National Academies Press: OpenBook

Guidebook for Understanding Urban Goods Movement (2012)

Chapter: Chapter 4 - Using Freight Data for Planning

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Suggested Citation:"Chapter 4 - Using Freight Data for Planning." National Academies of Sciences, Engineering, and Medicine. 2012. Guidebook for Understanding Urban Goods Movement. Washington, DC: The National Academies Press. doi: 10.17226/14648.
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Suggested Citation:"Chapter 4 - Using Freight Data for Planning." National Academies of Sciences, Engineering, and Medicine. 2012. Guidebook for Understanding Urban Goods Movement. Washington, DC: The National Academies Press. doi: 10.17226/14648.
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Suggested Citation:"Chapter 4 - Using Freight Data for Planning." National Academies of Sciences, Engineering, and Medicine. 2012. Guidebook for Understanding Urban Goods Movement. Washington, DC: The National Academies Press. doi: 10.17226/14648.
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Suggested Citation:"Chapter 4 - Using Freight Data for Planning." National Academies of Sciences, Engineering, and Medicine. 2012. Guidebook for Understanding Urban Goods Movement. Washington, DC: The National Academies Press. doi: 10.17226/14648.
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Suggested Citation:"Chapter 4 - Using Freight Data for Planning." National Academies of Sciences, Engineering, and Medicine. 2012. Guidebook for Understanding Urban Goods Movement. Washington, DC: The National Academies Press. doi: 10.17226/14648.
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Suggested Citation:"Chapter 4 - Using Freight Data for Planning." National Academies of Sciences, Engineering, and Medicine. 2012. Guidebook for Understanding Urban Goods Movement. Washington, DC: The National Academies Press. doi: 10.17226/14648.
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Suggested Citation:"Chapter 4 - Using Freight Data for Planning." National Academies of Sciences, Engineering, and Medicine. 2012. Guidebook for Understanding Urban Goods Movement. Washington, DC: The National Academies Press. doi: 10.17226/14648.
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Suggested Citation:"Chapter 4 - Using Freight Data for Planning." National Academies of Sciences, Engineering, and Medicine. 2012. Guidebook for Understanding Urban Goods Movement. Washington, DC: The National Academies Press. doi: 10.17226/14648.
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Suggested Citation:"Chapter 4 - Using Freight Data for Planning." National Academies of Sciences, Engineering, and Medicine. 2012. Guidebook for Understanding Urban Goods Movement. Washington, DC: The National Academies Press. doi: 10.17226/14648.
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Suggested Citation:"Chapter 4 - Using Freight Data for Planning." National Academies of Sciences, Engineering, and Medicine. 2012. Guidebook for Understanding Urban Goods Movement. Washington, DC: The National Academies Press. doi: 10.17226/14648.
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Page 39
Suggested Citation:"Chapter 4 - Using Freight Data for Planning." National Academies of Sciences, Engineering, and Medicine. 2012. Guidebook for Understanding Urban Goods Movement. Washington, DC: The National Academies Press. doi: 10.17226/14648.
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Suggested Citation:"Chapter 4 - Using Freight Data for Planning." National Academies of Sciences, Engineering, and Medicine. 2012. Guidebook for Understanding Urban Goods Movement. Washington, DC: The National Academies Press. doi: 10.17226/14648.
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Many factors contribute to the capacity challenges facing urban freight transportation net- works. The challenge of planning investments within an evolving system of both public and pri- vate facilities across multiple modes—and across local, state, interstate, and global geographies— is made more difficult because timely, accurate freight data is often fragmented, not compatible across sources, or simply not available. As freight planning has become more important to public agencies, freight data has become a topic of intense interest. The landscape of available public and private freight data resources is intricate and growing. Between 2006 and 2010, NCFRP funded 40 projects, with approximately one-fourth devoted to freight data and related topics, such as • NCFRP 03: Performance Measures for Freight Transportation, • NCFRP 06: Freight Demand Modeling to Support Public-Sector Decision Making, • NCFRP 11: Identification and Evaluation of Freight Demand Factors, • NCFRP 12: Specifications for Freight Transportation Data Architecture, • NCFRP 16: Representing Freight in Air Quality and Greenhouse Gas Models, • NCFRP 20: Guidebook for Developing Sub-national Commodity Flow Data, • NCFRP 25: Freight Trip Generation Land Use, • NCFRP 26: Freight Transportation Cost Data Elements, • NCFRP 27: Promoting Environmental Goals in Freight Transportation through Industry Benchmarking, • NCFRP 31: Overcoming Barriers to Sharing Freight Transportation Data. This chapter of the guidebook is intended only as a brief overview of freight data and its uses in a local planning context. The CD-ROM that accompanies this guidebook contains links to additional materials on freight data resources and more information about how these data sources can be used to address freight issues at a local level. The discussion here presents a geo- graphic framework for freight data categories, as well as general protocols for using primary and secondary data sources to address freight issues at the local planning level. For the public sector, reliable urban freight data can lead to better infrastructure and policy decisions that may improve urban freight operations and the livability of neighborhoods. For the private sector, supply chain reliability is crucial to business strategies that create competitive advantage. Multimodal transportation activities undertaken by MPOs strive for equilibrium between transport demand and community goals such as economic development, sustainable land use, environmental protection, and livable neighborhoods. Reliable data that addresses urban goods movement issues from multiple perspectives such as land use, infrastructure investment, traffic operations, safety, and economic development is often difficult to obtain because much of the most useful information resides with private-sector businesses providing transportation services or producing the products being delivered. 29 C H A P T E R 4 Using Freight Data for Planning

