Assessment of Fuel Economy Technologies for Medium and Heavy 

Duty Vehicles: Commissioned Paper on Indirect Costs and 

Alternative Approaches 

Draft final paper, Cambridge Systematics, Inc., September 17, 2009 

Introduction 

This paper was commissioned by the National Academy of Sciences in support of its committee 

on medium-duty and heavy-duty truck fuel economy standards as directed by the Energy 

Independence and Security Act of 2007. The Academy working group is charged with 

identifying the potential costs and other impacts of fuel economy technologies on the operation 

of medium-duty and heavy-duty trucks. The paper discusses two sets of issues related to 

potential Federal regulations to improve fuel economy of commercial motor vehicles. The first 

set of issues is the potential indirect costs and benefits of commercial vehicle fuel economy 

regulations – i.e., effects on vehicle costs, congestion, safety transportation service, and other 

factors in addition to the direct effects on fuel usage. The second set of issues is the potential for 

alternatives to fuel economy regulations for improving fuel efficiency. 

This paper, authored by Cambridge Systematics, Inc. (CS), examines a subset of these issues and 

complements a companion paper authored by Eastern Research Group, Inc. (ERG). Some of the 

issues are addressed by both authors. For example, for congestion effects, ERG’s paper discusses 

the relationship between technology and performance, while CS’ paper discusses the resulting 

impacts of performance on highway congestion. The issues were divided between the two firms 

based on their respective areas of expertise as follows: 

CS Paper

First subset of issues (indirect costs) 

• Vehicle-miles traveled and the rebound 

effect; 

• Vehicle class shifting; 

• Implications of transportation service and 

performance effects (with ERG input on 

performance); 

• Congestion impacts (with ERG input on 

performance); and 

• Safety impacts (of changes in traffic 

volume or performance). 

Second subset of issues (alternatives) 

• Fuel tax; 

• Congestion pricing; and 

• Intermodal transport. 

ERG Paper

First subset of issues (indirect costs) 

• Fleet turnover effects; 

• Environmental costs and co-benefits; 

• Transportation service and performance 

effects; and

• Safety impacts (of changes in fuel type).

Second subset of issues (alternatives) 

• Driver training.
 

This paper relies on findings from the literature on these issues but also includes new, sketchlevel analysis to translate literature findings (which may only indirectly address the issue) into 

results that directly address the issues being investigated. 

The key findings of this paper regarding indirect costs are as follows: 

• Vehicle-miles traveled (VMT) and the rebound effect – The “rebound effect” refers to an 

increase in VMT that may occur as a result of lower shipping costs caused by lower fuel costs. 

The additional fuel consumption would partially offset the fuel savings of more efficient 

trucks. Fuel economy regulations that achieve consumption reductions on the order of 4,500 

million gallons (17.6 percent of current fuel use) would reduce national Class 8 fuel 

consumption from 25,500 million gallons annually to 21,000 million gallons. This level of fuel 

efficiency gain would result in an average Class 8 per-mile operating cost reduction of $0.08 

(considering increased vehicle costs amortized over the life of the vehicle as well as decreased 

fuel costs), which could lead to an increase in truck demand and a decrease in rail demand. 

Using a range of assumptions regarding the elasticity of truck travel with respect to price, the 

increase in truck demand due to the decrease in truck operating costs would increase fuel 

consumption by 500 to 1,400 million gallons, diminishing the fuel consumption reduction to 

3,100 to 4,000 million gallons, or a rebound of 11 to 31 percent of the initial 4,500 million 

gallon reduction. The decrease in rail demand, due to the diversion of rail demand to truck, 

produces an additional fuel consumption reduction of 70 to 110 million gallons, counteracting 

the truck rebound effect by around 2 percent of initial 4,500 million-gallon reduction. The net 

rebound effect of the truck fuel consumption increase and the rail fuel consumption decrease 

is therefore about 9 to 30 percent—meaning that the actual reduction in fuel use would be 

about 3,200 to 4,100 million gallons (a 13 to 16 percent reduction in total truck fuel 

consumption). 

For larger fuel economy improvements (on the order of 39 percent), the rebound effect would 

be smaller on a percentage basis – in the range of 5 to 16 percent – for a net reduction in fuel 

use in the range of 32 to 37 percent. This is because the technologies used to achieve the 

higher fuel efficiency standard would be somewhat less cost-effective, raising the initial 

capital cost of the vehicle and leading to a lower per-mile operating cost savings compared to 

the most cost-effective technologies used to achieve the 17.6 percent reduction. 

• Vehicle class shifting – The potential for vehicle purchasers to shift to different classes of 

vehicles will depend upon the specific nature of the fuel economy regulations. However, 

hypothetical regulatory scenarios suggest that significant class-shifting is unlikely to occur in 

most conceivable situations. This is particularly true for long-haul trucks, which account for 

nearly three-quarters of total truck fuel consumption. 

• Transportation service and performance effects – Analysis in the concurrent paper 

developed by ERG suggests that fuel economy regulations would have no appreciable effect 

on vehicle performance. Therefore, impacts on transportation service (e.g., delivery times, 

reliability) will be minimal or non-existent. 

• Congestion impacts – Analysis in the concurrent paper developed by ERG suggests that fuel 

economy regulations would have no appreciable effect on vehicle performance and therefore 
no effect on congestion as a result of degraded performance. However, increased truck traffic 

as a result of the rebound effect could increase congestion. The effect is likely to be minimal 

on most roads, but could be significant at times and locations where the roadway is near 

capacity and where truck traffic represents a significant fraction of total traffic. An estimate 

using marginal per-mile congestion costs from the literature suggests that the increase in 

truck traffic volumes described above for the rebound effect could result in an increased 

congestion cost (to all road users) of approximately $13 billion nationwide. 

• Safety impacts – Analysis in the concurrent paper developed by ERG suggests that fuel 

economy regulations would have no appreciable effect on vehicle performance and therefore 

no effect on safety as a result of degraded performance. Increased truck traffic volumes from 

the rebound effect could be expected to cause a very small increase of less than one fatality 

per year and about two to eight injuries per year nationally. An alternative estimate using 

marginal safety costs from the literature suggests that the increased truck VMT could result in 

an increased safety cost (to all road users) of approximately $4 billion nationwide. 

Key findings regarding alternative approaches to reduce truck fuel consumption are as follows: 

• Fuel tax – To achieve reductions in fuel consumption of the same order of magnitude as 

assumed in the analysis of the rebound effect (20 to 40 percent) is estimated to require an 

increase in the fuel tax on the order of $1 to $2 per gallon, which would lead to a combination 

of both reductions in truck VMT and increases in vehicle fuel efficiency. However, elasticities 

of fuel consumption with respect to fuel price are not well-documented for freight trucks so 

this estimate should be considered particularly uncertainty. 

• Congestion pricing – No analysis has specifically examined the impacts of a comprehensive 

congestion pricing system on truck fuel consumption. Estimates for all vehicles have found 

modest impacts, on the order of a 0.5 to 1.1 percent reduction in total fuel consumption if 

congestion pricing were widely applied in the U.S., although this is based on data 

extrapolated from only two simulation studies. This is the result of a variety of effects, 

including a reduction in overall demand, shifting of demand to less congested periods, and 

more free-flowing traffic during peak travel periods. Analysis of cordon pricing in London 

and Stockholm has found very small impacts on truck fuel consumption, with minimal 

impacts on truck VMT but some benefits through reduced truck idling. 

• Intermodal transport – The potential for truck-to-rail mode shifting through investment in 

rail and intermodal infrastructure and other incentives has not been extensively studied. One 

study of the Mid-Atlantic region estimated that an investment of $12 billion could reduce fuel 

consumption by 10 to 26 billion gallons annually, at a cost of $0.47 to $1.26 per gallon of fuel 

saved. 

1. Indirect Costs and Benefits 

Little, if any, previous research has directly investigated the effects of commercial vehicle fuel 

economy standards on the factors addressed in these papers. There is a considerable amount of 

literature that can inform a general assessment of potential effects. The specific effects, however, 

depend greatly on how the fuel economy regulation is structured, as well as on external issues 

such as fuel prices, economic trends, and technology development. Therefore, the approach 

taken in this paper is to develop hypothetical scenarios that allow for examine what would 

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happen given various fuel economy regulation structures and other key factors. The objective is 

to provide a “what-if” assessment of the impact on fuel economy regulations on performance, 

congestion, costs, etc., rather than a definitive assessment of impacts. At a minimum, this will 

help determine whether or not each issue is significant enough to warrant consideration in 

designing a commercial vehicle fuel economy standard. 

