Applying TEAM in Regional
Sketch Planning:
A Case Study in A
United States
Environmental Protection
Mm. Agency
Office of Transportation and Air Quality
E PA-4 2 O-F-2 0-035
July 2020

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Applying TEAM in Regional
Sketch Planning:
A Case Study in
Transportation and Climate Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
United States
Environmental Protection
^1	Agency
EPA-420-F-20-035
July 2020

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Applying TEAM in Regional Sketch Planning: Austin, Texas
Acknowledgments
The U.S. EPA Office of Transportation and Air Quality would like to thank the Capital Area Council of
Governments for their partnership and support in providing the critical data and thoughtful input
required for the successful completion of this project and report.
ICF International provided technical support to the U.S. Environmental Protection Agency in the
development of the methodologies and analysis employed for this assessment.
Acronyms and Abbreviations
BAU
business as usual
CAPCOG
Capital Area Council of Governments
C02
carbon dioxide
C02e
carbon dioxide equivalent
EPA
U.S. Environmental Protection Agency
GHG
greenhouse gas
LRTP
Long Range Transportation Plan
MOVES
Motor Vehicle Emission Simulator (EPA's motor vehicle emissions model)
MPO
Metropolitan Planning Organization
NOx
nitrogen oxides
PM
particulate matter
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
VOCs
volatile organic compounds
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Applying TEAM in Regional Sketch Planning: Austin, Texas
Table of Contents
1	Introduction	1
2	TEAM Analysis Tools	3
3	Scenario Analysis	4
3.1	Baseline and BAU Details	4
3.2	Scenario 1: Improved Transit Frequency and Travel Times on Key Corridor	5
3.3	Scenario 2: Region-wide Transit Frequency Improvements	6
3.4	Scenario 3: Public Sector Worker Transit Subsidy	6
3.5	Scenario 4: Region-wide VMT Pricing	6
4	Scenario VMT and Emissions Analysis Results	8
4.1	VMT Analysis	8
4.2	Emissions Analysis	9
4.3	Discussion of Results	11
List of Tables and Figures
Figure 1: CAPCOG TEAM Analysis Geographic Scope	2
Table 1: CAPCOG TE Strategy Scenario Overview	4
Table 2: Baseline and BAU Regional Parameters	5
Table 3: Scenario 1 Improved Transit Frequency and Travel Times on Key Corridor Analysis Inputs	5
Table 4: Scenario 2 Region-wide Transit Frequency Improvements Analysis Inputs	6
Table 5: Scenario 3 Public Sector Worker Transit Subsidy Analysis Inputs	6
Table 6: Scenario 4 Trip Costs Calculations	7
Table 7: Scenario 4 Region-wide VMT Pricing Analysis Inputs	8
Table 8: Changes in VMT by Scenario Within Affected Geography/Population and at Regional Scale	9
Table 9: CAPGOG Emission Factors by Process and Analysis Year for Light-Duty Vehicles	9
Table 10: Daily VMT (mi) and Emission (kg) Reductions by Scenario Compared to the 2040 BAU	10
Table 11: Affected Scenario Geography or Population Percent Changes in Emissions for the 2040
Scenario Compared to the 2040 BAU	10
Table 12: Regionally Normalized Percent Changes in Emissions for the 2040 Scenario Compared to the
2040 BAU	11
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Applying TEAM in Regional Sketch Planning: Austin, Texas
1 Introduction
Air quality in the U.S. has improved over the years, as regulations and technologies have affected
emissions from all pollution sectors. Yet, even with improvements in vehicle technologies and fuels, the
transportation sector continues to be a major source of criteria pollutants and greenhouse gas (GHG)
emissions across the country. While emissions per mile traveled have decreased, growth in travel
activity has partially offset those reductions, and presents a challenge to achieving and maintaining
public health. For air quality and transportation planners interested in reducing transportation
emissions in their regions, the ability to estimate the emission reduction potential of a given strategy 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 the Travel Efficiency Assessment Method (TEAM) to quantify the potential emission
reduction benefits of travel efficiency strategies, and has worked with various state and local agencies to
apply TEAM in a series of case studies.1
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) listed in Section 108(f)(1)(A) of the Clean Air Act 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. Over the years, these types of strategies have been
promoted by non-governmental organizations, academics, and a variety of government agencies at the
local, state, and federal level.
EPA developed TEAM, an approach to quantify the potential emission benefits of travel efficiency