Source: Wilbur Smith Associates, adapted from The Geography of Transport Systems (Rodrigue, Comtois, and Slack 2009). Exhibit 4-1. Geographic dimensions of urban freight data. Primary and secondary data sources have strengths and limitations for supporting planning activities. Primary sources such as surveys or truck counts can provide the level of detail often needed for urban level planning but they can also require significant resources. Secondary freight data sources, both public and private exist, but often do not capture the levels of detail needed for urban freight planning (e.g., routing details). Used together, secondary freight data sources, supplemented with primary data often can be integrated to provide value to public planners addressing urban goods movement issues. The guidebook discussion of freight data is presented in a simple framework shown in Exhibit 4-1. Most public agencies that have undertaken a programmatic approach to freight planning have learned that there is seldom a “one size fits all” solution to urban freight data needs. Addressing multi- faceted issues typically requires multiple data sources and a variety of techniques. For example, reme- dies for congestion-related problems may require a combination of strategic projects pertaining to capacity enhancements, system preservation, operational improvements, demand management, and maintenance policies. The requisite data could encompass truck volumes, service perfor- mance, structural conditions, and cost information, not all of which may be limited to freight. Neighborhood Freight Data In NCHRP Synthesis Report 320: Integrating Freight Facilities and Operations with Community Goals, the author notes that integrating freight operations with the vision that most of us have for livable communities is a complex and multifaceted issue (Strauss-Wieder 2003). Just some 30 Guidebook for Understanding Urban Goods Movement

