applying the swim2 integrated model for freight planning in oregon
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Applying the SWIM2 Integrated Model For Freight Planning in Oregon. Prepared for the 13 th TRB Transportation Planning Applications Conference May 9, 2011. Presented by Becky Knudson Oregon DOT Transportation Planning Analysis Unit. Presentation Highlights. Describe Freight Plan analysis - PowerPoint PPT PresentationTRANSCRIPT
Applying the SWIM2 Integrated Applying the SWIM2 Integrated Model For Freight Planning in Model For Freight Planning in
OregonOregon
Prepared for the 13th TRB Transportation Planning
Applications ConferenceMay 9, 2011
Presented by Becky KnudsonOregon DOT Transportation Planning Analysis Unit
Presentation HighlightsPresentation 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 PlanOregon 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 PurposeFreight 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 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 Oregon StateWide Integrated Model (SWIM2) as Forecast ToolModel (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 ModelStateWide Integrated Model (SWIM2) (SWIM2)
ActivityLogsums
Employmentby Industry
Construction$Totals
LaborFlows$
Aggregate/EquilibriumMicro-simulationNext Time Period Feedback
Production Totals
SpaceInventory
TravelTime/Costs
OD Trip TablesTravelTime/Costs
Space PricesOccupied Space
Demand
Mode ChoiceLogsums
HH Labor
Commodity Flows$(internal, import, export)
ECONOMY
LANDDEVELOPMENT
SYNTHETICPOPULATION
ALLOCATION
TRANSPORT
ASSIGNMENT
Person Goods External
SPATIAL
StateWide Integrated ModelStateWide Integrated Model (SWIM2) (SWIM2)
ActivityLogsums
Employmentby Industry
Construction$Totals
LaborFlows$
Aggregate/EquilibriumMicro-simulationNext Time Period Feedback
Production Totals
SpaceInventory
TravelTime/Costs
OD Trip TablesTravelTime/Costs
Space PricesOccupied Space
Demand
Mode ChoiceLogsums
HH Labor
Commodity Flows$(internal, import, export)
ECONOMY
LANDDEVELOPMENT
SYNTHETICPOPULATION
ALLOCATION
TRANSPORT
ASSIGNMENT
Person Goods External
SPATIALEconomic Components
Population – Production/Consumption – Land Use
Spatially Represented
StateWide Integrated ModelStateWide Integrated Model (SWIM2) (SWIM2)
ActivityLogsums
Employmentby Industry
Construction$Totals
LaborFlows$
Aggregate/EquilibriumMicro-simulationNext Time Period Feedback
Production Totals
SpaceInventory
TravelTime/Costs
OD Trip TablesTravelTime/Costs
Space PricesOccupied Space
Demand
Mode ChoiceLogsums
HH Labor
Commodity Flows$(internal, import, export)
ECONOMY
LANDDEVELOPMENT
SYNTHETICPOPULATION
ALLOCATION
TRANSPORT
ASSIGNMENT
Person Goods External
SPATIALEconomic Components
Population – Production/Consumption – Land Use
Spatially Represented
Transportation Components
Workers – Commodities and Services (imports/exports, internal)
Traffic Assignment
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 - no jargon – get-to-the-point - relate findings to project goals
Example: Use this…
Group 2Production of Laborby Households
Group 3Use of Spaceby Non-Household Activities
Group 5Use of Spaceby Households
Group 6Consumptionof Commoditiesand Laborby Households
Group 7ProductionandConsumptionofCommoditiesbyNon-HouseholdActivities
Group 8Imports and Exportsof Commodities
Group 4Use of Laborby Non-Household Activities
Group 9Financial Flows
ALDSpace Development
Transport Demand and Supply
Even Better Use this…
Stronger economy generates more commodity flow in terms of value
Stronger economy generates more commodity flow in terms of tons
SWIM SWIM VIZVIZToolTool
1) Query
2) Tables
3) Visuals
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
21
Variation by region…
0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0%
ACT1 NW
ACT10 SW
ACT11 SE
ACT12 Lane
ACT2 Portland
ACT3 NE
ACT4 Scentral
ACT5 RV
ACT6 Lower JD
ACT7 Central
ACT8 SW
ACT9 CascadesW
Oregon
All Industry Compound Average Growth Rate 2012-2027
Pessimist
Reference
Optimist
22
Variation by industry sector…
0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0%
Ag, Mining, Food
Electronics, Durables, Construction
Forestry, Wood
Gov't, Education, Biz Services,Accommodations
Health
Paper, NonDurables
Retail, Personal Service, Communication
All Industries
Statewide Compound Average Growth Rate 2012-2027
Pessimist
Reference
Optimist
23
Variation by commodity type…
-1.0% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0%
ClayMineralStone
FoodorKindredProducts
ForestorWood
MachineryInstTranspMetals
OtherMisc
PetrolCoalChem
PulpPaper
All Commodities
Statewide Commodity ProductionCAGR 2012-2027
Pessimist
Reference
Optimist
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…For more information…
Becky KnudsonOregon DOT, Planning Analysis [email protected]
Alex BettinardiOregon DOT, Planning [email protected]
Oregon Freight Plan: http://www.oregon.gov/ODOT/TD/FREIGHT/FREIGHT_PLAN.shtml
Doug HuntUniversity of [email protected]
Tara WeidnerParsons Brinckerhoff
Erin WardellParsons [email protected]