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Implementing the Freight Transportation Data Architecture: Data Element Dictionary (2015)

Chapter: Chapter 7 - Resolving Differences in Data Element Definitions

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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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Suggested Citation:"Chapter 7 - Resolving Differences in Data Element Definitions." National Academies of Sciences, Engineering, and Medicine. 2015. Implementing the Freight Transportation Data Architecture: Data Element Dictionary. Washington, DC: The National Academies Press. doi: 10.17226/21910.
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92 C H A P T E R 7 7.1 Introduction The simple fact that the data elements contained in the various datasets may be defined, mea- sured, and reported differently does not indicate that the datasets cannot be used in tandem or combined in a single analysis. The differences simply require that some effort be expended to normalize the data for the intended application. This chapter provides guidelines to reconcile, harmonize, and create statistical bridges and crosswalks to resolve differences in data element definitions when combining those elements for an analysis. It also provides guidance on circum- stances in which crosswalks may not be statistically sound. 7.2 Methodology Five main topics were chosen for bridge development because of their importance in many different facets of freight data analyses. These include place names, units of measurement, com- modity and industry classification systems, and modes of transport. The reconciliation process involved identifying the nature of the differences, identifying commonalities within the differ- ences, and determining whether the differences are statistically significant, or whether they are inconsequential for freight data analysis. For statistically significant differences, recommenda- tions are provided on whether a bridge should or should not be applied, the parameters required for applying each bridge, and limitations of the crossover methodologies. Several sources were used in developing bridges or crosswalks for each topic, including the RBCS, user guides, and metadata associated with each data source. These documents provided detailed attribute descriptions and caveats for using the data. Along with these primary sources, various secondary sources such as academic papers and online data guides were also utilized. An example is the crosswalk between the North American Industry Classification System (NAICS) and the Standard Industrial Classification (SIC) system developed by the NAICS Association.183 Each discussion follows the same general format: 1. Topic (e.g., “[Place Name] Bridges”). 2. Keywords—Search terms related to the topic of discussion. 3. Type of Bridge, which may include: – Taxonomic Bridges (if applicable)—These bridges apply to data differences that result from how the data elements are classified. – Temporal Bridges (if applicable)—These bridges apply to data differences that result from how the definitions of data elements vary over time. – Methodological/Analytical Bridges (if applicable)—These bridges apply to data differences that result from how the data is collected, processed, and disseminated by the various reporting agencies. Resolving Differences in Data Element Definitions

Resolving Differences in Data Element Definitions 93 4. Data Sources Discussed—A list of data sources included in each bridge discussion for taxo- nomic, temporal, and methodological bridges. This list can be expanded in future work to include additional data sources not cited in a topic’s discussion. Disclaimers • Privately held freight data sources are excluded from the bridging discussions at this time because of confidentiality concerns and the unavailability of certain data sources. Incorporat- ing private data into the data discussions in the future will enhance the value of the Freight Data Dictionary in addressing the full range of potential problems data users might encounter when working with freight data. • Data element names as they exist in the actual data sources are represented in ALL CAPS: for example, ORIGINID, DESTINATIONID, and COMMODITYID. 7.3 Place Name Bridges Keywords: country, state, county, city, place, metropolitan area Place-identifier data elements identify the origin-destination of freight movement or the loca- tion of an event (e.g., an accident). Place identifiers from one data source often cannot be used with data elements from other data sources given the taxonomic or methodological differences between data sources. The tables presented in this discussion can be used to bridge such dif- ferences so that place identifiers from different data sources can be used with one another for freight data analysis. Within tables 7-1, 7-2, and 7-3, place-identifier data elements for each data source are catego- rized into columns based on their geographic classification system (e.g., two-letter postal code vs. FIPS [Federal Information Processing Standard] code vs. full text name). Data elements from different sources that are located in the same column can be used with one another with no bridging required. If data elements from multiple sources are in different columns (i.e., different classification systems), however, users can determine which type of bridge needs to be performed and then implement the bridge using the appropriate conversion tables, which are included at the end of this discussion. Assume a data user wants to compare U.S. Waterway data for the state of Texas with County Business Pattern data for Texas. The state data elements table (Table 7-2) shows that the U.S. Waterway data element “STATE” uses a two-letter postal code, whereas the County Business Pat- tern data element “FIPSSTATE” uses the FIPS system. To bridge these two state data elements, users can consult the State Name Crosswalk (Table 7-3) to determine which FIPS state numeric code corresponds with the U.S. Postal Service’s Texas postal code. State and county FIPS codes can be further paired with census tracts and block groups. These two subsequent geographies form part of the larger regions using a 12-digit numeric code (see Figure 7-1). In the figure, the first two digits signify the state of Texas (48), followed by Travis County (453), the tract—in this case, Tract 7 (0007.00), and the block group (1). The tract num- ber is a six-digit number with two digits after a decimal. The first four digits identify the tract number and the digits after the decimal identify changes in subdivisions.184,185 7.3.1 Country Data Elements Table 7-1 identifies country data elements across multiple databases with similar reporting codes.

94 Implementing the Freight Transportation Data Architecture: Data Element Dictionary The following resources provide information on how to bridge data elements that identify the same geographic unit but use different classification systems or codes: • FIPS (PUB 10-4) Country Code to County Name (full text) To convert between FIPS country codes and country name (full text), users can consult the Geopolitical Entities and Codes resource developed by the National Geospatial-Intelligence Agency. Users should note that FIPS publication 10-4 was withdrawn by the National Institute of Standards and Technology (NIST) in 2008 as a Federal Information Processing Standard. • Four-digit Schedule C or ISO Code to Country Name (full text) To convert between four-digit Schedule C or ISO Country Codes and the full text country name, users can consult the Schedule C Country Codes and Descriptions page maintained by the US Census Bureau. Figure 7-1. Sample 12-digit place identifier using numeric code. Two-letter Abbreviation (FIPS PUB 10-4) Four-digit Country Code (Schedule C or ISO Code) Full Name (text) Air Carrier Statistics ORIGINCOUNTRY DESTCOUNTRY OriginCountryName DestCountryName Carload Waybill Sample ORIGIN RAILROAD COUNTRY CODE FIRST (SECOND, THIRD FOURTH, FIFTH, SIXTH) INTERCHANGE RAILROAD COUNTRY CODE TERMINATION RAILROAD COUNTRY ROAD U.S. Waterway Data CTRYCODE CTRY_F CTRY_C ITCTRY National Agriculture Statistics Service (NASS) COUNTRY_CODE North American Transborder Freight Data (Transborder) COUNTRY National Ballast Information Clearinghouse Database Last Country Table 7-1. Country data elements.

Resolving Differences in Data Element Definitions 95 7.3.2 State Data Elements Table 7-2 identifies state data elements across multiple databases with similar reporting sche- mas. Consult Table 7-3 for a crosswalk between State Names, FIPS/ANSI/GSA Numeric State Codes, and two-character Postal Codes.186 7.3.3 County Data Elements Table 7-4 identifies county data elements across multiple databases with similar reporting codes. The Census 2010 FIPS Codes for Counties and County Equivalent Entities online data- base provides a crosswalk between County FIPS codes and counties and equivalent entities. Users should also consult the Census-published Substantial Changes to Counties and County Two-character Postal Code Two-digit FIPS/ANSI/ GSA Code Full Name (text) Air Carrier Statistics ORIGINSTATE DESTSTATE ORIGINSTATEFIPS DESTSTATEFIPS OriginStateName DestStateName Carload Waybill Sample ORIGIN STATE ALPHA TERMINATION STATE ALPHA County Business Patterns FIPSTATE Federal Railroad Administration Safety Database STATE Freight Analysis Framework STATE STATEFIPS DMS_ORGST DMS_DESTST U.S. Waterway Data STATE ORIGIN DEST Survey of Business Owners FIPST National Agricultural Statistics Service (NASS) STATE_ALPHA STATE_ANSI STATE_FIPS_CODEa state_name (State) Motor Carrier Management Information System (MCMIS) COUNTY_CODE_STATE ORIG_REPORT_STATE INSP_CARRIER_STATE SHIPPER_STATE REPORT_STATE STATE STATE_ISSUING_NUMBER PHY_ST Vehicle Travel Information System Documentation ABBREV STATECODE STATE FIPS CODE NAME Vehicle Inventory and Use Survey (VIUS) HB_STATE North American Transborder Freight Data (Transborder) USASTATE U.S. State CTAA Intermodal Terminals Database STFIPS Note: For state identifiers, FIPS, ANSI, and GSA codes are interchangeable. For a description of the relationship between American National Standards Institute (ANSI) Codes and FIPS Codes, users should consult the following resource: http://www.census.gov/geo/reference/ansi.html. a For this data source, GSA two-digit state codes also include “99” and “98” for US TOTAL and OTHER STATES, respectively; otherwise they match ANSI codes. Table 7-2. State data elements.

96 Implementing the Freight Transportation Data Architecture: Data Element Dictionary Name FIPS/ANSI/GSA Numeric Code Postal Code (USPS) Name FIPS/ANSI/GSA Numeric Code Postal Code (USPS) Alabama 01 AL Montana 30 MT Alaska 02 AK Nebraska 31 NE Arizona 04 AZ Nevada 32 NV Arkansas 05 AR New Hampshire 33 NH California 06 CA New Jersey 34 NJ Colorado 08 CO New Mexico 35 NM Connecticut 09 CT New York 36 NY Delaware 10 DE North Carolina 37 NC District of Columbia 11 DC North Dakota 38 ND Florida 12 FL Ohio 39 OH Georgia 13 GA Oklahoma 40 OK Hawaii 15 HI Oregon 41 OR Idaho 16 ID Pennsylvania 42 PA Illinois 17 IL Rhode Island 44 RI Indiana 18 IN South Carolina 45 SC Iowa 19 IA South Dakota 46 SD Kansas 20 KS Tennessee 47 TN Kentucky 21 KY Texas 48 TX Louisiana 22 LA Utah 49 UT Maine 23 ME Vermont 50 VT Maryland 24 MD Virginia 51 VA Massachusetts 25 MA Washington 53 WA Michigan 26 MI West Virginia 54 WV Minnesota 27 MN Wisconsin 55 WI Mississippi 28 MS Wyoming 56 WY Missouri 29 MO Table 7-3. State name crosswalk. FIPS/ANSI/GSA Code Full Name (text) Carload Waybill Sample ORIGIN FIPS CODE TERMINATION FIPS CODE Fatality Analysis Reporting System (FARS) COUNTY Federal Railroad Administration Safety Database CNTYCD COUNTY Freight Analysis Framework (FAF3) CTFIPS U.S. Waterway Data COUNTY1 National Agricultural Statistics Service (NASS) COUNTY_ANSI COUNTY_NAME (COUNTY) Motor Carrier Management Information System (MCMIS) COUNTY CODE (COUNTY_CODE) COUNTY NAME Highway Performance Monitoring System COUNTY_CODE Vehicle Travel Information System Documentation COUNTYCODE COUNTY FIPS CODE Note: For state identifiers, FIPS, ANSI, and GSA codes are interchangeable. For a description of the relationship between American National Standards Institute (ANSI) codes and FIPS codes, users should consult the following resource: http://www.census.gov/geo/ reference/ansi.html. Table 7-4. County data elements.

