92 In this study, a network equilibrium method is used to model the flows. GIS data from different sources are used, including satellite images of crop layers produced by the National Agricultural Statis- tics Service (NASS) and the U.S. Department of Agriculture (USDA). The highway network layer was derived from the GIS database of the North Dakota Department of Transportation (NDDOT) and the 2004 National Transportation Atlas, along with the findings from a grain elevator survey. These data were used to construct a comprehensive database on which this model was built. The agricultural freight model has many uses. Its initial application is the result of a legislatively mandated study in North Dakota. The North Dakota legislature directed the Upper Great Plains Transporta- tion Institute (UGPTI) to conduct a study of how improvements to the state’s transportation infrastructure might enhance its business climate and competitive position in economic development. Specific legislative language directed UGPTI to quantify the benefits and costs of removing spring highway load restrictions. This paper describes the component of the overall study that relates to grain movements. BACKGROUND A national summit sponsored by USDA and the St. Louis Regional Chamber and Growth Association, Agricultural Transportation Chal- lenges of the 21st Century, described the U.S. agricultural sector as the largest user of freight transportation services in the country (2). The conference proceedings suggest that the real challenge for U.S. farmers in the future will not be producing crops, but procuring and providing the transportation services needed to market the products. Trucks have a modal share of 45% of all agricultural freight, followed by railroads with a share of 32% of the flow (2). However, the truck share is increasing as a result of higher levels of domestic processing and off-farm feeding. The network of railroads is shrinking through the abandonment of branch lines. A Delphi survey by Vachal et al. predicted that railroad will abandon about 10% of the remaining route miles they operate by 2010, leaving only 155,000 route miles in the United States (3). In a previous study, Koo et al. used a spatial equilibrium model based on a linear programming algorithm to evaluate the effects of changes in transportation rates on the state’s grain distribution system (4). Russell et al. performed an in-depth study of agricultural freight flows in Kansas. However, the study was limited to movements of heavy combination vehicles (5). In this project, findings from earlier studies are used to develop a GIS database that stores the physical and behavioral attributes of demand and supply nodes, as well as the attributes of infrastructure facilities. However, instead of a spatial price equilibrium method, a Analyzing Effects of Spring Highway Load Restrictions on North Dakota’s Agricultural Freight Flows Subhro Mitra, Denver Tolliver, Amiy Varma, and Alan Dybing This paper describes a statewide agricultural freight transportation model that is used to estimate the benefits of improving North Dakota’s state highway system by removing spring load restrictions. The trans- portation model measures changes in freight flows caused by truck weight regulations during the spring thaw cycle. The primary focus is on grain transportation. A geographic information system (GIS) network of fed- eral, state, and county roads is developed to represent flows from fields to elevators and final destinations. The state is divided into 182 production zones on the basis of agricultural land use patterns. The data for the trip generation component of the model are derived from satellite imagery of crop layers in the state, with the use of GIS spatial analysis and algorithms developed for this purpose. The annual demand at elevators is estimated from grain movement reports filed with the State of North Dakota. Agri- cultural freight movement is modeled separately as two flows: internal- to-internal (i.e., from fields to elevators) and internal-to-external (i.e., from elevators to final destinations). CUBE transportation planning soft- ware is used to model the flows. In addition, the onion model concept used in Iowa is applied as a demand planning tool to capture the effects of spring load restrictions, which are dynamic and move from the southern to the northern part of the state during the spring thaw cycle. The costs of spring load limits are quantified as the increased distance and travel time caused by circuitous truck movements or as the reduced payload per trip. Statewide freight transportation is gaining impetus after the Trans- portation Equity Act for the 21st Century and the Intermodal Surface Transportation Efficiency Act of 1991, in which Congress encouraged the consideration of freight movements in statewide transportation planning (1). Many states have included freight in their long-range plans, and some states have developed stand-alone freight models. States have addressed the issue of freight planning differently based on their unique needs and budgets. Agriculture is the backbone of North Dakota’s economy. The agri- cultural freight generated in the state moves to both domestic and international destinations. This paper presents an agricultural freight demand model for assigning freight flows from farms to des- tinations. The model is customized to focus on the specific research objective, which is to analyze the effects of spring load restrictions on freight flows. S. Mitra, D. Tolliver, and A. Dybing, Upper Great Plains Transportation Institute, 430 IACC Building, and A. Varma, Department of Civil Engineering, North Dakota State University, Fargo, ND 58105. Corresponding author: S. Mitra, Subhro. Mitra@ndsu.edu. Transportation Research Record: Journal of the Transportation Research Board, No. 2008, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 92–99. DOI: 10.3141/2008-12