Applying TEAM in Regional
Sketch Planning:
Three Case Studies in:
ATLANTA
ORLANDO
ST. LOUIS
SEPA
United States
Environmental Protection
Agency
Office of Transportation and Air Quality
EPA-420-R-16-009
July 2016
-------
Applying TEAM in Regional
Sketch Planning:
Three Case Studies in:
ATLANTA
ORLANDO
ST. LOUIS
Transportation and Climate Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
Prepared for EPA by
ICF International
EPA Contract No. EP-C-12-011
Work Assignment No. 4-08
&EPA
United States
Environmental Protection
Agency
EPA-420-R-16-009
July 2016
-------
Acknowledgments
The U.S. EPA Office of Transportation and Air Quality would like to thank the following
organizations for their partnership and support in providing the critical data, information, and
thoughtful input required for the successful completion of this project.
Atlanta Regional Commission
MetroPlan Orlando Metropolitan Planning Organization
East West Gateway Council of Governments
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IV
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Contents
Acknowledgments iii
Tables vi
Executive Summary vii
1. Introduction l
2. Methodology 4
Strategies, Scenarios, and Baselines 4
Data Collection and Validation 6
Analysis 8
3. Case Study Results 19
MetroPlan Orlando 19
Atlanta Regional Commission 26
East-West Gateway 35
4. Results and Conclusions 47
Data for Use in TEAM 49
Strategy Analysis and Conclusions 50
MOVES Support of TEAM 53
Appendix A. Additional Technical Details for the TEAM Analysis A-l
A.1. Implications of Increased Transit A-l
A.2. Travel and Emissions Changes by Region and Scenario A-3
A.3. Additional Land Use Calculation Details A-4
Appendix B. MetroPlan Orlando B-l
B.1. Regional Comparison of VMT Reductions B-l
B.2. Emission Factors and Detailed Results B-l
Appendix C. Atlanta Regional Commission C-l
C.1. TDM Strategy Analysis Supplemental Information C-l
C.2. Regional Comparison of VMT Reductions C-2
C.3. Emission Factors and Detailed Results C-3
Appendix D. East-West Gateway D-l
D.1. Bicycle and Pedestrian Strategy Analysis Supplemental Information D-l
D.2. Regional Comparison of VMT Reductions D-l
D.3. Emission Factors and Detailed Results D-3
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Tables
Table ES-1. Percent VMT Change Relative to BAU ix
Table ES-2. Summary of Travel Efficiency Strategies Selected x
Table ES-3. Percent Regional VMT and Emissions Changes xii
Table 1. VMT Reduction Strategy Analysis Options inTRIMMS 5
Table 2. Other VMT Reduction Strategy Analysis Options (not in TRIMMS) 5
Table 3. Data Inputs for MOVES Runs 16
Table 4. MetroPlan Orlando Scenario Details 23
Table 5. MetroPlan Orlando Regionwide Percent VMT and Emissions Changes
for Light-Duty Vehicles 27
Table 6. ARC Scenario Details 32
Table 7. ARC Regionwide Percent VMT and Emissions Changes for Light-Duty
Vehicles 35
Tables. EWG Scenario Details 41
Table 9. EWG Regionwide Percent VMT and Emissions Changes for Light-Duty
Vehicles 45
Table 10. Percent Regional VMT and Emissions ChangesMetroPlan Orlando,
ARC, and EWG 47
Table A-1. ARC, EWG, and MetroPlan Orlando Transit Vehicle Percent VMT
and Emissions Changes A-3
Table A-2. Ewing and Cervero (2010) Elasticity Values for the Multivariate Land
Use Analysis A-4
Table B-1. MetroPlan Orlando Comparison of Regional VMT Reductions with
Cluster 2 B-l
Table B-2. Emission Factors for MetroPlan Orlando B-2
Table B-3. MetroPlan Orlando Comparison of VMT Reductions and Emission
Changes by Scenario B-2
Table C-1. ARC Transit Subsidies C-l
Table C-2. ARC Comparison of Regional VMT Reductions for Regional
Populations C-2
Table C-3. Emission Factors for ARC C-3
Table C-4. ARC Comparison of VMT Reductions and Emission Changes by
Scenario C-4
Table D-1. EWC Bicycle and Pedestrian Analysis D-l
Table D-2. EWG Comparison of VMT Reductions for Regional Populations D-2
Table D-3. Emission Factors for EWG D-3
Table D-4. EWG Comparison of VMT Reductions and Emission Changes by
Scenario D-4
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Executive Summary
EPA partnered with the metropolitan planning organizations in Atlanta, St. Louis, and Orlando to
understand the potential of travel efficiency strategies to reduce greenhouse gases (GHG) and
other air pollution. Travel efficiency strategies represent the broad range of strategies intended
to reduce travel activity, especially single-occupancy vehicle (SOV) travel. Examples of travel
efficiency strategies include travel demand management (e.g., telecommuting, transit
subsidies), public transit fare changes and service improvements, road and parking pricing, and
land use/smart growth. This builds on the description of Transportation Control Measures
(TCMs) listed in Section 108(f)(1)(A) of the Clean Air Act (CAA) by adding other strategies such
as pricing and smart growth. EPA has developed an approach to quantify the potential emission
benefits of these strategies, identified as the Travel Efficiency Assessment Method (TEAM).
TEAM uses available travel data and a transportation sketch model analysis to quantify the
change in vehicle miles travelled (VMT) resulting from the strategies and, emission factors from
EPA's MOVES2014 (Motor Vehicle Emission Simulator) model, to calculate corresponding
emission reductions. The TEAM case studies described in this report confirm EPA's belief that
travel efficiency strategies have the potential to significantly reduce GHGs and other pollutants
and provide an alternative to highway oriented SOV travel.
Beginning in 2012, EPA issued a solicitation of letters of interest to partner with transportation
planning agencies interested in applying TEAM in their area. Since that time, EPA has provided
technical assistance to six agencies to evaluate the emissions reduction potential of alternative
future transportation scenarios to compare against the emissions from the current transportation
plan (i.e., the business-as-usual (BAU) future base case). EPA quantified the potential
reductions of GHGs, fine particulate matter (PM2.s), nitrogen oxides (NOx), and volatile organic
compounds (VOCs) for several different scenarios.
After completing case studies with the initial three agencies1, the second group of three
agencies was selected in 2015 as case studies for testing the TEAM approach at a regional
level. The Atlanta Regional Commission (ARC), East West Gateway Council of Governments
(EWG), and MetroPlan Orlando Metropolitan Planning Organization (MetroPlan) partnered with
EPA for this effort. Each agency selected strategies for improving travel efficiency that are of
interest within the region and provided data to support the analysis. Strategies were grouped
into four scenarios for the analysis. Both the strategies and their underlying assumptions
represent a broad range of potential transportation futures for evaluating corresponding
emissions reductions.
1 The previous three case studies were completed in Boston, Kansas City, and Tucson. See more information in Estimating
Emissions Reductions from Travel Efficiency Strategies: Three Sketch Modeling Case Studies, EPA,
https://www.epa.qov/otaq/stateresources/policY/420r14003a.pdf
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With this second group of case studies, new approaches were also developed for TEAM to
estimate benefits of land use changes and bicycle/pedestrian networks, outside the sketch
model used for other strategies. EPA has a strong interest in understanding how land use
changes, particularly smart growth principles, support emissions reductions. In the current
study, two approaches to land use analysis were tested in the ARC and EWG analyses2. The
details of both land use approaches and how they were applied in the ARC and EWG regions
are included in this report. In addition, a bicycle and pedestrian network expansion was
evaluated with a method used by local entities in California, and its application to TEAM is
illustrated in the EWG case study. These new approaches supplement TEAM and further EPA's
goal of developing methods for estimating the GHG and other benefits of a range of travel
efficiency strategies in a simple, cost-effective way.
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
MOVES2014 was used to determine appropriate, regional average emission factors for all
regions. MOVES was run in inventory mode based on regionally provided inputs to produce
activity-weighted average emission factors for the four primary pollutants considered in this
analysis: CO2-Equivalent (CO2e), NOx, PM2.s, and VOCs.
EPA also used the TRIMMS sketch model developed by the Center for Urban Transportation
Research (CUTR) as part of the TEAM approach.3 TRIMMS offers a variety of features and is
easy to use, making it highly appropriate 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.
Land Use Analysis
Land use strategies are one of the most importantand one of the most complexmeans 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
2 MetroPlan Orlando elected not to evaluate an alternative land use strategy because they believe the most robust land use
possible for the region is already incorporated into their travel demand model data, and thus would be reflected in their future
BAU base case.
3 TRIMMS (Trip Reduction Impacts of Mobility Management Strategies) was developed by the National Center for Transit
Research and the Center for Urban Transportation Research at the University of South Florida under a grant from
the Florida Department of Transportation and the U.S. Department of Transportation.
viii July 2016
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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.
The land use analysis from the previous case studies produced results that diverged from EPA's
national study4 and raised questions about the land use algorithms in TRIMMS 3.0. Therefore,
EPA identified the need for a new approach to analyzing land use strategies within TEAM that is
both simple and reasonably accurate, within the constraints of a sketch modeling approach.
Several land use analytical approaches were considered for this second set of case studies with
respect to the following criteria:
Transparency of methodology
Availability and simplicity of inputs
Compatibility with inputs and elasticities used in other TEAM strategy analyses
Two land use analytical approaches were selected for testing in the participating regions. These
are identified as the Neighborhood Classification approach and the Multivariate Elasticity
approach. These analyses were conducted outside of the TRIMMS analysis. As with the
analysis of strategies conducted within TRIMMS, the resulting VMT reduction was used with the
emissions factors from the MOVES analysis to calculate emissions reductions. The results are
shown in Table ES-1. For complete details on the description and selection, see the
Methodology section of this report.
Table ES-1. Percent VMT Change Relative to BAU
Strategy
Atlanta: Smart Growth
St. Louis: Transit Oriented
Development
St. Louis: Work/Housing
Balance
2013 Approach
(TRIMMS)
-0.50%
-0.08%
-0.16%
2016 Approach
(Neighborhood)
-5.97%
-0.16%
-1.97%
2016 Approach
(Multivariate)
-6.43%
-0.54%
-1.12%
Selected Strategies
The combination of strategies in this report represents the broadest range tested to date, and
application to specific geographies and populations illustrates the versatility of TEAM. The
regions explored available data to support their strategy selection, and in the case of land use
1 https://www.epa.gov/otaq/stateresources/policY/420r11003.pdf
IX
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strategies, conducted additional analysis to provide required data. Table ES-2 provides an
overview of the selected strategies and their individual geographical and population application.
MetroPlan
(Orlando)
Table ES-2. Summary of Travel Efficiency Strategies Selected
Strategies
Expand employer programs
including transit pass
Improve transit access and travel
times
VMT pricing for entire region (6
cents/mile)
Unlimited transit pass with tuition
and university employment
Geographic Area Covered
Sub-population of 3 county area
Applied to
309,637 additional employees
covered by a variety of programs
Sub-population of 3 county area 515,425 employees
3 county area
3,301,256 (regional population)
Sub-population of 3 county area 205,000 students, faculty, and staff
ARC Expand telework and guaranteed
(Atlanta) ride home
Improve transit access times
Parking pricing ($7.50 for drive
alone trips)
Increase density and mixed use
land use
Employees in 5 county core area
of 20+ counties
5 county area
5 county area
5 county area
875,000+ additional employees
covered by a variety of programs
2 million+ regional employees
26% of all parking, public and private
4.4 million+ people (regional
population)
EWG
(St. Louis)
TOD near existing light rail stations 3 county core area
Increase residential density and Entire 5 county area
mixed development
1. 6 million+ people
2.3million+people
Complete bicycle and pedestrian
network
Complete light rail system
Entire 5 county area
Entire 5 county area
150% increase in bike lane miles
(1,951 miles)
Increase sidewalk coverage to 71% of
local and arterial roads (10,579
sidewalk miles)
761,887 additional people
Both ARC and MetroPlan Orlando selected strategies for analysis that have been associated
with TEAM since the beginning of its development: transportation demand management (TDM),
pricing, and transit. The EWG team was more interested in strategies to evaluate land use and
increased multimodal travel. This interest presented some new analytical challenges that have
demonstrated the flexibility of TEAM with respect to the analysis approach used.
The strategies selected by EWG in the current study demonstrate the potential interest in
alternative populations and geographies, as well as strategies that are not easily analyzed using
TRIMMS. This test of the TEAM approach illustrates that it can be used independent of the
analysis tool or method. As illustrated in the EWG results, the reduction in emissions may be
very limited; however, TEAM allows staff to view the comparison of potential outcomes of
various strategies. This allows informed decision making without the extensive cost and time of
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detailed travel demand model analysis - which may require a modeling sophistication beyond
current capabilities of many agencies.
As in the past, the greatest benefit in emissions reductions can be seen using a combination of
mutually supportive strategies over individual strategies modeled alone. All regions in this study
applied the strategies cumulatively. For example, land use can enhance both transit and TDM
improvements. However, when the strategy is applied to a subset of the population or
geography of the region, the effects may be large for the target population, but smaller for the
region as a whole. Applying the strategy to a larger geography or population would likely
produce larger reductions.
Results and Conclusions
This study estimated VMT and emission reductions for each of the future year scenarios,
expressed as a percent change as compared to the BAU baseline, as shown in Table ES-3.
Percent change is a useful measure when comparing strategy effectiveness across dissimilar
regions and is necessary for continued evaluation of TEAM performance in various regions with
specific interests. However, percent change does not fully represent the quantitative impact on
VMT and emissions, which is more important to the individual region. For example, the effect of
EWG scenarios appears relatively small in terms of percent change. For example, Scenario 4
represents a reduction of 1.9 million VMT/day, 440,000 kg/day CO2e, 16 kg/day PM2.s, 103
kg/day NOx, and 80 kg/day VOC - a notable improvement for the region, especially when
combined with other efforts to reduce pollutant emissions. Similarly, the other case study areas
could expect reductions of as much as:
12 million VMT/day, 2.8 million kg/day CO2e, 124 kg/day PM2.s, 535 kg/day NOx, and 414
kg/day VOC (Atlanta)
4.6 million VMT/day, 1.1 million kg/day CO2e, 39 kg/day PM2.5, 201 kg/day NOx, 117 kg/day
VOC (Orlando)
Participants in the case studies also pointed out that corresponding trip reduction is a measure
that is important for decision makers. These common performance metrics used by staff and
decision makers in a region can be a valuable way of observing the potential impact of
strategies that have often been difficult to quantify.
The results in Table ES-3 also show that the VMT and emissions changes are very similar
across each scenario. The regional average emission factors used in TEAM do not capture the
finer points of emissions modeling that would be exhibited with more detailed analyses,
including the differences between pollutants as functions of speed. However, this level of
resolution is appropriate for a sketch modeling approach. Total emissions reductions are
reported in Appendices B-D of this report.
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Transit changes were more regionally significant in the MetroPlan Orlando and ARC regions
than the effect of the light-rail buildout in EWG. The impact of a subarea strategy can be
significant at the subarea scale, but is reduced when reflected at the regional scale. Although
EWG transit expansion and TOD strategies support one another, the emissions reductions
modeled in this analysis are related to only the area around the rail stations.
Table ES-3. 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
GHGs (C02
equivalent)
PM2.5
NOx
VOC
MetroPlan Orlando
Scenario 1 : Expanded TDM
Scenario 2: Scenario 1 + Enhanced Transit
Scenario 3: Scenario 2 + Road Pricing
Scenario 4: Scenario 3 + University Transit
Pass
-0.65%
-0.92%
-4.75%
-6.08%
-0.65%
-0.92%
-4.75%
-6.07%
-0.65%
-0.92%
-4.75%
-6.07%
-0.65%
-0.92%
-4.74%
-6.06%
-0.65%
-0.92%
-4.73%
-6.05%
Scenario 1 : Expanded TDM
Scenario 2: Scenario 1 + Transit Frequency
Improvement
Scenario 3: Scenario 2 + Parking Pricing
Scenario 4: Scenario 3 + Land Use
(Neighborhood)
Scenario 4: Scenario 3 + Land Use
(Multivariate)
-0.69%
-0.86%
-2.85%
-8.82%
-9.28%
-0.68%
-0.86%
-2.85%
-8.81%
-9.27%
-0.68%
-0.86%
-2.85%
-8.81%
-9.27%
-0.67%
-0.85%
-2.82%
-8.79%
-9.25%
-0.66%
-0.83%
-2.81%
-8.78%
-9.24%
Scenario 1: Regional TOD (Neighborhood)
Scenario 1: Regional TOD (Multivariate)
Scenario 2: Scenario 1 + Workforce Housing
Balance (Neighborhood)
Scenario 2: Scenario 1 + Workforce Housing
Balance (Multivariate)
Scenario 3: Scenario 2 + Bike / Ped Network
(Neighborhood)
Scenario 3: Scenario 2 + Bike / Ped Network
(Multivariate)
Scenario 4: Scenario 3 + Transit Expansion
(Neighborhood)
Scenario 4: Scenario 3 + Transit Expansion
(Multivariate)
-0.16%
-0.54%
-2.13%
-1.66%
-2.21%
-1.73%
-2.54%
-2.07%
-0.16%
-0.54%
-2.13%
-1.66%
-2.22%
-1.75%
-2.56%
-2.11%
-0.16%
-0.54%
-2.13%
-1.66%
-2.24%
-1.76%
-2.57%
-2.13%
-0.16%
-0.54%
-2.13%
-1.66%
-2.37%
-1.89%
-2.70%
-2.39%
-0.16%
-0.54%
-2.13%
-1.66%
-2.56%
-2.08%
-2.90%
-2.79%
XII
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TEAM is a data-led approach. Data collection, validation, and analysis continue to consume the
majority of the effort. Agencies with easy access to the required data can support TEAM very
efficiently. The required MOVES data is readily available for areas that conduct a transportation
conformity analysis or otherwise develop mobile source emissions inventories for air quality
planning purposes. For areas where local data availability is an issue, the use of MOVES
default data is an adequate, although less precise alternative, to support analysis.
The results provide a very limited set of data points with which to compare the two land use
results. It is notable that results from the Neighborhood approach are within 1% of results from
the Multivariate approach for each strategy. The two approaches appear to produce results that
are very similar in magnitude.
As with the previous TEAM case study regions, these areas selected some transit strategies
that increase transit VMT. For these case studies, an off-model estimation approach was
developed to consider both passenger vehicle emissions reduced by increased transit, and
additional emissions produced by transit as a result of this increase. Estimating transit
emissions accurately from this increase in VMT requires more detailed information on transit
vehicle technology, fuels, and system operations than could be provided within the scope of this
analysis. The report notes where transit emissions are a factor so that regions that wish to use
the TEAM approach can identify how best to address these additional transit emissions.
EPA's previous TEAM case studies showed a range of technical capability among the regions
related to the use of MOVES. This was less true in the present iteration. Two regions were very
comfortable with the model in similar applications, and understood the relationship between the
present analysis and that conducted for regulatory purposes. The third region did not have
current experience using MOVES.
These case studies add new methods to the TEAM, furthering its ability to analyze strategies
that state and local governments could consider to reduce greenhouse gases and other air
pollution from the transportation sector. TEAM continues to be a viable, relatively low-cost
means for estimating the contribution that travel efficiency strategies can make to an area's
overall plan to reduce emissions and its carbon footprint, helping areas make choices in how
they allocate funding to potential strategies.
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1. Introduction
Over the past several years the U.S. Environmental Protection Agency (EPA) has supported
research aimed at substantiating the strong potential to reduce emissions by reducing single-
occupancy vehicle travel and correspondingly, vehicle miles traveled (VMT). Although this
research was initially focused on national-level benefits, the approach developed to quantify the
reductions was grounded in regional data that was then factored up to the national level. This
resulted in the report, Potential Changes in Emissions Due to Improvements in Travel
Efficiency5 (2010 national study). Because the methodology relied upon data typically available
in regional transportation planning and used a basic sketch-planning analysis technique, it
appeared to be readily adaptable for regions of varying sizes and technical sophistication.
EPA calls this approach the Travel Efficiency Assessment Method (TEAM). The term "travel
efficiency" is used to refer to those strategies defined in the Clean Air Act (CAA)6 such as
employer-based transportation management programs, transit improvements, smart growth and
related land use strategies, as well as road and parking pricing, and other strategies aimed at
reducing mobile source emissions by reducing vehicle travel activity. TEAM uses available
travel data and a sketch model analysis to quantify the change in VMT, combined with the EPA
MOVES (Motor Vehicle Emission Simulator) model's emission factors to calculate the emission
reductions that can reasonably be expected.