strategies without having to run an area's travel demand model, saving time and resources. TEAM uses
available travel data and a transportation sketch model analysis to quantify the change in vehicle miles
travelled (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 model (the Motor
Vehicle Emission Simulator, EPA's emissions model for both onroad and nonroad mobile sources). The
focus of a TEAM analysis is the effect of a strategy primarily on personal passenger vehicles. Therefore,
VMT and emissions impacts are estimated for personal passenger vehicles only (i.e., passenger cars,
passenger trucks, and motorcycles). Furthermore, potential increases in transit VMT and emissions
resulting from transit strategies are not accounted for in the VMT and emission results for this case
study.
This document details the TEAM analysis conducted in partnership with the Capital Area Council of
Governments (CAPCOG).2 CAPCOG represents a ten-county region in and around Austin, Texas. This
region is home to the Austin-Round Rock-Georgetown Metropolitan Statistical Area (MSA), which is the
fastest-growing large metropolitan area in the country.3 The geographic focus of the analysis, depicted
1	More information on EPA's Travel Efficiency Assessment Method, including past case studies, can be found at
www.epa.gov/state-and-local-transportation/estimating-emission-reductions-travel-efficiency-strategies.
2	More information on the Capital Area Council of Governments can be found at www.capcog.org/.
3	Keemahill, Dan, and Mary Huber. "Austin Region Fastest-Growing Large Metro in the Nation 8 Years Running,
Data Shows." Statesman, Austin American-Statesman, 18 Apr. 2019,
www.statesman.com/news/2019Q418/austin-region-fastest-growing-large-metro-in-nation-8-vears-running-data-
shows.
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Applying TEAM in Regional Sketch Planning: Austin, Texas
in the figure below, includes the five-County Austin-Round Rock-Georgetown MSA that includes Bastrop,
Caldwell, Hays, Travis, and Williamson Counties, as well as Burnet County.
Figure 1: CAPCOG TEAM Analysis Geographic Scope
BURNET
LLANO
LEE
BLANCO
FAYETTE
CAPCOG
Worsening traffic congestion is anticipated due to regional population growth, which is projected to
reach 4 million people by 2040. The most recent long-range transportation plan (LRTP) adopted by the
Capital Area Metropolitan Planning Organization (CAMPO) anticipates a 118% growth in VMT between
2010 and 2040, but only a 21% increase in road capacity.4 Austin is currently designated "attainment"
for all national ambient air quality standards (NAAQS).5 However, fast regional growth and increased
demand for travel has the potential to degrade Austin's air quality, thus prompting CAPCOG's interest in
conducting a TEAM analysis.
CAPCOG initially partnered with a large group of area stakeholders to discuss strategy selection and data
collection, including the Capital Metropolitan Transportation Authority (CapMetro), the Capital Area
Rural Transit System (CARTS), the Central Texas Regional Mobility Authority (CTRMA), City of Austin,
4	CAMPO 2040 Regional Transportation Plan, Table 7: Supply and Demand on Regional Roadways:
https://47kzwi6dnl447gy9z7dol6an-wpengine.netdna-ssl.com/wp-
content/uploads/2018/03/CAMPQ2040PlanFinal.pdf
5	Central Texas Air Pollution Levels Compared to National Standards, Capital Area Council of Governments,
http://aircentraltexas.org/en/regional-air-quality/how-is-the-air-in-central-texas.
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Applying TEAM in Regional Sketch Planning: Austin, Texas
Travis County, Capital Metropolitan Planning Organization (CAMPO), and the Federal Highway
Administration (FHWA). CapMetro is the primary transit provider for the Austin Urbanized Area. The
strategies evaluated in this analysis are the result of this stakeholder engagement.
2 TEAM Analysis Tools
A TEAM analysis is performed to determine the potential VMT and emissions reductions of various
strategies. The VMT component of the analysis in TEAM uses 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.6 TRIMMS relies on a number of user-supplied
parameters, or default parameters if user-supplied inputs are not available, to describe the regional
travel and transportation patterns. The parameters include:
	Mode Share - the percentage of travelers using a type (mode) of transportation.
	Average Vehicle Occupancy by Mode - the average number of people in each vehicle type.
	Average Trip Length - the average one-way trip length by mode of transportation.
	Demographic and Employment Characteristics - area-wide population and employment
