Potential for Mode Shift due to Heavy Duty 

Vehicle Fuel Efficiency Improvements 

A Case Study Approach 

Prepared for: 

U.S. Environmental Protection Agency 

Transportation and Climate Division 

Prepared by: 

GIFT Solutions, LLC 

Bryan Comer 

Erin Green 

Jordan Silberman 

James J. Corbett, Ph.D., P.E. 

James J. Winebrake, Ph.D. 

16 February 2012 

 
2 

Table of Contents 

Abstract ......................................................................................................................................................... 3 

Introduction .................................................................................................................................................. 4 

Methodology ................................................................................................................................................. 5 

Data and Assumptions .................................................................................................................................. 7

Results ......................................................................................................................................................... 10 

Case 1: Rubber Tires from Los Angeles to Fort Worth ............................................................................ 10 

Case 2: Plastic Pellets from Los Angeles to Fort Worth .......................................................................... 11 

Case 3: Newsprint from New Orleans to Chicago ................................................................................... 13 

Case 4: Gravel from Chattanooga to Indianapolis .................................................................................. 14 

Discussion and Conclusions ........................................................................................................................ 16 

Acknowledgements ..................................................................................................................................... 17

References .................................................................................................................................................. 17 
3 

Abstract 

 The U.S. Environmental Protection Agency (USEPA) has promulgated new regulations that 

increase heavy duty vehicle (HDV) fuel efficiency. One anticipated economic consequence of improving 

fuel efficiency is reduced fuel costs (in cents/ton-mile) for HDVs. GIFT Solutions, LLC has conducted a 

case study analysis to investigate how improved fuel efficiency may impact (i.e., reduce) freight rates in 

the HDV freight transport sector. Four cases were examined; the cases modeled the transport of rubber

tires, plastic pellets, newsprint, and gravel. The purpose of this report is to evaluate whether the new 

regulations could produce economic conditions (i.e., freight rates) that favor a modal shift from rail to 

truck as a direct result of lower HDV fuel costs for the cases analyzed. Specifically, we assumed that 

economic conditions would favor a shift from rail to truck if either the price per ton-mile to ship a 

commodity by truck, or the price to ship a given quantity of a commodity by truck, became lower

relative to rail transport options post-regulation. 

The results indicate that the new regulations do not seem to create economic conditions that 

would lead to a modal shift from rail to truck in all except one of the cases examined. However, these 

results are caveated knowing that the price of shipping a particular good from origin to destination is 

only one factor that a shipper must consider when making freight transport decisions. More research 

should be conducted with a focus on determining the generalizability of these results at the national 

level, as well as exploring the role of other factors that affect logistics decision-making in a multi-modal 

context. 
4 

Introduction 

New regulations promulgated by the USEPA will increase heavy duty vehicle (HDV) fuel 

efficiency. These regulations will result in reduced fuel consumption per mile and, presumably, lower

operating costs and freight rates for truck transportation. We have analyzed four cases to help 

understand how fuel efficiency improvements might impact operating costs and freight rates in the HDV 

freight transport sector. The cases explored in this report aim to determine if economic conditions 

would favor a shift from rail to truck transport as a direct result of the regulations. Specifically, we 

assumed that economic conditions would favor a shift from rail to truck if either the price per ton-mile 

to ship a commodity by truck, or the price to ship a given quantity of a commodity by truck, became 

lower relative to rail transport options post-regulation. 

The four cases we investigate model the transport of: 

1. Rubber tires from Los Angeles, CA to Fort Worth, TX

2. Plastic pellets from Los Angeles, CA to Fort Worth, TX 

3. Newsprint from New Orleans, LA to Chicago, IL 

4. Gravel from Chattanooga, TN to Indianapolis, IN. 

The commodity and the origin/destination (O/D) pairs for each case were chosen by the USEPA 

after GIFT Solutions, LLC carefully analyzed the 2007 Commodity Flow Survey (CFS) (U.S. Census Bureau,

2009) and the Freight Analysis Framework, Version 3 (FAF3

) (Oak Ridge National Laboratory, 2012) and 

recommended a number of potential O/D pairs that appeared suitable for analysis. 

The transport of rubber tires (Case 1), plastic pellets (Case 2), and newsprint (Case 3) were 

chosen because, as shown in Table 1, at least one-third of the annual tons in these commodity groups 

are transported greater than 500 miles, identified by USEPA as the range where modal shift would most 

likely occur (USEPA & NHTSA, 2011). Additionally, a significant proportion of total tons and ton-miles in 

these commodity groups are transported by rail—allowing the potential for a shift from rail to truck. 

These three case studies are intended to shed light on the impact of the rule on commodity flows that 

are most susceptible to shift from rail to truck. 

