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Suggested Citation:"Chapter 3 - Freight Data Uses." 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 3 - Freight Data Uses." 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 3 - Freight Data Uses." 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 3 - Freight Data Uses." 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 3 - Freight Data Uses." 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 3 - Freight Data Uses." 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 3 - Freight Data Uses." 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 3 - Freight Data Uses." 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 3 - Freight Data Uses." 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 3 - Freight Data Uses." 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 3 - Freight Data Uses." 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 3 - Freight Data Uses." 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 3 - Freight Data Uses." 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 3 - Freight Data Uses." 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 3 - Freight Data Uses." 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 3 - Freight Data Uses." 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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10 C H A P T E R 3 3.1 Introduction NCFRP Report 22: Freight Data Cost Elements compiled a comprehensive list of 18 public- sector planning and decision-making functions from a review of research publications, govern- ment documents, and other sources. Public-sector organizations captured in the identification and definition of the functions include federal and state departments of transportation (state DOTs), metropolitan planning organizations (MPOs), and port/airport/railroad authorities, as well as economic development and environmental agencies (Holguín-Veras et al. 2013). To ensure consistency between the definitions of public-sector functions found in the study for NCFRP Report 22 and in this study, a literature review was conducted using the initial list of public-sector functions identified in NCFRP 22 plus two additional functions: modal shift analysis and freight performance measurements. Therefore, 20 freight-related public-sector functions were used in conducting the literature review. On completion of the literature review, the final set of public-sector functions was reduced to 16, given that some examples of freight data uses were found to be captured in other public-sector functions, as shown in Table 3-1 and Table 3-2. This chapter summarizes the results of the literature search by the NCFRP Project 47 research team on how freight data is being used in an innovative or unique way to perform a function. Given the limitations on the scope of the study, this chapter provides examples that illustrate how freight databases are being used. It is suggested that over time, data elements from new or additional studies be added to and cited in the web-based freight data dictionary. 3.2 Methodology TRB’s TRID database was used in the literature search (TRB 2014). The various functions were separately searched using combinations of keywords, index terms, and subject headings derived from the descriptions provided of each function. Results were limited to nationally based studies written in the English language. Research in progress and international studies were not included. Searches were honed and publication years were limited until a manageable number of rel- evant results (100–300 results) were achieved. All searches began with the publication year limit of 1994–2014, but many searches were limited to the most recent decade or even the past 5 years, depending on the depth of the topic. The review covered approximately 1,000 publications found in the transportation literature. From the manageable number of relevant results, the most recently published items with readily available full-text PDFs were selected. The literature review then examined how freight-related sources were utilized for that study. Freight Data Uses

Function Description (adapted from NCFRP Report 22) 1 Congestion Management Identify and monitor recurring and non-recurring congestion along road corridors and evaluate and recommend mitigation strategies 2 Operations/Services Develop, operate, and maintain transportation modes; improve the movement of goods and people and increase the safety and efficiency of the transportation system through enhanced management and operations coordination 3 Safety Planning and Analysis Implement and maintain integrated multimodal safety and transportation planning; the ultimate goal is to reduce crashes, injuries, and fatalities 4 Freight Mobility Planning Incorporate goods movement into the regional transportation planning process 5 Emergency Preparedness and Security Planning Increase the safety and security of the transportation system through enhanced coordination and communications among emergency responders 6 Economic Development Planning Estimate the impacts of transportation planning on local population and employment 7 Freight Transportation and Land Use Planning Coordinate regional freight transportation planning and land use development 8 Environmental Planning Investigate activities involving mobile emissions planning, environmental protection, land use management, and air quality efforts 9 Regulation and Enforcement Conduct activities such as licensing, inspection, size and load specifications, work hours regulation, and taxes/fares 10 Intermodal Trade Corridor Planning Develop intermodal corridors to ensure efficient freight movement and reduce congestion 11 Terminal and Border Access Planning Manage terminals and borders to ensure efficient movement of people and goods across modes 12 Hazardous Materials Planning Improve safe movement and monitoring of hazardous materials transported using the freight system 13 Roadway Pavement and Bridge Maintenance Planning Study the effects of fleet use on infrastructure, such as expected pavement deterioration 14 Modal Shift Analysis Investigate policies and incentives that foster modal shift changes, including measuring the impact of shifting from one mode to another 15 Freight Performance Measurements Develop measures to monitor the performance of the freight transportation system, including its subsystems and components 16 Sustainable Transportation Investment Investigate ways to fund the existing transportation system and future projects Table 3-1. Freight planning and decision-making public-sector functions. Function Description (from NCFRP Report 22) 1 Financial Planning Investigate grants, loans, and subsidies to support the transportation system; also involves tax policy, road user fee assessment, and other activities such as public- private partnerships (partially captured in Economic Development Planning) 3 Interregional Connectivity Develop intermodal corridors to ensure efficient freight movement and reduce congestion (captured in Intermodal Trade Corridor Planning) 4 Security Planning Integrate emergency response and other calculations into transportation planning (captured in Emergency Preparedness Planning) 5 Transportation Equity Planning Incorporate transit equity principles and legislation such as SAFETEA-LU into regional transportation planning (excluded as no examples of freight data use were found ) Table 3-2. Additional functions identified but only partially covered in NCFRP Report 35.

