# 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 20122



## 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 Wo rth ............................................... ........................... 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 ........................................

................................................... ................................................... .... 173 

## Abstract The U.S. Environmental Protection Agency (USEPA) h as 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 fue l efficiency may impact (i.e., reduce) freight rate s 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 fo r the cases analyzed. Specifically, we assumed tha t economic conditions would favor a shift from rail t o truck if either the price per ton-mile to ship a commodity by truck, or the price to ship a given qu antity of a commodity by truck, became **lower** relative to rail transport options post-regulation. The results indicate that the new regulations do no t seem to create economic conditions that would lead to a modal shift from rail to truck in a ll except one of the cases examined. However, thes e results are caveated knowing that the price of ship ping a particular good from origin to destination i s only one factor that a shipper must consider when m aking 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 facto rs that affect logistics decision-making in a multi -modal
context.4



## Introduction New regulations promulgated by the USEPA will incre ase heavy duty vehicle (HDV) fuel efficiency. These regulations will result in reduc ed fuel consumption per mile and, presumably, lower operating costs and freight rates for truck transpo rtation. We have analyzed four cases to help understand how fuel efficiency improvements might i mpact operating costs and freight rates in the HDV freight transport sector. The cases explored in th is 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 shif t from rail to truck if either the price per ton-mi le 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 o f: 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) pair s for each case were chosen by the USEPA after GIFT Solutions, LLC carefully analyzed the 20 07 Commodity Flow Survey (CFS) (U.S. Census Bureau, 2009) and the Freight Analysis Framework, Version 3 (FAF 3 ) (Oak Ridge National Laboratory, 2012) and recommended a number of potential O/D pairs that ap peared suitable for analysis. The transport of rubber tires (Case 1), plastic pel lets (Case 2), and newsprint (Case 3) were chosen because, as shown in Table 1, at least one-t hird 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-mile s in these commodity groups are transported by rail—allo wing 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 stu dy that models the transport of a commodity that represents a significant portion of freight tr uck 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 s tone” commodity group is dominated by HDV truck transportation (89% of short tons are transported b y truck as shown in Table 1). Gravel and crushed stone also represents the largest portion of the fr eight transportation sector by weight, and ranks fi fth in terms of freight sector ton-miles transported (U.S. Census Bureau, 2009). Thus, Case 4 provides insi ght into how new fuel efficiency improvements for HDVs might impact commodity flows representing a significant portion of the truck freight sector. T able 1 summarizes the transportation characteristic s of each commodity group modeled in the cases.5 Table 1. Transportation characteristics of each com modity 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 FAF 3 . For each case, states with high “exporter ranks” for the commodity group being modeled were chosen as origin states; s imilarly, states with high “importer ranks” were chosen as destination states. The FAF 3 was used to verify that the origin state and desti nation state conducted a significant amount of trade with each o ther (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 reali stic freight transport activity. The FAF 3 provides state-level data; thus, it was important to choose a reasonable origin and destination city for each cas e. To do so, the Geospatial Intermodal Freight Transport (GIFT) model, described in detail in Wine brake, et al (2008) and Comer et al. (2010), was us ed 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 ori gins and destinations for each case. Table 2 summa rizes the characteristics of the O/D pairs for each case.

Table 2. Origin and destination exporter and import er rank by weight and value for each commodity grou p 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 (2w 1 , 2v 2 ) Fort Worth, TX (1w, 1v) 1,409 2 Plastics and rubber 3 Los Angeles, CA (2w, 2v) Fort Worth, TX (1w, 1v) 1, 409 3 Pulp, newsprint, etc. New Orleans, LA (2w, 4v) Ch icago, 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 b eing modeled. 2 v = rank by value ($) for the commodity group being modeled. 3 We model two cases with the same commodity group (p lastics and rubber) and O/D pair (Los Angeles to Fort Worth) because of the differen ces in how rubber commodities and plastic commoditi es may be shipped, despite being in the same commodity gro up.