information.
At the core of TRIMMS is the capability to estimate mode share changes through scenario analysis.
Scenario analysis in TRIMMS estimates impacts on travel patterns from adjusting the direct influences of
travel demand, namely trip cost, trip time, etc. Some data inputs need pre-processing before use in
TRIMMS. Pre-processing steps are described within the individual scenario discussions below. This
analysis was conducted with TRIMMS 4.0, the latest version of this sketch model available at the time
the case studies were undertaken.
A key user-supplied TRIMMS input for scenario analysis is the "commuters affected" value. The
commuters affected value represents the population affected by the policy or scenario under
consideration. A policy or scenario may affect the entire regional resident population or have a more
specific impact on a population subset, such as the employees of a certain industry or residents within a
defined project radius. While there is some subjectivity when specifying the commuters affected value,
it should comprehensively capture the VMT impacts of a given policy or scenario. The commuters
affected value is stated in the narratives for each of the scenarios evaluated in this analysis.
The emissions component of the analysis in TEAM uses MOVES2014a to determine regional average
emission rates for the study area. MOVES is EPA's state-of-the-science emissions modeling system that
estimates emissions for mobile source at the national, county, and project level for criteria pollutants,
greenhouse gases, and air toxics.7 MOVES was run in Inventory mode using inputs provided by CAPCOG
to produce activity-weighted composite light-duty emission rates for each pollutant evaluated in this
analysis. This process is described further in the Emissions Analysis section of this document. The
pollutants evaluated in this analysis include:
	C02-equivalent (C02e);
	nitrogen oxides (NOx);
	fine particulate matter (PM2.5); and
	volatile organic compounds (VOCs).
6The TRIMMS model and supporting documentation can be found at http://trimms.com/.
7For more information on EPA's MOVES model, visit https://www.epa.gov/moves.
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Applying TEAM in Regional Sketch Planning: Austin, Texas
3 Scenario Analysis
In a TEAM analysis, data is collected to represent conditions at a defined baseline year and a BAU future
year. BAU conditions are based on the CAMPO 2040 Plan, the current long-range regional transportation
plan.8 VMT and emissions are estimated in the future year BAU case. Then, future-year TE strategies are
selected for evaluation, and VMT and emissions are estimated in future TE strategy scenarios. These
estimates are compared against the BAU case. In addition to providing the baseline and BAU data,
CAPCOG selected the following four TE strategy scenarios for evaluation:
Table 1: CAPCOG TE Strategy Scenario Overview
Scenario
Description
Scenario 1: Improved Transit
Frequency and Travel Times
on Key Corridor
A hypothetical high-frequency transit service along a major
North/South corridor loosely based on the Orange Line route
highlighted in Project Connect, CapMetro's long-term service vision.
This transit service is expected to improve transit travel times and
access times for residents and commuters within the corridor
Scenario 2: Region-wide
Transit Frequency
Improvements
Region-wide transit frequency improvements that reduce transit
access and travel times, loosely based on what could be expected
from implementation of CapMetro's Project Connect Vision Plan
Scenario 3: Public Sector
Worker Transit Subsidy
Full transit fare subsidies for public sector workers.
Scenario 4: Region-wide VMT
Pricing
A hypothetical state VMT fee at a level needed to bring all modes up
to a "state of good repair" beyond existing revenue (see discussion
below in Section 3.6).
The following sections cover the analysis' baseline, BAU, and TE strategy scenarios in greater detail
including the primary TEAM analysis inputs.
3.1 Baseline and BAU Details
The data below defines the regional 2010 baseline and regional 2040 BAU modeling parameters used as
the basis for the VMT portion of the analysis. The parameters in Table 2 below were supplied by
CAPCOG unless otherwise noted.
8 https://www.campotexas.org/regional-transportation-plans/2040-plan/.
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Applying TEAM in Regional Sketch Planning: Austin, Texas
Table 2: Baseline and BAU Regional Parameters
Parameter Description
2010 Baseline
2040 BAU
Regional population
1,759,024
4,120,322
Regional employment
774,786
2,324,770
Mode share:


