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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2010. Guidance for Developing a Freight Transportation Data Architecture. Washington, DC: The National Academies Press. doi: 10.17226/14466.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2010. Guidance for Developing a Freight Transportation Data Architecture. Washington, DC: The National Academies Press. doi: 10.17226/14466.
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Page 12
Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2010. Guidance for Developing a Freight Transportation Data Architecture. Washington, DC: The National Academies Press. doi: 10.17226/14466.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2010. Guidance for Developing a Freight Transportation Data Architecture. Washington, DC: The National Academies Press. doi: 10.17226/14466.
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Page 14
Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2010. Guidance for Developing a Freight Transportation Data Architecture. Washington, DC: The National Academies Press. doi: 10.17226/14466.
×
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Page 15
Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2010. Guidance for Developing a Freight Transportation Data Architecture. Washington, DC: The National Academies Press. doi: 10.17226/14466.
×
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Page 16
Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2010. Guidance for Developing a Freight Transportation Data Architecture. Washington, DC: The National Academies Press. doi: 10.17226/14466.
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Page 16

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

10 Background In 2006, the nation’s transportation system moved more than 20 billion tons of goods valued at close to $15 trillion (1). The movement of freight in the country has more than doubled in the last 15 years, and it is expected to continue growing at a similar pace, with a projected level of 37 billion tons in 2035. This growth challenges the national transportation infrastructure, resulting in congestion along corridors and at the nodes of the network, including seaports, land ports of entry, truck and rail terminals, and airports. It is important to have accurate, comprehensive, and timely information in order to make sound investment decisions to improve and optimize the freight transportation system (2). A large number of stakeholders need access to freight trans- portation data. For example, federal, state, and local level trans- portation planning agencies require freight transportation information to identify operations and infrastructure improve- ments to the transportation system. Likewise, the private sector requires accurate, timely information on freight movements as well as accurate, timely information about the characteristics and operating conditions of the transportation network. Fre- quently, the need for data is on a real-time, or near-real-time, basis. High-quality data enable private-sector stakeholders to make informed investment decisions as well as informed operational decisions. Examples of real-world situations where the need for an integrated approach to freight data is critical include the following: • Commodity classification codes. A western state depart- ment of transportation (DOT) is currently developing a forecast of commodity flows by mode. Efforts by the state DOT to merge data from different studies had to address commodity code compatibility issues because state data used Standard Transportation Commodity Code (STCC) classifications, while regional forecasts used Standard Clas- sification of Transported Goods (SCTG) codes. This issue was resolved, but only after a laborious, expensive process. A unified commodity classification system would have avoided that problem. Around the country, states and met- ropolitan planning organizations (MPOs) that develop freight forecasting tools collect data from various sources, but the commodity classification codes contained within those sources are not always compatible, making analyses of commodity data difficult and time consuming. • Freight data and performance measure definitions. As part of FHWA’s Freight Performance Measurement (FPM) initiative, there was an interest in collecting border crossing travel time and delay data. Before collecting any data, it was necessary to agree on what border crossing “travel time” meant since different stakeholders might use different def- initions and data collection procedures. For example, one stakeholder would only measure the time for a freight ship- ment to go through the border crossing process on one side of the border. However, other stakeholders would consider the total time to go through both sides of the border. Obvi- ously, defining border crossing delay was only possible after agreeing to a common definition for border crossing travel time. Measuring delay also required the definition of a common reference against which travel times would be measured. The lack of standard definitions often leads to data incompatibilities and duplication of data collection efforts. • Federal, state, and local freight data collection efforts. A large Midwest MPO covers a metropolitan area that comprises 6 counties and over 10 million people. The area includes several Class I railroads, 2 passenger transit systems, over 20 multimodal terminals, toll roads, and the conflu- ences of several “smart” corridors. Many freight nodes gen- erate traffic that moves within county boundaries. These movements do not appear on purchased transportation databases. Collecting origin-destination (O-D) data is expen- sive and time consuming. Many state and local agencies are C H A P T E R 1 Introduction