## Methodology New regulations promulgated by the USEPA will resu lt in increased fuel efficiency for HDVs. The freight rates of HDVs will change (i.e., decrease) as fuel efficiency increases. A model was develope d by the authors, based on previous work by USEPA, to in vestigate how HDV freight rates would change as a6

result of the regulations. This section describes how the model estimates changes in HDV freight rate s 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 trac tor trailer), O/D pair, commodity group, and fuel surcharge (fuel surcharges vary based on the price of diesel fuel). These quotes were given in dollar s per truckload. The truckload rate quote was then divid ed by a quoted or estimated average tons of each commodity transported per truckload and then divide d 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-mil e for easier comparison. To calculate the post-regulatio n truck freight rate, the following equation was us ed:

 =  ∗ 100% −  
     +  ∗  
   ∗ (100% − %∆) + 




Where

**TR post**  = the truck freight rate post-regulation in cents per ton-mile (cents/ton-mi) **TR pre**  = the truck freight rate pre-regulation in cents/t on-mi **C fuel**  = the fuel cost component of the truck company’s o perating cost pre-regulation in cents/mi **C op**  = the truck company’s operating cost pre-regulatio n in cents/mi ∆η = the truck’s fuel efficiency (gal/mile) improveme nt in % C tech = the additional technology costs to comply with t he regulation (i.e., the higher incremental costs of a new Class 8 HDV that complies with the r egulation) in cents/ton-mi

The post-regulation truck freight rates ( **TR post**  ) were compared to rail freight rates on a cents pe r tonmile basis to determine if economic conditions sugg ested the potential for a modal shift from rail to truck. Rate quotes were also solicited from rail companie s 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 r ailcar used (varies based on the commodity being shipped), the O/D pair, the commodity group, and th e 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-mil e for easier comparison. In addition to calculating the truck and rail frei ght 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 c ommodity, they might be able to transport it using 14 trucks or three (3) rail cars. To estimate the tot al cost of transporting a given quantity of a commo dity from origin to destination pre-regulation, one simp ly divides the quantity to be shipped (in short ton s) by the capacity of the truck or rail car (in short tons) and rounds up to the nearest full truck or ra il car; this value is then multiplied by the quoted price p er truckload or price per carload. While partial t ruck or carloads may be possible, we only received quote s for full truckloads and full carloads. To estima te the total cost of transporting that same quantity b y truck post-regulation the same procedure is followed, but the price per truckload is adjusted t o reflect the effective price per truckload postregulation. The effective price per truckload post -regulation is estimated by multiplying the truck f reight rate post-regulation (in $/ton-mi) by the capacity of the truck (in short tons) and by the highway mil es. The result is a post-regulation price in $/truckloa d 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 as sumptions are shown in Tables 4 through 7.

Table 3. General model assumptions used in every ca se study.
Input Value Units Source Increase in HDV technology cost to meet new regulation $8,100 $ USEPA 1

Lifetime HDV vehicle miles travelled (VMT) 1,253,870 miles USEPA 2

C fuel 66.4 cents/mi diesel fuel cost ($/gal) from EIA ( 2012 ) *pre

regulation fuel efficiency (gal/mi) from USEPA 3

*100 cents/$

C op 158.2 cents/mi p re

- regulation operating costs based on ATRI (2008), EIA (2012), ICF (2010) and USEPA 4 C fuel /C op 4 2 % % Calculated Price of on-road diesel fuel $3.78 $/gal U.S. avg. for 1/2/2012 from EIA (2012) 5
F uel efficiency improvement ( ∆ η) 23% % USEPA 6

C tech 0.03 cents/ton-mi increase in HDV technology cost ÷ lifetime HDV VMT ÷ 19 short tons per truck 7