Resolving Differences in Data Element Definitions 97 Equivalent Entities187 and the Missouri Census Data Center Geographic Correspondence Engine (MABLE/Geocorr)188 when working with county place-identifier data elements to be aware of county boundary changes that have occurred since 1970 and may influence the accuracy of bridging county definitions between multiple years. In 2010, for example, Petersburg Borough (02-195) in Alaska was created from part of former Petersburg Census Area (02-195) and part of Hoonah-Angoon Census Area (02-105). In Virginia, Bedford (independent) city (51-515) was changed to town status and added to Bedford County (51-019).189 7.3.4 Statistical/Economic Area Data Elements Table 7-5 identifies statistical/economic area data elements across multiple databases with similar reporting codes. Below are recommendations for bridging data elements that identify the same geographic unit but use different classification systems or codes: • For a crosswalk between the five-digit U.S. DOT codes for ORIGINCITYMARKETID and DESTCITYMARKETID used in the Air Carrier Statistics database and corresponding metro- politan areas, users can consult the data definitions provided at BTS.gov.190 • For a crosswalk between three-digit Business Economic Area (BEA) codes used in the Carload Waybill Sample and corresponding metropolitan areas, see the STB BEA Codes (Table 7-5).191 • For appropriate concordances between Metropolitan Statistical Areas, Micropolitan Statisti- cal Areas, Metropolitan Divisions, and Combined Statistical Areas, see the BEA Statistical Areas online conversion tool.192 7.3.5 City/Other Place Name Data Elements Table 7-6 identifies city and other place name data elements across multiple databases with similar reporting codes. Following are recommendations for bridging data elements that identify the same geographic unit but use different classification systems or codes: • For a crosswalk applicable to FIPS/ANSI place codes, users can consult the 2010 ANSI Codes for Places online conversion tool.193 • Standard Point Location Codes use a six-digit system of nested two-digit codes with the fol- lowing pattern: STATE–COUNTY–CITY (POINT). These two-digit codes are based off FIPS codes; therefore, to derive the correct FIPS City code from the SPLC code, users can simply look at the last two digits.194 Metropolitan Statistical Area (Name) Combined Statistical Areas Business Economic Area (BEA) Codes Other Air Carrier Statistics ORIGINCITYMARKETID a DESTCITYMARKETID Carload Waybill Sample ORIGIN SMSA TERMINATION SMSA ORIGIN BEA AREA TERMINATION BEA AREA County Business Patterns MSA a Origin Airport, City Market ID. City Market ID uses an identification number assigned by US DOT to identify a city market. This field can be used to consolidate airports serving the same city market. Table 7-5. Statistical/economic area data elements.

98 Implementing the Freight Transportation Data Architecture: Data Element Dictionary 7.4 Units of Measurement Bridges Keywords: area, distance, value, monetary, volume, weight Temporal, methodological, or taxonomic differences exist between units of measurements in the various data sources. A user may wish, for example, to compare the shipping weight of a commodity in one data source that uses metric tonnage as the unit of measurement with that from another data source that uses hundredweight. Section 7.4.1 provides guidance on situa- tions in which it is appropriate to use similar data sources with disparate units of measurement, and presents several bridges and crosswalks that users can employ when conducting freight data analysis. 7.4.1 Area Foreign Trade Statistics (FTS) In 1989 the United States adopted the Harmonized Commodity Description and Coding System, commonly referred to as the Harmonized System (HS), to classify exports and imports. Given that this system collects information based on the metric standard, Table 7-7 can assist users in converting measures of area from other data sources to metric quantities and values for use with foreign trade statistics.195 Full Name (text) Standard Point Location (SPLC) Code FIPS Place Code Air Carrier Statistics ORIGINCITYNAME DESTCITYNAME Carload Waybill Sample ORIGIN SPLC a DESTINATION SPLC County Business Patterns CITY Federal Railroad Administration Safety Database CITYNAM city CITYCD Motor Carrier Management Information System (MCMIS) SHIPPER_CITY CITY CITY_CODE a City can be inferred from the SPLC database published by the National Motor Freight Traffic Association (NMFTA) – Information available at http://www.nmfta.org/pages/splc. Table 7-6. City and other place name data elements. Reported Units of Quantity Name (Abbreviation) HS Units of Quantity Name (Abbreviation) Multiplication Factor to Convert Reported Units to HS Units Square cenmeter (SCM) Square meter (SQM) 0.0001 Square meter (SQM) Square cenmeter (SCM) 10000 Square feet (SFT) Square meter (SQM) 0.0929 Square inch (SQT) Square meter (SQM) 0.0006452 Square inch (SQT) Square cenmeter (SCM) 6.452 Square yard (SYD) Square meter (SQM) 0.8361 Thousand square ­ (MSF) Square meter (SQM) 92.9 Table 7-7. Converting units of quantity to HS (metric) units.

Resolving Differences in Data Element Definitions 99 Topologically Integrated Geographic Encoding and Referencing (TIGER) The data elements ALAND and AWATER, which report land area and water area, respectively, report area in square feet. Table 7-8 provides a crosswalk between square footage and other com- monly used measures of area found in freight data sources.196 (Note: use Table 7-7 to convert to metric units of measurement). 7.4.2 Distance Air Carrier Statistics The Air Carrier Statistics database uses distance intervals to classify flight segment distances under the data element DISTANCEGROUP. Users wishing to compare Air Carrier Statistics flight segments distances to distance measurements from other data sources can use Table 7-9. Also included in this table is the equivalent distance measurement in kilometers.197 Area Unit of Measurement Area Equivalent 1 square mile 27,878,400 square feet 1 square mile 640 acres 1 square mile 258.999 hectares 1 acre 43,560 square feet 1 acre 0.0015625 square miles 1 acre 0.404686 hectares 1 hectare 2.47 acres 1 hectare 0.99386 square miles Table 7-8. Crosswalk between square footage and other measures of area. “DistanceGroup” Code Distance Interval Metric Equivalent “DistanceGroup” Code Distance Interval Metric Equivalent 1 < 500 miles 805 km 11 5,000 - 5,499 miles 8,046.7 - 8,850 km 2 500 - 999 miles 805 - 1,608 km 12 5,500 - 5,999 miles 8,851 - 9,654 km 3 1,000 - 1,499 miles 1,609.34 - 2,412 km 13 6,000 - 6,499 miles 9,656 - 10,459 km 4 1,500 - 1,999 miles 2,414 - 3,218 km 14 6,500 - 6,999 miles 10,461 - 11,264 km 5 2,000 - 2,499 miles 3,218.69 - 4,022 km 15 7,000 - 7,499 miles 11,265 - 12,068 km 6 2,500 - 2,999 miles 4,023 - 4,826 km 16 7,500 - 7,999 miles 12,070 - 12,873 km 7 3,000 - 3,499 miles 4,828 - 5,631 km 17 8,000 - 8,499 miles 12,875 - 13,678 km 8 3,500 - 3,999 miles 5,633 - 6,436 km 18 8,500 - 8,999 miles 13,679 - 14,482 km 9 4,000 - 4,499 miles 6,437 - 7,240 km 19 9,000 - 9,499 miles 14,484 - 15,287 km 10 4,500 - 4,999 miles 7,242 - 8,045 km 20 9,500 - 9,999 miles 15,289 - 16,092 km Table 7-9. Comparing flight segment distances.

100 Implementing the Freight Transportation Data Architecture: Data Element Dictionary Foreign Trade Statistics (FTS) In 1989 the United States adopted the Harmonized Commodity Description and Coding System, commonly referred to as the Harmonized System (HS), to classify exports and imports. Given that this system collects information based on the metric standard, Table 7-10 can assist users in converting measures of distance from other data sources to metric quantities and values for use with foreign trade statistics.198 7.4.3 Dimensions (length, width, and depth) Carload Waybill Sample The Carload Waybill Sample reports dimensions using a coding system unique to this data source. For the data elements OUTSIDE WIDTH, OUTSIDE HEIGHT, and EXTREME OUT- SIDE HEIGHT, dimensions are reported using a four-digit code, where the first two digits rep- resent feet and the last two digits represent inches, rounded up to the next inch in the case of a fraction. The data element OUTSIDE LENGTH reports length using a five-digit code, with the first three digits representing feet and the last two digits represent inches. Users can consult Table 7-11 Reported Units of Quantity Name (Abbreviation) HS Units of Quantity Name (Abbreviation) Multiplication Factor to Convert Reported Units to HS Units Cenmeter (CM) Meter (MTR) 0.01 Foot (FT) Meter (MTR) 0.3048 Linear feet (LFT) Meter (MTR) 0.3048 Meters (MTR) Thousand meters (THM) 0.001 Thousand meters (THM) Meters (MTR) 1000 Thousand linear feet (MLF) Linear meter (LNM) 304.8 Yard (YD) Meter (MTR) 0.9144 Table 7-10. Converting measures of distance for use with FTS distance measures. Data Element Definition Example dimension (length, width, height, etc.) Corresponding Carload Waybill Sample code OUTSIDE WIDTH Measurement of outside width of car, including aachments projecng to greatest extent. 2 feet, 7 inches (0.79 meters) 0207 0208 OUTSIDE HEIGHT Measurement from top of rail to top of eaves at side of car. 2 feet, 7 1/2 inches (0.80 meters) 0208 EXTREME OUTSIDE HEIGHT Measurement from top of rail to locaon where extreme height occurs. 2 feet, 8 inches (0.81 meters) 0208 OUTSIDE LENGTH Distance between pulling faces of the couplers in normal posion. 22 feet, 3 inches (6.78 meters) 22 feet, 3 1/2 inches (6.79 meters) 22 feet, 4 inches (6.81 meters) 02203 02204 02204 Table 7-11. Carload waybill sample measures of dimension.

