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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 FAF2 data
FAF2 Disaggregation method
Illustrative Example
Comparison with TRANSEARCH
Future Year Projections
Background
Growth in Florida
Increasing freight transportation
Capacity Constraints
FAF2 as potential data source
Audience of modelers and planners
Florida FAF2 Data
Florida FAF2 Data
2002
Mode Within State From State To State
Number Percent Number Percent Number Percent
Truck 487 85 50 68 85 42
Rail 60 11 17 23 37 18
Water (Domestic only) <0.1 <1 1 <1 37 18
Air, air & truck (Domestic only) <0.1 <1 0 <1 0 <1
Truck & rail <0.1 <1 0 <1 1 <1
Other intermodal 0 <1 1 1 5 3
Pipeline & unknown 27 5 5 7 36 18
Total 575 100 74 100 202 100
Florida FAF2 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
FAF2 Disaggregation Method
Data Sources
FAF2
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
FAF2 Disaggregation MethodSCTG Table NAICS Table
SCTG 2 to NAICS 3 Equivalency Table
CBP Data
NAICS 3 Employment Table
2002 Economic Census Data
2002 FAF2 Database
SCTG 2 to NAICS 3 Equivalency Table
County Level FAF2 Database by Value
SCTG 2 to NAICS 3 Equivalency Table
NAICS 3 Employment
Table
County Level FAF2 Database by Commodity
Census 2000 and QCEW Data
2002 FAF2 Database
DOMESTIC (kTon)
BORDER (kTon)
SEA (kTon)
Disaggregated to
Mode Split to
Truck (kTon)
Rail (kTon)
Water (kTon)
FAF2 Disaggregation Method
Mode Split to
Truck (kTon)
Rail (kTon)
Water (kTon)
SCTG 2 to NAICS 3 Equivalency Table
NAICS 3 Employment Table
County Level FAF2 Database by Commodity2002 FAF2 Database
County Level FAF2 Database by Commodity
2005 InfoUSA Data
TAZ Level FAF2 Database by FL Statewide Model Commodity Groupings
FAF2 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 FAF2 regional tonnage to obtain disaggregated FAF2 O-D database
Illustrative Example
Florida Statewide Freight ModelFlorida Commodity Code Commodity Group Name STCC Codes SCTG Codes
1 Agricultural Products 1,7,8,9 1,2,3
2 Minerals 10,13,14,19 14,16,10-13
3 Coal 11 15
4 Food 20 4,5,6,7,8
5 Non-Durable Manufacturing 21,22,23,25,27 9,30,39,29
6 Lumber 24 25,26
7 Chemicals 28 20-23
8 Paper 26 27,28
9 Petroleum Products 29 17-19
10 Other Durable Manufacturing 30,31,33-39 24,32-40
11 Clay, Concrete, Glass & Stone 32 31
12 Waste* 40 41
13 Miscellaneous Freight 41-47,5020,5030 42
14 Warehousing 5010 42
Illustrative Example
Paper (SCTG 27, 28)
Pulp, Newsprint, Paper, and Paperboard
Paper or Paperboard Articles
Production Equation
0.362 (21.53) x Paper Manufacturing (NAICS 322)• R2 = 0.80
Attraction Equation
0.064 (4.56) x Paper Manufacturing (NAICS 322) + 0.043 (4.59) x Printing and Related (NAICS 323)
• R2 = 0.76
Illustrative Example
Estimate the annual tonnage of paper produced Pc(i) or attracted Ac(j) for each County
Aggregate the county productions Pc(i) and attractions Ac(j) to their associated Florida FAF2 regions to create PFAF2(i) and AFAF2(j)
Expand the FAF2 Regions matrix, FAF2(k,l), to Florida counties matrix, County (i,j)
Illustrative Example
If origin i and destination j are in Florida then County(i,j)=[FAF2(k,l)*Pc(i)/PFAF2(i)* Ac(j)/ AFAF2(j)]
If origin i is in Florida and destination l, is outside Florida then County(i,l)=[ FAF2(k,l)*Pc(i)/PFAF2(i)]
If origin k is outside Florida and destination j is in Florida then County(k,j)=[ FAF2(k,l)*Ac(j)/AFAF2(j)]
Illustrative Example Paper 2002 (thousands of tons)
Origin Destination County FAF2 Zone
Disaggregation of Florida origins to Florida destinations
FAF2 Miami (20) FAF2 Jacksonville (19) #NA 16.27
Miami Dade County Baker County 0.16 #NA
Miami Dade County Clay County 0.11 #NA
Miami Dade County Duval County 5.51 #NA
Miami Dade County Nassau County 5.51 #NA
Miami Dade County St. Johns County 0.32 #NA
Palm Beach County Baker County 0.01 #NA
Palm Beach County Clay County 0.01 #NA
Palm Beach County Duval County 0.31 #NA
Palm Beach County Nassau County 0.31 #NA
Palm Beach County St. Johns County 0.02 #NA
Broward County Baker County 0.06 #NA
Broward County Clay County 0.04 #NA
Broward County Duval County 1.9 #NA
Broward County Nassau County 1.9 #NA
Broward County St. Johns County 0.11 #NA
Illustrative Example
Paper 2002 (thousands of tons)
Origin Destination County FAF22 Zone
Disaggregation of Florida origins to other US destinations
FAF2 Miami (20) GA Rem (25) #NA 6.64
Miami Dade County GA Rem 0.27 #NA
Palm Beach County GA Rem 4.74 #NA
Broward County GA Rem 1.63 #NA
Disaggregation of other US origins to Florida destinations
GA Rem (25) FAF2 Miami (20) #NA 199.63
GA Rem Miami Dade County 113.05 #NA
GA Rem Palm Beach County 26.11 #NA
GA Rem Broward County 60.47 #NA
Comparison with TRANSEARCH
TRANSEARCH STCC 26 (Pulp, Paper, or Allied Products)
TRANSEARCH is unlinked trips and FAF2 is linked trips
Origin (Production) Destination (Attraction)
FAF2 (SCTG
27, 28)TRANSEARCH
(STCC 26)FAF2 (SCTG
27, 28)TRANSEARCH
(STCC 26)
Broward 25% 7% 30% 4%
Miami-Dade 71% 87% 57% 84%
Palm Beach 4% 5% 13% 12%
Total 100% 100% 100% 100%
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
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