Creating a Supply-Chain Methodology for Freight Forecasting in Wisconsin

1. October 21, 2013 Jennifer Murray Traffic Forecasting Section Chief Wisconsin Department of Transportation Creating a Supply-Chain Methodology for FreightForecasting in Wisconsin 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. Multimodal Freight Fusion Forecasting Model

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 credibilitywith stakeholders

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

5. Wisconsin Freight Facilities

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

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

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

9. Draft Highway Freight Factor Scores

10. Continued Partnerships

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

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

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

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

15. Web-based Interactive Corridor Mapping Application

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

17. Schematic Business Plan for Fusion Concept • 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!