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Federal Highway Administration > Publications > Public Roads > Vol. 71 · No. 6 > Making the Case for Freight Investments

May/Jun 2008
Vol. 71 · No. 6

Publication Number: FHWA-HRT-08-004

Making the Case for Freight Investments

by Ed Strocko

A new model to capture the additive costs and benefits could help prioritize highway improvement projects. 

Movement of freight by trucks such as these is critical to the U.S. economy, and demand for more shipments is expected to grow significantly in the years ahead.
Movement of freight by trucks such as these is critical to the U.S. economy, and demand for more shipments is expected to grow significantly in the years ahead.

According to the Federal Highway Administration’s (FHWA) Freight Analysis Framework, the U.S. transportation network moved 17.5 billion metric tons (19.3 billion short tons) of goods in 2002. That equates to 48.1 million metric tons (53.0 million short tons) each day, worth more than $13 trillion. By 2035, freight volumes are expected to double to 33.6 billion metric tons (37.0 billion short tons), worth $38 trillion.

Businesses and individuals now demand more flexible and timely service, increasing the importance of an efficient and reliable freight transportation system. According to the FHWA report Freight Transportation: Improvements and the Economy, shippers (owners of freight being carried) and carriers (firms that carry freight) set a value of $25 to $200 per hour on freight transit time, depending on the product being carried. This value includes the cost of inventory in transit, such as insuring and holding the goods and any depreciation of perishable products — but excludes vehicle operating costs, such as driver wages or the opportunity cost of forgone revenue for the carrier. The value of transit time would be substantially higher if these costs were included. The fact that shippers and carriers value transit time so highly is indicative of the overall importance of logistics to business productivity and economic growth.

The growing demand for freight services contributes to congestion, and congestion in turn affects the timeliness and reliability of freight movement. Unexpected delays reduce productivity, increase operating costs, and decrease fuel efficiency. According to a 2001 FHWA white paper on the Highway Economic Requirements System (HERS), “Creating a Freight Sector within HERS,” unexpected delays can increase the cost of transporting goods by 50 to 250 percent.

Despite the importance of freight to the U.S. economy, the relationship between improvements in freight transportation and economic performance is not well understood. Traditional cost-benefit analyses typically measure the effects of roadway improvements on road users and carriers, but they do not take into consideration the effects on shippers, such as manufacturers, retailers, and other businesses. A smoother flowing corridor means faster trips for carriers and money saved on fuel and drivers’ wages. But manufacturers also benefit from more reliable transportation by adjusting their production schedules and becoming more efficient. Similarly, retailers are able to rely more on “just-in-time” delivery and leave less capital tied up in inventory or warehousing of goods.

Capturing these latter economic benefits, collectively referred to as “industrial reorganization” benefits, is at the core of a new FHWA modeling tool. The Highway Freight Logistics Reorganization Benefits Estimation Tool, now available from FHWA, estimates the broader economic benefits of freight improvements and promises to help increase the effectiveness of decisions about transportation investments.

“With an increasingly national and global economic base in America, freight movement has been growing faster than passenger travel and has become increasingly important for economic competitiveness,” says Glen Weisbrod, president of Economic Development Research Group and chair of the Transportation Research Board’s Committee on Transportation and Economic Development. “It has also become clearer to decisionmakers that it is shippers who are the true ‘users’ of freight transport and the beneficiaries of transport improvements. That fact alone calls for a very different kind of cost-benefit analysis for freight transportation.”

Paying Attention to Freight Transport

Understanding and improving freight flows is a high-priority issue for decisionmakers and planners at all levels of government. At the Federal level, the U.S. Department of Transportation (USDOT) is working to reduce congestion through the National Strategy to Reduce Congestion on America’s Transportation Network (known as the “Congestion Initiative”). The strategy involves a mix of approaches, such as using congestion pricing, expediting projects to optimize highway and aviation capacity, improving operations, and reducing barriers to private sector investment in transportation infrastructure.

States and localities also are focused on freight issues. A 2006/2007 survey under the Freight Partnership II, conducted by FHWA and the American Association of State Highway and Transportation Officials, found that 77 percent of policymakers at State departments of transportation (DOTs) identified freight as a priority for their organizations. This interest led many States to establish freight offices and develop statewide freight plans.