In support of TEAM, EPA provided opportunities for testing the methodology at the regional
scale. Analyzing Emission Reductions from Travel Efficiency Strategies: A Guide to the TEAM
Approach7 was issued in 2011 to support use of the methodology. This was followed in 2012
with a solicitation of letters of interest from agencies interested in applying TEAM in their local
context and evaluating their selected strategies. Three case studies are presented in the
resulting report, Estimating Emission Reductions from Travel Efficiency Strategies: Three
Sketch Modeling Case Studies8
For the latest effort and subject of this report, EPA provided technical assistance to implement
TEAM and conduct analyses for three additional agencies. The three agencies selected for
these case studies are the Atlanta Regional Commission (ARC), the metropolitan planning
organization (MPO) for Atlanta, GA; East West Gateway Council of Governments (EWG), which
does transportation planning for the St. Louis, MO-IL area; and MetroPlan Orlando Metropolitan
Planning Organization (MetroPlan), which covers Orlando, FL. Each agency selected strategies
of interest to the region and developed scenarios with these strategies based on their individual
goals for the case study. Four scenarios were identified for each agency by combining the
strategies of greatest interest to their region. The analysis was then conducted using the
5 http://www.epa.gov/otaq/stateresources/policY/420r11003.pdf
e CAA Section 108(f)(1)
7 http://www.epa.gov/otaq/stateresources/policY/420r11025.pdf
8 http://www.epa.gov/otaq/stateresources/policY/420r14003a.pdf
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TRIMMS (Trip Reduction Impacts of Mobility Management Strategies) sketch-planning model
and the MOVES2014 emissions model to estimate potential emissions reductions for the four
primary pollutants considered in this analysis: CCb -Equivalent, NOX, PM2.5, and VOC.
This report provides the results, conclusions, and recommendations from the experience in
conducting the analysis. The regions were diverse in their application of TEAM, and this
provided more insights into how the approach might best be applied. The analysis also pointed
out strengths and challenges of the analytical tools and methods used.
Ana lysis Tools
The TRIMMS model developed by the Center for Urban Transportation Research (CUTR) has
been a standard feature of the TEAM approach. TRIMMS offers a variety of features, is easy to
use, and is sensitive to many of the strategies that regions are interested in testing; making it
highly appropriate for this type of analysis. The regional VMT analysis for the majority of the
selected scenarios was conducted with TRIMMS 3.0, as used in the previous case study
analysis.9
MOVES201410 was used to determine appropriate, regional average emission factors for all
regions. MOVES was run in inventory mode based on regionally provided inputs to produce
activity-weighted average emission factors for the pollutants reported.
Land Use Analysis
Land use strategies are one of the most powerfuland also one of the most complexmeans
by which regions can reduce VMT. The land use analysis performed for the previous case
studies yielded results that were considered overly conservative when compared to other peer
reviewed studies, and raised questions about the reliability of the land use algorithms in
TRIMMS 3.0. Subsequently, EPA identified the need to evaluate alternative methods to
analyzing land use strategies within TEAM for this effort that is both simple and reasonably
accurate, within the constraints of a sketch modeling approach.
Several analytical approaches were considered with respect to the following criteria:
Transparency of methodology
Availability and simplicity of inputs
Compatibility with inputs and elasticities used in other TEAM strategy analyses
9 EPA 2014, EPA-420-R-14-003a. Available: http://www.epa.gov/otaq/stateresources/policY/420r14003a.pdf
10 EPA released an updated version of the MOVES model, MOVES2014a, in November, 2015. Due to the project's schedule, the
previous version, MOVES2014, was used in all analyses here. For criteria pollutant emissions from on-road vehicles, the
models predict essentially the same level of emissions. See EPA-420-F-15-046, November 2015.
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Accuracy of estimation
Among the approaches considered, two approaches were selected for testing in this analysis.
These are identified as the "Neighborhood Classification" approach and the "Multivariate
Elasticity" approach. These analyses were conducted outside of the TRIMMS analysis, and the
resulting VMT reduction was used with the emissions factors from the MOVES analysis. Both of
these approaches are explained in the next section.
Bicycle and Pedestrian Analysis
At the request of EWG, an additional strategy was analyzed outside of TRIMMS. Expanding
bicycle and pedestrian infrastructure is not incorporated in TRIMMS, but there are approaches
available in the literature which were adapted for application in TEAM. The approach is simple
enough to be used by any MPO. For additional information on this analysis, refer to Appendix
D.1.
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2. Methodology
Strategies, Scenarios, and Baselines
Strategies are the specific policy to be tested such as land use or transit changes. In contrast to
previous case studies, participating regions in this study selected a wide range of strategies for
analysis and were not constrained to those only appropriate for analysis using TRIMMS. This
adaptation was made in part due to the use of "off-model" land use analysis approaches and in
response to the availability of data within each region. This case study evaluation also allowed
further consideration of using a standard analysis tool, such as TRIMMS, and the flexibility that
TEAM might support with a variety of methods/tools. Some strategies of interest, such as
expanding bicycle infrastructure were not previously considered because these cannot be
modeled in TRIMMS. In this case study analysis, the bicycle system expansion strategy
selected by EWG was evaluated off-model. Details of this approach are covered in later
sections of the report.
Scenarios are groups of strategies to be 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 may increase with additional
actions over time. All agencies elected to use this approach. The specific strategies selected for
each scenario, along with the data needed to model them, are provided for each agency in
Section 3 of this report.
Strategy Selection for TEAM Analysis
The three regions selected individual strategies of interest that primarily fit within the TRIMMS
capabilities. In general, these fall into the three strategy categories identified in Table 1. Many of
the TRIMMS functions work by manipulation of travel times and travel costs, common factors in
travel demand modeling. For example, to model an expansion of HOV lanes in TRIMMS, the
user must input changes in typical travel times for carpool trips and possibly single occupancy
vehicle trips. In order to use TRIMMS effectively, users must consider how to translate their
strategy interest into the options within the model.
TRIMMS requires inputs such as target population, cost of parking or vehicle trips, transit travel
time, and transit access time. For example, to model the MetroPlan Orlando ridesharing
strategy (the TDM programs strategy in TRIMMS), data inputs were derived for number of
employees covered by the strategy, existing cost per trip for public transit and cycling, new cost
per trip for public transit and cycling based on per-employee subsidies for each mode, presence
of a guaranteed ride home program, and presence of a telework and flex work schedule
programs. Carefully translating the strategy into inputs that can be specified in the model is
essential to confidence in the results.
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Table 1 provides information about the analysis options that were used to analyze strategies
selected. The table shows the data needs for each strategy to conduct the TRIMMS analysis
(second column) and the TRIMMS modeling options relevant to each strategy (third column).
Other strategies of interest that are not easily transferable into model inputs were not suitable
for TRIMMS. Land use and multimodal strategies were analyzed outside of TRIMMS. Table 2
provides the corresponding data needs for those strategies.
Table 1. VMT Reduction Strategy Analysis Options in TRIMMS
Strategy Categories Strategies That Can Be Analyzed
TDM or Employer
Incentives
Transit
Pricing
Subsidies for alternative modes
Guaranteed ride home, ride match,
telework, and flexible work
schedules
Data Needs
share of regional employees covered
average subsidy offered to employees (by
mode) - OR - whether or not subsidies are
offered (by mode)
whether or not guaranteed ride home, ride
match, telework, and flexible work schedules
are offered
Free or bundled transit passes
Reduction in transit travel times or
wait times
Parking pricing
VMT pricing
share of regional population affected
average decrease in transit trip cost
transit travel time and access time
share of all parking (public and private) that is
priced
average increase in parking cost per trip
average increase in trip cost
Table 2. Other VMT Reduction Strategy Analysis Options (not in TRIMMS)
Strategy Categories Strategies That Can Be Analyzed
Land Use
Bicycle and
Pedestrian
Shifting population and employment
growth to more compact
neighborhoods/lower VMT
generating neighborhoods
Workforce-housing balance
initiative
TOD program
expand sidewalk coverage
expand bike lane coverage
Data Needs
Neighborhood approach:
share of regional population in affected areas
percent population by neighborhood type
Multivariate approach:
share of regional population in affected areas
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
share of regional population in affected areas
increase in sidewalk coverage
increase in miles of bicycle routes
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Baselines
TEAM results provide a comparison of potential emissions reductions from selected strategies
with the potential emissions from both a future business as usual (BAU) scenario, and baseline
scenario that represents the current transportation system. The results are presented as percent
reduction based on comparison with the area's BAU scenario. For this reason, the selected
BAU and baseline are a critical component of using this approach. The BAU scenario
represents likely emissions based on the future year anticipated transportation infrastructure
outlined in each region's long-range transportation plan (without implementation of the
additional TEAM strategies) along with future year demographic changes and may already
include some types of travel efficiency strategies, while the baseline represents nearly current
conditions. The general ability of agencies to provide this basic BAU is mixed, and the
participating agencies provided a range of BAU scenarios for use in this study, illustrating this
point. In most cases, the BAU represents the future year infrastructure and travel activity without
additional travel efficiency strategies. This is typically the scenario, or vision, in the adopted long
range transportation plan and is well suited for use with TEAM. It is essential to ensure that the
scenarios analyzed for comparison match the geographic bounds, fleet characteristics,
population, and other parameters of the BAU scenario.
It is important to note that the percentage reductions reported do not represent the full impact of
travel efficiency strategies compared to current conditions, as in each case some aspects of
travel efficiency are already included in the BAU. In the interest of completeness, the overall
impact that travel efficiency strategies can have compared to the baseline is included in the
Appendix.
Data Collection and Validation
Data collection and validation is the most important task within any analysis approach, and
TEAM is no exception. One of the efficiencies of using TEAM is that it relies primarily on inputs
or outputs from the travel demand model used for regional planning. The intent of TEAM is to
support early decision making by providing a comparison of different strategies under consistent
future conditions. Strategies that appear most effective and have the necessary public and
political support can then be more rigorously analyzed for precise impacts using the travel
demand model and other tools available to the region. When the data being used is consistent
across these levels of analysis, it is more likely that confidence in the results can be maintained
for the supported strategies.
Data collection involved receiving the actual data, processing as necessary, completing quality
assurance reviews of any provided inputs, creating databases of default data, performing any
necessary revisions to the data provided, and filling any gaps in the local data with default data
to create a complete dataset. Locally specific data is preferred to default data, and efforts were
made to obtain local data wherever possible. All three regions provided some local data that
generally was used by the agencies for other purposes. EWG and ARC performed data
collection unique to this analysis for land use.
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ARC'S Transit Frequency Improvement strategy provides a useful example to illustrate the level
of effort required to prepare a strategy for analysis. The policy to be tested is a 50% reduction in
average transit headways. ARC provided the following information:
average total trip time for public transit of 57.4 minutes (which includes time to get to the
vehicle, waiting time, and time to get to destination from vehicle);
average transit travel time of 33.45 minutes;
total system headway of 8.78 minutes; and
number of people living in the transit zone (46% of the total regional population).
In order to model this strategy in TRIMMS, the transit access time for the BAU and strategy
scenarios are needed. The average BAU access time was calculated by subtracting the total trip
time from the travel time (23.95 minutes). A 50% decrease in headways corresponds to a new
headway of 4.39 minutes; thus, the new total access time is 23.95 minutes minus 4.39 minutes
or 19.56 minutes. To calculate the affected regional population, the total regional population was
multiplied by 46% to determine a target population of 2,025,866 people. These calculated
values were reviewed and approved by ARC and EPA before running the TRIMMS model.
The MOVES analysis within TEAM uses regional data to determine average emission factors,
often from the same sources used for transportation conformity findings subject to regulation
under the CAA. ARC and EWG are subject to these CAA regulations. These emissions factors
are applied to the BAU case and future strategy VMT to estimate potential emissions reductions
in each scenario.
Data was provided from the most recent regional transportation conformity analysis in the two of
the participating regions, ARC and EWG. Therefore, while the TEAM analysis is a less rigorous
analysis than that required for transportation conformity, it is consistent with the interagency
policies and agreed-to approach for conformity. This consistency allows the results to inform the
conformity discussion about potential emission benefits of particular strategies, but the results
cannot be directly used in a regional transportation conformity analysis.
However, TEAM is not part of the transportation conformity process, and does not require the
use of transportation conformity data. MetroPlan Orlando is not designated as a CAA
nonattainment or maintenance area for any pollutant at this time and thus did not have locally
specific emissions inputs from a transportation conformity determination to provide. Default data
available in TRIMMS and MOVES2014 provided acceptable data to enable a relative
comparison of emissions among scenarios and with the BAU for this region.
MOVES input data provided by the regions required some processing for use in TEAM. For
example, for EWG two modifications were made to the provided data. In the first modification,
the MOVES input databases provided by EWG were those used for regional planning and
conformity by the responsible environmental organizations; the Missouri Department of Natural
Resources (MODNR) and Illinois Environmental Protection Agency (ILEPA). Neither
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organization had updated their inputs from the original MOVES2010b format, and therefore the
data required conversion to MOVES2014 format using EPA's converter tools. Second, the
"unfactored" VMT values provided represented average weekday VMT, stratified only by county
and HPMS facility (road) type. The EPA converter tool11 was used to translate these values into
annual VMT ("HPMSBaseYearVMT") for database input to MOVES.
Analysis
Results of this study, including reductions in trips, VMT, and emissions, are presented for light-
duty vehicles only to be consistent with and allow comparison to previous TEAM analyses.
VMT Ana lysis
The analysis for the 2013 regional case studies was conducted with TRIMMS 3.0, which
contained some significant changes from previous versions. The user interface was updated to
reduce the number of steps required to conduct the analysis, and the outputs were expanded in
detail. Most significantly, TRIMMS 3.0 added a new land use module that includes controls for
increasing residential densities, land use mixing, transit station accessibility, and transit-oriented
development (TOD). Although these changes to the land use component appear to provide
more utility for EPA's land use interest, the 2013 case studies did not show a reasonable
response to land use changes, when compared to other significant studies in the literature. This
is the basis for the focus on land use analysis in the current group of case studies. TRIMMS 3.0
remained useful for other selected strategies.
A number of scenarios involve transit improvements, such as reducing headways. These
improvements result in increased transit service with a corresponding increase in VMT, trips,
and emissions for transit vehicles. However, TRIMMS is limited in its ability to estimate the
corresponding increase in transit trips for transit strategies. Therefore, the impact of increased
usage of transit vehicles due to an increase in transit trips was estimated off model.
Actual VMT from the National Transit Database (NTD) in 2013 for each agency was used to
determine a corresponding increase in transit VMT for transit strategies. This was performed for
all transit strategies that involve increased transit service.12 Section A-1 of the appendices
provides further explanation and presents the changes in transit VMT and emissions for relevant
strategies. This supplemental analysis provides a more complete description of the impact of
transit strategies, which not only reduce light-duty VMT and emissions, but also increase transit
vehicle VMT and emissions.
Another limitation when using TRIMMS is that it does not consistently account for shifting trips
between modes. In particular, TRIMMS does not adequately capture mode shift for TDM
11 Available at http://www.epa.qov/otaq/models/moves/documents/aadvmt-converter-tool-moves2014.xlsx.
12 Not all transit strategies result in service increases. See the strategy detail table in each case study for those scenarios where
transit VMT is expected to increase.
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strategies where subsidies are offered for use of alternative modes. The "radio button" feature in
TRIMMS proved the most successful way to analyze TDM strategies without the use of actual
monetary values for subsidies. The radio button is a toggle that indicates if a strategy is present
or not; there are no additional parameters to enter. For the radio button, TRIMMS applies
reductions calculated in an evaluation of Washington State's Commute Trip Reduction program.
Strategies that can be modeled in TRIMMS using the radio button feature instead of cost inputs,
such as rideshare/transit trip subsidies, were modeled using this feature and not actual cost
figures from participating agencies.
Vehicle miles traveled and vehicle trips are needed to calculate emissions. TRIMMS outputs
person miles traveled and person trips, assuming that each person makes one round trip (two
one-way trips) per day. In order to derive VMT and vehicle trip estimates consistent with
regional travel models, person miles traveled and person trips from TRIMMS were multiplied by
a correction factor for each agency.
The correction factor was calculated as the ratio of the model estimated VMT per capita per day
to the TRIMMS-derived person miles traveled (in an automobile) per day. For example, the
TRIMMS modeled daily person miles traveled for EWG in 2045 is 38,885,791 for a population of
2,736,456. This equals a rate of 14.2 person miles per day. Actual total regional VMT are
74,336,862 (provided by EWG) for a daily rate of 27.2 VMT per person. The correction factor is
thus 1.91 (27.2 * 14.2). The calculated correction factors for each region were then applied to
the TRIMMS VMT results to scale modeled person miles and person trips to VMT and vehicle
trips for each agency.
Land Use Analysis
At the beginning of this second round of case studies, EPA identified the need for a new
approach to analyzing land use strategies within TEAM that is both simple and reasonably
accurate, within the constraints of sketch modeling. The land use analysis from the previous
case studies produced results that diverged from the reference national studies and raised
questions about the land use algorithms in TRIMMS.
The existing literature on the relationship between land use patterns and VMT generally focuses
on 'D' variables, particularly those that have become known in the field as the '5Ds':
Density
Diversity (land use mixing)
Design
Destinations (distance to regional destinations)
Distance to transit
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While the individual variables used vary from study to study, most fit under these five "D"
categories. Comparison of individual studies illustrated that each used between three and five
"D" variables. A 2010 Ewing and Cervero study13 provides elasticities that are calculated as a
weighted average of results from more than 50 studies, including both national and regional
studies. An elasticity is a measure of how a change in the price of one good affects the demand
for a related good. In the case of land use, elasticities measure how a change in a land use
measure (which effectively changes the "price" of walking, transit, and other car-free modes in
changing travel time) affects VMT. This approach of using a weighted average elasticity 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. The analysis approach based
on these elasticities is identified here as the "Multivariate Elasticity" approach.
Multivoriote Elasticity Approach
The Multivariate approach calculates the change in VMT from land use strategies by comparing
the following variables for the BAU and scenario cases:
Household/ population density
Job access by auto
Job access by transit
Distance to nearest transit stop
Land use diversity (typically defined as the level of mixing of different land use types such as
residential, commercial, and industrial)14
This method provides an estimate of reduced VMT by focusing on shifts in these land use
variables. In order to use this method, the agencies supplied each variable under both the BAU
and strategy scenarios.
The Design (street network density) variable is omitted for two reasons. First, regions whose
travel demand models do not include local roads (e.g. East West Gateway) will not be able to
accurately calculate this variable. Second, increasing street network density is a strategy that is
only applicable in select local circumstances, like redevelopment of large commercial and
industrial properties. It is beyond the scope of this regional analysis to estimate what increases
might be reasonable.
Data for all traffic analysis zones (TAZs) is needed in order to create population-weighted
averages for all 'D' variables. Population densities are first calculated for the Business as Usual
(BAU) and Scenario for all TAZs. Then, a single regional population density is calculated for the
13 Ewing and Cervero, "Travel and the Built Environment: A Meta-Analysis", Journal of the American Planning Association, 2010
14 Since there were no changes observed in land use diversity in the cases examined, this variable was not incorporated in the
current analysis.
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BAU by taking the population-weighted average of the TAZ population densities. The same
steps are followed to obtain the Scenario values. This approach allows for meaningful variations
in land use characteristics by shifting growth within the region; whereas calculating population
density at a gross regional level would show no change without increasing regional population
growth.
Gross Population Density vs. Population-Weighted Density
Suppose you have a region composed of two TAZs:
TAZ A has an area of 1 square mile and a population of 10
TAZ B has an area of 2 square miles and a population of 5
To measure the gross population density of the region, calculate the total regional area
and population: 3 square miles with a population of 15. Gross population density is 5
people per square mile (15 people / 3 square miles).
To measure population-weighted density of the region, first calculate the population
density of each TAZ individually:
TAZ A population density =10 people /1 square mile =10 people per square mile
TAZ B population density = 5 people / 2 square miles = 2.5 people per square mile
Next calculate the proportion of regional population in each TAZ:
TAZ A proportion of regional population = 10/15 = 0.66
TAZ B proportion of regional population = 5/15 = 0.33
Finally, calculate the region's population-weighted average density:
10 people per square mile * 0.66 + 5 people per square mile * 0.33 = 8.25 people per
square mile
Notice that the weighted density is higher than the gross density. That's because the
weighted density accounts for the fact that the residents of TAZ A, who live at a higher
density, make up a greater proportion of the region's residents. So 8.25 people per square
mile is more representative of what the typical regional resident experiences than 5 people
per square mile.