Auto, drive alone
48.75%
48.75%
Auto, rideshare
42.76%
42.76%
Transit
4.48%
4.48%
Vanpool
0.02%
0.02%
Bike
0.56%
0.56%
Walk
2.46%
2.46%
Other3
0.98%
0.98%
Average vehicle occupancy:


Auto, drive alone
1.00
1.00
Auto, rideshare
2.19
2.18
Vanpool
4.59
4.69
Public Transport
8.49
8.60
Other3
1.67
1.67
Average vehicle trip lengths (miles):


Auto, drive aloneb
11.07
11.07
Auto, rideshareb
13.26
13.26
Transitb
13.26
13.26
Vanpoolb
4.59
4.59
Bikeb
1.48
1.48
Walkb
0.68
0.68
3 This value reflects adjustments needed to make mode share sum to 100%.
b Denotes TRIMMS default
3.2 Scenario 1: Improved Transit Frequency and Travel Times on Key Corridor
This scenario assumes the implementation of a high-frequency transit service along a major
North/South corridor similar to the Orange Line in Project Connect, CapMetro's long-term service
vision.9 This scenario would improve transit travel times and access times for residents and commuters
within the corridor. Generally, transit strategies evaluated in TEAM are applied to the population within
Vz mile of transit stops, for whom travel choices are assumed to be influenced by the service
improvements. The affected population of 523,371 was determined through a spatial mapping of traffic
analysis zones (TAZs) along the corridor. The analysis inputs in Table 3 were supplied by CAPCOG unless
otherwise noted.
Table 3: Scenario 1 Improved Transit Frequency and Travel Times on Key Corridor Analysis Inputs

BAU
Scenario
Affected population (2040)
523,371
Public Transport - Access Time (min)
26.11
10.00
Public Transport -Travel Time (min)
28.66
13.70
9 The proposed Orange Line route as of 11/13/19 is available at:
https://capmetro.org/uploadedFiles/New2016/ProiectConnect Vision/Maps/Proiect-Connect-Vision-Plan-Map.pdf
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3.3 Scenario 2: Region-wide Transit Frequency Improvements
This scenario includes broader transit improvements at the region-wide level. This strategy assumes a
doubling in the region-wide public transport service frequency and a five-minute decrease in region-
wide average travel time, with no change in trip length. The full regional 2040 population was used to
represent the affected population. The analysis inputs in Table 4 were supplied by CAPCOG unless
otherwise noted.
Table 4: Scenario 2 Region-wide Transit Frequency Improvements Analysis Inputs

BAU
Scenario
Affected population (2040)
4,120,322
Public Transport - Access Time (min)
38.16
19.08
Public Transport -Travel Time (min)
28.66
23.66
3.4 Scenario 3: Public Sector Worker Transit Subsidy
Transit subsidies are a common way to encourage commuters to ride transit. As the capital of Texas and
home to several federal offices, the Austin region is home to a significant number of public sector
workers beyond the already-large number of local government employees, all of whom generate trips
within the region. Many of the local governments and state or federal agencies where these workers are
employed participate directly in the region's air quality planning efforts or otherwise play a supporting
role. Government entities can also directly influence commuting behavior for their own employees in
ways they might not be able to for private sector employees. Therefore, CAPCOG targeted public sector
employee commuting incentives as a possible strategy to reduce regional travel demand. For this
scenario, CAPCOG wanted to evaluate the effect of a full fare subsidy such that individual public sector
workers could use transit at no cost. The BAU public transport trip cost, $0.77, was assumed to be the
average fare paid across all trips provided and not necessarily the posted fare rate. This value was
calculated from the total fare revenue divided by the total number of unlinked trips. The affected
population provided by CAPCOG for this strategy is 398,107. This figure is calculated from a ratio of
public sector workers, from the sum of Local, State, and Federal government worker classes in the 2008-
2012 American Community Survey, to all workers within the region (approximately 17.12%) applied to
the CAMPO 2040 regional total jobs estimate (i.e., the 2040 BAU regional employment).10 Table 5
provides the analysis inputs for Scenario 3.
Table 5: Scenario 3 Public Sector Worker Transit Subsidy Analysis Inputs

