Office of Operations Freight Management and Operations

Freight Demand Modeling and Data Improvement Handbook

Chapter 1. Background

Freight transportation in the United States has been a topic of growing interest to policy makers, State departments of transportation (DOTs), metropolitan planning organizations (MPOs), and varied stakeholders. While Federal emphasis on freight has been expanded by the Moving Ahead for Progress in the 21st Century (MAP-21) Act and the Fixing America's Surface Transportation (FAST) Act, States and MPOs see freight as vital to supporting economic development. The overarching policy challenge for transportation agencies is to make informed investments in transportation infrastructure that support efficient freight mobility and access. To support economic development, long-range transportation plans and transportation improvement programs need to address freight. This need to better integrate freight with transportation planning recognizes the importance of goods movement, economic performance, and meeting consumer needs. Freight planning requires effective communication and coordination with the private sector (e.g., shippers, carriers, industry). It also requires close coordination with local government with respect to economic development and land use considerations. Although there has been increased emphasis on freight, accurate and timely freight data and forecasting still remain formidable challenges with substantial opportunity for improvement.

The second Strategic Highway Research Program, known commonly as SHRP2, was created by Congress as a component of the Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA- LU) of 2005. This legislation established four focus areas (see Table 1) to support research designed to improve highway safety, rehabilitate aging infrastructure, and reduce congestion. MAP-21 authorized additional funding to support implementation activities.

Table 1. Strategic Highway Research Program 2 focus areas.
Area Focus
Safety Prevent or reduce the severity of highway crashes by understanding driver behavior.
Renewal Address the aging infrastructure through rapid design and construction methods that cause minimal disruption and produce long-lived facilities.
Reliability Reduce congestion through incident reduction, management, response, and mitigation.
Capacity Integrate mobility, economic, environmental, and community needs in the planning and designing of transportation capacity.

Freight Demand Modeling and Data Improvement Strategic Plan

The SHRP2 C20: Freight Demand Modeling and Data Improvement Strategic Plan was developed as part of the Capacity focus area. This research initiative provides the strategic framework for making further advances in the areas of freight data collection, use and for accelerating innovative breakthroughs with the aim of integrating freight considerations into the planning process with confidence. It offers a road map that will lead to improved freight data sets and freight modeling practices. The plan outlines an organizational approach to help identify freight modeling and data priority needs, spur innovative ideas, and result in breakthrough solutions for wide application.

Efficient freight is essential to national, State, and local transportation infrastructure planning and our economic well-being. Transportation investments are capital intensive and represent long-term commitments. Understanding and forecasting freight flows is critical to planning for future transportation capacity, operation, preservation, safety and security, as well as energy and economic investment needs.

It is important that transportation planners possess both the tools and the skills to forecast freight demand and to analyze scenarios and investment alternatives as part of the overall transportation analysis. Travel demand forecasting, however, has historically been oriented toward passenger transportation. Passenger-oriented forecasting models draw on economic and demographic variables that are insufficient and sometimes irrelevant for estimating future freight demand, which is shaped by a much wider range of factors as a result of a complex logistics chain. The goal of the SHRP2 C20 program is to create better freight data and models that enable State, regional, and local planners to better predict trends in freight movement and make more informed project investment decisions.

Handbook Users and Purpose

This handbook provides information about better freight data and modeling tools through examples and pilot projects for State DOTs and MPOs to improve freight-related decisionmaking. It gives an overview of the projects funded by the SHRP2 Implementation Assistance Program (IAP) and highlights how these implementations address issues faced by freight planners. The primary audience for this product is practitioners—mainly those at State DOTs and MPOs. However, this handbook also recognizes that State DOTs and MPOs have different needs and capabilities; therefore, there is no "one size fits all" approach to freight planning—no one data or model solution that will satisfy the needs of all planning agencies.

By contrast, this handbook is designed to provide an overview of the data and tools available to practitioners across the spectrum of user types. These solutions range from the inexpensive and easily implementable to complex and analysis-rich options. The handbook is organized around the two subject areas of data and modeling tools and provides an overview of the options, benefits, and challenges of the tools identified. The subject matter draws from the original SHRP2 C20 Freight Demand Modeling and Data Improvement Strategic Plan, as well as the IAP projects, which serve as the foundation for the experience-based lessons learned and best practices guidance.