interested in O-D freight data, but do not have enough resources to collect this type of data. As a result, those agencies use O-D data collected at the national level, even though the national data do not provide a clear picture of O-D data at a county or city level. In some cases, state and local agencies end up developing customized tools to address their needs, frequently at great expense. However, not all state or local agencies have this capability. Access to finer resolution O-D data collected at the national level would provide state and local transportation planners with a valuable long-term analysis tool for making freight-related infrastructure improvements. • Regional freight data integration. In a large metropolitan area where three state boundaries are within a 60-mi radius, there are multiple sources of freight data, including data collected by the states, data collected by the MPO, and data purchased from a large commercial data provider. Recon- ciling or validating these separate databases is difficult due to the variability in time periods, data collection protocols, and potential overlaps, which, in turn, makes it difficult to build a common database with elements from each source. • Data to support public–private partnerships. In a rural part of the nation where the Class I rail carriers have “rational- ized” service and facilities, there is an interest in public– private partnership projects. These projects must have public support to attract private investment because the primary benefit is social and business development. In order to complete benefit-cost information to help attract private investment for these rural projects, it is necessary to obtain data to properly characterize rail traffic in the region. Unfortunately, it is virtually impossible for the ana- lysts to obtain this information. • Regional freight data understanding and integration. A paper manufacturer purchases logs from landowners in a tri-state area. The logs and resulting paper products are essentially commodities that compete primarily on price. Companies in the area would benefit from a pooled trans- portation program and an optimization approach with a multimodal solution (truck and rail) that addresses multi- state rules. In order to exchange transportation pricing data (which otherwise would not be allowed), it is neces- sary to form a cooperative or a shipper association. How- ever, the three states involved have different truck size and weight restrictions, do not have data forms for similar peri- ods, and cannot link O-D pair trips across state bound- aries. The railroad, due to recent mergers, wholesaling efforts, and a centralized sales approach, does not have a good understanding of local conditions and is closing rail access points due to the region’s “poor performance.” • Oversize/overweight permitting. A southern state that processes a large number of oversize/overweight permits is frequently tasked with permit requests for oversize or over- weight loads that must be unloaded at a seaport and then transported over state (and sometimes county) roads to another state. Routing is difficult because of the lack of rel- evant integrated information at both ends of the routing process, including information about acceptable routes in neighboring states. In one recent example, a load com- ing from Asia had to be transferred to another port first because land routes connecting to the first port where the load arrived were not adequate. • Short-haul trip optimization. In a port town where it is necessary to move international and domestic containers (using a combination of loads and empties, bare chassis, and bob tail trucks) between rail, port terminals, container yards, customers, and trucking terminals, data to help address empty miles and truck trip reduction needs are not available. Due to the intense competition for this short-haul business, primarily within a trucking/brokerage business model, efforts to create a shared data clearinghouse have not achieved desired results. • Freight transportation performance measures. Traffic con- gestion negatively affects freight mobility, causes huge losses to the private sector, and results in undesirable environ- mental impacts. However, there is no adequate database of performance measures nationwide that analysts could use to quantify those impacts accurately. The identification of those measures, and the underlying data that will be needed for their assessment, is a critical requirement for the iden- tification of sound freight transportation strategies around the country. • Truck routing. In a southern state, motor carriers have two options to travel through a very congested growing metro- politan area—they can either use the existing non-tolled Interstate highway (shorter distance) or use a new tolled facility that bypasses the metropolitan area (longer distance). Traffic conditions on the non-tolled facility can rapidly change from acceptable to stop-and-go. Because of intense competition and low profit margins, some carriers would like to be able to make routing decisions based on accurate current, as well as anticipated, traffic conditions. However, this information is not available. • Decisionmaking process in the private sector. The dynam- ics of domestic and international trade, influenced by the rapid growth of e-commerce, require an increasing num- ber of shipments in smaller quantities. Both shippers and carriers require information to optimize distribution net- works and supply chains, making it critical to have access to accurate, timely information on freight movements as well as accurate, timely information about the characteris- tics and operating conditions of the transportation network. Frequent updates on the operational status of the trans- portation system would allow the private sector to make routing decisions dynamically, thereby reducing delays, 11

