State and Local Freight Data

presented to

presented byCambridge Systematics, Inc.

Transportation leadership you can trust.

State and Local Freight DataUsing National Freight Data for Local Planning

FHWA Talking Freight Seminar

May 2010

Dike Ahanotu, Ph.D.

Topics

National freight data sources – CFS, FAF, TRANSEARCH» Considerations for local freight planning efforts» NCFRP 20 – preliminary findings and next steps

Developing local freight data» Potato production example» Diesel production example

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National DataBTS Commodity Flow Survey - Overview

Why focus on CFS?» Basis of FHWA FAF2 » Dominant source for short-distance truck trips in

TRANSEARCH

Shipper survey of select industry sectors» Over 100,000 responses across most significant industries» Survey data expanded to 117 regions, 7 modes, and

23 commodities

Very useful for freight planning efforts

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National DataBTS Commodity Flow Survey – Missing Data

Sectors included in the survey» Mining, manufacturing, wholesale trade, select retail trade

industries, auxiliary establishments

Sectors not included in the survey» Farms, forestry, fishing, utilities, construction, government-

owned entities, transportation, most retail and services industries, foreign-based businesses importing into the U.S.

Missing data due to » Cells with small or zero values» Proprietary concerns when a single firm dominates a

particular O-D-M-C combination

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National DataBTS Commodity Flow Survey – Survey Mechanism

40 shipments requested from each shipper» Shipments reported at regular intervals (e.g., every 20th

shipment)» Multistop tours recorded as single shipments from

shipper to each stop along the tour

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National DataBTS Commodity Flow Survey – Summary of Issues

Several sectors not included – many are local truck trips

Trip chaining not captured

Full supply chain of import flows not included

Open questions» How are firms handled that cross industries (e.g., Hewlett Packard)?

» How did individual firms interpret shipment information requests? For example, did they include parcels?

» How did firms respond when they had incomplete information?

» How did firms respond when they had unsophisticated shipment record storage systems?

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National DataFHWA Freight Analysis Framework

Key features» Built entirely from public data sources» Transforms CFS data to complete freight flow database» Log-linear modeling and IPF used to fill in “zero” cells» Employment, population, and VIUS truck VMT data used

for out-of-scope CFS sectors

Biggest issues» Technique to fill in zero cells is somewhat problematic

for data suppressed for proprietary reasons» No field data used to validate methods used for

out-of-scope sectors » Relationships will not hold true for all localities

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National DataTRANSEARCH

Privately maintained freight flow database

Off-the-shelf, county-level freight flow data available relatively quickly

Key features of methodology» Heavier reliance on economic data» Motor carrier data exchange used to distribute truck trips» CFS still heavily used for shorter distance truck trips

Biggest issues» Similar issues to CFS at local level

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NCFRP 20Summary of Preliminary Findings

Several attempts to disaggregate FHWA FAF

Less frequent efforts to develop ground-up freight flow data to supplement or substitute other sources

Very few efforts to validate the relationship between socioeconomic data and freight flows» Limited data indicate that this method works better for

some commodities rather than others

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NCFRP 20FAF2 Disaggregation Example

Method to correlate economic data to commodity tonnage flows (production data)

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SCTGSCTGCommodity Being Commodity Being

EstimatedEstimatedEmployment Data Used to Estimate the Employment Data Used to Estimate the

CommodityCommodity ““Fit”Fit”20-23 Various* Chemical Manufacturing 11%

10-15 Various** Mining (except oil and gas) 13%

9 Tobacco Products Beverage and Tobacco Product Manufacturing 15%

1 Live Animals/Fish Support Activities for Agriculture and Forestry 17%

16 Crude Petroleum Oil and Gas Extraction 21%

38 Precision Instruments Miscellaneous Manufacturing 34%

24 Plastics/Rubber Plastics and Rubber Products Manufacturing 43%

2 Cereal Grains Food Manufacturing, Farm Acres 48%

8 Alcoholic Beverages Beverage and Tobacco Product Manufacturing 50%

39 Furniture Furniture and Related Product Manufacturing 56%

4 Animal Feed Support Activities for Agriculture and Forestry 60%* SCTG 20-23 is Basic Chemicals, Pharmaceutical Products, Fertilizers, and Chemical Products and Preparations n.e.c.** SCTG 10-15 is Monumental or Building Stone, Natural Sands, Gravel and Crushed Stone, and Nonmetallic Minerals n.e.c.

NCFRP 20Next Steps (Preliminary)

Identify freight planning applications

Develop generic supply chain descriptions» High value, high volume, highly problematic

Assess methods for compiling subnational commodity flow data in terms of» Meeting needs of freight planning applications» Filling in gaps of existing commodity flow databases» Describing important supply chains

Collect small sample of new data to validate methods» May involve analysis of existing local databases

Develop guidebook on subnational commodity flow data11

Developing Local Freight Data

General methodology» Estimate value of commodity generated based on economic

output data from public sources or trade associations» Convert value to tonnages using sources such as CFS» Identify mode share from state or national data or

industry experts» Convert modal tonnage to vehicle data using sources

such as VIUS

Two Washington examples show range of applications for this methodology

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Washington Potato ExampleProduction

Potato production identified based on USDA and Washington State Potato Commission

13Source: WSDOT Development and Analysis of a GIS-Based Statewide Freight Data Flow Network; Goodchild, Jessup et al, 2009

Washington Potato ExampleProduction (continued)

Supply chain and potato processors identified by Washington State Potato Commission


Washington Potato ExampleProduction (continued)

Potato distribution identified by Commission survey

Freight flows were ultimately assigned to trucks and routed on the Washington highway network

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Major DestinationsMajor Destinations Lower BasinLower Basin Skagit ValleySkagit Valley Upper BasinUpper BasinEastern Washington 12.48% 2.03% 6.22%

Western Washington 14.29% 6.81% 6.40%

Oregon 2.31% 4.35% 1.25%

California 14.58% 40.72% 11.85%

Idaho 0.00% 0.00% 34.33%

States West of the Mississippi 22.01% 13.30% 12.76%

States East of the Mississippi 24.26% 23.58% 11.99%

Canada 8.85% 7.04% 2.91%

Mexico 0.14% 1.96% 0.25%

Other International 1.09% 0.20% 12.03%

Source: WSDOT Development and Analysis of a GIS-Based Statewide Freight Data Flow Network; Goodchild, Jessup et al, 2009

Washington Diesel Supply Chain Example

Diesel distribution data held in a multitude of locations by a number of agencies and private sector companies

WA DOA – regulates quality and quantity of fuel delivered at gas stations

WA Department of Ecology – regulates active underground storage tanks, publishes vessel entry data

U.S. EPA – regulates above-ground storage tanks

WA DOR – responsible for assessing and collecting fuel taxes at terminal locations

Other agencies monitor mode-specific activity (e.g., pipeline, waterborne activity, railroads)


Washington Potato ExampleProduction

Data stitched together from several different sources produced a multimodal freight flow picture


Conclusions on Collecting Local Freight Data

Some commodities will be much easier than others

May require a mix of actual data and estimated data» For example, could have good production data, but poor

distribution data» Trade associations and industry experts are critical

Likely cost-effective only for a select number of commodities or industries» Not cost-effective for developing entire commodity flow

databases

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State and Local Freight Data

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