Data and Modeling Best Practices—An Overview

The common, underlying objective of data and model use is to analyze and document baseline conditions related to freight movement and to estimate future activity based on metrics involving economic activity, demographic changes, employment by economic sector, supply and demand of raw materials and finished products by consumers and industries, commodity flows, and other factors. Different tools and data are used by practitioners for different geographic scales, depending on the issues and scale of needs. This section lays out those identified practices that most accurately address these metrics and offer potential innovations for future practices. The identified best practices:

  • Provide a baseline assessment of models and data.
  • Identify potentially innovative approaches to better understand goods movement in a variety of contexts for a variety of users.
  • Provide a springboard for better data and model development in the future.

The underlying methodology for most tools used in freight planning and forecasting includes using resources to:

  1. Document existing demographic and employment conditions and characteristics of freight transportation (including tonnage, geographic origins and destinations, and mode of transport).
  2. Estimate future measures of freight transportation for these same parameters (tonnage, origins, destinations, modes of transport) based on changes in population and employment; establishment, growth, and dissolution of firms; productivity improvements by industry; and other economic drivers.

Depending on the geographic scale, the ultimate objective of freight planning and forecasting is to forecast freight activity and its effects on local or regional conditions related to economic activity, traffic congestion, air quality, and other impacts. Freight planners must select appropriate methods based on their needs, capabilities, data sources, and the usefulness of the expected outcomes.

Depending on the type of planning being completed, the factors have varying levels of sensitivity. For example, a study to determine the need for an intermodal facility is more sensitive to data related to mode than an analysis of warehousing and distribution facilities. The sensitivity of these factors to data has been taken into account within these best practices, which makes them more robust than methods that use existing data and perform analyses.

Overview of National Data Best Practices

The data used in the freight planning and forecasting processes are predominantly drawn from public resources. Although national data sets are generally the most complete and accessible, they lack the detail required for regional or local freight analysis, although they are continually improving. Local data sources provide a more comprehensive scale for these analyses, but some of the data requires expensive, ongoing updates.

Although these sources are the best in terms of current general practices, insufficient and inferior- quality local data remains a critical challenge in the development of freight models. The principal data for predicting freight transportation demand are the commodity flows by truck, rail, and water and through border and marine ports available from the Federal Highway Administration (FHWA) in the 2012 Commodity Flow Survey. In addition, the FHWA Freight Analysis Framework (FAF) estimates commodity flows (tonnage and value) within, to, and from States and select regions by mode as well as freight movements among major metropolitan areas, States, regions, and international gateways.

One of the most commonly used databases for statewide analysis of freight movements is the commercial Transearch database developed by IHS Global Insight. Transearch estimates freight flows (i.e., commodity tonnage) by truck (i.e., for-hire truckload, for-hire less than truckload, and private truck), rail carload, rail-truck intermodal, water, and air at the county, business economic area, State, or Province level. The Transearch database is a proprietary source of detailed freight data available for purchase that includes assumptions (undisclosed) to estimate and forecast movements.

Some research relies on smaller freight data sets compiled by facility operators and owners, data collected by public and private entities, and data collected as part of a customized survey. Sources for these data sets range from the Waterborne Commerce and Vessel Statistics database to the U.S. Census Bureau's County Business Patterns and Economic Census databases to mail-out–mail-back surveys of freight shippers. Unfortunately, many of the data sources and databases available for statewide or MPO- level freight planning have considerable limitations as they focus on certain modes or commodities and are available at different geographic levels. Consequently, combining or integrating the data sources into a comprehensive, coherent, and consistent database is a challenging task.

Several new data best practices have emerged in recent years. These include real-time traffic information for all vehicles, including trucks, from HERE, INRIX, and the American Transportation Research Institute (ATRI). These identify where and when traffic congestion occurs and provides information about traffic conditions and incidents that could cause delays. By analyzing and processing real-time commercial vehicle probe data, users can identify highway system chokepoints, time of day congestion, and congestion speeds to use in conducting assessments of truck travel times, lost productivity, and vehicle ranges based on truck driver Federal hours of service regulations.

Overview of National Modeling Best Practices

The development of models is generally constrained by the data available to populate them. If a specific model is required for an analysis, the pertinent data must either be available or collected. The models identified as best practices range from complex to simplistic and have been used successfully for their given purpose. There is no one tool that is ideally suited for every application, and each has benefits and limitations.

