COMMODITY SPECIFIC DISAGGREGATION OF THE 2002 FAF 2 DATA TO THE COUNTY LEVEL FOR NEW JERSEY

The second generation of the Freight Analysis Framework, known as FAF, is a continuation of the original Freight Analysis Framework developed by the U.S. Department of Transportation, Federal Highway Administration. FAF provides trip interchanges for commodity flows between 114 domestic zones, 17 additional international gateways at which imports enter and exports depart the U.S., and 7 international regions. This paper presents methods for disaggregating the FAF data to the county level by developing different disaggregation factors for different commodity types. These new methods are also compared to the other disaggregation methods that were previously presented. The objective is to enable state and local governmental agencies to utilize FAF 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 FAF commodity data down to the New Jersey county level. The results of the disaggregation were then compared to 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. Opie, Rowinski and Spasovic 3 INTRODUCTION The second generation of the Freight Analysis Framework (FAF), known as FAF, is a continuation of the original Freight Analysis Framework developed by the U.S. Department of Transportation, Federal Highway Administration. Unlike the original FAF, which provided the public with generalized freight movement and highway congestion maps without disclosing the underlying data, FAF provides two separate products. The first product is the commodity flow origin-destination (O-D) data. The O-D data covers both the base year (2002) and future years between 2010 and 2035 with a 5-year interval. The second product is the freight movement data on all highway links within the FAF highway network. FAF is designed to enable the Federal Highway Administration (FHWA) to conduct investment/policy analysis and to support legislative activities. Since its inception, the application of FAF has permeated to all Administrations within the U.S. Department of Transportation. While FAF is currently undergoing further development, the FHWA has been collaborating with State Departments of Transportation, Metropolitan Planning Organizations, universities/colleges, and other institutions to develop methods and procedures to enable state and local government agencies to incorporate FAF data into their data analysis processes. The objective of this study is to support FAF by developing methods to disaggregate the large commodity origin-destination data covered in FAF into small geographic areas (county level) that will enable state and local governmental agencies to utilize FAF commodity origindestination data for freight flow trend and directional analysis and to devise further strategies for collecting and acquiring local commodity data. The focus area of this study is the State of New Jersey. The objective was to propose disaggregation methods that rely on readily available data and are reasonably easy to follow. This paper will present the work completed during the second year of this three year initiative and includes a discussion of the new commodity-based methods to estimate flows at the New Jersey county level, an improved treatment for overseas commodity flows by water, an overview of the disaggregation methods, and the potential future direction for the subsequent year of this study. Results are compared with the findings from the first year of this study (1).