We also felt it was important to include a case study that models the transport of a commodity 

that represents a significant portion of freight truck activity. Thus, we have included Case 4, which

models the transport of gravel. Even though gravel is not typically transported >500 miles (see Table 1), 

Case 4 is modeled because the “gravel and crushed stone” commodity group is dominated by HDV truck 

transportation (89% of short tons are transported by truck as shown in Table 1). Gravel and crushed 

stone also represents the largest portion of the freight transportation sector by weight, and ranks fifth in 

terms of freight sector ton-miles transported (U.S. Census Bureau, 2009). Thus, Case 4 provides insight 

into how new fuel efficiency improvements for HDVs might impact commodity flows representing a 

significant portion of the truck freight sector. Table 1 summarizes the transportation characteristics of 

each commodity group modeled in the cases. 

 
5 

Table 1. Transportation characteristics of each commodity group evaluated in the case studies. 

Case Commodity Group 2-digit 

Commodity 

Code 

Tons 

transported 

>500 mi (%) 

Tons 

Transported by 

Rail (R) and 

Truck (T) (%) 

Ton-mi 

Transported by 

Rail (R) and 

Truck (T) (%) 

Value 

Transported by 

Rail (R) and 

Truck (T) (%) 

1 & 2 Plastics and rubber 24 33% 20%R 

70%T 

25%R 

58%T 

9%R 

77%T 

3 

Pulp, newsprint, 

paper, and 

paperboard 

27 34% 19%R 

71%T 

29%R 

53%T 

13%R 

77%T 

4 Gravel and 

Crushed Stone 12 1% 4%R 89%T 

16%R 

55%T 

4%R 

90%T 

Source: U.S. Census Bureau (2009) 

We next selected O/D pairs where a relatively large share of shipments of each commodity 

group are likely to take place. We first looked at the exporter and importer rank of the origin or 

destination state according to FAF3

. For each case, states with high “exporter ranks” for the commodity 

group being modeled were chosen as origin states; similarly, states with high “importer ranks” were 

chosen as destination states. The FAF3 was used to verify that the origin state and destination state 

conducted a significant amount of trade with each other (e.g., Texas is the number-one trading partner

of California) (Oak Ridge National Laboratory, 2012) to help ensure that the O/D pairs reflected realistic 

freight transport activity. The FAF3

 provides state-level data; thus, it was important to choose a 

reasonable origin and destination city for each case. To do so, the Geospatial Intermodal Freight 

Transport (GIFT) model, described in detail in Winebrake, et al (2008) and Comer et al. (2010), was used 

to verify that intermodal transport facilities near each O/D city could receive both truck and rail. The 

analysis used these validated facilities as the origins and destinations for each case. Table 2 summarizes 

the characteristics of the O/D pairs for each case.

Table 2. Origin and destination exporter and importer rank by weight and value for each commodity group represented in 

the case studies. 

Case Commodity Group Origin 

(state exporter rank) 

Destination 

(state importer rank) 

Highway 

Distance 

(miles) 

1 Plastics and rubber Los Angeles, CA (2w1, 2v2) Fort Worth, TX (1w, 1v) 1,409 

2 Plastics and rubber3Los Angeles, CA (2w, 2v) Fort Worth, TX (1w, 1v) 1,409 

3 Pulp, newsprint, etc. New Orleans, LA (2w, 4v) Chicago, IL (2w, 1v) 926 

4 Gravel and Crushed Stone Chattanooga, TN (1w, 2v) Indianapolis, IN (1w, 2v) 419 

1 w = rank by weight (tons) for the commodity group being modeled. 2 v = rank by value ($) for the commodity 

group being modeled. 3We model two cases with the same commodity group (plastics and rubber) and O/D pair 

(Los Angeles to Fort Worth) because of the differences in how rubber commodities and plastic commodities may 

be shipped, despite being in the same commodity group. 

Methodology 

 New regulations promulgated by the USEPA will result in increased fuel efficiency for HDVs. The 

freight rates of HDVs will change (i.e., decrease) as fuel efficiency increases. A model was developed by 

the authors, based on previous work by USEPA, to investigate how HDV freight rates would change as a 
6 

result of the regulations. This section describes how the model estimates changes in HDV freight rates 

post-regulation. 

For each case, rate quotes were solicited from two trucking companies that accounted for the 

type of truck used (all quotes for 53’ Class 8 tractor trailer), O/D pair, commodity group, and fuel 

surcharge (fuel surcharges vary based on the price of diesel fuel). These quotes were given in dollars per 

truckload. The truckload rate quote was then divided by a quoted or estimated average tons of each 

commodity transported per truckload and then divided again by the highway miles between origin and 

destination to calculate a dollar per ton-mile rate; this rate was then converted to cents per ton-mile for 

easier comparison. To calculate the post-regulation truck freight rate, the following equation was used: 

 = 	
 ∗ 100% −




  + 	
 ∗




 ∗ (100% − %∆) + 


Where 

TRpost = the truck freight rate post-regulation in cents per ton-mile (cents/ton-mi) 

TRpre = the truck freight rate pre-regulation in cents/ton-mi 

Cfuel = the fuel cost component of the truck company’s operating cost pre-regulation in cents/mi 

Cop = the truck company’s operating cost pre-regulation in cents/mi 

∆η = the truck’s fuel efficiency (gal/mile) improvement in % 

Ctech = the additional technology costs to comply with the regulation (i.e., the higher incremental 

costs of a new Class 8 HDV that complies with the regulation) in cents/ton-mi 

The post-regulation truck freight rates (TRpost) were compared to rail freight rates on a cents per tonmile basis to determine if economic conditions suggested the potential for a modal shift from rail to 

truck. 

