scenario 4: Scenario 3 + Smart growth land use
5,393,019
-13,780,521
-808
-64,685
-27,282
The most comprehensive scenario, Scenario 4, was estimated to reduce VMT and emissions of each
major pollutant by 10-12%. Most of these reductions are attributed to the smart growth land use
strategy, which makes up the difference in performance between Scenarios 3 and 4.
The results of this case study were compared to previous TEAM case studies. Previous case studies
analyzed strategies similar to those selected by PSCAA. Table 5-9 compares the VMT reductions from
PSCAA's individual strategies to the range of reductions projected for other similar strategies.
Table 5-8. PSCAA Comparison of VMT Reductions Strategies and Previous Case Studies

% Light Duty VMT
Comparison Strategy
Previous Case Study Results for Category
Strategy
Reduction
PSCAA
Category
Min.
Avg.
Max
Expand CTR Program
-0.09%
Expanded
Transportation
Demand Management
-0.43%
-1.10%
-2.80%
Expand access to free
transit within EJ/low-
income populations
-1.78%
Transit pass
-0.99%
-1.16%
-1.33%
VMT Pricing
-3.24%
Road pricing
-3.83%
-6.70%
-9.56%
Smart growth land use
-6.80%
Land use
-0.16%
-2.70%
-6.43%
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PSCAA's TDM strategy produced smaller reductions compared to previous analyses, largely due to the
small population targeted in this TEAM analysis as compared to previous case studies. As stated
previously, the Puget Sound region already has a successful CTR program covering employers with 100
or more employees, with almost 25% of the region's employees falling under the current program. The
modeled strategy here would expand the program to cover 30% of regional employees. However, the
5% increase is smaller than the increase in TDM strategies analyzed in previous case studies.
The free transit pass strategy produced results above the previously observed range. The free transit
pass strategy would affect nearly 10%, a relatively large proportion, of the regional population. Previous
transit pass strategies analyzed in other case studies have applied to only about 5-6% of the regional
population.
The VMT pricing strategy produced results slightly lower than the previously observed range. This is
because this strategy represented an incremental increase on an assumed charge of $0.10 per mile that
is already included in the Draft Regional Transportation Plan 2018, and which is considered part of the
BAU scenario. As a result, increasing the charge from $0.10 to $0.15 had less influence on travel behavior
than increasing the charge from zero to $0.05, as most other case studies have done.
PSCAA's land use strategy produces results slightly above the range of previous results. This outcome is
not unexpected because PSCAA specified changes in the D variables that were more aggressive than other
regions have done in the past.
The unique approach to producing land use inputs in this case study, described in Section 1.5, resulted in
ambitious assumptions about land use across the entire region. In previous case studies, agencies have
generally considered the potential for land use changes on a TAZ-by-TAZ basis. The PSCAA analysis was
based on an aspirational regional average change, without consideration of the feasibility in individual
TAZs.
5,1, Transit VMT and Emissions
As discussed in section 1.5.3, shifting travel from light-duty vehicles to transit can increase transit VMT
and associated emissions. However, the impacts on transit VMT resulting from Scenarios 1 and Scenario
2 for this case study were assumed to be negligible, given the robust scale and scope of the BAU transit
system. In other words, the future transit system in the BAU case would be able to accommodate all the
additional transit riders. Therefore, no additional analysis of future scenario transit emissions was
necessary.
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Applying TEAM in Regional Sketch Planning: Four Case Studies
6. Results and Observations
6.1. Regional VMT and Emission Results
The analysis and results for each individual region were discussed in Sections 2 through 5 of this report.
This section shows the relative cumulative regional VMT and emissions changes for all the regions in
Table 6-1, and then provides an overview of the technical lessons learned in preparing for and
conducting the analyses.
Table 6-1. Percent Regional VMT and Emission Changes from the Case Study Areas
Percent Regional Emissions Changes for Future Year Business as Usual compared to Future Year Scenario
Scenario
Light-Duty
VMT
GHG (C02
equivalent)
PM2.5
NOx
voc
CUUATS
Scenario 1: Local Transit Hubs and Bus
Improvements + Bicycle and Pedestrian
Improvements
-2.96%
-3.39%
-4.35%
-5.59%
-7.48%
Scenario 2: Scenario 1 + Parking Pricing at the
University
-3.23%
-3.66%
-4.63%
-5.87%
-7.77%
Scenario 3: Scenario 2 + Smart Growth Land Use
-7.87%
-8.18%
-8.86%
-9.74%
-11.09%
Scenario 4: Scenario 3 + High Speed Rail -8.09% -8.38% -9.04% -9.88% -11.16%
IMCAL
Scenario 1: TDM Program for Petrochemical
Employees
-0.07%
-0.07%
-0.07%
-0.07%
-0.07%
Scenario 2: Scenario 1 + Transit Improvement in
North Lake Charles
-0.10%
-0.10%
-0.10%
-0.10%
-0.10%
Scenario 3: Scenario 2 + Parking Pricing in
Downtown Lake Charles
-0.24%
-0.24%
-0.24%
-0.23%
-0.22%
Scenario 4: Scenario 3 + Smart Growth Land Use -1.05% -1.04% -1.04% -1.01% -0.97%
NESCAUM
Scenario 1: Commuter Rail Improvements
-0.40%
-0.40%
-0.40%
-0.41%
-0.42%
Scenario 2: Scenario 1 + Local Bus
Improvements
-0.95%
-0.95%
-0.95%
-0.98%
-1.00%
Scenario 3: Scenario 2 + Smart Growth Land Use
-1.18%
-1.18%
-1.17%
-1.16%
-1.14%
Scenario 4: Scenario 3 + VMT Pricing -5.42% -5.44% -5.45% -5.54% -5.64%
PSCAA
Scenario 1: Expand Commute Trip Reduction
Program
-0.09%
-0.10%
-0.10%
-0.10%
-0.10%
Scenario 2: Scenario 1 + Expand access to free
transit within EJ/low-income populations
-1.87%
-1.89%
-1.91%
-2.00%
-2.17%
Scenario 3: Scenario 2 + VMT Pricing
-5.11%
-5.16%
-5.21%
-5.44%
-5.86%
Scenario 4: Scenario 3 + Smart growth land use
-11.91%
-11.82%
-11.71%
-11.28%
-10.49%
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The range of results reflect the strategies and level of implementation envisioned by the partner
agencies. As expected, the greatest reductions result from scenarios that represent a combination of
strategies that are mutually supportive and apply to a significant portion of the regional population.
