Embed Size (px)
Citation preview
Rapid Policy Assessment Training
October 2015
Maren Outwater and Erich Rentz
Section Schedule
1. Background 20 minutes
2. Scenario Planning 30 minutes
3. Urban Form 30 minutes
4. Accessibility 20 minutes
5. Induced Demand and Feedback 20 minutes
6. Performance Metrics 60 minutes
Overview of Presentation
Background
Learning Objectives
1. What is scenario planning?2. How does it help me evaluate
land use and transportation policies?
3. What are strategic models and how can they help?
4. How do I communicate results?
RPAT is a strategic model that conducts scenario planning to evaluate land use and transportation policies.
• What are the key components?
• What are factors that drive their development?
A group of participants who are engaged in a data driven communication process that seeks to:
– Ask questions – Develop answers– Come to agreement on common
problems and solutions
A Definition of Scenario Planning
Land Use Scenarios
Measures
• Proportion of Population and Employment by Place Type
• Population and Employment Densities by Place Type
• Built Environment– Location of population and employment by place type
• Demand Management Policies– Vanpool– Telecommuting
Area Type
DevelopmentType Urban Core Close in
Community Suburban Rural
Residential
Employment
Mixed-Use Transit Oriented
Development Rural/
Greenfield
- Ridesharing- Transit pass programs
Travel Demand
• Changes in population demographics
• Changes in personal income
• Changes in firm size or industry
• Auto and light truck proportions by year
• Induced demand –short term impacts
Transportation Policies
• Vehicle miles traveled charges
• Parking pricing programs
• Intelligent transportation system strategies for freeways and arterials
Transportation Scenarios
Transportation Supply
• Amount of regional transit service
• Amount of freeway and arterial capacity
• Support strategic planning efforts• Consider many possible scenarios • Combines higher level analysis of the transportation
supply with individual characteristics of travel demand, built environment, and policies– Growth by place type– Households (persons by age and income)– Firms (employees and industry)– Income growth– Truck and bus VMT– Accessibility– Congestion– Induced growth– Policy benefits
• Easy to apply and run quickly
• Bridge the Gap
Strategic Models for Integrated Land Use and Transportation
Regional Visioning
Strategic Models
TransportationPlans
Communicating Results
• Evaluate scenarios across a range of performance metrics– Community Impacts– Travel Impacts– Environmental and Energy Impacts– Financial and Economic Impacts– Location Impacts
• Compare multiple scenarios at a time graphically to quickly assess results
Comparison of Daily Vehicle Mile Traveled by Scenario
• Disaggregate policy model
• Predicts transportation impacts of growth patterns and transportation policies
Rapid Policy Assessment Tool Process
RPAT Model Components
• Captures individual household and firm characteristics
• Captures interactions between policies
• Spatial results are by place type
1. Household Synthesis2. Firm Synthesis3. Urban Form4. Accessibility5. Vehicles6. Auto Travel Demand7. Truck and Bus Travel Demand8. Congestion9. Induced Demand10.Policy Benefits
Household and Firm Synthesis
• Households– Persons by Age
from Census data– HH Income
from Bureau of Economic Analysis data
• Firms– Employees– Industry
from County Business Pattern data
• Data can be updated from local sources
• Predicts Place Types– Area Types (4)– Development
Patterns (4)• Based on Households
with – Working age persons– Children– Seniors
• Adjusted to fit regional totals
Urban Form Models
Inputs
• Freeway Lane Miles• Transit Revenue Miles
(annual bus and rail revenue miles per capita)
Outputs
• Freeway Lane Miles per Person
• Transit Revenue Miles per Person
Accessibility
• Relates both transit and auto accessibility to travel behavior.
• Used in vehicle ownership models and vehicle miles traveled models.