Each issue is addressed in the following format: 

• Key question – What is the key question of interest that is being investigated? 

• Background – What does the literature have to say relevant to this issue? 

• Approach – What quantification method was used to estimate potential impacts related to the 

key question? What key assumptions are made in the analysis? 

• Findings – What are the results of the analysis under alternative hypothetical scenarios 

generated for this report – i.e., what impact could commercial vehicle fuel economy standards 

potentially have on VMT and the rebound effect, vehicle class shifting, etc.? 

(ii) Vehicle-Miles Traveled (VMT) and the Rebound Effect 

Key Question: If fuel economy improvements reduce vehicle operating costs, will truck traffic 

volume increase? 

Background: A “rebound effect” in heavy-duty vehicle travel can be characterized as the extent 

to which cost savings from heavy-duty vehicle fuel efficiency improvements result in increased 

demand for truck shipping and decreased demand for rail shipping because truck travel is made 

less expensive per-mile due to reduced fuel costs and therefore reduced truck operating costs. 

This increased truck travel partially offsets the total fuel savings benefits. The rebound effect has 

been extensively studied for light-duty (personal) vehicle travel, but somewhat less so for freight 

truck travel.1 However, a number of studies have documented relationships between price and 

demand for truck travel. This literature is focused on long-haul freight truck movements because 

1) the effect is likely to be strongest for long-haul movements (due to available modal 

competition); 2) these movements represent the carriage of a significant volume of the nation’s 

freight; and 3) these movements account for the vast majority of the medium- and heavy-duty 

vehicle fuel consumption (78 percent).2 Little or no research exists to provide information on a 

rebound effect for medium-duty trucks. The sketch analysis presented herein will focus on long 

haul trucks, which are generally Class 8 trucks with 53-foot trailers and a gross vehicle weight 

(GVW) of up to 80,000 pounds. 

Caution should be exercised when considering the price-demand relationships found in the 

literature for freight trucks. The results appear to be heavily reliant on factors including the type 

of demand measures analyzed (vehicle-miles of travel, ton-miles, or tons), analysis geography, 

 

1

 The National Highway and Traffic Safety Administration (NHTSA), in its preliminary regulatory 

impact assessment for the CAFE standards rulemaking, selected a value of 15 percent as their best 

estimate, based on a review of the literature. The assessment also concluded that this effect could 

plausibly range from 10 to 20 or even 25 percent (NHTSA, 2008). 

2

 Oak Ridge National Laboratory, Transportation Energy Data Book, Edition 28, prepared for the US 

Department of Energy, 2009. See table 5.4 for relevant data. 

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trip lengths, markets served, and commodities transported.3 The literature does not treat 

differences between short- and long-run effects consistently, and, in general, presents 

inconsistent results across studies. To provide an envelope of potential effects, the analysis 

presented herein provides reasonable upper and lower bounds of potential effects as well as a 

mid-point or “likely” estimate. In nearly all cases, the price elasticities from the literature reflect 

the first effect of the price increase – the decrease of demand. However, they do not describe how 

firms will react to the cost savings resulting from lower fuel costs. A firm with higher income 

will purchase more inputs, produce more goods, and demand more freight transport. The 

figures presented here should be considered on the high side since the secondary effect would 

counter the primary effect in all cases. 

An additional issue is the difference between the average and marginal per-mile cost of truck 

shipping. The marginal cost considers only the fuel savings. The average cost also considers any 

increase in the capital cost of the vehicle, amortized over the lifetime of the vehicle. For the 

purposes of this assessment it is assumed that vehicle owners will pass these capital costs along 

to their customers, and therefore it is the average (net) cost or cost savings that is important. 

The rebound effect in heavy-duty vehicles works in three ways. The first two have been 

measured and characterized with price elasticities while the third is speculative: 

• An increase in overall demand for truck shipping, if total shipping costs are reduced (the selfprice elasticity effect); 

• A shift of some commodities from other modes (especially rail) to truck due to lower truck 

shipping costs (the cross-price elasticity effect); and 

• Less efficient utilization of trucks (e.g., lighter loads), because lower costs reduce cost 

pressures on industry to maximize efficiency. 

The self-price elasticity provides a measure for describing how the volume of truck shipping 

(demand) changes with its price while the cross-price elasticity provides a measure for describing 

how the volume of rail shipping changes with truck price. In general, an elasticity describes the 

percent change in one variable (e.g. demand for trucking) in response to a percent-change in 

another (e.g. price of truck operations). For example, a price elasticity of truck demand with 

respect to truck prices (self-price elasticity) of -0.5 implies that the demand for trucking will grow 

by 5 percent if truck operations costs decrease by 10 percent (+5 percent change in truck 

demand/-10 percent change in truck cost = -0.5). Similarly, a cross-price elasticity of rail demand 

with respect to truck prices (cross-price elasticity) of 0.5 implies that the demand for rail will 

decrease by 5 percent if truck operations costs decrease by 10 percent (-5 percent change in rail 

demand/-10 percent change in truck price = 0.5). 

 

3

 Graham and Glaister, “Road Traffic Demand Elasticity Estimates: A Review,” Transport Reviews 

Volume 24, 3, pp. 261-274, 2004 and Oum, Waters, and Jong Say Yong, “A Survey of Recent Estimates 

of Price Elasticities of Demand for Transport,” prepared for the World Bank, Infrastructure and Urban 

Development Department, January 1990. 

- 5 - 
Oum et al4 collected 17 estimates of freight transportation demand price elasticities. The authors 

focused on self-price elasticities and presented elasticity figures as both a range and a most likely 

estimate. The authors found that transportation is relatively inelastic since it is a derived demand 

– transportation demand only exists because there is demand for other products or services – 

with the exception of freight shipments that are subject to intermodal rail competition. The paper 

found truck self-price elasticity to fall in the range of -0.05 to -1.34 with the most likely range of 

 -0.7 to -1.10 (the measures of demand were not identified and varied by study).5 Furthermore, 

while the general literature on road transportation elasticities has distinguished short-run vs. 

long-run effects, this has not typically been done in the literature on truck elasticities. 

A more recent study by Christides and Leduc reviewed recent price elasticity literature for use in 

a truck size and weight study for the European Commission.6 The literature review collected 

self-price elasticities ranging from -0.3 and -1.75 and cross-price elasticities ranging from 0.11 to 

1.9. For use in their study, they selected values of -0.416 for truck self-price elasticity, measured 

in tons, and 0.38 for rail cross-price elasticity, measured in tons. 

Graham and Glaister,7 in a comprehensive review of recent price elasticity results, found that 

truck self-price elasticity is likely to fall between -0.5 and -1.5, but did not identify a demand 

measure. The Federal Highway Administration (FHWA) used a self-price elasticity of -0.97, 

measured in vehicle-miles, for a freight benefit and cost study.8 A study for the National 

Cooperative Highway Research Program9 cited a rail cross-price elasticity of 0.52 based on the 

Intermodal Competition Model10 and identified a range of cross-price elasticities between 0.35 

and 0.59 from an analysis of Class 1 railroads.11 

Approach: To estimate the potential rebound effects and resulting effects on fuel consumption it 

is necessary to describe a base case and to develop potential fuel economy improvement 

alternatives. It is necessary for the base case to describe current truck and rail volumes; truck and 

rail fuel economy; truck and rail fuel consumption; average truck operating cost per-mile; and 

truck purchase price. For alternatives, it is necessary to describe the potential fuel consumption 

 

4

 Oum, Waters, and Jong Say Yong, “A Survey of Recent Estimates of Price Elasticities of Demand for 

Transport,” prepared for the World Bank, Infrastructure and Urban Development Department, January 

1990. 

5

 While the authors find that demand is “relatively inelastic” this is not always the case, as an elasticity 

of 1.0 or -1.0 can be considered a dividing line between “elastic” and “inelastic,” with higher (absolute) 

values considered elastic. 

6

 Christidis and Leduc, “Longer and Heavier Vehicles for freight transport,” European Commission Joint 

Research Center’s Institute for Prospective Technology Studies, 2009. 

7

 Graham and Glaister, “Road Traffic Demand Elasticity Estimates: A Review,” Transport Reviews 

Volume 24, 3, pp. 261-274, 2004. 

