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1 1.1 Introduction U.S. state and metropolitan planning agencies are now expected to incorporate freight demand into their strategic transportation policies. While many had always recognized trucking demand in their highway needs, the inclusion of other modes was stimulated by federal legislation that began in 1991 with the passage of the Intermodal Surface Transportation Efficiency Act (referred to as ISTEA). This began the process of switching the planning focus from highway departments and networks to transportation agencies and modal systems. Subsequent legislative actions include the Transportation Equity Act for the 21st century (TEA-21) in 1998 and Moving Ahead for Progress in the 21st Century Act (MAP-21) in 2012, which further strengthened the criti- cal role played by freight transportation in supporting the goals of economic competitiveness, safety, and sustainability. MAP-21 includes provisions to improve the condition and performance of the multi-modal national freight network and also requires that all state departments of transportation (DOTs) direct resources toward improving freight movement through several initiatives, such as the following: ⢠Assessing the condition and performance of the national freight network ⢠Identifying highway bottlenecks that cause significant freight congestion ⢠Forecasting freight volumes ⢠Identifying major trade gateways and national freight corridors ⢠Reducing barriers that impact freight transportation performance (FHWA 2012) These initiatives require an understanding of both current and future freight demand and the different modal transportation networks utilized, which is arguably most efficiently determined using robust models accessing accurate, consistently defined freight data (Walton et al. 2014). Moreover, current modeling effectiveness and potential is already limited by data constraints related to focus, structure, definitions, sampling designs, timing, and relevance. Improved and more robust data sets are needed to allow freight models to adequately capture the determinants of freight demand, accurately measure the impact of freight on the transportation infrastructure, and effectively support the decision-making processes of public and private stakeholders at the national, state, regional, and local levels (Chase et al. 2013). A national freight data architecture linking various freight data sources across modes, sub- jects, and levels of geography has been proposed to enhance data inputs, thus improving cur- rent modeling (Chase et al. 2013). The many current challenges in linking multiple freight data sources (as identified in the literature) include the following: ⢠Different origin and destination definitions and geographic units that do not directly correlate ⢠Different commodity classifications ⢠Different assumptions to estimate data or deal with missing data C H A P T E R 1 Background
2 Implementing the Freight Transportation Data Architecture: Data Element Dictionary ⢠Different expansion factors and control totals ⢠Differing procedures used for data aggregation or disaggregation ⢠Difficulty in obtaining proprietary data from private sources ⢠Inconsistency of data across different modes of transport ⢠Inconsistency of data collection efforts across different modes of transport (rail versus high- way versus air cargo versus intermodal) ⢠Inaccurate or nonexistent local-level commodity flow data ⢠Different vehicle classifications ⢠Different data storage formats and dictionary definitions Slight or subtle variants in data definitions and metadata structures across datasets, and some- times temporally within the same data sources, pose challenges to the compilation and use of freight data. Data analysts, regulators, and policy analysts frequently face challenges when com- bining data from multiple sources into a single national or state-level analysis, or when using the data for program development and administration that spans multiple geographic areas. Organizations may call the same freight data element by different names or call different data elements by the same name. In some cases, freight data elements thought to be equivalent are combined, leading to incorrect investment decisions based on invalid information. A dictionary that organizes the many current freight data elements, provides a method for identifying dif- ferences in definitions, and offers a set of homogeneous approaches for bridging gaps between definitions would constitute a critical tool to strengthen freight planning. The need for such a dictionary identifies the primary focus of this research project. 1.2 Research Objective The key objective of the research was to produce a searchable and sustainable web-based freight data element dictionary for transportation analysis, with an accompanying set of rec- ommendations for identifying differences in definitions and developing statistical and harmo- nization bridges between definitions, as appropriate for resolving differences. The dictionary, designed for a wide range of potential users, is capable of supporting a variety of future freight planning initiatives at metropolitan, state, regional, and national levels. It is structured to benefit from user feedback and database updates as well as helping frame greater consistency in terms of definitions, content, and temporal sampling of current databases when they are updated. 1.3 Study Approach The study consisted of the following tasks: ⢠Identify âreadily availableâ data sources associated with freight. ⢠Provide examples of freight data uses and applications. ⢠Compile and classify an inventory of data elements and glossary terms found in the selected sources into a uniform typology. ⢠Identify differences in data element definitions. ⢠Provide metadata tools and resources to guide data users on the appropriate steps and procedures for combining data from multiple freight data sources. ⢠Develop a searchable and sustainable web-based application containing the study findings, an inventory of freight data dictionaries, and a discussion feature to be used by practitioners to exchange ideas and information. NCFRP Report 35 presents the findings from each of the above tasks. More than 40 U.S. freight- related data sources were identified in the literature, and their data elements were organized into
Background 3 a typology across databases so that similar data elements could be identified. Classifying similar data elements facilitated the identification of differences in their definitions and aided in the development of harmonization or statistical bridges, as appropriate, for resolving those differ- ences. Examples of freight data uses were also compiled from the literature to demonstrate how freight data sources are currently being utilized by agencies and the research community. All the information contained in this report is available on the searchable web-based Freight Data Dictionary application. The purpose of this web-based tool is to provide an avenue where the information gathered from this study can be updated as newer data sources and methods for resolving data heterogeneity become available. The web-based application provides an oppor- tunity for practitioners to exchange ideas and information to support the effective and accurate use of freight data. It also widens the utility of freight data sources by assisting less experienced planners to derive more accurate output and widen data use. This report is organized into eight chapters, including the introduction. Chapter 2 describes the development of a web-based data dictionary framework that would result in a searchable and sustainable product. Chapter 3 identifies the wide range of activities in which freight data are used, including operations, congestion, safety, security, economic development, and land use. Chapter 4 provides an inventory of freight data sources and dictionaries and provides a glossary of terms. Chapter 5 considers the challenge of classification and validation of data ele- ments across databasesâfactors that limit models and their application. Chapter 6 examines differences in data element definitions across a wide set of databases, while Chapter 7 addresses the challenge of resolving the differences, which is critical to meeting the prime research objec- tive of the project. Chapter 8 provides suggestions for implementing the product of this work and undertaking a variety of additional activities related to strengthening the model and extend- ing its use to analyze and support freight planning programs across a wide range of public and private uses.
