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Historically, public sector transportation planning activities, especially in urban areas, have focused primarily on the movement of passengers. Recent emphasis, however, has been placed on statewide planning, and the movement of freight has received increased attention. This research developed a layered, statewide freight model of Iowa. In the layered approach, each model layer represents a commodity grouping or economic sector. The primary commodity data used in the model was Reebie Associates' TRANSEARCH database. Fifteen commodity groups were selected to be included in the layered model, at the 3-digit STCC level. The TRANSEARCH data, as available for the model, was at a level too coarse to support intrastate modeling purposes. Consequently, these data were disaggregated to the county level proportional to selected indicator variables chosen after careful inspection of input-output accounts. For each commodity model, the disaggregated TRANSEARCH O/D data were synthesized into a production/attraction table. The production and attractions data were distributed with a gravity model and assigned to the network using an all-or-nothing assignment algorithm. The assigned flows were then converted to truck or carloads, as appropriate, using commodity specific factors. Current practice is to validate statewide freight models with volume counts. The layered models, however, do not include all freight demand, limiting the effectiveness of this validation method. The innovation of this research was to create a validation data set of volumes for each commodity group at locations in Iowa based on observations of trucks and railcars in the field. To support the validation method developed in this research, an extensive data collection effort was undertaken. Some 11,400 trucks and 4,400 railcars were observed at 20 locations around the state. Data on observed cargo, truck configuration, trailer type, and carrier information were collected for trucks. Similar data were collected for rail. All of the observed data were compiled into a single database. For trucks, the observed carrier name and number were matched to carrier information in the Motor Carrier Management Information System (MCMIS) database, which was used to estimate the commodity being carried. For highway flows, commodity estimates were made for each observed truck, based on the type of trailer observed. Commodities were estimated for approximately 50 percent of the trucks observed. This technique compared well with an independent source, the 1991 Iowa Truck Survey, which stopped trucks to determine their commodities. For rail, commodity flows were estimated based upon the observed car type and commodity. The commodity flow data were then used to validate the model. The data contains volumes of commodity groups in numbers of vehicles (trucks, railcars) which were compared to the model output. Results of the validation varied, depending on the commodity group. For highways, the technique was most effective for validating flows where specialized equipment was required (automobiles, chemicals, farm machinery, etc.). Average model errors for these commodity groups ranged from 8% to 70%. Other commodities transported in more general equipment had a larger variation in model error. For rail, model errors ranged from 20% to 90% for commodities that could be validated. The sensitivity of the model to changes in model parameters was also examined. Finally, to demonstrate the model's applicability, a simple case study was developed that forecasted distribution patterns of the selected commodity groups ten years in the future.

A GIS-based multi-commodity freight model: typology, model refinement and field validation