Population weighted averages can be calculated for other D variables as well. The
weighting factor is always proportion of regional population, while the calculated variable
can be diversity, distance to transit, or something else.
The Multivariate approach was applied outside of TRIMMS in a simple spreadsheet analysis.
Results from the spreadsheet analysis can be readily combined with results from other
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strategies analyzed in TRIMMS in a post-processing spreadsheet, a standard part of the TEAM
approach. In the post-processing spreadsheet, percentage reductions in VMT calculated for
each scenario or strategy are applied sequentially to the business as usual VMT projection for
the region. These reductions were calculated by multiplying changes in land use variables (such
as population density) by elasticity values. For specific elasticity values see Appendix Table A-2.
Neighborhood Classification Approach
The Neighborhood approach is significantly simpler in terms of data collection and calculation
than the Multivariate approach. It relies on the idea that individual neighborhoods can be
classified in terms of typical driving habits of their residents, and that land use planning can shift
growth patterns away from more driving-intensive neighborhood types towards less driving-
intensive ones. Using this approach identifies the change in VMT from land use strategies by
comparing the percent of household, population, and jobs within separate neighborhood types
before and after each strategy. Each neighborhood type has a unique VMT metric (such as
VMT/ household or VMT/day). The land use strategies shift households, population, and/or jobs
to from neighborhood types with higher VMT to neighborhood types with lower VMT, producing
a net reduction in VMT. This method provides an estimate of reduced VMT just by focusing on
shifts in populations among neighborhood types.
The approach is similar to that used by the Atlanta Regional Commission in its 2014 study,15
where all TAZs in the region were classified into quintiles based on average annual household
automobile CCbe emissions. Automobile CO26 emissions are highly correlated with household
VMT, and household VMT data is readily available in the same format. Neighborhoods closer to
the urban core tend to have lower emissions per household, while neighborhoods further away
have higher emissions per household.
In order to use this method, the agencies supplied average VMT metrics for each neighborhood
type and the percent of households, population, and/or jobs within each neighborhood type
under both the BAU and strategy scenarios. The resulting VMT reductions are very simple to
calculate outside of TRIMMS. Calculating a weighted average of VMT per capita across the five
classifications for both BAU and Scenario and then comparing these yields the percentage
reduction in VMT for the entire analysis area. As with the Multivariate approach, this approach is
conducted in a simple spreadsheet analysis, and the results combined with the results of other
strategies in a post-processing spreadsheet.
15 Atlanta Regional Commission, "Understanding the regulatory environment of climate change and the impact of community
design on greenhouse gas emissions," 2014.
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Special Considerations in Sketch Planning Analysis
Elasticities
TRIMMS contains default elasticities that measure the relationship of travel costs and access
times to travel patterns. Direct elasticities describe relationships between travel by one mode
and the cost and time characteristics of that mode. For example, a direct elasticity of drive alone
travel with respect to trip cost of -0.5 means that a 1% increase in drive alone trip cost is
associated with a 0.5% decrease in drive alone travel. Cross elasticities describe relationships
between different modes of travel. A cross elasticity of drive alone travel with respect to transit
access times of 1.0 indicates that a 1% decrease in transit access times is associated with a 1%
decrease in drive alone travel.
TRIMMS does not provide defaults for all elasticities. For example, TRIMMS 3.0 does not have
a default elasticity to represent the shift in drive alone travel relative to the cost of carpool or
vanpool travel. TRIMMS assumes that the values of these elasticities are zero, whereas their
true values are most likely non-zero. TRIMMS allows the substitution of elasticities for its default
values. The national-level research for EPA identified elasticities that are well supported in the
literature. The research team relied upon these values to supplement and support what was
available in TRIMMS. The TEAM user guidance 16 identifies the desired elasticities for a
regional analysis as well as provides a list of elasticities in its Appendix C.
Still, there are several instances in which elasticities not supplied by either TRIMMS or the
literature, and which therefore default to zero, can produce unreasonable analysis results. If the
model is run with these values set to zero, the TRIMMS model may predict increases in trips for
one mode of travel without corresponding decreases for another mode. TRIMMS automatically
rebalances total trip numbers when evaluating employer-based strategies, but not for other
strategy types. The user can correct for this by adjusting the outputs of TRIMMS to rebalance
total trip numbers (the approach taken in this study, as described below) or by supplying a more
complete set of elasticity values.
Alternative Populations and Geographies
Many strategies do not affect the entire regional population but instead focus on a sub-region
and/or a subset of the total population, or target population. TEAM results are reported for the
entire region in each case study analysis in order to make comparisons to previous case
studies. However, all three agencies requested modeling strategies for application in a subset of
the population and a limited geography within the region. For each region, there was a smaller
core set of counties, sub-geographies, or sub-populations where application of the strategies is
16 EPA 2011, EPA-420-R-11-025. Available: http://www.epa.gov/otaq/stateresources/policY/420r11025.pdf
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most reasonable and will most likely produce the greatest reduction in VMT and vehicle
emissions.
Each strategy was modeled for the affected subarea and for the target population only. Once
the impact on the subarea was determined, VMT reductions were compared to the total regional
VMT to determine a reduction in regional VMT. For example, a strategy may reduce VMT by
10% for the target population but only 1% for the total regional population (the target population
represents 10% of the total regional population).
Using TEAM for sub-geographies and sub-populations is more complicated, but certainly
possible. Sub-geographies and populations sometimes require different baseline assumptions
(e.g. mode shares and trip lengths for downtown vs. region), and sub-populations can be
difficult to isolate (e.g. traveler population for a specific corridor). Combining strategies that
apply to different sub-populations or sub-geographies requires that the effects of the strategies
be summed together outside of TRIMMS as a post-processing step.
The value of the results using sub-geographies and sub-populations can be obscured when
results are reported at the regional scale. The corresponding percent VMT and emission
reductions are often quite small when applied to the entire region. For example, a strategy that
reduces VMT by 0.1% among 10% of the regional population will reduce total regional VMT by
just 0.01%. However, VMT and emission reductions can be significant for the sub-geography
covered by a strategy even if the reductions are minor at the regional level. For example, ARC'S
parking pricing strategy reduces VMT by 11.4% for the target population, but regional VMT is
reduced by only 2%.
The strategies selected in the current study demonstrate the potential interest in alternative
populations and geographies, as well as strategies that are not easily analyzed using TRIMMS.
This test of the TEAM approach shows that it can be used independent of the analysis tool or
method. TEAM allows staff to compare potential outcomes of various strategies to inform
decision making without the extensive cost and time of detailed travel demand model analysis.
TEAM also does not require extensive reliance on modeling staff; instead, it uses data inputs
that can be obtained from travel demand model inputs and outputs without additional analysis.
Throughout this report, it is essential to consider the target population for individual strategies
when evaluating the impact. TDM strategies are an example of TEAM applied consistently to an
employee sub-population. The number of employees or the geography of interest may vary, but
TDM always applies to the working population. Other strategies may limit the penetration or the
geographic area to which it applies. This information is available in the scenario descriptions.
Results are reported as percent change at a regionwide scale with the corresponding impact in
VMT/trips for the sub-geography or sub-population noted.
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MOVES Analysis
MOVES is EPA's Motor Vehicle Emission Simulator, a state-of-the-science emission modeling
system that estimates emissions for mobile sources at the national, county, and project level for
criteria pollutants, greenhouse gases, and air toxics. It can estimate the total on-road emissions
for a particular county or set of counties for a specific period. It can also answer "what if"
questions, such as: "how would particulate matter emissions decrease in my state on a typical
weekday if truck travel was reduced during rush hour?", or "how does the total hydrocarbon
emission rate change if my fleet switches to gasoline from diesel fuel?" The purpose of MOVES
is to provide an accurate estimate of emissions from cars, trucks and non-highway mobile
sources under a wide range of user-defined conditions.17
MOVES2014 is a major new revision to EPA's mobile source emission model, replacing all
versions of MOVES2010. It represents EPA's most up-to-date assessment of on-road mobile
source emissions. MOVES2014 includes the regulations promulgated since the release of
MOVES2010b that can affect the present analysis, including the Tier 3 emission standards that
phase in beginning in 2017 for gasoline cars and light-duty trucks (among other categories) and
the second phase of light-duty vehicle GHG regulations that phase in for model years 2017-
2025 cars and light trucks. MOVES2014 also incorporates new and up-to-date emissions data
from a wide range of test programs and other sources, and updates findings on the effects of
fuel properties such as gasoline sulfur and ethanol, and new analyses of particulate matter (PM)
data related to PM speciation and temperature effects, and substantial new data and updates
for default vehicle population and activity, including new vehicle miles travelled (VMT) estimates
based on updated FHWA Highway Performance Monitoring System values, new national
average speed distributions based on global positioning system (GPS) data, new state supplied
data from the 2011 National Emission Inventory, and many other population and/or activity
related updates.18'19
MOVES runs were conducted, quality assured, reviewed internally and by EPA, and corrected
as needed for any data issues. Data collection focused on each element required to perform the
analysis at the county, multi-county, or custom domain resolution as identified in Table 3.
17 User Guide for MOVES2014, EPA-420-B-14-055, July 2014
https://www.epa.aov/otaq/models/moves/documents/420b14055.pdf.
18 EPA Releases MOVES2014 Mobile Source Emissions Model, EPA-420-F-14-049, July 2014
https://www.epa.aov/otaq/models/moves/documents/420f14049.pdf
19 Note that, while the MOVES2014a model was released during this period of performance, it was not used in the analyses. This
should have a negligible effect on emissions. MOVES2014a includes minor updates to the default fuel tables, and provides a
small correction to brake wear emissions. Other criteria pollutant emissions remain essentially the same as MOVES2014.
https://www.epa.gov/otaq/models/moves/documents/420f15046.pdf.
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Table 3. Data Inputs for MOVES Runs
Data Type Description
Fields without default values
available at the county scale1
Data Elements
Local data, as available MOVES
default data when local is
unavailable2
Source (Vehicle) Type Population
Vehicle Type VMT
Road Type Distribution
Meteorological Data
Age Distribution
Month, Day, Hour VMT Fractions
Modeling decision elements,
typically not requiring local data
Average Speed Distribution
Domain/Scale
Calculation Type
Time Aggregation
Calendar Year
Evaluation Month
Type of Day
Evaluation Hour
Ramp Fraction
Fuel Supply, Formulation, Usage, Region and Alternative
Vehicle and Fuel Technology (AVFT)
I/M (Inspection and Maintenance) Program
Start Distribution Information
Geographic Bounds
Vehicle Type
Road Type
Pollutants and Processes
Strategies
Activity
Emissions Detail
1MOVES2014 includes Retrofit Data in this category, which is not a required field and is not used in this analysis, so is not shown here.
2MOVES2014 updates inputs to include Hotelling Information, which applies only to heavy vehicles and is not applied or shown here.
Geographic Considerations for MOVES
The geographic scale selected for MOVES modeling was the county scale, consistent with EPA
Guidance,20 but the approach varied across the regions. Both EWG and ARC represent their
multi-county domains through "representative" county inputs. In the case of ARC, inputs were
provided for a single representative county, modeled as Fulton County, Georgia, representing
the inputs for the 5-county domain considered here.21 This representative county uses averaged
inputs such as speed and meteorology to cover the modeled region and period. EWG
performed a similar analysis, but provided inputs aggregated into 2 representative counties to
accommodate the bi-state region: St. Louis, Missouri, and St. Clair, Illinois. The MetroPlan
Orlando analysis was performed for each of the three counties in their planning domain
individually and aggregated outside the model. Details about the collection, processing,
sources, and quality assurance of each of these data items for each region appear in the
regional discussions.
20 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). https://www.epa.aov/otaq/stateresources/documents/420b16059.pdf
21 Note that ARC generally plans for a 13 cou nty inner and 7-county outer domain for the 20-county nonattainment area.
16
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Deriving Emissions Factors
The TEAM approach derives regionally specific emission factors from MOVES. It does not use
MOVES in "Emissions Rates" mode to directly calculate emission rates nor does it directly
calculate regional emissions within the model for the given scenarios. Instead, MOVES is run in
"Inventory" mode for each of the regions with the available regional data, and then rates are
calculated from a ratio of the resulting totals of emissions and activities. This ratio provides an
activity-weighted, average emission factor for the region in the specified year. These emission
factors are then applied to the strategy-determined VMT changes to indicate emission changes.
Four primary pollutants were considered in this analysis: CO26, NOx, PM2.5, and VOC. Other
pollutants necessary for the model to compute these four were also included in the analysis as
done in the initial study. These are provided in Appendix A tables for each agency.
As noted above, TRIMMS uses composite vehicle types. Emission factors were derived by
combining MOVES vehicle and fuel types to match the TRIMMS composite definitions. For the
TRIMMS vehicle categories auto drive alone and auto rideshare, composite emission factors
representing motorcycles, passenger cars, and passenger trucks were calculated from the
MOVES model's output. For the TRIMMS vanpool category, MOVES vehicle types for
passenger truck and light commercial trucks were combined, representing the different types of
vehicles that could be used.
All road types were included. Only running and starting emission processes are included: Start
Exhaust, Crankcase Running Exhaust, Crankcase Start Exhaust, Running Exhaust, Brakewear,
and Tirewear emission processes were included in the emissions totals from the MOVES
runs.22 All MOVES runs for this project were run without pre-aggregation; hourly analysis was
performed. All analyses were made at the annual scale.
Emission factors were calculated as total running emissions from hourly outputs (in grams per
year) divided by total running activity (in miles per year). A similar analysis was made for
starting emissions. In both cases, the resulting emission factors, in grams of pollutant per
average mile driven or grams of pollutant per average start were produced.
A single emission factor was derived for each (TRIMMS) vehicle type, year, and pollutant for
both the starting and running processes. All MOVES emissions processes that contribute to
these two aggregate processes, such as tailpipe and crankcase exhaust emissions, were
summed to produce these totals. The resulting overall average emission factor was used for
every scenario, pairing current year emission factors with baseline activity for baseline
emissions, and future year emission factors with BAU activity and all scenario alternatives
activity, for future year emissions. All calculations were made off model, with a combination of
22 Notably, evaporative emissions are not included. Evaporative emissions add significant complexity and run times to the
analysis, and are not expected to be affected by any of the strategies considered here. Thus they would have negligible effect
on the comparison of alternatives.
17 July 2016
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MySQL and spreadsheet calculations. Both starting emissions - based on TRIMMS-calculated
number of trips - and running emissions - based on TRIMMS-calculated VMT - were included
in computing the total emissions and emission changes. The calculation of total emissions was
done in a simple, off-model spreadsheet calculation that assembled outputs from both the
emission factor and VMT modeling results. This method is a very efficient approach to
determining impacts of the strategies with the given resolution of the input data. Regionally
specific details and results are presented in the following section.
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3. Case Study Results
Three agencies were selected as case studies for testing the TEAM approach at a regional
level: Atlanta Regional Commission (ARC), East West Gateway Council of Governments
(EWG), and MetroPlan Orlando Metropolitan Planning Organization (MetroPlan Orlando). Each
agency selected strategies for improving travel efficiency that are of interest within the region
and provided data to support analysis.
The lead agencies worked with stakeholder groups to support their strategy selection and data
collection as a part of their common agency practice. The selection of strategies has been
grouped into four scenarios for each region based on their individual interests and data
availability. For this study all agencies applied strategies to achieve a progressive increase in
VMT reduction across scenarios. Both the strategies and their underlying assumptions
represent a broad range of potential futures for evaluation of corresponding emissions
reductions. Each of the regional analyses are examined in detail in the following sections.
In each of the three regions sub-geographies and sub-populations were used to target individual
strategies. The results are reported at the regional level, consistent with previous TEAM case
studies. In addition, the potential scenario impact for the target population or subarea is reported
in the discussion. Additional detail on VMT and specific pollutant emissions reductions is
provided in the Appendices, as noted in the text.
It is important to keep in mind that TEAM is used for the purpose of comparing potential travel
efficiency strategies, and is not intended to replace the detailed technical analysis used in
transportation planning. However, because the TEAM data inputs are consistent with the
region's travel demand modeling and transportation conformity process (where applicable),
results are sufficiently accurate for decision making.
MetroPlan Orlando
Background
MetroPlan Orlando is a metropolitan planning organization (MPO) and regional planning
partnership of three counties, two urbanized areas, 22 municipalities, two expressway
authorities, two international airports, and a regional transit authority. The MPO region has
doubled in population over the past 25 years, and strong population growth continues to be the
trend. The diverse population of the region is accompanied by heavy tourist and business travel.
In 2014 a new record was established with 59 million visitors.
The transportation sector contributes 40 percent of Florida's greenhouse gas (GHG) emissions.
More than 60 percent of the air pollution in Central Florida is related to transportation emissions;
however, the region is well within current air quality standards. An Air Quality emissions
inventory and Contingency Plan was developed in partnership with the University of Central
Florida. The 2040 Long Range Transportation Plan focuses heavily on transit and improving
19 July 2016
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bicycle and pedestrian access in recognition of the need to diversify transportation options.
These factors led MetroPlan Orlando staff to consider using TEAM to evaluate air quality
concerns as a proactive way to consider transportation-related emissions.
The MetroPlan Orlando base year is 2009 with a future year of 2040. MetroPlan Orlando no
longer maintains a separate trend and vision forecasting process in their modeling. All forecasts
are based on the vision included in the Long Range Transportation Plan (LRTP) which includes
land use. MetroPlan Orlando has considered the impacts of land use changes during LRTP
development; based on a no-build and on a cost-feasible case. The agency considers it unlikely
that land use changes beyond what was included in the LRTP would be acceptable to decision
makers. With no need to perform a land use analysis, all strategies were able to be modeled
using TRIMMS. MetroPlan Orlando staff provided all local data that was included in the TEAM
analysis.
Scenario Development
Scenarios were identified by MetroPlan Orlando staff based on policies that were likely to be
considered in future planning activities; however, specific data to inform the analysis was very
limited. Instead, default data within TRIMMS representing the Orlando-Kissimmee-Sanford
Metropolitan Statistical Area (MSA) was used. The TRIMMS default data is drawn from two
sources. Mode share data are drawn from the American Community Survey for the Orlando
MSA. Trip length data by mode are drawn from the National Household Travel Survey (NHTS).
These are the best national sources for the data; therefore TRIMMS default data points can be
used with confidence.
Scenario 1 - Expand ridesharing and TDM programs
This scenario represents three policy changes that MetroPlan Orlando may consider in the
future. In the 2040 BAU case, these policies apply to a subset of the regional commuter
population, which equals 114,000. In the 2040 scenario case, this subset of the total commuter
population is increased to 309,367. Each of the policies in this scenario is described below.
Policy 1: Subsidize work trips using alternate modes by $150 per employee per month for public
transportation and by $35 per employee per month for cycling. Offer subsidies to 10% of
employees. In order to attain this policy goal, several actions would be required.
Increase transit and cycling subsidies offered to employees
Expand the number of employees offered subsidies for alternate mode work trips from 2.8% to
10% of total employees
Expand carpool/vanpool/bike/walk incentive programs
Policy 2: Offer telework and flex work schedule programs to 10% of employees. To implement
this policy, it will be necessary to expand the number of employees with access to telework and
flex work from 6.3% to 10% of total employees
20 July 2016
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Policy 3: Expand guaranteed ride home program from 2.8% of total employees to 20%.
This scenario required three model runs with individual populations represented. The individual
strategies represent both monetary and non-monetary benefits, and therefore must be
considered individually and summed for the full VMT reduction impact. In each case, the target
population is the number of additional employees offered the benefit in the future beyond the
BAU case.
For non-monetary strategies, the radio buttons for TDM programs and subsidies for Guaranteed
Ride Home and Ride Match and Telework and Flexible Work Schedules were used. The radio
button feature is an option in TRIMMS to analyze TDM strategies without the use of actual
monetary values for subsidies. The radio button method more accurately captures mode shift
than the monetary method. For monetary subsidies, the BAU trip cost was provided by
MetroPlan Orlando, and the strategy trip cost was calculated based on the policy. These values
were entered into TRIMMS to estimate changes on VMT. MetroPlan Orlando did not provide trip
costs for cycling.