• Predicts number of vehicle for each household– Autos– Bikes– Light Trucks
• Predicts vehicles by age/ fuel efficiency• Based on
– Number of persons of driving age– Elderly persons– Household income– Population density– Freeway and transit supply– Urban mixed-use area
Vehicle Models
Travel Demand Models
• Truck VMT is based on changes in regional household income
• Bus VMT is calculated from bus revenue miles
• Predicts Vehicle Miles Traveled for each Household– Autos and Light Trucks– Heavy Trucks– Buses and Passenger Rail
• Based on– Household income– Population density– Number of household vehicles– Freeway and transit supply– Driving age persons in household– Elderly persons in household– Mixed use development
Three aspects are represented:1. VMT is allocated to freeways and
arterials by congestion level2. Speeds and fuel economies are
calculated for freeways and arterials3. Congestion in local areas is
estimated from increased activity
Congestion is part of a feedback loop between changes in each scenario and induced growth
Accounting for Congestion
• Definition– Additional demand resulting from adding
transportation supply• Short Term – Induced Demand
– Changes in road supply, function of speed– Potential mode and route shift
• Long Term – Induced Growth– Changes in growth patterns resulting from changes
in travel patterns
Induced Demand
Direct Travel Impacts• Daily VMT• Daily Vehicle Trips• Daily Transit Trips• Peak Travel Speeds
by Facility Type• Vehicle Hours of
Travel• Vehicle Hours of
DelayCommunity Impacts• Public Health
Impacts and Costs• Equity Impacts
Environment and Energy Impacts• Fuel Consumption• Greenhouse Gas
Emissions• Criteria EmissionsFinancial and Economic Impacts• Regional Highway
Infrastructure Costs• Regional Transit
Infrastructure and Operating Costs
• Annual Traveler Cost
Performance Metrics
Land Market and Location Impacts
• Regional Accessibility
Scenario Planning
Scenario Planning Framework
• Scenario planning provides a framework for developing a shared vision for the future by analyzing various forces that affect growth– Health– Transportation– Economics– Environmental– Land use
• Helps agencies engage in a more informed and strategic transportation decision-making process.
• Used in conjunction with a charrette or chips games, can help stakeholders better understand and visualize future transportation and land use patterns.
• Scenario planning software programs can also help develop and assess scenarios, visualize the differences between alternatives, and encourage stakeholder participation.
Benefits of Scenario Planning
Scenario Planning at FHWA/FTA
• Workshops and Peer Exchanges• Webinar series to promote Scenario
Planning• Innovative Research• Publications, Case Studies
– Scenario Planning Guidebook•Six-Phases
– State of the Practice Report•Survey of MPOs on current practices
Needs for a Data Driven Process
• Understand critical decision points in the transportation planning process and how land use affects demand for transportation capacity.
• Represent the dynamics and inter-relationships of land use strategies with the performance of a transportation investment.
• Facilitate improved communication, interaction, and partnerships between decision-makers and planners in transportation and land use arenas.
Land Use
Transportation
• Process maps for State DOTs and MPOs
• Areas where smart growth levers can be used
– Policy Studies– Planning studies– Programming– Implementation
Decision Points for Smart Growth in Planning Process
Practitioner Information Needs Survey
• Develop a tool that can be used by land use and transportation planners to provide opportunities for interaction on common goals
• Most agencies are interested in scenario planning as a strategy for evaluating land use policies
• Many agencies need coordination, cooperation, and communication with local governments on land use policy, since land use regulations are governed by local governments
• Agencies also want to understand– Induced demand– Travel demand management– Urban form– Congestion reduction– Outcomes and performance
Topic Well-EstablishedRelationships Gaps in Research
Built environment impact on peak auto demand
Impact on daily travel Impact by time of day
Mobility by mode and purpose Impact on daily travel Impact by trip purpose
Induced traffic and induced growth
Capacity expansion on an expanded facility
Route shifts, time of day shifts, mode shifts,induced trips, new destinations, growth shifts on the network, effects of operational improvements, land use plans
Relationship between smart growth and congestion
Localized effects Macro-level or regional effects
Smart growth and freight Freight is necessary for population centers
Impacts of loading docks, truck routing, full-cost pricing, freight facilities and crossings, inter-firm cooperation, stakeholder communication
Gaps in Research
Breakout #1: Applying Scenario Planning with RPAT
Step #1: Identify Your Scenarios
6 DCHC Tested Scenarios:1. 2040 MTP - Baseline2. E+C: 18% Reduction of Roadway Construction3. Hwy: 9.8% Increase of Roadway Construction4. TRN: 276% Rail Mile Increase, 12% Bus mile
Reduction and 9.4% Reduction of roadway construction
5. Shift 15% Growth to Dense Areas6. Shift 15% Growth to Dense Areas with 15% lane mile
ITS treatment
Sample Scenarios
6 DCHC Tested Scenarios:1. 2040 MTP - Baseline2. E+C: 18% Reduction of Roadway Construction3. Hwy: 9.8% Increase of Roadway Construction4. TRN: 276% Rail Mile Increase, 12% Bus mile
Reduction and 9.4% Reduction of roadway construction
5. Shift 15% Growth to Dense Areas6. Shift 15% Growth to Dense Areas with 15% lane mile
ITS treatment
Sample Scenarios
Step #2: Create New Scenario in RPAT
Create New Scenario in RPAT
View Existing Scenarios
Existing Scenarios
Add a New Scenario
Click to add a new scenario
Create New Scenario
Add Scenario Name
Enter a name for your new scenario
Copy Settings from Existing Scenario
Choose an existing scenario to copy from
Create New Scenario
View List of Scenarios
Step #3: Update Scenario Input Variables
Update Scenario Input Variables
Click “Scenarios” to adjust your new scenario
Update Scenario Input Variables
Select New Scenario
Use the “Active Scenario” menu to select your new scenario
View Parameters
The “Parameters” are inherited from the base year; developed from extensive background research, adjust with caution.
