1. Commodity Origin-Destination Provisional Estimates Edward Fekpe, Ph.D., PEng. Research Leader Transportation Market Sector

2. 2 Project Team Battelle –Water –Pipeline MacroSys Research and Technology Inc. –Highway –Air Univ. of Tennessee Center for Transportation Research –Rail

3. 3 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

4. 4 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

5. 5 Principal Data Sources - Rail Weekly Railroad Traffic Carload Waybill Sample Surface Transborder Freight Database County Business Pattern Database Producer Price Index

6. 6 Principal Data Sources - Air Form 41T-100 air traffic data Census Bureau Foreign Trade Division - International Air data

7. 7 Principal Data Sources - Water Waterborne databank Internal U.S. Waterway Monthly Indicators Waterborne tonnage by state and ports

8. 8 Principal Data Sources - Pipeline Petroleum Supply Annual Petroleum Supply Monthly

9. 9 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

10. 10 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

11. 11 Data Sources Useable data Estimation methodologies by mode Data validation Estimation Architecture Provisional estimates by mode 1 2 3

12. 12 Benchmark 2002 FAF2 Database Data quality assessment Origin- destinations Tonnage Value Quality Control Process Provisional estimates by mode vetted Provisional estimates by mode vetted Provisional estimates by mode “unvetted” 4

13. 13 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

14. 14 Highway Rail Database Development Water Pipeline Air 5 Domestic database (320,000) Sea Database (93,000) Air Database (51,000) Landborder database (314,000)

15. 15 Database Structure OriginDestination CommodityMode Kilo Tons Million dollars FAF -zoneStateFAF zoneState AL remALVA WashiVAOther foodstuffsTruck13.577.62 AL remALVA WashiVABase metalsTruck8.882.05 AL remALVA WashiVAArticles-base metalTruck5.340.66 AL remALVA WashiVAMixed freightTruck19.591.15 AL remALVT Newsprint/paperTruck12.473.91 AL remALWA remWAPrinted prods.Truck1.60.48 AL remALWA remWAWood prods.Truck2.7140.11 AL remALWA remWATextiles/leatherTruck22.4914.81

16. 16 State and National Summaries Domestic database Landborder database Air database Sea database AL WV WY National Summary AK AR State Summaries

17. 17 Example of State Summary (tonnage) 2005 (million tons) Mode Within StateFrom StateTo State Number% % % Total51,146.610088,051.810074,220.8100 Truck50,704.59980,068.99164,405.887 Rail319.5<13,978.754,001.55 Water4.9<1447.2<145.8<1 Air, air & truck103.2<1716.6<1810.71 Truck and rail<0.1<11,676.82214.4<1 Other intermodal14.4<1216.4<1611.7<1 Pipeline & unknown<0.1<1947.214,130.96

18. 18 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

19. 19 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@dot.gov

20. 20 “Burning” Questions?