Transit service is not changing under this scenario, so transit VMT will not increase. The shift in
drive-alone and rideshare trips to transit trips will be supported by existing transit system
capacity in the 2040 BAU scenario. Although VMT for non-motorized modes will increase, there
are no emissions associated with these modes.
Scenario 2 - Expand ridesharing and TDM programs along with transit network
expansion and improvement
This scenario adds transit expansion and improvement to the TDM scenario by reducing transit
trip times by one third. This improvement is accomplished by increasing the average speed of
transit trips from 14.6 to 20 miles per hour and reducing average headways from 25.5 minutes
to 15 minutes.
The target population for Scenario 2 is the same as the BAU population covered (515,425
commuters) because the change is with respect to travel time only, without increase in the
number of commuters using public transit. Under this scenario, average transit travel time is
reduced from 22.5 minutes (BAU) to 16.5 minutes and waiting time is reduced from 25.5
minutes (BAU) to 10.5 minutes due to the reduction in headways.
The impact of expanded ridesharing and TDM programs from Scenario 1 was summed with
these TRIMMS model runs as a post-processing step.
Scenario 3 - Expand ridesharing and TDM programs plus transit network
expansion and improvement and road pricing
Scenario 3 adds VMT pricing at 6 cents per mile to the effects of Scenarios 1 and 2. The charge
is anticipated to apply to the full BAU regional population of 3,301,256. Current trip costs for
21 July 2016
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drive-alone and rideshare are increased by 6 cents per mile within TRIMMS to represent the
strategy change from the BAU condition. Per mile cost based on fuel, tires, and maintenance
were obtained from the RITA database for 2013.23
The impact of Scenario 2 policies was summed with the road pricing results from the TRIMMS
model runs as a post-processing step. VMT pricing does not result in a change in transit service
and therefore transit VMT will not increase. The shift in drive-alone and rideshare trips to transit
trips due to road pricing will be supported by the BAU transit system capacity. Although VMT for
non-motorized modes will increase, there are no emissions associated with these modes.
Scenario 4 - Expand ridesharing and TDM programs plus transit network
expansion and improvement and road pricing along with a university transit pass
The final scenario adds a university transit pass at no perceived cost to the user. Free transit is
bundled with tuition and university employment, making it available to 205,000 students, faculty,
and staff. This scenario does not result in any increased transit service VMT. The shift in drive-
alone and rideshare trips to transit trips will be supported by the BAU transit system capacity.
The impact of previously considered policies was summed with the university transit pass
TRIMMS model runs as a post-processing step.
Scenario Summary
Input parameters are provided in Table 4 below for current conditions, a business as usual
(BAU) future, and the four scenarios selected by MetroPlan Orlando. Specific input values are
provided for each of the scenarios. The MetroPlan Orlando scenarios apply across the entire
region without any sub-geographies considered. The resulting VMT reductions for each
scenario are included in the table. In each scenario, the specific target population is identified.
For each case study region, a comparison Cluster from the 2010 national study was selected
based on regional population size and transit mode share. This comparison was performed to
validate the reasonableness of the results in this analysis. In addition, the first group of case
studies provides a basis for comparison. Results for each strategy type for the case study
regions were expected to be similar to these previous results. Where results for case study
regions were dissimilar to those of the comparison region, discrepancies are noted. In some
cases, the comparison helped to identify necessary adjustments to the TRIMMS inputs.
MetroPlan Orlando is compared to San Diego, California in Cluster 2. The MetroPlan Orlando
strategies were matched as closely as possible with the strategies in the 2010 national study,
and the results of this comparison are presented in of Appendix B. This comparison does not
imply that MetroPlan Orlando values will match exactly those of San Diego. However,
23 United States Department of Transportation. 2015. National Transportation Statistics. Table 3-17. Available:
http://www.rita.dot.gov/bts/sites/rita.dot.qov.bts/files/NTS Entire 15Q4 O.pdf.
22 July 2016
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comparisons made between the two regions supported the reasonableness of the MetroPlan
Orlando results.
Table 4. MetroPlan Orlando Scenario Details
Scenario
Current
Conditions
Business as
Usual
Description
Existing conditions across
all strategies in 2009
Data Inputs
2040 conditions with BAU
levels of employer
program, transit, and road
pricing
mode shares
average vehicle occupancy
average vehicle trip lengths
regional population and employment
mode shares
average vehicle occupancy
2040 regional population and employment
current employer-based incentives for alternative
commute modes
current travel times (22.52 minutes for transit)
current access times (25.5 minutes for transit)
average trip costs ($1.98 for drive alone, $0.61 for
rideshare, $3.75 for transit without current
employer-based incentives, and $2.05 for transit
with current employer-based incentives)
Daily Regional VMT
Reduction
NA
NA
Scenario 1:
Expanded TDM
Expand access to
telework and flexwork
programs, Guaranteed
Ride Home and
ridematching services.
Scenario 2: Improve transit and
Scenario 1 + expand transit pass
Enhanced Transit program.
share of additional regional employees covered 0.65% auto VMT
(162,891 for guaranteed ride home program, 80,934 reduction (502,039)
for guaranteed ride home and monetary subsidies,
and 65,182 for guaranteed ride home, monetary
subsidies, and telework and flexwork programs;
309,637 total additional employees covered)
average monthly subsidy offered to employees
($150 for transit and $35 for bike)
guaranteed ride home program toggle buttons
telework and flexible work program toggle buttons
27% transit travel time reduction (to 16.5 minutes)
41% transit access time reduction (to 15 minutes)
Number of regional employees covered (515,425)
0.92% auto VMT
reduction (708,069);
Note: transit VMT
increase
Scenario 3:
Scenario 2 +
Road Pricing
Scenario 4:
Scenario 3 +
University Transit
Pass
Implement mileage
pricing.
Implement unlimited
transit access pass to be
bundled with student
tuition and given to all
faculty and staff as an
employee benefit
Regional population covered (3,301,256)
6 cents per mile charge for drive alone and
rideshare trips
31 % increase in average cost per trip (from $1 .98 to
$2.61 for drive alone and from $0.61 to $0.80 for
rideshare)
Number of regional students, faculty, and staff
covered by university transit pass program
(205,000)
average transit trip cost for university transit pass
(from $3.75 to $0)
4.75% auto VMT
reduction
(3,649,898)
6.08% auto VMT
reduction
(4,666,465)
23
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Emissions Analysis
Regions that have used the MOVES model to conduct a transportation air quality conformity
analysis or develop an emissions inventory required by the CAA will have readily available data
to provide for the TEAM emissions analysis. However, those requirements do not apply to
MetroPlan Orlando. As a result, the MPO has little experience with MOVES analyses, and does
not have input data readily available for such analyses. While TEAM is able to rely on all default
inputs within the MOVES model, EPA's GHG Guidance notes that relying exclusively on
MOVES default database information for an analysis reduces the precision of the emissions
analysis since the default data may not be the most current or best available information for any
specific county.24 Nevertheless, default data is acceptable for an analysis to allow comparisons
of relative emissions reductions among different scenarios.
MOVES2014 was run at the county scale for each of the three counties (Osceola, Seminole,
and Orange) and each year (2009 and 2040). The emissions and activity for each county were
then aggregated to regional totals and these sums ratioed to determine the representative
emission factor. The final emission factors were then extracted and applied to the regional VMT
reductions to estimate the corresponding emissions reductions. The data used in the MOVES
simulations is listed in the previous section (see Table 3.); for MetroPlan Orlando, all MOVES
data is default.
The post-processing of MOVES outputs to determine the activity weighted, average emission
factors representing the MetroPlan Orlando region was done with database and spreadsheet
analysis methods. In these methods, emissions and activities are aggregated into the source
types required by TRIMMS, and the ratio of emissions and activities is taken. The emissions
and activity values are first summed from the outputs of the three individual counties from the
MOVES simulation. This is done using database methods to combine the separate tables from
each county's simulation into a single set of emissions and activities. Those resulting composite,
regional emission and activity values are then ratioed, to produce regional emission rates for the
composite vehicle types. These database values are then output into spreadsheets where the
presentation of values is simplified into tables and presented for use in the spreadsheet models
used in the scenario analyses. The resulting average emission factors for the MetroPlan
Orlando region and TRIMMS vehicle types are shown in Table B-2.
MOVES was run in inventory mode for each of the regions to produce activity-weighted,
average emission factors each the region. These emission factors were coupled with the
predicted changes in activity to produce net emissions changes by scenario for each region.
The corresponding relative reductions are presented for each region in this section of the report.
24 Because there is no federal requirement for creating on-road GHG inventories or conformity analyses for attainment areas,
EPA guidance including its GHG Guidance, is entirely voluntary. However, it may be considered best practice to follow this
guidance for analyses such as these.
24 July 2016
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MetroPlan Orlando Scenario Results
The results of the VMT and emissions analysis for light-duty vehicles at a regionwide scale are
presented below in Table 5, following a discussion of the results for each scenario.
Scenario 1 for Expanded TDM has a moderate impact on a target population of approximately
300,000. The regional VMT reduction represents shifts from drive-alone to rideshare, cycling,
walking and transit. For MetroPlan Orlando this represents a reduction of approximately
500,000 VMT and 50,000 trips for light-duty vehicles, and is consistent with results for TDM
strategies for other regions. Scenario 1 represents a total regional VMT reduction of 0.65% for
light-duty vehicles.25 To increase the impact of this strategy, MetroPlan Orlando may consider
increasing the number of regional employees to receive these benefits.
Scenario 2 adds transit improvements to the Expanded TDM scenario. This has a relatively
small impact on the target population (515,425) for transit trip times and access time reductions.
This is equivalent to a reduction in light-duty VMT of 206,000 and light-duty auto trips of 22,000,
which are shifted to public transit.26 The combined strategies under Scenario 2 represent a total
regional VMT reduction of 0.9% for light-duty vehicles.27
The reported VMT reduction does not include any potential increase in transit VMT, although
this is anticipated to occur. Increasing the frequency of transit service reduces wait times and
travel times for vehicles and makes riders more likely to choose transit. The strategy will also
result in additional transit vehicle trips and VMT. However, the limited penetration of the
improved transit system (only 16% of the total regional population is affected) reduces its
regionwide impact. Reducing transit wait times or travel times even further will not induce many
more trips by transit with this limited population. Land use changes to bring more people closer
to transit stops could be more effective than service improvements alone. For the potential
impact of the anticipated transit VMT increase, see Table A-1 in Appendix A.
From previous analyses using TEAM, it is clear that pricing strategies are consistently the most
effective at reducing emissions, and that is confirmed with the MetroPlan Orlando Scenario 3
results. Road pricing is applied to the full population of the region, and demonstrates a 3.8%
regional VMT reduction for light-duty vehicles (see Table B-1 in the Appendix) for a total
combined VMT reduction of 4.75% for the scenario. This equals 2.9 million VMT and 300,000
trips for mileage pricing with shifts from drive-alone and rideshare to transit, vanpool, cycling,
and walking.
In Scenario 4, there is a large impact on the specific target population for the university transit
pass. Again, the target population is small, but the effect is significant to that group. This results
25 For the target population, the VMT reduction is 7%.
26 An increase in transit vehicle VMT is anticipated (see Appendix A)
27 For the target population, the VMT reduction is a moderate 1.7%.
25 July 2016
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in a decrease of 1.0 million VMT and 100,000 trips for light-duty vehicles with shifts from drive-
alone and rideshare to transit. The target population includes 205,000 university students,
faculty, and staff. A free transit pass provides a very strong incentive for the target population,
but at a regional level, the auto VMT reduction is only 1.3% (see Table B-1 in the Appendix) for
a total combined VMT reduction of 6.08% for the scenario.28
The incremental reduction for the university transit pass is stronger than that of the employer-
based TDM strategy because the university transit pass makes taking transit essentially free for
any given trip. Even if there is a cost increase bundled with tuition, the perceived cost is free,
since the cost has been paid. When both TDM and the university transit pass are combined with
a highly successful strategy like road pricing, the outcome is optimal - as Scenario 4 illustrates.
The MetroPlan Orlando case study is a good example of how TEAM can be employed in a
region without any MOVES capabilities or experience. No locally specific MOVES input data
was available from MetroPlan Orlando, and thus all defaults were used. Recent, local data is
always preferable over defaults, since it is expected to offer the best representation of local
conditions. The method used in TEAM of deriving emission factors from MOVES that are
coupled with TRIMMS VMT is an acceptable approach to support comparison of strategies,
even with the use of default data.
Table 5 provides the results of the VMT and emissions analysis for light-duty vehicles at a
regionwide scale. As previously discussed, individual strategies were applied to the appropriate
population group that is targeted by the strategy, which is often a subset of the regional
population. For example, TDM strategies are always applied to a sub-population of regional
employees, targeted by the geography or the limitations of the strategy (such as 10% of all
employees).
Atlanta Regional Commission
Background
The Atlanta Regional Commission staffs a 20-county MPO that supports a population over 5
million. The Atlanta region has a strong TDM community focused on improving both air quality
and quality of life. The agency is a recognized leader in transportation data collection and
analysis, and has both developed and used sketch planning tools. ARC expressed an interest in
TEAM to test strategies not easily captured using travel demand modeling, and because the
agency has an ongoing interest in climate change at the regional level. ARC is also interested in
comparing TEAM to an off-model emissions calculator developed for use in CMAQ project
selection. The calculator has some similarities to the TEAM approach, but is applied at a project
scale.
28 For the target population, the VMT reduction is large at 21.3%.
26 July 2016
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Table 5. MetroPlan Orlando Regionwide Percent VMT and Emissions Changes for Light-Duty
Vehicles
Percent Regionwide Emissions Changes for 2040 BAD Compared to 2040 Scenario
Scenario
Scenario 1: Expanded TDM
PM,.S
NO*
VOC
10% of employees -0.65% -0.65% -0.65% -0.65% -0.65%
Scenario 2: Scenario 1 +
Enhanced Transit
Scenario 3: Scenario 2 + Road
Pricing
Scenario 4: Scenario 3 +
University Transit Pass
Kegionwiae ^us,oo/j
16% of employees
Regionwide (51 5,425)
3,301,256
Regionwide population
Students, faculty, and
staff
(205,000)
-0.92%
-4.75%
-6.08%
-0.92%
-4.75%
-6.07%
-0.92%
-4.75%
-6.07%
-0.92%
-4.74%
-6.06%
-0.92%
-4.73%
-6.05%
The identified target population is for the additional strategy only. For the total Scenario population, previous
scenario populations must be considered.
With such a large planning region, ARC often focuses analysis at a sub-area or project level.
TDM strategies are generally carried out in scattered locations across the region. A 13-county
core is designated for ozone non-attainment, and MOVES has been used for air quality planning
purposes and performing transportation conformity analyses for that area since 2012. TEAM is
best applied at a regional scale, and determining the appropriate geography for this case study
required some discussion. Ultimately, it was determined that strategies would apply to a 5-
county geography (Fulton, DeKalb, Cobb, Gwinnett, and Clayton) within the 13 county "core." In
order to conform to the approach used in the region's conformity analysis, emission factors were
determined from the "representative" county approach described earlier in this report. These five
counties represent 64% of the population and 75% of the jobs for the full MPO region in the
base year. In the BAU, the numbers are 56% and 70% respectively.
Stakeholders are an essential part of the ARC decision making process. Staff engaged the
technical transportation committee, a stakeholder TDM committee, and the policy committee on
the selection of strategies. Other stakeholders were engaged by staff on an as-needed basis.
The TDM committee provided feedback and pricing information for some of the related
strategies. The other two committees were informed and provided answers to questions raised.
The ARC base year is 2015 with a future year of 2040 to match the current regional
transportation plan (RTP). Data was readily available for the TEAM analysis from existing travel
demand modeling, and the emissions analysis was consistent with conformity analysis
assumptions and data. Base year data was provided by the activity-based model, and some
transit data was provided by the Metropolitan Atlanta Regional Transit Authority (MARTA).
27
July 2016
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Scenario Development
The TEAM analysis considers travel activity in 5 out of 20 counties modeled by ARC. ARC'S
travel demand model provided parameters for BAU mode shares, trip lengths, and trip costs by
mode. Regional models estimate much greater population growth in the outer counties, but job
growth is predicted to stay more centralized. The models predict slight decreases in average trip
lengths, possibly due to higher densities of population and employment and increasing
congestion.
Scenario 1 - Expand ridesharing and TDM programs
The three policies in this scenario represent potential changes from the BAU in the 5-county
subarea under consideration. The commuter population of the five counties is 873,193 in the
BAU case. The total commuter population in 2040 targeted to this scenario is increased to
1,746,386. This scenario required three model runs with individual populations represented.
1. The first policy is to require all employers with 100 employees or more to provide
carpool/vanpool/transit/bike/walk incentives. Incentive programs currently offered by
employers would be expanded without changing the dollar amount of subsidies. This policy
expands the share of regional employees with access to these incentives by 92%.
The second policy is to expand alternate work schedule programs by doubling the number of
employees with access to telework and flex work.
The final policy is to expand the guaranteed ride home program by increasing the number of
employees with access to the program by 50%.
TRIMMS uses a simple approach to analyzing TDM strategies. As in the previous case studies,
the radio button feature was used to collect basic information about the TDM strategy features.
However, this approach does not account for trips shifting to other modes
Since ARC is able to provide actual trip costs and subsidy amounts to support a more rigorous
TDM analysis, the agency was interested in a rebalancing analysis using TRIMMS to account
for trips that shift to other modes. However, the rebalancing analysis overestimated the number
of drive-alone trips reduced. As such, the radio button feature in TRIMMS proved the most
successful way to account for these changes. Appendix C discusses the rebalancing process
and presents the subsidy values that ARC provided for this scenario in Table C-1. This
information was not used in the analysis.
The individual TDM strategies represent both monetary and non-monetary benefits, and
therefore must be considered individually and summed for the full VMT reduction impact. In
each case, the target population is the number of additional employees offered the benefit in the
future beyond the BAU case. For non-monetary strategies, the radio buttons for TDM programs
and subsidies for Guaranteed Ride Home and Ride Match and Telework and Flexible Work
28 July 2016
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Schedules were used. For monetary subsidies, the radio buttons for Program Subsidies by
mode were used.
Transit service does not change under this scenario, so transit VMT will not increase. The shift
in drive-alone and rideshare trips to transit trips will be supported by the capacity of the BAU
transit system. Although VMT for non-motorized modes will increase, there are no emissions
associated with these modes.
Scenario 2 - Expand ridesharing and TDM programs along with transit frequency
improvement
This scenario adds transit expansion and improvement to the TDM scenario by reducing transit
access time; accomplished through reduced headways. In this strategy, average transit
headways are reduced by 50%, which corresponds to a decrease in access time of 18%. There
is no change in transit travel time assumed.
As previously noted, increased transit service results in increased VMT, trips, and emissions for
transit vehicles that are not accounted for in the light-duty analysis. Transit service does not
include demand response service or paratransit.
The target population for transit improvement is the same as the BAU population. This number
represents the total 5-county regional population living within 1/4 mile of a transit stop
(2,025,866). This transit strategy does not increase the population covered by transit, it just
reduces headways and access time for the BAU population. MARTA service has recently been
added in one of the five counties, and the suburban systems are expected to continue to grow.
These changes are the basis for the reduction in access and egress time from 23.95 minutes to
19.56 minutes; 18.3% reduction in access time consistent with a 50% decrease in headways.
Scenario 3 - Expand ridesharing and TDM programs plus transit frequency
improvement and parking pricing
Scenario 3 adds a policy to increase average cost of parking by 74% and remove parking
subsidies in major activity centers. This increased parking cost is $165 per month for drive alone
($7.50 per day for 22 days) and $2.73 for rideshare ($2.73 per person per day for 22 days and
2.75 persons per vehicle) consistent with the national average. The assumption is that 100% of
employers in major activity centers will be subject to parking pricing.
This scenario required two TRIMMS model runs to separate those currently paying for parking
(30,110) from those not currently paying who will pay in the future (738,662). ARC provided
monthly parking costs for both drive alone and rideshare which were divided by 22 workdays to
determine the daily cost input needed for TRIMMS.
Transit service is not changing under this scenario, so transit VMT will not increase. The shift in
drive-alone and rideshare trips to transit trips will be supported by the capacity of the BAU
29 July 2016
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transit system. Although VMT for non-motorized modes (e.g., walking, bicycle) will increase,
there are no emissions associated with these modes.