of the complex issues of concern to urban planners at the neighborhood level are outlined in Exhibit 4-2. In NCFRP Report 8: Freight-Demand Modeling to Support Public-Sector Decision Making (Cambridge Systematics, Inc. and GeoStats, LLP 2010) the authors reported that interviews and surveys conducted with public decisionmakers identified “existing routing” as the primary freight data need identified by those surveyed. At the neighborhood level, route choices can often be complex and non-intuitive, requiring at least some level of primary data collection to understand truck driver decisions. Traffic counts and trip diaries are two traditional means of understanding route choices made by truck drivers, but new methods such as GPS vehicle track- ing also are gaining prominence (GPS data collection methods are discussed more in the sec- tion on Freight Network Data). One issue with all such approaches is that they reveal the result of routing decisions but not the reasons for them. Because many truck fleets rely on stop- sequencing software to construct their routes, familiarity with such tools can help interpret the primary data findings. Safety is an important consideration for both citizens and freight operators. Freight vehi- cles are not necessarily more unsafe than other vehicles, but because of blind spots, slower vehicle reaction times, larger loads, or loads of hazardous materials, freight should always be considered in the planning process. In a neighborhood, it may be especially important to understand how freight vehicles interact on local streets where pedestrians and children are present. For example, truck drivers often complain that tree trimming and landscaping blocks the line of sight for drivers sitting high off the road. Safety issues can also arise when large trucks using local streets encounter construction or inadequate infrastructure such as low or narrow bridges. Understanding safety issues at the local level typically requires data and information about actual conditions, such as route preferences, truck counts, and vehicle speeds. Given that freight operations often generate large volumes of truck traffic, air quality and emissions has become an increasingly important safety issue to many communities. Many states and communities have adopted idling regulations for residential areas. The American Trans- portation Research Institute (ATRI), the non-profit research arm of the American Trucking Associations has compiled a compendium of these regulations, which can be found on the resource CD. Some communities also are becoming more active in monitoring neighborhood emissions. For instance, the Port of Los Angeles has installed four monitoring stations that con- tinuously measure air quality in the port complex and in communities downwind of the port. Using Freight Data for Planning 31 Source: Wilbur Smith Associates. Exhibit 4-2. Freight data issues affecting neighborhoods.

The EPA has developed the Smartway® Program to encourage the adoption of new engine tech- nologies and other activities to reduce diesel emissions. Finally, the dispersion of emissions can be as much, or more, important than their originating points, in terms of where effects are felt and how to alleviate them. Modeling that captures topography and meteorology in addition to traffic is required for this, and some is accessible through EPA. Another issue gaining prominence in freight planning is the need to address environmental justice. Environmental justice (EJ) refers to the concept that over time, geographic areas with larger-than-average concentrations of minority populations or populations at or below the poverty line suffer disproportionately negative environmental impacts from transportation- related development. Since 1994, federal agencies have been required to identify and address potential or actual disproportionately adverse environmental effects on minority and low- income populations. For example, when the Atlanta Regional Commission defined a regionwide truck route plan, it conducted a demographic analysis of the region, with a special emphasis on identifying EJ populations. EJ census blocks were mapped in relation to the proposed truck routes, in order to address environmental justice issues concerning existing and potential future freight traffic impacts during subsequent outreach sessions. For each of the neighborhood freight issues described there are likely some sources of second- ary data collected for traffic monitoring, land-use compliance, or travel demand modeling that can be adapted to address safety, air quality, or environmental justice issues. However, to better understand the actual conditions in a neighborhood, some primary data collection will be required. Exhibit 4-3 provides a framework for how primary and secondary data sources might be integrated to address common freight issues at a neighborhood level. 32 Guidebook for Understanding Urban Goods Movement Exhibit 4-3. Integrating freight data to address neighborhood issues.

