The Iowa Department of Transportation (DOT) is in the process of a major project to update their statewide model, known as iTRAM, aided in part by a grant from the Federal Rail Administration. The update aims to add both passenger and freight rail modes to the model and while making substantial updates to all of the major model components. This update also includes passenger destination choice models and moving to a general commodity flow based framework for both truck and rail freight.

The project stands to benefit from a variety of new data sources, including an add-on sample to the 2009 National Household Travel Survey on the passenger side. This presentation, however, will focus primarily on the use of a number of different sources of data for developing commodity flows by mode for detailed geography such as iTRAM’s traffic analysis zones.

While FHWA’s Freight Analysis Framework (FAF3) provides flows at a national scale, several different data sources were considered and used to validate and disaggregate these flows in and around Iowa. The analysis included some common datasets, such as detailed proprietary employment and establishment data and other specialized databases such as Transearch not uncommon in this type of analyses. However, several new or less common data sources were also studied, including IMPLAN’s econometric data on commodity productions and consumptions, REMI model data on labor productivity, truck GPS data from the American Transportation Research Institute (ATRI) and the Surface Transportation Board’s confidential carload waybill sample which was acquired by Iowa DOT for special use on the project. The Iowa DOT also has developed and maintained significant datasets in-house on commodities and freight facilities of special importance to Iowa. The presentation will highlight why, or in some cases why not, each data source was chosen for a particular use in developing the model, and how each dataset that was used contributed value to the model. We’ll also discuss lessons learned along the way, (“do’s and don’ts”) with practical advice on the strengths and weaknesses of each data set relative to use in a statewide model .