Report #4 in a Series of Transportation and Trade Studies of the Appalachian Region 

 

Meeting the Transportation Challenges of the 21st Century: 

Intermodal Opportunities in the Appalachian Region 

Economic Benefits of Intermodal Efficiencies

Prepared for: 

Appalachian Regional Commission 

Prepared by: 

Rahall Transportation Institute, Marshall University 

And 

Wilbur Smith Associates 

December 2004 
 1

INTRODUCTION 

As has been demonstrated in the ARC Region, investment in transportation infrastructure can 

produce significant economic benefits. In the instance of freight-related intermodal investments, 

these economic impacts arise as a result of reducing the cost of doing business in a given region. 

Benefits may also arise as a result of the strategic significance of alternative mode choice as a 

business location determinant. 

The first two parts of the current intermodal research effort consisted of an in-depth commodity 

flow analysis and the identification and profiling of specific intermodal case studies in the ARC 

Region. The purpose of this section of the study is to estimate the potential economic impacts of 

increased transportation efficiencies that can result from intermodal improvements. 

Three of the intermodal case studies identified in the earlier phases of the research were selected 

for economic impact analysis, including the Trans-Tennessee Railroad, the Prichard Intermodal 

Yard (part of the Central Corridor Doublestack Initiative), and the Port of Pittsburgh Containeron-Barge projects. The selection was based on the availability of data, the diversity of the 

projects, and the commitment of sponsoring agencies regarding the projects. The economic 

modeling was conducted using the ARC’s REMI model, which reports results on a north, central, 

and south regional basis. It is important to note that only the projected efficiency or savings in 

transportation costs were included as input into the REMI model. No construction impacts, 

safety benefits, or long-term strategic development impacts are included in this analysis. 

Therefore, the purpose of the current analysis is to provide some insight into the potential 

benefits of intermodal investment in the ARC Region, rather than serve as a major investment 

study. The following sections summarize the methodologies and results of an economic 

modeling exercise for each of the selected case studies. 
 2

TRANS-TENNESSEE RAILROAD 

The analysis of the economic impact of transportation efficiencies arising from the proposed 

Trans-Tennessee Railroad is based on the findings of a report commissioned by the Tennessee 

Department of Transportation (TDOT).1 An evaluation of the findings of that report was not 

conducted as a part of the current effort; rather the findings are assumed to be reasonable and 

serve as the initial input into the modeling process. 

The TDOT report consisted of a benefit-cost analysis based on different truck-to-rail diversion 

scenarios. These estimated benefits included fuel savings, annual shipping costs savings, travel 

time efficiencies, accident savings, etc. The projected shipping cost savings and travel time 

efficiencies form the starting point of the current analysis. The estimated savings were combined 

with detailed information on commodity flow, statewide industrial employment, gross state 

product, and BEA direct requirement coefficients (which describe the importance of 

transportation costs relative to total costs across industries) to develop the input for the REMI 

model in terms of lower transportation cost by industry. The process is summarized in the 

following flow chart. 

 

1

 “Task 8: Evaluation of Rail Infrastructure Proposals, Basic Freight Connection, Project 1, An East-West Rail Connection.” 

Arcadis and Don Breazeale & Associates, Revised May 2003. 

Distribute projected savings across commodities based on commodity 

flow data for the region 

Cross reference commodities to industries and allocate savings to 

industries using Gross state product based ratios 

Using BEA direct requirements, calculate cost associated with affected 

industries 

Calculate transportation cost savings by industry for each of the 

diversion scenarios 
 3

Three scenarios were evaluated: 5% diversion, 10% diversion and 20% diversion, where 

diversion refers to the diverting of commodities currently being (and forecasted to be) shipped by 

truck to being shipped by rail. The projected annual savings in shipping costs for these diversion 

scenarios, obtained from the TDOT report, are $43.9 million, $87.9 million and $175.8 million, 

respectively. Exhibit 1 displays the results for the ARC Region. 