The models discussed below may be used in different ways, depending on the situation. They could be used alone when modeling one discrete element or factor or they may be used in combination to model different parts of a larger model, such as one for an MPO or state DOT.

The current state-of-the-practice of freight modeling is to move in the direction of behavior-based models that reflect the behaviors, economic principles, and business practices that dictate the movement of freight. To support behavior-based modeling, all four of the modeling projects described in Chapter 3 are behavior-based models.

Trend and Time Series Analyses

Trend and Time Series Analyses

Benefits

  • Is simple to use.
  • Requires historical information but is not data intensive.
  • Supports mode, commodity, and Origin-Destination analyses.

Limitations

  • Accuracy is suspect if based on short-term historical data.
  • Assumes past trends are indicative of future activity.
  • Lacks the dynamics of explanatory factors that affect freight demand.

Trend analysis and time series analysis methods forecast freight demand using historical trends. Depending on the data available, this category of freight demand modeling can consider varying levels of complexity and aggregation. The simplest trend analysis model involves the development of a growth factor from historical aggregate freight data that represents the annual compound growth rate of freight shipments that is applied to project future freight shipments. To account for temporal variations and interdependencies, trend analysis is often implemented using more advanced statistical time series analysis techniques, including smoothing, autocorrelation, autoregressive moving average models, and the use of neural networks.

Elasticity Methods

Elasticity methods are specifically used to estimate how freight commodities are split among transportation modes. These models assume that mode-choice decisions are based on the total logistics costs (TLC) associated with using various modes or modal combinations that are practical for a set of freight shipments. TLC includes the actual transport costs (or carrier charges) and other logistics costs (e.g., inventory costs, stock-out costs) incurred. The models assume that increases in TLC result in the diversion of some freight traffic to competing modes.

Planners use price elasticity, defined as shippers' sensitivity to TLC associated with a mode, to study how changes in TLC affect the quantity of freight demand shipped by each mode. Elasticity is calculated in two ways: 1) change in demand for a mode with respect to its own price, known as a direct elasticity, and 2) change in demand for a certain mode with respect to a change in price of a competing mode, referred to as a cross elasticity. Either way, planners can calculate point elasticity, arc elasticity, or shrinkage factors from field observations on price and quantity before and after a price change or from knowledge of the functional relationship between quantity and price.

Elasticity Models

Benefits

  • Observed or estimated data may be used.
  • Can be applied for short- or long-term analyses.
  • Can be useful for sketch planning applications.

Limitations

  • Not applicable for using data prior to deregulation in the 1970s.
  • Difficult to collect data for inputs.
  • Difficult to correlate multiple factors that affect demand.

Because this method separates freight demand by mode, elasticity measures must be used in conjunction with other models of total freight demand. Modal diversion may be estimated using disaggregate data for a sample, or by using more aggregate data when the total volume of movements is summarized by key variables (such as commodity). The diversion estimates can then be derived from estimated changes in TLC, or when other logistics costs are unaffected by cost changes.

Logistic Network Models

Logistic network models forecast how freight demand is divided between modes (or carriers) and travel corridors between a specific origin and destination. Like elasticity methods, logistic network models can be used in conjunction with other models of total freight demand. These models are recognized because they consider the freight transportation system as a whole, which is defined by interactions among producers, consumers, shippers, carriers, and the government. In particular, logistic network models assign commodity flows to a mode (or combination of modes) and specific route within a network that minimizes total transport costs, taking into account the location of activities within the network. Depending on the factors in which they are most interested, planners have two main options for modeling logistic networks.

The first approach, known as freight network equilibrium modeling, focuses on shipper-carrier interactions. In these models, the generation of trips from each region is assumed to be known; shipper transportation needs are determined and are then routed to minimize the carrier's costs.

Logistic Network Models

Benefits

  • Good for larger geographic areas.
  • Consider the defined interactions among producers, consumers, shippers, carriers, and the government.

Limitations

  • Require an assumption of shared perfect knowledge of freight movements between the shipper and carrier.
  • Complex to implement.

The second approach, known as spatial price equilibrium, focuses on producer, consumer, and shipper interactions. These models estimate trip generation by including commodity supply and demand functions. Transportation costs are fixed values or functions of the flows on the network. Producer and consumer behaviors are incorporated through a supply and demand function for each zone. The shippers are assumed to behave according to the following two equilibrium principles:

  • If there is a flow of commodity i from region A to region B, then the price of commodity i in region A plus the transportation costs from A to B will equal the price of the commodity in region B.
  • If the price of commodity i in region A plus the transportation costs from A to B is greater than the price of commodity i in region B, then there will be no flow from A to B.

