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Disaggregate Disaggregate State Level State Level Freight Data to Freight Data to County LevelCounty Level
October 2013
Shih-Miao Chin, Ph.D.Ho-Ling Hwang, Ph.D.Francisco Moraes Oliveira Neto, Ph.D.Center for Transportation AnalysisOak Ridge National Laboratory
OutlineOutline
Background Freight Analysis Framework (FAF) Major data sources
Methodology Disaggregation process Example
Results & Validations FAF Ton-miles Comparison with other freight data programs
Remarks
Background: Background: Freight Analysis Framework (FAF)Freight Analysis Framework (FAF)
Manages by the Office of Freight Management and Operations, Federal Highway Administration (FHWA)
Provides a comprehensive picture of freight movement among states and major metropolitan areas by all modes
Most current release is FAF3.4 database
South, Central & Western Asia
Eastern Asia
Mexico
Europe
Africa
Canada
Rest of Americas
Mexico
SE Asia & Oceania
Eastern Asia
SW & Central Asia
Geography 123 domestic regions 8 foreign regions
Modes of transportation Truck Rail Water Air/air-truck Multiple mode/mail Pipeline Others/unknown
43 Commodities
Background: Background: Major Data SourcesMajor Data Sources
Commodity Flow Survey (CFS) Conducted under the partnership of U.S. Census and Bureau of Transportation
Statistics (BTS) Sample survey of business U.S. establishments & classified according to North
American Industry Classification System (NAICS) codes Latest available data: 2007 (i.e., base year data for FAF3)
County Business Patterns (CBP) An annual data series from U.S. Census Provides economic data by industry (# establishments, employment, payroll) Latest available data: 2011
Industry Input-Output (I-O) Accounts Annual I-O tables produced by the Bureau of Economic Analysis (BEA) Make and Use Tables, by industry according to NAICS codes Latest available data: 2011
FAF3 Disaggregation: FAF3 Disaggregation: Estimation of Ton-MilesEstimation of Ton-MilesTonnage and value of goods moved are important measures of the freight
activity, but they do not necessarily reflect the usage of transportation systems Environmental impact (emissions and fuel efficiency) of freight activity can be
assessed using measures normalized by ton-miles
The revenue of transportation firms is related to the amount of freight in tones transported per mile
Main disaggregation steps Linking freight activities with economic activities
Disaggregate FAF3 database (ODCM tonnage matrix) to county level
Estimate average shipment distance by mode on the multimodal network systems
Freight Flow Disaggregation Freight Flow Disaggregation ApproachApproach
ωOrigin county / Commodity, Mode ωDestination county / Commodity, Mode
ωcounty-to-county by commodity & mode
ProductionCBP
Information theory
o d
i j
Where (o, d) – FAF OD pair & (i, j) – County pair
f FAF zone-to-zone, Commodity, Mode
AttractionCBP BEA I-O Accounts (apq)
ωO/ C, M = ∑ωO / I ωI / C, M ωD/ C, M = ∑ωD / I ωI / C, M
Methodologies/ModelsMethodologies/ModelsLog-linear regression models for linking freight activity with economic
activity by industry sector at stateProduction: freight tonnage shipped & payroll of producing industryAttraction: freight tonnage received & payroll of receiving industry
Estimates of county-level production/attraction shares by industrySpatial distribution by matrix balancing procedures (or doubly constraint
gravity model)
0 1000 2000 3000 4000 5000 6000 70000
1
2
3
4
5
6
7
8
9x 10
4
Payroll of food manufacturing by state (millinos of dollars)
To
tal
ton
s sh
ipp
ed b
y s
tate
(th
ou
san
ds)
Production curve for food manufacturing
y = 6.52x1.09
R2 = 0.85
Distance MatricesDistance Matrices
Terminal links
Terminal Access/ Egress Links
Origin of movement
Highway access link
Highway Network #1
Highway Network #2
Rail Network
Movement destination
http://cta.ornl.gov/transnet/
Highway: Contains 500,000 miles of roadway in the US, Canada, and Mexico