1 Consistent with values used in the final HDV rule f or MY2018 Class 8 Sleeper Cab trucks with a High Roo f found in Table 2-39 in USEPA & NHTSA (USEPA & NHTSA, 2011). 2 The USEPA estimated lifetime VMT for HDVs using outp ut from the MOtor Vehicle Emissions Simulator (MOVES) model. 3 The pre-regulation fuel efficiency for Class 8 Slee per Cab trucks with a High Roof is estimated at 0.18 gal/mi by the USEPA. 4 All operating cost components besides fuel and truc k purchase costs come from ATRI’s 2008 analysis of truck operating costs at http://www.atri-online.org/research/results/economi canalysis/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 pur chase price operating cost components are derived f rom ICF’s (2010) preregulation new vehicle price estimate (consistent w ith values used in the final HDV rule) and USEPA’s lifetime HDV VMT estimate obtained from the MOVES model. 5 The U.S. Energy Information Administration (EIA) po sts the average on-road diesel price weekly at http://www.eia.gov/petroleum/gasdiesel/

. 6 It is expected that fuel efficiency for Class 8 Sle eper Cab trucks with a High Roof will improve from 0.18 gal/mi to 0.14 gal /mi, a ~23% improvement. 7 The USEPA assumed an average payload of 19 short tons for class 8 HDVs as found in USEPA & NHT SA (2011).8 Table 4. Case 1 assumptions: Rubber tires from Los Angeles to Fort Worth.
Input Value Units Source Truck Co. “A” freight r ate pre

regulation $3,500 $/ truckload Con

- Way Sales Rep

Truck Co. “B” f reight r ate pre

regulation $3,300 $/ truckload YRC Sales Rep

Short t ons/53’ t railer 13.9 tons Calculated 1 Highway miles between origin and destination 1409 miles Google Maps TM

Rail Co. “A” freight rate (shipper

owned equipment) $5,380 $/ carload BNSF Online Rate Calculator 2 Rail Co. “A” freight rate (railroad

owned equipment) $8, 1 80 $/ carload BNSF Online Rate Calculator Rail Co. “B” freight rate (shipper

owned equipment) $8,390 $/ carload Union Pacific Rate Calculator 3 Rail Co. “B” freight rate (railroad

owned equipment) $8,390 $/ carload Union Pacific Rate Calculator Tons/60’ box car 27.5 tons Calculated 4 Rail miles between origin and destination 1,658 miles GIFT model

1 Based on the volume of a 53-foot trailer (~3,800 cu bic feet) (Ship North America Transportation, 2011) and the density of a typical rubber automobile tire (~7.33 pounds per cu bic feet); the density of a tire was calculated usi ng data found at tirerack.com (Tire Rack, 2011). 2 Available at http://www.bnsf.com/customers/prices-and-tools/carl oad-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 cu bic feet) (BNSF, 2011) and the density of a typical rubber automobil e tire.

Table 5. Case 2 assumptions: Plastic pellets from L os Angeles to Fort Worth.
Input Value Units Source Truck Co. “A” freight rate pre

regulation $3,430 $/truckload Con

- Way Sales Rep

Truck Co. “B” freight rate pre

regulation $3,300 $/truckload YRC Sales Rep

Short tons/53’ trailer 22.5 1 tons YRC Sales Rep Highway miles between origin and destination 1409 miles Google Maps TM

Rail Co. “A” freight rate (shipper

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

owned equipment) N/A $/carload BNSF Online Rate Calculator Rail Co. “B” freight rate (shipper

owned equipment) $ 10,450 $/carload Union Pacific Online Rate Calculator Tons/large covered hopper car 76 tons Calculated 2 Rail miles between origin and destination 1,658 miles GIFT model

1 Though more than 22.5 tons of plastic pellets coul d fit in a 53’ trailer, the maximum freight weight is 45,000 pounds (22.5 short tons). 2 Based on the volume of a large covered hopper (~4,7 50 cubic feet) (BNSF, 2011) and the density of poly propylene pellets (~32 pounds per cubic feet) (ASI, 2011).9 Table 6. Case 3 assumptions: Newsprint from New Orl eans 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 22.5 1 tons 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 Calculator 2 Rail Co. “C” freight rate (railroad-owned equipment) $6,350 $/carload CSX Online Rate Calculator

Tons/60’ boxcar 104.5 tons BNSF 3 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). 2 Available at http://shipcsx.com/public/ec.shipcsxpublic/Main?modu le=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, 201 1).