Scenario 4 - Expand ridesharing and TDM programs plus transit frequency
improvement, parking pricing, and smart growth land use
ARC'S land use strategy is to encourage new residents in the five county core area to locate in
compact areas with the outcome of producing lower transportation emissions as investigated in
their 2014 climate change study.29 The strategy does not include shifting population growth from
outlying counties to the inner 5 counties. The target population for the land use strategy is equal
to the BAU population for the 5-county subarea (4.4 million). ARC has been using a land use
analysis approach that is similar to the Neighborhood Classification method tested in this study.
As a result, the agency was able to provide the required data for this analysis. The Multivariate
approach was new to the agency and they were equally interested in providing data and getting
results for both approaches.
This scenario combines all previous strategies tested with population growth more concentrated
in travel efficient neighborhood types. For the Neighborhood Classification analysis it was
assumed that 50% of total population will be living in the two neighborhood types with the lowest
average VMT per person (out of 5 total neighborhood types), versus 37% in the BAU. These
neighborhood types include areas close to major activity centers, areas with good transit
access, and/or areas that are walkable. They also have the fewest average daily miles traveled
per person (9.8 and 15.9), while the other neighborhood types range from 18 to 31 daily
miles/person.
The Multivariate approach analyzes the same policy stated in terms of specific land use
variables. In this analysis, four specific changes were anticipated:
Increase population-weighted density by 59%
Increase job access by auto by 3%
Increase job access by transit by 56%
Reduce distance to nearest transit stop by 12%
Both land use analysis methods were conducted outside of TRIMMS. Transit VMT and non-
motorized modes were not included in the VMT reduction. ARC provided the average VMT per
person per day for each of the five neighborhood types as well as the percent of households in
each type for the BAU and scenario.
29 See: http://atlantareqional.com/environment/air/climate-chanqe
30 July 2016
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For the Multivariate approach analysis, elasticity values from Ewing and Cervero (2010) were
used to determine total percent VMT change for each type of change. See Table A-2 in
Appendix A for the elasticity values used for this analysis.
Transit service is not changing under this scenario, so transit VMT will not increase.
Scenario Summary
Input parameters are provided in Table 6 for current conditions, a business as usual future, and
the four scenarios selected by ARC. Specific input values are provided for the scenarios. The
ARC scenarios were applied only to the 5-county region. The resulting VMT reductions for each
scenario are included in the table. In each scenario, the specific target population is identified.
ARC is compared to San Diego, California in Cluster 2. The ARC strategies were matched as
closely as possible with the strategies in the 2010 national study; the results of this comparison
are presented in Table C-2 of Appendix C. This comparison does not imply that ARC values will
match exactly those of San Diego. All comparisons made between the two regions supported
the reasonableness of the ARC results.
Emissions Analysis
Many regions have now conducted a transportation conformity analysis using the MOVES
model; however, for many regions the 2014 version seems to be less widely used at present.
ARC had developed extensive databases for use in its transportation conformity analyses to
comply with Clean Air Act requirements and therefore had existing data to provide for the
emissions analysis; it provided this data as a series of individual spreadsheets as well as a
custom set of statewide inputs in MOVES2014 format for this analysis. These model input
values are consistent with those used by the region in its conformity process.
ARC is a participant in the Interagency Committee for air quality planning as part of the
conformity process. The agency has been using MOVES to model emissions since 2012, but
has not yet performed conformity analysis with MOVES2014.
In TEAM the MOVES analysis is focused on extracting activity-weighted, regional average
emissions factors from the model that represent the general parameters of the study region, as
discussed above. ARC initially provided a custom database built on custom extractions of
activity data from their travel demand model within the five-county study area. ARC provided
inputs based on its travel demand model and its current conformity analyses along with a new
Georgia-specific MOVES default database to use in these simulations; illustrating the
consistency of the TEAM analysis with other analytical processes in the region. These were all
loaded into new, MOVES2014 input databases. MOVES2014 input runspec files were created
to match the provided inputs. Model simulations were then conducted for years 2015 and 2040
with these input databases and runspecs.
31 July 2016
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Scenario Description
Current
Conditions
Business as
Usual
Existing conditions
across all
strategies in 2010
2040 conditions
with BAU levels of
employer program,
parking pricing,
land use, and
transit
Table 6. ARC Scenario Details
Data Inputs
mode shares
average vehicle occupancy
average vehicle trip lengths
regional population and employment
mode shares
average vehicle occupancy
2040 regional population and employment
current travel times (33.45 minutes for transit)
current access time (23.95 minutes for transit)
average parking costs ($4.32 for drive-alone and $1.57 for
rideshare)
Scenario 1: Expand access to
Expanded TDM telework and
flexwork programs,
Guaranteed Ride
share of additional regional employees covered (271,194 for
flexwork and telework programs, 590,236 for flexwork and
telework and monetary subsidies, and 11,763 for telework
and flexwork, monetary subsidies, and guaranteed ride home;
Daily Regional
VMT Reduction
NA
NA
0.69% auto VMT
reduction (867,544)
Scenario 2:
Home and
ridematching
services.
Reduce transit trip
Scenario 1 + times
Transit
Frequency
Improvement
Scenario 3: Increase and
Scenario 2 +
Parking Pricing
expand coverage
of parking costs.
873,193 total additional employees covered)
No change in average monthly subsidy offered to employees
Program Subsidies radio buttons by mode for monetary
subsidies
18.3% access time reduction (to 19.6 minutes) based on 50% 0.86% auto VMT
decrease in headways reduction
Number of regional employees covered (2,025,866) (1,093,477); transit
VMT will increase
26% of all parking (public and private) is priced 2.85% auto VMT
74% increase in average parking cost per trip (to $7.50 for
drive-alone and to $2.73 for rideshare)
reduction
(3,602,286)
Scenario 4: Increase Neighborhood approach applies to entire regional population 8.82% auto VMT
Scenario 3+ residential density (4,404,057 people): reduction
LandUse andmixeduse . 50% of population in neighborhood types 1 (9.76 average (11,1 47,396) for
land uses for entire dgi|y per capitg VMT) gnd 2 (15 g average dai|y per capita Neighborhood
regional population \/N\~[) approach; 9.28%
auto VMT reduction
50% of population in neighborhood types 3 (18.8 average /.< ^ 703 oj?\ for
daily per capita VMT), 4 (21 .82 average daily per capita Muitivar'iate
VMT), and 5 (3 1.10 average daily per capita VMT) accroach
Mulitvariate approach applies to entire regional population:
Increase population-weighted density by 59%
Increase job access by auto by 3%
Increase job access by transit by 56%
Reduce distance to nearest transit stop by 12%
32
July 2016
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ARC includes the effects of inspection and maintenance (I/M) programs in their MOVES
modeling for transportation conformity analysis, including for future years. To be consistent with
the ARC transportation conformity process, the I/M program described in the input dataset
provided by ARC was used. Previous TEAM case studies have omitted I/M in the analysis.
Although we did not conduct sensitivity simulations to assess the impact of including this
program on the emissions, the affect is expected to be small, and likely to somewhat reduce the
predicted overall emissions change. Relative emission changes should not be affected.
All data used for this analysis was as provided by ARC. The agency is familiar with MOVES
analysis and was prepared to provide all required inputs. Although ARC has not yet conducted
formal analyses in MOVES2014, the data provided have been modified to include any
necessary changes from MOVES2010 to MOVES2014. All regional inputs were provided in
spreadsheet format and imported into new MOVES2014 input databases. Some custom inputs
were crafted by ARC for use in the smaller, five-county domain considered here, such as VMT,
while all others, such as fuels and meteorology, are consistent with ARC'S approach in its
conformity analyses and have undergone development and review through the Interagency
Planning process.
The post-processing of hourly MOVES outputs to determine the activity weighted, average
emission factors representing the ARC region was done with database and spreadsheet
analysis methods. In these methods, emissions and activities are aggregated into the source
types required by TRIMMS, and the ratio of emissions and activities is taken. These database
values are output into spreadsheets where the presentation of values is simplified into Table
C-3 and presented for use in the spreadsheet models used in the scenario analyses. The
resulting average emission factors for the ARC region and TRIMMS vehicle types are shown in
Table C-3.
ARC Scenario Results
The results of the VMT and emissions analysis for light-duty vehicles at a regionwide scale are
presented below in Table 7, following a discussion of the results for each scenario.
Scenario 1, expanding the Atlanta region's existing TDM programs, would reduce regional VMT
by 0.7% for light-duty vehicles (representing approximately 870,000 VMT and 66,000 trips).30 As
discussed previously, the radio-button feature in TRIMMS used for TDM strategies simplifies
the analysis. Although an alternate approach using actual dollar values for monetary subsidies
was attempted, this did not provide reasonable results from TRIMMS. With improved
rebalancing across modes, the strategy may produce greater reductions for the entire region.
Another factor restricting this strategy is limited deployment. Only about half of the region's
employees will be offered subsidies as the strategy is currently specified. However, reaching
30 For the target population, the VMT reduction is a moderate 3.5%.
33 July 2016
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more employees would be increasingly challenging, as it would require engaging businesses
under 100 employees.
Scenario 2 adds system-wide transit frequency improvements, which provide an additional
regional VMT reduction of 0.2% for light-duty vehicles. The total combined regional VMT
reduction is 0.86% for the scenario.31 This is equivalent to a 43,000 reduction in light-duty auto
trips or 226,000 VMT, which are shifted to public transit.32 Increasing the frequency of transit
service reduces wait times for vehicles and makes riders more likely to choose transit. However,
the regionwide impact is still limited by the extent of the transit system.
In Scenario 3, adding parking pricing reduces regional VMT by an additional 2% for light-duty
vehicles, which is equivalent to a 475,000 decrease in light-duty auto trips and 2.5 million VMT.
The total combined regional VMT reduction is 2.85% for the scenario.33 Pricing policies tend to
be an effective strategy, because this can affect a large proportion of trips. In this case, ARC'S
proposed strategy would price approximately 25% of all employment-based parking at around
$4 per trip. The remaining 75% of employment-based parking is unlikely to be priced, given its
location outside of major activity centers. For potentially larger reductions using a pricing
strategy approach, ARC may consider VMT or congestion pricing.
In Scenario 4, adding land use strategies reduces regional VMT by an additional 6% to 6.4%
for light-duty vehicles, depending on the analysis method used. This is equivalent to a reduction
of 1.5 to1.6 million light-duty auto trips or 7.5 to 8.1 million VMT. The total regional scenario
VMT reduction is 8.8% to 9.3%, again based on the land use analysis method.
Land use produces the largest reduction of any strategy examined by ARC, thanks to an
aggressive shift towards compact development patterns. ARC'S land use strategy would
increase the density of the average resident's neighborhood by 59% and increase the average
number of jobs accessible by transit within 45 minutes for each resident by 56%. Those
represent dramatic changes in the land use environment and transportation options available to
many residents.
ARC could potentially reduce VMT even further through land use by considering increasing land
use mixing. The current multivariate analysis showed no projected increase in land use mixing.
Adding explicit measures to collocate housing, retail, and job opportunities in mixed-use districts
reduces the need to make long car trips and helps to encourage shorter trips by walking and
biking.
31 For the target population, the VMT reduction is 0.4%.
32 For the corresponding increase in transit vehicle VMT see Appendix A
33 For the target population, the VMT reduction is 11.4%.
34 July 2016
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Table 7. ARC Regionwide Percent VMT and Emissions Changes for Light-Duty Vehicles
Scenario
Scenario 1 : Expanded TDM
Scenario 2: Scenario 1 + Transit
Frequency Improvement
Scenario 3: Scenario 2 + Parking
Pricing
Scenario 4: Scenario 3 + Land Use
(Neighborhood Approach)
Scenario 4: Scenario 3 + Land Use
(Multivariate approach)
Target Population*
20% of Regionwide
population (873, 193)
46% of Regionwide
population (2,025,866)
17% of Regionwide
population (768,772)
4,404,057 Regionwide
population
4,404,057 Regionwide
population
Light-Duty
VMT
-0.69%
-0.86%
-2.85%
-8.82%
-9.28%
GHGs (C02
equivalent)
-0.68%
-0.86%
-2.85%
-8.81%
-9.27%
PM2.5
-0.68%
-0.86%
-2.85%
-8.81%
-9.27%
NOx
-0.67%
-0.85%
-2.82%
-8.79%
-9.25%
voc
-0.66%
-0.83%
-2.81%
-8.78%
-9.24%
The identified target population is for the additional strategy only. For the total Scenario population,
previous scenario populations must be considered.
East-West Gateway
Background
East-West Gateway Council of Governments (EWG) is the metropolitan planning organization
(MPO) for the St. Louis bi-state region. EWG serves a region of approximately 2.6 million
people over a five-county geography in Missouri and Illinois. The agency is well known for its
data-driven planning process, and EWG staff supports data collection and analysis for all
planning activities in the region including Geographic Information Systems (GIS), Land use
Evolution and Assessment Model (LEAM), the regional travel demand model, and the MOVES
model. Recent efforts in sustainability and climate change make it an ideal test for TEAM.
EWG participated in the OneSTL planning process, funded by a HUD Sustainable Communities
Grant, from 2010 to 2013. EWG's Board of Directors approved the plan, which provides a
framework of goals and strategies for fostering sustainable development in the St. Louis region.
Unlike a long range transportation plan, OneSTL does not include a program of investments or
assumptions about land use patterns.
EWG's latest long range transportation plan (LRP), Connected2045, was approved in 2015.
Many of the metrics, goals, and strategies included in OneSTL are incorporated in
Connected2045. As required of LRPs, Connected2045 adds a program of transportation
investments including roads, transit, and bicycle facilities.
The EWG stated interest in participating in the TEAM case study is to allow decision makers
and partners in the region to make more informed decisions in efforts to meet the goals outlined
in the LRP and in OneSTL. The goals in these plans are not binding in a decision-making or
35
July 2016
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investment context, nor are there specific targets set for future years. The TEAM analysis
provides an opportunity to quantify the GHG benefits of specific policies and investments, which
will in turn help build support among regional stakeholders for taking action in support of the
plans' stated goals.
Concurrent with the TEAM analysis, EWG has received a grant from the Federal Highway
Administration (FHWA) to facilitate GHG analysis of transportation related emissions using the
MOVES model. Both of these efforts will support a better understanding of GHG emissions and
potential strategies to reduce them.
EWG plays a significant role in the region by facilitating and coordinating interagency groups
focused on air quality issues and emissions reduction. The Missouri Department of
Transportation has expressed an interest in the TEAM analysis to inform a current Planning and
Environmental Linkage study on a 40-mile corridor along 1-70. EWG staff work closely with local
air quality agencies and established the Air Quality Advisory Committee along with the Inter-
Agency Consultation Group that supports the air quality conformity process. The region is
designated non-attainment for ozone. EWG has coordinated with these broad partnerships in
the selection of strategies and data inputs to inform the TEAM analysis.
EWG has no experience using sketch planning tools, and this is one reason for the interest in
TEAM. Requests for GHG planning support from local municipalities as well as from the two
State DOTs conducting corridor studies, provides the agency with an incentive to analyze at the
sub-regional scale. Sketch planning tools are a natural fit for this interest by supporting decision
making while requiring limited detailed analysis. This approach also fits well for implementing
the OneSTL plan for sustainable development.
Scenario Development
During initial discussion of potential strategies to test, EWG expressed an interest in expanding
the light rail system, and this was ultimately selected for analysis. Because the agency works
across two-states, analysis at the county scale was also an interest.
Scenarios selected by EWG represent a combination of geographies: at both the regional (5-
county) scale and for a sub-region of three counties. The three-county area includes the City of
St. Louis (which functions as its own county), St. Louis County, and St. Clair County. Together
these three counties contain the entire urban core of the region, along with all of the existing
and planned rapid transit infrastructure.
The 5-county area includes the 3 core counties along with St. Charles and Madison Counties.
Growth patterns in the region are expected to shift towards these counties in the future. While
the 2 outlying counties are not slated for rapid transit infrastructure, they are ripe for inclusion in
other types of land use strategies.
The strategies selected for analysis reflect a strong multimodal interest with supporting land
use. Two different land use strategies were selectedone covering the three-county core and
36 July 2016
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one covering all five counties, and EWG agreed to test both the Neighborhood Classification
and the Multivariate analysis approach for both. This resulted in 4 different ways of analyzing
the potential for land use to contribute to reducing emissions in the EWG region. The two other
strategies, one for transit and one for bicycle and pedestrian transportation, represent a full build
out of the light rail and bike/ped networks. Scenarios were developed by adding strategies to
see the combined effect. Only the transit strategy was appropriate to model using TRIMMS. The
bicycle and pedestrian analysis was accomplished with a simple spreadsheet analysis.
Scenario 1 - Regional Transit Oriented Development Initiative
This scenario is aimed at creating pedestrian and bicycle-friendly communities as outlined in
OneSTL, and increasing population and jobs located near light rail stations, with most of the
growth going to transit oriented development (TODs). The policy support required for this
strategy is to increase the population and jobs located near light rail stations in a three-county
core. This is a land use strategy and therefore two approaches were applied for analysis.
For the Neighborhood Classification approach, the analysis shifts 1% of population and 2% of
jobs within the 3 core counties to more compact neighborhood types clustered around light rail
stations.
For the Multivariate approach, access and density were increased as follows:
Increase population-weighted density by 4%
Increase job access by auto by 2%
Increase job access by transit by 7%
Reduce distance to nearest transit stop by 2%
This scenario required two separate spreadsheet analyses using data provided by EWG. Both
land use analysis methods were conducted outside of TRIMMS. The TOD scenario was
analyzed to determine potential VMT reductions for light-duty vehicles and did not consider
increased transit service as part of the scenario. Although VMT for non-motorized modes will
increase, there are no emissions associated with these modes.
The Neighborhood Classification approach is based on the total employment and population for
each area type assumed in the scenario. Total VMT is calculated by summing the average daily
VMT for each area type. This allows comparison to the VMT in the BAU case to determine VMT
reductions. EWG provided the average VMT per person per day for each of the five
neighborhood types as well as the percent households in each type for the BAU and the
scenario.
For the Multivariate approach the percent change between the BAU and the scenario in each of
the parameters, provided by EWG and listed above, was determined and multiplied by the
elasticity values from Ewing and Cervero (2010) to identify a total percent change in VMT.
37 July 2016
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For both approaches, the shift in drive-alone and rideshare trips to transit trips will be supported
by BAU transit system capacity, without additional transit VMT. Although VMT for non-motorized
modes will increase, there are no emissions associated with these modes.
Scenario 2 - Regional Transit Oriented Development Initiative and Workforce
Housing Balance Initiative
The Workforce Housing Balance Initiative is an attempt to bring jobs and workers closer
together by providing good balance of residential, retail and non-retail land uses in the five-
county region. While the Regional TOD initiative is focused at existing centers of high transit use
in the inner 3 counties, the Workforce Housing Balance applies to the broader five-county area.
The additional policy implication for this scenario is to focus growth in jobs and population in a
manner that increases the local balance in individual counties.
As with the Regional TOD scenario, both land use analysis methodologies were applied to this
scenario and the same analysis approach was used. As in Scenario 1, the two analysis
approaches yielded comparable results.
For the Neighborhood Classification approach, the analysis shifts 2% of the population to more
compact neighborhood types near existing job centers. In addition, 17% of jobs are shifted to
neighborhood types that have higher concentrations of housing.
For the Multivariate approach, access and density (provided by EWG) were increased as
follows:
Increase population-weighted density by 2%
Increase job access by auto by 3%
Increase job access by transit by 2%
Reduce distance to nearest transit stop by 11 %
The same approach described above for Scenario 1 was used to calculate VMT reductions for
Scenario 2. As in Scenario 1, the shift in drive-alone and rideshare trips to transit trips will be
supported by the BAU transit system capacity, without additional transit VMT.
Scenario 3 - Regional Transit Oriented Development Initiative and Workforce
Housing Balance Initiative with support for alternative modes of travel
Scenario 3 adds a complete bicycle and pedestrian network in the five-county region. The policy
basis for this scenario is to increase sidewalk coverage on local and arterial roads from 56% to
71% and to expand miles of bicycle facilities by 150%. This represents a full build out of the
bicycle and pedestrian networks currently in local and regional plans. As such, it includes
construction of some facilities that are already included in the LRP and, therefore, the BAU. We
allowed some overlap in this case for several reasons. First, there are additional bicycle facilities
38 July 2016
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included in this strategy that are not included in the LRP. Second, the LRP makes clear that
inclusion of bicycle facilities is not a commitment to funding. Presumably then, the case still
needs to be made for building those facilities. Third, removing the bicycle facilities in question
from the BAU would not result in any measurable difference in BAU travel patterns.