Resolving Differences in Data Element Definitions 101 as a guide for comparing measurements such as length, width, and height from other data sources with those reported by the Carload Waybill Sample. Vehicle Inventory and Use Survey (VIUS)199 The data element LENGTHTOTAL reports the total length of the vehicle or vehicle/trailer combination using a coding system unique to VIUS. Table 7-12 provides a crosswalk between the VIUS codes for vehicle length and length ranges that can be compared with vehicle lengths from other data sources; also included is the metric unit equivalent. 7.4.4 Monetary Carload Waybill Sample The Carload Waybill Sample classifies railroads based on their annual operating revenues as either Class I ($250 million or more), Class II ($20 million or more), or Class III ($0–$20 million). Users can apply the following formula to adjust a railroad’s operating revenues to eliminate the effects of inflation: ’ . ’ .( ) = × Annual Operating Revenue Current Year s Revenues 1991 Avg Index Current Year s Avg Index The average index (deflator factor) is based on the annual average Railroad Freight Price Index for all commodities.200 Commodity Flow Survey (CFS) Users should note that the total value of shipments—as measured by the CFS with the data element VALUE (MILLION $) and by the U.S. Gross Domestic Product (GDP)—provides dif- ferent measures of economic activity in the United States. These measures are not directly com- parable, and no bridge exists to directly correlate the two. The value of shipments, as measured by the CFS, is the market value of goods shipped from manufacturing, mining, wholesale, and select retail and service establishments, as well as warehouses and managing offices of multiunit LENGTHTOTAL code Length Metric Equivalent 01 < 16.0 feet < 4.88 meters 02 16.0 to 19.9 feet 4.877 to 6.07 meters 03 20.0 to 27.9 feet 6.10 to 8.50 meters 04 28.0 to 35.9 feet 8.53 to 10.94 meters 05 36.0 to 40.9 feet 10.97 to 12.47 meters 06 41.0 to 44.9 feet 12.50 to 13.69 meters 07 45.0 to 49.9 feet 13.72 to 15.21 meters 08 50.0 to 54.9 feet 15.24 to 16.73 meters 09 55.0 to 59.9 feet 16.76 to 18.26 meters 10 60.0 to 64.9 feet 18.29 to 19.78 meters 11 65.0 to 69.9 feet 19.812 to 21.31 meters 12 70.0 to 74.9 feet 21.34 to 22.83 meters 13 75.0 to 79.9 feet 22.86 to 24.35 meters 14 > 80.0 feet > 24.38 meters Table 7-12. VIUS codes and vehicle length measurements.

102 Implementing the Freight Transportation Data Architecture: Data Element Dictionary establishments. Broader in scope, the GDP is the value of all goods produced and services per- formed by labor and capital located in the United States.201 Foreign Trade Statistics (FTS) Users should note that the Census constant dollar series data used in FTS data does not match U.S. Bureau of Economic Analysis constant dollar series data because of the underlying cover- age differences between the current dollar National Income and Product Accounts (NIPA) and Census data.202 Users should note that adjustments are required to use Canadian import data to produce U.S. export data to make the two comparable with one another. Canadian imports are recorded at their U.S. point of origin and do not include inland freight to the port of exit in the United States. On the other hand, U.S. exports include inland freight to the U.S. port of exit and are recorded at the U.S. seaport, airport, or border port of export inside the United States. Canada adds an estimated 4.5% of the value to each transaction to cover inland freight to compensate for this discrepancy. Average monthly exchange rates as quoted by the Federal Reserve Board are applied to adjust the Canadian import data to U.S. dollars. A formula for converting U.S. total exports to cor- responding Canadian imports is provided at the U.S. International Trade in Goods and Services (FT900) web page. Air Carrier Statistics Beginning in 2006, numbers in the Schedule B-1, B-11, P-11, and P-12 data tables began fol- lowing the format of common public financial documents, such as reports filed with the Securities and Exchange Commission (SEC) or company financial statements.203 When using data from Air Carrier Statistics, use Table 7-13 to reconcile reporting differences between the pre- and post- 2006 financial reports:204 7.4.5 Time 7.4.5.1 Quarterly Reporting To compare quarterly data across data sources, users must ensure that the definition of quar- ters is consistent between each data source. Table 7-14 provides a crosswalk between the defi- nitions and coding systems for freight data sources that report quarterly data. Note that most freight data quarter definitions are different from those used by the Federal government for its fiscal year. Attribute After October 2006 Before October 2006 Revenues Report revenues as posive Report revenues as negave Expenses Report expenses as negative Report expenses as posive Profits Report profits as posive Report profits as negave Losses Report losses as negave Report losses as posive Net Income Report profits as posive Report losses as negave Report profits as negave Report losses as posive Table 7-13. Reconciling financial data before and after October 2006 in the Air Carrier Statistics.

Resolving Differences in Data Element Definitions 103 7.4.5.2 Military Time Fatality Analysis Reporting System (FARS) FARS records the time of death using two data elements, DEATH_HR and DEATH_MN, indicating the hour of death and minute of death, respectively. These times are reported using military time, also known as the 24-hour clock format. Data users can consult Table 7-15 for a conversion between military time (24-hour time) and the 12-hour time. As an example for the FARS database, if DEATH_HR = 14 and DEATH_MN = 55, the time of death would be 1455, or 2:55 p.m. Federal Railroad Administration (FRA) Safety Database The FRA Safety Database uses a similar convention to identify the hour and minute of highway-rail crossing accidents using the data elements TIMEHR and TIMEMIN, respectively. Consequently, users can use Federal Railroad Administration (FRA) Safety data with data from Air Carrier Statistics205 Carload Waybill Sample206 County Business Patterns207 Service Annual Survey208 U.S. Government Fiscal Year209 Quarter Definion Code Quarter Definion Code 210 Quarter Definion Quarter Definion Quarter Definion 1st Quarter Jan. 1 – March 31 1 Jan. 1 – March 31 13 Jan. 1 – March 31* Jan. 1 – March 31 Oct. 1 – Dec. 31 2nd Quarter April 1 – June 30 2 April 1 – June 30 14 – April 1 – June 30 Jan. 1 – March 31 3rd Quarter July 1 – Sept. 30 3 July 1 – Sept. 30 15 – July 1 – Sept. 30 April 1 – June 30 4th Quarter Oct. 1 – Dec. 31 4 Oct. 1 – Dec. 31 16 – Oct. 1 – Dec. 31 July 1 – Sept. 30 *County Business Patterns reports quarterly payroll estimates for the first quarter only. Table 7-14. Crosswalk of time definitions and coding systems used in freight data sources. 12-Hour Time Military Time (24 Hour) 12-Hour Time Military Time (24 Hour) Midnight 0000 or 0000 hours Noon 1200 or 1200 hours 1:00 a.m. 0100 or 0100 hours 1:00 p.m. 1300 or 1300 hours 2:00 a.m. 0200 or 0200 hours 2:00 p.m. 1400 or 1400 hours 3:00 a.m. 0300 or 0300 hours 3:00 p.m. 1500 or 1500 hours 4:00 a.m. 0400 or 0400 hours 4:00 p.m. 1600 or 1600 hours 5:00 a.m. 0500 or 0500 hours 5:00 p.m. 1700 or 1700 hours 6:00 a.m. 0600 or 0600 hours 6:00 p.m. 1800 or 1800 hours 7:00 a.m. 0700 or 0700 hours 7:00 p.m. 1900 or 1900 hours 8:00 a.m. 0800 or 0800 hours 8:00 p.m. 2000 or 2000 hours 9:00 a.m. 0900 or 0900 hours 9:00 p.m. 2100 or 2100 hours 10:00 a.m. 1000 or 1000 hours 10:00 p.m. 2200 or 2200 hours 11:00 a.m. 1100 or 1100 hours 11:00 p.m. 2300 or 2300 hour Table 7-15. Converting military time to 12-hour time.

104 Implementing the Freight Transportation Data Architecture: Data Element Dictionary the Fatal Analysis Reporting System (FARS) with no conversion necessary. Consult Table 7-15 for a conversion between military time (24-hour time) and the 12-hour time. 7.4.6 Volume Foreign Trade Statistics (FTS) In 1989 the United States adopted the Harmonized Commodity Description and Coding System, commonly referred to as the Harmonized System (HS), to classify exports and imports. As this system collects information based on the metric standard, Table 7-16 can assist users in converting measures of volume from other data sources to metric quantities and values for use with foreign trade statistics.211 U.S. Waterway Data212 The data element NRT or “Vessel Net Tonnage” reports the volume of space available for the accommodation of passengers and the stowage of cargo. NRT can be determined using the fol- lowing formula, expressed in units of 100 cubic feet for each net ton: , , , ( ) = −Vessel Net Tonnage gross tonnage volume of space used for accommodating vessel master officers crew navigation and propelling machinery in100 cubic feet per ton Users should note that NRT should not be confused with a tonnage capacity, as it simply expresses a volume capacity for passengers and cargo. 7.4.7 Weight Foreign Trade Statistics (FTS) In 1989 the United States adopted the Harmonized Commodity Description and Coding System, commonly referred to as the Harmonized System (HS), to classify exports and imports. Reported Units of Quantity Name (Abbreviation) HS Units of Quantity Name (Abbreviation) Multiplication Factor to Convert Reported Units to HS Units Cord (CD) Cubic meter (CBM) 2.550 Cubic cen meter (CC) Liter (LTR) 0.001 Cubic meter (CBM) Liter (LTR) 1000. Cubic meter (CBM) Thousand cubic meters (TCM) 0.001 Gallon (GAL) Liter (LTR) 3.785 Gallon (GAL) Barrel (BBL) 0.02381 Liter (LTR) Cubic meter (CBM) 0.001 Proof gallon (PFG) Proof liter (PFL) 3.785 Thousand cubic meters (TCM) Cubic meters (CBM) 1000. Thousand cubic feet (MCF) Thousand cubic meters (TCM) 0.02832 Wine gallon (WG) Liter (LTR) 3.785 Table 7-16. Conversion measures for volume.

Resolving Differences in Data Element Definitions 105 Given that this system collects information based on the metric standard, Table 7-17 can assist users in converting measures of weight from other data sources to metric quantities and values for use with foreign trade statistics.213 Vehicle Inventory and Use Survey (VIUS) The data elements VIUS_GVW (Gross Vehicle Weight Based on Reported Average Weight) and WEIGHTAVG (Average Weight of Vehicle or Vehicle/Trailer Combination) use the coding system shown in Table 7-18 to report average weights. In addition, the data element WEIGHT_ SIZE classifies the average weight of the vehicle or vehicle/trailer combination in four categories: “Light,” “Medium,” “Light-Heavy,” and “Heavy-Heavy.”214 Use Table 7-18 to find the equiva- lent VIUS codes, Weight Range, and VIUS Weight Size class, or to compare other weight-related measurements when using data elements from other data sources. Reported Units of Quantity Name (Abbreviation) HS Units of Quantity Name (Abbreviation) Multiplication Factor to Convert Reported Units to HS Units Barrel (BBL) Thousand cubic meters (TCM) 0.000159 Barrel (BBL) Liter (LTR) 159 Clean yield pound (CYP) Kg dry rubber content (KDR) 0.4536 Content pound (CLB) Content kilogram (CKG) 0.4536 Content pound (CLB) Clean yield kilogram (CYK) 0.4536 Clean yield pound (CYP) Clean yield kilogram (CYK) 0.4536 Cubic foot (CF) Cubic meter (CBM) 0.02832 Cubic yard (CYD) Cubic meter (CBM) 0.7646 Content ton (CTN) Content metric ton (CTN) 0.9072 Content short ton (CST) Content metric ton (CTN) 0.9072 Gram (GM) Kilogram (KG) 0.001 Gross pound (GLB) Gram (GM) 453.6 Gross pound (GLB) Gross kilogram (GKG) 0.4536 Gross pound (GLB) Kilogram (KG) 0.4536 Gross metric ton (GTN) Metric Ton (TON) 1 Hundredweight (CWT) Kilogram (KG) 45.36 Hundredweight (CWT) Metric Ton (TON) 0.04536 Kilogram (KG) Metric ton (TON) 0.001 Kilogram (KG) Gram (GM) 1000 Long ton (LTN) Kilogram (KG) 1016 Long ton (LTN) Metric Ton (TON) 1.016 Metric ton (TON) Kilogram (KG) 1000 Metric ton (TON) Gross metric ton (GTN) 1 Metric ton (TON) Barrel (BBL) 7.33331 Ounces (OZ) Kilogram (KG) 0.02835 Ounces (OZ) Grams (GM) 28.35 Table 7-17. Conversion measures for weight.

