Assessment of Fuel Economy Technologies for Medium and Heavy ...

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., revised September 21, 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 1 to 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) in the range of $0.3 to $1.4 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 an increase in the range of 80 to 360 fatalities
per year and about 1,600 to 7,700 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 in the range of $0.09 to $0.4 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. An upper bound can
be placed on the potential fuel savings from congestion pricing (or other highway congestion
mitigation strategies) by estimating the total nationwide fuel ―wasted‖ by trucks in
congestion. This figure is estimated to be no more than 640 million gallons, or about 2
percent of total truck fuel consumption.
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 42 to 114 million gallons annually, at a cost of $110 to $290 per gallon of fuel
saved. The findings of one study of the I-81 corridor suggest that an investment of $7.9
billion could reduce fuel consumption by 145 million gallons annually, at a cost of $54 per
gallon of fuel saved, while another suggests that an investment of $2.5 billion for the Norfolk
Southern ―Crescent Corridor,‖ also paralleling I-81 between New York and the Southeast,
could save 170 million gallons annually.
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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
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
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).
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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,
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.
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).
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.
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.
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Oum et al 4 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 Program 9 cited a rail cross-price elasticity of 0.52 based on the
Intermodal Competition Model 10 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.
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eduction; 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 mpg 13 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)
report 17 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 CO 2 : 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 CO 2 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
fuel-oil 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 billion VMT for alternative 1
and 5.0 - 15.0 billion 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.
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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. 18 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 beneficial 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
18
Cummins, ―Heavy Duty Fuel Efficiency Regulations,‖ July 2009.
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e 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
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. 19 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 engine power output will be minimal, if any.
Therefore, the impacts of fuel economy regulations on quality of service are likely to be minimal
19
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 -

or non-existent. One 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), and not those that ―cube out‖ (i.e., are limited by volume). It is also conceivable
that some truck purchasers might purchase smaller engines due to the higher cost of meeting
more stringent fuel economy standards. If this is the case, the purchaser has still determined that
the reduced performance will not be a significant detriment to their ability to conduct business,
and therefore, by definition, there is no significant impact to shippers or to the overall economy.
(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 billion 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. While degraded truck
performance could also impact congestion, the concurrent white paper produced by ERG
suggests that the impacts of fuel economy regulations on truck performance will be minimal, if
any.
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-to-freeway 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). 20 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. An increase in the total truck traffic PCEs on the road could be attributable to a
number of effects, most significantly an increase in total traffic, although there could also be
modest impacts if trucks have lower power output, higher weight, or regulations that encourage
class-shifting.
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
20
Transportation Research Board, Highway Capacity Manual, Washington, D.C., 2000.
- 12 -

other roadway characteristics such as lane width or distance to obstructions. 21 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 rural 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:
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. 22 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.
21
Battelle, ―Traffic Operations and Truck Size and Weight Regulations, Working Paper 6,‖ prepared for
the Federal Highway Administration, February 2005.
22
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.
- 13 -

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);
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
- 14 -

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: The increase in rural and urban Class 8 truck VMT is 1.9 to
8.7 billion VMT (58 percent of the previously estimated 3.2 to 15 billion VMT increase) and 1.3 to
6.3 billion VMT (42 percent of the 3.2 to 15 billion VMT increase), respectively. 23 Using Parry‘s
marginal congestion costs of $0.037 per-mile in rural areas and $0.168 per-mile in urban areas, the
total increase in cost ranges from $0.3 billion to $1.4 billion (e.g. $0.3 billion = 1.9 billion rural
VMT * $0.037 per-mile + 1.3 billion urban VMT * $0.168 per-mile).
(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 billion 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. 24 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
23
Federal Highway Administration, Highway Statistics, 2009 and Bureau of Transportation Statistics,
Pocket Guide to Transportation, January 2009.
24
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.
- 15 -

million VMT for injury crashes. 25 Based on these average rates, the rebound effect analysis
(3.2 - 15 billion additional VMT annually) implies a national increase of 80 - 360 fatalities per year
and 1,600 – 7,700 injuries per year.
For estimation of change in marginal cost: The increase in rural and urban Class 8 truck VMT is 1.9 to
8.7 billion VMT (58 percent of the previously estimated 3.2 to 15 billion VMT increase) and 1.3 to
6.3 billion VMT (42 percent of the 3.2 to 15 billion VMT increase), respectively. 26 Using Parry‘s
marginal congestion costs of $0.034 per-mile in rural areas and $0.018 per-mile in urban areas, the
total increase in cost ranges from $0.09 billion to $0.4 billion (e.g. $0.09 billion = 1.9 billion rural
VMT * $0.034 per-mile + 1.3 billion urban VMT * $0.018 per-mile)
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.
25
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).
26
Federal Highway Administration, Highway Statistics, 2009 and Bureau of Transportation Statistics,
Pocket Guide to Transportation, January 2009.
- 16 -

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
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 Sperling 27
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
27
Sperling, Dan, ―Consumer Response to Fuel Price Changes: Implications for Policy,‖ January 15, 2008.
- 17 -