For sidewalk coverage, EWG staff estimated aspirational increases based on expected
urbanization patterns in each county.
Analysis of this scenario is based on resources and assumptions developed by the San Diego
Association of Governments (SANDAG).34 The method is simple to apply, applicable at the
regional level, and based on empirical research. It therefore fits with the overall TEAM concept.
The general concept of this approach is to predict mode shifts based on the increased
infrastructure provided for bicycling by expanding the lane miles. The bike lane method
assumes a 1% increase in bike mode share for every 1 bicycle lane mile per square mile of
area.35 In this case, bicycle lane miles were increased by 150% for the same area.
The pedestrian method assumes the increase in pedestrian commuters (and 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). After the bicycle and pedestrian strategy is applied, new mode
shares are computed for all modes. This redistribution of trips supports an increase in bike and
walk trips and a decrease in drive-alone trips. See Table D-1 in Appendix D for comparisons.
The national average bike and walk trip lengths from the 2009 National Household Travel
Survey (NHTS) (2.26 miles and 0.70 miles, respectively) was then used to calculate the change
in VMT by mode. For the purposes of this analysis, only the reduction in auto-drive alone trips
and VMT were included (other modes were not included). For both bicycle and pedestrian
infrastructure, the total decrease in drive-alone, one-way trips is 20,062 with a corresponding
total decrease in drive-alone VMT of 29,255.
For both approaches, the shift in drive-alone and rideshare trips to transit trips will be supported
by the BAU transit system capacity, without additional transit VMT. Although VMT for non-
motorized modes will increase, there are no emissions associated with these modes.
34 San Diego Association of Governments. 2011. 2050 Regional Transportation Plan. Technical Appendix 15: SANDAG Travel
Demand Model Documentation. Available: http://www.sandag.org/uploads/2050RTP/F2050RTPTA15.pdf
35 Dill, J., and T. Carr. 2003. Bicycle Commuting and Facilities in Major U.S. Cities: If You Build Them, Commuters Will Use
Them - Another Look. Transportation Research Board 1828, National Academy of Sciences, Washington, D.C. Available:
http://www.ltrc.lsu.edu/TRB 82fTRB2003-002134.pdf
39 July 2016
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Scenario 4 - Regional Transit Oriented Development Initiative and Workforce
Housing Balance Initiative with support for alternative modes of travel and transit
expansion
The final scenario adds the light rail expansion after all other supportive policies are in place.
The strategy represents a complete build-out of the planned light rail system in the five-county
region. The impact for the region is an increase of the population living within 1 mile of light rail
from 257,000 to 1,019,000.
The transit strategy is the only EWG strategy that was modeled using TRIMMS. The approach
was to determine access and travel time improvements for public transportation that reduce
VMT. National data from the 2009 NHTS for average travel and access time for transit was used
because specific travel data from EWG was not available. An evaluation using the assumed
difference in service levels between the light rail system expansion and bus service in the BAU
scenario proposed that both access time and travel time are reduced by 25%. This assumption
is supported by EWG staff as reasonable.
According to EWG, the transit buildout strategy would involve a 354% increase in light rail miles
from 61 miles to 275 miles. The 354% increase in light rail miles was used to estimate new VMT
and trips and associated emissions (see Appendix A for a detailed discussion of these
methods).
The analysis also includes an evaluation of changes in transit-supportive land use using the
Multivariate approach. In this analysis, it was assumed that the population-weighted density for
the 1-mile transit buffer zone36 increased by 1% (based on accompanying population
projections provided by EWG). The land use analysis method was conducted outside of
TRIMMS. Elasticity values from Ewing and Cervero (2010) were used to determine total percent
VMT change.
Scenario Summary
Input parameters are provided in Table 8 for current conditions, a business as usual future, and
the four scenarios selected by EWG. Specific input values are provided for the scenarios. The
EWG scenarios were applied only to the three-county or five-county region. The resulting VMT
reductions for each scenario are included in the table. In each scenario, the specific target
population is identified.
36 The 1-mile transit buffer zone is defined as the area within 1 mile of the expanded light rail system.
40 July 2016
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Table 8. EWG Scenario Details
Scenario Description Data inputs v^CuS
Current
Existing
Conditions conditions
across all
strategies in
2010
Business as 2040
Usual conditions
with BAU
Scenario 1 :
levels of
employer
program,
land use,
and transit
Increase
mode shares
average vehicle occupancy
average vehicle trip lengths
regional population and employment
782 bike lane miles
8,374 sidewalk miles
vehicle ownership (1 .7 vehicles/household)
residential population density (578 persons per square mile)
retail establishment density (3.1 per square mile)
mode shares
average vehicle occupancy
2040 regional population and employment
current travel times (12.78 minutes for transit)
current access times (14.92 minutes for transit)
vehicle ownership (1 .7 vehicles/household)
residential population density (612 persons per square mile)
retail establishment density (3.5 per square mile)
Share of regional commuters covered (1,646,886)
NA
NA
0.1 6% auto VMT
Regional
Transit
Oriented
Development
transit
oriented
development
Neighborhood approach
11.6% of population and employment in neighborhood type 1
(20.9 average daily VMT per population + job)
19.9% of population and employment in neighborhood type 2
(15.9 average daily VMT)
26.1 % of population and employment in neighborhood type 3
(14.8 average daily VMT)
25.8% of population and employment in neighborhood type 4
(12.1 average daily VMT)
9.3% of population and employment in neighborhood type 5
(18.4 average daily VMT)
7.3% of population and employment in neighborhood type 6
(21.2 average daily VMT)
Mulitvariate approach:
Increase population-weighted density by 4%
Increase job access by auto by 2%
Increase job access by transit by 7%
Reduce distance to nearest transit stop by 2%
reduction (121,121)
for Neighborhood
approach; 0.54%
auto VMT reduction
(400,252) for
Multivariate
approach
41
July 2016
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Scenario Description
K
Scenario 2:
Scenario 1 +
Workforce
Housing
Balance
Increase
residential
density and
mixed use
land uses for
nonuSon
population.
Data Inputs
K
Share of regional commuters covered (2,387,956)
Neighborhood approach
13.2% of population and employment in neighborhood type 1
(20.9 average daily VMT per population + job)
20% of population and employment in neighborhood type 2 (15.9
average daily VMT)
26.6% of population and employment in neighborhood type 3
(14.8 average daily VMT)
30.1 % of population and employment in neighborhood type 4
(12.1 average daily VMT)
5% of population and employment in neighborhood type 5 (18.4
average daily VMT)
5.1 % of population and employment in neighborhood type 6
(21.2 average daily VMT)
Mulitvariate approach:
Increase population-weighted density by 2%
Increase job access by auto by 3%
Increase job access by transit by 2%
Reduce distance to nearest transit stop by 11 %
,,r»-rr, j »
VMT Reduction
2.13% auto VMT
reduction
(1,584,406) for
Neighborhood
approach; 1.66%
auto VMT reduction
approach
Scenarios: Complete 150% increase in bike lane miles (to 1,951) 2.21% auto VMT
Scenario 2 + bicycle and Increase in sidewalk coverage on local and arterial roads to 71 % reduction
Bike / Red pedestrian (to 10,579 sidewalk miles) | (1 ,640,332) for
Network network in
the five-
Scenario 4:
county
region
Light rail
25% travel time reduction (to 9.58 minutes)
Scenario 3 + expansion . 25% access time reduction (to 1 1 . 1 9 minutes)
Transit
Expansion
share of additional regional population covered by transit
expansion (761,887)
For the Multivariate approach, an increase in population-
weighted density of 1 %
Neighborhood
approach; 1.73%
auto VMT reduction
(1,287,248) for
Multivariate
approach
2.54% auto VMT
reduction
(1,890,772) for
Neighborhood
approach; 2.07%
auto VMT reduction
(1,537,687) for
Multivariate
approach; 66.2%
Transit VMT will
increase
42
July 2016
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EWG strategies are compared to those of Sacramento, California in Cluster 4. The EWG
strategies were matched as closely as possible with the strategies in the 2010 national study.
The results of this comparison are presented in Table D-2 of Appendix D. All comparisons made
between the two regions supported the reasonableness of the EWG results.
Emissions Analysis
The emissions analysis in a bi-state region has many stakeholders. EWG has experience
working with both the Missouri and Illinois agencies for environmental protection: Missouri
Department of Natural Resources (MODNR) and Illinois Environmental Protection Agency
(ILEPA), respectively. The two agencies have different data to contribute, but the transportation
conformity interagency agreements establish a context for jointly considering emissions.
In this process, a single representative county in each state is modeled. This is identical to the
process described for ARC, except that two counties are used, one for each represented state.
Those are St. Louis (County), Missouri, and St. Clair, Illinois. EWG provided these aggregated
inputs consistent with their conformity analysis (other than VMT). Accordingly, this same
approach and data was used in the TEAM analysis.
Only two modifications were made to the data provided by EWG: MOVES input databases were
converted from MOVES 2010b format to MOVES2014 using EPA's converter tool, and current
regional VMT data provided by EWG were used in the MOVES input databases. The updated
MOVES input databases include 2015 and 2045 values from the regional travel demand model;
representing current conditions and including the addition of a new unrestricted access
roadway. Calibration factors have not been determined, and EWG confirmed that minor effects
from calibration will be minimized by the emission factor method used in the TEAM approach.
However, these data represent average weekday VMT stratified only by county and HPMS
facility (road) type. The EPA converter tool37 was used to translate data into annual VMT values
("HPMSBaseYearVMT") for the input databases. As with ARC, the existing inspection and
maintenance ("I/M") program was included in this analysis.
MOVES was run at the county scale for both representative counties and years (2015, 2045).
The final emission factors were determined from the MOVES inventory-mode outputs using
MySQL and spreadsheet analyses to create tables for further analysis. All data used for this
analysis was provided by EWG.
Although EWG provided all required data inputs, the analysis is slightly complicated by the fact
that it is a bi-state region, requiring two distinct MOVES analyses to determine emissions and
activities. However, these two sets of values were aggregated off-model to produce combined,
regional average emission factors. Also, the analysis relies on a "representative county"
37 Available at http://www.epa.qov/otaq/models/moves/documents/aadvmt-converter-tool-moves2014.xlsx.
43 July 2016
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approach to simulate the counties in each state. The fact that the two representative counties
are in two states adds negligibly to the complexity of the MOVES modeling.
In addition, this case provided an exercise in use of some ancillary tools EPA provides for
MOVES analysis. The two state agencies provided all inputs from prior transportation conformity
modeling. However, neither agency had updated the inputs to MOVES2014 format. This
modification was straightforward with the converter application in MOVES2014. Additionally,
using EPA's VMT converter tool was straightforward once the methodology was agreed upon
with EWG.
The post-processing of hourly MOVES outputs to domain-average emission factors for the EWG
region was done with database and spreadsheet analysis methods. In these methods,
emissions and activities are aggregated into the source types required by TRIMMS, and the
ratio of emissions and activities is taken. The emissions and activity inputs are first pre-
aggregated into a single value from the outputs of the two individual representative counties
from the MOVES simulation. This is done using database methods to combine the separate
tables from each state's simulation into a single set of emissions and activities. Those are then
ratioed into emission rates for the composite vehicle types. These ratios are then output into
spreadsheets where the presentation of values is simplified into tables and presented for use in
the spreadsheet models used in the scenario analyses. The resulting average emission factors
for the EWG region and TRIMMS vehicle types are shown in Table D-3 of Appendix D.
EWG Scenario Results
Table 9 provides the results of analysis for light-duty vehicles. Additional explanation is provided
below and detailed numerical results are included in Appendix A and D.
For the EWG analysis, both land use approaches yielded small reductions in total VMT. The
multivariate approach requires TAZ level data and was a good methodological fit for EWG. The
agency's modeling staff were comfortable manipulating land use projections at the TAZ level in
order to specify their land use strategies.
Applying the neighborhood approach was less straightforward for EWG. Since EWG does not
have an activity-based travel demand model,38 it was impossible to produce estimates of total
VMT generated by the traveler's place of residence. As a workaround, EWG analyzed VMT
generated by trips originating from both homes and workplaces to provide the inputs needed. In
addition, aggregating population and job projections by neighborhood types required several
more steps beyond the TAZ level analysis.
38 EWG uses a traditional 4-step model, which estimates simple 1-way trips by zone of origin and destination. Newer activity-
based models treat trips as a function of activities such as work, school, and shopping. These models are capable of
estimating trips taken by people that live in a certain zone, regardless of whether the trip itself begins or ends in that zone.
44 July 2016
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Table 9. EWG Regionwide Percent VMT and Emissions Changes for Light-Duty Vehicles
Percent R
Scenario
Scenario 1: Regional TOD
Scenario 2: Scenario 1 +
Workforce Housing Balance
Scenario 3: Scenario 2 +
Bike / Red Network
Scenario 4: Scenario 3 +
Transit Expansion
egionwide Emissions C
Target Population*
60% of Regionwide
population (1,646,886)
87% of Regionwide
population (2,387,956)
87% of Regionwide
population (2,374,467)
37% of Regionwide
population (1,018,960)
Changes for 20'
Approach
Type
Neighborhood
Multivariate
Neighborhood
Multivariate
Neighborhood
Multivariate
Neighborhood
Multivariate
tO BAU Co
Light-
Duty
VMT
-0.16%
-0.54%
-2.13%
-1.66%
-2.21%
-1.73%
-2.54%
-2.07%
mpared to 21
GHGs
(C02
equivalent)
-0.16%
-0.54%
-2.13%
-1.66%
-2.22%
-1.75%
-2.56%
-2.11%
)40 Scenar
PM2.5
-0.16%
-0.54%
-2.13%
-1.66%
-2.24%
-1.76%
-2.57%
-2.13%
io
NOx
-0.16%
-0.54%
-2.13%
-1.66%
-2.37%
-1.89%
-2.70%
-2.39%
voc
-0.16%
-0.54%
-2.13%
-1.66%
-2.56%
-2.08%
-2.90%
-2.79%
The identified target population is for the additional strategy only. For the total Scenario population, previous
scenario populations must be considered.
Scenario 1 regional TOD land use has a small impact with a 0.2% to 0.5% reduction in regional
VMT for light-duty vehicles. This is equivalent to a 15,000 to 51,000 decrease in light-duty auto
trips and 120,000 to 400,000 VMT.39 The target population includes 60% of the regional
population. The impact of this strategy is relatively small, because the policy change affects a
small population. This strategy shifts only 2% of regional jobs and 1% regional population to a
small number of light rail station areas. The vast majority of jobs and population in the region
remain unaffected.
Scenario 2 adds workforce housing balance as a land use strategy to regional TOD land use
for an additional moderate 1.1% to 2.0% reduction in regional VMT for light-duty vehicles. This
is equivalent to a 106,000-186,000 decrease in light-duty auto trips and 830,000 to 1.5 million
VMT.40 The combined strategies under Scenario 2 represent a total regional VMT reduction of
1.7% to 2.1%. The reduction for this strategy is larger than that for the TOD strategy, because
this strategy shifts jobs and population throughout a five-county area, affecting a much higher
share of the regional population.
Scenario 3 adds bicycle and pedestrian network expansions. This has a small additional impact
of 0.08% reduction in regional VMT for light-duty vehicles, equivalent to a 38,000 decrease in
light-duty auto trips and 56,000 VMT.41 The combined strategies under Scenario 3 represent a
total regional VMT reduction of 1.7% to 2.2%.
39 For the target population, the VMT reduction is 0.3-0.9%.
40 For the target population, the VMT reduction is 1.3-2.3%.
41 For the target population, the VMT reduction is 0.09%.
45
July 2016
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This modest reduction reflects empirical research on the impact of bicycle and pedestrian
infrastructure. Studies of infrastructure's impact are necessarily conservative, and analyzing the
impact of infrastructure alone tends to produce very small results. However, there are many
examples of regions worldwide that have seen much higher growth in bicycling and walking
through a combination of infrastructure investment, safety campaigns, and cultural trends
shifting towards a preference for non-motorized modes.
Scenario 4 adds transit expansions, and the accompanying transit supportive land use.
Changes include reductions in transit travel and access times along with changes in household /
population density, along with shifts from drive-alone and rideshare trips to transit trips. This has
an additional impact on regional light-duty VMT of 0.3% reduction.42 This is equivalent to a
decrease in VMT of 250,000 and 29,000 trips for light-duty vehicles for a total combined
regional VMT reduction of 2.1% to 2.5% for the scenario.43
This is a relatively small impact, consistent with the results for similar strategies. Reduced
transit travel and access times only affects 29% of the total regional population, so the regional
VMT and emissions reduction is limited. Likewise, this strategy only includes a 1% change in
household / population density, which produces a small regional effect on VMT and emissions.
42 For the target population, the VMT reduction is 1.1%.
43 For the corresponding increase in transit vehicle VMT (see Appendix A)
46 July 2016
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4. Results and Conclusions
Analysis results for each individual region were evaluated in Section 3 of this report. In this
section, the larger comparisons and lessons learned across regions is provided. Significant
changes in the analytical approach are also noted along with related conclusions. Table 10
provides an overview of the relative cumulative regional VMT and emissions changes across all
regions studied for the pollutants of primary interest to support these comparisons.
Table 10. Percent Regional VMT and Emissions ChangesMetroPlan Orlando, ARC, and EWG
Percent Regional Emissions Changes for Future Year Business as Usual Compared to Future Year Scenario
Scenario
Light-Duty GHGs (C02
VMT equivalent)
PM2.5
NOx
VOC
MetroPlan Orlando
Scenario 1 : Expanded TDM
Scenario 2: Scenario 1 + Enhanced Transit
Scenario 3: Scenario 2 + Road Pricing
-0.65%
-0.92%
-4.75%
-0.65%
-0.92%
-4.75%
-0.65%
-0.92%
-4.75%
-0.65%
-0.92%
-4.74%
-0.65%
-0.92%
-4.73%
Scenario 4: Scenario 3 + University Transit Pass
-6.08%
-6.07%
-6.07%
-6.06%
-6.05%
Scenario 1 : Expanded TDM
Scenario 2: Scenario 1 + Transit Frequency
Improvement
Scenario 3: Scenario 2 + Parking Pricing
Scenario 4: Scenario 3 + Land Use (Neighborhood)
Scenario 4: Scenario 3 + Land Use (Multivariate)
-0.69%
-0.86%
-2.85%
-8.82%
-9.28%
-0.68%
-0.86%
-2.85%
-8.81%
-9.27%
-0.68%
-0.86%
-2.85%
-8.81%
-9.27%
-0.67%
-0.85%
-2.82%
-8.79%
-9.25%
-0.66%
-0.83%
-2.81%
-8.78%
-9.24%
Scenario 1: Regional TOD (Neighborhood)
Scenario 1: Regional TOD (Multivariate)
Scenario 2: Scenario 1 + Workforce Housing Balance
(Neighborhood)
Scenario 2: Scenario 1 + Workforce Housing Balance
-0.16%
-0.54%
-2.13%
-1.66%
-0.16%
-0.54%
-2.13%
-1.66%
-0.16%
-0.54%
-2.13%
-1.66%
-0.16%
-0.54%
-2.13%
-1.66%
-0.16%
-0.54%
-2.13%
-1.66%
Scenario 3: Scenario 2 + Bike / Ped Network
(Neighborhood)
Scenario 3: Scenario 2 + Bike / Ped Network
(Multivariate)
Scenario 4: Scenario 3 + Transit Expansion
(Neighborhood)
Scenario 4: Scenario 3 + Transit Expansion
(Multivariate)
-2.21%
-1.73%
-2.54%
-2.07%
-2.22%
-1.75%
-2.56%
-2.11%
-2.24%
-1.76%
-2.57%
-2.13%
-2.37%
-1.89%
-2.70%
-2.39%
-2.56%
-2.08%
-2.90%
-2.79%
47
July 2016
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Of the three regions, ARC'S scenarios achieved the greatest reductions in VMT and
emissions9%. Most of these reductions come from an aggressive land use strategy. Scenario
4 increases the percent of the population living in low-VMT neighborhoods from 37% to 50%, in
terms of the Neighborhood Approach. In Multivariate terms, population-weighted density
increases by nearly 60%. This scale of change in land use is made feasible by the rapid growth
expected in the region. ARC expects the five core counties, which are the subject of the
analysis, to add 900,000 residents and 1.2 million jobs by 2040.