106 Implementing the Freight Transportation Data Architecture: Data Element Dictionary 7.5 Commodity Classification Bridges Keywords: bridge, crosswalk, Harmonized System (HS), NAICS (North American Industry Classification System), Standard Transportation Commodity Code (STCC), Standard Classification of Transported Goods (SCTG), Standard International Trade Classification (SITC), Hazardous Materials, time, temporal 7.5.1 Commodity Code Resolution Users wishing to compare commodity data from one data source with another source may have difficulties because different data sources often report commodities at varying levels of resolution, even when they use the same classification system. Although a commodity from data source A may be reported at the two-digit level, data source B may report that commodity at the six-digit level. This section provides guidance on when it is appropriate to use data elements that use the same commodity classification systems with one another, and presents methods for bridging data resolution discrepancies. Within each table, commodity codes for each data source are categorized into columns by data resolution. Data elements from different sources that are located in the same column can be used with one another with no bridging required. If data elements from multiple sources are in different columns (i.e., different data resolutions), these data elements can be bridged at the lowest data resolution (i.e., two-digit). Harmonized System (HS) Codes HS codes provide an increasing level of detail about a given commodity as the number of digits increases. Use the table below to identify which HS codes from the listed data sources can be used with one another for data analysis. As an example, Table 7-19 shows that Foreign Trade Statistics (FTS) commodity data can be bridged with Transborder freight commodity data at the six-digit level with no data manipulation required. If a user wants to bridge 10-digit FTS data VIUS CODE Weight Range Weight Size Class 01 Less than 6,001 pounds Light (10,000 pounds or less) 02 6,001 to 8,500 pounds 03 8,501 to 10,000 pounds 04 10,001 to 14,000 pounds Medium (10,001 to 19,500 pounds) 05 14,001 to 16,000 pounds 06 16,001 to 19,500 pounds 07 19,501 to 26,000 pounds Light-Heavy (19,501 to 26,000 pounds) 08 26,001 to 33,000 pounds Heavy-Heavy (26,001 pounds or more) 09 33,001 to 40,000 pounds 10 40,001 to 50,000 pounds 11 50,001 to 60,000 pounds 12 60,001 to 80,000 pounds 13 80,001 to 100,000 pounds 14 100,001 to 130,000 pounds Table 7-18. VIUS codes for weight range and weight size class.

Resolving Differences in Data Element Definitions 107 with six-digit Transborder freight data, however, they would need to remove the last four digits from the FTS commodity code to bridge the two data sets. Standard Classification of Transported Goods (SCTG) Codes SCTG codes provide an increasing level of detail about a given commodity as the number of digits increases. Use Table 7-20 to identify which SCTG data elements from the listed data sources can be used with one another for data analysis. As an example, the table shows that CFS commodity data can be bridged with FAF2 and VIUS commodity data at the two-digit level by truncating the five-digit data element COMMODITY down to two digits. Standard International Trade Classification (SITC) Codes SITC codes provide an increasing level of detail about a given commodity as the number of digits increases. Use Table 7-21 to identify which SITC data elements from the listed data sources can be used with one another for data analysis. As an example, the table shows that commodities reported in the Foreign Trade Statistics (FTS) database can only be used in conjunction with U.S. Waterway Data commodities at the two-digit level. Hazardous Material Codes Hazardous Material codes provide an increasing level of detail about a given commodity as the number of digits increases. Use Table 7-22 to identify which Hazardous Material code data elements from the listed data sources can be used with one another for data analysis. As an example, the table shows that MCMIS data can be bridged with FARS data at both the one- and four-digit resolutions after truncating the MCMIS four-digit codes. Harmonized System (HS) Data Source Two-Digit Four-Digit Six-Digit Ten-Digit Foreign Trade Statistics (FTS)215 available* available* COMMODITY COMMODITY North American Transborder Freight Database216 COMMODITY available* COMMODITY unavailable * Data at this resolution can be derived by truncating the longer commodity codes. Table 7-19. Bridging foreign trade commodity data with Transborder freight commodity data. Standard Classification of Transported Goods (SCTG) Data Source Two-Digit Three-Digit Four-Digit Five-Digit Commodity Flow Survey (CFS) 217 available* available* available* COMMODITY Freight Analysis Framework (FAF2)218 SCTG2 unavailable unavailable unavailable Vehicle Inventory and Use Survey (VIUS) 219 PRODUCT_PRINCPL unavailable unavailable unavailable * Data at this resolu‰on can be derived by trunca‰ng the longer commodity codes. Table 7-20. Using Standard Classification of Transported Goods (SCTG) data elements from various sources.

108 Implementing the Freight Transportation Data Architecture: Data Element Dictionary Miscellaneous Codes Related to Commodities The data elements presented in Table 7-23 provide specific details for commodities that are beyond what the Harmonized System (HS), Standard Transportation Commodity Codes (STCC), Standard Classification of Transported Goods (SCTG), Standard International Trade Classifica- tion (SITC), and hazardous material codes provide. Users should note that it is inappropriate to compare these data elements directly with similar data elements from other sources without further investigation. 7.5.2 Temporal Bridges within Classification Systems Data related to commodities may change over time as classification systems are refined and updated. This page provides methods for bridging temporal differences within the Harmonized System (HS), North American Industry Classification System (NAICS), Standard Classification of Transported Goods (SCTG), and Standard International Trade Classification (SITC) com- modity classification systems. The applicable freight data sources are listed under each classifica- tion system heading. Harmonized System (HS) United Nations HS Conversion Tables The HS is regularly updated by the World Customs Organization (WCO) to accommodate the emergence of new and disappearance of previously existing products, with major revisions occurring in 1996, 2002, 2007, and 2012. The United Nations (UN) Comtrade database pro- vides concordance tables between current HS codes and previous versions, which are available online at http://unstats.un.org.222 Figure 7-2 shows which concordance tables are available for each HS version-pair. The concordance tables, which are available in separate Microsoft Excel files, provide direct con- versions for newer codes with codes for earlier versions. In addition to showing the corresponding Standard International Trade Classification (SITC) Data Source Two-Digit Three-Digit Four-Digit Five-Digit Foreign Trade Stascs (FTS)220 available* available* available* SITC SITC_CODE U.S. Waterway Data221 PMS_COMM unavailable unavailable unavailable * Data at this resoluon can be derived by truncang the longer commodity codes. Table 7-21. Using SITC data elements from various sources. Hazardous Material Codes Data Source One-Digit Four-Digit Fatality Analysis Reporng System (FARS)223 HAZ_CNO PHAZ_CNO HAZ_ID PHAZ_ID Motor Carrier Management Informaon System (MCMIS) 224 available* HAZMAT MATERIAL ID * Data at this resoluon can be derived by truncang the longer commodity codes. Table 7-22. Using Hazardous Material Code data elements from various sources.

Resolving Differences in Data Element Definitions 109 Data Source Data Element Definition Carload Waybill Sample225 UNIQUE SERIAL NUMBER To allow for unique idenficaon of waybills, the AAR/Railinc assigns a six-digit number to all waybills processed. Hardcopy waybills are assigned serial numbers in the 100,000 to 199,999 range. MRI waybills are assigned serial numbers in the 200,000 to 999,999 range and the 000,000 to 099,999 range. WAYBILL NUMBER The waybill number is the number an originating railroad document assigns to each waybill. Center for Transporta on Analysis Intermodal Terminals Database226 CARGO A three-digit code for the type of cargo or commodity group involved in the intermodal connecon Foreign Trade Sta s cs (FTS)227 USDA One-digit agriculture or non-agriculture product code Motor Carrier Management Informaon System (MCMIS)228 CARGO Description of cargo hauled by this carrier. A maximum of three cargo types are printed. HAZARDOUS MATERIALS CARRIED/SHIPPED Iden‚fies the type of hazardous material transported or shipped by the en‚ty and whether bulk (B), non- bulk (N), or all (A).Note: The conversion of the Hazardous Materials Data elements of the new Census File to the old is as follows: Bulk (B) = Tank (T), Non-Bulk (N) = Package (P), and All (A) = Both (B). HAZMAT S Type of hazardous material shipped by interstate and intrastate shippers. Coded same as HAZMAT C. Up to three hazardous materials may be printed. "B" indicates that the cargo is shipped in Bulk quan‚ties. "N" indicates that the cargo is shipped in Non-Bulk. "A" indicates cargo is shipped both in Bulk and Non- Bulk quan‚‚es. HAZMAT C Type of hazardous material carried by interstate and intrastate motor carriers. Up to three hazardous materials may be printed. "B" indicates that the cargo is carried in Bulk quan‚‚es. "N" indicates that the cargo is carried in Non-Bulk quan es. "A" indicates cargo is carried both in Bulk and Non-Bulk quantities. Naonal Agricultural Stastics Service (NASS)229 SECTOR Five high level, broad categories useful to narrow down choices (CROPS, ANIMALS & PRODUCTS, ECONOMICS, DEMOGRAPHICS, and ENVIRONMENTAL). GROUP Subsets within sector (e.g., under sector = CROPS, the groups are FIELD CROPS, FRUIT & TREE NUTS, HORTICULTURE, and VEGETABLES). COMMODITY The primary subject of interest (e.g., CORN, CATTLE, LABOR, TRACTORS, OPERATORS). U.S. Waterway Data230 CONTAINER Container Indicator PRINC_COMM Principal Commodity List AAR = Association of American Railroads Table 7-23. Detailed data elements that require further investigation before making comparisons.