12 Implementing the Freight Transportation Data Architecture: Data Element Dictionary The initial strategy was to develop a weighting system by which freight data uses could be categorized as serving one of three functions: 1. Cleaning data and using simple graphs and tables to show relevant information, such as using the Freight Analysis Framework (FAF) for commodity flows. 2. Combining a database with another database to perform a selected function; for example, integrating place-based databases (e.g., the Commodity Flow Survey) with network-based databases (e.g., the Highway Performance Monitoring System). 3. Combining a database with other databases to perform extensive transformation through statistical analysis and assumptions to perform a selected function, such as using the FAF to determine roadway emissions. On completing the literature review, the research team found that many of the studies fell into the first category. Though relevant in validating the need for performing a public-sector function, studies from the first category were excluded from this report because of the sheer volume of such publications. It was also thought to be more beneficial to present a less redun- dant and more diverse set of examples of how currently available freight databases are being utilized. The literature selected for discussion in NCFRP Report 37 is therefore based on how freight data was used in an innovative or unique way to perform a public-sector function. The research team recognizes that modeling attempts in some of these studies may have been limited; how- ever, these studies were included in the report to serve as examples and to demonstrate the current limitations of most of our databases, especially in areas such as freight modeling at dis- aggregate county and sub-county levels. The idea is that the methodologies used in some of these studies can be adopted and enhanced as newer and richer data becomes available to researchers. The current design of the Freight Data Dictionary enables future studies to be easily incorpo- rated into the system using the Discussion Wall feature. 3.3 Congestion Management In this report, congestion management is defined as the identification and monitoring of recur- ring and non-recurring congestion along road corridors. It involves evaluating and recommend- ing strategies that mitigate traffic congestion and facilitate a reliable and efficient flow of personal and commercial vehicles. Freight-related congestion management studies found in the literature mostly focused on mitigating the effects of truck movements along urban corridors. Recurring topics included examining the impact of truck lane restrictions, roadway pricing strategies, and the cost of urban freight congestion. The most commonly cited nationally available freight-related database for congestion man- agement studies is the Highway Performance Monitoring System (HPMS) dataset. In this data- base, truck traffic counts are combined with other data sources, such as roadway geometry data, incident data, weather data, vehicle registration data, and truck GPS data. In some instances, the HPMS was combined with the FAF to estimate future truck flows and truck freight value. However, most congestion management studies utilized field data collected specifically for the study area or data provided by local traffic agencies. Data from these studies were sometimes complemented with state DOT traffic data. Sources of field data collected for most of the studies include manual traffic counts, video monitoring, and surveys specifically designed for a study area. The data from these individual studies, though relevant, is rarely available to or acces- sible by others on completion of a study. Furthermore, no central data collection repository is available where the locally collected data can be stored or shared with other data users in the transportation community.

Freight Data Uses 13 These examples from the literature demonstrate the use of nationally available data sources for congestion management studies: • Eisele et al. (2013a) combined traffic volume and roadway inventory data from the HPMS with historical speed data from INRIX (a traffic information provider) speed data to estimate urban truck freight delay and diesel fuel consumption, and the associated costs for trucks in urban congestion. A geographic matching process was performed to assign traffic speed data from INRIX to each HPMS road section, and traffic volumes for each hour time interval from daily volume data were estimated. Congestion performance measures were calculated using calculated average travel speed and total delay for each hourly interval. • Guo et al. (2010) utilized the HPMS, National Highway Planning Network (NHPN), and the Lock Performance Monitoring System (LPMS) from the U.S. Waterway database to identify and assess transportation infrastructure bottlenecks in the Mississippi Valley Region. The HPMS database was used in analyzing regional highway traffic conditions and freight bottle- necks, and the LPMS database was used in identifying the location of lock delays on the inland waterway system in the region. HPMS data was mapped onto the NHPN network through a dynamic segmentation process, and detailed traffic information on sampled sections was extrapolated to universe sections for freeways. Truck unit delay, measured in hours of delay for trucks per 1,000 miles, was used in identifying bottleneck locations on the network. A con- gestion corridor growing method was also incorporated in the analysis framework to account for the systematic congestion caused by interchange bottlenecks. • Cambridge Systematics (2005) developed a methodology to identify freight bottlenecks using HPMS, NHPN, and FAF data. Highway bottlenecks were located by scanning the HPMS database for highway sections that were highly congested, as indicated by a high volume of traffic in proportion to the available roadway capacity (the volume-to-capacity ratio). Using the FAF and the HPMS sample databases, the volume of trucks passing through the identified bottlenecks were also estimated, and truck-hours of delay was calculated. • Eisele et al. (2013b) also developed estimated state and urban-area commodity values by inte- grating the commodity value supplied by FAF with truck vehicle-miles traveled (VMT) calcu- lated from the HPMS roadway inventory database. Truck VMT is computed as the product of average daily traffic, percent trucks, and link length. To obtain the truck VMT-based com- modity values, predetermined state and urban truck VMT percentages were multiplied by the U.S. truck commodity values from the FAF. 3.4 Operations/Services Operations and services functions involve the development, management, and maintenance of transportation modes to improve the movement of goods and people and increase the safety and efficiency of the transportation system. The studies within the literature relating to this function also included infrastructure planning, prioritizing needs, and assessing network vulnerability and resiliency. In some instances, FAF data were combined with data from the HPMS, the U.S. Bureau of Economic Analysis (BEA), and the National Transportation Atlas Database (NTAD). These examples from the literature show how nationally available data sources were utilized for operations/services: • Jansuwan et al. (2010) developed a decision support tool to assess the vulnerability of the transportation network and conducted a case study based on disruption scenarios of defi- cient highway bridges on the Utah highway network. State-specific commodity flows within, out of, into, and through Utah were extracted from FAF version 2.2 (FAF2.2) and converted into truck origin-destination (O-D) trips. To generate the case study scenarios, data from the National Bridge Inventory (NBI) database and Utah’s seismic hazard map were utilized.