MetroPlan has the second highest reductions, at 6%. Most of these reductions come from a
road pricing policy. Road pricing policies have consistently produced the highest reductions in
TEAM analyses, so it is not surprising that MetroPlan's road pricing scenario adds significantly
to its total reductions.
EWG achieved the smallest reductions, at 2% for two reasons. First, EWG chose to include
bicycle and pedestrian infrastructure as a strategy. While this is an important policy for multi-
modal accessibility, current analytical methods do not predict a strong causal effect between
bike/ped infrastructure and VMT reduction.
Second, EWG's land use strategies are limited in scope by the expected slow growth in the
region. EWG expects to add 150,000 residents and 150,000 jobs to the five-county area by
2045about 1/5 of the growth forecast in the fast growing ARC region. In both the
Neighborhood Approach and the Multivariate Approach, EWG's land use scenarios result in
shifts of population and population-weighted density of only a few percent each. Greater
population shifts would require relocating existing jobs and housing, which the agency does not
anticipate. The TOD policy is also limited by the extent of the light rail system, which is only
planned to serve the three-county core.
Comparison of the strategy results across regions illustrates that TEAM continues to provide
similar results with respect to strategy effectiveness for each specific strategy type. For
example, road pricing is much more aggressive than parking pricing, and continues to make the
most significant changes in the region.
Other noteworthy comparisons are:
Transit changes were more significant in the MetroPlan Orlando and ARC regions than the
effect of the light-rail buildout in EWG. This outcome is the impact of a subarea strategy
change reflected at the regional scale. The transit expansion and TOD strategies in the
MetroPlan Orlando and ARC regions support one another, but the emissions reductions of
the light-rail buildout in EWG are related to the area around the rail stations and not the full
EWG region.
The university pass change in MetroPlan Orlando is similar to the impact of parking pricing
in Atlanta. Again, the sub-population effect is large, but not regionally significant.
48 July 2016
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The EWG strategies focused on the multimodal aspect of their transportation system
coupled with supportive land use changes in a geographical subarea of the region. The
combination of these strategies does a good job of representing what can reasonably be
expected from this type of approach.
MetroPlan Orlando strategies build on their underlying land use assumptions already
included in the BAU through the data drawn from the travel demand model; however,
without a direct comparison of land use strategy, it is impossible to see what the land use is
contributing to emissions reductions.
Data for Use in TEAM
VMT Ana lysis
TEAM is a data-led approach. Data collection, validation, and analysis continue to comprise the
majority of the effort. In the current study, EWG and MetroPlan Orlando represented opposite
ends of the data-intensity spectrum. With respect to data, ARC represents a strong example of
how TEAM can be applied most efficiently.
EWG's process was led by the modeling team, and all strategies were defined in a data-rich
environment. As a result, EWG provided a very high level of detail for input data. The volume of
input data provided by EWG, along with custom approaches to several strategies, required extra
analytical effort. This effort was justified because the analysis yielded more reasonable results
consistent with detailed strategy data from EWG, which would have otherwise been overlooked.
MetroPlan Orlando was not able to engage modelers in its process. The MPO contracts for
modeling services and including these modelers would have added significant cost for
participating in the TEAM study. As a result, the MetroPlan Orlando inputs to TEAM were
informed estimates in most cases. Although the analysis time was decreased by using the
limited data available, the MetroPlan Orlando inputs are also more aspirational and less
reflective of actual policy and planning discussions.
MOVES Analysis
Both ARC and EWG were familiar with MOVES analyses and had data available from recent
transportation conformity determinations to adapt for this analysis. The agencies were able to
describe their typical process as well as provide necessary data. ARC prepared and delivered
study-specific data, while EWG provided previously available data and guidance for its use. No
unique data collection was performed for this MOVES analysis.
MetroPlan Orlando is not an air quality nonattainment area engaged in air quality planning and
analysis. Relevant data from the region's travel demand model was not available during the
TEAM study period, and therefore the agency relied on default data for the MOVES analysis.
49 July 2016
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As expected from the previous analysis, MOVES familiarity and expertise is becoming less of an
issue as agencies become better coordinated around model needs. TEAM could become an
even more relevant and useful tool as the barriers to its use are identified and addressed.
Strategy Analysis and Conclusions
The current study has continued to test and refine the TEAM approachin many cases pushing
TEAM beyond the limits of TRIMMS. As of 2013, CUTR planned to develop an enhanced web-
based version of TRIMMS. However, further updates to the model have not occurred. As a
result, an important function of this study has been to consider several points of departure from
TRIMMS. In addition to providing meaningful analyses for the three regions involved, this
analysis also suggests ways that TEAM can continue to evolve even if TRIMMS does not. While
TEAM has relied on using TRIMMS to determine potential VMT reductions, other sketch models
may also prove useful, depending on the strategies of interest. Other tools are available for
sketch planning and some regions use in-house analysis techniques for this purpose. In
selecting an appropriate tool for use in the TEAM analysis, agencies may want to evaluate their
options based on the individual strategies of interest.
Areas of refinement for TEAM tested or considered in this study include:
Two new land use analysis approaches
A simple approach to analyze bicycle and pedestrian infrastructure expansion
Consideration of an alternate approach for analyzing TDM subsidies
A new approach to account for estimated emissions increases resulting from transit
expansion
Two New Land Use Analysis Approaches
This study introduced the Neighborhood Classification approach and the Multivariate Elasticity
approach. Two regions, ARC and EWG, tested both approaches in order to provide a direct
comparison of the methods.
ARC previously conducted a land use visioning process that used an approach very similar to
the Neighborhood approach. As a result, this approach was a natural fit for the agency.
ARC also agreed to test the Multivariate approach; however, doing so required ARC to
reanalyze its land use scenario using the land use and travel demand models in order to derive
values for the 'D' variables required for this approach. This placed a significant amount of
additional effort on the part of ARC staff. Because the primary contact at ARC for the TEAM
process was not in the modeling group, the Multivariate approach also required additional intra-
agency coordination.
50 July 2016
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For EWG, the situation was nearly reversed. EWG had not conducted a prior land use visioning
study, and TEAM provided the impetus for considering future land use scenarios. The primary
contact for TEAM at EWG was the head of the modeling group. As a result, EWG tended to take
a very data intensive approach to providing inputs to TEAM. The Multivariate approach was a
natural fit for EWG, because the staff lead was very comfortable working with TAZ level data
and deriving inputs from the travel demand model. To create land use scenarios, modeling staff
moved projected growth across individual TAZs; effectively predicting population shifts that
would impact land use.
In contrast to ARC, it was the Neighborhood approach that required extra work for EWG. EWG
translated the land use scenarios described at the TAZ level (using the Multivariate approach)
into neighborhood types for that purpose. However, translating TAZ level land use changes into
higher-level shifts between neighborhood types proved challenging and confusing. The logic of
shifting growth between neighborhood types was not transparent to staff that were used to
dealing with land use at the TAZ level.
Results from the two approaches are comparable, because each approach was applied to the
same underlying policy and land use scenarios. In the case of ARC, results from the two
approaches were almost identical: 6.0% VMT reduction from the Neighborhood approach and
6.4% from the Multivariate approach.
Differences were more pronounced in EWG's analysis. The regional TOD land use strategy
produced VMT reductions of 0.2% and 0.5% from the Neighborhood and Multivariate
approaches, respectively. The workforce-housing balance strategy produced reductions of 2.3%
and 1.3% from the Neighborhood and Multivariate approaches, respectively.
These results provide a very limited set of data points with which to compare the two results. It
is notable that results from the Neighborhood approach are within 1% of results from the
Multivariate approach for each strategy. It appears that the two approaches produce results that
are very similar in magnitude.
The most important distinction between the two approaches was the 'fit' with existing agency
knowledge and resources. Practitioners using TEAM may simply select whichever land use
approach matches best with the data and modeling practices in use at the agency.
Bicycle/Pedestrian Infrastructure
A new approach to analyzing bicycle and pedestrian infrastructure improvements as part of this
study was implemented at the request of EWG. As highlighted in the previous TEAM study,
bicycle and pedestrian improvements cannot be analyzed in TRIMMS, but they are of interest to
many MPOs. The approach used is based on recent improvements in bicycle and pedestrian
analytical processes, as described in the Methodology section of this report. The approach can
be applied by other MPOs desiring to calculate the impacts of bicycle and pedestrian
infrastructure on VMT and emissions. For details, see Table D-1 in the Appendix.
51 July 2016
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Analyses of the impacts of bicycle and pedestrian infrastructure produce consistently
conservative results. As expected, the VMT reductions from EWG's strategy were very small
0.1%. However, EWG believes that there is value in demonstrating quantifiable impacts from
bicycle and pedestrian improvements to support decision making.
Investments in bicycle/pedestrian infrastructure generally produce very small GHG reductions
and are rarely justified on the basis of emissions reductions alone. However, the current
transportation interest in quality of life benefits such as access to healthy transportation modes
will continue to increase practitioner interest in examining bicycle and pedestrian improvements.
Analyzing TDM Subsidies
As in the previous study, two different approaches to analyzing TDM subsidies were considered;
using subsidy dollar amounts, and using the radio button analysis function for subsidies. The
radio button function was used for both ARC and MetroPlan Orlando, because using the dollar
amounts tends to produce unreasonable results. This happens because the elasticity values
provided with TRIMMS are incomplete, and TRIMMS does not contain any inherent controls for
the total number of trips made by a given population.
The radio button function produces consistently reasonableif relatively smallVMT
reductions. For both ARC and MetroPlan Orlando, the total impact of TDM strategies including
both subsidies and non-monetary benefits was a 0.7% reduction in regional VMT.
Developing a reliable option to analyze actual dollar amounts of TDM subsidies may enhance
confidence in the results. It is relatively simple to conduct such an analysis and compare the
results to those of TRIMMS. Similar to the land use comparison, this may be considered for
further improvements to TEAM.
Emissions from Transit Expansion
The previous study stopped short of quantifying emission increases from expanded transit
service. Many of the case study regions have analyzed a strategy to enhance transit service in a
way that would increase the number of transit vehicle miles traveled. However, TRIMMS does
not reliably predict transit VMT resulting from these strategies. In TRIMMS, change in transit
VMT is proportional to change in transit passenger miles traveled. This convention misses the
distinction between strategies that add passengers to existing transit vehicles and strategies
that increase the amount of transit service provided.
When transit is considered as a GHG reducing strategy, it is important to consider both
emissions reduced (displaced) by transit and emissions produced by transit. In an attempt to
begin consideration of both, an off-model estimation of transit emissions was developed. Adding
this information advances the analysis of transit strategies, but it will also place heightened
scrutiny on the net impact of transit strategies. This study estimated emissions generated from
increased transit service using data from the National Transit Database. However, several
52 July 2016
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complications were encountered in obtaining data needed to estimate the percent increase in
transit service.
While this study does account for both sides of the transit equation, and is therefore an
improvement on the previous study, we consider the approach used in this study a work in
progress. Future studies should clarify the inputs needed to estimate transit emissions and
request these from the regions at the beginning of the TEAM process. It will also be necessary
to scrutinize all parts of the analytical process for transit strategies. This includes the elasticities
that have been used with TEAM for several years. Elasticities toward the low (conservative) end
of the range were selected for use in TRIMMS for this analysis. Further consideration of whether
these elasticities best represent the benefits of transit improvement strategies is warranted.
See Appendix A for a detailed discussion of the methods used to asses transit VMT and
emissions outside of TRIMMS.
MOVES Support of TEAM
MOVES is the best model for estimating emissions for TEAM. MOVES2014 is EPA's current
mobile source emission inventory tool, and is required to be used by many regions to develop
related regulatory analyses, such as for State Implementation Plans (SIP) and transportation
conformity determinations.44 These uses of the model are addressed in other EPA guidance,
and this project has shown that regions are becoming increasingly familiar with its use.
Accordingly, many are developing their own, custom MOVES inputs.
Although the inputs developed for those purposes may be used for TEAM, the use of the
MOVES model for TEAM differs somewhat.45 Primarily, as a sketch analysis, detailed emission
factors are not required, and the additional complexity in producing and using them is not
warranted. Instead, overall regional, average emission factors representing the activity-weighted
mean of all starting and running emissions activities in the region is produced and coupled with
the TRIMMS VMT and trips outputs. These emission factors are calculated as total running
emissions from hourly outputs (in grams per year) divided by total running activity (in miles per
year) across the modeled region; a similar analysis is made for starting emissions. Output
emission factors have units of grams of pollutant per average mile driven or grams of pollutant
per average start. This is explained further in the TEAM User's Guide and in Section 2.3,
above. Additionally, EPA has provided tools to assist with MOVES analyses. Together, this
approach and available tools greatly simplify the required MOVES analysis.
44 E.g., MOVES2014 Technical Guidance: Using MOVES to Prepare Emission Inventories for State Implementation Plans and
Transportation Conformity, EPA-420-B-15-007, January 2015.
https://www.epa.gov/otaq/models/moves/documents/420b15007.pdf
45 More explanation in the use of this approach is given in: Analyzing Emission Reductions from Travel Efficiency Strategies: A
Guide to the TEAM Approach, EPA-420-R-11-025, September 2011. Available at:
http://www.epa.gov/otaq/stateresources/policy/420r11025.pdf.
53 July 2016
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Previously, we found a range of technical capability among the regions related to the use of
MOVES. This was less true in the present iteration. Although MetroPlan Orlando was unfamiliar
with the model and had no available inputs, this was the exception. The other two regions were
very comfortable with the model in similar applications, and understood the relationship between
the present analysis and that conducted by regulation to meet air quality conformity.
Selection and Use of Base and Future Years
Base and future years in MOVES should be selected to agree with those of the strategies being
analyzed. A common issue encountered was the discrepancy between baseline year selected
for the TRIMMS and strategy analysis, and information that has been prepared by the regions
for regional emissions analysis. In these cases, the emission inputs for the baseline year had to
be modified (or default values extracted) to agree with the strategy baseline year. In the present
analysis, TEAM strategies were selected to agree with inputs prepared for other MOVES-based
analyses, as expected under the previous case studies.
54 July 2016
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Appendix A. Additional Technical Details for the TEAM
Analysis
A.I. Implications of Increased Transit
The results presented in the main document include reductions in trips, VMT, and emissions for
light-duty vehicles for making comparisons to previous TEAM analyses. Changes in transit VMT
and emissions for relevant strategies were not included above, but are presented in this
appendix for informational purposes. Doing so provides a more complete description of the
impact of transit strategies, which not only reduce light-duty VMT and emissions, but also
increase transit vehicle VMT and emissions.
Transit improvement strategies will result in increased transit service and increases in VMT,
trips, and emissions for transit vehicles. Because TRIMMS is limited in its ability to estimate the
corresponding increase in transit trips for transit strategies, the impact on transit trips was
estimated off model using actual VMT from the National Transit Database (NTD) in 2013 for
each agency. These data were used to determine a corresponding increase in transit VMT for
all transit strategies that involve increased transit service (see discussion in Section 3 above).
Total fuel consumption and VMT for 2013 was obtained from the NTD for the following transit
agencies for each study agency:
MetroPlan Orlando:46
» Central Florida Regional Transportation Authority (LYNX)
ARC:
» Cobb County Department of Transportation Authority (CCT)
* Georgia Regional Transportation Authority (GRTA)
» Gwinnett County Board of Commissioners (GCT)
» Hall Area Transit (HAT)
* Metropolitan Atlanta Rapid Transit Authority (MARTA)
EWG:
* Madison County Transit District(MCT)
» Rock Island County Metropolitan Mass Transit District(MetroLink)
46 Although SunRail also operates within the MetroPlan Orlando region, it wasn't included in this analysis because there was no
data for SunRail in the NTD.
A-1 July 2016
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» Bi-State Development Agency of the Missouri-Illinois Metropolitan District, d.b.a.(St.
Louis) Metro(METRO)
To estimate transit VMT for these agencies in the year 2040, NTD VMT data was multiplied in
by the growth rate in transit VMT modeled in TRIMMS for each agency (MetroPlan Orlando =
2.69; ARC = 1.43; EWG = 1.05). Although the transit expansion strategies apply to only a
portion of the regional population, the entire transit system in each region is affected. So, the
growth factors were multiplied by the total NTD VMT for 2013 to estimate future BAU 2040
transit VMT for each agency.
The increase in transit VMT for each agency was calculated as follows:
MetroPlan Orlando: the 41% reduction in transit access time for Strategy 2 (25.5 min for
BAU and 15.0 min for strategy), represents a 70% increase in transit vehicles per hour (2.35
vehicles/hr for BAU and 4.0 veh/hr for strategy). 2040 transit VMT was multiplied by a 70%
increase to determine new VMT for the new transit vehicle service.
ARC: the 50% reduction in headways for Strategy 2, corresponding to an 18% decrease in
access time (23.95 min for BAU and 19.56 min for strategy), represents a 22.47% increase
in transit vehicles per hour (2.51 vehicles/hr for BAU and 3.07 veh/hr for strategy). 2040
transit VMT was multiplied by a 22.4% increase to determine new VMT for the new transit
vehicle service.
EWG: only light rail emissions were considered. This is because the strategy only includes
expansion of the light rail system. The expansion in light rail miles under this strategy is
354% (representing an increase from 61 miles to 275 miles), provided by EWG. 2045 transit
VMT was multiplied by a 354% increase to determine new VMT for the new transit vehicle
service.
Total transit VMT for each agency was split into two categories: buses and rail (electric). VMT
for rail was taken from the NTD; the portion of total VMT that is due to electric rail was
calculated for each agency (MetroPlan Orlando = 0%; ARC = 33.5%; EWG = 18.7%). The total
increase in transit VMT for each agency, as described above, was multiplied by these fractions
to determine rail VMT and bus VMT. The MOVES EFs presented above were applied to the
increase in bus VMT. For rail VMT increases, new electricity emission factors were derived and
applied. The electricity emission factors were calculated as follows:
1. The regional electricity emission factor in pounds CO2 per megawatt hour (MWh) was
obtained from the EPA eGRID database.47 The SERC South region (1,154.32
lbsCO2e/MWh) was used for ARC and the SERC Midwest region (1,719.68 lbsCO2e/MWh)
was used for EWG.
47 See: https://www.epa.qov/sites/production/files/2015-10/documents/eqrid2012 summarytables O.pdf
A-2 July 2016
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2. Total electricity use for rail from the NTD for 2013 was multiplied by the eGRID emission
factors to determine grams CCbe emitted for each agency.
3. Total rail CCbe emissions were divided by total rail miles to calculate an electricity emission
factor in grams per mile for each agency (2,275.8 gCCbe/mile for ARC and 4,213.3
gCO2e/mileforEWG).
4. These emission factors were multiplied by the percent reduction in transit bus emission
factors from MOVES to estimate a future year electricity EF incorporating increased fuel
efficiency.
5. The resulting CCbe emission factors for rail were then applied to the increase in rail VMT as
indicated above. Only CO26 emissions for rail was estimated; other pollutants were not
included.
Table A-1. ARC, EWG, and MetroPlan Orlando Transit Vehicle Percent VMT
and Emissions Changes
Agency
ARC
EWG
MetroPlan
Orlando
Strategy
Transit Frequency
Improvement
Transit Buildout
Transit Improvement
Change in Regional
Transit VMT
22.4%
66.2%
70.0%
GHGs (C02
equivalent) kg/day
258,517
514,674
160,857
PM2.5
(kg/day)
128
0
133
NOx
(kg/day)
4
0
4
VOC
(kg/day)
9
0
8
A.2. Travel and Emissions Changes by Region and Scenario
As discussed previously, MOVES was run in inventory mode for each of the regions to produce
activity-weighted, average emission factors each the region. These emission factors were
coupled with the predicted changes in activity to produce net emissions changes by scenario for
each region. The corresponding relative reductions are presented for each region in the body of
the report.
All available MOVES vehicle and fuel types were included in the simulations. In characterizing
the vehicle types used in the TRIMMS model, the following were included:
Passenger Car
Passenger Truck
Transit Bus
Light Commercial Truck
Motorcycle
A-3
July 2016
-------
All available fuels were included. The available list includes E-85, Gasoline, Diesel, LPG, CNG,
and electricity. Note, however, that not all fuel-vehicle combinations are included in the MOVES
model, and thus would not be included in the final results.