Aggregate Demand Models

Aggregate demand models estimate freight traffic using aggregate data that include limited information on the multitude of factors affecting freight transportation demand. They attempt to model the aggregate volume of commodity flow rather than the number of individual trips. These methods support modeling for freight shipments by mode, commodity, origin-destination (O-D) pair, or a combination of these parameters.

Aggregate Demand Models

Benefits

  • Availability of required data.
  • Easily applied to commodity, time, and costs to determine freight demand.

Limitations

  • Does not consider route choice.
  • Does not estimate overall freight demand.

The simplest aggregate demand models use a total flow approach, which uses regression-based statistical methods to calculate an overall aggregate measure of freight travel demand in an economy. The main factor considered in this model is the predicted output of economy (commonly prepared in conjunction with time series or cross-sectional data). Total flow measures of demand are typically measured in tons or ton-miles for a specific mode over a given period of time.

Another approach to aggregate freight demand models is to consider relative flows, attempting to determine the proportion of total traffic carried by each discrete mode. An advantage of this type of model is that, in some contexts, it may be more appropriate to use a single equation that estimates a single aspect of freight traffic demand. This method uses regression techniques to model the relative flow of one mode when compared against another.

Disaggregate Demand Models

Disaggregate demand models take the methods of the aggregate models one step further, a process which offers several theoretical and empirical advantages. Specifically, these models attempt to estimate the number of individual trips by mode and the links for each of these trips on the freight transportation network. Unlike aggregate models, disaggregate demand models can distinguish freight demand across different routes and trips. In addition, disaggregate demand models are more accurate at identifying freight shipments by mode, commodity, and O-D pair. Planners have a variety of disaggregate models to choose from that parallel the four-step urban transportation modeling process.

Disaggregate Demand Models

Benefits

  • Estimates freight demand by mode over a network.
  • Is more accurate than aggregate demand models.

Limitations

  • Cost-intensive.
  • Difficult to collect the necessary data inputs.

The market survey approach involves the distribution of detailed market surveys to shippers. Shippers are asked to rank various factors with respect to their importance in the modal decisionmaking process. These factors include certainty of delivery time, charge, speed, safety, regularity, service to customer, packing requirements, length of haul, location of firm, method of payment, and intermodal capability, among others. In addition, shippers may be asked to rate different modes on ordinal ranking scales with respect to these factors. The survey results are used to construct a modal preference matrix to indicate the mode chosen for a shipment of certain characteristics. This matrix is then used to determine freight shipments by mode for various O-D pairs.

Alternatively, the behavioral mode split model predicts freight demand by focusing on the mode choice decisions made by the manager of the receiving or shipping firm. The advantage of this approach is that choice is observed at the most disaggregate level possible; namely, with respect to individual shipments dispatched by individual firms. In contrast to the market survey approach, these models are estimated using revealed choices without depending on the shipper explaining how he or she chooses a mode. Behavioral mode split models are based on the assumption that the shipper is concerned with maximizing utility (i.e., satisfaction) with respect to the various explanatory variables that affect the mode choice decisionmaking process. These decisions incorporate mode characteristics, consignment characteristics, firm characteristics, and shipper characteristics.

A third inventory-based approach attempts to integrate the mode choice and production decisions made by a shipper. Variables related to production, such as shipment size, mode choice, and frequency of shipments, are treated as internal decisions. The rationale of the inventory approach is that freight in transit can be considered to be, in effect, an inventory of goods on wheels, similar to goods in process in the factory. The model predicts the expected total annual variable cost of hauling the commodity.

Input–Output Models

Input–Output Models

Benefits

  • Simple and quick to implement.

Limitations

  • Is not comprehensive.

Input–output models are the simplest and, consequently, least descriptive methods for forecasting freight demand. They are used primarily in sketch planning applications, regional planning studies at an aggregate level, and when data are extremely scarce.

Input–output analysis involves using economic input and output indicators to determine the levels of economic activity that may drive freight transportation demand. Inputs (e.g., capital, labor, land) are entered into an input–output analysis matrix to determine the various economic outputs. These may include the quantity of goods and services produced by type, geographic location, and temporal frame; the demand for goods and services by type, geographic location, and temporal frame; and other such measures of economic output. The outputs are converted into estimates of freight transportation demand that would satisfy the demand for goods and services.