110 Implementing the Freight Transportation Data Architecture: Data Element Dictionary HS codes between given years, the tables also indicate the relationship between the two HS versions that informed the method by which the conversions were performed (see Column D in Figure 7-3). The four types of relationships are as follows: • For a 1:1 relationship, the HS subheading is correlated with one and only one subheading in the previous HS. • For a 1:n relationship, the HS subheading is the result of merging several subheadings in the previous classification. • For an n:1 relationship, the HS subheading is a result of a split of one subheading in the previ- ous classification into several subheadings. • For an n:n relationship, the subheading is the result of a split and merge of several subheadings in the previous classification. A more detailed discussion on the methodology used to create the concordance tables, along with the potential shortcomings of these conversions, is available from Comtrade in an explana- tory document, Correlation and Conversion Tables Used in UN Comtrade.231 Concording U.S. Harmonized System (HS) Categories Over Time In Concording U.S. Harmonized System Categories Over Time, Pierce and Schott (2010) developed an algorithm to track changes in product codes to construct a comprehensive con- cordance of HS codes over time.232 Concordance files for HS codes from 1989–2009 are provided in an appendix that accompanies the paper and is available online.233 With sufficient knowledge of data analysis and statistical software, data users can use the algorithm code to customize or extend it to incorporate future revisions of HS categories. The state code used to build the concordance also is provided in the appendix to the paper by Pierce and Schott, and the data used in the algorithm are available within a .ZIP file located at Schott’s International Economics Resource Page, Trade Data, and Concordances.234 Figure 7-2. Concordance tables, HS 2007–2012. Figure 7-3. Conversion detail from concordance table.

Resolving Differences in Data Element Definitions 111 Standard International Trade Classification (SITC) The SITC system was introduced in 1950 by the United Nations (UN).235 The UN Comtrade database provides concordance tables between current SITC codes and previous versions, which are available online at unstats.un.org.236 Figure 7-4 shows which concordance tables are available for each SITC version-pair. The conversion tables, which are available in separate Microsoft Excel files, provide direct con- versions to newer codes from codes used with earlier versions. In addition to showing the cor- responding SITC codes for the given years, the tables indicate the relationship between the two SITC versions that informed the method by which the conversions were created (see the “Relation- ship” column in Figure 7-5). As with the HS, four types of relationships are possible in the SITC, as follows: • For a 1 to 1 (1:1) relationship, the SITC subheading is correlated with one and only one subhead- ing in the previous classification. • For an n to 1 (n:1) relationship, the SITC subheading is a result of a split of one subheading in the previous classification into several subheadings. • For a 1 to n (1:n) relationship, the SITC subheading is the result of merging several subheadings in the previous classification. • For the n to n (n:n) relationship, the subheading is the result of a split and merge of several sub- headings in the previous classification. A more detailed discussion of the methodology used to create the conversion tables, along with the potential shortcomings of these conversions, is provided in the document Correlation and Conversion Tables used in UN Comtrade.237 Figure 7-4. Conversion and correlation tables, SITC 4–SITC 1. Figure 7-5. Screenshot from correlation table between SITC revision 3 and SITC revision 2.

112 Implementing the Freight Transportation Data Architecture: Data Element Dictionary 7.5.3 Bridges across Classification Systems The discussions below provide methods for reconciling data elements across data sources that use different commodity classification systems. Harmonized System (HS) to Standard Classification of Transported Goods (SCTG) The SCTG has not been updated since 1996, when it was first introduced as a replacement to the Standard Classification of Goods system.238 The Freight Analysis Framework (FAF) reports annual tonnage and dollar valued freight flows using the same 43 two-digit SCTG classes used by the 2007 U.S. Commodity Flow Survey (CFS). Commodities reported using the 10-digit Har- monized Tariff Schedule (Schedule B for exports) must be translated to SCTG using a crosswalk developed for the purpose: Users can consult the crosswalk provided in Appendix D of The Freight Analysis Framework Version 3 (FAF3): A Description of the FAF3 Regional Database and How It Is Constructed (2011).239 Harmonized System (HS) to NAICS to Standard International Trade Classification (SITC) Bridge The U.S. Census Bureau provides a concordance table that allows for quick bridging between NAICS and HS commodity codes.240 Users can follow the instructions provided below to bridge these two systems for use in freight data analysis. Step 1. Open a web browser and go to: http://censtats.census.gov/. Step 2. Once at the Censtats Databases website, go to the International Trade Data subhead- ing and click on the link to the Concordances (see Figure 7-6). Step 3. Choose either the Import Concordance table or the Export Concordance table (see Figure 7-6). Step 4. Notice that the concordance tables contain the following dropdown menus (see Figure 7-7). • Classification system: End-Use,241 NAICS, SITC (Standard International Trade Classifica- tion, shown in Figure 7-7), HI-TECH242 categories, HS Codes • Year: 2008–2014 • Options to “Browse SITC code” or “Search code/description for SITC” for specific codes based on the chosen classification system Step 5. From the dropdown menus, select the classification system and year and press “Go,” then select the commodity of interest (e.g., under “Browse SITC code” as shown in the figure) and press “Go.” The table automatically generates a concordance table showing the corresponding Figure 7-6. Screenshot showing links on Censtats website.

Resolving Differences in Data Element Definitions 113 codes and descriptions used in the other classification systems. Figure 7-8 shows an example concordance table for soybean imports for each of the classification systems in 2013. Standard Classification of Transported Goods (SCTG) to Standard Transportation Commodity Code (STCC) A concordance bridge between SCTG and STCC classification codes is available for purchase as an online subscription through Railinc, a for-profit subsidiary of the Associate of American Railroads (AAR).243 A discussion about the difficulties associated with bridging SCTG with other commodity classification systems is available at the Statistics Canada website.244 7.6 Industry Classification Bridges Keywords: bridge, NAICS (North American Industry Classification System), concordance table, time, temporal, SIC, County Business Patterns, Commodity Flow Survey (CFS), industry, revision Users wishing to compare industry data from one data source with another source may have difficulties because different data sources often use different industry classification systems. The temporal differences within the same industry classification system across multiple years can also make freight data analysis difficult for users. This section provides guidance on when it is appropriate to bridge temporal differences across data sources using the same classification system, as well as crosswalks for converting between classification systems. 7.6.1 Temporal Bridges within Industry Classification Systems Data related to freight-related industries may change over time as classification systems are refined and updated. This page provides methods for bridging temporal differences in the North American Industry Classification System (NAICS). Figure 7-7. Screenshot of Imports Concordance table showing dropdown menus. Figure 7-8. Sample concordance table for soybean imports in 2013.

114 Implementing the Freight Transportation Data Architecture: Data Element Dictionary North American Industry Classification System (NAICS) The North American Industry Classification System (NAICS) is reviewed every 5 years for potential revisions so that the classification system can keep pace with the changing economy. The U.S. Census Bureau provides concordance tables in spreadsheet form to bridge changes in NAICS codes over time.245 These tables provide detailed descriptions of the direct relationships between classification sys- tems for each version of the NAICS from 1987 to 2012. Table 7-24 provides links to the available concordance tables from the US Census website.246 Data users should note that not all versions of NAICS can be bridged with one another (e.g., the 2012 NAICS can only be bridged directly with the 2007 NAICS), as additional concordances are needed to bridge larger gaps in time between NAICS versions. Figure 7-9 presents an example of how the concordance tables appear when opened. This fig- ure shows the relationship between 2012 and 2007 NAICS codes for the farming industry. Note Figure 7-9. Screenshot showing a sample concordance table. 2012 NAICS 2012 NAICS to 2007 NAICS247* 2007 NAICS 2007 NAICS to 2012 NAICS248 2007 NAICS to 2002 NAICS249 2002 NAICS 2002 NAICS to 2007 NAICS250 2002 NAICS to 1997 NAICS251 2002 NAICS to 1987 NAICS252 1997 NAICS 1997 NAICS to 2002 NAICS253 * Print readers are referred to the end- notes for Chapter 6 and Chapter 7, which include urls for the online documents that contain the concordances. Table 7-24. Links to concordance tables from U.S. Census website.*

Resolving Differences in Data Element Definitions 115 that the concordance table includes six-digit NAICS codes for both 2012 and 2007, as well as col- umns labeled “NAICS Title,” describing the industry/piece of the industry (e.g., potato farming). NAICS Changes in County Business Patterns As Table 7-25 shows, the 1998, 2003, and 2008 County Business Patterns datasets lagged by 1 year in terms of the NAICS classification system used. Adoption of the 2012 NAICS system changed the way industries are classified in the County Business Patterns dataset. The update added several new industries, and realigned a significant number of other industries. The major changes to the 2012 NAICS are discussed in the balance of this section, along with methods that users can employ to bridge the new and old classification systems when using County Business Patterns data.254 New Industries For the 2012 NAICS, five new industries were derived from 2007 NAICS Code 22119, Other Electric Power Generation (see Table 7-26). Realignment and Consolidation of Industries The 2012 NAICS included a comprehensive review of the manufacturing sector, which resulted in the consolidation of more than 20% of the manufacturing industries coded in 2007. Industry realignment, consolidation, and other changes also affected other industrial sectors, including Construction, Wholesale Trade, Retail Trade, and Accommodation and Food Ser- vices. Table 7-27 shows how several 2007 NAICS electronics store categories were combined into a single 2012 NAICS industry. A more detailed discussion about the classification changes and realignment of industries introduced in the 2012 County Business Patterns can be found at the Economic Census, Industry Classification Updates web page.255 Year Range of Data Data Classified 2012 to present NAICS 2012 2008 to 2011 NAICS 2007 2003 to 2007 NAICS 2002 1998 to 2002 NAICS 1997 Table 7-25. County Business Patterns datasets, 1998, 2003, 2008. 2007 NAICS 2012 NAICS NAICS Industry Title 22119 Other Electric Power Genera on 221114 Solar Electric Power Genera on 221115 Wind Electric Power Genera on 221116 Geothermal Electric Power Genera on 221117 Biomass Electric Power Genera on 221118 Other Electric Power Genera on Table 7-26. Multiple 2012 NAICS industries derived from one 2007 NAICS code.

116 Implementing the Freight Transportation Data Architecture: Data Element Dictionary Bridging 2012 NAICS and 2007 NAICS To bridge 2012 NAICS industries with 2007 NAICS industries when using County Business Pattern data, users should consult the 2012 to 2007 NAICS Concordance File, which is provided on the NAICS website.256 7.6.2 Bridging across Industry Classification Systems Different freight data sources employ different classification systems to identify industries. It is often unclear, however, whether disparate systems can be used with one another for freight data analysis. This page helps fill this gap by providing conditions when it is appropriate to apply crosswalks and bridges for linking different industry classification systems with one another. Particular attention is paid to data sources that have undergone changes related to the North American Industry Classification System (NAICS) and the Standard Industrial Classification (SIC) system. County Business Patterns Since 1998, County Business Patterns industry data has been tabulated based on the North American Industry Classification System (NAICS), whereas prior releases were tabulated accord- ing to the Standard Industrial Classification (SIC). Table 7-28 provides the industry classifica- tion system used for specific versions of County Business Patterns data. Users can consult the Bridge between NAICS and SIC (1997), which was published as part of the 1997 Economic Census, to bridge 1998–2002 County Business Pattern data with prior releases that used the SIC. The sample table in Figure 7-10 shows how this resource bridges NAICS and SIC codes for elements of the construction industry. NAICS codes appear in bold 2007 NAICS 2012 NAICS NAICS Industry Title 443142 Electronics Stores 443112 Radio, Television, and Other Electronics Stores 443120 Computer and Soware Stores 443130 Camera and Photographic Supplies Stores 451220 Prerecorded Tape, Compact Disc, and Record Stores Table 7-27. Consolidation reflected in changes from the 2007 NAICS to 2012 NAICS. Year Range of Data Data Classified 2012 to present NAICS 2012 2008 to 2011 NAICS 2007 2003 to 2007 NAICS 2002 1998 to 2002 NAICS 1997 1988 to 1997 SIC 1987 1974 to 1987 SIC 1972 Table 7-28. Industry classification system for County Business Patterns data.