14 Implementing the Freight Transportation Data Architecture: Data Element Dictionary Disruption scenarios from an earthquake, based on assumed impassable status of bridges after a strong earthquake, were selected for structurally deficient bridges in or near high seismic hazard areas. Changes in travel distance and VMT as a result of trucks using alternative routes were measured. Applications of the tool include developing recommendations for prioritiz- ing bridges for maintenance, retrofitting, and detour route planning for freight movements, among others. • Schroeder et al. (2012) used BEA and FAF data to develop a freight-based prioritization framework to identify freight infrastructure needs critical to maintaining economic vitality by incorporating economic metrics associated with infrastructure performance and roadway level of service. The framework first evaluated infrastructure needs on a specified highway network, then prioritized those needs using a decision model to balance developed economic metrics that estimate regional corridor-wide benefits of the local improvement with sever- ity of needs as quantified with conditional performance measures. The BEA input-output model was used to identify the most transportation-dependent industrial sectors, which were then linked with commodity flows using the FAF. A set of conditional performance measures was selected to identify critical locations meriting improvements, including National Bridge Investment Analysis System (NBIAS) outputs, International Roughness Index (IRI), truck fatality crash rate and truck crash rate, and deficiencies in geometric standards. • Kersh et al. (2012) developed a risk-based approach to identifying and prioritizing Interstate segments for planning alternate route diversions for trucks, and a method for selecting pre- ferred alternative truck routes when diversion is required. The methodology used traffic data from the Tennessee DOT’s travel information system, the Tennessee Department of Safety, and the NTAD-NBI to rank all Tennessee Interstate segments on the basis of route restric- tions, relative truck traffic, history of severe accidents, and congestion levels. Alternate routes were generated in a GIS environment that considered both trucks and passenger vehicles and took into account criteria for roadway grades, clearances, bridge design loads, school zones, capacity, and demand. 3.5 Safety Planning and Analysis Safety planning and analysis are defined here as implementing and maintaining integrated multimodal safety and transportation planning. The ultimate goal is to reduce crashes, injuries, and fatalities. Nationally available freight-related data sources found in the literature for safety planning and analysis studies include the Fatal Analysis Reporting System (FARS) and the Federal Railroad Administration (FRA) Safety Database. FHWA uses injury and fatality data from the FARS database, combined with VMT data from HPMS, to report the number and rate of injuries and fatalities involving large trucks in its safety performance measures criteria (FHWA 2000). These examples from the literature demonstrate the use of nationally available data sources for safety planning and analyses: • Hall and Mukherjee (2008) carried out analytical and statistical analyses to identify and quan- tify the factors that contribute to freight-related crashes using FARS and an additional dataset called Trucks Involved in Fatal Accidents (TIFA), which provides coverage of all medium and heavy trucks recorded in FARS (Jarossi et al. 2011). Researchers linked the crash time, date, day, month, year, and age of driver from the FARS database, as well as the number of hours driven and the trip type from TIFA, to study the safety impact of the length of time drivers have been operating their vehicles and the effect of hour-of-service regulations on enhancing safety. • Liu et al. (2013a) developed a methodology for quantifying the relationship between train derailment severities and their associated affecting factors, such as residual train length, derailment speed, train power distribution, and proportion of loaded railcars in the train, using the Rail Equipment Accident (REA) database maintained by the FRA.

Freight Data Uses 15 • Liu et al. (2013b) also used the REA data on broken-rail-caused car derailments to develop a statistical model that considers a combination of risk-reduction strategies to assist decision- makers in improving the safety of transporting hazardous materials by rail. 3.6 Freight Mobility Planning Freight mobility plans are created by states and other planning agencies to incorporate goods movement into the region’s transportation planning process. These plans promote an under- standing of the relationships between freight movement, economic growth, and the transpor- tation infrastructure. Most plans seek to determine the adequacy of current infrastructure in meeting the needs of the industry and to assess the impacts of future demand. State agencies have developed freight mobility plans using a combination of the following databases: FAF, Vehicle Inventory and Use Survey (VIUS), HPMS, IHS Global Insight’s Transearch (Transearch), Com- modity Flow Survey (CFS), U.S. Census data, U.S. Waterway data, and the Carload Waybill Sample. The Florida Statewide Freight and Goods Mobility Plan used VIUS with the gross state product in determining transportation demand factors that influence freight. The CFS was used to complement data obtained through Transearch to estimate commodity flows (Cambridge Systematics 2007). The Alabama Statewide Freight Study and Action Plan utilized the FAF, U.S. Waterway, Alabama DOT traffic count data, and industry cluster information from the U.S. Census Bureau and other data sources to develop and validate disaggregated commodity flows in the state (Anderson and Harris 2011). Ohio’s Freight Impacts on Roadway System Study uti- lized the Transearch, CFS, VIUS, and Ohio truck count databases (Beagan and Grenzeback 2002). Transearch was used to obtain freight shipments traveling to, from, or through Ohio. Annual ton- nage flows were converted to daily truck trips using VIUS, then assigned to the highway network and compared with truck counts from the Ohio DOT. These examples from the literature demonstrate the use of nationally available data sources for freight mobility planning: • The West Coast Corridor Coalition Trade and Transportation Study (Cambridge Systemat- ics 2008) involved an assessment of the characteristics of the region’s freight transportation system to identify key physical chokepoints that currently hinder the ability of a region’s trade and transportation system from effectively serving current and future growth in freight traf- fic. The FAF commodity O-D database was used as the initial source of data for estimating international commodity flow demand through West Coast seaports and inland movements of international shipments. The study team compared FAF base-year and forecast estimates with existing port demand estimates to determine and address inconsistences in individual port demand estimates. The FAF also was used for the estimation of base-year and forecast North American Free Trade Agreement (NAFTA) freight demand between the United States and Canada/Mexico through border-crossing locations in the study area. FAF NAFTA freight flow estimates were compared against the North American Transborder Freight Database (Transborder) and other border-crossing traffic flow data from Canada and Mexico. The FAF highway network was used for the analysis of base-year and forecast highway system charac- teristics in the study area pertaining to highway network capacity constraints and bottlenecks. HPMS truck traffic data was used as the base-year truck traffic count. Internal and external truck growth rates for forecasting were developed from the FAF and converted to truck trips using payload factors from VIUS data. Base-year air cargo demand through major airports in the study area relied on cargo data reported by airlines to individual airports. The airline- reported data was then compared and vetted against the Air Carrier Statistics database and the U.S. Foreign Trade Data. Forecast air cargo demand data was derived from the airport master plans available from major airports in the study area. Rail network demand and forecast were based on data from available regional/statewide rail studies in the study area.