Freight Node Data Freight nodes represent the consolidated or individual endpoints that generate or receive freight flows. Nodes are also the key points of production, consumption, or intermediate han- dling for goods. Freight facility/node information can be useful in a planning context for travel demand modeling, land-use planning, and environmental analysis. Quick Response Freight Manual II, (Beagan et al. 2010) a freight modeling resource published by FHWA, points out that the first step in traditional four-step travel forecasting is to understand trip generation rates. There are two approaches for understanding trip generation rates at a local level: (1) conduct local surveys of vehicles generated by major freight nodes in a given travel analysis zone (TAZ) or (2) apply national default generation rates based on industry employ- ment by TAZ. The former are more expensive, but the latter suffer from a variety of problems affecting industry specificity, productivity, mode usage, and supply chain design. Exhibit 4-4 shows GIS mapping of the estimated tons produced by individual freight nodes within a TAZ. Freight facility/node information can be useful for site planning (i.e., understanding the traf- fic impacts of a new or expanded freight facility). Finally, node data also is important in a plan- ning context for understanding “last mile” needs for designating truck routes, and for use in travel demand modeling, land-use planning, and environmental analysis. ISTEA placed new emphasis on developing inventories of nodes where freight or people tran- sitioned from one mode to another. Specifically, the planning regulations that were promulgated as a result of ISTEA required states to develop Intermodal Management Systems (IMS), a data- base requirement that was later dropped because of the onus it placed on state planning agen- cies. Although MPOs were not required by ISTEA to develop comparable IMS datasets at the urban area level, they were encouraged to work with states and utilize information from state IMS data in developing their own transportation plans. When IMS data became an option as opposed to a requirement, some states continued to maintain IMS data for freight and some MPOs have also developed freight facility datasets. At the federal level, an intermodal terminal facilities database has been created that is now available through the National Transportation Atlas Database (NTAD) series through the Bureau of Transportation Statistics (BTS). The NTAD file for 2009 contains 3,280 records of facilities nationwide. Using Freight Data for Planning 33 Source: Wilbur Smith Associates. Exhibit 4-4. Freight node data—tonnage production by facility.

Exhibit 4-5. Integrating node data for travel demand modeling and other planning issues. Proprietary sources of freight facility data also are available and include specific estimates about the volume of shipments produced and received at each facility. Typically these datasets are modified versions of business establishment data from sources such as InfoUSA™, Harris InfoSource, or ThomasNet®. Typically, secondary data sources about facilities can be supplemented through online, phone, or mail surveys. There are field examples of study efforts wherein the largest facilities (top 20 per- cent by number) produced 80 percent or more of the total freight flow volumes for a given study area. These instances of the Pareto principle (the 80/20 rule) suggest that concentrating survey efforts on the very largest commodity producing facilities in an urban area is an efficient, cost- effective manner for improving the quality of freight facility data. Large freight facilities also can be productive locations for conducting surveys to determine local bottlenecks or other opera- tional issues affecting truck movements in a local area. Agency studies have had success conduct- ing break-room surveys at local truck terminals to hear from drivers about operational issues they face in making regional deliveries. Exhibit 4-5 provides some guidance on how primary and secondary node data can be integrated to address urban freight planning needs. Freight Network Data Freight network data helps define major route patterns and critical infrastructure being used to convey freight shipments through the various modal systems. Truck counts are probably the most common data element collected by public planning agencies that contribute network information about freight. Heavy truck counts can provide information about the key network elements used for freight movements and the associated infrastructure demands on various 34 Guidebook for Understanding Urban Goods Movement

highway segments. Other elements of network data include rail line capacity, inland waterway capacity (including locks and dams), port throughput capacity, posted speeds, and weight and dimension limitations on bridges and pavements. See Exhibit 4-6. In the urban context, it is likely that freight network data is most pertinent to highway net- works that service the key freight nodes across all modes, such as rail intermodal yards, ports, airports, manufacturing facilities, and distribution centers. From a freight movement stand- point, network roles should be a central part of planning a region’s transportation system and should be managed both developmentally and operationally. Developmentally, freight networks should be protected by proper zoning, building permits, and enforcement, so that key network elements are capable of sustaining truck traffic volumes effi- ciently. Road geometry, pavement structures, and bridge designs should be planned to accommo- date large or heavy vehicles, with appropriate turning radii, height clearances, and passing points. Operationally, freight networks should be managed for productive freight movements. In urban areas traffic signals on freight network routes should be timed for truck movements from known freight generators and receivers. Construction activities should avoid disrupting primary and relief routes simultaneously, and construction, as far as practical, should be coordinated with industry, avoiding commercially sensitive time periods (like month’s end) and understanding the time pat- terns of line-haul and city freight schedules. Exhibit 4-7 provides an example of how primary and secondary data sources can be used to help identify bottlenecks on urban freight networks. To support the developmental and operational elements of freight networks requires data such as average speed by route, time of day, and seasonal truck traffic. Most of this data is collected by planning agencies through roadside data collection, surveys, or increasingly through advanced technology. A new form of network data has emerged on the market because of such technolog- ical changes. Today, many trucking companies and private carrier fleets use global positioning systems (GPS) to keep track of driver and equipment movements. Vendors of GPS and fleet man- agement software are packaging the data in formats that allow public agencies to examine the net- work choices made by truck drivers operating in urban areas and across the country. FHWA has been working with ATRI to present truck performance data on some of the nation’s key Interstate highways. The FHWA/ATRI “Freight Performance Measures” (FPM) project now provides access to online performance data that can be accessed at http://www.freightperformance.org. Private sources of vehicle tracking data also are emerging as GPS data become more available via cell phone networks. INRIX® is one example of a proprietary vendor that can develop customized Using Freight Data for Planning 35 Source: Wilbur Smith Associates. Exhibit 4-6. Freight network data.