Resolving Differences in Data Element Definitions 117 type, with the corresponding SIC codes appearing in regular type just below. Tables from this resource also include the number of establishments, sales/receipts/revenue/shipments, annual payroll, and paid employees for both NAICS and SIC industries.257 To compare 2012 County Business Pattern industries with SIC industries from prior releases, users can consult the NAICS to SIC Crosswalk provided by the NAICS Association.258 The cross- walk directly compares industries between the two classification systems. Users should note that for Puerto Rico County Business Patterns data, the change from SIC to NAICS occurred in 2003.259 Commodity Flow Survey (CFS) The CFS has been updated four times since it was introduced in 1993. In addition to improve- ments to the design of the survey, sample size, survey methodology, and modes of transport, these updates have involved changes in industry classification related to the switch from the Standard Industrial Classification (SIC) system to NAICS beginning after 1997. Table 7-29 compares industry coverage between different versions of the CFS. A full description of changes between the various CFS versions is provided at the Bureau of Transportation Statistics CFS website.260 NAICS to SIC Crosswalk To use NAICS industries from the 2002, 2007, and 2012 CFS with those from the 1993 and 1997 CFS for freight data analysis, users can consult the NAICS to SIC Crosswalk provided by the NAICS Association.261 These tables provide direct comparisons of industries between the two classification systems. 7.7 Mode of Transport Bridges Keywords: mode, transport, air, rail, pipeline, truck, waterway, vessel, vehicle, multimodal, intermodal, unknown Definitions of freight transport modes tend to be consistent among freight data sources; however, taxonomic and temporal differences still exist in their naming and scope. For exam- ple, within the truck mode, trucks may be classified as “commercial trucks” or “large trucks” to differentiate them from “passenger pickup trucks.” Similarly, vessels may be referred to as “carriers,” “ships,” or “water mode of transport”; and rail mode may be referred to as “railroad” or “train.” Figure 7-10. Bridging NAICS and SIC data.

118 Implementing the Freight Transportation Data Architecture: Data Element Dictionary To illustrate these differences, Table 7-30 presents a summary of data element values related to mode of transport as reported in the most recent versions of the respective databases. For each mode of transport, the various names used in the database are listed. 7.7.1 Taxonomic Mode of Transport Bridges Different databases use different names for the same transport mode. Sometimes information on subcategories of transport modes is provided; the information provided may also vary among 1993 CFS and 1997 CFS 2002 CFS 2007 CFS 2012 CFS Establishments classified based on the 1987 Standard Industrial Classificaon System (SIC) Establishments classified based on 1997 North American Industry Classificaon System (NAICS) Establishments classified based on 2002 North American Industry Classificaon System (NAICS) Establishments classified based on 2007 North American Industry Classificaon System (NAICS) Publishers in Manufacturing Sector Not covered a Publishers in Informaon Sector a Publishers in Informaon Sector a Logging in Manufacturing Sector Not in scope. Classified in Agriculture (NAICS 113) Not in scope. Classified in Agriculture (NAICS 113) Not in scope. Classified in Agriculture (NAICS 113) Other Manufacturing (excluding Prinng Trade Services [SIC 279]) Other Manufacturing (excluding Prepress Services [NAICS 323122]) Other Manufacturing (excluding Prepress Services [NAICS 323122]) Other Manufacturing (excluding Prepress Services [NAICS 323122]) Mining (except mining services [SICs 108, 124, 138, 148] and oil and gas extracon [SICs 131 and 132]) Mining (except support acvies [NAICS 213] and oil and gas extracon [NAICS 211]) Mining (except support acvies [NAICS 213] and oil and gas extracon [NAICS 211]) Mining (except support acvies [NAICS 213] and oil and gas extracon [NAICS 211]) Wholesale (merchants and manufacturers' sales branches and government-owned liquor stores) Wholesale (merchants’ and manufacturers' sales branches and government liquor wholesales) Wholesale (merchants’ and manufacturers' sales branches and government liquor wholesales) Wholesale (merchants’ and manufacturers’ sales branches and government-owned liquor wholesales) Retail - catalog and mail order houses Retail (electronic shopping and mail order houses) Retail (electronic shopping and mail order houses, fuel dealers) Retail (electronic shopping and mail order houses, fuel dealers) Auxiliaries (e.g., warehouses) Auxiliaries (e.g., warehouses) Auxiliaries (e.g., warehouses) b Auxiliaries (e.g., warehouses) b Generalized and Specialized Freight Trucking General Freight Trucking (NAICS 4841)c and Specialized Freight Trucking (NAICS 4842)c a b c I Under NAICS, publishers were reclassified from Manufacturing (SIC 2711, 2721, 2731, 2741, and part of 2771) to Information (NAICS 5111 and 51223) and were excluded in the 2002 CFS. However, for the 2007 CFS, publishers were restored as an in-scope industry. ncludes only captive warehouses that provide storage and shipping support to a single company. Warehouses offering their services to the general public and other businesses are excluded. For tabulation and publication purposes, NAICS 484 is grouped with NAICS 4931. Although they are included in all surveys, the procedures for identifying in-scope auxiliary establishments have changed over the years. For the 1997 CFS, a managing office was considered in scope only if it had sales or end-of-year inventories in the 1992 Census. Research conducted prior to the 2002 CFS showed that not all managing offices with shipping activity in the 1997 CFS indicated sales or inventories in the 1997 Economic Census. Consequently the 1997 Economic Census results were not used to determine scope for managing offices in the 2002 CFS. For 2002, an auxiliary was included if it supported an in-scope or retail company. For the 2007 CFS, an advance survey of approximately 40,000 auxiliary establishments was conducted in 2006 to identify auxiliary establishments with shipping activity. Those that indicated that shipping was performed (as well as non-respondents) were included in the CFS sample universe. Table 7-29. Industry coverage between versions of the CFS.

Air Carrier Stascs Air b Border Crossing/Entry Data Truck Train Containers Carload Waybill Sample Ex-lake Lake cargo Intercoastal Coastwise Inland waterways Intermodal trailer- on-flat-car (TOFC) and container-on- flat-car (COFC) Unknown Commodity Flow Survey (CFS) Truck Private truck For-hire truck Railroad Water Inland water Deep sea Great lakes Mulple waterways Air (includes truck to/from airport) Pipeline Mulple modes Parcel, USPS, or courier Other and unknown modes Freight Analysis Framework (FAF3) Truck Rail Water Air Pipeline Mulple modes and mail Other and unknown No domesc mode Fatal Analysis Reporng System (FARS) Medium truck Heavy truck Single unit truck Combinaon truck EMS air EMS unknown mode Transported unknown source Other Not transported Not reported Highway Rail Water Air Pipeline Intermodal a Multimodal a Other/ Unknown Table 7-30. Summary of freight mode of transport values in the various freight data sources. (continued on next page)

Highway Rail Water Air Pipeline Intermodal a Multimodal a Other/ Unknown Vehicle Travel Informaon System (VTRIS) Single unit trucks Single trailer trucks Mul-trailer trucks a For purposes of Table 7-30, the category “multimodal” means freight movement by multiple modes (which may sometimes include transit), and the category “intermodal” means movement of a container, as defined by MARAD. b For a complete list of aircraft types, see the “aircraft type” field in the Air Carrier Statistics database. c Self-propelled vessel types include dry bulk carrier, container ship, general cargo carrier, specialized carrier, tanker, push boat, and tugboat. d Non-self-propelled vessel types include dry covered barge, dry open barge, deck barge lash /Seabee barge, other dry barge, single hull tank barge, double hull tank barge, and other tank barge. U.S. Waterway Data Self-propelled c Non-self- propelled d Vehicle Inventory and Use Survey (VIUS) Straight truck Truck-tractor Foreign Trade Stascs (FTS) Vessel Air All methods North American Transborder (Transborder) Truck Rail Vessel Air Pipeline Mail Free trade zones Other Table 7-30. (Continued).

Resolving Differences in Data Element Definitions 121 databases. The following sections provide further discussions on the data values that exist for the freight mode of transport data elements. Figures 7-11 and 7-12 show the inherent relationships between data value definitions for truck and vessel transport modes, respectively, as defined in the databases. These figures serve as a guide to identifying and bridging the different data values as used in their respective databases. Data users are advised to note that these values are restricted to freight modes of transport. Individual databases may contain other modes of transport such as buses, privately owned vehi- cles, and pedestrians. In addition, only the most recent versions of the databases are used in the following table. Users should consult the discussion of temporal differences in Chapter 6 for changes in individual databases over time. Air Carrier Statistics The Air Carrier Statistics database provides information for a single mode of transport: air. The data element AIRCRAFT TYPE, available only in the T-100 Domestic Segment, T-100 International Segment, and T-100 Segment data tables, contains a list of more than 250 aircraft models. Figure 7-11. Relationships between highway modes of transport as provided in the various data sources. * Self-propelled vessel types include dry bulk carrier, container ship, general cargo carrier, specialized carrier, tanker, push boat, and tugboat. ** Non-self-propelled vessel types include dry covered barge, dry open barge, deck barge lash /Seabee barge, other dry barge, single hull tank barge, double hull tank barge, and other tank barge. Figure 7-12. Relationships between “water” mode of transport definitions as provided in the various data sources.

