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 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) Activity Logsums Employment by Industry Construction$ Totals Labor Flows$ Aggregate/Equilibrium Micro-simulation Next Time Period Feedback Production Totals Space Inventory Travel Time/Costs OD Trip Tables Travel Time/Costs Space Prices Occupied Space Demand Mode Choice Logsums HH Labor Commodity Flows$ (internal, import, export) ECONOMY LAND DEVELOPMENT SYNTHETIC POPULATION ALLOCATION TRANSPORT ASSIGNMENT Person Goods External SPATIAL

StateWide Integrated Model (SWIM2) Activity Logsums Employment by Industry Construction$ Totals Labor Flows$ Aggregate/Equilibrium Micro-simulation Next Time Period Feedback Production Totals Space Inventory Travel Time/Costs OD Trip Tables Travel Time/Costs Space Prices Occupied Space Demand Mode Choice Logsums HH Labor Commodity Flows$ (internal, import, export) ECONOMY LAND DEVELOPMENT SYNTHETIC POPULATION ALLOCATION TRANSPORT ASSIGNMENT Person Goods External SPATIAL Economic Components Population – Production/Consumption – Land Use Spatially Represented

StateWide Integrated Model (SWIM2) Activity Logsums Employment by Industry Construction$ Totals Labor Flows$ Aggregate/Equilibrium Micro-simulation Next Time Period Feedback Production Totals Space Inventory Travel Time/Costs OD Trip Tables Travel Time/Costs Space Prices Occupied Space Demand Mode Choice Logsums HH Labor Commodity Flows$ (internal, import, export) ECONOMY LAND DEVELOPMENT SYNTHETIC POPULATION ALLOCATION TRANSPORT ASSIGNMENT Person Goods External SPATIAL Economic 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 2 Production of Labor by Households Group 3 Use of Space by Non-Household Activities Group 5 Use of Space by Households Group 6 Consumption of Commodities and Labor by Households Group 7 Production and Consumption of Commodities by Non-Household Activities Group 8 Imports and Exports of Commodities Group 4 Use of Labor by Non-Household Activities Group 9 Financial Flows ALD Space 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 VIZ Tool 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…

22 Variation by industry sector…

23 Variation by commodity type…

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… Becky Knudson Oregon DOT, Planning Analysis Unit Alex Bettinardi Oregon DOT, Planning Analysis Oregon Freight Plan: Doug Hunt University of Calgary Tara Weidner Parsons Brinckerhoff Erin Wardell Parsons Brinckerhoff