datasets from a variety of cell phone and other GPS devices from vendors who supply GPS services to the trucking industry. Information gained from network data can be used to identify current truck routes as well as potential alternatives that may serve additional destinations or facilitate a faster trip. Network data can also include critical information regarding routes that are inaccessible to trucks because of weight limits, vertical clearance, truck prohibitions, or other reasons. Freight Flow Data Commodity flows are typically used in freight planning to provide insights about the economic and trade environment of a region. Commodity flow attributes help tie goods movement to eco- nomic development by providing information about urban consumption dependencies such as raw material or service input markets (imports), and markets for finished manufacturing prod- ucts (exports). As such, commodity flow information also is used to generate trip estimates in some traffic modeling applications. Commodity data at the urban level can provide insight about interdependencies between goods and services such as retail trade and food services. Commodity flow data also can help identify those industries in a regional economy that are highly dependent on transportation (e.g., those industries producing high-volume movements and/or high-value products). When combined with other economic and demographic informa- 36 Guidebook for Understanding Urban Goods Movement Source: Wilbur Smith Associates. Exhibit 4-7. Example of integrated data sources for evaluating urban freight networks.

tion, freight flow data can help depict how an urban area is connected through trade to other regions of the nation or the world. Freight flow data is origin-destination information about commodity shipments (see Exhibit 4-8). The typical commodity flow record will contain an origin-destination, type of commodity, weight and/or value of the commodity shipment, and mode of shipment. There are secondary sources of freight flow data from both public and commercial sources. Exam- ples include • Commodity Flow Survey (BTS); • Freight Analysis Framework, Version 3 (FHWA); • Railroad Waybill (Surface Transportation Board [STB]); and • TRANSEARCH® (IHS Global Insight). The Commodity Flow Survey (CFS) is conducted as an element of the Economic Census conducted by the U.S. Census Bureau every 5 years. The CFS is produced by surveying business establishments in mining, manufacturing, wholesale trade, and selected retail industries. The CFS provides estimates of tons shipped and dollar values for all major modes and covers the entire United States. However, the CFS has a number of well-researched weaknesses. For example, not all commodities are covered by the CFS—the survey does not survey establishments classified as government, farms, forestry, fisheries, construction, or transportation. The CFS does not capture the first leg of imports. Although the CFS plays an important role in providing data on domestic freight movements, gaps in shipment and industry coverage, and a lack of geographic and com- modity detail, limit the direct usefulness of the CFS data, especially at the urban level. The Freight Analysis Framework (FAF) dataset produced by FHWA, now in its third genera- tion, uses the CFS as a starting point, and then uses statistical modeling methodologies to remedy most of the deficiencies associated with the CFS. The FAF-3 database includes measurements of freight flows by weight and value between 131 unique geography zones. The United States is divided into 123 domestic zones while areas outside the United States are represented in 8 distinct foreign zones. The database yields freight flow origin and destination (O/D) pairs, identifying Using Freight Data for Planning 37 Source: Wilbur Smith Associates. Exhibit 4-8. Freight flow data.

