Applying the SWIM2 Integrated Model
For Freight Planning in Oregon
13th
Prepared for the
TRB Transportation Planning
Applications Conference
May 9, 2011
Presented by Becky Knudson
Oregon DOT Transportation Planning Analysis Unit
Presentation Highlights
• Describe Freight Plan analysis
• Brief overview of the Statewide
Integrated Model (SWIM2)
• Key factors of effective modeling for
long range planning
• Contribution of analysis to planning
process
Description of Freight Plan Analysis
Oregon Freight Plan
• First statewide freight plan
• Scope of analysis was well matched
to SWIM2 model
• ODOT modeling staff served role as
internal consultants
• Consultant staff served as extension
of ODOT staff
– Support of Freight Plan analysis
– Continue model development
Freight Plan Analysis Purpose
• Forecast range of likely economic
conditions to gain understanding of effects
on freight movement
– Illustrate variation in statewide and regional
activity and commodity flows
– Provide information to support development
of freight strategies
Analytical Approach
• Plan for freight flows given an uncertain
economic future
• Use scenarios to evaluate range of possible
futures
Reference: “business-as-usual” (2.0%*)
Optimistic: more economic growth (2.7%*)
Pessimistic: less economic growth (1.2%*)
High Transportation Costs: Pessimistic scenario
with 3-fold increase in variable operating costs
* Compound Annual Growth Rates
Overview of Statewide Model
Oregon StateWide Integrated Model
(SWIM2) as Forecast Tool
• SWIM is dynamic
– integrates the dynamic interactions of land use,
the economy and transportation infrastructure
• SWIM1 used successfully on several
statewide analyses
– Proved its value repeatedly
– Generated support for SWIM2 development
• SWIM2 has greater spatial acuity
– more detailed inputs and components
– Can evaluate more policy options
StateWide Integrated Model
(SWIM2)
SPATIAL
Employment
by Industry
ECONOMY
Construction$
Totals
Production Totals
Activity
Logsums
Space Prices
Occupied Space
Demand
SYNTHETIC HH Labor ALLOCATION Space LAND
Inventory
DEVELOPMENT
POPULATION Mode Choice
Travel
Time/Costs
Logsums
Labor
Commodity Flows$
Flows$ (internal, import, export)
TRANSPORT
Travel
Time/Costs
Person
Goods
External
OD Trip Tables
ASSIGNMENT
Aggregate/Equilibrium
Micro-simulation
Next Time Period Feedback
StateWide Integrated Model
(SWIM2)
ECONOMY
SPATIAL
Employment
Construction$
Economic
Components
by Industry
Totals
Production Totals
Activity
Logsums
Space Prices
Occupied Space
Demand
Spatially
Represented
SYNTHETIC HH Labor ALLOCATION Space LAND
Inventory
Population – Production/Consumption
– Land Use
DEVELOPMENT
POPULATION Mode Choice
Travel
Time/Costs
Logsums
Labor
Commodity Flows$
Flows$ (internal, import, export)
TRANSPORT
Travel
Time/Costs
Person
Goods
External
OD Trip Tables
ASSIGNMENT
Aggregate/Equilibrium
Micro-simulation
Next Time Period Feedback
StateWide Integrated Model
(SWIM2)
ECONOMY
SPATIAL
Employment
Construction$
Economic
Components
by Industry
Totals
Production Totals
Activity
Logsums
Space Prices
Occupied Space
Demand
Spatially
Represented
SYNTHETIC HH Labor ALLOCATION Space LAND
Inventory
Population – Production/Consumption
– Land Use
DEVELOPMENT
POPULATION Mode Choice
Logsums
Travel
Time/Costs
Labor
Commodity Flows$
Flows$ (internal, import, export)
TRANSPORT
Transportation Components
Person
Goods
External
OD and
Trip Tables
WorkersTime/Costs
–Travel
Commodities
Services (imports/exports, internal)
ASSIGNMENT
Traffic Assignment
Aggregate/Equilibrium
Micro-simulation
Next Time Period Feedback
Key Factors of
Effective Modeling Analysis
Effective Modeling for Long
Range Planning
• Myth: Sophistication of the
modeling tool is the number one
component of effective modeling
Watson on Jeopardy
Key Factors of Effective
Modeling Analysis
• Use models to inform and manage risk
– The true value of modeling comes from how the
analyst uses the tool, not the tool itself
• Direct access to analyst
– Answer questions immediately, alleviate
misconceptions
– Gain understanding of goals and objectives
• Analyst must have good communication skills
– Use good visuals
– get-to-the-point
- no jargon
- relate findings to project goals
Example: Use this…
Group 7
Production
and
Consumption
of
Commodities
by
Non-Household
Activities
Group 8
Imports and Exports
of Commodities
ALD
Space Development
Group 2
Production of Labor
by Households
Group 3
Use of Space
by Non-Household Activities
Group 6
Consumption
of Commodities
and Labor
by Households
Group 9
Financial Flows
Group 4
Use of Labor
by Non-Household Activities
Transport Demand and Supply
Group 5
Use of Space
by Households
Even
Better
Use this…
Stronger
economy
generates
more
commodity
flow in terms
of value
Stronger
economy
generates
more
commodity
flow in terms
of tons
1) Query
2) Tables
3) Visuals
SWIM
VIZ
Tool
Contribution of Analysis to the
Planning Process
Contribution of Modeling Analysis
to the Planning Process
• Source of descriptive data used to frame
discussion
– Described economic conditions
– Illustrated regional differences
– revealed patterns of activity
• Use model scenarios to address risk
– facilitates planning despite many unknowns
• Helped identify core issues
• Reduced perception of bias
Oregon
ACT9 CascadesW
ACT8 SW
Variation by
region…
ACT7 Central
ACT6 Lower JD
ACT5 RV
Pessimist
ACT4 Scentral
Reference
Optimist
ACT3 NE
ACT2 Portland
ACT12 Lane
ACT11 SE
ACT10 SW
ACT1 NW
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
21Rate 2012-2027
All Industry Compound Average Growth
6.0%
All Industries
Retail, Personal Service, Communication
Variation by
industry
sector…
Paper, NonDurables
Health
Gov't, Education, Biz Services,
Accommodations
Pessimist
Forestry, Wood
Reference
Electronics, Durables, Construction
Optimist
Ag, Mining, Food
0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0%
Statewide Compound Average Growth Rate
2012-2027
22
All Commodities
PulpPaper
Variation by
commodity
type…
PetrolCoalChem
OtherMisc
MachineryInstTranspMetals
Pessimist
ForestorWood
Reference
Optimist
FoodorKindredProducts
ClayMineralStone
-1.0%
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
Statewide Commodity Production
CAGR 2012-2027
23
6.0%
7.0%
8.0%
Conclusions
• Models are powerful tools
– Effectiveness is determined by how they are used
– Good sources of descriptive data
– Good for evaluating “what if” scenarios, assessing risk
• Using them for long range planning takes time
and forethought
• Planners and modelers must work together to
realize the full potential of using these tools
• The extra time used for analysis pays off in the
end with a more productive outcome and
smoother process
Having our own Watson
would be really, really cool
For more information…
Oregon Freight Plan:
http://www.oregon.gov/ODOT/TD/FREIGHT/FREIGHT_PLAN.shtml
Becky Knudson
Oregon DOT, Planning Analysis Unit
[email protected]
Doug Hunt
University of Calgary
[email protected]
Alex Bettinardi
Oregon DOT, Planning Analysis
[email protected]
Tara Weidner
Parsons Brinckerhoff
[email protected]
Erin Wardell
Parsons Brinckerhoff
[email protected]
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