Abstract
The second generation of the Freight Analysis Framework, known as FAF2, is a continuation of the original Freight Analysis Framework developed by the U.S. Department of Transportation, Federal Highway Administration. FAF2 provides trip interchanges for commodity flows between 114 domestic zones, 17 additional international gateways at which imports enter and exports depart the United States, and seven international regions. This paper presents methods for disaggregating the FAF2 data to the county level by developing different disaggregation factors for different commodity types. These new methods are also compared with other disaggregation methods. The objective is to enable state and local governmental agencies to utilize FAF2 commodity origin–destination data for a quick desktop analysis and to devise further strategies in collecting and acquiring local commodity data. The focus area of this study is the state of New Jersey. The study developed and applied different methods to disaggregate FAF2 commodity data down to the New Jersey county level. The results of the disaggregation were then compared with Global Insight's Transearch Database and other disaggregation methods previously developed and presented as part of this study. Findings indicate that no one disaggregation method produces the best results for trip productions and attractions. Disaggregating each commodity using commodity-specific industry employment data yielded the best results in matching the Transearch database for flow origins. However, simple non-commodity-specific factors, such as truck vehicle miles traveled, total employment, or adjusted population data, generally yielded better results in disaggregating flow attractions.
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