Summary and Implications

Freight demand is characterized by a variety of factors including, quantity, geographic scale, time period, source, transportation mode, and commodity. It is intrinsically interrelated with regional, national, and international economic and demographic characteristics, operational factors and logistics, infrastructure, public policy and regulations, technology, and environmental factors, all of which have varying data sets that are incomplete or contain inaccuracies, or both.

Forecasting and understanding the movement of goods, regardless of geographic scope, requires assembling information from a variety of data sources. Despite the current data deficiencies, several best practice modeling methods and techniques have been developed and successfully applied within a variety of planning processes. Nevertheless, the lack of useful freight forecasting data has several serious implications. One of which is that freight forecasters are often hindered by data deficiencies and, thus, they cannot completely analyze complex freight supply chains. This limits the development of forecasting models that answer the questions asked by today's elected officials, transportation professionals, and public regarding the impact of goods movement. Freight demand model developers use a variety of methods to account for this missing information, such as making assumptions or narrowing the modeling focus when selecting a freight model.

It is important that freight models reflect the transportation system, land use, economic factors, and logistics patterns. Excluding logistics is one of the shortcomings of otherwise advanced forecasting techniques. There are quite a few efforts underway that include private sector transportation practices that can support future data and tools. The current efforts to enhance the data and models based on logistics are primarily oriented toward supporting enhancements in private sector logistics practices (often specific to certain industries) and are rarely used to support and inform current public-sector freight forecasting techniques. However, this is changing as public sector freight planners see the benefits of understanding the private sector practices for public sector freight infrastructure decisionmaking.

Completing logistics-based freight planning assists in the understanding of long-haul and local freight movements. The integration of local touring and trip chaining is comprised primarily of local truck distribution and deliveries. This type of local truck activity is not captured in national data sets and, while difficult to accurately incorporate into regional freight models, would be of extreme benefit to local and regional freight planning.

Decisionmaking Needs and Gaps

Through the development of the Freight Demand Modeling and Data Improvement Strategic Plan, an extensive review and outreach process was undertaken with a focus on State DOTs, MPOs, county and municipal planners, toll road authorities, and port infrastructure owners and operators. Through this effort, a list of needs and the gaps associated with those needs were determined in order to lay the foundation for a programmatic approach to meeting the identified needs. These needs were used to develop the implementation process to ensure that the pilot projects developed addressed issues germane to meeting those needs and improving data and models throughout the process. Needs vary among agencies; however, some themes do recur.

Standardized Data and Tools

The general belief is that freight forecasting and analysis should be enhanced through a recognized and valid inventory of standardized data sources with common definitions. Satisfying the need for standardizing data sources across different geographic levels and transport modes and establishing consistency among data sources for truck, rail, marine, and air transport will make comparisons at different geographic scales more accurate, improving multimodal freight planning overall. The development of a statistical sampling of truck shipment data, similar to the Carload Waybill Sample for railroads, would also enhance available data. This would enable planners to get a microscopic view of trucking activity that would be comparable to the level of detail available for the railroad industry.

Not surprisingly, a range of standardized analytic tools and applications is needed to address diverse decisionmaking needs. There is a generally recognized need for some standardization of planning tools and methods for different geographic scales, including large regions, States, metropolitan areas, and corridors.

Incorporate Behavior-Based Freight Aspects

Behavior-based facets of freight decisionmaking should be incorporated into modeling, or at least better understood as an important factor in freight movements. One of the major deficiencies in current freight planning practice is that the tools and data are based on the movement of freight as measured in unit loads (e.g., trucks, railcar loads, tonnage) transported between origin and destination points. Freight planning and forecasting must undergo a dramatic transformation to include provisions for all of the complex factors that are involved in decisionmaking by freight shippers and carriers. This relates to a general need to expand the knowledge base of public sector planners and decisionmakers to include a more thorough understanding of private sector supply chain and delivery systems.

Need for More Granular/Local Freight Data and Analysis

Industry-level freight data are needed at the sub-regional level, and there is also a need to better understand local deliveries in urban areas. Current freight data are best suited for large geographic scales that do not translate well to local planning efforts. In addition, even the best tools and data do not accurately model local touring aspects of freight deliveries. Better information is needed to understand the nature, volume, and trends associated with intermodal transfers as well as the movement and repositioning of empty trucks, vessels, and rail equipment.

