Commodity Origin-Destination Provisional Estimates

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Commodity Origin-Destination Provisional Estimates

• Edward Fekpe, Ph.D., PEng.
• Research Leader
• Transportation Market Sector

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Project Team

• Battelle
• Water
• Pipeline
• MacroSys Research and Technology Inc.
• Highway
• Air
• Univ. of Tennessee Center for Transportation
  Research
• Rail

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Goal

• Develop provisional estimates of commodity O-D
  for 2005, 2006, 2007
• Updates 2002 FAF2 database (benchmark)
• Modes
• Air
• Highway
• Pipeline
• Rail
• Water
• Public domain data sources
• Develop estimation methodology for each mode

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Principal Data Sources Highway

• Surface Transborder Freight database
• County Business Pattern database
• Monthly Trucking Tonnage Report
• Gross State Product
• State Personal Income
• Monthly Manufacturers Shipments, Inventories,
  and Orders (M3) Survey
• Monthly Wholesale Trade Survey
• Producer Price Index

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Principal Data Sources - Rail

• Weekly Railroad Traffic
• Carload Waybill Sample
• Surface Transborder Freight Database
• County Business Pattern Database
• Producer Price Index

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Principal Data Sources - Air

• Form 41T-100 air traffic data
• Census Bureau Foreign Trade Division -
  International Air data

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Principal Data Sources - Water

• Waterborne databank
• Internal U.S. Waterway Monthly Indicators
• Waterborne tonnage by state and ports

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Principal Data Sources - Pipeline

• Petroleum Supply Annual
• Petroleum Supply Monthly

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Challenges

• Inconsistencies in data from different sources
• Non-availability of data e.g.,
• Commodity value data not available for all modes
• T-100 excludes information for some all-cargo
  carriers
• O-D information removed from public use waybill
  sample
• For pipeline, data available by PAD Districts
• Crosswalk between commodity codes
• Expansion of state level data to FAF regions
• Calibration of estimation models

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Estimation Methodologies

• Mode specific
• Estimation approach determined by data
• Different approaches for domestic vs
  international
• Examples of estimate methods
• Growth rates
• State level
• FAF region
• O-D pair
• Simple moving averages
• Weight/value ratio

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Estimation Architecture
Data Sources
Useable data
Data validation
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Estimation methodologies by mode
Provisional estimates by mode
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Quality Control Process
Provisional estimates by mode unvetted

• Data quality assessment
• Origin-destinations
• Tonnage
• Value

Provisional estimates by mode vetted
Benchmark 2002 FAF2 Database
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Provisional O-D Databases

• Domestic movements
• origin and destination within U.S.
• International movements via land border crossings
• import and export between the U.S. and Mexico,
  and between the U.S. and Canada
• International movements via seaports
• import and export between U.S. and other
  countries
• International movements via airports
• international air cargo covering import and export

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Database Development
Landborder database (314,000)
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Highway
Rail
Domestic database (320,000)
Water
Sea Database (93,000)
Pipeline
Air
Air Database (51,000)
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Database Structure
Origin Origin Destination Destination Commodity Mode Kilo Tons Million dollars
FAF -zone State FAF zone State Commodity Mode Kilo Tons Million dollars
AL rem AL VA Washi VA Other foodstuffs Truck 13.57 7.62
AL rem AL VA Washi VA Base metals Truck 8.88 2.05
AL rem AL VA Washi VA Articles-base metal Truck 5.34 0.66
AL rem AL VA Washi VA Mixed freight Truck 19.59 1.15
AL rem AL VT VT Newsprint/paper Truck 12.47 3.91
AL rem AL WA rem WA Printed prods. Truck 1.6 0.48
AL rem AL WA rem WA Wood prods. Truck 2.71 40.11
AL rem AL WA rem WA Textiles/leather Truck 22.49 14.81
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State and National Summaries
Domestic database
Landborder database
Sea database
Air database
National Summary
State Summaries
AL
AK
WV
AR
WY
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Example of State Summary (tonnage)
  2005 (million tons) 2005 (million tons) 2005 (million tons) 2005 (million tons) 2005 (million tons) 2005 (million tons)
Mode   Within State Within State From State From State To State To State
Mode   Number Number Number
Total 51,146.6 100 88,051.8 100 74,220.8 100
Truck 50,704.5 99 80,068.9 91 64,405.8 87
Rail 319.5 lt1 3,978.7 5 4,001.5 5
Water 4.9 lt1 447.2 lt1 45.8 lt1
Air, air truck 103.2 lt1 716.6 lt1 810.7 1
Truck and rail lt0.1 lt1 1,676.8 2 214.4 lt1
Other intermodal 14.4 lt1 216.4 lt1 611.7 lt1
Pipeline unknown lt0.1 lt1 947.2 1 4,130.9 6
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Lessons Learned and Future Estimates

• Familiarity with structure and nuances of
  available data sources
• Methodologies have been tested
• SQL queries developed for compiling databases
• No guarantees of data quality
• Limitations data quality, multi-modal, time
  budget
• No revisions to provisional estimates expected
• Provisional estimates not competing with private
  industry

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Lessons Learned and Future Estimates

• Provisional estimates give big picture
• Improvements in estimates for subsequent years
  expected
• Comments and suggestions welcome
• Send to
• Dr. Tianjia Tang
• Tianjia.Tang_at_dot.gov

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Burning Questions?