 Rate quotes were also solicited from rail companies for each O/D pair and commodity group. 

These quotes were obtained from online rail company rate calculators and validated further through 

discussions with rail company sales representatives when possible. The rate quotes were given in 

dollars per carload and accounted for the type of railcar used (varies based on the commodity being 

shipped), the O/D pair, the commodity group, and the fuel surcharge (fuel surcharges vary based on the

price of diesel fuel). The carload rate quote was then divided by an estimated average tons of each 

commodity transported per carload and then divided again by the rail miles between origin and 

destination to calculate a dollar per ton-mile rate; this rate was then converted to cents per ton-mile for 

easier comparison. 

 In addition to calculating the truck and rail freight rates in cents/ton-mi, one can also estimate 

how the total cost of shipping a specific quantity of goods might change post-regulation. For example, if 

a shipper wanted to transport 300 short tons of a commodity, they might be able to transport it using 14 

trucks or three (3) rail cars. To estimate the total cost of transporting a given quantity of a commodity 

from origin to destination pre-regulation, one simply divides the quantity to be shipped (in short tons) 

by the capacity of the truck or rail car (in short tons) and rounds up to the nearest full truck or rail car; 

this value is then multiplied by the quoted price per truckload or price per carload. While partial truck 

or carloads may be possible, we only received quotes for full truckloads and full carloads. To estimate 

the total cost of transporting that same quantity by truck post-regulation the same procedure is 

followed, but the price per truckload is adjusted to reflect the effective price per truckload postregulation. The effective price per truckload post-regulation is estimated by multiplying the truck freight 

rate post-regulation (in $/ton-mi) by the capacity of the truck (in short tons) and by the highway miles. 

The result is a post-regulation price in $/truckload for transporting the commodity. 
7 

Data and Assumptions 

 This section details the data and assumptions used in the case study analysis. General model 

assumptions are shown in Table 3. Case-specific assumptions are shown in Tables 4 through 7. 

Table 3. General model assumptions used in every case study. 

Input Value Units Source

Increase in HDV technology 

cost to meet new regulation $8,100 $ USEPA1 

Lifetime HDV vehicle miles 

travelled (VMT) 1,253,870 miles USEPA2 

Cfuel 66.4 cents/mi 

diesel fuel cost ($/gal) from EIA (2012)*preregulation fuel efficiency (gal/mi) from USEPA3

*100 cents/$

Cop 158.2 cents/mi pre-regulation operating costs based on ATRI 

(2008), EIA (2012), ICF (2010) and USEPA4

Cfuel/Cop 42% % Calculated

Price of on-road diesel fuel $3.78 $/gal U.S. avg. for 1/2/2012 from EIA (2012)5

Fuel efficiency 

improvement (∆η) 23% % USEPA6 

Ctech 0.03 cents/ton-mi increase in HDV technology cost ÷ lifetime HDV 

VMT ÷ 19 short tons per truck7 

1 Consistent with values used in the final HDV rule for MY2018 Class 8 Sleeper Cab trucks with a High Roof found in Table 2-39 

in USEPA & NHTSA (USEPA & NHTSA, 2011). 2The USEPA estimated lifetime VMT for HDVs using output from the MOtor Vehicle 

Emissions Simulator (MOVES) model. 3The pre-regulation fuel efficiency for Class 8 Sleeper Cab trucks with a High Roof is 

estimated at 0.18 gal/mi by the USEPA. 4All operating cost components besides fuel and truck purchase costs come from ATRI’s 

2008 analysis of truck operating costs at http://www.atri-online.org/research/results/economicanalysis/Operational_Costs_OnePager.pdf; 

fuel operating cost components are derived from EIA’s (2012) diesel fuel price and USEPA and NHTSA’s (2011) pre-regulation 

fuel efficiency estimates; pre-regulation truck purchase price operating cost components are derived from ICF’s (2010) preregulation new vehicle price estimate (consistent with values used in the final HDV rule) and USEPA’s lifetime HDV VMT 

estimate obtained from the MOVES model. 5The U.S. Energy Information Administration (EIA) posts the average on-road diesel 

price weekly at http://www.eia.gov/petroleum/gasdiesel/. 

6

It is expected that fuel efficiency for Class 8 Sleeper Cab trucks with a 

High Roof will improve from 0.18 gal/mi to 0.14 gal/mi, a ~23% improvement. 7The USEPA assumed an average payload of 19 

short tons for class 8 HDVs as found in USEPA & NHTSA (2011). 