122 Implementing the Freight Transportation Data Architecture: Data Element Dictionary Carload Waybill Sample The data element TYPE OF MOVE VIA WATER is a classification of water movement in the Carload Waybill Sample. Users should take note of the following water movement definitions as provided in the STB Waybill Reference Guide:262 • 0—Not a water movement • 1—Ex-Lake (from Great Lakes to reporting railroad) • 2—Lake Cargo (from rail to Great Lakes) • 3—Intercoastal (a continuous movement by U.S. rail that involves an Atlantic Ocean [or Gulf of Mexico] and Pacific Ocean movement, in either direction) • 4—Coastwise (a continuous movement involving rail at either end of a coastwise movement between ports on the East Coast [including the Gulf of Mexico] or between ports on the West Coast) • 5—Inland Waterways (a rail movement in combination with a barge movement on rivers and canals [waterways other than the Great Lakes] that is not considered a part of the rail move- ment [e.g., rail-car ferry]) • 9—Unknown Border Crossing/Entry The Border Crossing/Entry database contains several freight-related surface mode definitions of which users should be aware: • Container—Any conveyance entering the United States that is used for commercial purposes, either full or empty (including containers moving in-bond for the port initiating the bonded movements) • Rail container crossings (loaded and empty)—The number of full or empty rail containers arriving at a port • Train crossings—The number of arriving trains at a particular port • Truck container crossings (loaded and empty)—The number of full or empty truck containers arriving at a port • Truck crossings—The number of arriving trucks (does not include privately owned passenger pickup trucks) Commodity Flow Survey (CFS) The CFS provides information on mode of transport for a variety of modes and at various levels of detail. Below are the modes included in the database. Users should pay attention to the mode definitions when using mode-related data from other data sources.263 • Single mode shipments—Shipments transported by only one of the following modes: private truck, for-hire truck, rail, any water mode, pipeline, or air – Private truck—Trucks operated by employees of the establishment or the buyer/receiver of the shipment; includes trucks providing dedicated services to the establishment – For-hire truck—Trucks operated by common or contract carriers made under a negotiated rate – Rail—Any common carrier or private railroad – Inland water—Vessels or barges operating primarily in navigable waters, both within and along the borders of the United States, such as: � Rivers (Mississippi River, Saint Lawrence Seaway, etc.) � Lakes (excluding Great Lakes) � Along the shoreline but actually in the ocean (e.g., Intracoastal Waterway along the Atlantic and Gulf coasts, Inside Passage of Alaska, etc.) � Canals, harbors, major bays, and inlets

Resolving Differences in Data Element Definitions 123 – Great Lakes—Vessels or barges operating on the Great Lakes – Deep sea—Vessels or barges operating primarily in the open waters of the ocean, outside the borders of the United States – Multiple waterways—Shipments sent by any combination of Inland water, Great Lakes, and Deep sea; usually involving a transfer between vessels – Pipeline—Movement (of oil, petroleum, gas, slurry, etc.) through pipelines that extend to other establishments or locations beyond the shipper’s establishment; does not include aqueducts for the movement of water – Air—Any shipment sent via air mode to its destination, including shipments carried by truck to or from an airport • Multiple mode shipments—Shipments for which two or more of the following modes of transportation were used AND parcel delivery/courier/U.S. Parcel Post shipments: – Private truck or for-hire truck – Railroad – Water (inland water, Great Lakes, deep sea, and multiple waterways) – Pipeline – Air – Other mode – Parcel delivery/courier/U.S. Parcel Post—Includes ground and air shipments of pack- ages and parcels that weigh 150 pounds or less, and were transported by a for-hire carrier. (Parcel delivery/courier/U.S. Parcel Post shipments are all considered multiple mode because this category includes all parcel shipments [whether via ground or air] tendered to a parcel or express carrier. In defining this mode, these shipments were not combined with any other reported mode because, by their nature, parcel delivery/ courier/U.S. Parcel Post shipments are already multimodal. For example, if a respondent has reported a shipment’s mode of transportation as parcel and air, the shipment is treated as parcel only) – Other multiple modes—Shipments sent by any other mode combinations not specifically listed in the tables • Other mode(s)—Shipments for which no mode of transportation were reported, or were reported by the respondent as “other” or “unknown”; also includes shipments with a mode other than any of the listed modes (e.g., conveyor belt, animal power, and so forth). Foreign Trade Statistics (FTS) Mode of transport information in the FTS database is reported based on the method of trans- portation by which the merchandise arrives in or departs from the United States. Modes con- tained in the database include:264 • Air—Shipments leaving or arriving in the United States only by air • Vessel—Shipments leaving or arriving in the United States only by vessel • All Methods—Exports and general imports leaving or arriving in the United States by vessel, air, truck, rail, air mail, parcel post, and other methods of transport, including the following (which are excluded from the vessel and air statistics): – Mail and parcel post shipments (including those transported by vessel or air) – Imports and exports transported by (a) vessels moving under their own power or afloat and (b) aircraft flown into or out of the United States – Low-value shipments Freight Analysis Framework (FAF3) The FAF3 reports three main mode categories: domestic mode (DMS_MODE), foreign inbound mode (FR_INMODE), and foreign outbound mode (FR_OUTMODE). As the names

124 Implementing the Freight Transportation Data Architecture: Data Element Dictionary imply, domestic mode signifies the mode of transport used only within the United States. For- eign inbound and outbound modes represent the modes of transport for shipments entering the United States and exiting the United States, respectively. The mode of transport definitions used in the FAF3 include:265 • Truck—Private and for-hire truck; does not include truck moves categorized under “multiple modes and mail” or truck moves in conjunction with domestic air cargo. • Rail—Any common carrier or private railroad; does not include rail moves categorized under “multiple modes and mail.” • Water—Shallow draft, deep draft, Great Lakes, and intra-port shipments; does not include water moves categorized under “multiple modes and mail.” • Air (includes truck-air)—Shipments typically weighing more than 100 pounds that move by air or by a combination of truck and air in commercial or private aircraft; includes air freight and air express, but does not include shipments weighing 100 pounds or less, which are typi- cally categorized under “multiple modes and mail.” In the case of imports and exports by air, domestic moves by ground to and from the port (airport) of entry to or exit from the United States are categorized under truck mode. • Multiple modes and mail—Shipments by multiple modes and by parcel delivery services, U.S. Postal Service, or couriers. This category is not limited to containerized or trailer-on-flatcar shipments. • Pipeline—Crude petroleum, natural gas, and product pipelines; includes flows from offshore wells to land (which are counted as water moves by the U.S. Army Corps of Engineers); does not include pipeline moves categorized under “multiple modes and mail.” • Other (and unknown)—Shipments not classified elsewhere, such as flyaway aircraft, and shipments for which the mode cannot be determined. • No domestic mode—Shipments that have an international mode but no domestic mode; limited to import shipments of crude petroleum transferred directly from inbound ships to a U.S. refinery at the zone of entry. This is done to ensure a proper accounting of import flows while avoiding assigning flows to the domestic transportation network that are not used. North American Transborder Freight Database (Transborder) Transborder uses the DISAGMOT data field to identify mode of transportation for ship- ments entering and exiting the United States. The specific number codes for mode of trans- portation are:266 • 1 = Vessel • 3 = Air • 4 = Mail (U.S. Postal Service and courier shipments; cannot be further subdivided into specific modes such as air, rail, or truck) • 5 = Truck • 6 = Rail • 7 = Pipeline • 8 = Other, a category that includes: – Flyaway aircraft (aircraft moving under their own power from the aircraft manufacturer to a customer and not carrying any freight) – Powerhouse (electricity) – Vessels (moving under their own power) – Pedestrians carrying freight – Unknown and miscellaneous “other” • 9 = Foreign trade zones

Resolving Differences in Data Element Definitions 125 Before April 1995, the actual modes of transport for imports into foreign trade zones (FTZs) were unknown and were therefore categorized under DIGAMOT 8 (other). Beginning in April 1995, as the result of inquiries from users, DIGAMOT 9 (foreign trade zones) was added as a mode of transport. Although FTZs are treated as a mode of transport, the actual mode for a spe- cific shipment into or out of the FTZ remains unknown because U.S. Customs does not collect this information. Vehicle Inventory and Use Survey (VIUS) VIUS contains multiple data element fields describing highway transport modes. • BODYTYPE—This data element distinguishes between truck tractors and non-truck tractors. • TRUCK_SORTER—This data element distinguishes between small trucks (pickups, mini- vans, other light vans, and sport utilities) and large trucks. • AXLE_CONFIG—This data element provides the best option to determine truck types based on the number of axles on the power unit and the number of axles on any trailer(s) pulled. The following parent categories are available: 1. Straight Trucks (not pulling a trailer) and Truck Tractors (not pulling a trailer - not in use) 2. Straight Trucks (pulling a trailer) 3. Truck tractors (pulling a trailer) Data element fields that provide additional information on vehicle and trailer types are: • TRAILER—Single trailer pulled, double trailers pulled, or triple trailers pulled • TRAILERTYPE and TRUCKTYPE—Tractor or other truck • WEIGHT_SIZE—Average weight of vehicle or vehicle/trailer combination (light, medium, light-heavy, heavy-heavy) • VIUS_GVW—Gross vehicle weight based on reported average weight Vehicle Travel Information System (VTRIS) The VTRIS uses the FHWA vehicle classification system for highway transport modes. The categories described in the database are: 1. Motorcycles 2. Passenger cars 3. Single unit trucks (2-axle, 4-tire) 4. Buses 5. Single unit trucks (2-axle, 6-tire) 6. Single unit trucks (3-axle) 7. Single unit trucks (4-axle or more) 8. Single trailer trucks (4-axle or less) 9. Single trailer trucks (5-axle) 10. Single trailer trucks (6-axle or more) 11. Multi-trailer trucks (5-axle or less) 12. Multi-trailer trucks (6-axle) 13. Multi-trailer trucks (7-axle or more) 7.7.2 Temporal Mode of Transport Bridges Temporal mode of transport bridges are generally provided by the individual databases in an effort to reconcile changes in data collection efforts and reporting over time. The balance of this section provides additional information on these bridges.

126 Implementing the Freight Transportation Data Architecture: Data Element Dictionary Air Carrier Statistics Numerous airline mergers and acquisitions have occurred in the history of air freight. Such changes can alter how a particular airline is labeled in the Air Carrier Statistics. These mergers and acquisitions affect the data elements related to carrier identification within the Air Carrier Statistics (e.g., AIRLINEID, UNIQUECARRIER, and UNIQUECARRIERNAME). Users are advised to consult documents containing information on airline mergers. An example is the “List of airline mergers and acquisitions” webpage on Wikipedia.267 Commodity Flow Survey (CFS) The CFS has undergone changes over time in the way it describes water mode of transport. Table 7-31 provides a temporal bridge.268 The main changes over the years are the definition of the water mode of transport values. In 1993, these were classified as “inland water and/or Great Lakes” and “deep sea water.” As of 2012, the new names are “inland water” and “deep sea” modes of transport. Fatality Analysis Reporting System (FARS) In the FARS database, multiple data elements in the VEHICLE data file can be used to identify the highway transport mode of the commercial vehicle that was involved in a fatal crash. • V_CONFIG—This data element describes the general configuration of this vehicle. • BODY_TYP—This data element identifies a classification of the vehicle based on its general body configuration, size, shape, doors, and so forth. These data elements have undergone changes throughout the years to provide additional detail information of vehicle configuration and classification. Table 7-32 summarizes changes to the attribute codes of the vehicle configuration (V_CONFIG) data element over the years as provided in the FARS Analytical User’s Guide:269 Table 7-33 summarizes NHTSA’s vehicle body type classifications. The data elements BODY_ TYP (body type) and TOW_VEH (vehicle trailing) are used to determine vehicle categories, and their attribute codes can be found in the FARS Analytical User’s Guide.270 The data element “Transported to Medical Facility By” has undergone changes throughout the years to provide additional detail on the method of transportation provided to move an indi- vidual to a hospital or medical facility. The FARS Analytical Reference Guide lists these changes. 1993 1997, 2002, and 2007 2012 For-hire truck For-hire truck For-hire truck Private truck Private truck Private truck Rail Rail Rail Air Air Air Inland water and/or Great Lakes Shallow dra vessel Inland water Deep sea water Deep dra vessel Deep sea Pipeline Pipeline Pipeline Parcel delivery, courier, or U.S. Parcel Post Parcel delivery, courier, or U.S. Parcel Post Parcel delivery, courier, or U.S. Parcel Post Other mode Other mode Other mode Unknown Unknown Unknown Table 7-31. Bridging temporal changes in CFS mode names.