Table 7. Case 4 assumptions: Gravel from Chattanoog a 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 Sal es Rep Tons/53’ trailer 22.5 1 tons YRC Sales Rep Highway miles between origin and destination 419 mi les 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 BNSF 2 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 hol d 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 freigh t rates pre- and post-regulation and compare them to quoted rail freight rates. The purpose of comparing the pre- and post-regulation truck freigh t rates to the rail freight rates is to determine whe ther or not the new HDV regulation would reduce tru ck freight rates below competing rail rates post-regul ation. If the price of shipping goods by truck bec omes 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 t o rail for these cases. A number of important cave ats 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 Fi gure 1 provides a visual representation of the truck and rail routes from origin to destination. The GIFT model was used to generate the optimal rai l and truck routes for the case, and the truck routes were validated using Google Maps TM

. Freight rates were available from two trucking comp anies and two rail companies for Case 1, as discussed above. The results indicate that even be fore fuel cost savings, the truck freight rates wer e 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, t he 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-reg ulation in this case study.

Table 8. Case 1 results showing no economic potenti al 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

- O 2 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

- O 2 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 t he railcars are owned by the shipper and transporte d 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 transp ort of rubber tires from Los Angeles, CA to Fort Wo rth, TX for Case 1. 

### Case 2: Plastic Pellets from Los Angeles to Fort Wo rth The results of Case 2 are found in Table 9, and Fig ure 2 provides a visual representation of the truck and rail routes from origin to destination. The GIFT model was used to generate the optimal rai l and truck routes for the case, and the truck routes were validated using Google Maps TM

. Freight rates were available from two trucking comp anies and two rail companies for Case 2. The results indicate that the fuel cost savings did not result in any truck rate becoming less expensi ve than any rail rate **post-regulation** in this case study. The truck rates were always m ore expensive than the rail rates.12 Table 9. Case 2 results showing no economic potenti al 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 transp ort of plastic (polypropylene) pellets from Los Ang eles, 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 Fig ure 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 Maps TM

. Freight rates were available from two trucking comp anies and three rail companies for Case 3. The pre-and post-regulation truck rates for Truck C o. “A” and Truck Co. “B” were cheaper than the quoted rate for shipper-owned and railroad-owned eq uipment 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 potent ial 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

- regulat ion ) 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 equipment14

Figure 3. Truck and rail routes modeling the transp ort of rolls of newsprint from New Orleans, LA to C hicago, IL for Case 3. 

### Case 4: Gravel from Chattanooga to Indianapolis The results of Case 4 are found in Table 11, and Fi gure 4 provides a visual representation of the truck and rail routes from origin to destination. The GIFT model was used to generate the optimal rai l and truck routes for the case, and the truck routes were validated using Google Maps TM

. Freight rates were available from two trucking comp anies and two rail companies for Case 4. The truck rates in cents/ton-mi pre- and post -regu lation 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 expe nsive than the rates quoted from Rail Co. “A” and always more expensive, or as expensive, as the rates quote d from Rail Co. “B.” However, the total cost of tran sporting 300 short tons of gravel by truck became l ess expensive than rail post-regulation when comparing Trucking Co. “A” to Rail Co. “A” when railroadowned equipment (i.e., rail cars) was used for tran sport; the rail rate remained less expensive than t he truck rate post-regulation when assuming shipper-ow ned equipment. Further the truck rate for Trucking Co. “B” became less expensive than the rat e for Rail Co. “C” when assuming **shipper-owned** equipment; the truck rate pre and post-regulation w as less expensive than the rail rate when assuming railroad-owned equipment. The results indicate tha t 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 b ecame less expensive than some rail rates when considering the transport of 300 short tons of grav el in this case study.15 Table 11. Case 4 results showing no economic potent ial for shift from rail to truck post-regulation wh en considering cents/ton-mi freight rates; however, there may be p otential for shift when considering transporting 30 0 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 transp ort of gravel from Chattanooga, TN to Indianapolis, IN for Case 4.16