Freight models should also incorporate local land use policies and controls to increase the accuracy of freight forecasting at the local level. Since freight transportation is a derived economic activity that is ultimately driven by consumption and production at a local level, local land-use decisions have an enormous impact on freight transportation demand. The current planning tools based on population and industry employment trends should be enhanced by incorporating a wide variety of land uses by industry, especially those that are major generators of freight traffic (e.g., manufacturing, warehousing, retail sales, transportation terminals).

Need for Better Tools

There is a need to develop a full multimodal, network-based freight demand model that incorporates all modes of transport (vehicle, railcar, vessel) to a similar level of detail for various geographic scales. To be truly effective, this ambitious effort would have to address some of the other needs identified in this section: namely, the need to more fully understand the underlying economic drivers in freight transportation and the need to incorporate real-world supply chain and logistics practices in the planning process. The development of such a model is the ultimate goal of the freight planning and modeling community.

In addition, highway authorities have a strong interest in developing tools that would let them use freight forecasts to support their infrastructure design processes. This need relates particularly to the relationship between truck volumes and weights and highway infrastructure (e.g., bridge and pavement design). Agencies with oversight responsibilities for inland waterway systems have similar needs for marine infrastructure, such as ports and locks.

Need to Tie Freight to Economics

There is a need to better understand the correlation between freight activity and various economic influences such as fuel price, currency valuation, and macroeconomic trends. One of the major challenges facing many public agencies is their inability to accurately predict important changes in freight transportation activity that result from external influences and underlying economic forces. In addition, the subjective influence of passenger traffic on shipper and carrier efficiency and decisions related to routing, mode choice, and time-of-day freight shipments needs to be more clearly understood. Conversely, decisions in industries involved in freight transportation (e.g., manufacturing, trucking, warehousing), such as site selection, production schedules, and mode choice produce economic and demographic impacts that need to be quantified.

Of great interest are benefit-cost analysis tools that go beyond traditional financial measures by including other direct and indirect benefits and costs (public and private). Tools would include metrics to assess environmental and economic development policy initiatives on a comparable basis with standard financial measures. More effective methodologies are also needed to apply freight forecasts to funding and finance analyses, such as revenue projections. These types of tools are of great interest to toll road authorities, owners, and operators of freight infrastructure such as port terminals, whose future needs and financial stability are tied to the ability of the owners and operators to develop accurate forecasts of demand by mode and commodity.

Need for Enhanced Freight Planning Skills and Outreach

Stakeholders consistently emphasized the importance of a concentrated effort to develop the requisite knowledge and skills to support freight analysis. The factors that drive freight transportation demand are complex and require an understanding of a wide range of topics, such as economics, political science, demographics, transportation planning, engineering, finance, information technology, and organizational skills. This need for knowledge and skills also relates to the need to understand the goals and objectives of shippers and carriers in the private sector and planners in the public sector. Bridging the gaps between the needs of the public and private sectors would help facilitate more effective planning and forecasting.

Intelligent Transportation Systems and Big Data

Intelligent transportation system (ITS) resources and related vehicle-based technologies, such as global positioning system (GPS) and connected/automated vehicles should be used to generate data to support freight planning and modeling. Technology provides large volumes of complex and variable data that require advanced techniques and technologies to capture, store, distribute, manage, and analyze the information.

Visualization

The need for enhanced visualization tools for public outreach related to the freight planning process is important to communicate results. Visualization tools provide a powerful means of communicating complex concepts and data.

Freight Decisionmaking Gaps

Table 2 shows the decisionmaking needs, the gaps between the needs and the current modeling and data practices, and the data and modeling requirements that will close the gaps. Articulating the capabilities of the current state-of-the-art models and data sets and comparing them with the needs of decisionmakers sets the stage for identifying the modeling and data needs to fill the gaps. These needs are the foundation for the actions incorporated in the Strategic Plan.