Where strategies affect only a small subset of the regional population or only apply in a designated
subarea of the region, the impacts were smaller in comparison.
It is important to note that the percent change in VMT and emissions shown in Table 6-1 are relative to
the future year BAU case. If a strong program of travel efficiency strategies is already included in the
LRTP for the region, the incremental addition or strengthening of strategies would result in modest
changes compared to the BAU. Where a scenario represents an aggressive departure from the BAU and
was applied broadly across the region, the reductions were more significant.
6.2. Observations and Lessons Learned
In each round of case studies, the application of TEAM for new strategies is based on the interests and
capabilities of the partner agencies, and when necessary, new methods for assessing strategies can be
developed. In this round of case studies, the interests of the partner agencies presented an opportunity
to develop and test methods not previously considered with the TEAM approach. The new strategies
and methods provide an opportunity to better understand the application and capabilities of TEAM and
provide some lessons learned as detailed below.
6.2.1.	Accessibility to Different Organizations
TEAM is accessible to a wide variety of organizations with varying degrees of topical and technical
expertise. In past TEAM case studies, EPA partnered with larger organizations traditionally involved in
transportation planning, such as MPOs or state DOTs. For this round of case studies, EPA partnered with
an air quality agency (PSCAA), an air quality association (NESCAUM), and a smaller MPO (IMCAL). Both
NESCAUM and PSCAA represent new types of partner agencies with a different set of core competencies
and technical expertise. These organizations generally had less transportation data at their disposal.
However, these organizations could still engage in the TEAM analysis with meaningful results. In some
cases, the new partners coordinated with appropriate stakeholders. For example, NESCAUM worked
closely with CTDOT and CTDEEP to gather the needed data. For IMCAL, surrogate data, such as data
from various national datasets, were used to complete the work. EPA will continue to consider the
applicability of national data sources when local data is not available. In short, TEAM has shown itself as
a tool accessible to a variety of different organizations.
6.2.2.Flexibility	of Strategy and Scenario Evaluation
TEAM is unique in its flexibility to explore an array of different travel efficiency strategies. Throughout
the various case studies, partner agencies could select four future year scenarios for evaluation with
each scenario comprised of one or more selected travel efficiency strategies of their choosing. In many
instances, partners have suggested new strategies to evaluate in TEAM and we have developed methods
to evaluate them. For example, this round of case studies included two agencies interested in rail
strategies. CUAATS selected a high-speed rail strategy running from St. Louis to Chicago with a stop in
Champaign-Urbana. NESCAUM explored a rail strategy that included potential improvements to reduce
average travel times and shorter wait times on trains that serve the New York-New Haven corridor.
Both strategies involved developing rail emission rates and ridership estimates that captured the travel
activity and emissions that occur within the analysis area.
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In previous rounds of case studies, we developed new methods to estimate VMT impacts of bicycle and
pedestrian improvements, as well as evaluate land use changes. These methods were employed in this
latest set of case studies as well.
Throughout the rounds of case studies, TEAM has been used to explore hypothetical "what-if" exercises,
evaluate program level decisions, and been tested on numerous new strategy applications, yet, TEAM
was flexible enough in each case to produce useful results.
6.2.3.Scalability of Affected Population and Geographic Area of Analysis
TEAM has been successfully used to evaluate the VMT and emission reduction benefits of strategies
applied to a specific corridor, city or county, or entire state, and thus has been used to evaluate
strategies targeted to a specific sub-population such as employees of specific industrial sector or people
living along a specific corridor, as well as to a region's entire population. The analysis for the State of
Connecticut, facilitated by NESCAUM, provided an opportunity to apply TEAM to a new geographic scale
- an entire state. Previous TEAM case studies have focused on the MPO boundaries as the geographic
scale of analysis, with some strategies applied to a sub-geography or to a sub-population within the
region. The Connecticut-NESCAUM case study considered a state-wide analysis area, with a VMT pricing
strategy applied state-wide and the remaining strategies applied in a defined transportation corridor
extending from New York to New Haven. This involved developing separate travel data and weighted
emission rates for each sub-geography. Though TEAM had not been previously applied at this
geographic scale, the methodology was easily adapted. NESCAUM's case study paves the way for
NESCAUM to apply the TEAM methodology to other states in its consortium, as well as for other states
to consider applying travel efficiency strategies statewide.
6.3. Conclusion
These four case studies once again demonstrate the value of adopting travel efficiency strategies to
reduce emissions of air pollutants. Federal regulations on vehicles and fuels have achieved great
benefits, but when state and local agencies are looking to improve air quality further, the contribution of
travel efficiency strategies cannot be overlooked. Of course, travel efficiency strategies have other
benefits as well, such as reducing congestion and accidents.37 EPA believes these case studies provide
examples for other state and local agencies to consider to significantly reduce emissions.
37 See EPA, Potential Changes in Emissions Due to Improvements in Travel Efficiency - Supplemental Report:
Analysis of Potential Co-Benefits, EPA-420-R-11-04, September 2011.
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