Update Scenario Input Variables
The “Scenario Inputs” are inherited from the base year; adjust to create your scenario!
Update Scenario Input Variables
Adjust Transit Supply
First, adjust population and jobs by place type
Adjust Transit Supply
Review Documentation
Documentation for the input table will open automatically
Adjust Bus and Rail Supply
Variables of interest to be adjusted
Update Scenario Input Variables
Highlight variables and update their values
Make the following changes:• Sub R – PopGrowth =
0.15• Sub E – EmpGrowth =
0.05• UC R – PopGrowth =
0.25• UC E – EmpGrowth =
0.25
Save Scenario Inputs
Remember to save!
Update Scenario Input Variables
Adjust ITS Policy
Now adjust the ITS policy
Adjust ITS Policy
Adjust ITS Policy
Adjust ITS Policy
Variable of interest to be adjusted
Adjust ITS Policy
Update Scenario Input Variables
Remember that we do not need to update the
‘% Growth…’ variable because it was inherited from S6 when we created
this scenario
Step #4: Run the Scenario
Run the Scenario
Click to navigate to the page to run the model for the updated scenario
Run the Scenario
Run the Scenario
Click to run the model for the updated scenario
Run the Scenario
Run the Scenario
Congratulations! You have setup and run a scenario in RPAT!
Urban Form
Place Types
• Urban Core– High-density mixed use places– High job to housing ratios– Well connected streets– High levels of pedestrian activity
• Close in Community– Primarily housing– Some mixed use centers and arterial corridors– Adjacent to downtown areas
• Suburban – Low level of integration of housing with jobs, retail,
and services– Poorly connected street networks– Low levels of transit service– Limited pedestrian facilities
• Rural– Widely dispersed towns and residential uses– Little or no transit– Limited pedestrian facilities
A place type refers to all of the characteristics of a developed area including the types of uses included, the mix of uses, the density, and the intensity of uses.
• Residential– Primarily housing– Limited employment and retail opportunities– All place types except rural
• Commercial– Focused on employment– Limited retail and residential– All place types except rural
• Mixed-Use– Mix of residential, commercial, and retail uses– Found in Close in Communities and Urban Core
place types• Transit Oriented Development (TOD)
– Greater access to transit– All place types except rural
Greenfields are undeveloped land in a rural area either used for agriculture, landscape design, or left to evolve naturally. These areas of land are properties being considered for urban development.
Development Patterns
• Smart Growth Transect– Thomas Comitta
Associates, 2010• Caltrans Mobility
Handbook– 2010
• Reconnecting America– Center for Transit-
Oriented Development, 2010
Urban Form Sources
• Percent Growth in each Place Type is developed to achieve growth targets.– This evaluation is
outside RPAT.
Building a Land Use Scenario
Area Type
DevelopmentType Urban Core Close in
Community Suburban Rural
Residential
Employment
Mixed-Use Transit Oriented
Development Rural/
Greenfield
• More compact development would have higher increases in both population and employment in the urban core and close in communities and no development in suburban and rural areas.
• Trend development would have higher increases in residential suburban areas, assuming this was the trend for a particular region.