 
8 

Table 4. Case 1 assumptions: Rubber tires from Los Angeles to Fort Worth. 

Input Value Units Source

Truck Co. “A” freight rate preregulation $3,500 $/truckload Con-Way Sales Rep

Truck Co. “B” freight rate preregulation $3,300 $/truckload YRC Sales Rep

Short tons/53’ trailer 13.9 tons Calculated1

Highway miles between origin and 

destination 

1409 miles Google MapsTM

Rail Co. “A” freight rate (shipperowned equipment) $5,380 $/carload BNSF OnlineRate 

Calculator2 

Rail Co. “A” freight rate (railroadowned equipment) $8,180 $/carload BNSF Online Rate 

Calculator

Rail Co. “B” freight rate (shipperowned equipment) $8,390 $/carload Union Pacific Rate 

Calculator3 

Rail Co. “B” freight rate (railroadowned equipment) $8,390 $/carload Union Pacific Rate 

Calculator 

Tons/60’ box car 27.5 tons Calculated4

Rail miles between origin and 

destination 

1,658 miles GIFT model

1

Based on the volume of a 53-foot trailer (~3,800 cubic feet) (Ship North America Transportation, 2011) and the density of a 

typical rubber automobile tire (~7.33 pounds per cubic feet); the density of a tire was calculated using data found at 

tirerack.com (Tire Rack, 2011). 2Available at http://www.bnsf.com/customers/prices-and-tools/carload-shipping-advisor/

3

Available at https://c02.my.uprr.com/pic/jas/index.jas 

4

 Based on the volume of a 60-foot boxcar (~7,500 cubic feet) (BNSF, 

2011) and the density of a typical rubber automobile tire. 

Table 5. Case 2 assumptions: Plastic pellets from Los Angeles to Fort Worth. 

Input Value Units Source

Truck Co. “A” freight rate preregulation $3,430 $/truckload Con-Way Sales Rep

Truck Co. “B” freight rate preregulation $3,300 $/truckload YRC Sales Rep

Short tons/53’ trailer 22.51tons YRC Sales Rep

Highway miles between origin and 

destination 

1409 miles Google MapsTM

Rail Co. “A” freight rate (shipperowned equipment) $8,210 $/carload BNSF Online Rate 

Calculator 

Rail Co. “A” freight rate (railroadowned equipment) N/A $/carload BNSF Online Rate 

Calculator 

Rail Co. “B” freight rate (shipperowned equipment) $10,450 $/carload Union Pacific Online 

Rate Calculator 

Tons/large covered hopper car 76 tons Calculated2

Rail miles between origin and 

destination 

1,658 miles GIFT model

1

 Though more than 22.5 tons of plastic pellets could fit in a 53’ trailer, the maximum freight weight is 45,000 pounds (22.5 

short tons). 2Based on the volume of a large covered hopper (~4,750 cubic feet) (BNSF, 2011) and the density of polypropylene 

pellets (~32 pounds per cubic feet) (ASI, 2011). 
9 

Table 6. Case 3 assumptions: Newsprint from New Orleans to Chicago. 

Input Value Units Source

Truck Co. “A” freight rate pre-regulation $1,750 $/truckload Con-Way Sales Rep 

Truck Co. “B” freight rate pre-regulation $1,740 $/truckload YRC Sales Rep 

Tons/53’ trailer 22.51tons YRC Sales Rep

Highway miles between origin and 

destination 

910 miles Google MapsTM

Rail Co. “A” freight rate (shipper-owned 

equipment) 

$8,260 $/carload BNSF Online Rate 

Calculator 

Rail Co. “A” freight rate (railroad-owned 

equipment) 

$8,260 $/carload BNSF Online Rate 

Calculator 

Rail Co. “B” freight rate (shipper-owned 

equipment) 

$5,000 $/carload Union Pacific Online Rate 

Calculator 

Rail Co. “B” freight rate (railroad-owned 

equipment) 

$5,000 $/carload Union Pacific Online Rate 

Calculator 

Rail Co. “C” freight rate (shipper-owned 

equipment) 

$6,350 $/carload CSX Online Rate 

Calculator2 

Rail Co. “C” freight rate (railroad-owned 

equipment) 

$6,350 $/carload CSX Online Rate Calculator 

Tons/60’ boxcar 104.5 tons BNSF3

Rail miles between origin and destination 894 miles GIFT model 

1

 Though more than 22.5 tons of newsprint could fit in a 53’ trailer, the maximum freight weight is 45,000 pounds (22.5 short 

tons). 2Available at http://shipcsx.com/public/ec.shipcsxpublic/Main?module=public.pricing 

3

The density of newsprint is ~44 

pounds per cubic foot (TAPPI, 2003), meaning that ~165 tons could fit into a 7,500 cubic foot boxcar; however, the maximum 

load for a 60-foot box car is 104.5 tons (BNSF, 2011).

Table 7. Case 4 assumptions: Gravel from Chattanooga to Indianapolis. 