Increasingly, State and local transportation planners are looking for input from the private sector as well. According to the Freight Partnership II survey, 45 States receive some type of input from the freight industry during long-range and project planning.

Ultimately, congestion increases the cost of consumer goods and reduces U.S. competitiveness in the global marketplace, says Tony Furst, director of Freight Management and Operations Office at FHWA. “We’re committed to working with States, the freight industry, and other stakeholders to develop the best means of meeting the Nation’s freight transportation needs.”

Congestion similar to what is shown here poses a challenge for just-intime freight deliveries.
Congestion similar to what is shown here poses a challenge for just-intime freight deliveries.

Need for a New Tool

Many of the transportation planning tools currently available do not systematically incorporate freight. For years, transportation planners used four-step travel demand models to predict the demand for transportation. These models involve divid-ing a study area into subareas, then  (1) estimating how many trips will originate in each area, (2) projecting the areas where trips will terminate, (3) determining the modes of transportation, and finally (4) determining the routes for trips on the transportation network.

A significant downside of the four-step travel demand models is that they focus on passenger travel patterns, but freight movements do not behave the same way as passenger travel. “The context is changing,” said Kathleen Hancock, associate professor of civil and environmental engineering at Virginia Polytechnic Institute and State University, at a February 2007 freight webinar sponsored by FHWA. “Traditionally, many approaches have used the four-step model paradigm, which came out of the passenger modeling background. This does not fit the freight paradigm. We need to shift out of that mode and into some other types of models, which include logistics modeling, and hybrids of these.”

This is not to say that travel demand models should not be used, but freight managers are rethinking the older models and developing freight-specific and freight-sensitive models for cost-benefit analyses.

For highway projects, most cost-benefit analyses calculate the benefits of improvements based on reductions in travel time, vehicle operating costs, crash costs, and emissions. These are considered “first-order” benefits. They measure the direct effects of improvements in travel time and reliability on current users of the transportation system, and are effective at gauging impacts on both passengers and freight.

This graphic shows how the first-order benefits of transportation improvements lead to second- and third-order benefits. At the top is a rectangle labeled First-Order Benefits, which consist of ‘cost reductions on current freight times’ and ‘reduced transit times and increased reliability.’ From this rectangle an arrow points down and to the right to another rectangle, labeled Second-Order Benefits, which contains: ‘firms improve logistics,’ ‘firms serve larger market and output increases,’ and “freight miles increase.’ From this rectangle an arrow points down and to the right to another rectangle, Third-Order Benefits, which contains ‘improved products’ and ‘new products.’
Cascading benefits from transportation improvements: first-order benefits occur immediately, while second- and third-order benefits accrue over time.

Models attempting to capture the benefits to trucking typically do not look beyond first-order benefits. The result is that the benefits of a better road network for freight transportation are assumed to be limited to the carrier and other road users. Benefits to shippers (which include manufacturers and retailers), such as faster and more reliable delivery, have not been taken fully into account even in the more sophisticated models.

Benefits to the shippers are more subtle and complex and are considered “second-order” or reorganizational benefits. These occur when shippers invest in improved logistics in response to transportation improvements. For instance, a manufacturer might consolidate and streamline its distribution network. Improved transportation might enable existing suppliers, warehouses, or production facilities to serve larger market areas, making it possible for a manufacturing firm to increase its output. Companies can reduce the amount of inventory they carry, freeing up capital for more productive uses.

Typically, investments make in-cremental improvements to the transportation system. The business benefits of these second-order  improvements also are incremental, occur over time, and are spread across many industrial sectors and retail companies, making the benefits difficult to quantify.

These second-order benefits spill over into the increasingly complex logistics industry, which is responsible for managing the sourcing, handling, transportation, and storage of intermediate products and finished goods along the supply chain. In addition to the physical movement and storage of goods, logistics activities entail sophisticated information management and exchange activities, which can be managed in-house by shippers or outsourced to third-party companies specializing in logistics activities. The scope of services offered by third-party logistics providers varies, as some providers are strictly transportation firms, while others manage every aspect of the contracting firm’s (shipper’s) logistics activities.

Transportation improvements can lead to logistics cost savings, in terms of fewer trucks, fewer drivers, or less fuel needed to make a shipment. Typically, carriers pass along logistics cost savings to shippers, who in turn pass the benefits to consumers or investors and/or reinvest the money in their own logistics needs. When transporting goods becomes easier, manufacturers build and maintain fewer warehouses. Moreover, the carrying cost of maintaining production input stock on hand is reduced, as businesses can better rely on the shipping system to deliver goods when needed.