• Households are allocated based on – 1 person of working age– 2+ people of working age– With children– All persons 65+ years old– Data from National Household Travel Survey (NHTS)
• Firms are allocated randomly– Regional data may be useful to identify these
relationships
Urban Form Models
• Allocates households and firms individually to a place type until control totals are matched.
Urban Form Model Process
Step 1 and 2Household and
Firm Models
Step 3Urban Form
Models
Total Households and Firms by Place
Type
% Growth by Place
Type
Individual Households and Firms by Place
Type
Model Component
Data Input
Model Output
Urban Form Effects on Vehicle Ownership
Urban Area
Urban Mixed-Use
Area
Pop-ulationDensity
House-hold
Income
Elderly Pop-
ulations
Transit Revenue
Miles
Freeway Lane Miles
Urban Area Urban Mixed-Use Area
Population Density Household Income Elderly Populations Transit Revenue Miles
Freeway Lane Miles
• Some effects are interacted with other variables to include the effects from a combination of variables.
Urban Form Effects on Vehicle Ownership
Vehicle Age Model
Household Income
Population Data
Zero Vehicle Models
Highway and Transit
Supply
Model Component
Data Input
Model OutputVehicles by Age
Vehicle Type Model
More Drivers than Vehicles
Models
Equal Drivers and Vehicles
Models
Less Drivers than Vehicles
Models
Urban Form Data
Vehicles per Household
Light Truck Proportions
Non-Motorized Vehicle Model
Non-Motorized Vehicles
Fuel Efficiency
• Predicts the change in travel for each household due to changes in urban form.
Urban Form Effects on Travel
Category Urban Form Description
Elasticity for Change in
VMT
Vehicle trip decrease
Transit trip increase
Density Household/Population Density -0.04 -0.043 0.07
Diversity Land Use Mix (entropy) -0.09 -0.051 0.12
Design Intersection/Street Density -0.12 -0.031 0.23
Distance to Transit
Distance to Nearest Transit Stop -0.05 -0.036 0.29
Case Study #1: Oregon Department of Transportation
Accessibility and Travel Impacts
• Definition– Amount of bus and electrified rail and highway
supply• Allocated to each household
– Relative to demographics and urban form• Affects household decisions
– Vehicle ownership– Travel demand
Accessibility
• Supply used in vehicle and travel models
Transportation Supply
Step 1 and 2Household and
Firm Models
Step 3Urban Form
Models
Step 4Accessibility
Models
Highway and Transit Supply
Step 5Vehicle Models
Step 6 and 7Travel Demand
Models
Step 8Congestion
% Increase in Highway and
Transit Supply
Step 9 and 10Policy Adjusted Travel Demand
Feedback for Policy Benefits
Feedback for Induced
Growth and Travel
Scenario Input
Model Component
Data Input
Feedback Loop
• Transit accessibility– Transit revenue miles allocated to each household
• Auto accessibility– Freeway lane miles allocated to each household
Accessibility Process
Transit Revenue Miles Interacted with
1. Household Income2. Population Density3. Elderly Populations4. Freeway Lane Miles5. Urban Areas6. Urban Mixed Use
Areas
Freeway Lane Miles Interacted with
1. Population Density2. Elderly Populations3. Transit Revenue
Miles4. Urban Mixed Use
Areas
Accessibility Measures
Each of these is also measured on a per capita basis
Vehicle Miles Traveled Model Process
1. Predicts households that don’t travel by car2. Predicts vehicle miles traveled for car travelers
– Higher incomes, more vehicles, more drivers, more freeways increase VMT
– No vehicles, higher population density, more transit service, living in urban mixed use areas decrease VMT
Auto Travel Demand Models
Household Budget Model
• Households make their travel decisions within money and time budget constraints– Travel within budget is less sensitive to
changes in price– Travel exceeding budget is very sensitive to
changes in price
• Household budgets are assumed at 10% of income– Budget cost per mile estimated from base
average miles traveled
• Allows pricing strategies to be tested for each household
Household budgeting provides a necessary reality to making travel decisions. As fuel price increases, household travel will decrease at or above household budgets.