Input Value Units Source

Truck Co. “A” freight rate $900 $/truckload Con-Way Sales Rep 

Truck Co. “B” freight rate $560 $/truckload YRC Sales Rep 

Tons/53’ trailer 22.51tons YRC Sales Rep

Highway miles between origin and destination 419 miles Google MapsTM

Rail Co. “A” freight rate (shipper-owned equipment) $3,600 $/carload BNSF Online Rate 

Calculator 

Rail Co. “A” freight rate (railroad-owned equipment) $4,000 $/carload BNSF Online Rate 

Calculator 

Rail Co. “C” freight rate (shipper-owned equipment) $2,410 $/carload CSX Online Rate 

Calculator 

Rail Co. “C” freight rate (railroad-owned equipment) $2,810 $/carload CSX OnlineRate 

Calculator 

Tons/open top hopper car 100 tons BNSF2

Rail miles between origin and destination 515 miles GIFT model 

1

 Though more than 22.5 tons of gravel could fit in a 53’ trailer, the maximum freight weight is 45,000 pounds (22.5 short tons). 

2

 Based on the volume of an open top hopper (~4,000 cubic feet) (BNSF, 2011) and the density of gravel (~95 pounds per cubic 

feet) (RF Cafe, 2011), an open top hopper could hold 190 tons of gravel; however, the maximum load for an open top hopper is 

100 tons (BNSF, 2011). 
10 

Results 

 The results of these cases report the truck freight rates pre- and post-regulation and compare 

them to quoted rail freight rates. The purpose of comparing the pre- and post-regulation truck freight 

rates to the rail freight rates is to determine whether or not the new HDV regulation would reduce truck 

freight rates below competing rail rates post-regulation. If the price of shipping goods by truck becomes 

lower than the price of shipping goods by rail post-regulation, then we can claim that economic 

conditions may exist for a modal shift from truck to rail for these cases. A number of important caveats 

to the interpretation of the results are discussed in the conclusion section. 

Case 1: Rubber Tires from Los Angeles to Fort Worth

 The results of Case 1 are found in Table 8, and Figure 1 provides a visual representation of the 

truck and rail routes from origin to destination. The GIFT model was used to generate the optimal rail 

and truck routes for the case, and the truck routes were validated using Google MapsTM. 

Freight rates were available from two trucking companies and two rail companies for Case 1, as 

discussed above. The results indicate that even before fuel cost savings, the truck freight rates were less 

expensive than every rail rate except for the quote from Rail Co. “A” for shipper-owned equipment 

(which was 11.8 cents per ton-mi). Nevertheless, the truck freight rates did not become less than 11.8 

cents per ton-mi after regulation. Therefore, the fuel cost savings did not result in any truck rate

becoming less expensive than any rail rate post-regulation in this case study. 

Table 8. Case 1 results showing no economic potential for shift from rail to truck post-regulation. 

Freight Rate

(cents/ton-mi) Mode Details 

18.4 Rail Rail Co. “B” quote for S-O

1 or RR-O2 equipment

18.0 Rail Rail Co. “A” quote for RR-O equipment

17.8 Truck (pre-regulation) Truck Co. “A” quote pre-regulation

16.8 Truck (pre-regulation) Truck Co. “B” quote pre-regulation

16.1 Truck (post-regulation) Truck Co. “A” rate (calculated) post-regulation

15.2 Truck (post-regulation) Truck Co. “B” rate (calculated) post-regulation

11.8 Rail Rail Co. “A” quote for S-O equipment

Freight Rate to Ship 

300 Short Tons ($) 

Mode Details

$92,250 Rail Rail Co. “B” for S-O

1 or RR-O2 equipment

$89,960 Rail Rail Co. “A” for RR-O equipment

$77,000 Truck (pre-regulation) Truck Co. “A” pre-regulation

$72,590 Truck (pre-regulation) Truck Co. “B” pre-regulation

$69,720 Truck (post-regulation) Truck Co. “A” post-regulation

$65,740 Truck (post-regulation) Truck Co. “B” post-regulation

$59,190 Rail Rail Co. “A” for S-O equipment

1

S-O means “shipper-owned” equipment, meaning that the railcars are owned by the shipper and transported by the rail 

company. 2

RR-O means “railroad-owned” equipment, meaning that the railcars are owned by the rail company.
11 

Figure 1. Truck and rail routes modeling the transport of rubber tires from Los Angeles, CA to Fort Worth, TX for Case 1. 

Case 2: Plastic Pellets from Los Angeles to Fort Worth 

The results of Case 2 are found in Table 9, and Figure 2 provides a visual representation of the 

truck and rail routes from origin to destination. The GIFT model was used to generate the optimal rail 

and truck routes for the case, and the truck routes were validated using Google MapsTM. 

Freight rates were available from two trucking companies and two rail companies for Case 2. 

The results indicate that the fuel cost savings did not result in any truck rate becoming less expensive 

than any rail rate post-regulation in this case study. The truck rates were always more expensive than 

the rail rates. 