three underlying movement pairs (foreign to domestic, domestic to domestic, and domestic to foreign) and each type of O/D pair is further described by seven types of modal movement or combination of modes. In addition, the database provides forecast matrices out over 2 decades. Even with the greater detail in geographic zones, the database must be further disaggregated and supplemented with local data for use in many urban planning applications. However, given the robust promise of the FAF-3 for planning applications, research into techniques for disaggrega- tion continues, and some additional tools and techniques are readily available for planners to begin commodity analysis for their area. NCFRP Project 20, “A Guidebook for Developing Sub- National Commodity Flow Data,” a project in progress at the time of this report, will provide additional resources and methods for local planners seeking to use freight flow data. Additional information about FAF-3 can be found at http://faf.ornl.gov/fafweb/Default.aspx. TRANSEARCH® is a proprietary database produced by IHS Global Insight. TRANSEARCH® is a nationwide commodity O/D database produced on an annual basis for freight flows between U.S. geographies at the county, Bureau of Economic Analysis (BEA) area, or state level. TRANSEARCH® employs the CFS but also various primary and secondary data sources covering commodity volume, value, and modal flow, including a long-term, proprietary motor carrier traf- fic sample, railroad waybill samples, and numerous commercial and federal government surveys. The comprehensive geographic, commodity, and modal coverage of this database has made it a popular source of freight flow information for state and metropolitan transportation planners. TRANSEARCH® data is not free, and the price varies with the level of customization and cover- age. One benefit of TRANSEARCH over FAF-3 is that if errors or inaccuracies in the O/D matrix are discovered, they are corrected in a timely manner. Nonetheless, as many governments strug- gle with budgetary issues, many find data purchases for freight flows difficult to justify. Whether public or private, secondary sources of freight flow information can be enhanced by primary data collection activities, such as truck intercept surveys or shipper interviews. Because the prominent freight flow data sources are based on periodic national survey samples and data modeling techniques, localized surveys can serve to validate flows at a more localized level. Techniques such as truck intercept surveys can be used to examine the validity of O/D data, length of haul, and other attributes of third-party datasets. These data sources and meth- ods for disaggregation to smaller geographic areas will be discussed further in the next section. See Exhibit 4-9. Freight Data Protocols As discussed at the beginning of this chapter, freight data has become a topic of intense inter- est. Many planners, analysts, and academics hold strong opinions about freight data sources, techniques for applying freight data to planning issues, and the usefulness of products resulting from freight data and analysis. Freight data is a complex subject requiring information about both public and private facilities. Unlike passenger travel, a freight trip is far more likely to cross multiple modes and journey through multiple geographic jurisdictions. Although methods for collecting passenger information have become highly standardized, freight data sources are often fragmented and incompatible. For instance, the primary sources of commodity flow data use dif- ferent industry classification schemes: FAF-3 and CFS use the Standard Classification of Trans- ported Goods (SCTG), and TRANSEARCH® and the Rail Waybill Sample use the Standard Transportation Commodity Classification (STCC) system. Both FAF-3 and TRANSEARCH® produce statistics for seven transportation modes or movement types; however, FAF movement types include modal combinations representing intermodal movements, while TRANSEARCH® movement types disaggregate specific modes like trucking into truckload, less-than-truckload, and private fleets. 38 Guidebook for Understanding Urban Goods Movement