16 Implementing the Freight Transportation Data Architecture: Data Element Dictionary • NCFRP 14: Guidebook for Understanding Urban Goods Movement (Rhodes et al. 2012) provides information on how multiple freight data sources can be used to address freight issues at the local level. Examples of issues discussed include safety, congestion, land use planning, emis- sions, environmental justice, commercial vehicle routing, and travel demand modeling. Data sources cited in the guidebook and grouped by geographical coverage include the following: – Freight node data, which represent consolidated or individual endpoints that generate or receive freight flows and are the key points of production, consumption, or intermediate handling for goods. Example data sources are the NTAD, InfoUSA™, Harris InfoSource, or ThomasNet®. – Freight network data, which define major route patterns and critical infrastructure being used to convey freight shipments through the various modal systems. Examples include the HPMS, NTAD, and NHPN. – Freight flow data, which provide information on commodity flows and provides insight on the economic and trade environment of regions. Typical commodity flow records will contain information on the O-D of shipments, type of commodity, weight, and/or value of the commodity shipment, and mode of shipment. Example data sources include CFS, FAF, Carload Waybill Sample, and Transearch. – Neighborhood freight data, which provide information on safety, congestion, land use, and emissions. Example data sources are HPMS and FARS. 3.7 Emergency Preparedness and Security Planning For NCFRP Project 47, emergency preparedness and security planning were defined as increasing the safety and security of the transportation system through enhanced coordina- tion and communications among emergency responders. Few specific freight-related emergency preparedness planning studies were found in the literature, as most studies focused on first responders, mass transit, and natural disaster response (e.g., earthquake and strategic military response). Moreover, many studies were overview reports that did not undertake data analysis. This finding may reflect both post-9/11 priorities set by the U.S. Congress and the paucity of reliable data available, as noted in the studies. A nationally available data source was utilized for emergency preparedness and security plan- ning in the Freight Planning Support System for Northern New Jersey study (Fallat et al. 2003). This study examined interruptions in freight movement caused by the September 11, 2001, terrorist attack on New York City, as well as potential freight system impacts, redundancies, and appropriate strategies to respond to or prevent system failure in the event of another major disaster within the northern New Jersey region. The study utilized Transearch data for commod- ity flows, traffic and highway network data from the New Jersey DOT, the Center for Transpor- tation Analysis (CTA) national rail network, Port Import/Export Reporting Service (PIERS) Maritime Database, FRA Rail Waybill Sample, and other public and private data sources, which were all integrated into GIS for analyses. 3.8 Economic Development Planning Economic development planning seeks to tie the impacts of freight-related infrastructure projects to economic growth—specifically, to increases in employment opportunities, resource consumption, property values, wealth accumulation, and productivity (Litman 2010). Though it is possible to measure the direct impact of transportation improvements on travel time, dif- ficulties arise when attempting to estimate “the indirect nature and relevance” of transpor- tation improvements to logistics operations, inventory management, and overall business decision-making (AECOM 2001). The 2001 FHWA report found that studies relating highway

Freight Data Uses 17 improvements to logistics decision-making have been found to be more qualitative than empiri- cal because of a lack of data resulting from privacy concerns and operational competitiveness (AECOM 2001). Following are some examples of the qualitative evidence of highway improve- ments on logistics operations cited by Jack Faucett Associates (1994): • Reduced inventory costs resulting from faster and more reliable replenishment delivery times. • Economies of scale in larger volumes of output per plant given access to wider distribution markets. • Reductions in regional warehouse operations resulting from more direct deliveries from plants to retailers, wholesale distributors, and customers as a result of more reliable delivery times direct from manufacturers (Jack Faucett Associates 1994). Measuring the impacts of transportation planning on logistics is further complicated by the rapid adoption of information technology tools in the supply chain. As stated by AECOM (2001), “the impacts of highway improvements on transit time as well as technological changes in the trucking industries suggest that distinguishing causal relationships of highway improve- ments on logistics has become more complex.” Therefore, attempts to empirically quantify the “explicit linkages” between infrastructure projects and economic growth “are [often] character- ized by assumptions or hypothetical situations” (AECOM 2001). These examples from the literature demonstrate the use of nationally available data sources for economic development planning: • AECOM (2001) developed a methodology to relate the demand for freight transportation to freight transport charges and highway performance. This methodology is based on the assump- tion that freight charges are dependent on highway performance since average vehicle speed and speed cycling directly affect carrier’s costs and, presumably, shipping rates. The study utilized data from several sources, including performance and traffic volume data from the HPMS, com- modity flow data from the FAF, and regional economic activity data from the Bureau of Labor Statistics and the BEA. • Sage et al. (2013) developed a process to address the need for an improved method to ana- lyze freight benefits associated with proposed highway and truck intermodal improvements. Regional travel demand models (TDMs) are used in calculating transportation benefits associ- ated with freight investments, including truck travel time savings, truck operating cost savings, and truck emission changes. The freight transportation-related benefits from the TDM are then used in performing regional economic impacts analysis with IMPLAN’s Input-Output and Washington State computable general equilibrium models, which were generated with IMPLAN data. • NCFRP Report 12: Framework and Tools for Estimating Benefits of Specific Freight Network Investments (Cambridge Systematics et al. 2011) also developed the Freight Evaluation Frame- work, which seeks to (1) enhance public planning and decision-making processes regarding freight; (2) supplement benefit/cost assessment with distributional impact measures; and (3) advance public-private cooperation for infrastructure facility financing, development, operation, and maintenance. Though no specific analysis or calculations were performed as part of the study, databases identified for implementing the framework include utilizing HPMS for estimating VMT, and the Carload Waybill Sample, Air Carrier Statistics, and waterborne data for estimating mode specific services and market shares. 3.9 Freight Transportation and Land Use Planning For NCFRP Project 47, freight transportation and land use planning has been defined as the coordination of regional transportation plans with land use development. An effective and well- integrated freight and land use plan results in both public and private sector benefits, such as