 
12 

Table 9. Case 2 results showing no economic potential for shift from rail to truck post-regulation. 

Freight Rate

(cents/ton-mi) Mode Details 

10.8 Truck (pre-regulation) Truck Co. “A” quote pre-regulation

10.4 Truck (pre-regulation) Truck Co. “B” quote pre-regulation

9.8 Truck (post-regulation) Truck Co. “A” rate (calculated) post-regulation

9.4 Truck (post-regulation) Truck Co. “B” rate (calculated) post-regulation

8.3 Rail Rail Co. “B” quote for S-O equipment

6.5 Rail Rail Co. “A” quote for S-O equipment

Freight Rate to Ship 

300 Short Tons ($)

Mode Details

$47,960 Truck (pre-regulation) Truck Co. “A” pre-regulation

$46,190 Truck (pre-regulation) Truck Co. “B” pre-regulation

$43,490 Truck (post-regulation) Truck Co. “A” post-regulation

$41,890 Truck (post-regulation) Truck Co. “B” post-regulation

$41,790 Rail Rail Co. “B” for S-O equipment

$32,850 Rail Rail Co. “A” for S-O equipment

Figure 2. Truck and rail routes modeling the transport of plastic (polypropylene) pellets from Los Angeles, CA to Fort Worth, 

TX for Case 2. 
13 

Case 3: Newsprint from New Orleans to Chicago 

The results of Case 3 are found in Table 10 and Figure 3. The GIFT model was used to generate 

the optimal rail and truck routes for the case, and the truck routes were validated using Google MapsTM. 

Freight rates were available from two trucking companies and three rail companies for Case 3. 

The pre-and post-regulation truck rates for Truck Co. “A” and Truck Co. “B” were cheaper than the 

quoted rate for shipper-owned and railroad-owned equipment from Rail Co. “A.” However, the results 

indicate that the fuel cost savings did not result in any truck rate becoming less expensive than any rail 

rate post-regulation in this case study. 

Table 10. Case 3 results showing no economic potential for shift from rail to truck post-regulation. 

Freight Rate

(cents/ton-mi) Mode Details 

8.8 Rail Rail Co. “A” quote for S-O or RR-O equipment

8.5 Truck (pre regulation) Truck Co. “A” quote pre regulation

8.5 Truck (pre regulation) Truck Co. “B” quote pre regulation

7.8 Truck (post-regulation) Truck Co. “A” rate (calculated) post-regulation

7.7 Truck (post-regulation) Truck Co. “B” rate (calculated) post-regulation

6.8 Rail Rail Co. “C” quote for S-O or RR-O equipment

5.3 Rail Rail Co. “B” quote for S-O or RR-O equipment

Freight Rate to 

Ship 300 Short 

Tons ($)

Mode Details

$24,770 Rail Rail Co. “A” for S-O or RR-O equipment

$24,500 Truck (pre regulation) Truck Co. “A” pre regulation

$24,390 Truck (pre regulation) Truck Co. “B” pre regulation

$22,240 Truck (post-regulation) Truck Co. “A” (calculated) post-regulation

$22,140 Truck (post-regulation) Truck Co. “B” (calculated) post-regulation

$19,040 Rail Rail Co. “C” for S-O or RR-O equipment

$14,900 Rail Rail Co. “B” for S-O or RR-O equipment
14 

Figure 3. Truck and rail routes modeling the transport of rolls of newsprint from New Orleans, LA to Chicago, IL for Case 3. 

Case 4: Gravel from Chattanooga to Indianapolis 

The results of Case 4 are found in Table 11, and Figure 4 provides a visual representation of the 

truck and rail routes from origin to destination. The GIFT model was used to generate the optimal rail 

and truck routes for the case, and the truck routes were validated using Google MapsTM. 

Freight rates were available from two trucking companies and two rail companies for Case 4. 

The truck rates in cents/ton-mi pre- and post -regulation for Truck Co. “A” were always more expensive

than the rail rates. The truck rates pre- and post-regulation for Truck Co. “B” were always less expensive 

than the rates quoted from Rail Co. “A” and always more expensive, or as expensive, as the rates quoted 

from Rail Co. “B.” However, the total cost of transporting 300 short tons of gravel by truck became less 

expensive than rail post-regulation when comparing Trucking Co. “A” to Rail Co. “A” when railroadowned equipment (i.e., rail cars) was used for transport; the rail rate remained less expensive than the 

truck rate post-regulation when assuming shipper-owned equipment. Further the truck rate for 

Trucking Co. “B” became less expensive than the rate for Rail Co. “C” when assuming shipper-owned

equipment; the truck rate pre and post-regulation was less expensive than the rail rate when assuming 

railroad-owned equipment. The results indicate that the fuel cost savings did not result in any truck rate 

becoming less expensive than any rail rate post-regulation when considering the cents/ton-mi freight 

rate; however, the truck rate post-regulation did became less expensive than some rail rates when 

considering the transport of 300 short tons of gravel in this case study. 