Consider, for example, a highway that is repaired and reinforced to carry more weight from truck traffic. The logistics managers take into account weight maximums and regulations when they make shipment decisions. Higher weight maximums mean that trucks can be loaded more heavily. Heavier loads mean fewer trucks are needed for shipping the same volumes, and fewer trucks could lower expenditures on gas and driver wages.

In the short term, shippers and carriers have little freedom in responding to changes in the transportation network; delivery schedules and routings can be changed, but origins and destinations are fixed. In the somewhat longer run, carriers can modify the characteristics of the truck fleet by using one truck instead of two or using fewer drivers. Over the long term, shippers can alter the number, size, and location of factories and warehouses.

In the long term, transportation improvements also produce “third-order” benefits, when improved service enables businesses to deliver new or improved products. For instance, fresh pineapples and canned pineapples are essentially different products. Improved transportation service that enables a company to deliver fresh pineapples to the market instead of the processed fruit would create a new product market. Similarly, it might be feasible to use a piece of heavy construction equipment in a remote area only if replacement parts are available on a timely basis to ensure reliable operation. Improved transportation access to a remote area could make it possible for a manufacturer to guarantee a level of reliability and thus sell its products there. Customers also might be willing to pay more for equipment that would be more reliable.

To ensure that investments in infrastructure are sufficient to support economic growth, policymakers need tools and models that accurately measure all three types of benefits. State and local transportation planners also need tools that enable them to understand the national impacts of their policies. Recognizing these needs, FHWA began investigating ways to capture the range of economic benefits associated with transportation improvements.

This graphic shows the positive economic effects of transportation investments. At the top is a rectangle labeled Efficient Transportation Infrastructure Investment. An arrow points down from it to another rectangle, labeled Reduced Transportation Cost. An arrow points down from this rectangle to another, labeled Enhanced Productivity. Finally, an arrow points down from this rectangle to another, labeled Economic Growth.

The Road to Tool Development

In 2001, FHWA assembled an expert panel to review a white paper titled “Benefit-Cost Analysis of Highway Improvements in Relation to Freight Transportation: Microeconomic Framework,” which laid out the theory behind benefits associated with industrial reorganization.

Then, in 2002, FHWA held a roundtable on the importance of freight transportation to the Nation’s economy. There, 36 transportation professionals from the public and private sectors discussed two other papers — “Economic Effects of Transportation: The Freight Story” and “Transportation Infrastructure, Freight Services Sector, and Economic Growth: A Synopsis” — that were the underpinning for the reorganizational benefit.

Following feedback garnered from the roundtable, FHWA undertook a national analysis of the long-term benefits of highway freight improvements by examining the interactions between transportation demand, costs, and the condition and performance of the Nation’s highway system. The analysis looked beyond the travel time savings captured in the conventional cost-benefit analysis framework and developed a methodology for quantifying the effects of improvements to the transportation system in relation to (1) immediate cost reductions to carriers and shippers, (2) the impacts of improved logistics while keeping output fixed, and (3) additional gains from reorganization such as increased demand and new or improved products.

FHWA researchers subsequently used this methodology to construct a regional analysis of freight demand with respect to highway performance, with a goal of determining regional differences in the demand structure. Ultimately, the aim was to develop regional estimations of the additive value of performance improvements beyond the value of time savings.