18 Implementing the Freight Transportation Data Architecture: Data Element Dictionary reduced congestion, improved air quality and safety, enhanced community livability, improved operational efficiency, reduced transportation costs, and greater access to facilities and markets (Hartshorn and Lamm 2012). Examples of data required in performing this function include data on truck trip generation, delivery tours, transportation network characteristics, and eco- nomic characteristics and spatial distribution of participating agents. The most commonly cited databases found in the literature were CFS, U.S. Customs and Border Protection (CBP), Dun and Bradstreet, and industry-related databases. These data sources often were combined with locally collected data (often via surveys). For example, nationally available data sources were cited for freight transportation and land use planning in NCHRP Report 739/NCFRP Report 19: Freight Trip Generation and Land Use. This study found that some freight trip generation data relating to land use was collected over the years, but most of the data was either outdated or insufficient for current planning needs (Holguín-Veras et al. 2012). As part of the study, shipper and carrier surveys were conducted and used in the development of the freight trip generation models. NCHRP Report 739/NCFRP Report 19 provides additional information on the freight and land use data needs and suggests data collection techniques to address those needs. The report cites CFS and zip code business patterns as useful sources of data for freight trip generation modeling. It further recommends the use of the CFS micro-data to estimate commodity movement parameters for freight demand models. 3.10 Environmental Planning Environmental planning involves activities such as mobile emissions planning, environmen- tal protection, land use management, and air quality efforts. Freight-related data required for environmental planning includes vehicle type and vehicle trip information, route information, air quality data, and network information. EPA’s Motor Vehicle Emission Simulator (MOVES) model is generally utilized in performing regional emission modeling studies (EPA 2014). Input data required by MOVES includes vehicle population, age distribution, VMT by vehicle type, and average speed distribution, among others. VMT data from HPMS is the primary source; however, the required speed data for MOVES is taken from other sources, such as INRIX or GPS equipment (Eisele et al. 2013c; Boriboonsomsin et al. 2012). These examples from the literature demonstrate the use of nationally available data sources for environmental planning: • Ostria (1996) developed a methodology by which intercity trucking emissions can be assessed using emission factors documented in state implementation plans (SIPs) and data from VIUS (formerly TIUS—Truck Inventory and Use Survey). Using the gross vehicle weight classifica- tion and area of operation variables housed in VIUS, intercity VMT were calculated, and the dis aggregated emission estimates reported in SIP documents were utilized in isolating the intercity freight VMT. • Vanek and Morlok (1998) estimated total freight energy consumption for a range of com- modity groups using an activity-based approach to energy consumption. Total freight activity was decomposed into components by mode and by commodity group, and each compo- nent was multiplied by an intensity estimate to calculate total energy use for that commodity group. Fourteen commodity groups as defined in the CFS were used, and total energy use for each commodity group was based on the modal volumes for truck, rail, truck-rail intermodal, marine, and air. • Ang-Olson and Cowart (2014) explored current and future air quality effects that result from the development of North American trade and transportation corridors, and strategies to mitigate their impacts. The analysis focused on five specific binational corridor segments:

Freight Data Uses 19 Vancouver–Seattle, Winnipeg–Fargo, Toronto–Detroit, San Antonio–Monterrey, and Tucson– Hermosillo. Current and future levels of trade, transportation, and emissions were estimated for each corridor segment using commodity flow and traffic volume data. Commodity flows, developed from an analysis of the Transborder surface freight data, were used to analyze trade origin and destination patterns, changes in trade levels in particular industries, changes in vehicle size and weight, and shifts in mode share. The Transborder surface freight data was supplemented with traffic volume data for cross-border truck and rail movements from the U.S. Customs Service, the Canada Customs and Revenue Agency, and private bridge and tun- nel operating authorities. • Corbett et al. (2010) developed the California Geospatial Intermodal Freight Transportation (GIFT) model to analyze energy and environmental impacts of goods movement through California’s marine, highway, and rail systems. The GIS-based model incorporates informa- tion from energy and environmental variables into segments of the national highway, rail, and waterway network, to enable the reporting of environmental performance measures associ- ated with freight flows on the network. It also enables the comparison of alternative cargo flow patterns that minimize energy consumption and emissions when least cost or shortest path routes are considered. Road, rail, and waterway network features and facility locations used in GIFT are from NTAD, U.S. Army Corps of Engineers (USACE) waterways, and other private and public data sources. O-D Freight Flow Data used in this study were from the FAF ver- sion 2 (FAF2) and CFS data sets, which were supplemented with USACE Entrance and Clear- ance data. The Entrance and Clearance data, which contains a vessel’s International Maritime Organization identification number, can be used in quantifying the volume of container traf- fic entering and leaving a port. When linked to data compiled by classification societies such as Lloyd’s Registry of Ships, operational characteristics of vessels can be further examined. 3.11 Regulation and Enforcement Planning Regulation and enforcement planning seeks to improve the safety of freight operations through the implementation and management of activities such as vehicle licensing, inspec- tions, size and weight specifications, work hours regulation, and taxes/fares. Databases found in the literature for performing this function include the Motor Carrier Management Information System (MCMIS) and FARS. These examples from the literature demonstrate the use of nationally available data sources for regulation and enforcement planning: • Gillham et al. (2013), in cooperation with FMCSA, developed the Intervention Model to mea- sure the effectiveness of roadside inspections and traffic enforcement in terms of crashes and injuries avoided, and lives saved. Roadside inspections as recorded in the MCMIS database are converted into crash risk probabilities with the assumption that an inspection violation implies a certain degree of crash risk. Thus, for each inspection that is uncovered and cor- rected, it is assumed that there is a reduced risk of an accident occurring. “By summing the crash risk probabilities for all violations corrected over all inspections, the model estimates the number of crashes avoided as a result of the FMCSA Roadside Inspection and Traffic Enforce- ment Programs” (Gillham et al. 2013). • Dang (2007) used FARS and state data files to determine the effectiveness of Electronic Stabil- ity Control (ESC) systems in reducing fatal run-off-road crashes and vehicle rollovers. Vehicle identification number (VIN) data from the crash files were matched with VIN data obtained from the Passenger Vehicle Identification Manual (published annually by the National Insur- ance Crime Bureau) to obtain vehicle make, model, and year information. The final analysis database contained records of each vehicle involved in a crash and the vehicle make, model,

20 Implementing the Freight Transportation Data Architecture: Data Element Dictionary and year. A series of statistical analyses were then performed with an emphasis on testing the effectiveness of ESC systems. 3.12 Intermodal Trade Corridor Planning For purposes of NCFRP Project 47, intermodal trade corridor planning was defined as the monitoring and development of policies and strategies that facilitate the efficient movement of goods by a variety of modes along key national and international trade corridors. Data sources cited in the literature included Transborder, Foreign Trade Database, USA Trade Online, and the Bureau of Transportation Statistics (BTS). These examples from the literature demonstrate the use of nationally available data sources for intermodal trade corridor planning: • Figliozzi et al. (2001) examined alternate methods for estimating loaded NAFTA truck vol- umes between the United States and Mexico. The first method utilizes truck volume counts reported in the Transborder database and estimates loaded trucks by applying a factor of empty trucks to the total number of trucks crossing the bridge. Corrections for intermodal freight shipments, single-unit trucks, and local trade are also applied. In the second method, truck volumes are estimated using U.S. international trade data from the Foreign Trade Data- base. Truckload weight per commodity is calculated by multiplying a commodity group’s density by the capacity volume of various truck types, based on the assumption that high density commodities will weigh out and low density commodities will cube out. • Harrison et al. (2010) examined major trends in intermodal shipping that impact Texas inter- modal trade corridors, including an analysis of key supply-and-demand forces that underpin intermodal service and routing options. A review of current and future trade corridors used for handling international intermodal trade was performed to show the comparative strengths and weaknesses of different routing options for intermodal cargo shipping. Transborder trade data from BTS and foreign trade data from USA Trade Online were used in examining trade patterns between Texas and its top trading partners. 3.13 Terminal and Border Access Planning Terminal and border access planning involves the management and maintenance of inter- modal freight terminals and border facilities to ensure efficient movement of goods and people. Related topics in this area include border and port congestion, security, terminal access to rail and port facilities, and port efficiency and throughput. Studies in this area tend to utilize project- specific survey data for analytical purposes. Data on cargo volume and terminal traffic can be obtained from terminal operators and port authorities (Shafran and Strauss-Wieder 2003). These examples from the literature demonstrate the use of nationally available data sources for terminal and border access planning: • Turnquist and Rawls (2010) developed a multimodal network model that performs a vulner- ability assessment of border trade flows to disruptions at one or more of the major bridges and tunnels on the border between the United States and Canada. The model’s O-D table was estimated using freight flows from the Transborder database and validated with Canadian data and bridge-specific truck and count information. The assessment is performed by construct- ing a multimode equilibrium model of international freight flows in the Lake Erie corridor, and the model is subjected to disruptions (closure of one or more border-crossing facilities), which results in shifts in traffic flow and congestion in the remaining facilities. The economic costs of disruptions are then measured.

