Disaggregate
State Level
Freight Data to
County Level
October 2013
Shih-Miao Chin, Ph.D.
Ho-Ling Hwang, Ph.D.
Francisco Moraes Oliveira Neto, Ph.D.
Center for Transportation Analysis
Oak Ridge National Laboratory
Outline
 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:
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
 Geography
 123 domestic regions
 8 foreign regions
Canada
 Modes of transportation
Eastern Asia
SW & Central
Asia
Europe
Africa
South, Central
Eastern
Asia & Western Asia
SE Asia &
Oceania
Mexico
Mexico
Rest of
Americas







Truck
Rail
Water
Air/air-truck
Multiple mode/mail
Pipeline
Others/unknown
 43 Commodities
Background:
Major 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:
Estimation of Ton-Miles
Tonnage 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 Approach
o
Production
f FAF zone-to-zone, Commodity, Mode
j
i
d
Attraction
CBP
BEA I-O Accounts (apq)
CBP
ωOrigin county / Commodity, Mode
ωDestination county / Commodity, Mode
Information theory
ωcounty-to-county by commodity & mode
ωO/ C, M = ∑ωO / I ωI /
C, M
ωD/ C, M = ∑ωD / I ωI /
Where (o, d) – FAF OD pair
& (i, j) – County pair
C, M
Methodologies/Models
Log-linear regression models for linking freight activity with economic
activity by industry sector at state
Production: freight tonnage shipped & payroll of producing industry
Attraction: freight tonnage received & payroll of receiving industry
Total tons shipped by state (thousands)
9
x 10
4
Production curve for food manufacturing
8
y = 6.52x1.09
R2 = 0.85
7
6
5
4
3
2
1
0
0
1000
2000
3000
4000
5000
6000
7000
Payroll of food manufacturing by state (millinos of dollars)
Estimates of county-level production/attraction shares by industry
Spatial distribution by matrix balancing procedures (or doubly constraint
gravity model)
Distance Matrices
Intermodal Network
Highway
Network #2
Movement
destination
Terminal Access/Egress
Links
Rail Network
Terminal links
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
Origin of
movement
Highway access
link
Highway
Network #1
http://cta.ornl.gov/transnet/
Baltimore Example:
Destination County FIPS
241
Origin
County 24003 24005 24013 24025 24027 24035 24510
FIPS
242
D=
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
Estimated using the highway network system in GIS
9
Managed by UT-Battelle
for the U.S. Department of Energy
FAF zone to county disaggregation –
generation and attraction by county
NAICS 311
FIPS
Total
24009
0
24017
145
24021
20,300
24031
11,798
24033
29,754
24037
292
FAF O-D Flow
(short tons)
t242,241,truck = 171,747
Production Model
(Production Share)
Attraction Model
(Attraction Share)
Annual payroll
($ 1000) in the
origin counties
yˆ rm ,311,truck
10
Managed by UT-Battelle
for the U.S. Department of Energy
NAICS 311
FIPS
Total
24003
144,451
24005
292,850
24013
40,136
24025
52,675
24027
88,878
24035
10,939
24510
393,440
Share of annual payroll
($ 1000) in the
destination counties
PRODUCTIONS
FIPS
Tons
24009
0
24017
222
24021
55,562
24031
30,297
24033
85,180
24037
486
Total
171,747
ATTRACTIONS
FIPS
Tons
24003
22,614
24005
51,059
24013
5,169
24025
7,071
24027
12,922
24035
1,156
24510
71,755
Total
171,747
yˆ rm ,311,truck
FAF to county disaggregation –
distribution and spatial interaction
yˆ rm ,311,truck
NAICS 311
yˆ rm ,311,truck
11
24003
24005
24013
24025
24027
24035
24510
FIPS
22,614
51,059
5,169
7,071
12,922
1,156 71,755
Tons
FIPS
Tons
24009
0
0
0
0
0
0
0
0
24017
222
32
65
6
9
16
2
93
24021
55,562
6,548 16,893
2,073
2,263
4,144
330 23,312
24031
30,297
3,868
8,997
928
1,205
2,404
199 12,697
24033
85,180
12,096 24,963
2,150
3,574
6,324
622 35,451
24037
486
12
20
34
Managed by UT-Battelle
for the U.S. Department of Energy
71
142
4
202
yˆ rsm ,311  f ( yˆ r ,311,truck , yˆ s,311,truck , d rs ,truck )
Matrix of Total Tons by Truck
Destination County FIPS
Origin
County
FIPS
24003
24005
24013
24025
24027
24035
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
1,122,386 1,698,387
269,881
303,239
811,074
Total Tons
Matrix of Tons * Distance Matrix
12
Managed by UT-Battelle
for the U.S. Department of Energy
24510 Total Tons
57,583
251,699
63,981 1,296,054 5,565,002
Matrix of Ton-miles
FAF Ton-miles Estimates
Share of Ton-miles by Mode
6%
10%
1%
0%
44%
0%
14%
25%
Average Shipment Distance
239
185
Other and
unknown
Truck
534
Pipeline
600
Air (include
truck-air)
636
Water
705
Rail
Multiple modes
& mail
800
Value/ Ton-miles ($)
Truck
4.67
Rail
0.40
Pipeline
0.91
Multiple modes & mail
3.67
Water
0.48
Other and unknown
4.94
Air (include truck-air)
140.90
Include all domestic, exported, and imported
shipments transported within the U.S.
Comparisons with Other Freight
Data Programs
U.S. Network
Data source / Modes
Sub-system
Highway
FAF3 (Truck single mode only)
2007 CFS (Truck single mode only)
FAF3 (Rail single mode plus rail portion of
Railway
multiple modes)
2007 CFS (Rail single mode and portion of
multiple modes which includes rail)
2007 AAR report (all rail activities)
FAF3 (water and the water portion of
Waterway
multiple modes)
2007 CFS (water and the portion of multiple
modes which includes water)
2007 USACE waterborne commerce (all
water activities)
Ton-miles
(billions)
2,473
1,342
1,726
1,530
1,820
554
348
506
Concluding Remarks
 To carry out national transportation freight analysis and planning at a
level of detail
 The 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.