microsimulation of commodity flow in the mississippi valley region the microsimulation team of the...

Microsimulation of Commodity Flow in the Mississippi Valley Region

The Microsimulation Team of the Center for Freight Infrastructure Research and EducationSeptember 14, 2010

Freight Microsimulation

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


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]


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


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


Iowa Crop Land


Synthetic Farms for Iowa


Major Steps: Crops Elevator shipment generation

Similar attributes to farm shipments Destination choice

Shipment distances Establishment employments, types


SpatialDimensions of Crops Model


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.


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


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


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=producerC=consumerW=transshipment point


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


Network


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


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


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


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


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

microsimulation of commodity flow in the mississippi valley region the microsimulation team of the...

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