Exhibit 1 

Economic Impact of Intermodal Efficiencies of Trans-Tennessee Railroad 

ARC Region-wide Results 

 Region Wide 

 Job Years GRP* 

Real Dis 

Income 

 (in 1000s) 

(bil fixed 

96$) (bil fixed 96$) 

5 % Diversion Scenario 

 35 year total 156.92 14.76 11.72 

 Annual average 4.48 0.42 0.33 

 

10% Diversion 

Scenario 

 35 year total 253.70 23.85 18.98 

 Annual average 7.25 0.68 0.54 

 

20% Diversion 

Scenario 

 35 year total 510.64 48.01 38.23 

 Annual average 14.59 1.37 1.09 

 * Gross Regional Product 

It appears that the Trans-Tennessee Railroad could provide substantial economic benefits as a 

result of increased intermodal efficiencies. Furthermore, the model results at the sub-region level 

suggest that the benefits would be distributed throughout Appalachia, including the north, central 

and south regions. The south region, in which the actual investment is physically located, would 

experience the most significant share of the benefits (51.0%), with 42.5% and 6.5% of the 

benefits projected to accrue to the north and central regions, respectively. In summary, 

preliminary benefits modeling suggest that all regions have the potential of experiencing a net 

gain in terms of employment, output, and income as result of the proposed Trans-Tennessee 

Railroad. 
 4

It should be noted that the 35-year total employment estimates represent the number of job years 

and is not cumulative jobs. For example, in the 20% diversion scenario for the south region, the 

79.96 thousand total jobs does not mean that there will be 79.96 thousand new jobs in any given 

year. Rather, there will be an additional 79.96 thousand full-time equivalent jobs over the course 

of 35 years. If a job is created in year 1 and sustained for the entire 35 years, that 1 job represents 

35 total full-time equivalent job years. Therefore, the better measure of employment is the 

average annual job estimate, which represents in any given year the number of full-time 

equivalent jobs the project is projected to support. 

PRICHARD, WV INTERMODAL YARD 

The analysis of the economic benefits of the transportation efficiencies arising from the proposed 

Prichard Intermodal Yard are based on an in-depth benefit-cost study of the Central Corridor 

Doublestack Initiative conducted by Marshall University in 2003. An evaluation of the findings 

of that report was not conducted as a part of the current effort; rather the findings are assumed to 

be reasonable and serve as the initial input into the modeling process. 

 

The Marshall University study included estimates of container traffic and estimated 

transportation cost savings over a West Virginia facility at Prichard. Those estimates were 

compared to the commodity flow and drayage data, which were part of an earlier phase of this 

study to determine the likely types and volumes of commodities that would be flowing through 

the facility. The resulting commodity flow mix, combined with other data including 

employment, BEA transportation direct requirements, and GRP was used to translate the 

estimated transportation cost-savings from the Marshall University report into transportation 

savings by industry. From this, the input for the REMI modeling exercise was derived. The 

following flowchart summarizes the process. 
 5

Projected intermodal containers distributed across 

commodities based on ratios derived from detail commodity 

flow data

Commodities cross-referenced to industries

Total transportation costs of affected industries calculated 

using GRP data and BEA transportation direct requirement 

coefficients

Potential transportation cost savings by industry calculated by 

allocating total estimated transportation costs savings based 

on each industry’s share of total transportation costs

Projected total transportation cost savings calculated by 

multiplying projected number of containers by projected 

savings per container form Marshall report

Projected intermodal containers distributed across 

commodities based on ratios derived from detail commodity 

flow data

Commodities cross-referenced to industries

Total transportation costs of affected industries calculated 

using GRP data and BEA transportation direct requirement 

coefficients

Potential transportation cost savings by industry calculated by 

allocating total estimated transportation costs savings based 

on each industry’s share of total transportation costs

Projected total transportation cost savings calculated by 

multiplying projected number of containers by projected 

savings per container form Marshall report

Both an upper bound and lower bound scenario are modeled. The lower bound used the first year 

projections of traffic and the upper bound used maximum terminal capacity. Again, the 

economic impacts represent only the impacts arising from transportation costs savings and do not 

include any strategic economic development impacts. The impacts are modeled for the entire 

ARC Region as a whole, as well as the north, central and south regions. The results for the 

region are presented in Exhibit 2. 

Exhibit 2 

Economic Impact of Transportation Efficiencies arising from the 

Prichard Intermodal Yard 

Lower Bound Upper Bound 

Employment 

Job Years GRP* 

Per Dis 

Income 

Employment

Job Years GRP* 

Per Dis 

Income 

(in 1000s) 

(bil fixed 

96$) 

(bil fixed 

96$) (in 1000s) 

(bil fixed 

96$) 

(bil fixed 

96$) 

Region Wide 

30 year total 14.01 1.32 1.04 287.02 26.99 21.47 

 Annual 

average 0.40 0.04 0.03 8.20 0.77 0.61 

* Gross Regional Product 
 6

Similar to the Trans-Tennessee railroad, the Prichard intermodal yard displays potential for 

significant economic benefits for all three sub-regions within the ARC Region. Over fifty percent 

of the estimated benefits are projected to accrue to the southern region, while the northern and 

central regions are projected to harness 43.0 percent and 6.5 percent of the estimated impacts, 

respectively. 