Given the complexity of freight data and its applications, the following protocols are offered for selecting and using freight data in planning applications: • Clearly define the issue(s) to be addressed: While planners always start with a plan, it cannot be overstated that in the case of freight data, clearly defining specific data needs to support the planning effort will save considerable time and resources. A good first step in identifying the data needed to support a larger planning effort is the creation of a data synthesis and collec- tion plan. Putting together the issues to be addressed, and the specific data needed to support those efforts, allows for greater interaction (and potentially buy-in) from the planning team and colleagues. The data plan should be a flexible document that is modified as conditions change or new information is identified. • Collect only the information you need (and can support): Although “only collect the data you need” is a common rule, there is often a tendency to extract “nice to know” as opposed to “need to know” information from private-sector freight stakeholders. It is also a good idea to assess internal capabilities for data analysis and maintenance, so that efforts made to collect freight data don’t go unused or become outdated if the planning effort requires periodic updates to the supporting information. • Seek out partners who can open doors for your data collection efforts: Many successful freight data efforts result from public agencies partnering with business groups, trade associations, or economic development agencies. Chambers of commerce and industry associations like state trucking or rail associations can be crucial partners for data collection. Getting the sup- port of industry groups can provide access to their membership and often these groups will Using Freight Data for Planning 39 Source: Wilbur Smith Associates. Exhibit 4-9. Example of integrated data sources for customizing freight flow data.

assist in disseminating surveys and add legitimacy to your efforts. In addition, they can be a source of expert advice and rules of thumb, which may substitute when data is inaccessible. Public or quasi-public economic development agencies also can be of great help identifying appropriate stakeholders for primary data collection efforts with various industry groups. • Remember the 80/20 rule: Pareto’s principle suggests that for many things a few (20 percent) are vital and many (80 percent) are trivial. Because freight is often consolidated at significant nodes, getting good information for the most vital facilities (i.e., largest 20 percent) can provide information about a majority of volume. • There is no “one size fits all” data solution for most freight planning efforts: Once your data needs are identified there are likely to be local-, regional-, and state-level data that can help support your efforts. There are also a growing number of national-level secondary freight data sources avail- able online, as well as documentation and research through FHWA and TRB that can identify and explain secondary freight data sources. With a comprehensive list of secondary data that can support your project, conduct a gap analysis to identify where primary data collection may be needed to address your specific issues. • Design a data program that fits your needs, and be creative: There are many opinions about what constitutes good freight data. Often opinions have been formed by using inappropriate data sources for the problem being addressed, misguided expectations, or personal biases. Before using secondary freight data sources do the research required to understand potential shortcomings and be realistic about what data gaps may need to be filled. Be prepared to think outside the box in seeking ways to collect primary data. 40 Guidebook for Understanding Urban Goods Movement

Next: Chapter 5 - Regulations Impacting Urban Goods Movement »
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TRB’s National Cooperative Freight Research Program (NCFRP) Report 14: Guidebook for Understanding Urban Goods Movement is designed to help facilitate decisions that accommodate and expedite urban goods movement while minimizing the environmental impact and community consequences of goods movement.

The guidebook and cases studies are designed to help decision makers better understand the potential impacts of their urban goods movement decisions on transportation infrastructure and operations; land use and site design; and laws, regulations, and ordinances applicable to urban areas.

The guidebook includes case studies that explore how urban supply chains connect to the urban economy, infrastructure, and land use patterns; their impacts on land use codes and regulations governing metropolitan goods movement of private-sector freight providers; and planning strategies for potentially improving mobility and access for goods movements in urban areas.

The print version of the NCFRP Report 14 includes a CD-ROM that includes a report and appendices on the process that developed the guidebook, and two PowerPoint presentations with speaker notes that transportation planners may use to help explain how local decision makers might enhance mobility and access for goods movement in their area.

The CD-ROM is also available for download as an ISO image. Links to the ISO image and instructions for burning a CD-ROM from an ISO image are provided below.

Help on Burning an .ISO CD-ROM Image

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(Warning: This is a large file and may take some time to download using a high-speed connection.)

An article on NCFRP Report 14 was published in the January-February 2013 version of the TR News.

CD-ROM Disclaimer - This software is offered as is, without warranty or promise of support of any kind either expressed or implied. Under no circumstance will the National Academy of Sciences or the Transportation Research Board (collectively “TRB”) be liable for any loss or damage caused by the installation or operation of this product. TRB makes no representation or warranty of any kind, expressed or implied, in fact or in law, including without limitation, the warranty of merchantability or the warranty of fitness for a particular purpose, and shall not in any case be liable for any consequential or special damages.

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