Table 2. Current freight decisionmaking needs and gaps.
Decisionmaking Needs Gaps Between Needs and Current Practices Data or Modeling Requirements to Close Gaps
Standardized data sources with common definitions.
  • Various data sources collected through different programs result in extensive inconsistencies.
  • Homogeneous data for ease of incorporation into freight models and for consistency of freight models in different regions.
  • Reduction in data manipulation to improve accuracy.
Statistical sampling of truck shipments.
  • Detailed knowledge of truck movements in local areas.
  • Understanding of current truck activity by different industry segments (long-haul, local, drayage).
  • An ongoing standard data-collection program to gather local truck movements.
  • Compilation of truck data to a level comparable to rail industry data (i.e., Carload Waybill Sample).
Standardized analytic tools and applications.
  • Wide range of various tools that require unique data sets.
  • Consistency in modeling approaches and data needs for similar geographic scales.
Capacity.
  • Current practices use truck movements and commodity flows, but should be based on the behaviors, economic principles, and business practices that dictate the movement of freight.
  • Current modeling tools do not accurately reflect real world supply chains and logistics practices.
  • Determination of the influencing behavioral factors that affect freight movement and ongoing data collection to inform models.
  • Behavior-based freight modeling tools to take advantage of newly collected data sets for various geographic analyses.
  • Incorporation of intermodal transfers, consolidation and distribution practices, and other shipper and carrier practices in modeling tools.
Inclusion of behavior- based elements into freight models.
  • Public sector access to intermodal transfer data of containers, bulk material, and roll-on–roll-off cargo is lacking for most transfer facilities other than those of large ports and rail yards.
  • Data sets developed through collaboration with the private sector to inform the planning practice knowledge base and models on intermodal transfers.
  • Protocols to collect data on a regular basis.
  • Industry-level forecasts that are sensitive to the unique factors of different industries.
Data development to understand the nature, volume, and trends of intermodal transfers.
  • Freight data are generally not industry-specific, which translates into forecasts that are not sensitive to the unique industry trends that are critical to regions that rely heavily on specific industries.
  • Tools and data at a disaggregated level (local) that can be aggregated for larger geographic analyses.
  • Tools and models to take advantage of the new data sets.
  • Enhanced understanding of land use decisions and their implications on freight activity.
Industry-level freight data development at a sub-regional level and within urban areas.
  • Current freight data and models lack local detail related to the generation of freight activity, which hampers local efforts to effectively plan for the last mile.
  • Resources for local organizations to incorporate land-use considerations into freight planning data and models.
  • Freight generation models by industry classes.
Incorporation of local land-use policies and controls for better local forecasting accuracy.
  • Freight models are typically based on population, employment, and industry level productivity forecasts, with no consideration for the impacts of other economic factors.
  • Enhanced models that incorporate a wide array of economic factors in forecasting freight demand.
Development of a correlation between freight activity and various economic influences and macroeconomic trends
  • Freight models are typically based on population, employment, and industry level productivity forecasts, with no consideration for the impacts of other economic factors.
  • Enhanced models that incorporate a wide array of economic factors in forecasting freight demand.
Better accuracy of freight forecasts.
  • Freight models rarely (if ever) are reviewed to see how accurately they are forecasting, calling into question their reliability and validity.
  • A systematic approach for freight model and data owners to review and evaluate forecasts (every three to five years) and adjust models and data methods accordingly.
Development of a process to routinely generate new data sources and problem- solving methods.
  • The improvement of freight planning nationally depends on continuing innovation and steady progress in the development of models, analytic tools, and knowledge acquisition.
  • A value-adding and sustainable process to generate new and innovative ideas.
  • Acknowledgment of failed practices that can contribute to the knowledge base of practitioners.
Use of intelligent traffic system (ITS) resources to generate data for freight modeling.
  • Technologies that can be used to collect freight data have not been used to their potential.
  • Data can provide a wealth of information related to current conditions and diversions as a result of traffic incidents.
  • An understanding of the information needed by the modeling community and the standard to which it can be used.
  • An accessible data bank for freight modeling developed with the cooperation of GPS device providers, ITS infrastructure owners, and other data providers.

Implementation Assistance Program Overview

The Implementation Assistance Program (IAP) was developed to help State DOTs, MPOs, and other interested organizations deploy SHRP2 Solutions. A range of opportunities is available to raise awareness of SHRP2 Solutions and to encourage early adoption of these products. Seven rounds of the IAP were offered between February 2013 and April 2016.

Each product selected for implementation assistance has the potential to deliver more efficient, cost- effective programs to meet the complex challenges facing transportation today. SHRP2 initiated the C20 project to advance fresh ideas and new approaches to freight demand modeling and data collection so that freight considerations would be more fully integrated into transportation decisionmaking. To that end, in March 2014 the FHWA awarded four projects focused on behavior-based freight modeling project and seven projects focused on innovations in local freight data. Modeling projects looked to develop forecasting methods and tools that can address decisionmaking needs. Local freight data projects advanced the collection and use of freight data at the sub-regional levels.