costs, and emissions. Access to up-to-date benchmarking metrics and statistics would also facilitate the decisionmak- ing process in the private sector. However, freight trans- portation data and indicators are frequently dated. • Truck trip generation rates. Truck trip generation is an essential metric in a public planner’s tool kit. Existing data include warehouse locations and traffic volumes on the links that connect these facilities to other supply chain locations. However, other pieces of information are miss- ing (e.g., whether a warehouse facility is a live load/unload business, provides for drop and hook trucking operations, or is supported by truck load [TL] or less-than-truckload [LTL] service). Likewise, although it is possible to measure or estimate the square feet of warehouse space, there is no information about its cubic capacity. However, the num- ber of trucks generated from a facility can vary greatly depending on the ceiling height. To respond effectively to current and anticipated freight data requirements, public and private decisionmakers must understand the freight transportation system, its use, and its role in economic development. As Figure 2 suggests, one way to understand freight transportation is by analyzing commod- ity movements, trade, and relationships among different sec- tors of the economy. In reality, as the real-world examples above demonstrate, understanding freight transportation requires taking into consideration many other aspects, a small sample of which includes operating conditions of the trans- portation network, traffic congestion, environmental impacts, and safety. Although there are many ongoing freight data collection efforts, these efforts are frequently inadequate in terms of scope, coverage, geographic and/or temporal resolution, quality, and access to data. Efforts to bridge these gaps with analytical tech- niques and/or additional data collection programs tend to be ad hoc and cover only limited aspects of the entire freight transportation data spectrum. The transportation community has recognized the urgent need to address this problem. For example, the TRB’s 2003 Special Report 276: A Concept for a National Freight Data Pro- gram, recommended a framework for developing national commodity movement data, with a goal to facilitate data fusion and fill data gaps in order to develop a comprehensive picture of freight flows (4). This report evolved from a 2001 conference in Saratoga Springs, NY, which concluded that cur- rently available data were inadequate to support the require- ments of analysts and policymakers and recommended a framework for the development of national freight data (5). 12 Figure 2. Example of movement of goods from port to consumer (3).

The proposed framework in Special Report 276 included an advisory committee to oversee the detailed design of a multi- faceted survey program, a comprehensive survey and data gathering program, a national freight database, a freight data synthesis program to fill data gaps, and supplemental data collection activities (Figure 3). Special Report 276 recognized the availability of data sources such as the Commodity Flow Survey (CFS), the Vehicle Inventory and Use Survey (VIUS) (now discontinued), the Carload Waybill Sample, and the Waterborne Commerce of the United States (WCUS) database. The report recognized the potential for data availability resulting from the imple- mentation of initiatives such as the Freight Analysis Frame- work (FAF) and the Automated Commercial Environment/ International Trade Data System (ACE/ITDS). The report also recognized the increasing importance of alternative data collection methods (e.g., through EDI programs and intelligent transportation system [ITS] implementations), and recommended the implementation of strategies to encour- age data collection and synthesis by public- and private- sector organizations. Noting the unique position of the fed- eral government to provide the necessary leadership to ensure a successful implementation of a framework for national commodity movement data, the report recommended that the Bureau of Transportation Statistics (BTS) assume that leadership role. In general, the report highlighted the need to conduct an assessment of the strengths and weaknesses of a wide range of data sources as a prerequisite for the devel- opment of the national framework. Special Report 276 described the proposed framework for national commodity movement data at a high conceptual level. As a result, it would be inappropriate to treat the report as a prescription for detailed framework data components or requirements. For example, the report recommended capturing the following data items to describe important commodity movement characteristics: origin and destina- tion; commodity characteristics, weight, and value; modes of shipment; routing and time of day; and vehicle/vessel type and configuration. However, it only briefly addressed other critical related issues such as privacy and data confidential- ity issues, data fusion challenges, agency roles, and security considerations. In 2009, NCHRP Project 8-36, Task 79, proposed a high- level framework for a prototype web-based freight data exchange network (Figure 4) (6). In the framework, the data exchange network (Figure 4) would be a centralized data repository where data providers and users enter and/or access commodity movement-related datasets, metadata, data quality reports, and reference materials. The web-based data exchange network would enable users to extract, transform, 13 Source: Department of Homeland Security. Figure 3. TRB’s Special Report 276: Proposed Framework for a National Freight Data Program (4).

