Disaggregating faf2 data for California
Sarah E. Aly and
No 207721, 50th Annual Transportation Research Forum, Portland, Oregon, March 16-18, 2009 from Transportation Research Forum
The Freight Analysis Framework Version 2 (FAF2) data estimates commodity flows and related freight transportation activity among states, sub-state regions, and major international gateways (FHWA 2006). The FAF2 commodity flow origin-destination database includes tons and value of commodity movements among regions by mode of transportation and type of commodity. The FAF2 data for the State of California is represented through a Commodity Origin-Destination Database comprising aggregated data for five major FAF2 zones. These zones are: Los Angeles, San Diego, Sacramento, San Jose, and “the remainder of California” and they encompass a total of 58 counties. Disaggregating such data from a 5 zone level to a 58 county level can significantly enhance the understanding of freight flow volume and distribution in California which can be used to improve state freight flow planning. A variety of methods for the disaggregation of the FAF2 data have been developed. Some techniques use socioeconomic factors such as employment and population as a basis for their disaggregation procedures, while others use a Truck Vehicle Miles Traveled (TVMT) approach. This paper presents an analysis of a revised Truck Vehicle Miles Traveled (TVMT) based method (Rowinski et al 2007) to disaggregate the FAF2 data pertaining to California for the year 2002. This work is part of a larger project aimed towards developing a freight analysis framework for the State of California. The main factor used for this disaggregation is the ratio of TVMT within the county level to the TVMT within the respective FAF2 zone level. The analysis was carried out using this factor for both the Origins and Destinations as illustrated in a numerical example. The results of this work provide a unique insight to freight flow volume and distribution within the State of California. The methodology has proven to be an efficient approach to disaggregating FAF2 data and can be easily applied to future projections.
Keywords: Research Methods/ Statistical Methods; Resource /Energy Economics and Policy (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:ndtr09:207721
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