FAF2 Data Disaggregation Methodology and Results presented to Model Task Force presented by Vidya Mysore, Florida DOT Krishnan Viswanathan, Cambridge Systematics,

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Presentation transcript:

FAF2 Data Disaggregation Methodology and Results presented to Model Task Force presented by Vidya Mysore, Florida DOT Krishnan Viswanathan, Cambridge Systematics, Inc. November 28, 2007

Presentation Overview Background Florida FAF 2 data FAF 2 Disaggregation method Illustrative Example Comparison with TRANSEARCH Future Year Projections

Background Growth in Florida Increasing freight transportation Capacity Constraints FAF 2 as potential data source Audience of modelers and planners

Florida FAF 2 Data

2002 ModeWithin StateFrom StateTo State NumberPercentNumberPercentNumberPercent Truck Rail Water (Domestic only)<0.1< Air, air & truck (Domestic only)<0.1<10 0 Truck & rail<0.1<10 1 Other intermodal0<11153 Pipeline & unknown Total

Florida FAF 2 Data Forecasts based on overall economic changes Mode shares are assumed same in future 98 percent increase in commodity flows from 2002 to 2035 Truck increase is 108 percent Domestic Water will decline by 51 percent

FAF 2 Disaggregation Method Data Sources FAF 2 County Business Pattern (CBP) Public Use Microdata Samples (PUMS) Census 2000 Three digit NAICS employment at county level CBP most complete at MSA Therefore use PUMS for employment allocation Census 2000 for government and self-employed

FAF 2 Disaggregation Method SCTG TableNAICS Table SCTG 2 to NAICS 3 Equivalency Table CBP Data NAICS 3 Employment Table 2002 Economic Census Data 2002 FAF 2 Database SCTG 2 to NAICS 3 Equivalency Table County Level FAF 2 Database by Value SCTG 2 to NAICS 3 Equivalency Table NAICS 3 Employment Table County Level FAF 2 Database by Commodity Census 2000 and QCEW Data 2002 FAF 2 Database DOMESTIC (kTon) BORDER (kTon) SEA (kTon) Disaggregated to Mode Split to Truck (kTon) Rail (kTon) Water (kTon)

FAF 2 Disaggregation Method Mode Split to Truck (kTon) Rail (kTon) Water (kTon) SCTG 2 to NAICS 3 Equivalency Table NAICS 3 Employment Table County Level FAF 2 Database by Commodity 2002 FAF 2 Database County Level FAF 2 Database by Commodity 2005 InfoUSA Data TAZ Level FAF 2 Database by FL Statewide Model Commodity Groupings

FAF 2 Disaggregation Method Develop relationships between commodity and employment and population data Rationale is commodities end up in Zones that produce or consume them Use relationships to develop factors for each commodity for freight flow disaggregation Apply share of county tonnage to FAF 2 regional tonnage to obtain disaggregated FAF 2 O-D database

Illustrative Example Florida Statewide Freight Model Florida Commodity CodeCommodity Group NameSTCC CodesSCTG Codes 1Agricultural Products1,7,8,91,2,3 2Minerals10,13,14,1914,16, Coal1115 4Food204,5,6,7,8 5Non-Durable Manufacturing21,22,23,25,279,30,39,29 6Lumber2425,26 7Chemicals Paper2627,28 9Petroleum Products Other Durable Manufacturing30,31, , Clay, Concrete, Glass & Stone Waste* Miscellaneous Freight41-47,5020, Warehousing501042

Illustrative Example Paper (SCTG 27, 28) Pulp, Newsprint, Paper, and Paperboard Paper or Paperboard Articles Production Equation (21.53) x Paper Manufacturing (NAICS 322) R 2 = 0.80 Attraction Equation (4.56) x Paper Manufacturing (NAICS 322) (4.59) x Printing and Related (NAICS 323) R 2 = 0.76

Illustrative Example Estimate the annual tonnage of paper produced P c (i) or attracted A c (j) for each County Aggregate the county productions P c (i) and attractions A c (j) to their associated Florida FAF 2 regions to create P FAF2 (i) and A FAF2 (j) Expand the FAF 2 Regions matrix, FAF 2 (k,l), to Florida counties matrix, County (i,j)

Illustrative Example If origin i and destination j are in Florida then County(i,j)=[FAF 2 (k,l)*P c (i)/P FAF2 (i)* A c (j)/ A FAF2 (j)] If origin i is in Florida and destination l, is outside Florida then County(i,l)=[ FAF 2 (k,l)*P c (i)/P FAF2 (i)] If origin k is outside Florida and destination j is in Florida then County(k,j)=[ FAF 2 (k,l)*A c (j)/A FAF2 (j)]

Illustrative Example Paper 2002 (thousands of tons) OriginDestinationCountyFAF 2 Zone Disaggregation of Florida origins to Florida destinations FAF 2 Miami (20)FAF 2 Jacksonville (19)#NA16.27 Miami Dade County Baker County 0.16 #NA Miami Dade County Clay County 0.11 #NA Miami Dade CountyDuval County 5.51 #NA Miami Dade CountyNassau County 5.51 #NA Miami Dade CountySt. Johns County 0.32 #NA Palm Beach County Baker County 0.01 #NA Palm Beach CountyClay County 0.01 #NA Palm Beach CountyDuval County 0.31 #NA Palm Beach CountyNassau County 0.31 #NA Palm Beach CountySt. Johns County 0.02 #NA Broward County Baker County 0.06 #NA Broward CountyClay County 0.04 #NA Broward CountyDuval County 1.9 #NA Broward CountyNassau County 1.9 #NA Broward CountySt. Johns County 0.11 #NA

Illustrative Example Paper 2002 (thousands of tons) OriginDestinationCountyFAF2 2 Zone Disaggregation of Florida origins to other US destinations FAF 2 Miami (20)GA Rem (25)#NA6.64 Miami Dade CountyGA Rem0.27#NA Palm Beach CountyGA Rem4.74#NA Broward CountyGA Rem1.63#NA Disaggregation of other US origins to Florida destinations GA Rem (25)FAF 2 Miami (20)#NA GA RemMiami Dade County #NA GA RemPalm Beach County26.11 #NA GA RemBroward County60.47 #NA

Comparison with TRANSEARCH TRANSEARCH STCC 26 (Pulp, Paper, or Allied Products) TRANSEARCH is unlinked trips and FAF 2 is linked trips Origin (Production)Destination (Attraction) FAF 2 (SCTG 27, 28) TRANSEARCH (STCC 26) FAF 2 (SCTG 27, 28) TRANSEARCH (STCC 26) Broward25%7%30%4% Miami-Dade71%87%57%84% Palm Beach4%5%13%12% Total100%

Future Year Projections Establish national control totals by commodity Apply specific shipment growth by market and commodity Apply specific purchasing and consumption growth by market and commodity

Future Year Projections Summarize and compare results with national control totals Adjust resulting freight flows so that volumes correspond with national level as follows: For each market & commodity, adjust so shipments match purchases For each commodity, adjust so that national control totals are satisfied

Future Year Projections Use same methodology for Florida using CBP and Woodes & Poole (WP) data WP data available at county level Since we are focused on only tonnage, the data available to use can be used in a similar manner

Discussion