This graph depicts the additive freight benefit. The x, or horizontal, axis is labeled Transportation Units (where Q stands for quantity). The y, or vertical, axis is labeled Transportation Cost per Unit (where C stands for cost). The x-axis contains three labeled quantity units: Q subscript 0, marking the initial quantity; Q1, marking a greater quantity shipped as a result of highway improvements or a policy change; and Q2, marking an even greater quantity shipped as a result of the shipper changing its logistics (reorganization) in response to a reduction in the cost for transportation. On the y-axis, the following units are labeled: C subscript 1, marking the new unit generalized cost of transportation following highway improvement or policy change, and C subscript 0, marking the initial unit generalized cost of transportation. Two curves—D subscript 0 and D subscript 1—are plotted on the graph, starting high and very close to the y-axis (above the C subscript 0 mark) and arcing down and to the right toward the right side of the x-axis. D0 indicates the shipper’s original transportation demand and D  subscript 1indicates the shipper’s new transportation demand, created as a result of reduced costs for transportation. The D subscript 1 arc is a little shallower than D subscript 0, starting below it on the left and ending above it on the right. A box, a, for ‘short-run benefits (cost reduction)’ is bounded on the left side by the y-axis, on the top by the C subscript 0 mark, and on the bottom by the C1 mark. The box is bounded on the right by the Q0 mark on the x-axis. The D subscript 0 and D subscript 1 arcs intersect at the top right corner of the box. A rough triangle, b, for ‘medium-run benefits (buy more transport),’ appears to the right of box a, also bounded on the top by C subscript 0 and the bottom by C subscript 1. The triangle’s bottom side extends to the right to Q subscript 1. The D subscript 0 arc meets the intersection of C subscript 1 and Q subscript 1 and forms the third “side” of the triangle. A more acute, rougher triangle, c, for “long-run benefits (supply-chain evolution),’ appears to the right of triangle b, that is, arc D subscript 0 forms the left’side’ of triangle c. Triangle c is also bounded at the top by C0 and bottom by C subscript 1. The D subscript 1 arc meets the intersection of C subscript 1 and Q subscript 2 and forms the third side of the triangle.
The figure illustrates a shipper’s responses to a reduction in the cost of freight transportation. In the short run, the shipper continues to buy the same number of vehicle-miles of freight transportation (shown as Q0). The benefit to the shipper is a reduction in the cost of moving freight over the same number of vehiclemiles (area a). In the next phase, the shipper may react to the cost reduction by taking advantage of the lower cost and buying more freight transportation (shown as Q1). This response provides additional benefits (area b). The shipper’s original demand curve (D0), however, does not change because responses to cost reductions occur over a considerable period of time. In this phase, a shipper does not change the firm’s basic logistics. After a shipper has had time to consider the cost reduction, the firm might choose to adjust its logistics. When this occurs, the shipper’s demand for transportation changes, and a new demand curve (D1) emerges. The area between the old and new demand curves (area c) reflects an additional benefit resulting from a change in logistics (reorganization). Thus, the full benefit of a freight improvement (realized by the reduction in cost of transportation) is reflected in the sum of areas a, b, and c.

Concurrently, in 2004, USDOT initiated the Freight Model Improvement Program (FMIP) to enhance the state of the art and state of the practice in freight modeling at the national, regional, and local levels. FMIP is a forum for transportation planners and practitioners to share best practices and gain a better understanding of current and future freight movements in their areas. Several States, including Florida, Indiana, Iowa, Kentucky, Minnesota, Ohio, Oklahoma, Oregon, and Washington, already have developed dedicated freight models.

Using the data gathered through the national analysis, FHWA researchers developed the Highway Freight Logistics Reorganization Benefits Estimation Tool, a planning model that estimates the additive benefits of freight reorganization. The Microsoft® Excel®-based tool and the economic framework underlying it can help highway planners achieve  a more complete picture of the  returns on highway investments.  The tool is available free on FHWA’s Web site at http://ops.fhwa.dot.gov/freight/freight_analysis/econ_methods.htm.

The Underlying Economic Framework

The Highway Freight Logistics Reorganization Benefits Estimation Tool estimates the benefits associated with highway investments by establishing a relationship between two factors. First the model uses estimates of the elasticity of demand for truck freight movement with respect to highway performance. As a highway becomes more congested, this will tend to reduce the demand for freight movement. For instance, some firms might keep more inventory and schedule fewer deliveries if there are frequent congestion delays. Another important relationship is the elasticity of demand with respect to price and a set of other region-specific variables. In the same way that a consumer who is faced with a higher price for oranges might purchase fewer oranges, as the price of freight movement rises, shippers may choose to purchase less transportation.

If the price of a good or service increases, consumers will typically use less of it, but how much less depends on how “elastic” (or flexible) their demand is. Demand for a commodity is influenced not only by its price but also by the prices of suitable substitute goods or services. For example, in a freight corridor offering both rail and truck options, rail rates will influence demand elasticity for truck service, and vice versa. However, in a corridor with only truck service, the demand elasticity for truck service will be less because there is no available alternative to trucking.