For all specific emissions changes in each region, see Tables B-3, C-4, and D-4.
A3. Additional Land Use Calculation Details
For the Multivariate approach analysis used to estimate VMT and emission reductions for the
land use strategies, elasticity values from Ewing and Cervero (2010) were used to determine
total percent VMT change for each type of land use change.48 This study provides elasticities
that 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 A-2.
Table A-2. Ewing and Cervero (2010) Elasticity Values for the
Multivariate Land Use Analysis
"D" Category
Density
Diversity
Design
Destinations
Distance to Transit
Variable
Household/ population density
Job density
Land use mix (entropy)
Jobs-housing balance
Intersection/ street density
% 4-way intersections
Job access by auto
Job access by transit
Distance to downtown
Distance to nearest transit stop
Elasticity Value
-0.04
0
-0.09
-0.02
-0.12
-0.12
-0.2
-0.05
-0.22
0.05
1 Ewing and Cervero, "Travel and the Built Environment: A Meta-Analysis", Journal of the American Planning Association, 2010
A-4 July 2016
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Appendix B. MetroPlan Orlando
B.I. Regional Comparison of VMT Reductions
The MetroPlan Orlando strategies were matched as closely as possible with the strategies in
the 2010 national study for San Diego, California in Cluster 2. The results of this comparison, in
terms of percent VMT reduction, are presented in Table B-1 below.
Cluster 2 represents regions with a population greater than 2.9 million (the MetroPlan Orlando
regional 2040 population is 3.3 million) and a transit share of 9% or less (MetroPlan Orlando
2040 transit share is 2.9%). The representative area for Cluster 2 used in this comparison is
San Diego, which had a future population of 4.0 million in 2030 and a transit share of 1.6% in
the national study.
Table B-1. MetroPlan Orlando Comparison of Regional VMT Reductions with Cluster 2
Strategy
% Auto Regional VMT Reduction - % Auto Regional VMT Reduction - 2010
MetroPlan Orlando National Study, Cluster 2, San Diego
Strategy 1 : Expanded TDM
Strategy 2: Enhanced Transit
Strategy 3: Road Pricing
Strategy 4: University Transit Pass
0.65%
0.27%
3.83%
1.32%
1.01%
0.18%
3.94%
1.16%
The values in the table represent the regional VMT reductions for each individual strategy
component, not for the cumulative scenarios, to ensure comparability between strategies from
TEAM and strategies from the 2010 national study. As shown in the table, all four strategies are
very similar to Cluster 2 in terms of regional VMT reductions. This validates the approach taken
for MetroPlan Orlando. Auto VMT reductions for expanded transportation demand management
(TDM) programs are similar to Cluster 2: 0.65% versus 1.01%. The enhanced transit VMT
reductions are also reasonably close: 0.27% compared to 0.18%. Road pricing has a very
similar percent VMT reduction to Cluster 2 at 3.83% versus 3.94%, and the University transit
pass strategy reduces VMT by 1.32% while the Cluster 2 transit fare strategy reduces VMT by a
similar 1.16%.
B.2, Emission Factors and Detailed Results
Table B-2 presents the emission factors used for MetroPlan Orlando for the base year (2009)
and the future year (2040) for both vehicle travel and vehicle starts.
B-1
July 2016
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Table B-2. Emission Factors for MetroPlan Orlando
Categories
Grams per mile
Grams per start
Base Year (2009) Future Year (2040) Base Year (2009) Future Year (2040)
Auto (Motorcycles+Passenger Car
GHGs (C02-equivalent)
NOx
PM2.5
VOCs
s+Passenger Trucks]
454.46
0.87
0.01
0.23
239.33
0.03
0.01
0.01
114.60
1.60
0.01
1.98
69.11
0.14
0.004
0.16
Vanpool (Passenger Trucks+Light Duty Trucks)
GHGs (C02-equivalent)
NOx
PM2.5
VOCs
554.30
1.16
0.02
0.30
286.15
0.04
0.01
0.01
138.91
2.07
0.02
2.57
79.68
0.16
0.005
0.17
Transit Vehicles (buses)
GHGs (C02-equivalent)
NOx
PM2.5
VOCs
1,491.17
14.01
0.73
1.00
1,344.28
1.12
0.03
0.06
115.60
0.04
0.02
0.17
120.26
0.01
0.003
0.07
Table B-3 presents the calculated total emission and travel changes for each of the main
pollutants for MetroPlan Orlando.
Table B-3. MetroPlan Orlando Comparison of VMT Reductions and Emission Changes by
Scenario
Resulting Travel (VMT/day) and Emissions Changes for Selected Pollutants (kg/day), relative to BAD or Baseline Level,
by Scenario
Travel and Emissions Changes-2040 BAU to Travel and Emissions Changes-2009 Baseline to 2040
Scenario
Scenario 1 : Expanded TDM
Scenario 2: Scenario 1 +
Enhanced Transit
Scenario 3: Scenario 2 +
Road Pricing
Scenario 4: Scenario 3 +
University Transit Pass
Light-Duty
VMT
-502,039
-708,069
-3,649,898
-4,666,465
2040 Set
GHGs (C02
equivalent)
-123,515
-174,236
-898,018
-1,148,238
mario
PM2.5
-4
-6
-31
-39
NOx
-22
-30
-157
-201
voc
-13
-18
-91
-117
Light-Duty
VMT
29,581,583
29,375,554
26,433,725
25,417,158
Scenario1
GHGs (C02 DM
i L\ PM25
equivalent)
-2,969,454 -81
-3,020,175
-3,743,956
-3,994,177
-83
-107
-116
NOx
-44,434
-44,443
-44,570
-44,613
VOC
-17,924
-17,929
-18,003
-18,029
1 Emissions decrease even though VMT increase because emissions per mile are much lower in 2040 than in 2009.
B-2
July 2016
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Appendix C. Atlanta Regional Commission
Appendix C presents supplemental information about the TDM strategy analysis for ARC, along
with a regional comparison of VMT reductions to the 2010 national study.
C.I. TDM Strategy Analysis Supplemental Information
ARC provided actual trip costs and subsidy amounts to support a more rigorous TDM analysis;
therefore, the agency was interested in a rebalancing analysis using TRIMMS to account for
trips that shift to other modes. The rebalancing process is discussed below.
The steps below outline a rebalancing analysis attempted using the values in Table C-1,
provided by ARC. The rebalancing analysis overestimated the number of drive-alone trips
reduced, and therefore this analysis was unsuccessful (the radio button feature in TRIMMS was
used instead). The information is provide here only for reference.
1. Transit/rideshare cross-elasticities in TRIMMS were set to zero to remove the effect of
shifting trips from transit to rideshare.
2. Trips were rebalanced by redistributing the increase in rideshare/transit trips to other modes
based on the BAU mode shares for ARC.
3. The results were compared using the TRIMMS radio buttons for trip subsides (rideshare,
vanpool, transit, and cycling). The rebalancing analysis showed a decrease in drive-alone
trips of 182,000 and an increase in rideshare trips of 209,000, while the TRIMMS radio
button approach showed a decrease in drive-alone trips of 31,000 and an increase in
rideshare trips of 17,000.
Table C-1. ARC Transit Subsidies
Mode
Auto- Drive Alone
Auto- Rideshare
Vanpool
Public Transport
Cycling
Monthly
Subsidy
$0
$48
$60
$75
$20
Average Trip Cost
-BAU
$1.12
$0.99
$4.50
$2.34
$0.00
Average Trip Cost -
Scenario
$1.12
$0.00
$3.14
$0.64
$0.00
C-1
July 2016
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C.2. Regional Comparison of VMT Reductions
The ARC strategies were matched as closely as possible with the strategies in the 2010
national study for San Diego, California in Cluster 2. The results of this comparison, in terms of
percent VMT reduction, are presented in Table C-2 below.
Cluster 2 represents regions with a population greater than 2.9 million (the ARC regional 2040
population is 4.4 million) and a transit share of 9% or less (ARC 2040 transit share is 2.8%). The
representative area for Cluster 2 used in this comparison is San Diego, which had a future
population of 4.0 million in 2030 and a transit share of 1.6% in the national study.
The values in the table represent regional VMT reductions for each individual strategy
component, not for the cumulative scenarios, to ensure comparability between strategies from
TEAM and strategies from the 2010 national study.
As shown in Table C-2, regional auto VMT reductions for ridesharing and TDM programs are
similar to the Cluster analysis, 0.7% versus 1.0%. The transit improvement VMT reductions are
essentially identical at 0.18%. Parking pricing has a larger effect for ARC than for the Cluster
analysis (2% versus 0.6%), which is likely because San Diego had a relatively high baseline
parking charge ($5.94 for drive-alone and rideshare) compared to the baseline parking charge
for ARC ($4.32 for drive-alone and $1.57 for rideshare).
Table C-2. ARC Comparison of Regional VMT Reductions for Regional Populations
Strategy
Strategy 1: Expand Ridesharing and TDM
programs
% Auto Regional VMT Reduction - % Auto Regional VMT Reduction - 2010
ARC National Study, Cluster 2, San Diego
0.69%
1.01C
Strategy 2: Transit Frequency
Improvement
0.18%
0.18%
Strategy 3: Parking Pricing
Strategy 4: Smart Growth Land Use
1.98%
5.97% (Neighborhood)
6.43% (Multivariate)
0.63%
2.66%
The reduction in VMT for the land use strategy varies widely between ARC and the Cluster: 6-
6.4% reduction for ARC compared to a 2.7% reduction for the Cluster. The large variation in
VMT reductions for the land use strategy may reflect the new land use analysis methods used in
this case study. Results are not directly comparable to the results from the 2010 analysis
because a different methodology was used. The 2010 land use analysis was done in TRIMMS
2.0, while this land use analysis was performed outside of TRIMMS using the Neighborhood
and Multivariate approaches.
C-2
July 2016
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C.3. Emission Factors and Detailed Results
Table C-3 presents the emission factors used for ARC for the base year (2009) and the future
year (2040) for both vehicle travel and vehicle starts.
Categories
Table C-3. Emission Factors for ARC
Grams per mile
Grams per start
Base Year (2010) Future Year (2040) Base Year (2010) Future Year (2040)
Auto (Motorcycles+Passenger Cars+Passenger Trucks)
GHGs (C02-equivalent)
NOx
PM2.5
VOCs
391.73
0.34
0.01
0.07
230.24
0.02
0.01
0.01
115.72
0.94
0.01
1.14
81.43
0.16
0.005
0.20
Vanpool (Passenger Trucks+Light Duty Trucks)
GHGs (C02-equivalent)
NOx
PM2.5
VOCs
Transit Vehicles (buses)
GHGs (C02-equivalent)
NOx
PM2.5
VOCs
462.08
0.53
0.02
0.11
1,301.93
6.52
0.34
0.57
277.35
0.03
0.01
0.01
1,238.12
1.19
0.04
0.07
136.39
1.39
0.01
1.59
131.78
0.02
0.01
0.21
93.26
0.17
0.01
0.19
129.89
0.01
0.003
0.12
C-3
July 2016
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Table C-4 presents the calculated total emission and travel changes for each of the main pollutants for ARC.Table C-4. ARC
Comparison of VMT Reductions and Emission Changes by Scenario
Table C-5. ARC Comparison of VMT Reductions and Emission Changes by Scenario
Resulting Travel (VMT/day) and Emissions Changes for Selected Pollutants (kg/day), relative to BAD or Baseline Level, by Scenario
Travel and Emissions Changes-2040 BAU to 2040 Scenario
Scenario Light.Duty GHGs(C02
VMT equivalent) 25 NUX VUU
Scenario 1 : Expand
Ridesharing and TDM
programs
Scenario 2: Scenario 1 +
Transit Improvement and
Promotion
Scenario 3: Scenario 2 +
Parking Pricing
Scenario 4: Scenario 3 + Smart
Growth Land Use
(Neighborhood Approach)
Scenario 4: Scenario 3 + Smart
Growth Land Use (Multivariate
Approach)
-867,544
-1,093,477
-3,602,286
-11,147,396
-11,728,876
-209,507
-264,204
-871,534
-2,698,504
-2,839,303
-9
-11
-38
-117
-124
-39
^9
-163
-508
-535
-29
-37
-126
-394
-414
Travel and Emissions Changes-2015 Baseline to 2040 Scenario1
Light-Duty GHGs(C02 pM25 NQ VQC
VMT equivalent) KM" NUX VUU
29,753,678
29,527,744
27,018,936
19,473,826
18,892,346
-8,710,582
-8,765,278
-9,372,608
-11,199,579
-11,340,378
-121
-123
-149
-229
-235
-39,158
-39,169
-39,283
-39,628
-39,655
-18,180
-18,188
-18,277
-18,545
-18,565
1 Emissions decrease even though VMT increase because emissions per mile are much lower in 2040 than in 2015 (see Table C-3).
C-4
July 2016
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Appendix D. East-West Gateway
Appendix D presents supplemental information about the bicycle and pedestrian strategy
analysis for EWG, along with a regional comparison of VMT reductions to the 2010 national
study.
D.I. Bicycle and Pedestrian Strategy Analysis Supplemental
Information
Table D-1 presents the mode shares associated with Scenario 3 for the bicycle and pedestrian
network. The table shows the BAU mode shares, the mode shares after the bike lane expansion
only, and the mode shares after the bike lane expansion and increased sidewalk coverage
combined (for the year 2040). As shown in the table, the redistribution of trips supports an
increase in bike and walk trips and a decrease in drive-alone trips.
Table D-1. EWC Bicycle and Pedestrian Analysis
Mode
Auto-drive alone
Auto-rideshare
Vanpool
Public transit
Cycling
Walking
Other
BAU Mode
Share
52.41%
38.00%
0.00%
3.43%
0.34%
5.55%
0.27%
Mode Share after Bike
Lane Expansion
52.21%
37.86%
0.00%
3.42%
0.73%
5.52%
0.27%
Mode Share after Bike Lane Expansion
and Increased Sidewalk Coverage
51.99%
37.70%
0.00%
3.41%
0.72%
5.92%
0.27%
D.2. Regional Comparison of VMT Reductions
The EWG strategies were matched as closely as possible with the strategies in the 2010
national study for Sacramento, California in Cluster 4. The results of this comparison, in terms of
percent VMT reduction, are presented in Table D-2 below.
The comparison for EWG is Cluster 4 based on comparable population and transit share.
Cluster 4 represents regions with a population of 1.5-2.9 million (EWG's regional 2045
population is 2.7 million) and a transit share of 4% or less (EWG's 2045 transit share is 3.4%).
The representative area for Cluster 4 used in this comparison is Sacramento, which had a future
population of 1.9 million in 2030 and a transit share of 3.3% in the national study.
D-1
July 2016
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Table D-2. EWG Comparison of VMT Reductions for Regional Populations
Strategy
% Auto VMT Reduction - EWG
% Auto VMT Reduction - 2010 National
Study, Cluster 4, Sacramento
Strategy 1 : Regional Transit Oriented
Development (TOD)
Strategy 2: Workforce Housing Balance
Strategy 3: Bike / Red Network
Strategy 4: Transit Expansion
0.1 6% (Neighborhood)
0.54% (Multivariate)
2.26% (Neighborhood)
1.28% (Multivariate)
0.09%
0.90%
1.86%
1.86%
N/A
0.15%
The values in the table represent the regional VMT reductions for each individual strategy
component, not for the cumulative scenarios, to ensure comparability between strategies from
TEAM and strategies from the 2010 national study. As shown in Table D-2, regional auto VMT
reductions populations for land use programs vary compared to the Cluster analysis; for TOD,
EWG's strategies reduce auto VMT by 0.16%-0.54%, while the Workforce Housing Balance
reduces auto VMT by 1.28%-2.26%, versus 1.86% for Sacramento. The Workforce Housing
Balance strategy is much closer to the Sacramento regional VMT reduction, likely because this
strategy applies to most of the EWG region (87% of the regional population is affected) while
the TOD strategy applies to a smaller part of the region (60% of the regional population is
affected). The Sacramento land use strategy applies to the entire region. In addition, the large
variation in VMT reductions for the land use strategy reflects the new land use analysis methods
used in this case study. Results are not directly comparable to the results from the 2010
analysis because a different methodology was used. The 2010 land use analysis was done in
TRIMMS, while this land use analysis was performed outside of TRIMMS using the
Neighborhood and Multivariate approaches described in Section 2.3 below.
The transit expansion VMT reduction for EWG is much higher than the Sacramento reduction:
0.9% for EWG versus 0.15% for Sacramento. This is likely because the EWG strategy includes
an aggressive reduction in headways of 25%, while the Sacramento strategy is much less
aggressive. TRIMMS 2.0 was used for the Sacramento analysis and the 3.0 version for the
EWG analysis. An analysis to determine the differences in results between these two versions
of TRIMMS was not conducted, so it is unknown what affect the different models have on
results.
There was no comparable Cluster strategy for bike/pedestrian measures. The comparison
between EWG and Sacramento is useful for EPA to consider with respect to analytical
approaches and improvements in TEAM over time.
D-2
July 2016
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D.3. Emission Factors and Detailed Results
Table D-3 presents the emission factors used for EWG for the base year (2009) and the future
year (2040) for both vehicle travel and vehicle starts.
Table D-3. Emission Factors for EWG
Grams per mile
Grams per start
Categories
Base Year (2015) Future Year (2045) Base Year (2010)
Vanpool (Passenger Trucks+Light Duty Trucks)
Future Year
(2040)
Auto (Motorcycles+Passenger C
GHGs (C02-equivalent)
NOx
PM2.5
VOCs
ars+Passenger True
404.83
0.39
0.01
0.08
ks)
220.80
0.03
0.01
0.01
133.42
0.97
0.02
1.36
95.33
0.18
0.01
0.30
GHGs (C02-equivalent)
NOx
PM2.5
VOCs
478.02
0.58
0.02
0.12
263.51
0.04
0.01
0.01
155.60
1.27
0.02
1.72
109.28
0.20
0.01
0.30
Transit Vehicles (buses)
GHGs (C02-equivalent)
NOx
PM2.5
VOCs
1,436.48
12.93
0.72
0.96
1,306.60
1.31
0.04
0.05
118.91
0.04
0.02
0.48
115.64
0.01
0.003
0.25
D-3
July 2016
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Table D-4 presents the calculated total emission and travel changes for each of the main pollutants for EWG.Table D-4. EWG
Comparison of VMT Reductions and Emission Changes by Scenario.
Resulting Travel (VMT/day) and Emissions Changes for Selected Pollutants (kg/day), relative to BAD or Baseline Level, by Scenario
Scenario
Land Use
Travel and Emissions Changes - 2045 BAD to
2045 Scenario
Travel and Emissions Changes - 2015 Baseline to
2045 Scenario1
Scenario 1 : Land
Use: Regional TOD
(3 counties)
Scenario 2: Scenario
1 + Land Use:
Workforce-Housing
Balance (5 counties)
Scenario 3: Scenario
2 + Bike/Ped Network
(5 counties)
Scenario 4: Scenario
3 + Transit Buildout
(5 counties)
Approach
Neighborhood
Multivariate
Neighborhood
Multivariate
Neighborhood
Multivariate
Neighborhood
Multivariate
Light-Duty
VMT
-121,121
-400,252
-1,584,406
-1,231,322
-1,640,332
-1,287,248
-1,890,772
-1,537,687
GHGs (C02
equivalent)
-27,859
-92,060
-364,423
-283,212
-380,428
-299,216
-438,030
-356,819
PM2.5
-1
-3
-13
-10
-14
-11
-16
-13
NOx
-6
-21
-81
-63
-90
-72
-103
-85
voc
-5
-15
-59
-46
-71
-58
-80
-67
Light-Duty
VMT
9,353,096
9,073,966
7,889,811
8,242,895
7,833,885
8,186,969
7,583,445
7,936,530
GHGs (C02
equivalent)
-10,087,033
-10,151,235
-10,423,597
-10,342,386
-10,439,602
-10,358,390
-10,497,205
-10,415,993
PM2.5
-355
-357
-367
-364
-367
-364
-369
-366
NOx
-28,166
-28,181
-28,241
-28,223
-28,250
-28,232
-28,263
-28,245
VOC
-11,402
-11,412
-11,456
-11,443
-11,468
-11,455
-11,477
-11,464
Emissions decrease even though VMT increase because emissions per mile are much lower in 2040 than in 2015 (see Table D-3).
D-4
July 2016
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