14 Figure 4. NCHRP Project 8-36—Task 79’s proposed freight data exchange network framework (6).

and query datasets. The data warehouse would include meta- data for the transformed datasets and pre-processed sum- mary tables. As in the case of the framework proposed in TRB’s Special Report 276, the focus of the NCHRP Project 8-36 Task 79 freight data exchange network was commodity movement data. As part of the Upper Midwest Freight Corridor Study, which covered several upper Midwest states as well the provinces of Ontario and Manitoba in Canada, researchers developed a system called Midwest FreightView that enables users to connect remotely to freight datasets located at the University of Toledo (7). The system includes a viewer that depicts features such as highways, rail lines, ports, and inter- modal terminals. The system contains datasets from agencies at the federal, state (or provincial), and regional levels. In addi- tion, the database contains regional economic data, including employment figures, number and locations of establishments, and types of commodities produced within each portion of the region. The need for reliable freight transportation data also has been identified in the U.S. DOT’s proposed Framework for a National Freight Policy, which includes the following objectives (8): • Objective 1: Improve the operations of the existing freight transportation system. • Objective 2: Add physical capacity to the freight transporta- tion system in places where investment makes economic sense. • Objective 3: Better align all costs and benefits among par- ties affected by the freight system to improve productivity. • Objective 4: Reduce or remove statutory, regulatory, and institutional barriers to improved freight transportation performance. • Objective 5: Proactively identify and address emerging transportation needs. • Objective 6: Maximize the safety and security of the freight transportation system. • Objective 7: Mitigate and better manage the environ- mental, health, energy, and community impacts of freight transportation. Each objective, strategy, and tactic in the freight policy framework requires collecting, maintaining, and using reli- able data. Recognizing this need, the framework includes the following strategies to address freight data needs: • Strategy 4.4: Actively engage and support the establishment of international standards to facilitate freight movement. • Strategy 5.1: Develop data and analytical capacity for mak- ing future investment decisions. Clearly, the range of data needs to support the national freight policy framework is quite wide and covers a variety of freight-related business processes, including trade and supply chain; planning, design, construction, operations, and main- tenance of freight transportation networks; environmental and energy impacts; safety; and security. Research Objectives The overarching theme behind NCFRP Project 12 was the need for accurate, comprehensive, timely freight transporta- tion data at different levels, as well as the need for a holistic approach to freight transportation data. More specifically, NCFRP 12 was set up to identify specifications for a national freight data architecture that would facilitate freight-related statistical and economic analyses; support the decisionmak- ing process by public and private stakeholders at the national, state, regional, and local levels; and enable the acquisition and maintenance of critical data needed to identify freight-related transportation needs. Specific NCFRP Project 12 objectives included the following: • Develop specifications for content and structure of a national freight data architecture that serves the needs of public and private decisionmakers at the national, state, regional, and local levels; • Identify the value and challenges of the potential data archi- tecture; and • Specify institutional strategies to develop and maintain the data architecture. In providing a frame of reference for the rest of this report, it is worth noting that the scope of NCFRP Project 12 was to develop requirements and specifications for a national freight data architecture, not to develop the data architec- ture (which would be a logical next step after identifying those requirements and specifications). In addition, although Chapter 4 includes a formal definition and scope for a national freight data architecture, it may be useful at this point to clarify what is, and what is not, a data architecture. In gen- eral, a data architecture can be defined as the manner and process used to organize and integrate data components. This definition is similar to others found in the literature. It follows that a data architecture is not a database (databases may be built based on data architectures); a data model, a data standard, a specification, or a framework (these elements could be components of a data architecture); a system archi- tecture (a system architecture could use data architecture components); a simulation or optimization model; or an institutional program. 15

The remainder of this report summarizes the research find- ings as follows: • Chapter 2 includes a discussion of systems, databases, and architectures that might be used as a potential reference for the development of a national freight data architecture; • Chapter 3 includes a summary of data needs and the results of online questionnaires and subsequent interviews with freight stakeholders; • Chapter 4 provides an outline and draft requirements for a national freight data architecture, as well as challenges and strategies related to the implementation of a national freight data architecture; and • Chapter 5 includes relevant conclusions and recommen- dations. • Appendix A of the contractor’s final report, available on the project webpage, provides freight transportation data sources. 16

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TRB’s National Freight Cooperative Research Program (NCFRP) Report 9: Guidance for Developing a Freight Transportation Data Architecture explores the requirements and specifications for a national freight data architecture to link myriad existing data sets, identifies the value and challenges of the potential architecture, and highlights institutional strategies to develop and maintain the architecture.

The report also includes an analysis of the strengths and weaknesses of a wide range of data sources; provides information on the development of a national freight data architecture definition that is scalable at the national, state, regional, and local levels; and offers readers a better understanding of the challenges that might block the implementation of a national freight data architecture as well as candidate strategies for developing, adopting, and maintaining it.

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