8

 HDR-HLB Decision Economics, Inc. and ICF International, Freight Benefit/Cost Study: Phase III Analysis 

of Regional Benefits of Highway-Freight Improvements, prepared for the Federal Highway Administration 

Office of Freight Management and Operations, February 2008. 

9

 Cambridge Systematics, Inc., Characteristics and Changes in Freight Transportation Demand: A Guidebook 

for Planners and Policy Analysts Phase II Report, National Cooperative Highway Research Program 

Project 8-30, June 1995. 

10 Scott M. Dennis, The Intermodal Competition Model, Association of American Railroads, September 1988. 11 J. Jones, F. Nix, and C. Schwier, The Impact of Changes in Road User Charges on Canadian Railways, 

prepared for Transport Canada by the Canadian Institute of Guided Ground Transport, Kingston, 

Ontario, September 1990.

- 6 - 
reduction; logical fuel economy technologies to achieve the reduction; improved fuel economy; 

increased truck purchase price; resulting operating cost (including savings from fuel economy 

improvements and increased capital cost from increased purchase price); and self-price and 

cross-price elasticities. 

Scenarios: Three scenarios are outlined below: 

• Base case: Total affected truck volume is 142,706 million VMT and total affected rail volume is 

1,852 billion ton-miles;12 truck fuel economy is 5.59 mpg13 and average rail fuel economy is 

436 gallons per ton-mile;14 truck fuel consumption is 25,500 million gallons and rail fuel 

consumption is 4,250 million gallons (based on estimates of travel and fuel economy); total 

truck operating costs are $1.73 per-mile ($247 billion nationally = $1.73/mile * 142,706 million 

miles), including $0.70 fuel-oil related costs (fuel costs and fuel tax costs) and $0.206 per-mile 

truck and/or trailer lease or purchase payments;15 and the current purchase price of a Class 8 

tractor-trailer is $100,000.16 

• Alternative 1, 17.8 percent reduction in fuel use: In alternative 1, a set of technologies are 

implemented to achieve a 17.8 percent reduction in fuel consumption. The selection of 

technologies and this target reduction were based on the forthcoming Northeast States Center 

for a Clean Air Future (NESCCAF) and International Council on Clean Transportation (ICCT) 

report17 that outlines technologies, their impacts and their costs. The technologies include 

best-available aero-kit (fully aerodynamic mirrors, cab side extenders, integrated sleeper cab 

roof fairings, aerodynamic bumper, full fuel tank fairings, side skirt fairings, and boat tails), 

wide single base tires, aluminum wheels, idle reduction, and improved lubricants. The 

technology improves fuel economy from 5.59 to 6.80 mpg at a cost of $22,930 per vehicle. 

Accounting for decreases in operating cost and increased capital cost, the net per-mile 

operating cost will be reduced to $1.65, $0.57 of which is fuel-oil related costs, down from 

$0.70 in the base case, and $0.25 of which is truck/trailer payments, up from $0.21 in the base 

case. The total national operating cost is $236 billion (142,706 vehicle-miles * $1.65/mile). The 

chosen range of self-price elasticity of truck demand with respect to truck price is between -

0.5 and -1.5 and the cross-price elasticity of rail demand with respect to truck price is between 

0.35 and 0.59. These ranges were chosen to include the majority of the estimates while 

excluding the outliers. 

• Alternative 2, 38.6 percent reduction in fuel use: In alternative 2, a set of technologies, again 

based on the ICCT report, are implemented to achieve a 38.6 percent reduction in fuel 

 

12 Bureau of Transportation Statistics, Pocket Guide to Transportation, January 2009. 

13 Energy Information Administration, “Annual Energy Outlook, 2009,” March 2009. 

14 Association of American Railroads, “Freight Railroads and Greenhouse Gas Emissions,” June 2008. 

15 American Transportation Research Institute, An Analysis of the Operational Costs of Trucking, December 

2008. 

16 Anthony Grezler, “US Heavy Duty Vehicle Fleets Technologies for Reducing CO2 : An Industry 

Perspective,” Volvo Powertrain Corporation, August 23, 2007.

17 Northeast States Center for a Clean Air Future, Southeast Research Institute, TIAX, LLC., and 

International Council on Clean Transportation, Reducing Heavy-Duty Long Haul Truck Fuel Consumption 

and CO2 Emissions, September 2009. 

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consumption. This set of technologies includes all alternative 1 technologies plus continued 

streamlining of the cab, a reshaped trailer, boat tail, full skirting of the cab and trailer, tractortrailer gap fairing, very low resistance tires, bottoming cycle, parallel hybrid system, and road 

speed governors set to 60 mph. The technology improves fuel economy from 5.59 to 9.10 

mpg at a cost of $71,630 per vehicle. Accounting for the reduced fuel cost and increased 

capital cost, the net per-mile operating cost will be reduced to $1.61, $0.43 of which are fueloil related costs, down from $0.70 in the base case, and $0.35 of which are truck and trailer 

payments, up from $0.21 in the base case. The total national operating cost is $230 billion 

(142,706 vehicle-miles * $1.61/mile). The elasticity ranges are the same as those employed in 

the alternative 1 analysis. 

Findings: The impact of the three potential rebound effects on the alternatives are described 

below: 

The first rebound effect – truck VMT increase 

• Before accounting for the rebound effect, the fuel economy improvements of alternatives 1 

and 2 will reduce fuel consumption from 25,500 million gallons to 21,000 and 15,700 million 

gallons, respectively. This represents a reduction of 4,500 - 9,800 million gallons of fuel. 

• Class 8 long-haul demand will increase (rebound) by 3.2 - 9.5 million VMT for alternative 1 

and 5.0 - 15.0 million VMT for alternative 2, based on the projected net decrease in operating 

costs associated with fuel efficiency improvements of $0.08 and $0.12 cents per mile, 

respectively. 

• The VMT increase will cause a rebound in fuel use. The rebound for alternative 1 will reduce 

the total fuel savings from 4,500 million gallons to 3,100 to 4,000 million gallons. Put another 

way, the rebound effect for Class 8 vehicle travel will range from 11 to 31 percent if the 

technologies in alternative 1 are implemented. The rebound for alternative 2 will reduce the 

total fuel savings from 9,800 million gallons to 8,200 to 9,300 million gallons, depending on 

the elasticity. This will cause a rebound ranging from 5 to 16 percent. 

• The rebound effect varies depending on the elasticity and the cost-effectiveness of the 

technology. If the technology is more cost-effective, then there will be a larger rebound effect 

because the cost of trucking will come down to a larger degree, increasing the total growth in 

demand for trucking. Alternative 2 shows a lower rebound effect (on a percentage basis) than 

alternative 1 because alternative 1 deploys the most cost-effective technologies, with 

diminishing returns gained from the technologies deployed under alternative 2. 

The second rebound effect: diversion from rail 

• Some of the increased truck travel will be diverted from rail. With truck per-mile operating 

costs dropping from $1.73 in the base case to $1.65 and $1.61 in alternatives 1 and 2, shippers 

will shift 28.7 to 48.4 billion ton-miles from rail carriers to truck carriers in alternative 1 and 

45.4 to 76.5 billion ton-miles from rail shippers in alternative 2. 

• The shift from rail to truck will reduce rail fuel consumption by 66 to 111 million gallons for 

alternative 1 and 104 to 176 million gallons for alternative 2. 

• Considering truck fuel savings, the truck rebound effect, and the rail fuel savings for 

alternative 1 together, the fuel reductions of 4,500 million gallons will drop to 3,200 to 4,100 

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million gallons, for a combined rebound effect of about 9 to 30 percent (compared to 11-31 

percent without rail fuel savings). Considering the same for alternative 2, the fuel reductions 

of 9,800 million gallons will drop to 8,300 to 9,500 million gallons, for a combined rebound 

effect of 3 to 15 percent (compared to 5-16 percent without rail fuel savings). 

The third rebound effect: utilization reduction 

• Trucking is a cost-competitive business and profits are historically razor-thin. While trucking 

companies work diligently to reduce operating costs out of competitive necessity, continued 

cost increases force fleet owners to continue to look for ways to reduce operating costs. The 

relief that comes from decreased operating cost (4 percent in alternative 1 and 7 percent in 

alternative 2) is not likely to create a significant change in utilization optimization. However, 

there is no evidence in the literature to quantify the degree to which trucking companies 

maximize the utilization of their equipment or to describe how this might change with total 

truck operating costs. 