BAU
Scenario
Affected population (2040)
398,107
Public Transport -Trip Cost ($)
0.77
0.00
3.5 Scenario 4: Region-wide VMT Pricing
VMT pricing is a strategy to levy a distance-based fee on vehicle use. This kind of program is expected to
reduce single occupancy vehicle travel and encourage commuters to use other forms of transportation,
including public transit, carpool, or vanpool where costs are spread across multiple riders. In recent
years, states have increasingly been considering alternative sources of revenue to make up shortfalls in
funds available to support their transportation plans. CAPCOG noted discussion in the Texas State
10 United States Census Bureau. "Summary File." 2008 - 2012 American Community Survey. U.S. Census Bureau's
American Community Survey Office, 2018. Web. 1 November 2019. www.census.gov/programs-
survevs/acs/data/summarv-file.html.
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Legislature about the potential need to collect transportation funding from alternate mechanisms than
fuel taxes due to increased fuel efficiency and the increasing market penetration of electric vehicles
(EVs). CAPCOG also noted that the Texas Department of Transportation's (TxDOT's) long-range plan
identified $15.5 billion per year in additional statewide funding that would be needed to bring all modes
of transportation up to a "state of good repair."11 By considering a pricing strategy, the analysis can
inform local transportation planners and state leaders about the VMT and emissions impacts of shifting
to VMT pricing to compliment gas tax revenue. An analysis of VMT pricing is also useful to the region for
understanding the impacts of tolled versus untolled roadway capacity projects, which have been a hot
topic within the region and across the state.
Consideration of a specific price to apply for this strategy was challenging without any previous
discussion on what might be reasonable in the region. Options considered were:
1.	Fuel Tax Replacement: A price equivalent to the revenue collected from the fuels tax for FY
2018; considering $3,674,997,000 in motor fuels tax revenue and 97,234,128,089 VMT in FY
2018. This price would be $0.0186 per mile. This would offer a substitute for EVs not paying fuel
tax, but for most people in the area, driving behavior is already impacted by paying the gas tax.
2.	State of Good Repair: The extra revenue needed to achieve TxDOT goals: an extra $15.5 billion
(2014 dollars) was identified as needed for "state of good repair" for all modes beyond 2014
revenue of $5.5 billion. The funding gap was updated to 2019 dollars for a total of $16.7 billion
using the Bureau of Labor Statistics' Consumer Price Index calculator. Comparing this amount to
FY 2018 VMT, this price would be $0.0846 per mile and represent revenue not currently
included in the TxDOT budget.12
3.	Comparable VMT Fees: Other options included comparison to previous TEAM analyses, using
the highest or the lowest price per VMT.
CAPCOG selected the second option described above for the TEAM analysis. Pricing was applied to the
full anticipated regional population in 2040 of 4,120,322. TRIMMS uses the change in costs between the
BAU and scenario as the basis for the calculation of the change in VMT. The current cost for the affected
modes, auto-drive alone and auto-rideshare, was input as zero to reflect the absence of VMT pricing in
the BAU scenario. Note, for the purposes of this analysis, the VMT fee was only applied to light-duty
vehicle travel from passenger cars, passenger trucks, and motorcycles (i.e., MOVES sourcetypes 11, 21,
31). VMT from commercial vehicles was outside the scope of the analysis. Additionally, for this analysis,
services such as vanpool and public transit were assumed to not be subject to the VMT fee. The new trip
costs input reflects the new marginal costs associated with the VMT fee for the given mode. The new
costs were derived by multiplying the VMT price of $0.0846/mile by the 2 times the "average one-way
trip length" values by mode. Table 6 provides the components of the trip cost calculation.
Table 6: Scenario 4 Trip Costs Calculations
Mode
VMT Price ($/mi)
(A)
One-way trip
Length (mi) (B)
New Trip Cost ($)
= (A x (B x 2))
Auto-Drive Alone
$0.0846
11.071
$1.87
Auto-Rideshare
$0.0846
13.261
$2.24
denotes TRIMMS de'
ault
11	Texas Department of Transportation "Texas Transportation Plan 2040 - Executive Summary" Exhibit ES-4
available at: http://ftp.dot.state.tx.us/pub/txdot-info/tpp/2040/plan/exec-summarv.pdf.
12	Texas Department of Transportation 'Texas Transportation Plan 2040 - Executive Summary" available at:
http://ftp.dot.state.tx.us/pub/txdot-info/tpp/2040/plan/exec-summarv.pdf.
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Table 7 provides the analysis inputs for Scenario 4.
Table 7: Scenario 4 Region-wide VMT Pricing Analysis Inputs