Freight Data Uses 21 • Bhamidipati and Demetsky (2009) described a general methodological framework for evalu- ating the impacts of intermodal terminals on the transportation system and applied it to the highway system of Virginia. Models developed for the framework were calibrated with commodity flow, socioeconomic, and other data for the Commonwealth of Virginia. Data sources included Transearch, Carload Waybill Sample data, the Oak Ridge National Labora- tory GIS database (now part of the NHPN), Census data, IMPLAN County Wise Employment Data, and the Virginia DOT’s Crash Database and GIS Integrator. Truck diversions were then estimated from the data by using distance/travel time models and discrete choice models to estimate freight demand and drayage activities on the network. 3.14 Hazardous Materials Planning Hazardous materials planning involves improving safe movement and monitoring of hazardous materials transported on the freight network. Hazardous materials movement data, such as what is reported in the CFS, is used in policy development, the rule-making process, program planning, and identification of emergency response and preparedness needs (Duych et al. 2011). The data- bases most frequently cited within the literature included the Pipeline and Hazardous Material Safety Administration’s (PHMSA) database, the Hazardous Materials Incident Reporting System (HMIRS), and CFS. Estimates of daily hazardous materials shipments can be derived and the safety of the hazardous materials transportation assessed from the CFS data (Duych et al. 2011). These examples from the literature demonstrate the use of nationally available databases for hazardous materials planning: • Restrepo et al. (2009) examined the causes and economic consequences of hazardous liquid pipeline accidents in the United States. Data on accidents related to hazardous liquid pipe- lines from the PHMSA was utilized in the analysis. Regression models were used to determine what factors were associated with product-loss cost, property damage cost, and cleanup and recovery costs. Factors examined included the system part involved in the accident, location characteristics, and type of incident. • Ellis (2011) examined factors contributing to the release of packaged or dangerous container- ized goods during marine transport. Data from the HMIRS database and the UK’s Marine Accident Investigation Branch accident databases were utilized in identifying factors contrib- uting to the release of dangerous containerized goods. 3.15 Roadway Pavement and Bridge Maintenance Planning Roadway pavement and bridge maintenance planning involves examining the effects of freight movement, typically truck traffic and heavy vehicles, on the pavement and bridge infrastruc- ture. Several studies focused on examining the structural integrity of bridges or pavement con- dition and design and were found to utilize data collected through controlled lab experiments. Databases frequently cited in the literature for roadway pavement and bridge maintenance planning include HPMS for traffic volume and pavement condition information, the NBI data- base for bridge information (Jansuwan et al. 2010), and FAF and Transearch for commodity flow data. These examples from the literature demonstrate the use of nationally available data sources for roadway pavement and bridge maintenance planning: • Fortowsky and Humphreys (2006) examined the cost impact of higher truck weight lim- its being allowed on Interstate routes in Maine. Two methodologies were developed for the

22 Implementing the Freight Transportation Data Architecture: Data Element Dictionary assessment. The first methodology estimated the changes in freight truck traffic volumes as a result of the increased weight limits, and subsequent changes in VMT, truck configurations, and equivalent single-axle loads (ESALs). The second methodology estimated road cost per ESAL using the predetermined ESAL calculations from the first methodology. Truck O-D flows were estimated using Transearch data and supplemented with weigh-in-motion sta- tion data, vehicle classification counts, and network data from the Maine DOT’s TIDE road database system. • Schroeder et al. (2012) developed a freight-based prioritization framework to identify freight infrastructure needs critical to maintaining economic vitality. The framework incorporates economic metrics associated with infrastructure performance and level of service. Using BEA data, an input-output model is used to identify transportation-dependent industrial sectors, which are then linked with FAF commodity flows. A set of conditional performance measures is selected to identify critical locations meriting improvements, including (1) the structural integrity of bridges from NBIAS outputs; (2) IRI from HPMS; (3) truck fatality crash rate and truck crash rate from the Virginia DOT crash database; and (4) other geometric standards deficiencies. The framework’s outputs are a prioritized list of economically critical highway infrastructure needs selected in consideration with regional economic impacts, safety, and mobility improvements. 3.16 Modal Shift Analysis Modal shift is recognized to occur “when one mode has a comparative advantage in a similar market over another” (Rodrigue et al. 2013). Incentives for modal shift in freight networks include cost savings, travel time reductions, network reliability, and implementing strategies to mitigate energy usage and greenhouse gas emissions (Nealer et al. 2012; Eisele et al. 2012). Feasible modal shift considerations for freight are mainly composed of shifts between road (trucks), rail, and water modes (Corbett et al. 2010). Modal shift analysis includes not only measuring the impact of shifting from one mode to another but also examining the policies and incentives that foster modal shift changes. The most commonly cited nationally available freight-related databases for modal shift studies are the FAF, CFS, and PIERS. These examples from the literature demonstrate the use of nationally available data sources for modal shift analysis: • Nealer et al. (2012) compared energy usage and emissions across multiple freight transporta- tion modes to determine opportunities for modal shift. A transportation flow input-output model was developed for more than 400 U.S. economic sectors using freight transport data from CFS commodity categories and input-output use tables from the BEA. Sector-specific mode choice shifts were analyzed, and large-scale reductions in emissions and fuel con- sumption also were examined. Multiple scenarios were analyzed, including (1) quantifying the reasonable bounds of energy and emissions to be reduced by a complete modal shift from truck to rail; (2) determining foregone energy and emissions when modal shift occurs for the top 20% supply chain sectors; and (3) the effect of an increased truck efficiency on modal shift. • The Maritime Administration study, Impact of High Oil Prices on Freight Transportation: Modal Shift Potential in Five Corridors (Transportation Economics & Management Systems 2008), sought to evaluate the impact of oil prices on U.S. domestic freight transportation and assess how prices impact transportation logistics chains. The analysis was performed using the GOODS™ demand and supply model, which is calibrated to identify the potential for water- borne transportation to capture containerized traffic. FAF O-D traffic data was used in cali- brating the model’s demand parameters. FAF data was further augmented with Transborder

