1. October 21, 2013 Jennifer Murray Traffic Forecasting Section Chief Wisconsin Department of Transportation 1 TRB – SHRP2 Symposium: Innovations in Freight Demand Modeling and Data Improvement

2. Create a statewide freight forecasting framework that integrates travel demand modeling with freight analysis tools, provides performance metrics and analyzes alternative strategies to move freight. 2

3. Objectives for Multimodal Freight Fusion Forecasting Model  Use forecasting model day-to-day  Implement national best-practices  Visualize the data in one place  Align transportation investment with needs  Build forward thinking and credibility with stakeholders 3

4. 4 WISCONSIN Chicago Twin Cities Lake Superior Lake Michigan Mississippi River

5. 5 Wisconsin Freight Facilities

6. 6  Governor’s Freight Industry Summit  Freight Mobility Action Agenda  Transportation Finance & Policy Commission  Connections 2030: Wisconsin’s Long-range Transportation Plan  Stakeholder Meetings Freight Industry Partners

7. 7 Top Commodity Profiles - Economic Drivers  Tonnage  Value  Economic Importance  Flows  Forecasts Commodity Tons Mode  Transportation issues associated with each commodity

8. Draft Highway Freight Factors on Southeast State Trunk Highways 8 Criteria Thresholds Weight/ Hierarchy Traffic segments assigned draft highway “Freight Factor” scores

9. 9 Draft Highway Freight Factor Scores

10. Continued Partnerships 10

11. Multimodal Freight Fusion Forecasting Model  Freight supply-chain forecasting tool based on traditional statewide 4-step model  Economics of moving freight  Business production locations, product types, availability and general business development timeframes  System performance measures 11

12. Data Improvements Needed  Vehicle classification count data  Data disaggregation investigation Commodity information ○ Shipping costs ○ Commodity weights Freight supply-chain Intermodal terminal supply-chain data New business data  Diesel fuel consumption data  Non-highway modes 12

13. 13 Permanent Count Stations (ATRs) Continuous Weight-in-Motion Continuous Class Continuous Length Portable Count Stations Short-term Length Miscellaneous Counts Manual Centralized Processing Data Analysis Data Collection Standards FHWA/WisDOT Standards Binning Data Collection Traffic Forecast/Projection Vehicle-Miles of Travel Modeling Meta Manager Travel Demand Model Microsimulation Identified Project Need Budget Capacity Analysis Accountability Expertise in Review and Development of Products Sufficient Truck Counts Standard WisDOT Approach Statewide Data-Driven Concept for Freight Fusion Forecasting and Modeling (as represented by Vehicle Classification Count Data) Data Refinement / Improvement

14. Freight Forecasting with Fusion Concept  Concept continuing to evolve – use the data, contribute to the data – “PLUG-IN”  Flexibility and tailored to needs Air quality modeling Mechanistic Empirical Pavement Design software inputs Oversize, over-weight vehicles  Multimodal aspect provides insights  Survey businesses for data 14

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16. Fusion Model Role  Analysis  Transportation project planning and programming  MAP-21 opportunities  Last-mile connections  Partnering  Good stewardship 16

17. Schematic Business Plan for Fusion Concept 17  Outline long-range goals, expectations  Specific uses for the model  Guidelines for development, technology, transportation modes, tool and data updates  Budget  Performance measures  Implementation - “the everyday”

18. Thank You! 18