Railway: Contains every railroad route in the US, Canada, and Mexico that has been active since 1993
Waterway: Contains inland and off-shore links
Intermodal Network
9 Managed by UT-Battellefor the U.S. Department of Energy
Estimated using the highway network system in GIS
Baltimore Example:Baltimore Example:
Destination County FIPSOrigin County FIPS
24003 24005 24013 24025 24027 24035 24510
24009 49 76 96 93 67 73 69
24017 51 74 94 91 62 79 67
24021 70 62 29 88 47 100 58
24031 44 50 42 76 23 71 44
24033 27 50 68 67 31 54 43
24037 71 99 118 116 86 95 91
242242
241241
D =
10 Managed by UT-Battellefor the U.S. Department of Energy
FAF zone to county disaggregation – FAF zone to county disaggregation – generation and attraction by countygeneration and attraction by county
Annual payroll ($ 1000) in the origin counties
Share of annual payroll ($ 1000) in the destination counties
NAICS 311FIPS Total
24009 024017 14524021 20,30024031 11,79824033 29,75424037 292
NAICS 311FIPS Total
24003 144,45124005 292,85024013 40,13624025 52,67524027 88,87824035 10,93924510 393,440
PRODUCTIONS FIPS Tons
24009 024017 22224021 55,56224031 30,29724033 85,18024037 486Total 171,747
ATTRACTIONSFIPS Tons
24003 22,61424005 51,05924013 5,16924025 7,07124027 12,92224035 1,15624510 71,755Total 171,747
truckrmy ,311,ˆ truckrmy ,311,ˆ
11 Managed by UT-Battellefor the U.S. Department of Energy
FAF to county disaggregation – FAF to county disaggregation – distribution and spatial interactiondistribution and spatial interaction
0 0 0 0 0 0 0
32 65 6 9 16 2 93
6,548 16,893 2,073 2,263 4,144 330 23,312
3,868 8,997 928 1,205 2,404 199 12,697
12,096 24,963 2,150 3,574 6,324 622 35,451
71 142 12 20 34 4 202
NAICS 311
FIPS Tons
24009 0
24017 222
24021 55,562
24031 30,297
24033 85,180
24037 486
24003 24005 24013 24025 24027 24035 24510 FIPS
22,614 51,059 5,169 7,071 12,922 1,156 71,755 Tons
),ˆ,ˆ(ˆ ,,311,,311,311, truckrstruckstruckrrsm dyyfy
truckrmy ,311,ˆ
truckrmy ,311,ˆ
12 Managed by UT-Battellefor the U.S. Department of Energy
Matrix of Total Tons by TruckMatrix of Total Tons by TruckDestination County FIPS
Origin County FIPS
24003 24005 24013 24025 24027 24035 24510 Total Tons
24009 32,842 33,744 3,978 7,524 16,094 2,232 26,197 122,611
24017 75,066 90,196 8,747 18,270 37,555 4,554 65,519 299,907
24021 202,845 445,228 102,333 69,463 180,529 10,952 302,784 1,314,134
24031 385,372 613,635 102,795 106,944 342,452 22,376 482,374 2,055,948
24033 363,469 436,776 45,599 87,047 206,271 19,945 361,597 1,520,703
24037 62,792 78,809 6,429 13,991 28,173 3,922 57,583 251,699
Total Tons 1,122,386 1,698,387 269,881 303,239 811,074 63,981 1,296,054 5,565,002
Matrix of Tons * Distance Matrix Matrix of Ton-miles
FAF Ton-miles Estimates FAF Ton-miles Estimates
4.67
0.40
0.91
3.67
0.48
4.94
140.90
Value/ Ton-miles ($)
Include all domestic, exported, and imported shipments transported within the U.S.
Comparisons with Other Freight Comparisons with Other Freight Data ProgramsData Programs
U.S. Network Sub-system
Data source / ModesTon-miles (billions)
Highway FAF3 (Truck single mode only) 2,4732007 CFS (Truck single mode only) 1,342
RailwayFAF3 (Rail single mode plus rail portion of multiple modes)
1,726
2007 CFS (Rail single mode and portion of multiple modes which includes rail)
1,530
2007 AAR report (all rail activities) 1,820
WaterwayFAF3 (water and the water portion of multiple modes)
554
2007 CFS (water and the portion of multiple modes which includes water)
348
2007 USACE waterborne commerce (all water activities)
506
Concluding RemarksConcluding RemarksTo carry out national transportation freight analysis and planning at a
level of detailThe disaggregation methodology will provide more data at a more
geographic detailed level for: Environmental impact assessment
Vulnerability and resilience of freight multimodal network
Modal shift analysis
Truck weight and size studies
Further work is required to estimate freight flow models through FAF regions, by commodity, by mode.