• Transit revenue miles increased to account for non-revenue service miles (12%)
Assumptions can be adjusted with local data sources
Transit and Truck Miles
• Heavy truck VMT is a function of growth in regional income
Induced Demand and Congestion
• Induced Demand is – Calculated for changes in roadway supply– As a function of speed – Based on potential mode and route shifts
• Represents short term induced demand– Sensitivity for induced demand is based on
research from Robert Cervero (JAPA, 2003)• Long term effects are not included
– Research has not clearly quantified effects
Induced Demand
• Induced demand is included as a feedback from changes in supply and congestion levels
• Predicts changes in vehicle miles traveled
Induced Demand Process
• For regional facilities– By functional class (freeways and arterials)– By average speed (5 categories)
• For local facilities– By place type (4 types)– Intersection density– Node density
Congestion
Congestion on Regional Facilities
Congestion on Local Streets
• Increases in local traffic congestion due to concentration of activity– Compact mixed use areas can
manage traffic effectively with connected street grids
• Elasticities for Local Congestion
Variable Number of nodes
Weighted intersections
Vehicle MilesTraveled -0.380 -0.211
Vehicle HoursTraveled -1.05 -0.58
Percent of VMT on Arterials -1.295 -0.718
A Comparative Assessment of Travel Characteristics for NeotraditionalDesign by McNally and Ryan, 1993
Local Road Network Designs
• NeotraditionalNetwork Design
• Conventional Network Design
Diversity Rural Suburban Close In Community Urban Core
Mixed Use Not Applicable
18%increase No change No change
Homogenous 36%increase
36%increase No change 22%
decrease
Effects of Urban Form on Local Congestion
• Higher increases in rural and suburban places• Increases in urban core homogenous areas• Increases in suburban mixed use areas
Feedback for policy benefits and induced demand
Congestion Feedback Process
Performance Metrics
Direct Travel Impacts
Daily Vehicle Trips
Daily Transit Trips
Description Vehicle Trip Decrease
Density Household/Population Density -0.043Diversity Land Use Mix (entropy) -0.051Design Intersection/Street Density -0.031Regional Accessibility Job Accessibility By Auto -0.036Distance to Transit Distance to Nearest Transit Stop 0
Description Transit Trip Increase
Density Household/Population Density 0.07Diversity Land Use Mix (entropy) 0.12Design Intersection/Street Density 0.23Regional Accessibility Job Accessiblity By Auto 0Distance to Transit Distance to Nearest Transit Stop 0.29
More Direct Travel Impacts
• Daily Vehicle Miles Traveled- Light VMT for each household and place type- Regional VMT for heavy trucks and buses
• Peak Travel Speeds by Facility Class- VMT by speed bin and class (freeway, arterial, other)- Average speeds by class
• Vehicle Hours of Travel, Delay- Vehicle hours of travel at free flow- Vehicle hours of travel in congestion
Energy Impacts
• Fuel Consumption- Calculated from VMT and fuel economy, split into fuel
types- Calculated for light vehicles, heavy trucks, and buses
Year
Auto Proportion
Diesel
Auto Proportion
CNG
Lt. Truck Proportion
Diesel
Lt. Truck Proportion
CNG
Gas Proportion
Ethanol
Diesel Proportion Biodiesel
1990 0.007 0 0.04 0 0 0 1995 0.007 0 0.04 0 0 0 2000 0.007 0 0.04 0 0 0 2005 0.007 0 0.04 0 0.1 0.01 2010 0.007 0 0.04 0 0.1 0.05 2015 0.007 0 0.04 0 0.1 0.05 2020 0.007 0 0.04 0 0.1 0.05
Environmental Impacts
• Greenhouse Gas Emissions- Light vehicles calculated by household- Regional heavy truck and transit emissions
• Criteria Emissions- Emission rates from MOVES 2010a database - Volatile organic compounds (VOC)- Carbon monoxide (CO)- Oxides of nitrogen (NOx) - Sulfur dioxide (SO2)- Particulate matter (PM)
Fuel Type Carbon Intensity (gm per mega joule)
Ultra-low sulfur diesel (USLD) 77.19 Biodiesel 76.81 Reformulated gasoline (RFG) 75.65 CARBOB (gasoline formulated to be blended with ethanol) 75.65 Ethanol 74.88 Compressed natural gas (CNG) 62.14
Financial & Economic Impacts
• Regional Highway Infrastructure Costs FHWA Highway Economic Requirements System (HERS) Construction costs per lane mile in 2002 dollars