BAU
Scenario
Commuters Affected (2040)
4,120,322
Auto-Drive Alone - Trip Cost ($)
0.00
1.87
Auto-Rideshare - Trip Cost ($)
0.00
2.24
4 Scenario VMT and Emissions Analysis Results
TEAM results combine VMT analysis from TRIMMS with the emission rates derived from MOVES. Results
can be contextualized both in terms of reduction within the affected scenario geography, or population,
and in terms of the reduction in VMT on a regional basis. Contextualizing the results in these two ways
avoids the possibility of misrepresenting a strategy that may be highly effective within the geographic
scale in which they are implemented but has a smaller regional impact. For example, transit service
improvements can be effective and could significantly affect VMT in a corridor or other sub-geography
of a region, however, without additional public investment, may only impact a small sub-population of
the entire region. Conversely, if public transit was available everywhere across a region, these strategies
could be more competitive with the driving (auto-drive alone and auto-rideshare) mode shares.
In TEAM, the focus of the analysis is the effect of a strategy on passenger vehicle travel activity. For the
VMT analysis, this activity is captured as the Auto-Drive Alone and Auto-Rideshare modes in TRIMMS.
For the MOVES emissions analysis, this activity is represented by the passenger car, passenger truck, and
motorcycle source types. Potential increases in transit VMT and emission resulting from mode shift to
transit are not accounted for in the VMT and emission results presented below. For transit service
improvement strategies based on reduced wait and trip times (Scenarios 1 and 2), headway and trip
time reductions are assumed to be achieved by increasing the number of buses and thus route-miles
traveled in a given route. For example, halving the headway would require doubling the buses running
that route. Additional transit vehicle type, fuel type, and operational details of the transit frequency
improvement strategies are needed to assess the transit VMT and emission impacts of these strategies
and is beyond the scope of this analysis.
4.1 VMT Analysis
TRIMMS runs were used to determine the changes in light-duty VMT (Auto-Drive Alone and Auto-
Rideshare) for each scenario. The VMT analysis results are provided both in terms of the percent change
in VMT reduction within the affected scenario geography, or population, and in terms of the percent
change in regional VMT. As noted above, contextualizing the results this way allows the reader to see
the direct effect of a strategy on the affected scenario geography or population and the effect on the
region as a whole. Table 8, below, provides the scenario total reduction in daily light-duty VMT, and the
percent changes in light-duty VMT at the affected geography or population level and at the regional
level compared to the 2040 Regional BAU.
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Applying TEAM in Regional Sketch Planning: Austin, Texas
Table 8: Changes in VMT by Scenario Within Affected Geography/Population and at Regional Scale
Scenario
Affected Geography
or Population
Reduction
in Light-
Duty VMT
(mi)
Percent
Change in
Light-Duty
VMT within
Affected
Geography
or
Population
Percent
Change in
Light-Duty
VMT within
Region
Scenario 1: Improved Transit
Frequency and Travel Times on
Key Corridor
Population within Vz
mile of transit stop
along corridor
-56,671
-0.76%
-0.10%
Scenario 2: Region-wide Transit
Frequency Improvements
Region-wide
population
-233,425
-0.40%
-0.40%
Scenario 3: Public Sector Worker
Transit Subsidy
Public sector
workers
-587,977
-10.42%
-1.01%
Scenario 4: Region-wide VMT
Pricing
Region-wide VMT
-2,443,044
-4.18%
-4.18%
4.2 Emissions Analysis
In TEAM, the MOVES analysis is focused on generating activity-weighted, regional average emission
factors to represent the general conditions of the study region. Fuel efficiency and emission standards
are assumed to improve for vehicles over time consistent with assumptions in MOVES, thus emission
rates are lower in future years. Data used for this analysis was adapted from a Texas Commission on
Environmental Quality file transfer protocol site supplied by CAPCOG.13 EPA compiled MOVES input
databases for Bastrop, Burnet, Caldwell, Hays, Travis, and Williamson counties for both the 2010
baseline year and 2040 BAU analysis year. Activity-weighted emission factors were generated
developed by summing total emissions, by pollutant and associated process, and dividing by the
appropriate activity value, whether starts or VMT for the light-duty vehicles included in the analysis (by
passenger cars, and passenger trucks, and motorcycles source types). Activity-weighted emission
factors were generated by summing total emissions, by pollutant and associated process, and dividing
by the appropriate activity value, whether starts or VMT for the light-duty vehicles included in the
analysis (passenger car, and passenger truck, and motorcycle source types). Table 9 provides the
activity-weighted emission factors generated for the entire 6-county analysis region by activity type and
analysis year for light-duty vehicles.
Table 9: CAPGOG Emission Factors by Process and Analysis Year for Light-Duty Vehicles
Pollutant
Emissions per mile (g/mi)
Emissions per start (g/start)
Base Year (2010)
Future Year (2040)
Base Year (2010)
Future Year (2040)
C02e
459.75
218.47
97.49
55.47
NOx
0.85
0.02
1.64
0.12
PM2.5
0.01
0.01
0.01
0.004
VOCs
0.27
0.01
2.30
0.16
13 Texas Commission on Environmental Quality FTP site available at ftp://amdaftp.tceq.texas.gov/pub/EI/onroad/.
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MOVES emission factors for 2040 were combined with scenario-level light-duty VMT changes for 2040
to estimate emissions reductions for both the scenario affected geography, or population, and at the
regional level. Table 10, below, provides the daily VMT reductions, in miles, and emission reductions, in
kilograms, by scenario.
Table 10: Daily VMT (mi) and Emission (kg) Reductions by Scenario Compared to the 2040 BAU
Scenario
Light-Duty
VMT
C02e
PM2.5
NOx
voc
Scenario 1: Improved Transit
-56,671
-12,672
-1.90
-0.37
-1.14
Frequency and Travel Times on Key





