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.