 

It should be noted that the 35-year total employment estimates represent the number of job years 

and is not cumulative jobs.2

 

In addition to modeling the economic impacts of the transportation cost savings, a simplified 

analysis of strategic economic development impacts was conducted. The analysis represents a 

sketch level “what-if” analysis and should not be interpreted as an investment grade analysis. 

The major assumption used in the strategic economic development analysis was that the 

intermodal yard would increase the accessibility of ARC’s north and central regions. 

Specifically, it was assumed that the intermodal facility would increase accessibility by .005 

percent between the ARC counties in the north and central regions. This assumption is based on 

the fact the proposed intermodal yard would open more land for development, given access to a 

new mode of transportation, by increasing access to that land an average of .005 percent. The 

combined results from reduced transportation costs and increased accessibility are displayed in 

Exhibit 3. 

As with the results from the transportation cost savings alone, the total impacts resulting from the 

Prichard Intermodal yard are spread across all three sub-regions in Appalachia. The northern 

region is projected to garner the largest share of the benefits (55.3 percent), while the southern 

and central regions capture 43.2 percent and 7.5 percent of the estimated benefits, respectively. 

 

2 For example, for the lower bound scenario, the 14.01 thousand total jobs does not mean that there will be 14.01 thousand new 

jobs in any given year. Rather, there will be an additional 14.01 thousand full-time equivalent jobs over the course of 35 years. If 

a job is created in year 1 and sustained for the entire 35 years, that 1 job represents 35 total full-time equivalent jobs. 
 7

Exhibit 3 

Economic Impact of Intermodal Efficiencies and Increase Accessibility 

Resulting from the Prichard Intermodal Yard 

 Lower Bound Upper Bound 

 Employment GRP* 

Per Dis 

Income Employment GRP* 

Per Dis 

Income 

 (in 1000s) 

(bil fixed 

96$) 

(bil fixed 

96$) (in 1000s) 

(bil fixed 

96$) 

(bil fixed 

96$) 

Region Wide 

 30 year total 165.44 15.38 11.60 517.04 46.80 30.95

 Annual 

average 4.73 0.44 0.33 14.77 1.34 0.88

* Gross Regional Product 

Although speculative in nature, the results of modeling an increase in accessibility arising from 

the Prichard Intermodal Yard illustrate the potential significance in terms of business attraction 

of the project. Despite the speculative nature of the modeling, the assumption of increase 

accessibility itself is plausible. Even though the site is currently in close proximity to Interstate 

64, via US 52, there are very few proximal residential or commercial structures, indicating 

access to the site is not sufficient enough to support significant development. By providing 

access to intermodal rail, the access to the site itself is increased. The addition of a 

complementary mode of transportation will arguably make the site more attractive for industrial 

development. The addition of rail access will make the site more accessible for intermodal 

container traffic, leading to increased development opportunities. For example, the annual 

employment impact with strategic economic development considerations is ten-fold the 

estimated impact arising from the transportation costs alone. These results indicate that more indepth analysis of the potential strategic benefits of this project is worth pursuing. 
 8

PORT OF PITTSBURGH CONTAINER-ON-BARGE SERVICE 

The premise of the analysis for the Port of Pittsburgh (PoP) Container-on-Barge (COB) initiative 

is that transportation cost savings can be expected from taking advantage of moving freight in 

heavier containers on river barges. The starting premise of these estimates is the fact that 

containers moved by barge can be loaded to full weight capacity, whereas when moved by truck, 

containers are subject to road weight restrictions and can be loaded to only about 80 percent 

capacity. This gives the container-on-barge (COB) service about a 20 percent relative savings 

advantage. It should be noted that due to the time and limited data availability constraints, the 

savings estimates for Pittsburgh were developed applying a number of assumptions to allow for 

the development of a plausible, although speculative, scenario. 

For the purposes of reader’s ease, the following diagram summarizes the steps applied in the 

derivation of the COB service related cost savings ratio. A more detailed description is presented 

in the narrative that follows. 

