A layered architecture for freight transportation demand modeling entails the construction of a statewide freight transportation demand model by separately simulating traffic for one commodity at a time. Layers can then be added together to construct a comprehensive model that includes the most significant freight flows. Most state or regional economies are dominated by a few economic sectors, and models can be constructed for those sectors that generate the most freight traffic and/or are the most important to the regional economy. Freight traffic demand modeling in intercity applications is more likely to focus on economic development, local infrastructure improvements, maintenance, and similar policy and planning concerns than on system capacity issues. Thus, it is more important to understand changes in traffic growth by economic sector than as the composite of all freight traffic. This method is less data intensive and more easily understood by transportation professionals than previous approaches. The layered approach is therefore more likely to achieve the desired objectives than would general models, which attempt to forecast heterogeneous freight transportation demands simultaneously. This approach is demonstrated through a case study using the meat products and farm machinery industries in Iowa. Other commodities will be added in the future to complete a model of Iowa’s statewide freight transportation demand. A framework is presented for organizing and identifying planning goals, key issues, and predominant commodities for intercity freight transportation. Although examples are provided, specific recommendations addressing the full range of issues, data collection activities, tools, and urban applications are suggested for further study. A case study demonstrates the approach used for one issue, one mode, and two commodities, which could be repeated elsewhere for similar applications.
Transportation Energy Futures Series: Freight Transportation Demand: Energy-Efficient Scenarios for a Low-Carbon Future