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 $0.634 28 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
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-and-trade 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. 29 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
28
American Transportation Research Institute, An Analysis of the Operational Costs of Trucking, December
2008.
29
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
- 18 -

truck-specific 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
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
off-peak 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
mid-range 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).
While reducing congestion can save fuel, there is an implicit limit on these savings, bounded by
the total fuel wasted in existing congestion. In this analysis, a sketch-level estimate is made of the
total fuel ―wasted‖ by trucks, and therefore the potential upper bound benefits of congestion
relief strategies. This analysis only considers the fuel savings from improved traffic operations,
and not any reduction in fuel use due to an overall decrease in demand.
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.
- 19 -

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. 30 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. 31 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
United States. 32 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. 33 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, 34
the truck fuel consumption reduction from congestion pricing would have been 560 gallons
annually – a very small amount.
30
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.
31
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.
32
Cambridge Systematics, Inc., Moving Cooler: An Analysis of Transportation Strategies for Reducing
Greenhouse Gas Emissions, Urban Land Institute: Washington, D.C, July 2009.
33
Transport for London, Central London Congestion Charging: Impacts Monitoring, Fourth Annual Report, June
2006.
34
U.S. Environmental Protection Agency, ―A Glance at Clean Freight Strategies: Idle Reduction,‖
February, 2004.
- 20 -

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.
To estimate the maximum potential fuel savings from congestion reduction, it is necessary to
estimate total freight truck delay as well as total fuel consumption by hour of delay. Winston and
Langer 35 estimate the cost of congestion to freight trucks to be approximately $10 billion in the
year 2000, based on the Texas Transportation Institute‘s measures of urban delay in 2000 and the
FHWA Freight Analysis Framework estimates of total origin-destination delay. Using the
authors‘ value of time of $30 per hour of delay, the results imply a total of 333 million hours of
delay ($10 billion/$30 per hour). According to a 1993 FHWA study, which produced fuel
consumption estimates for the Highway Performance Monitoring System, a combination truck
consumes 1.934 gallons of fuel per hour in congestion. 36 These values imply that, in 2000, the
total excess national fuel consumption resulting from highway congestion was at most 500
million gallons (333 million hours * 1.51 gallons/hour), or 2.0 percent of (current) truck fuel
consumption. 37 This value provides the upper bound for the potential fuel consumption savings
from congestion reduction strategies (including congestion pricing), assuming that only
operational (and not demand) effects are considered.
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.
(iii) Intermodal Transport
Key Question: What impact would public-sector initiatives to improve intermodal transport
have on fuel consumption
35
Winston and Langer, ―The Effect of Government Highway Spending on Road Users‘ Congestion
Costs,‖ AEI-Brookings, May 2006.
36
Science Applications International Corporation, Speed Determination Models for the Highway Performance
Monitoring System, prepared for the Federal Highway Administration, 1993.
37
Two factors may cause this estimate to be overstated – first, the use of the combination truck fuel
consumption rate slightly overestimates the total wasted fuel since this rate will be less for single-unit
trucks (1.607 gallons/hr); and second, trucks have become more fuel-efficient since that time (average
truck fuel efficiency increased by 22 percent between 1993 and 2007, according to the Transportation Energy
Data Book Edition 28, Table 5.1). On the other hand, total congestion has increased since the Winston and
Langer study was conducted in 2000.
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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. 38 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 Model 39 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
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.
38
Cambridge Systematics, Inc., Transportation’s Role in Reducing Greenhouse Gas Emissions, Forthcoming,
prepared for Federal Highway Administration.
39
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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Findings: The following studies either estimated diversion potential from rail investment in
specific corridors.
Mid-Atlantic Rail Operations (MAROps) study: 40 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 42 to 114 million 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 $110 and $290 in investment per gallon of fuel saved based on 2008 dollars and
2035 traffic.
Norfolk Southern Crescent Corridor: 41 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 annually. Over an assumed 30 year life of the project, this implies a cost
of $83 per truck trip ($2.5 billion / (1 million trips * 30 years), and a savings of more than 170
million gallons annually.
The Northeast-Southeast-Midwest Corridor Marketing Study: 42 This study used the Reebie
Associates‘ Diversion Model, a detailed statistical model, to estimate diversion from truck to
rail based on reductions in rail operating costs stimulated by public investment in rail
infrastructure. The study found that a long-term (13 to 17 years), corridor-wide (the entire I-
81 corridor), public investment in rail intermodal infrastructure of $7.9 billion could divert
approximately 812 million truck VMT. The study did not estimate the probable reduction in
fuel consumption but the findings imply that truck fuel consumption would be reduced by
145 million gallons annually (812 million VMT/5.59 mpg), implying $54 in investment costs
per gallon of fuel saved in 2003 dollars.
40
Cambridge Systematics, Inc., Mid-Atlantic Rail Operations Study, prepared for the I-95 Corridor
Coalition, forthcoming.
41
http://www.nscorp.com/nscportal/nscorp/Media/News%20Releases/2009/greencastle.html
(Accessed September 16, 2009)
42
Reebie Associates, The Northeast-Southeast-Midwest Corridor Marketing Study, prepared for Virginia
Department of Rail and Public Transportation, December 2003.
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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
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-of-way as well as added costs for mitigation of impacts. These barriers will pose challenges
to large-scale improvements in freight infrastructure sufficient to leverage significant truck-rail
mode shift.
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Figure A-1 Vehicle Passenger Car Equivalents on Rural 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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