Summary of the Container on Barge (COB) Savings Ratio Derivation 

Use Port of Baton Rouge COB volume as a proxy for the first 

4 years of PoP potential COB service 

Apply PA freight growth rates to obtain PoP container 

volume till year 20 

Multiply the annual PoP COB container throughput by the 

weight of a COB container to derive the total COB 

containerizable tonnage 

Use the difference between COB and truckable container 

weight (20%) to derive the extra COB containrazable tonnage 

at PoP 

Multiply the proportion of the key targeted industries (i.e., 

plastics, chemicals, and steel) in the PA Gross State Product 

by the total annual (e.g., in year 2010) freight tonnage to 

obtain the total forecast tonnage in the key industries 

Divide the extra COB containerizable tonnage by the total 

forecast tonnage in the key industries to obtain the cost 

savings ratio 
 9

In order to estimate the savings, one of the first steps was to project the number of containers 

and tonnage of freight that can result from a container-on-barge service at the Port of Pittsburgh. 

Since there were no specific forecasts developed for this type of a transport at the Port of 

Pittsburgh, the throughput of the Port of Baton Rouge was used as a proxy for what can be 

expected at Pittsburgh. The two ports handle similar volumes of freight3

, and have very good 

inland waterway system access. The Port of Baton Rouge commenced its weekly COB 

scheduled service, operated by Osprey Line to New Orleans and Houston, in the beginning of 

2004. According to Port of Baton Rouge officials, about 2,200 containers were handled in the 

January to May period, and the Port is on course to ship 5,000 containers by the end of the year. 

The Port recently signed agreements with the Ports of Memphis and Shreveport, and is 

expecting to reach additional agreements with Chicago and other Midwestern facilities within a 

year. The Port officials stated that the Port “experienced a sharp increase in demand after just a 

few months of service”, and they “believe the container-on-barge service will steadily increase 

in popularity”. The officials are expecting a doubling of container throughput volumes each 

year over the next few years, after which it is expected to level off. 

 

3 According to the US Army Corps of Engineers Waterborne Commerce Statistics Center, the 2002 tonnages for the Port of 

Greater Baton Rouge, and the Port of Pittsburgh were 60.6 million, and 52 million tons, respectively. 
 10

Based on these observations from the Port of Baton Rouge, similar volumes of containers were 

applied to the Port of Pittsburgh (please see Exhibit 4). Year 2005 (with its 5,000 containers) 

was assumed to be the first year of operations and, following the doubling of growth for two 

years and halving that pace in year four, the growth rate in Pennsylvania freight shipment 

tonnage was applied for the rest of the twenty-year time horizon4

. 

Exhibit 4: Projected Container on Barge Throughput 

at the Port of Pittsburgh 

Year COB Volume (# of 

containers) 

1 5,000

2 10,000

3 20,000

4 30,000

5 30,395

6 30,795

7 31,200

8 31,611

9 32,027

10 32,448

11 32,875

12 33,308

13 33,746

14 34,191

15 34,641

16 35,097

17 35,558

18 36,026

19 36,501

20 36,981

 

4 Source: Freight Analysis Framework (FAF) State Profiles – PA. 
 11

The average weight of a truckable container is typically up to 25 tons. Since containers on barge 

can be loaded to about 20 percent more weight than truckable containers, an additional 20 

percent was added to this weight. This COB containerizable weight was applied to the projected 

container throughput to obtain a forecasted total COB tonnage for the Port of Pittsburgh (please 

see Exhibit 5) 

 Exhibit 5: Projected Container on Barge Tonnage 

 at the Port of Pittsburgh 

Year 

COB Volume 

(tons) 

1 150,000

2 300,000

3 600,000

4 900,000

5 911,845

6 923,846

7 936,005

8 948,324

9 960,805

10 973,451

11 986,263

12 999,243

13 1,012,395

14 1,025,719

15 1,039,219

16 1,052,896

17 1,066,754

18 1,080,794

19 1,095,018

20 1,109,430
 12

The projected annual difference between the COB and truckable tonnage is the extra 

containerizable tonnage that can be moved on barges through the Port relative to the trucking 

mode (see Exhibit 6). 