Corridor





Scenario 2: Region-wide Transit
-233,425
-52,197
-7.81
-1.52
-4.69
Frequency Improvements





Scenario 3: Public Sector Worker
-587,977
-132,398
-21.64
-3.90
-14.42
Transit Subsidy





Scenario 4: Region-wide VMT Pricing
-2,443,044
-552,762
-95.53
-16.38
-67.42
Table 11, below, provides the percent changes compared to the 2040 BAU in light-duty VMT and by
pollutant for each scenario for the affected geography, or population, of the scenario. Note, pollutant
percent changes generally track closely with VMT percent changes.
Table 11: Affected Scenario Geography or Population Percent Changes in Emissions for the 2040 Scenario
Compared to the 2040 BAU
Scenario
Light-Duty
VMT
C02e
PMz.5
NOx
VOC
Scenario 1: Improved Transit
-0.76%
0.76%
0.68%
0.75%
0.59%
Frequency and Travel Times on Key





Corridor





Scenario 2: Region-wide Transit
-0.40%
0.40%
0.35%
0.39%
0.31%
Frequency Improvements





Scenario 3: Public Sector Worker
-10.42%
10.40%
10.15%
10.37%
9.89%
Transit Subsidy





Scenario 4: Region-wide VMT Pricing
-4.18%
4.19%
4.33%
4.21%
4.47%
Table 12, below, provides the regional percent changes compared to the 2040 BAU in light-duty VMT by
pollutant for each scenario. Note, pollutant percent changes generally track closely with VMT percent
changes.
U.S. Environmental Protection Agency
10

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Applying TEAM in Regional Sketch Planning: Austin, Texas
Table 12: Regionally Normalized Percent Changes in Emissions for the 2040 Scenario Compared to the
2040 BAU
Scenario
Light-Duty
VMT
C02e
PMz.5
NOx
VOC
Scenario 1: Improved Transit
-0.10%
-0.10%
-0.09%
-0.09%
-0.08%
Frequency and Travel Times on Key





Corridor





Scenario 2: Region-wide Transit
-0.40%
-0.40%
-0.35%
-0.39%
-0.31%
Frequency Improvements





Scenario 3: Public Sector Worker
-1.01%
-1.00%
-0.98%
-1.00%
-0.96%
Transit Subsidy





Scenario 4: Region-wide VMT Pricing
-4.18%
-4.19%
-4.33%
-4.21%
-4.47%
4.3 Discussion of Results
Among the scenarios selected, Scenario 4: Region-wide VMT pricing is the most effective strategy for
VMT reduction at the regional level. This strategy may be increasingly important as states grapple with
transportation funding shortfalls and as vehicle fleets shift to alternative fuel sources, such as electricity,
that do not contribute towards fuel tax revenues. However, transit service improvements or subsidies
can be effective and could significantly affect VMT in a corridor or other geography or population of a
region. Scenario 3: Public Sector Worker Transit Subsidy had the single largest impact on VMT and
emissions within the affected geography or population.
U.S. Environmental Protection Agency
11

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