Pilot projects were selected through a competitive process with assistance being administered jointly by FHWA and American Association of State Highway and Transportation Officials (AASHTO). Funding for data projects was limited to $150,000 and modeling projects were limited to $350,000.

Pilot Projects and Case Study Overview

Implementation assistance was awarded to a total of 12 transportation agencies throughout the country for their projects. Seven agencies received funding for data projects, and four agencies for modeling projects.

Data IAP Agencies:

  • Capital District Transportation Committee—New York.
  • Delaware Valley Regional Planning Commission—Pennsylvania.
  • Florida Department of Transportation.
  • Mid-America Regional Council—Missouri.
  • South Dakota Department of Transportation.
  • Washington State Department of Transportation.
  • Winston-Salem Metropolitan Planning Organization—North Carolina.

Modeling IAP Agencies:

  • Maricopa Association of Governments (MAG)—Arizona
  • Maryland State Highway Administration and Baltimore Metropolitan Council
  • Metro—Oregon
  • Wisconsin Department of Transportation

Chapter 2 of the report will describe in greater detail the data projects, while Chapter 3 will cover modeling projects. Table 3 shows the agencies and a description of the IAP project for each.

Table 3. SHRP2 C20 pilot project overview.
Agency Type Description
Capital District Transportation Committee—New York Data The Capital District Transportation Committee created a unified data set for the region at the zip code or transportation analysis zone level by integrating diverse data sources that will support mitigating impacts of truck traffic, determining the impact of freight on quality of life, improving safety and security, prioritizing investments, and performance measurement.
Delaware Valley Regional Planning Commission— Pennsylvania Data The Delaware Valley Regional Planning Commission advanced a freight data planning and visualization program to better understand intermodal transportation for freight. This effort integrated data for distribution supply chains and for performance management, with an aim to help other MPOs by developing a method that has the potential to be replicated elsewhere.
Florida Department of Transportation Data The Florida Department of Transportation improved the accuracy of freight forecasts in order to support State, regional, and local freight plans and projects by collecting data representing the supply and demand chain for petroleum commodities distributed throughout South Florida.
Mid-America Regional Council—Missouri Data The Mid-America Regional Council used a combination of existing data and new sources of commercial waybill data to address future freight planning needs by enhancing its ability to establish targets that properly demonstrate impacts to the cost of freight movement.
South Dakota Department of Transportation Data The South Dakota Department of Transportation studied the growth in commodity demand and production and analyzed needs and impacts on the State and local transportation systems. This project enhances the understanding of how the growth of agricultural production and the current and expected location, timing, and impact of commodity shipping affect South Dakota's highways.
Washington State Department of Transportation Data The Washington State Department of Transportation collected detailed behavioral response information from industry and local urban truck volume data for the State's food distribution supply chain to accurately model key State supply chain behavioral responses to different State policy scenarios aimed at reducing emissions.
Winston-Salem Metropolitan Planning Organization—North Carolina Data The Winston-Salem metropolitan planning organization, as part of the Piedmont Triad Regional Model Team, collected data and conducted a travel diary survey that will be used to support development of an advanced freight model.
Maricopa Association of Governments (MAG)—Arizona Modeling The Maricopa Association of Governments developed a multi-modal freight model to better replicate the economic behaviors of establishments, shippers, and carriers by modeling travel and tour formations in Arizona's Sun Corridor mega-region.
Maryland Department of Transportation and Baltimore Metropolitan Council Modeling The Maryland Department of Transportation and Baltimore Regional Transportation Board developed a regional tour-based truck model covering intra-local distribution with sensitivity to the long-distance truck flows represented in the statewide freight model.
Metro—Oregon Modeling Metro (Portland, Oregon's metropolitan planning organization) used a hybrid approach to understand the local portion of the region's supply chains as  well as the tour-based behavior of individual trips to address economic policy questions and depict truck volume and flow of goods.
Wisconsin Department of Transportation Modeling The Wisconsin Department of Transportation developed a hybridized model for freight demand that, through integration with regional travel demand models, addresses deficiencies in the current statewide freight forecasting and establishes a robust platform for quantifying how different scenarios affect freight transportation in the region.

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