Resolving Differences in Data Element Definitions 127 The following list summarizes changes to the data element definitions based on the years the data was released: • 1977–2000 – 0 = No – 1 = Yes – 7 = Died at the scene (1999–2000) – 8 = Died en route (1999–2000) – 9 = Unknown • 2001–2006 – 0 = No – 1 = Yes – 9 = Unknown Aribute Codes 1991– 1994 1995– 2000 2001– 2009 2010– later 0 0 -- -- Not applicable, not a medium/heavy truck or bus -- -- 00 -- Not applicable, not a medium/heavy truck or bus or vehicle displaying a hazardous material placard -- -- -- 00 Not applicable 1 1 01 -- Single unit truck (2 axles, 6 res) -- -- -- 01 Single unit truck (2 axles and GVWR more than 10,000 pounds) 2 2 02 02 Single unit truck (3 or more axles) -- 3 03 -- Single unit truck (unknown number of axles, res) 3 4 04 -- Truck/trailer(s) -- -- -- 04 Truck pulling trailer(s) 4 5 05 05 Truck-tractor (bobtail, i.e., tractor only, no trailer) 5 6 -- -- Truck-tractor/semi-trailer -- -- 06 -- Truck-tractor/semi-trailer (one trailer) -- -- -- 06 Truck-tractor/semi-trailer -- -- 07 -- Truck-tractor/doubles (two trailers) -- -- -- 07 Truck-tractor/double -- -- 08 -- Tractor/triples (three trailers) -- -- -- 08 Truck-tractor/triple -- -- -- 10 Vehicle 10,000 pounds or less placarded for hazardous materials 6 7 19 -- Medium/heavy truck, cannot classify -- -- -- 19 Truck more than 10,000 pounds, cannot classify 7 8 -- -- Bus -- -- 20 -- Bus (seats for 9–15 occupants, including driver) -- -- -- 20 Bus/large van (seats for 9–15 occupants, including driver) -- -- 21 -- Bus (seats for more than 15 people, including driver, 2001–2006) -- -- 21 -- Bus (seats for 16 or more people, including driver, 2007–2009) -- -- -- 21 Bus (seats for more than 15 occupants, including driver, 2010 and later) -- -- 70 -- Light truck (van, mini-van, panel, pickup, sport ulity vehicle displaying a hazardous material placard) -- -- 80 -- Passenger car (only when displaying a hazardous material placard) -- -- -- 98 Not reported 9 -- -- 99 Unknown -- 9 99 -- Unknown if light or medium/heavy truck/bus Table 7-32. Temporal changes to attribute codes of V_CONFIG in FARS.

128 Implementing the Freight Transportation Data Architecture: Data Element Dictionary • 2007–2009 – 0 = Not transported – 1 = Yes, EMS – 2 = Yes, law enforcement – 3 = Yes, other – 4 = Yes, transported by unknown source – 9 = Unknown Classification (BODY_TYP) Data Year and Code 1975–1981 1982–1990 1991–Later Passenger Cars 9-Jan 01-11, 67 01-11, 17 (since 2010) Light Trucks & Vansd 43, 50-52, or (60 and tow_veh=0) 12, 40, 41, 48-51, 53-56, 58, 59, 68, 69, or (79 and tow_veh=0 or 9) 14-22, 24a, f, 25b,f, 28-41, 45-49, or (79 and tow_veh =0 or 9) Large Trucks 53-59, or (60 and tow_veh=1) 70-72, 74-76, 78, or (79 and tow_veh in 1-5h) 60-64, 66, 67e, 71, 72, 78, or (79 and tow_vehg in 1-4) Motorcycles 15-18 20-29 80-89 Buses 25-29 30-39 50-59 (55 van-based >10k lb. since 2011) Other/Unknown Vehicles 35-42, 44, 45, 99 13, 14, 42, 52, 73, 77, 80, 81, 82, 83, 88, 89, 90, 99 12, 13, 23f, 42, 65, 73, 90, 91, 92, 93, 94c, 95 (since 2012), 97, 99 Also, since 2004 (79 and tow_vehg =5 or 6) or 98 (since 2010) Passenger Vehicles 01-09, 43, 50-52, or (60 and tow_veh=0) 01-12, 40, 41, 48-51, 53- 56, 58, 59, 67-69, or (79 and tow_veh-0 or 9) 01-11, 14-22, 24a, 25b, 28-41, 45-49, or (79 and tow_veh=0 or 9), or 17 (since 2010) Ulity Vehicles (a.k.a. On/Off Road) 43 12, 56, 68 14-16, 19 Pickups 50 50, 51 30-39 Vans 51 40, 41, 48, 49 20-22, 24a, f, 25b,f, 28, 29 Medium Trucks 53, 54, 56 70, 71, 75, 78 60-62, 64, 67e, 71 Heavy Trucks 55, 57-59, or (60 and tow_veh=1) 72, 74, 76, or (79 and tow_veh in 1-5h) 63, 66, 72, 78, or (79 and tow_vehg in 1-4) Combina„on Trucks ((53-56, 60) and tow_veh=1), or 57-59 ((70-72, 75, 76, 78, 79) and tow_veh in 1-5h) or 74 ((60-64, 71, 72, 78, 79) and tow_vehg in 1-4) or 66 Single Unit Trucks (53-56, 60) and tow_veh =0 (70-72, 75, 76, 78, 79) and tow_veh in (0,9) (60-62,63,64,67,71,72,78,79) and tow_veh in (0,5,6g, 9) Notes: a Body type code 24 (van-based school bus) was added in 1993. When solely defining School Buses, be sure to include body type code 24. b Body type code 25 (van-based transit bus) was added in 1993. When solely defining Transit Buses, be sure to include body type code 25. c Body type code 94 (motorized wheelchair) was added in 1997 and deleted in 1998. d “Light trucks & vans” is frequently referred to as just “light trucks.” e Body type code 67 (medium/heavy pickup [e.g., Ford Super Duty 450/550]) was added in 2001. For the purposes of medium and heavy truck classifications, this body type will be considered a medium truck. f Body type codes for van-based bus (24, 25) and van-based motor home (23) were deleted in 2003. These attributes were removed because a review of coding used by FARS analysts revealed that these body types were rarely being captured. g New code was added in 2004 for Vehicle Trailing (tow_veh) - 5 (vehicle towing another motor vehicle). In 2009 the attribute was split into two to distinguish between fixed and non-fixed linkages (5 and 6). This attribute is not part of the selection criteria for the classifications “light,” “large,” “heavy,” or “combination truck.” Beginning with 2004, an unknown truck type (light/medium/heavy) that was towing another vehicle (BODY_TYP=79 and TOW_VEH=5,6) should be classified as Other/Unknown. This classification is subject to change. h From 1982 to 1990, Vehicle Trailing (TOW_VEH) attribute value 5 (yes, two or more trailing units) existed in 1982 only. Including “5” in the range from 1982 to 1990 does not affect the classification. Table 7-33. NHTSA’s vehicle body type classifications.

Resolving Differences in Data Element Definitions 129 • 2010–Current – 0 = Not transported – 1 = EMS air – 2 = Law enforcement – 3 = EMS unknown mode – 4 = Transported unknown source – 5 = EMS ground – 6 = Other – 8 = Not reported – 9 = Unknown North American Transborder Freight Database (Transborder) Temporal changes to the DISAGMOT mode of transport field in the Transborder freight database include the following: • July 1995—U.S. foreign trade zones (FTZs) were added as a mode of transport, recognizing the increased activity of FTZs with regard to imports from Mexico and Canada. Although FTZs are treated as a mode, the actual mode of transportation for a specific shipment into or out of the FTZ is unknown because the data is not collected by U.S. Customs. Before July 1995, FTZ shipments had been incorrectly included as rail shipments. • January 2004—Air and vessel modes of transport were added. • January 2007—The database was consolidated from 12 tables to three tables to improve import and export reporting by U.S. state, port of entry/exit, and commodity. The consolida- tion did not affect reporting on mode of transport. Vehicle Inventory and Use Survey (VIUS) The Vehicle Inventory and Use Survey (VIUS) program documentation provides a list of changes made to the data source over time with specific descriptions of the change and why it was implemented.271 The U.S. Census Bureau provides a “comparability” Excel spreadsheet that allows users to compare each data release to the previous data release by variable and valid response.272 Users should consult these references when bridging temporal differences within the data source. The following bullet points describe an example from the document showing the changes made to the AXLE CONFIGURATION and BODY/TRAILER TYPE data element fields from 1997 compared to 2002:273 Axle Configuration—The 2002 VIUS broke out additional axle response options and collapsed “util- ity” and “full” trailer. Truck tractors were allowed to indicate no trailer (or trailer axles) in the 2002 VIUS, whereas in the 1997 VIUS truck tractors were required to have a trailer and trailer axles present. Reason for Change—The additional axle and utility/full trailer changes were done at the request of data users. The Census Bureau attempted to correct erroneous 1997 VIUS editing by allowing truck tractors not in use to not report a trailer (and trailer axles). Body/Trailer Type—The 1997 VIUS asked respondents to classify their truck by selecting from a list of body types. If the vehicle was a truck-tractor, the respondent was asked to make their selection based on the trailer type most often pulled. The 2002 VIUS separated these, allowing single units to report both a body type and a trailer type (if applicable). Response options for both questions were modified. Reason for Change—Some body and trailer types are not interchangeable, so using separate questions reduced respondent error. The response option changes for both questions were based on data user input and questionnaire testing. Endnotes for both Chapter 6 and Chapter 7 are listed in the References section.

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TRB's National Cooperative Highway Research Program (NCFRP) Report 35: Implementing the Freight Transportation Data Architecture: Data Element Dictionary provides the findings of the research effort to develop a freight data dictionary for organizing the myriad freight data elements currently in use.

A product of this research effort is a web-based freight data element dictionary hosted by the U.S. Department of Transportation’s Bureau of Transportation Statistics (BTS).

The project web page includes a link to supporting appendices not printed with the report.

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