 
15 

Table 11. Case 4 results showing no economic potential for shift from rail to truck post-regulation when considering 

cents/ton-mi freight rates; however, there may be potential for shift when considering transporting 300 short tons 

Freight Rate

(cents/ton-mi) Mode Details 

9.5 Truck (pre regulation) Truck Co. “A” quote pre regulation

8.7 Truck (post-regulation) Truck Co. “A” rate (calculated) post-regulation

7.7 Rail Rail Co. “A” quote for RR-O equipment

6.9 Rail Rail Co. “A” quote for S-O equipment

6.0 Truck (pre regulation) Truck Co. “B” quote pre regulation

5.4 Truck (post-regulation) Truck Co. “B” rate (calculated) post-regulation

5.4 Rail Rail Co. “C” quote for RR-O equipment

4.6 Rail Rail Co. “C” quote for S-O equipment

Freight Rate to Ship 

300 Short Tons ($)

Mode Details

$12,600 Truck (pre-regulation) Truck Co. “A” pre-regulation

$11,980 Rail Rail Co. “A” for RR-O equipment

$11,430 Truck (post-regulation Truck Co. “A” post-regulation

$10,780 Rail Rail Co. “A” for S-O equipment

$8,420 Rail Rail Co. “C” for RR-O equipment

$7,910 Truck (pre-regulation) Truck Co. “B” pre-regulation

$7,220 Rail Rail Co. “C” for S-O equipment

$7,190 Truck (post-regulation) Truck Co. “B” post-regulation

Figure 4. Truck and rail routes modeling the transport of gravel from Chattanooga, TN to Indianapolis, IN for Case 4. 
16 

Discussion and Conclusions 

New regulations promulgated by the USEPA improve the fuel efficiency of HDVs. One 

consequence of increased HDV fuel efficiency would be reduced fuel costs, potentially resulting in lower 

truck freight rates. Results indicate that the fuel cost savings did not result in any truck rate becoming 

less expensive than any rail rate post-regulation on a cents/ton-mi basis in the cases examined in this 

report. In each case, the truck freight rates in cents/ton-mi pre-and post-regulation were already 

cheaper than some or all of the quoted rail rates, or remained more expensive (with the exception of 

Case 4, where the post-regulation truck freight rate became as expensive as the rail freight rate); 

however, external price factors, such as the price of diesel fuel, could affect these freight rate estimates. 

The freight rates for both truck and rail will likely change as the price of diesel fuel changes and is passed 

on via fuel surcharges. As the price of diesel increases, fuel surcharges increase. The model developed 

by GIFT Solutions, LLC is capable of adjusting these freight rates based on actual fuel surcharge data for 

select truck and rail transportation companies; i.e., as the price of diesel changes, we can model how the 

freight rates for truck and rail will change since price quotes from these companies begin with a base 

rate and then are adjusted by the fuel surcharge. 

When considering the potential impact of the regulations on the total cost of shipping a given 

quantity (in short tons) of a commodity, we found that economic conditions for modal shift did not 

occur in Cases 1-3. However, results indicate that potential for modal shift might exist for Case 4 

(shipping gravel from Chattanooga, TN to Indianapolis, IN). In Case 4, the total cost of shipping 300

short tons of gravel by truck became less expensive than rail post-regulation when comparing Trucking 

Co. “A” to Rail Co. “A” when railroad-owned equipment (i.e., rail cars) were used for transport; the rail 

rate remained less expensive than the truck rate post-regulation when assuming shipper-owned 

equipment. Further the truck rate for Trucking Co. “B” became less expensive than the rate for Rail Co. 

“C” when assuming shipper-owned equipment; the truck rate pre- and post-regulation was less 

expensive than the rail rate when assuming railroad-owned equipment. Case 4 shows that when 

considering the transport of 300 short tons of gravel, truck may be the least expensive option as a direct 

result of fuel savings from the regulation. We concede that modeling the transport of 300 short tons of 

a commodity provides limited insight into how the total freight transport price might change depending

on the volume of goods transported. There may be an opportunity to explore how the volume of 

shipments impacts modal choice. 

We would now like to discuss some of the caveats one must consider in interpreting the results. 

We assume that the full fuel cost savings as well as the increased vehicle costs for new HDVs will be 

passed on to shippers via changes in truck freight rates. It is clear that carriers (both truck and rail) 

generally adjust their freight rates according to the price of on-road diesel fuel; however, it is unclear 

what portion of the additional HDV technology costs to adhere to regulations will be passed along to 

shippers and over what time frame. Moreover, results suggest that trucking companies may not adjust 

their freight rates to account for differences in actual fuel consumption; rather, they only adjust their 

rates to reflect changes in the price of on-road diesel fuel. For example, we noticed that the truck 

companies did not adjust their freight rates to account for increased fuel consumption due to hauling a 

heavier load; the price to ship 14 short tons of tires was essentially the same as the price to ship 22.5 

short tons of plastic pellets from Los Angeles, CA to Fort Worth, TX. Instead, the change in price seems 

to be mainly attributable to the O/D pair and the per-mile fuel surcharge. 