Exhibit 6: Projected Extra Container Tonnage 

at the Port of Pittsburgh 

Year 

COB Volume 

(tons) 

1 25,000

2 50,000

3 100,000

4 150,000

5 151,974

6 153,974

7 156,001

8 158,054

9 160,134

10 162,242

11 164,377

12 166,541

13 168,732

14 170,953

15 173,203

16 175,483

17 177,792

18 180,132

19 182,503

20 184,905

According to the Port's COB Pre-feasibility Study, plastics, chemicals, and steel were identified 

as the key commodities that the Port should be targeting for this type of service. It should be 

noted that plastics products, in addition to other commodities such as food (rice), are also the 

key commodities transported on barges through the Port of Baton Rouge; thus, there already 

appears to be a market for and established flows of these commodity groupings at both ports. 

Next, the expected extra COB tonnage in year 6 (2010) – 154 thousand tons – was divided by 

the total forecasted Pennsylvania tonnage in the plastics, chemicals, and steel industries in 2010. 
 13

The latter (valued at over 63 million tons) was approximated by applying the proportion of 

these three industries in the Pennsylvania Gross Domestic Product5 to the total state tonnage in 

2010 (1,217 million tons) as forecasted in the FAF freight profile for the state. The resulting 

ratio of 0.24 percent is used to approximate the future transportation cost savings ensuing from 

the extra weight capacity that container-on-barge service at the Port of Pittsburgh may allow6

. 

Even though this scenario focused on the three major regional industries, it is possible that some 

of the other tonnage could also benefit from the COB service. 

Based on the input calculations described above, the economic impacts in the ARC Region of 

these estimated transportations costs were modeled using REMI for both an upper bound and 

lower bound number of containers. The results are displayed in Exhibit 7. 

Exhibit 7 

Economic Impacts of Intermodal Efficiencies Arising from 

Container-on-Barge Operations at Port of Pittsburgh

 Lower Bound Upper Bound 

 Employment GRP* 

Per Dis 

Income Employment GRP* 

Per Dis 

Income 

 (in 1000s) 

(bil fixed 

96$) 

(bil fixed 

96$) (in 1000s) 

(bil fixed 

96$) 

(bil fixed 

96$) 

Region Wide 

 20 year total 79.51 5.87 2.64 159.14 11.76 5.29

 Annual 

average 3.98 0.29 0.13 7.96 0.59 0.26

 

* Gross Regional Product 

 

5 Source: BEA Regional Economic Accounts, May 2003. 6 It should be noted that container-on-barge service is typically results in a number of other benefits such as fuel, emissions, and 

safety relative to the truck mode, but these were not the subject of the current analyses. 
 14

As can be seen, there is indication that the COB operations at Port of Pittsburg could lead to a net 

gain in economic activity and the magnitude suggests that the project could result in significant 

benefits in terms of job and income for the region. It should be noted that the sub-regional 

modeling suggests the potential for a slight redistribution of economic activity from the central 

and southern regions to the northern region of Appalachia. 

From a strategic economic development perspective, the Port of Pittsburgh COB service shows 

promise. Examining the estimated employment impacts by industry (see Exhibit 8) reveals that 

key industries within the ARC Region would be stimulated by the proposed investment. 

Exhibit 8 

Region-wide Employment Effects of Port of Pittsburgh Container-on-Barge Service 

 by Sector 

7% 5%

0%

8%

5%

7%

20% 4%

42%

2% Durables

Non-Durables

Mining

Construction

Trans.&Public Util.

Fin&Ins&Real Est

Retail Trade

Wholesale Trade

Services

Agri&For&Fish Serv

The Transportation and Public Utilities industry is projected to experience the largest stimulus, 

arguably driven by the fact that the new service would give rise to increased demand for logistic 

services that are not typically captured in the transportation and public utility industrial 

classification. Other key industries projected to capture significant portions of the employment 

increase include retail trade, construction, durable and nondurable manufacturing, and wholesale 

trade. This is not surprising given the experience with the impact of similar services at the Port of 

Baton Rouge. There, the area surrounding the port has experienced increased employment in 

those manufacturing and distribution firms that utilize the new service. 
 15

Conclusion 

The preceding has provided the results of preliminary economic impact modeling for three 

potential freight intermodal projects in Appalachia. Although numerous assumptions were 

required in some cases, the results provide valuable information and insight into the economic 

development impact that freight transportation efficiencies can have as a result of increasing the 

competitive position of Appalachia by lowering the cost of doing business. 

While the results produced are not meant to guide investment decision making, they do help to 

indicate the potential and provide support for more in-depth analysis and consideration of these 

and other transportation development opportunities in Appalachia.