Freight Significant Corridors Included in the Regional Analysis

East Region (18 Corridors) Central Region (18 Corridors) West Region (23 Corridors)

Atlanta-Jacksonville

ATL-JAX

Amarillo-Oklahoma City

AMA-OKL

Barstow-Amarillo

BAR-AMA

Atlanta-Knoxville

ATL-KNX

Billings-Sioux Falls

BIL-SIO

Barstow-Bakersfield

BAR-BAK

Atlanta-Mobile

ATL-MOB

Chicago-Cleveland

CHI-CLE

Barstow-Salt Lake City

BAR-SAL

Birmingham-Nashville

BGH-NSH

Cleveland-Columbus

CLE-COL

Dallas-El Paso

DAL-ELP

Birmingham-Chattanooga

BIR-CHA

Dayton-Detroit

DAY-DET

Dallas-Houston

DAL-HOU

Detroit-Pittsburgh

DET-PIT

Indianapolis-Chicago

IND-CHI

Denver-Kansas City

DEN-KAN

Harrisburg-Philadelphia

HAR-PHI

Indianapolis-Columbus OH

IND-COL

Denver-Salt Lake City

DEN-SAL

Knoxville-Harrisburg

KNX-HAR

Kansas City-St Louis

KNC-STL

Galveston-Dallas

GAL-DAL

Miami-Atlanta

MIA-ATL

Knoxville-Dayton

KNX-DAY

Laredo-San Antonio

LAR-SAN

Miami-Richmond

MIA-RIC

Louisville-Columbus

LOU-COL

Los Angeles-Tucson

LAX-TUC

Mobile-New Orleans

MOB-NOR

Louisville-Indianapolis

LOU-IND

Nogales-Tucson

NOG-TUC

New Orleans-Birmingham

NOR-BIR

Memphis-Dallas

MEM-DAL

Portland-Salt Lake City

POR-SAL

Boston-New York City

NYC-BOS

Memphis-Oklahoma City

MEM-OKL

Portland-Seattle

POR-SEA

New York City-Cleveland

NYC-CLE

Nashville-Louisville

NSH-LOU

San Antonio-Dallas

SAN-DAL

New York City-Harrisburg

NYC-HAR

Nashville-St Louis

NSH-STL

San Diego-Los Angeles

SDG-LAX

Philadelphia-New York City

PHI-NYC

Omaha-Chicago

OMA-CHI

San Francisco-Los Angeles

SFO-LAX

Pittsburgh-Columbus

PIT-COL

St Louis-Oklahoma City

STL-OKL

San Francisco-Portland

SFO-POR

Richmond-Philadelphia

RIC-PHI

St Louis-Indianapolis

STL-IND

San Francisco-Salt Lake City

SFO-SAL

 

 

 

 

San Antonio-Houston

SAN-HOU

 

 

 

 

Seattle-Billings

SEA-BIL

 

 

 

 

Seattle-Blaine

SEA-BLA

 

 

 

 

Seattle-Sioux Falls

SEA-SIO

 

 

 

 

Tucson-San Antonio

TUC-SAN

Similarly, for supply chain managers, the demand elasticity for transportation services is influenced not only by freight transport costs but also by the relative costs of other production inputs, such as warehousing. When transport costs go down, transport becomes a cheaper input to the greater supply chain and therefore might be substituted for warehousing. These kinds of changes in demand happen over the long run and can influence substantially the level of benefits accruing from transportation improvements.

FHWA developed the tool to  accommodate outcomes from consumer surplus models, where the practitioner (the model user) explicitly accounts for induced demand using standard estimates for transportation demand elasticity. In essence, the model helps practitioners estimate the change in consumer surplus resulting from a candidate highway investment. Consumer surplus is the benefit that consumers receive from being able to purchase a product for a price that is less than they would be willing to pay. For example, if a consumer is willing to pay $1 to use a road but is charged only $0.50, the consumer surplus associated with use of the road is $0.50 for this consumer. Traditional cost-benefit models calculate the benefits of transportation improvements based on estimates of consumer surplus.

The outputs of the Highway Freight Logistics Reorganization Benefits Estimation Tool indicate the additional benefit related to reorganizing logistics that can be expected following a planned improvement in the transportation infrastructure. A practitioner then can add these outputs to cost-benefit analyses that do not independently account for the value of improved freight movement.