Freight Data Uses 23 data for cross-border flows and with Transport Canada data for Canadian domestic flows. Historical energy price data, short-term outlook data, and long-term growth rate scenarios developed by the Energy Information Administration (EIA) were used in developing the sce- narios to be tested. 3.17 Freight Performance Measurement Performance measurement is defined by the U.S. Government Accountability Office (2011) as “the ongoing monitoring and reporting of program accomplishments, particularly progress toward pre-established goals.” Freight performance measures examine the transportation system’s effi- ciency, safety, and condition in meeting freight demand, including the impact on energy use and the environment (Gordon Proctor & Associates et al. 2011). It provides a greater insight into the performance of the current transportation system and allows agencies to “rank capital investments and evaluate alternative programs,” “provide a rationale for allocating resources,” and “assist in monitoring progress toward achieving specific transportation goals and targets” (Prozzi et al. 2011). Freight performance measures found in the literature tend to rely on disaggregated data from sources such as GPS devices for monitoring truck movements (Sage et al. 2013). These data sources tend to be proprietary in nature; however, the American Transportation Research Insti- tute (ATRI), in partnership with FHWA, provides aggregated truck GPS data to evaluate travel time and travel time reliability measures along critical freight corridors (ATRI 2014). McMullen et al. (2010) and NCFRP Report 10: Performance Measures for Freight Transporta- tion (Gordon Proctor & Associates et al. 2011) identified several national and readily available freight databases that can assist in developing performance measures (Table 3-3). Though these may not be sufficient in meeting current demands, knowledge of their capabilities and current limitations can inform how future data collection efforts should be tailored to supple- ment these data sources. Freight network and node data sources also are available from the National Transportation Atlas, Waterways Facilities data, and FAF network databases. 3.18 Sustainable Transportation Investment Sustainable transportation investment involves investigating ways to fund both the existing transportation system and future projects. For example, nationally available data sources were utilized for sustainable transportation investments in the development of a baseline roadmap by the California Hybrid, Efficient and Advanced Truck Research Center’s (CalHEAT) Research and Market Transformation Roadmap for Medium- and Heavy-Duty Trucks. The roadmap is intended to guide the advancement and demonstration of efficient truck technologies and systems to meet or exceed the 2020 goals for California in air quality, energy security, petro- leum reduction, and greenhouse gas reductions. Data sources used for the vehicle technologies characterization map and baseline included the R.L. Polk Co., U.S. Census, 2002 VIUS, 2009 Climate Registry Reporting Protocol, a 2008 CARB truck and bus study that used department of motor vehicle (DMV) data, the Transportation Energy Data Book (TEDB), and fuel use estimates from Argonne National Lab (California Energy Commission 2013). The initial truck inventory study used a baseline inventory from R.L. Polk consisting of 2009 vehicle registration data from 1.5 million commercial medium- and heavy-duty trucks. The vehicles were then grouped by weight and application. Additional data was gathered on average VMT, fuel consumption, and emissions per mile to determine average fuel use, NOx, and CO2e emissions for each of the truck categories. These averages were then multiplied by the vehicle population inventory to develop baseline fuel consumption, CO2e, and NOx by average VMT and vehicle category (California Energy Commission 2013).

24 Implementing the Freight Transportation Data Architecture: Data Element Dictionary Performance Measure Potential Source of Data 1. Safety Highway Accident Crash Reporting Systems (state level) Fatality Analysis Reporting System Motor Carrier Management Information System Safety Measurement System Safety and Fitness Electronic Records Rail FRA State Freight Rail Safety Statistics Air Accident/Incident Data System Aviation Safety Reporting System Near Midair Collision System Runway Safety Office Runway Incursion Database Ports/Marine Marine Information for Safety and Law Enforcement 2. Maintenance/Preservation Highway Pavement Management System (state level) National Bridge Inventory Rail Rail Network Data (state level) Air Airport Pavement Management System (state level) Ports/Marine USACE Navigation Data Center 3. Mobility, Congestion, and Reliability Highway Highway Performance Measurement System ATRI N-CAST INRIX Probe Vehicle Data Weigh-in-motion Data Rail Association of American Railroads’ Railroad Performance Measures Air Air Carrier Statistics Ports/Marine USACE Lock Performance Measurement System Maritime Safety and Security Information System Port Import and Export Reporting System 4. Accessibility and Connectivity Highway State, regional, or MPO-level GIS databases Rail Carload Waybill Sample Ports/Marine USACE Lock Performance Measurement System Air Air Carrier Statistics Commodity Flow Data State-level commodity flow models Freight Analysis Framework Transearch Database Commodity Flow Survey 5. Environment Highway The EPA’s MOVES2010 Source: Adapted from McMullen et al., 2010, and NCFRP Report 10. Table 3-3. Freight performance measurement data sources.

Freight Data Uses 25 3.19 Findings from the Literature Review on Freight Data Uses The literature review identified key studies showing innovative and unique examples of how available freight data sources are utilized by agencies and the research community. The following points summarize the findings from the review: • Studies limited by the availability of disaggregated data typically rely on field- or project- specific survey data to perform the task at hand. Data from these project-specific studies, though relevant, is rarely available to or accessible by others on completion of a study. • Several studies used state and regional databases and/or models to either supplement or replace nationally available freight data sources, where possible. • Additional sources of data utilized by practitioners include data from local and regional plan- ning agencies, marine port and airport authorities, and industry sources. • Data sources such as the CFS and FAF, though popular, tend to be outdated for performing specific tasks—a limitation cited in several of the studies reviewed. • Data reliability and validity of nationally available freight data sources remain items of con- cern. Thus, there is a shift toward the use of relatively new and more reliable intelligent trans- portation system (ITS)-related data sources such as GPS data and vehicle-to-infrastructure connected devices. • Several freight-related models were found to be theoretical in nature, requiring data that is currently either unavailable or insufficient and, therefore, necessitating certain assumptions and transformations for the models to be used. • A need may exist for a central data collection repository at which locally collected or project- specific data can be stored or shared with other data users in the transportation community. These project-specific data sources could complement currently available freight data sources as well as provide additional opportunities to test or validate freight-related models.

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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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