(iii) Vehicle Class Shifting 

Key Question: If regulations or new technologies change the relative costs and performance 

characteristics of various classes of medium and heavy vehicles, will some buyers choose larger 

or smaller vehicles than they would have before the change? 

Background: Past regulations have had an impact on the choice of vehicle characteristics. Prior to 

deregulation of the trucking industry, 4-axle trucks were common. After deregulation, most 

carriers shifted to 5-axle trucks since these trucks could carry more freight and therefore operate 

more economically, given weight per axle restrictions. 

To induce class-shifting, truck fuel economy regulations would need to significantly increase the 

cost (or decrease the performance) of one class of truck relative to another. The specific nature of 

a vehicle class-shifting effect will therefore depend upon how the regulations are structured. A 

“class-neutral” regulation could be conceived that does not lead to any shifts. 

There is little or no literature that describes the cross-class mode shift between truck types based 

on cost. The potential for class-shifting appears limited, however, because most equipment is 

chosen to fit the physical requirements of a particular shipment, not because of changes in fuel 

price. A shipper will hire carriers to minimize total logistics cost and the carrier will deliver a 

service at the minimum possible cost (to maximize their profits). The shipment could be longhaul (more than 500 miles, the one-day round-trip distance), short-haul (less than 500 miles), a 

drayage shipment between an intermodal facility and the final destination, or a local shipment 

between a regional warehouse and a grocery store. Shippers and carriers will give additional 

consideration to whether the commodity is high-value and time-sensitive or low-value and less 

time sensitive, whether the commodity will weigh-out or cube-out, and the labor cost. Other 

vocational medium- and heavy-duty truck owners have very specific carrying and sizing needs 

(e.g. dump trucks, garbage trucks, buses, and utility trucks). A shipper and carrier will examine 

the set of requirements, negotiate a rate, and choose the optimal truck for the shipment. Shifting 

between trucks and truck classes is complicated, but it is possible to conceive of some examples 

where certain policies would create cases where a truck from one class might become fungible 

with a truck from another class. 

- 9 - 
Approach: Analysis first requires the identification of hypothetical regulations and, second, the 

identification of specific truck classes and shipment type that have potential for shifting given the 

regulations. 

Findings: The following is a sample of potential regulatory approaches and their possible 

impacts. The impacts are intended only to show the likely direction of shifting. A quantitative 

analysis would require a more specific understanding of alternative regulations, the distribution 

of vehicle classes, the development of complete weight and operating cost tables for each vehicle 

type and class, and potentially, research with vehicle owners to determine how these impacts 

might affect their purchase decisions. 

Regulations that would increase or decrease the weight of a vehicle. If fuel economy regulations were to 

require all trucks to implement weight-increasing or –decreasing technologies, there are several 

potential outcomes, depending on the current use of the truck. Two potential outcomes are 

described below: 

• Trucks used for certain types of specialized shipments that have similar trucks in different 

classes are the most likely to be interchangeable. For example, a FedEx delivery truck can be 

one of three classes, including Class 3, 4, or 5 (Cummins). If regulations were to increase the 

weight of a Class 3 FedEx delivery truck and make it Class 4, FedEx might decide to simply 

purchase more Class 4 trucks going forward. Other truck types that are fungible between 

Classes include city delivery (Classes 3, 4, and 5), conventional van (Classes 3 and 4), and 

conventional tractors (Classes 7 and 8). In the long-run, these changes might result in larger 

(or smaller) trucks being used for deliveries of all sorts, which would lead to an increase in 

travel. 

• Trucks used for certain types of specialized shipments that do not have similar trucks in 

different classes and are not likely to be interchangeable may still be impacted by a policy that 

would increase or decrease the gross vehicle weight. License requirements for Class 7 

vehicles make marginal Class 6 vehicles most likely to experience class-shift pressure from a 

regulation that would increase general gross vehicle weight. For example, if regulations were 

to require school buses to add a significant amount of weight, it is possible that they would 

become Class 7 vehicles. If that were to happen, school bus drivers would need to gain 

additional licensure or buses would need to counteract the weight gain and lose capacity. 

Other Class 6 vehicles that are marginal now (school bus, beverage trucks, rack trucks, and 

single axle vans) might bump over to Class 7, which would require special driver licensure. 

Regulations that would be implemented based on geography of truck travel (e.g., requiring aero fairings on 

trucks traveling outside of the city): 

• Some fuel-saving technologies are only cost-effective for certain types of uses. For example, 

aerodynamic improvements are most beneficial for high-speed, long-haul travel, while 

hybridized drivetrains are most benefits for stop-and-go duty cycles. Fuel economy could be 

directly regulated depending upon the intended use of the vehicle, or technology 

requirements imposed based on intended use. Under the assumption that a regulation would 

be defined as something similar to ‘percent of miles of travel on roads within Census Urban 

Area boundaries,’ trucks that travel half in the city and half outside of the city represent a 

likely marginal case. Trucks meeting the marginal description are likely to be trucks that, for 

- 10 - 
example, carry goods between warehouses on the urban fringe and urban retailers. These 

carriers would likely make the argument that their travel was most often inside of the urbanarea boundary and, therefore, they should not be required to install fairings. They are not 

likely to shift classes to avoid regulation, as their shipments are not likely to change. 

Monitoring and enforcement of such a regulation would likely be a challenge, especially 

without the deployment of technologies such as global positioning systems (GPS) monitors as 

a basis for charging VMT fees. Additionally, urban area boundaries change after each 

decennial census, further reducing the practicality of this type of regulation. 

Regulations that impact a certain engine size: 

• Regulations that would impact only a certain engine size would implicitly impact a certain 

class truck more than another. For example, if engine regulations were implemented on any 

engine with peak horsepower greater than 400, this would select for Class 8 trucks—either 

line-haul or other vocational loads. However, any regulations that would increase the 

operating cost for an engine sized for Class 8 use would be doing so for the class of trucks 

that are least likely to switch. Vocational trucks would still be used for job-specific purposes 

and other users of Class 8 trucks are extremely unlikely to shift to smaller trucks because 

labor costs would still far outweigh the increased operating cost that would accompany the 

required increase in shipments. 

For example, a steering column manufacturer in Vermont fills four 53-foot trailer-loads of 

steering columns bound for an automobile manufacturing plant in Mississippi. If he were to 

shift this into a smaller Class 7 carrying a 28-foot pup trailer, it would take nearly twice as 

many truckloads, doubling the total number of shipments. The impact of switching to Class 7 

trucks will significantly increase total cost because fuel costs of Class 7 trucks are roughly 75 

percent of Class 8 fuel costs ($0.48 compared to $0.634 per mile) and labor is likely to remain 

similar since the shipment requirements are comparable.18 Total labor costs will double 

(eight trucks compared to four) and fuel expense will increase by about 50 percent (eight 

trucks * $0.48 - four trucks * $0.634), resulting in a much more expensive shipment. 

(v) Transportation Service and Performance Effects 

Key Question: Are regulations likely to reduce the quality of the services provided by vehicles, 

for example by affecting speed, reliability, or cargo capacity? If so, what are the implications for 

costs and benefits? 

Discussion: Initial research conducted by ERG for the concurrent white paper suggests that the 

impacts of fuel economy regulations on truck performance will be minimal, if any. Therefore, the 

impacts of fuel economy regulations on quality of service are likely to be minimal or non-existent. 

The exception is that some regulations may lead to the use of technologies that add modestly to 

the weight of the vehicle, which will modestly reduce cargo capacity on a weight basis. This will 

only be an issue for shipments that “weigh out” (i.e., are limited by the maximum load allowed), 

 

18 Antich, “Medium-Duty Operating Costs Increase in 2008-CY,” reprinted from Work Truck Magazine, 

May/June 2009 and American Transportation Research Institute, An Analysis of the Operational Costs of 

Trucking, December 2008. 

- 11 - 
and not those that “cube out” (i.e., are limited by volume). [Details to be added pending completion 

of ERG paper – what potential weight increase? What impact on %cargo capacity? Could this require 

more trucks on the road to meet the same demand?]

- 12 - 
(vi) Congestion Impacts 

Key Question: If regulations reduce truck performance and/or reduce or increase truck VMT, 

what are the potential implications for congestion? 

Background: Section (ii) describes how the demand for long-haul trucking could increase if fuel 

economy regulations effectively reduce the net operating cost of long-haul trucking. Truck 

demand is estimated to increase by 3.2 to 15.1 million VMT (an increase of 2.2 to 10.5 percent), 

depending on the technology alternative selected, the prevailing national truck price elasticity 

and rail cross price elasticity, regulation, and technology response. The concurrent white paper 

produced by ERG suggests that the impacts of fuel economy regulations on truck performance 

will be minimal, if any, and therefore performance impacts on congestion are not considered 

further. 