Because freight benefits accrue to businesses outside a specific region of the Nation, FHWA’s research analyzed data at the corridor level. FHWA focused on 59 freight-significant corridors in the eastern, central, and western United States.

FHWA researchers collected data on corridor performance, demand for freight movement, freight prices, and regional economic activity. They then applied a regression analysis and estimated elasticities of demand for each of the three regions.

The regression analysis indicated that demand for shipping services varies with the expected speed of delivery, and the level of variance differs by geographic area. Overall, as highway performance improves, demand for freight movement increases.

From the regional estimates, FHWA developed the underlying data inputs for use on individual projects. Using State-specific data and regionally estimated elasticities, FHWA customized the model to provide assessments of future infrastructure improvements. In addition, the model incorporates data on commodities (goods being moved on the road) as influencing variables (volume and value).

Using the Tool

The model features three screens: Estimation Inputs, Conventional CBA (cost-benefit analysis) Inputs, and Summary of Results. The Estimation Inputs screen gathers key information from the model user regarding the specific roadway improvement being analyzed. The user inputs information such as the State where the project is located, the roadway segment length, baseline truck traffic, classes or types of trucks (mix of vans, 18-wheelers, etc.), and average speed.

A transportation improvement project, the tool assumes, will have a beneficial impact on freight traffic that travels along the segment. Because business reorganization benefits depend on the volume and type of freight being hauled in a specific corridor, the tool requires practitioners to enter segment-specific information that captures the likely dollar value of time associated with different types of freight movement and vehicle operating costs. A segment improvement project can affect any of the value components identified in the Estimation Inputs section: value of time, vehicle operating costs, and reliability of travel time.

Changes can be entered as either percentages (changes in vehicle operating cost, travel time, and reliability) or specific changes to detailed inputs (for vehicle operating cost, changes could include fuel efficiency or reduced tire wear) after the roadway improvement is completed. The tool offers predefined values for all input data, but users should key in data specific to their segments, as these values vary greatly segment by segment.

The tool’s second screen enables the user to input freight benefits from a conventional highway cost-benefit analysis. These benefits would come from an analysis through an existing cost-benefit analysis model. Users can obtain simple results without completing this screen; however, entering project-specific data in this section facilitates taking full advantage of the new tool’s capabilities. Outputs from an existing cost-benefit analysis model help determine baseline demand and performance, expected improvement, and currently measured freight-specific benefits. Only benefits for freight should be entered on this screen, not highway user benefits.

This screen grab from the “Summary of Results” screen in the Highway Freight Logistics Reorganization Benefits Estimation Tool shows a bar graph that depicts the conventional and reorganizational benefits of a sample freight project. The x-axis lists years from 2005 to 2023 in increments of 1 year. The y-axis lists millions of 2006 dollars, starting from $0.0 million and ranging up to $12.0 million in $2 million increments. The first bar at the left, for 2005, shows the conventional benefit at $0.25 million and the reorganization benefit at $0.0 million, for a total benefit of $0.25 million. In 2006, the conventional benefit is $0.25 million and the reorganization benefit at $0.01 million, for a total benefit of $0.26 million. In 2007, the conventional benefit is $0.50 million and the reorganization benefit is $0.02 million, for a total benefit of $0.52 million. In 2008, the conventional benefit is $2.00 million and the reorganization benefit is $0.09 million, for a total benefit of $2.09 million. In 2009, the conventional benefit is $3.00 million and the reorganization benefit is $0.14 million, for a total benefit of $3.14 million. In 2010, the conventional benefit is $4.00 million and the reorganization benefit is $0.19 million, for a total benefit of $4.19 million. In 2011, the conventional benefit is $4.00 million and the reorganization benefit is $0.19 million, for a total benefit of $4.19 million. In 2012, the conventional benefit is $5.00 million and the reorganization benefit is $0.24 million, for a total benefit of $5.24 million. In 2013, the conventional benefit is $6.00 million and the reorganization benefit is $0.28 million, for a total benefit of $6.28 million. In 2014, the conventional benefit is $7.00 million and the reorganization benefit is $0.33 million, for a total benefit of $7.33 million. In 2015, the conventional benefit is $8.00 million and the reorganization benefit is $0.38 million, for a total benefit of $8.38 million. In 2016, the conventional benefit is $9.00 million and the reorganization benefit is $0.42 million, for a total benefit of $9.42 million. In 2017, the conventional benefit is $10.00 million and the reorganization benefit is $0.47 million, for a total benefit of $10.47 million. In 2018, the conventional benefit is $10.00 million and the reorganization benefit is $0.47 million, for a total benefit of $10.47 million. In 2019, the conventional benefit is $9.00 million and the reorganization benefit is $0.42 million, for a total benefit of $9.42 million. In 2020, the conventional benefit is $9.00 million and the reorganization benefit is $0.42 million, for a total benefit of $9.42 million. In 2021, the conventional benefit is $8.00 million and the reorganization benefit is $0.38 million, for a total benefit of $8.38 million. In 2022, the conventional benefit is $7.00 million and the reorganization benefit is $0.33 million, for a total benefit of $7.33 million. In 2023, the conventional benefit is $6.00 million and the reorganization benefit is $0.28 million, for a total benefit of $6.28 million.
This screen grab from the “Summary of Results” screen in the Highway Freight Logistics Reorganization Benefits Estimation Tool shows a bar graph that depicts the conventional (in blue) and reorganizational (in red) benefits of a sample freight project. In this hypothetical example, the benefits increase for the first 18 years due to improved system performance, and then decrease for the last 5 years due to increasing congestion and decreasing performance.