• Regional Transit Infrastructure and Operating Costs National Transit Database (NTD) Net Cost to supply an unlinked passenger trip by mode (2009)
Functional
Classification
Small Urban Small
Urbanized Large
Urbanized
Freeways $3.1 - $11.1 $3.4 - $12.1 $5.7 - $60.0
Principal Arterial $2.6 - $9.4 $2.9 - $10.2 $4.2 -$15.0
Minor Arterial/ Collector $2.0 - $7.0 $2.1 - $7.4 $2.9 - $10.2
Mode Capital Cost Operating Cost Total Cost Fare Revenue Net Cost
Bus $0.71 $3.40 $4.11 $0.91 $3.20
Heavy Rail $1.78 $1.80 $3.58 $1.09 $2.49
Commuter Rail $5.74 $9.80 $15.54 $4.69 $10.85
Light Rail $7.82 $3.00 $10.82 $0.78 $10.04
More Financial & Economic Impacts
• Annual Traveler Cost - Fuel $0.585 per mile in 2010 for auto users Includes variable costs (gas, oil, maintenance,
and tires) and fixed costs (insurance, license, registration, taxes, depreciation, and finance charges)
• Annual Traveler Cost – Travel Time Urban Mobility Report (TTI) Average travel delay for each metropolitan region Only includes auto users
Location & Community Impacts
• Relative increase in jobs accessibility by auto Function of distribution of growth by place type Weighted by population and employment growth
• Livability FTA criteria Use of alternative modes
― transit vehicle trips― bike ownership
Mixed use land use― mixed use place type― transit oriented development place type
Household transportation expenditures as a function of budget Use of alternative modes by low income households
• Equity Impact Regional accessibility by income group
Community Impacts
• Public Health Impacts and Costs Road safety impacts (daily VMT * 347 = annual VMT)
― Fatal = 1.14 per 100 million VMT― Injury = 51.35 per 100 million VMT― Property damage = 133.95 per 100 million VMT
Amount of walking (proxy for physical fitness)
Emissions (PM, NOX, VOC)
D Description Walking Increase
Density Household/Population Density 0.07Diversity Land Use Mix (entropy) 0.15Design Intersection/Street Density 0.39Regional Accessibility Job Accessibility By Auto 0Distance to Transit Distance to Nearest Transit Stop 0.15
In this example, scenarios 3 and 8 have the highest reduction in vehicle hours of delay (23-24%) due to additional lane miles. Scenario 4 includes ITS treatments, which also reduce congestion a significant amount.
Visualizing Performance Metrics
Title, including Performance
Metric (Vehicle Hours of Delay)
Percent Change in Performance
Metric for each Scenario
Title, including Performance
Metric (Vehicle Hours of Delay)
Scenarios compared against the Base Scenario
Percent Change in Performance
Metric for each Scenario
Axis adjusted for each Performance
Metric
RPAT’s charting is very easy to use and follows a template so each chart can be easily interpreted.
Case Study #2: Durham-Chapel Hill-Carrboro Metropolitan Planning Organization
Breakout #2: Generate Performance Measures
Step #1: Locating Performance Metrics
Locating Performance Metrics
Locating RPAT Output Data
RPAT’s output data tables can be found
here
Locating Output Data Charts
RPAT’s output data
can be charted
here
Step #2: Navigating the Outputs Directory
Navigating the Outputs Directory
Community Impacts
Direct Travel Impacts
Environment and Energy Impacts
Financial and Economic Impacts
Location Impacts
Summaries of Inputs
Daily Vehicle Miles Traveled by Place Type
Daily VMT Documentation
Documentation for the output table will open automatically
Step #3: Creating Reports
Creating Reports
Move to the Reporting
tab
Creating Reports
Choosing Scenarios
Choose the Scenarios for your report
Choosing Measures
Determine the measures to report
Choosing Performance Metrics
Determine the performance metrics to report
Report Options
Running the Report
Creating Charts
Congratulations! You have used RPAT to generate
a set of custom reports!
www.rsginc.com
Contacts
www.rsginc.com
Contacts
MAREN OUTWATER, PEVice President
ERICH RENTZ, GISPSenior Analyst
Reports generated by RPAT can be found within the RPAT directory (…/RPAT/projects/project/reports):
Bonus Material!
RPAT performance metrics can be found in their raw form as .RData files and comma separated value (CSV) files within the RPAT directory (…\RPAT\projects\#####\outputs):
Bonus Material!