The remainder of this analysis focuses on basic freeway sections because fuel economy 

regulations are most likely to affect long-haul truck traffic, which spend the vast majority of their 

time on freeways. (Trucks have a significant impact on congestion at other traffic control 

locations, such as signalized and unsignalized intersections, merge sections, and freeway-tofreeway off-ramps, but the data to analyze these impacts is not readily available.) In the highway 

capacity manual (HCM), in basic freeway analysis, trucks are represented as “passenger car 

equivalents” (PCE).19 The PCE concept is meant to capture the effect that heavy vehicles have on 

traffic flow on a freeway because heavy vehicles occupy more space, travel more slowly up steep 

grades and more quickly down them, accelerate more slowly, brake more slowly, and change 

lanes more slowly than passenger cars. Furthermore, different passenger car operators react 

differently to the presence of trucks; for example, following further behind trucks than cars. 

The HCM increases the PCE for trucks based on roadway conditions such as grade, number of 

lanes, distance to roadside obstructions, and width of lanes. It does not, however, distinguish 

between truck PCEs under congested and uncongested conditions. FHWA simulated the effect 

of combination trucks as part of its Truck Size and Weight Study to estimate how effective truck 

PCEs change based on the weight-to-horsepower ratio of the truck itself, the grade of the 

roadway, the road type, geography, and congestion levels. They did not estimate the impacts of 

other roadway characteristics such as lane width or distance to obstructions.20 The tables from 

the FHWA report are reproduced in this paper and to show how different scenarios change how 

trucks impact roadway volumes. Generally, steeper grades, longer hills, fewer lanes, and a 

higher weight-to-horsepower ratio increase the number of PCEs for a single truck. Truck PCE 

conversions are necessary to calculate a volume-to-capacity (V/C) ratio, which can be used to 

estimate congestion and delay measurement. Figure A-1 at the end of this document shows PCEs 

for trucks on urban highways. 

While different formulas have been developed to estimate traffic speeds based on V/C ratios, an 

illustrative example can be provided through the use of the Bureau of Public Roads (BPR) 

formula: 

 

19 Transportation Research Board, Highway Capacity Manual, Washington, D.C., 2000. 

20 Battelle, “Traffic Operations and Truck Size and Weight Regulations, Working Paper 6,” prepared for 

the Federal Highway Administration, February 2005. 

- 13 - 
Congested Speed = (Free-Flow Speed) / (1 + 0.15 * [volume/capacity] ^ 4) 

The formula implies that as a basic freeway segment approaches capacity (as the V/C ratio 

approaches 1.0), traffic speed will be reduced. To calculate delay, one must calculate both 

congested and free-flow travel time from the segment length and congested and free-flow speeds, 

then measure the difference between the free-flow travel time and the congested travel time. 

An alternative perspective on congestion measurement uses estimates from literature of the 

marginal cost of one combination truck on overall congestion. In one study, Parry estimates the 

marginal congestion cost of combination trucks to be $0.168 per mile in urban areas and $0.037 

per mile in rural areas.21 The marginal congestion cost describes the cost, measured in lost travel 

time, that a single additional combination truck imposes on the rest of the traffic already on the 

roadway. Generally, as congestion increases, the marginal cost increases. In his analysis, Parry 

uses an estimate of PCEs that is consistent with the HCM but does not take into account the full 

impacts of trucks on congestion. To wit, the marginal cost estimates are likely to be larger. 

Approach: 

For estimation of change in delay: The approach taken is to conceptualize an average high-truck 

traffic basic freeway scenario, create a comparison scenario with additional 2.2 - 10.5 percent 

truck traffic (from Section (1)(ii)), estimate the V/C ratio for each scenario, estimate congested 

segment speeds based on the BPR formula, and calculate delay for each scenario. This approach 

assumes that the average nationwide increase in truck traffic occurs on this hypothetical 

congested segment. 

For estimation of change in marginal cost: An alternative approach uses Parry’s marginal congestion 

costs. To estimate total increased marginal congestion costs, the current urban and rural Class 8 

truck VMT is identified, and the percent change in VMT calculated in Section (1)(ii) applied to 

estimate the total marginal costs. 

Scenarios for estimation of change in delay: A basic four-lane freeway segment (two lanes in either 

direction) is at capacity when there are at least 2,200 passenger cars per lane per hour, or 4,400 

passenger cars traveling in either direction. The roadway has a free-flow travel speed of 70 mph, 

no horizontal obstructions, two 12-foot travel lanes, no grade, and is 10 miles in length. The two 

scenarios below illustrate the impact of trucks on highway capacity under a condition of 

moderately heavy demand: 

• Base scenario: A roadway that carries a significant number of trucks and passenger vehicles 

but currently operates at an acceptable level of service, and does not experience many hours 

of delay. The volume (demand) consists of 3,400 total vehicles on the basic freeway segment 

per hour. Ten percent of them (340 vehicles) are trucks and they have, on average, a 500 

horsepower engine and a fully-loaded gross-vehicle weight of 80,000 lbs, which produces a 

weight to horsepower ratio of 160 lb/hp (80,000/500); 

 

21 Parry, Ian, “How Should Heavy-Duty Trucks Be Taxed?” Resources for the Future, April 2006. See 

Table 1 for his benchmark parameter values for mileage-related marginal external costs. 

- 14 - 
• Truck VMT increase scenario: This scenario is identical to the base scenario described above 

except that trucks are increased between 2.2 and 10.5 percent to grow with the increase in 

traffic described in Section (1)(ii). 

Findings:

Base scenario for estimation of change in delay: To estimate the volume to capacity ratio, take the 

3,400 vehicles and add the additional PCEs created by the trucks. Each truck on this segment 

occupies the same capacity as 2.6 passenger vehicles (From Figure A-1, each truck is worth 2.6 

PCEs). The road operates as if there are 3,060 autos plus 884 PCEs (340*2.6), identical to a 

scenario where there are no trucks and 3,994 passenger cars. The estimated V/C ratio is 

3,944/4,400, or 0.90. Using the BPR formula, the congested speed for this segment is 63.8 mph 

((70) / (1 + 0.15 * [3,944/4,400] ^ 4) =63.8 mph). It takes 8.6 minutes to travel the 10-mile 

roadway in free-flow conditions and 9.4 minutes in congested conditions, which implies a delay 

of 0.8 minutes per vehicle under the operating conditions described in the scenario. For all 3,400 

vehicles, this implies a total delay of 2,720 minutes of delay, approximately 45 hours. 

Truck VMT increase scenario for estimation of change in delay: In this scenario, truck volumes increase 

by 2.2 - 10.5 percent over the base scenario conditions, an increase of 8 - 36 trucks or an increase 

in total vehicles of 0.2 – 1.1 percent. The road now operates as though there are 3,060 autos plus 

904 - 978 PCEs (348*2.6; 376*2.6), identical to a scenario where there are no trucks and 3,964 - 

4,038 passenger cars. The V/C ratio is 3,964/4,400 – 4,038/4,400, or 0.90 - 0.92. Using the BPR 

formula, the congested speed for this segment is 63.7 - 63.3 mph. This marginally increases the 

travel time for the 10-mile segment to 9.4 - 9.5 minutes in congested conditions, which implies a 

delay of 0.8 - 0.9 minutes per vehicle. For all 3,408 - 3,436 vehicles, this implies a total delay of 

2,726 - 3,092, approximately 45 - 52 hours – an increase in delay of 0 – 16 percent compared to the 

base scenario. 

This increase in delay is small compared to the overall travel time on this segment (although it is 

valid only at this particular point on the BPR curve.) Furthermore, the majority of VMT on U.S. 

freeways and arterial roadways occurs at acceptable levels of service (71 percent of urban VMT 

and 92 percent of rural VMT currently operates at level of service D or better) and impacts on 

these roadways are likely to be minimal. However, the marginal impact on delay will become 

larger as the capacity of the road is approached. For some roadways operating at or near 

capacity and with high truck volumes, it is possible that increased truck travel as a result of the 

rebound effect from higher fuel efficiency could have an impact on delay that is not insignificant. 