The USEPA (2011) discusses the potential for aggregate fuel savings from the rule to be partially 

offset by increased HDV VMT post-regulation due to the rebound effect; however the cases analyzed 

here are not impacted by increases in VMT because they model the transport of commodities between 

fixed O/D pairs with fixed commodity payloads. Thus, we assume that truck and rail routes and the 

payload for each truck and rail car will not change post-regulation. We use fixed O/D pairs and fixed
17 

commodity payloads to control for any other variables that might impact the economics of modal choice 

and, therefore, isolate the effects of fuel efficiency improvements post-regulation on truck freight rates. 

Additional costs that a shipper would bear to prepare the commodity for shipment are not 

explicitly accounted for in the analysis. For example, the cost to the shipper (in both dollars and time) to 

package (e.g., palletize), load, and unload the shipment could impact their decision-making when 

considering which mode (rail or truck) to choose to transport the commodity. Additionally, the analysis 

does not consider any additional costs that may be associated with drayage to or from an origin or 

destination when considering rail transport. Each facility situated at each O/D pair selected in this

analysis has the capability to load and unload both trucks and railcars; however, for other freight 

movements, this may not be the case. Thus, the cases examined here may underestimate the true price 

of transporting goods from origin to destination depending on the needs of the shipper. Other factors, 

including the volume and frequency of shipments, would also affect modal choice. For example, if a 

shipper needs to transport multiple truckloads or carloads of goods, the shipper would need to consider 

the tradeoffs between price, time-of-delivery, and O/D modal capacity (i.e., the ability of the origin or 

destination location to load or unload trucks or rail cars) when choosing truck or rail modes. 

We recommend future research to investigate how varying the price of diesel fuel might affect 

truck and rail freight rates differently. Additionally, we recommend future work focusing on how other 

factors, besides the freight rate, impact shippers’ freight transportation decisions, and how the influence 

of these other factors can be estimated, parameterized, and used in probabilistic decision functions that 

could add insights into the potential for modal shift in response to improved truck fuel efficiency. There 

may be an opportunity for the USEPA and GIFT Solutions, LLC to team with SmartWay shippers to 

investigate how freight transport decisions are made and what factors influence those decisions. 

Acknowledgements 

The authors wish to thank the Transportation and Climate Division of the USEPA for their 

support of this project. Additionally, the authors are grateful to the joint research collaborative 

between the Rochester Institute of Technology and the University of Delaware for the use of the GIFT 

model. 

References 

American Transportation Research Institute (ATRI). (2008). An analysis of the operational costs of 

trucking. Atlanta, GA. 

ASI. (2011). Bulk density and specific gravity chart Retrieved January 3, 2012, from 

http://www.asiinstr.com/technical/Material_Bulk_Density_Chart_P.htm

BNSF. (2011). Individual railcar Retrieved December 23, 2011, from http://bnsf.com/customers/howcan-i-ship/individual-railcar/#%23subtabs-3

EIA. (2012). Gasoline and diesel fuel update Retrieved January 2, 2012, from 

http://www.eia.gov/petroleum/gasdiesel/

ICF International. (2010). Investigation of costs for strategies to reduce greenhouse gas emissions from 

heavy-duty on-road vehicles. 

Oak Ridge National Laboratory. (2012). FAF3 summary statistics Retrieved January 9, 2012, from

http://faf.ornl.gov/fafweb/FUT.aspx

RF Cafe. (2011). Density of some common building materials Retrieved December 27, 2011, from 

http://www.rfcafe.com/references/general/density-building-materials.htm
18 

Ship North America Transportation. (2011). Equipment: Truck, truck trailers and van specifications 

Retrieved December 27, 2011, from 

http://www.shipnorthamerica.com/htmfiles/equipment.htm

TAPPI. (2003). Weight of newsprint rolls Retrieved December 22, 2011, from 

http://www.tappi.org/Downloads/unsorted/UNTITLED---0108050211pdf.aspx

Tire Rack. (2011). Tires Retrieved December 27, 2011, from 

http://www.tirerack.com/tires/tires.jsp?tireMake=Michelin&tireModel=Harmony&tab=Specs&t

irePageLocQty=

U.S. Census Bureau. (2009). 2007 commodity flow survey. Washington, DC. 

USEPA, & NHTSA. (2011). Final rulemaking to establish greenhouse gas emissions standards and fuel 

efficiency standards for medium- and heavy-duty engines and vehicles: Regulatory impact 

analysis. Washington, DC. 

Winebrake, J. J., Corbett, J. J., Falzarano, A., Hawker, J. S., Korfmacher, K., Ketha, S., & Zilora, S. (2008). 

Assessing energy, environmental, and economic tradeoffs in intermodal freight transportation. 

Journal of the Air and Waste Management Association, 58, 1004-1013.