MPC
Research Projects (1999-00)

Identifying Number

MPC-190

Project Title

Grain Highway Network Analysis: Use of Satellite Imagery and USDA Data to Forecast Heavy Truck Trips Generated from Rural Land Use Zones

University

North Dakota State University

Project Investigator

Denver Tolliver
Upper Great Plains Transportation Institute, NDSU
(701)231-7767
denver.tolliver@ndsu.edu

External Project Contact

N/A

Project Objective

1. Develop a prototype model for analyzing grain and other traffic generated by agricultural processing plants;
2. Link the model to GIS software used by state transportation departments in Region 8;
3. Describe the use of satellite imagery in mapping crop production in rural land use zones;
4. Develop the prototype model in such a way that it can be extended to provide statewide coverage of all agricultural commodities in the future;
5. Advance the state of modeling and analytical capabilities in freight and heavy truck analysis by developing new impedance functions and network algorithms for modeling grain flows;
6. Produce useful traffic and highway planning information for a case study in southeastern North Dakota; and
7. Describe how the model could be adapted for use by other state transportation departments in Region 8.

Project Abstract

The location of new facilities such as agricultural processing plants is significantly altering truck traffic patterns in rural areas. Large processing plants create substantial inbound truck flows that typically are concentrated on several collector or arterial highways. After a facility begins operation, the annual equivalent single axle loads (ESALs) on key access highways may be significantly higher than the design values. This project will build on an existing study (MPC168). A prototype network model is being developed of a large corn processing plant in southeastern ND. The model will simulate flows of corn based on forecasted supply, demand, and farmer delivery criteria. It will function within a GIS environment (Arcview) and will utilize three main GIS database layers: (1) corn production; (2) elevator capacities and demands, and (3) plant demand and capacity. The model will forecast grain flows from production zones to elevators, satellites, and processing plants; assign the predicted flows to truck types and highways; and estimate the ESALs on key arterial and major collector highways. At the start of MPC168, corn production data were available only at the county level. Now, much better data can be derived. Using satellite images and field data provided by USDA, corn production forecasts will be developed for 1,500 sub-county strata.

In addition to better data, the project will add value to MPC168 by illustrating how USDA data and NASA satellite images can be used to estimate zonal grain production and forecast the annual truck trips generated from rural land use zones.

Task Descriptions

• Task 1 – In cooperation with corn plant officials, define corn procurement strategies and logistical practices that affect transportation demand and truck use.
• Task 2 – Create a supply data layer from USDA county production statistics and satellite images, and allocate the crop production to zones within counties.
• Task 3 – Create an elevator shipment file of corn and other grains handled historically by each facility, that also includes information on mode distribution and facility characteristics.
• Task 4 – Create a highway layer in which centroids and elevators are referenced to state routes and reference points.
• Task 5 – Write procedures and code to represent link impedance functions that reflect trucking costs, seasonal load limits, and other factors.
• Task 6 – Write spatial interaction and optimization procedures to distribute corn traffic over highway links so as to optimize decision criteria such as net price to farmers.
• Task 7 – Survey farmers and elevators to determine truck type usage and economic distance ranges of various trucks.
• Task 8 – Write procedures to allocate flows among truck types and estimate annual trucking miles, costs, and other variables.
• Task 9 – Write procedure to calculate ESALs for each highway link.
• Task 10 – Perform traffic simulations and summarize results.
• Task 11 – Publish a draft report.
• Task 12 – Hold demonstration and training sessions for NDDOT personnel.
• Task 13 – Publish final project report.
• Task 14 – Deliver a TEL8 seminar to interested state transportation departments and other universities in region 8.

Milestones, Dates

• Starting Date: November 1, 1999
• Project Milestones:
  • Working paper on the logistical strategies and practices of corn plants - January 1, 2000
  • Demonstration of GIS database functions to NDDOT - March 1, 2000
  • Interim Report to NDDOT - June 1, 2000
  • Draft report- September 1, 2000
  • Demonstration/training session for NDDOT - September 15, 2000
  • TEL8 seminar for state transportation departments, universities, and other interested groups in region 8 - October 1, 2000.
  • Final report- October 20, 2000
• Ending Date: October 31, 2000

Yearly and Total Budget

$70,000

Student Involvement (e.g. Thesis, Assistantships, Paid Employment)

At least two graduate and one undergraduate students will be working on the project. A thesis also may result.

Relationship to Other Research Projects

The project builds upon a current Year 10 project (MPC168). It will add considerable value to MPC168 by using satellite images and USDA crop production information to generate detailed production forecasts by sub-county strata. This project also will illustrate how these data sources can be used in agricultural states to generate commodity flow and truck traffic forecasts for highway planning.

Technology Transfer Activities

A seminar will be presented for transportation practitioners that document the data sources, modeling process, and potential impacts of large processing plants on highway and truck use.

Potential Benefits of the Project

State transportation and local transportation departments and agricultural groups will be the primary beneficiaries of the research. The data, model, and information can be used for: (1) Short-run highway planning and programming by state DOTs; (2) Long-term planning and forecasting of highways demands and truck traffic patterns resulting from agricultural processing plants facilities; (3) Optimal facility location decisions; and (4) Economic impacts of seasonal loads limits on industry.

TRB Keywords

Agricultural, roads, planning, truck, traffic