For estimation of change in marginal cost: Rural and urban Class 8 truck VMT is 82,768 (58 percent of 

142,703 million VMT) and 59,935 (42 percent of 142,703 million VMT), respectively.22 Using 

Parry’s marginal costs of $0.037 per-mile in rural areas and $0.168 per-mile in urban areas, the 

total increase in cost is $13,131 million ($3,062 million rural + $10,069 urban). 

 

22 Federal Highway Administration, Highway Statistics, 2009 and Bureau of Transportation Statistics, 

Pocket Guide to Transportation, January 2009. 

- 15 - 
(vii) Safety Impacts 

Key Question: If regulations reduce truck performance and/or reduce or increase truck VMT, 

what are the potential implications for safety? 

Background: Section (1)(ii) describes how the demand for long-haul trucking will increase if fuel 

economy regulations effectively reduce long-haul truck operating costs. Truck traffic is estimated 

to increase between 3.2 and 15.1 million VMT (an increase of 2.2 - 10.5 percent), depending on the 

technology alternatives and assumed demand elasticities. Literature shows that truck traffic has 

a direct correlation with injuries and fatalities. Estimates of increased truck traffic can be 

multiplied by the injury and fatality crash rates to estimate the range of potential deaths and 

injuries caused by the increase in travel. 

The concurrent white paper produced by ERG suggests that the impacts of fuel economy 

regulations on truck performance will be minimal, if any, and therefore performance impacts on 

safety are not considered further. 

The safety impacts of changes in truck traffic can also be valued using marginal safety cost 

estimates from the literature. The marginal cost that one combination truck has on the safety of 

the population describes the cost, measured in property damage, medical costs, value of a 

statistical life, etc. that a single additional combination truck imposes on the rest of the traffic 

already on the roadway. The marginal safety cost of combination trucks has been estimated by 

Parry to be $0.018 per mile in urban areas and $0.034 per mile in rural areas.23 Generally, the 

marginal costs are higher in rural areas than in urban areas because the speeds are higher and 

there is a higher likelihood of a larger speed differential, a key determining factor in highway 

accident severity and likelihood. 

Findings: 

For estimation of increase in injuries and fatalities: Recent data for highway crashes indicate crash 

rates for truck tractors with trailers of 2.4 per 100 million VMT for fatal crashes and 51.1 per 100 

million VMT for injury crashes.24 Based on these average rates, the rebound effect analysis (3.3 - 

15.1 million additional VMT annually) implies a national increase of 0.08 - 0.36 fatalities per year 

and 1.7 – 7.8 injuries per year. 

For estimation of change in marginal cost: Rural and urban Class 8 truck VMT is 82,768 (58 percent of 

142,703 million VMT) and 59,935 (42 percent of 142,703 million VMT), respectively.25 Using 

Parry’s marginal costs of safety of $0.034 per-mile in rural areas and $0.018 per-mile in urban 

areas, the total increase in cost is $3,893 million ($2,814 million rural + $1,079 urban). This is a 

much larger cost than would be expected based on the estimated change in injury and fatality 

 

23 Parry, Ian, “How Should Heavy-Duty Trucks Be Taxed?”, Resources for the Future, April 2006. See 

Table 1 for his benchmark parameter values for mileage-related marginal external costs. 

24 Federal Motor Carrier Safety Administration, “Commercial Vehicle Facts,” November 2007 

http://www.fmcsa.dot.gov/facts-research/facts-figures/analysis-statistics/cmvfacts.htm 

(Accessed September 16, 2009). 

25 Federal Highway Administration, Highway Statistics, 2009 and Bureau of Transportation Statistics, 

Pocket Guide to Transportation, January 2009. 

- 16 - 
rates shown above (the latest value of life estimates are in the range of $5 million and injury costs 

would be considerably less; while there would be more property damage only crashes, costs for 

these crashes are also much less than average injury or fatality costs). Potential reasons for these 

differences have not been investigated. 

2. Alternative Approaches to Improving Fuel Efficiency 

Three alternative approaches to improving fuel efficiency are discussed in this section: a fuel tax, 

congestion pricing, and improvements in intermodal transport to divert freight from truck to rail. 

(i) Fuel Tax 

Key Question: What impact would an increase in the fuel tax (assessed on diesel fuel, all motor 

vehicle fuels, or an all fuels based on carbon content) have on truck fuel consumption? 

Background: A fuel tax would affect truck fuel consumption in two ways: first, it would reduce 

truck traffic volumes, and second, it would encourage the purchase of more fuel-efficient vehicles 

and the retrofitting of existing vehicles with fuel-saving technology. It is possible to estimate the 

first effect, truck VMT reduction due to operating cost increase, using the elasticities of VMT with 

respect to price of truck travel as described in section (1)(ii). 

The second effect can similarly be evaluated using evidence on the elasticity of truck fuel 

efficiency with respect to fuel price. While there is considerable literature on this subject as 

applied to overall on-road vehicle traffic, this literature generally does not distinguish between 

personal and commercial vehicle elasticities and therefore the primary effects represented are for 

personal vehicles and travel. For example, Small and Van Dender (2007) provide a review of 

previous studies as well as recent estimates using data through 2004. The authors estimate 

elasticities of VMT, fuel intensity (gallons/mile), and total fuel consumption (VMT multiplied by 

fuel intensity) with respect to fuel price changes. Elasticities are estimated both over a historical 

time period (1966 to 2004) and within the past few years (2000 to 2004) to examine how elasticities 

might be changing. These findings are shown in Table 1. 

Table 1 Historical and Recent Long-Run Elasticities With Respect to 

Fuel Price 

Calculated Long-Run Price Elasticities With 

Respect to Fuel Price of: Elasticity 1966 to 2004 Elasticity 2000 to 2004 

Vehicle Miles Traveled -0.210 -0.057 

Fuel Intensity -0.193 -0.191 

Fuel Consumption -0.363 -0.237 

Rebound Effect (Percentage) 21.0% 5.7% 

Source: Small and Van Dender (2007). The values shown in this table are “long-run” elasticities – i.e., response 

over a multi-year period after the price change. Long-run elasticities will be greater than the immediate or short-term response since travelers are able to make more fundamental adjustments to their 

- 17 - 
activity patterns, such as changing residence or worksite locations or changing the number and types 

of vehicles owned. 

The findings imply, for example, that based on data from the 1966 to 2004 period, a 100 percent 

increase in fuel price should lead to a 21 percent reduction in VMT and a 19 percent increase in 

the fuel efficiency of vehicles, for an overall net decrease in fuel consumption of 36 percent. For 

the more recent period of 2000 to 2004, the elasticity of VMT with regard to fuel price declined 

substantially, such that a 100 percent increase in fuel price would lead to only a 6 percent long 

term decline in VMT. Table 1 shows that historically, about half of the impact of a fuel price 

increase would be on VMT reduction and the other half on fuel efficiency; but that in recent 

years, the VMT response to price increases has become much lower – less than one-third the 

magnitude of the fuel efficiency effect. Another recent review of elasticities by Sperling26

suggests that elasticity values are higher, but also reaches a similar conclusion that elasticities 

with respect to VMT have declined in recent years. Sperling found that long run elasticities of 

fuel consumption with regard to fuel price “may be as low as -0.2.” 

Converting Small and van Dender’s fuel intensity elasticity, which is measured in response to 

fuel prices, to an elasticity based on overall vehicle operating expenses, would imply about a two 

to three times higher elasticity (since fuel cost represents only about one-half to one-third of total 

operating costs, as discuss in Section (1)(ii)), or up to around two to three times -0.19 (about -0.4 

to -0.6). Doing the same with the 1966-2004 VMT elasticity produces a similar result, which falls 

at the lower end of the range identified previously through studies specific to truck traffic, and 

about half the value selected above based on FHWA’s report (-0.97). 

Approach: 

The question investigated here is what level of fuel tax would be required to reduce truck fuel 

consumption by roughly 20 to 40 percent, the same range of benefits assumed in the scenarios 

evaluated in Section (1)(ii). It is not known how well the elasticities reported by Small and Van 

Dender represent responses for the commercial vehicle sector. However, if it is assumed that the 

fuel intensity response is about the same magnitude as the VMT response, the previous analysis 

of VMT response can be used as a basis for estimating the combined response, by answering the 

question of what fuel price increase would be required to reduce VMT by 10 to 20 percent (and 

assuming that a similar decrease in fuel intensity of 10 to 20 percent is also achieved, for a net 

effect in the range of 20 to 40 percent). The analysis is simplified by selecting an elasticity for 

both rail and truck demand used in relevant U.S. studies. The truck price elasticity of -0.97 used 

by FHWA and rail cross-elasticity of 0.52 from AAR were selected. 

