# Presentation: 010675

## Commodity Origin-Destination Provisional Estimates

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

## Project Team

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

## 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

## 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

## Principal Data Sources - Rail

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

## Principal Data Sources - Air

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

## Principal Data Sources - Water

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

## Principal Data Sources - Pipeline

- Petroleum Supply Annual
- Petroleum Supply Monthly

## 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

## 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

## Estimation Architecture

- Useable data

-  Estimation methodologies
- by mode

- Data validation

- Provisional estimates by mode

- 1

- 2

- 3

## Quality Control Process

- 2002 FAF2 Database

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

- Provisional estimates
- by mode
- vetted

- Provisional estimates by mode
- “unvetted”

- 4

## 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

## Database Development 

- Rail

- Database Development

- Water

- Pipeline

- Air

- 5

- Domestic database
- (320,000)

- Sea
- Database
- (93,000)

- Air
- Database
- (51,000)

- Landborder database
- (314,000)

## Database Structure

| Origin |  | Destination |  | Commodity | Mode | Kilo
Tons | Million
dollars |
| --- | --- | --- | --- | --- | --- | --- | --- |
| FAF -zone | State | FAF zone | State |  |  |  |  |
| 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 |

## State and National Summaries 

- State and National Summaries

- Domestic database

- Landborder database

- Air
- database

- Sea
- database

- AL

- WV

- WY

- National
- Summary

- AK

- AR

**State Summaries**

## Example of State Summary (tonnage)

|   | 2005 (million tons) |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
| Mode
  | Within State |  | From State |  | To State |  |
|  | 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 | <1 | 3,978.7 | 5 | 4,001.5 | 5 |
| Water | 4.9 | <1 | 447.2 | <1 | 45.8 | <1 |
| Air, air & truck | 103.2 | <1 | 716.6 | <1 | 810.7 | 1 |
| Truck and rail | <0.1 | <1 | 1,676.8 | 2 | 214.4 | <1 |
| Other intermodal | 14.4 | <1 | 216.4 | <1 | 611.7 | <1 |
| Pipeline & unknown | <0.1 | <1 | 947.2 | 1 | 4,130.9 | 6 |

## 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

## 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

## “Burning” Questions?