Microsimulation of Commodity Flow in the Mississippi Valley Region

1. Microsimulation of Commodity Flow in the Mississippi Valley Region The Microsimulation Team of the Center for Freight Infrastructure Research and Education September 14, 2010

2. Idea • Descriptive model • Exploit multiple freight-related databases that would ordinarily be difficult to link together • Preserve, as much as possible, the richness and detail, both spatial and economic, of the underlying databases • Focus on 5 key indicator commodities • Work mainly at the shipment level • Focus on trucks

3. Commodities • Chosen from commodities suggested by MVFC states • Greater detail than FAF • Commodities with many origins and destinations in region [SCTG] • Corn [02200] • Soybeans [03400] • Dairy products [all 071xx] • Motor vehicle parts [all 364xx] • Articles of plastics [all 242xx]

4. Databases • Dun & Bradstreet establishments • Commodity Flow Survey • Census of Agriculture • Agricultural surveys • Crop maps • Benchmark IO table • Freight Analysis Framework • Ontario Commercial Vehicle Survey • Oak Ridge national highway network, enhanced • Others

5. Major Steps: Crops • Farm synthesis • Crop • Harvested acres • Location (long/lat) • Harvest dates • Planting dates • On-site storage • Truck ownership • Farm shipment generation, by date • Number of shipments • Size • Truck type • Destination type (elevator, ethanol, feed lot, etc.) • Time of Day Most of Cedar County, IA

6. Iowa Crop Land

7. Synthetic Farms for Iowa

8. Major Steps: Crops • Elevator shipment generation • Similar attributes to farm shipments • Destination choice • Shipment distances • Establishment employments, types

9. SpatialDimensions of Crops Model

10. Major Steps: Crops • Some simplifying assumptions, e.g., • All farm-based shipments go by truck • All exports from elevators do not go by truck • A single farm has just one crop (corn, soybeans, other) • No transshipment points, except elevators. • Empties are ignored.

11. Major Steps: Manufactured Products • Shipments move from establishment to establishment, perhaps through a transshipment point. • Actual establishments within the region, “super-establishments” outside region • One super-establishment for each FAF zone for each 6-digit NAICS • Producing establishments limited to those which produce the three indicator industrial commodities • Any establishment can be a consumer. • No households • No empties

12. Major Steps: Manufactured Products • Shipment generation • Size • Mode • Need one truck? Needs multiple trucks? • Destination • Distance range (CFS) selection • Within range, establishment is selected randomly based on: • Fraction of US employment within 6-digit industrial category • Distance within range • Industry’s share of commodity purchases from IO tables

13. Major Steps: Manufactured Products • Tour structure selection • P-C • P-W—C • P—W-C • P-C-C • P-W-W-C • P-W—C-C • P—W-C-C • P-C-C-C • Transshipment point selection • Time of day for tour legs P=producer C=consumer W=transshipment point

14. Route Choice and Traffic Assignment • Sensitive to time of day (“dynamic”) • Link travel times for route choice • FAF zones used only for keeping trips on correct sides of rivers/borders, otherwise no use of TAZs in the assignment step. • Aggregated to nodal catchment areas (about 43,000) • Multiclass • Not capacity restrained • Not a traffic microsimulation

15. Network

16. 24-Hour Assignment, All Classes, All Commodities, Late October

17. 24-Hour Static Assignment, All Classes, All Commodities, Detail

18. Traffic Dynamics • Trial simulations underway • One hour intervals • 60 hours of simulated time to allow shipments to arrive from west and east coasts • E.g., 6 am on Monday to 6 pm on Wednesday • Departure times drawn from uniform probability distributions, with logic to keep leg sequences correct given previous leg departure times and trip times. • Need to account for driver rest periods • Need to account for time zones • Very long execution times

19. Lessons So Far • D&B not perfect but very good, needed considerable help in the agricultural sectors. • High degree of spatial, temporal, economic detail is achievable • Concept is expandable to the full US • Concept could be expanded to most, if not all, commodities • Better representation of the supply chain than found in typical regional models • Simulation times are long but not unreasonable. • Limited applicability to long-term forecasts

20. The Team • Data management: University of Toledo, Pete Lindquist and staff • Data synthesis: University of Wisconsin—Madison, Jessica Guo and staff • Software development: University of Wisconsin—Milwaukee, Alan Horowitz and staff • Policy: Ernie Wittwer, University of Wisconsin—Madison