Findings: 

The fuel tax necessary to achieve 20 - 40 percent lower fuel consumption in the average long-haul 

truck is $0.18 - $0.36 per mile. Because the cost of fuel per mile is currently $0.63427 at an average 

fuel economy of 5.59 mpg, the data imply a fuel price of $3.54 gallon ($0.634 per mile * 5.59 miles 

per gallon). Using the same conversion, the increase in the per-mile fuel tax translates to an 

increase in the per-gallon fuel tax of $1.01 - $1.98, for the 20 and 40 percent scenarios 

 

26 Sperling, Dan, “Consumer Response to Fuel Price Changes: Implications for Policy,” January 15, 2008. 

27 American Transportation Research Institute, An Analysis of the Operational Costs of Trucking, December 

2008. 

- 18 - 
respectively. This will reduce truck fuel consumption by 5,100 - 10,100 million gallons (20 - 40 

percent from current fuel consumption levels of 25,500 million gallons). 

This corresponds to a decrease in truck traffic from current annual VMT of 71,400 to 67,000 or 

43,000 for the 20 and 40 percent reduction cases, respectively. Based on the cross-price elasticity 

of 0.52, rail ton-miles will increase from 1,852 billion ton-miles to 1,953 - 2,053 billion ton miles in 

the 20 - 40 percent cases. This increase in rail traffic will increase rail fuel consumption by 230 - 

460 million gallons. This will reduce the impact of the truck fuel tax increase from 5,100 - 10,100 

to 4,870 - 9,640 million gallons, reducing the effectiveness of the reduction by approximately 5 

percent in both cases. 

Potential for Government Promotion: An increase in the Federal diesel motor fuel tax would be 

easy to administer as the mechanism already exists for levying and collecting this tax. However, 

the increase required to achieve significant fuel efficiency benefits – estimated to be in the range 

of $1 to $2 per gallon – is much higher than would be acceptable in the current policy 

environment (where, in fact, even small increases of five to 10 cents per gallon may be considered 

intolerable). It also significantly exceeds the likely fuel price increase under a national cap-andtrade program. Analysis by the Energy Information Administration (EIA) of cap and trade 

legislation under the previous Congress showed that a $50 per tonne allowance price – at the 

upper range of what would be expected in 2030 – corresponds to a gasoline price increase of 

about 45 cents per gallon in 2030.28 A lower allowance price of $30 per tonne (27 cents per gallon) 

is consistent with the range in the draft American Clean Energy and Security Act of 2009. Using 

the same assumptions as the above analysis, this price would result in a decrease in commercial 

vehicle fuel consumption of only about 5 percent. 

(ii) Congestion Pricing 

Key Question: What impact would congestion pricing have on fuel consumption? 

Background: Congestion pricing could take different forms, such as area-wide network pricing 

on freeways and possibly arterials, “cordon” or area pricing in central business districts, or truckspecific congestion pricing such as the varying time-of-day gate fees implemented at the Ports of 

Los Angeles and Long Beach. 

Congestion pricing could affect truck fuel consumption by: 

• Shifting trips to less-congested off-peak hours; 

• Reducing congestion for trucks continuing to operate during peak periods; 

• Reducing overall goods movement and related truck traffic due to higher costs; and 

• Shifting logistics patterns – e.g., leading industries to establish consolidation centers on the 

edges of urban areas to reduce truck activity within the congested area. 

Area-wide congestion pricing is applicable to freeways and major arterials where there is 

significant congestion. Cordon pricing strategies are only applicable in major urban areas with 

 

28 U.S. Department of Energy, Energy Information Administration, “Energy Market and Economic 

Impacts of S. 2191, the Lieberman-Warner Climate Security Act of 2007,” 2008. 

http://www.eia.doe.gov/oiaf/service_rpts.htm 

- 19 - 
significant congestion. The limited geographic applicability of these two scenarios limits the fuel 

reduction potential. Area-wide congestion pricing has greater potential since it is estimated that 

nearly 30 percent of urban VMT occurs at level of service E or F. Cordon pricing of metropolitan 

area central business districts, however, is estimated to affect only 3 percent of total VMT 

nationwide. Furthermore, evidence suggests that there will be little, if any, overall impact on 

total truck traffic (as the added costs are likely to be marginal, or the option of moving to the offpeak period acceptable), but rather that the benefits will occur from trucks operating under 

improved flow conditions, and therefore using less fuel due to idling or stop-and-go operations. 

This will have a larger impact on smaller urban trucks since larger long distance trucks operate 

mostly on uncongested highways. 

It should be noted that while reducing congestion should improve fuel economy up to a point 

(e.g., increasing average speeds from 10 to 20 or 20 to 30 mph), truck fuel consumption rates also 

tend to increase at higher speeds (over 45-55 mph), and therefore, increasing speeds from midrange congestion (30-40 mph) to free-flow highway speeds may have a negative fuel economy 

impact. Congestion is likely to affect urban service and delivery movements more than long-haul 

freight, and therefore it is the fuel consumption characteristics of smaller trucks that are most 

important. 

If congestion pricing is implemented only on a limited basis (e.g., only freeways), diversion of 

traffic to other nontolled facilities is likely to be a significant concern because of the impacts on 

neighborhood and local traffic. Increases in VMT on alternate routes could offset the fuel savings 

achieved from reductions in VMT and congestion on the facility itself. Therefore, congestion 

pricing will be most effective at reducing fuel consumption if it is implemented universally (on 

all major roads in an area). 

Findings: Most studies of the impact of congestion pricing have focused on all traffic, rather than 

distinguishing impacts on personal vs. commercial vehicle traffic. A study for the U.S. 

Department of Energy used travel demand models in Minneapolis-St. Paul and Seattle, in 

conjunction with speed-fuel efficiency relationships, to evaluate the combined benefits of travel 

reductions and operating efficiencies from areawide systems of managed lanes.29 The results 

from different scenarios ranged from an 0.1 to 2.5 percent impact on fuel consumption and GHG 

emissions depending upon the scenario. Extrapolating these results to a national level based on 

projected 2030 congestion levels in different urbanized areas led to an overall estimated reduction 

in national fuel consumption ranging from 0.5 to 1.1 percent.30 Another national study of 

greenhouse gas (GHG) emission reduction strategies estimated that cordon pricing could 

potentially reduce VMT on the order of 3 percent if applied to all metropolitan areas in the 

 

29 These systems included high-occupancy/toll (HOT) lanes on freeways, in which drivers of singleoccupancy vehicles can use the lane if they pay a fee which depends upon the congestion on the 

untolled travel lanes. Depending upon the scenario, either existing/planned high-occupancy vehicle 

(HOV) lanes were converted to HOT lanes, or a new HOT lane was constructed alongside an 

existing/planned HOV lane to form two HOT lanes. 

30 Energy and Environmental Analysis, Inc., “Market-Based Approaches to Fuel Economy: Summary of 

Policy Options,” prepared for National Energy Technology Laboratory, U.S. Department of Energy, 

2008.

- 20 - 
United States.31 These are rough estimates for all vehicles, however, that may not be transferable 

to truck traffic. 

Evaluations of cordon pricing schemes implemented in both London and Stockholm examined 

effects specifically on truck traffic. Experience in London suggests that the reduction in overall 

vehicle-kilometers of travel (VKT) has come almost exclusively from passenger vehicles rather 

than trucks. While the cordon pricing scheme reduced total VKT by 16 percent within the pricing 

zone, the data show that truck demand did not change once the scheme was introduced, 

remaining at a constant level of 0.07 million VKT in 2006. Truck travel speeds also did not 

change significantly. On the other hand, truck trips crossing the cordon declined, suggesting that 

each truck makes more deliveries, generating an equivalent truck VKT. Furthermore, these 

trucks have benefited from reduced queuing and subsequently, truck idling and associated fuel 

consumption was reduced. 

To measure the impact of London’s cordon pricing scheme on truck fuel consumption, it is 

necessary to compare truck idling before and after the scheme was introduced. Once the scheme 

was introduced, excess delays were reduced by 26 percent, from 2.3 to 1.7 minutes per 

kilometer.32 Given 70,000 truck-kilometers traveled and a reduction in excess idling delay of 0.6 

minutes per kilometer (2.3-1.7 minutes per kilometer), the scheme reduced truck idling by a total 

of 42,000 minutes (700 hours). With each truck hour of idling consuming 0.8 gallons per hour,33

the truck fuel consumption reduction from congestion pricing would have been 560 gallons 

annually – a very small amount. 

