FAF2 Data Disaggregation Methodology and Results

1
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

2
Presentation Overview

• Background
• Florida FAF2 data
• FAF2 Disaggregation method
• Illustrative Example
• Comparison with TRANSEARCH
• Future Year Projections

3
Background

• Growth in Florida
• Increasing freight transportation
• Capacity Constraints
• FAF2 as potential data source
• Audience of modelers and planners

4
Florida FAF2 Data
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Florida FAF2 Data
  2002 2002 2002 2002 2002 2002
Mode Within State Within State From State From State To 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) lt0.1 lt1 1 lt1 37 18
Air, air truck (Domestic only) lt0.1 lt1 0 lt1 0 lt1
Truck rail lt0.1 lt1 0 lt1 1 lt1
Other intermodal 0 lt1 1 1 5 3
Pipeline unknown 27 5 5 7 36 18
Total 575 100 74 100 202 100
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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

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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

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FAF2 Disaggregation Method
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FAF2 Disaggregation Method
SCTG 2 to NAICS 3 Equivalency Table
County Level FAF2 Database by Commodity
2002 FAF2 Database
NAICS 3 Employment Table
County Level FAF2 Database by Commodity
TAZ Level FAF2 Database by FL Statewide Model
Commodity Groupings
2005 InfoUSA Data
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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

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Illustrative Example

• Florida Statewide Freight Model

Florida 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
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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

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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)

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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)

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Illustrative Example
    Paper 2002 (thousands of tons) Paper 2002 (thousands of tons)
Origin Destination County FAF2 Zone
Disaggregation of Florida origins to Florida destinations Disaggregation of Florida origins to Florida destinations Disaggregation of Florida origins to Florida destinations 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
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Illustrative Example
    Paper 2002 (thousands of tons) Paper 2002 (thousands of tons)
Origin Destination County FAF22 Zone
Disaggregation of Florida origins to other US destinations Disaggregation of Florida origins to other US destinations Disaggregation of Florida origins to other US destinations 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 Disaggregation of other US origins to Florida destinations Disaggregation of other US origins to Florida destinations 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
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Comparison with TRANSEARCH

• TRANSEARCH STCC 26 (Pulp, Paper, or Allied
  Products)
• TRANSEARCH is unlinked trips and FAF2 is linked
  trips

  Origin (Production) Origin (Production) Destination (Attraction) 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
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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

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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

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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

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Discussion
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