In Minnesota, technological and economic shifts in the grain supply chain have altered the way grain producers and sellers navigate their local freight network. In particular, many producers have been increasing their personal trucking capacity and taking longer trips to intermodal and domestic market options. This logistical reshaping of local grain supply chains pressure transportation officials to reconsider the consequences for road infrastructure and congested freight corridors. Studies are discussing the potential of disaggregated commodity flow survey (CFS) data as a critical tool in understanding small-scale freight movement and informing infrastructural investment decisions. Utilizing ArcGIS’s Network Analyst and Hot spot tools to analyze inter-county grain trucking, our study effectively differentiates highly active freight corridors. The model is used to further inform an ongoing infrastructure development project in the Twin Cities metro area by contextualizing road usage within the economic framework of the grain supply chain. However, this study finds CFS data alone fails to account for shifting supply chain conditions, and their consequent impact on the road network. Employing United States Department of Agriculture crop production and cropland data, this study additionally builds an original, computational model that simulates corn producer shipment reaction to market price competition within two key grain-producing counties. Results visualize how producers, during spot months, may be incentivized to haul longer distances to more competitive markets—especially emerging biofuel industries. This lesson proves crucial for state and local transportation officials who wish to identify freight infrastructure development opportunities that invigorate and accommodate growth in Minnesota’s expanding agricultural industry cluster.