In Stockholm, the cordon pricing scheme reduced total GHG emissions from vehicles by 10 to 14 

percent in the pricing zone. According to the manual counts of travel within the zone, there has 

been a 13 percent reduction of truck trips, but only a 7.8 percent reduction in truck VMT once the 

scheme was implemented. The total VMT is 2,185,000 and heavy truck VMT is 55,994, 2.8 percent 

of total VMT. If the GHG were reduced by 14 percent, then trucks were responsible for 0.4 

percent of the total reductions, a modest amount. 

Potential for Government Promotion: Congestion pricing has been experimented with in a 

number of areas, primarily on existing tolled facilities, but has not yet gained widespread 

popularity. From a technical standpoint, congestion pricing is relatively easy to implement on 

facilities that already are tolled. The broader-scale application of this strategy beyond existing or 

proposed toll highway facilities, however, is likely to require the universal deployment of 

electronic toll collection technologies. This will require coordination by a state or regional 

transportation agency. The U.S. DOT is encouraging greater experimentation in this area. In 

2007, the Department awarded $853 million in funding to five metro areas for Urban Partnership 

Agreements to reduce congestion, which include a significant focus on tolling/pricing strategies. 

 

31 Cambridge Systematics, Inc., Moving Cooler: An Analysis of Transportation Strategies for Reducing 

Greenhouse Gas Emissions, Urban Land Institute: Washington, D.C, July 2009. 

32 Transport for London, Central London Congestion Charging: Impacts Monitoring, Fourth Annual Report, June 

2006. 

33 U.S. Environmental Protection Agency, “A Glance at Clean Freight Strategies: Idle Reduction,” 

February, 2004. 

- 21 - 
(iii) Intermodal Transport 

Key Question: What impact would public-sector initiatives to improve intermodal transport 

have on fuel consumption? 

Background: Improvements to intermodal transport, such as rail capacity improvements and 

bottleneck relief, intermodal (truck-rail) terminals, and financial/pricing incentives, could 

potentially encourage shippers to make greater use of rail in place of truck, increasing the 

efficiency of freight movement on a ton-mile basis. 

Work in progress for U.S. DOT on transportation GHG reduction strategies includes a literature 

review of the potential GHG benefits of shifting freight from truck to rail through intermodal 

improvements.34 Reductions in fuel consumption on the order of 60 percent per ton-mile are 

typical for shifts from trucking (trailers or containers) to long-haul intermodal rail, with 

reductions decreasing with shorter distances. Savings can vary significantly, however, 

depending upon the distance of the movement and type of cargo. 

Estimates of total potential freight mode-shifting have been aspirational in nature, rather than 

based on empirical data, due in large part to the complex nature of competition between trucks 

and rail. The potential for mode-shifting is limited to certain types of commodities—those that 

are heavy, low-value, and do not have an acute need for reliable and timely delivery—e.g., 

building stone and waste, as well as certain movements—in particular, long-haul movements 

where the efficiency benefits of rail outweigh the additional handling/logistics costs and time at 

either end, generally shipments longer than 1,000 miles. Furthermore, market demand both 

affects and is dependent upon the quality of service. Rail service improves significantly as 

demand between market pairs increases – increased traffic (trains per day) increases the level of 

service that railroads provide to customers, and means that improved access is possible since 

(shippers need access to rail facilities to ship via rail). In short, shippers choose a mode that 

minimizes their total logistics cost. 

There are numerous ways to estimate diversion, but each has its flaws. In general, simple 

techniques (e.g., the ‘Delphi Method,’ comparative market analysis, and elasticity methods) rely 

on simplifying assumptions and sketch planning techniques while complicated techniques (such 

as FHWA and the Federal Railroad Administration’s Intermodal Transportation and Inventory 

Cost Model35 and econometric models) require significant data resources, time resources, and 

computation power. Furthermore, complicated techniques are very sensitive to inputs and the 

inputs are often modeled. For example, public truck flow data, by commodity, do not exist while 

rail data is sampled, proprietary, and requires traffic modeling for model estimation, all of which 

decrease the reliability of results. 

Despite the difficulties of estimating the size of diversion impacts, it is generally accepted that 

this phenomenon exists and that there are a consistent set of variables that impact the outcome. 

Actions that can affect a truck-rail mode shift include investment in rail and intermodal terminal 

 

34 Cambridge Systematics, Inc., Transportation’s Role in Reducing Greenhouse Gas Emissions, Forthcoming, 

prepared for Federal Highway Administration. 

35 Federal Highway Administration and Federal Railroad Administration, Intermodal Transportation and 

Inventory Cost Model, Highway-to-Rail Intermodal User’s Manual, March 2005. 

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infrastructure, direct operating subsidies for railroads, land use regulations (for example, to 

preserve rail sidings for rail-oriented businesses), and taxes to increase the cost of truck travel, as 

previously discussed. 

Findings: The following studies either estimated diversion potential from rail investment in 

specific corridors (Crescent Corridor and Mid-Atlantic Rail Operations Study). 

• Norfolk Southern Crescent Corridor:

36 The Crescent Corridor will provide premium service 

intermodal trains to compete with I-81 long-haul truck markets between the New York area 

and the Southeast. This market is underserved by rail today and has significant potential for 

growth. Improvements include new track, upgrading signals, and removing choke points. 

Norfolk Southern estimates that a $2.5 billion investment will result in the diversion of 

1,000,000 truck trips, implying a cost of $2,500 per truck trip, and saving more than 170 

million gallons annually. 

• Mid-Atlantic Rail Operations (MAROps) study:

37 The MAROps study used the comparative 

market approach to estimate the diversion potential in the Mid-Atlantic region. The 

comparative market approach looked at every market pair in the country and compared rail 

travel distance to truck travel distance (a measure of the circuity of rail), commodity, traffic 

density (tons of good shipped), and, finally, mode-share of truck and rail. Mid-Atlantic 

markets were identified that, given similar operating conditions to other markets nationally, 

could carry more rail traffic, which would reduce truck traffic on the parallel highway 

corridor. The study estimated the potential diversion from projects representing a $12 billion 

investment in the Mid-Atlantic rail network to be somewhere between 0.67 and 1.8 million 

truck trips annually, or 237 to 638 million VMT (4 to 11 percent of total regional truck VMT) 

in 2035 (unpublished result). This diversion would save 9.5 to 25.6 billion gallons of diesel 

fuel in trucks (based on today’s 5.59 mpg fuel economy), not accounting for increased rail 

traffic. This implies a range of $7,000 to $18,000 per diverted truck trip in the Mid-Atlantic 

and between $0.47 and $1.26 in investment per gallon of fuel saved based on 2008 dollars and 

2035 traffic. 

Potential for Government Promotion: The government could promote rail diversion through the 

promotion of freight villages; aiding in the siting of intermodal terminals, transload facilities, and 

bulk storage facilities; expand market reach for regional railroads; and the continued 

improvement in rail infrastructure, including signal, track, bridge, terminal, and clearance 

upgrades. 

While freight rail infrastructure investment has traditionally been left to the private sector, the 

Federal government as well as a number of states have increasingly become involved in this issue 

for purposes of economic development and road traffic reduction. There are several state and 

Federal programs that will fund rail improvement to help bridge the gap between investment 

 

36 http://www.nscorp.com/nscportal/nscorp/Media/News%20Releases/2009/greencastle.html

(Accessed September 16, 2009) 

37 Cambridge Systematics, Inc., Mid-Atlantic Rail Operations Study, prepared for the I-95 Corridor 

Coalition, forthcoming. 

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needs and the availability of private capital. The Federal-aid highway funding program also 

allows some flexibility in using funds for non-highway freight transportation projects. 

To date most of the easier rail capacity improvement projects have been built, leaving primarily 

the more difficult and expensive projects. In addition to being expensive, many of the remaining 

critical needs are set in urban environments where there are substantial constraints on right-ofway as well as added costs for mitigation of impacts. These barriers will pose challenges to largescale improvements in freight infrastructure sufficient to leverage significant truck-rail mode 

shift. 

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Figure A-1 Vehicle Passenger Car Equivalents on Urban Highways 

Source: Battelle, “Traffic Operations and Truck Size and Weight Regulations, Working 

Paper 6,” prepared for the Federal Highway Administration, February 2005. 

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