The Summary of Results screen presents the outcomes based on the user inputs from the previous two screens. The model uses the regional elasticities to estimate long-run demand shift and calculate the ratio of long-run benefit to cost savings and consumer surplus. In this way, the model adds the reorganization benefit to the traditional benefit and presents the results as tables and graphs for the estimates of consumer surplus and reorganization effects and the reorganization and conventional freight benefits.

Looking Ahead

The tool is an off-the-shelf product for planners, but it also can be used as the starting point for customized analyses of freight impacts. More accurate measurements of benefits will help planners make the case for critical freight-related projects that will improve reliability and predictability of travel times.

“The important thing is not only the tool itself, but to get planners thinking about how to evaluate freight in a project,” says Rolf Schmitt, freight analysis team leader in FHWA’s Office of Freight Management and Operations. “Freight is different from passenger transportation, and transportation planners are looking for new approaches to incorporate freight into their analyses.”

Adds Sergio Ostria, a senior vice president at ICF International who helped develop the tool, “All the literature on business logistics reorganizations suggests that firms will take advantage of improved transportation by reorganizing their business processes to become leaner, reduce inventories, and use more transportation. The results of this study provide a way to quantify this effect with publicly available data. More importantly, the freight tool provides an off-the-shelf resource for planners to use.”

Although improving cost-benefit analysis models will provide much-needed help in assessing the links between transportation and the economy, it is not the only answer. Cost-benefit analysis should be supplemented with insights from other analytical perspectives, such as macroeconomic modeling, and other new approaches that are sensitive to the economic and geographical context of new transportation investments and their effects.

FHWA’s Office of Freight Management and Operations continues to seek ways to better incorporate freight in the transportation planning process through policy discussions, outreach, public awareness, technical analysis, and tool development. Making good investment choices for the transportation system remains critical to enhancing the Nation’s economic productivity and global connectivity.

A better understanding of the link between transportation investments and the economy will help decisionmakers select the most cost-effective investments and prioritize freight projects. However, improved freight transportation can-not be achieved through technical expertise alone. Enhanced collaboration between the private and public sectors also is needed. Transportation planners need to understand how policies at different levels of government and between jurisdictions interact. In addition, private sector involvement is necessary to help understand freight needs and build support for critical freight projects.

“With freight becoming a priority in so many policymaking institutions, a consensus for better freight planning is emerging,” says FHWA’s Furst.


Ed Strocko is a transportation specialist in FHWA’s Office of Freight Management and Operations. He focuses on freight transportation infrastructure projects, economics, and finance to support FHWA, State DOTs, and metropolitan planning organizations in implementing and modifying initiatives and programs to improve freight operations. He also supports the Federal-Aid Highway Program, primarily in the areas of eligibility, grant mechanisms and agreements, Transportation Improvement Programs, Statewide Transportation Improvement Programs, and public-private partnerships.

For more information, visit http://ops.fhwa.dot.gov/freight/freight_analysis/econ_methods.htm or contact Ed Strocko at 202–366–2997 or ed.strocko@fhwa.dot.gov.

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