Disaggregate State Level Freight Data to County Level

Transcription

1 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

2 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

3 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 SW & Central Eastern Asia Asia Eastern South, Central Asia & Western Asia SE Asia & Oceania Mexico Mexico Canada Rest of Americas Africa Europe Geography 123 domestic regions 8 foreign regions Modes of transportation Truck Rail Water Air/air-truck Multiple mode/mail Pipeline Others/unknown 43 Commodities

4 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

5 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

6 Freight Flow Disaggregation Approach o f FAF zone-to-zone, Commodity, Mode d Production CBP i j Attraction CBP BEA I-O Accounts (a pq ) ω Origin county / Commodity, Mode ω Destination county / Commodity, Mode Information theory ω county-to-county by commodity & mode ω O/ C, M = ω O / I ω I / C, M ω D/ C, M = ω D / I ω I / C, M Where (o, d) FAF OD pair & (i, j) County pair

7 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 9 x 104 Production curve for food manufacturing Total tons shipped by state (thousands) y = 6.52x 1.09 R 2 = 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)

8 Distance Matrices Intermodal Network Highway Network #2 Movement destination Terminal Access/Egress Links Terminal links Rail Network 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

9 Baltimore Example: Origin County FIPS Destination County FIPS D = Estimated using the highway network system in GIS 9 Managed by UT-Battelle for the U.S. Department of Energy

10 FAF zone to county disaggregation generation and attraction by county NAICS 311 FIPS Total , , ,754 Annual payroll ($ 1000) in the origin counties 10 Managed by UT-Battelle for the U.S. Department of Energy FAF O-D Flow (short tons) t 242,241,truck = 171, Production Model Attraction Model (Production Share) (Attraction Share) ˆ y rm,311, truck PRODUCTIONS FIPS Tons , , , Total 171,747 ATTRACTIONS FIPS Tons , , , , , , ,755 Total 171,747 NAICS 311 FIPS Total , , , , , , ,440 Share of annual payroll ($ 1000) in the destination counties ˆ y rm,311, truck

11 FAF to county disaggregation distribution and spatial interaction ˆ y rm,311, truck FIPS NAICS ,614 51,059 5,169 7,071 12,922 1,156 71,755 Tons FIPS Tons ˆ y rm,311, truck , , , ,548 16,893 2,073 2,263 4, ,312 3,868 8, ,205 2, ,697 12,096 24,963 2,150 3,574 6, , Managed by UT-Battelle for the U.S. Department of Energy y ˆ rsm 311 f ( yˆ r,311, truck, yˆ s,311, truck, drs,, truck )

12 Matrix of Total Tons by Truck Origin County FIPS Destination County FIPS Total Tons ,842 33,744 3,978 7,524 16,094 2,232 26, , ,066 90,196 8,747 18,270 37,555 4,554 65, , , , ,333 69, ,529 10, ,784 1,314, , , , , ,452 22, ,374 2,055, , ,776 45,599 87, ,271 19, ,597 1,520, ,792 78,809 6,429 13,991 28,173 3,922 57, ,699 Total Tons 1,122,386 1,698, , , ,074 63,981 1,296,054 5,565,002 Matrix of Tons * Distance Matrix Matrix of Ton-miles 12 Managed by UT-Battelle for the U.S. Department of Energy

13 FAF Ton-miles Estimates Multiple modes & mail Rail Water Air (include truck-air) Pipeline Other and unknown Truck Share of Ton-miles by Mode 14% 10% 6% 1% 0% 0% 44% Truck Value/ Ton-miles ($) 4.67 Rail % Pipeline Multiple modes & mail Average Shipment Distance Water Other and unknown Air (include truck-air) Include all domestic, exported, and imported shipments transported within the U.S.

14 Comparisons with Other Freight Data Programs U.S. Network Ton-miles Data source / Modes Sub-system (billions) Highway FAF3 (Truck single mode only) 2, CFS (Truck single mode only) 1,342 Railway FAF3 (Rail single mode plus rail portion of multiple modes) 1, CFS (Rail single mode and portion of multiple modes which includes rail) 1, AAR report (all rail activities) 1,820 Waterway FAF3 (water and the water portion of multiple modes) CFS (water and the portion of multiple modes which includes water) USACE waterborne commerce (all water activities) 506

15 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.