## Discussion and Conclusions New regulations promulgated by the USEPA improve th e fuel efficiency of HDVs. One consequence of increased HDV fuel efficiency would be reduced fuel costs, potentially resulting in low er truck freight rates. Results indicate that the fuel cost savings did not result in any truck rate beco ming less expensive than any rail rate **post-regulation** on a cents/ton-mi basis in the cases examined in t his report. In each case, the truck freight rates in c ents/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 rat e became **as** expensive as the rail freight rate); however, external price factors, such as the price of diesel fuel, could affect these freight rate est imates. The freight rates for both truck and rail will like ly change as the price of diesel fuel changes and i s passed on via fuel surcharges. As the price of diesel inc reases, fuel surcharges increase. The model develo ped by GIFT Solutions, LLC is capable of adjusting thes e 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 regula tions on the total cost of shipping a given quantity (in short tons) of a commodity, we found t hat 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 Indianapol is, 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 equipme nt (i.e., rail cars) were used for transport; the r ail rate remained less expensive than the truck rate po st-regulation when assuming shipper-owned equipment. Further the truck rate for Trucking Co. “B” became less expensive than the rate for Rail C o. “C” when assuming shipper-owned equipment; the truc k 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 grav el, truck may be the least expensive option as a di rect result of fuel savings from the regulation. We con cede that modeling the transport of 300 short tons of a commodity provides limited insight into how the t otal freight transport price might change depending on the volume of goods transported. There may be a n opportunity to explore how the volume of shipments impacts modal choice. We would now like to discuss some of the caveats on e must consider in interpreting the results. We assume that the full fuel cost savings as well a s 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 r ail) generally adjust their freight rates according to t he price of on-road diesel fuel; however, it is unc lear what portion of the additional HDV technology costs to adhere to regulations will be passed along to shippers and over what time frame. Moreover, resul ts suggest that trucking companies **may not** adjust their freight rates to account for differences in a ctual fuel consumption; rather, they only adjust th eir rates to reflect changes in the price of on-road di esel fuel. For example, we noticed that the truck companies did not adjust their freight rates to acc ount for increased fuel consumption due to hauling a heavier load; the price to ship 14 short tons of ti res was essentially the same as the price to ship 2 2.5 short tons of plastic pellets from Los Angeles, CA to Fort Worth, TX. Instead, the change in price se ems to be mainly attributable to the O/D pair and the p er-mile fuel surcharge. The USEPA (2011) discusses the potential for aggreg ate 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 t hey model the transport of commodities between fixed O/D pairs with fixed commodity payloads. Thu s, 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 fixed17

commodity payloads to control for any other variabl es that might impact the economics of modal choice and, therefore, isolate the effects of fuel efficie ncy improvements post-regulation on truck freight r ates. Additional costs that a shipper would bear to prepa re the commodity for shipment are not explicitly accounted for in the analysis. For exam ple, the cost to the shipper (in both dollars and t ime) to package (e.g., palletize), load, and unload the shi pment could impact their decision-making when considering which mode (rail or truck) to choose to transport the commodity. Additionally, the analys is 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 cas es examined here may underestimate the true price of transporting goods from origin to destination de pending on the needs of the shipper. Other factors
, including the volume and frequency of shipments, wo uld also affect modal choice. For example, if a shipper needs to transport multiple truckloads or c arloads of goods, the shipper would need to conside r 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 ra il cars) when choosing truck or rail modes. We recommend future research to investigate how var ying the price of diesel fuel might affect truck and rail freight rates differently. Addition ally, we recommend future work focusing on how othe r factors, besides the freight rate, impact shippers’ freight transportation decisions, and how the infl uence of these other factors can be estimated, parameteri zed, and used in probabilistic decision functions t hat could add insights into the potential for modal shi ft in response to improved truck fuel efficiency. There may be an opportunity for the USEPA and GIFT Soluti ons, LLC to team with SmartWay shippers to investigate how freight transport decisions are mad e and what factors influence those decisions.



## Acknowledgements The authors wish to thank the Transportation and Cl imate 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 t he University of Delaware for the use of the GIFT model.



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