High Speed Rail Line in the United States:
A Feasibility Study
Concentrations:
Authors:
P HYSICS
A LEXANDER B ENNETT
E CONOMICS
JAMES E BERT
P OLITICAL S CIENCE
B ENJAMIN H ERST
E CONOMICS & S TATISTICS
DANIEL K RAFT
E CONOMICS & EALC
S COTT S OUTHERN
Energy & Energy Policy
Professors Stephen Berry & George Tolley
The University of Chicago
December 8, 2013
Abstract
The goal of this study is to identify the optimal location for the implementation of a high
speed rail line in the United States and assess the economic, technical, and political feasibility
of such a project. We focus on the current political climate and existing policy initiatives to
establish a framework for how a high speed rail project would be received by the general public
as to develop a realistic scenario for the endeavor were it started today. Taking into account the
currently existing travel options between major metropolitan areas, we ultimately chose to focus our study on a rail connecting Chicago and major cities in Texas via St. Louis. Through an
analysis of the required infrastructure, historical economic trends in the transportation sector,
and potential financing options, we project the cost for developing and executing this project
to be between $118bn and $125bn. Modeling the operational performance of the proposed
HSR system under different scenarios, we conclude that the proposed HSR system requires a
minimum 10% diversion rate from other modes of transportation and a variable cost structure
of less than or equal to $0.30 per passenger mile in order to be operationally profitable. Lastly,
we also calculate that the present value of all future environmental benefits, measured in US$,
only accounts for 1% to 2% of the overall construction costs. Hence, based on a five-legged
cost-benefit analysis, which is outlined in detail in Section 11, we conclude that the economic
and environmental viability of this proposed high-speed rail line remains questionable at the
current point in time.
i
Contents
1
Introduction
1
2
Route Selection & Rationale
2
2.1
City Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
2.2
Route Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4
2.3
Route Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
3
4
5
6
Infrastructure Required for Proposed Route
8
3.1
Logistical Merits of a Midwest/Texas Route . . . . . . . . . . . . . . . . . . . . .
8
3.2
Infrastructural Overview & Estimated Cost . . . . . . . . . . . . . . . . . . . . .
8
National Political Climate
13
4.1
Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2
Post-2008 Developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.3
Recent Barriers to Federal Funding for High-Speed Rail . . . . . . . . . . . . . . 17
State-Level Political Climate
17
5.1
Illinois . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
5.2
Missouri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
5.3
Arkansas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
5.4
Louisiana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
5.5
Texas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Energy Requirements and Considerations
21
6.1
Current Energy Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
6.2
Proposed Utilization of High-Efficiency High-Speed Electric Trains . . . . . . . . 22
6.3
Energy Considerations of Proposed High-Speed Rail System . . . . . . . . . . . . 23
ii
6.4
7
8
9
Opportunities for Energy Efficiency: Regenerative Braking Technology . . . . . . 24
Operational Performance
24
7.1
Estimation of the Expected Ridership . . . . . . . . . . . . . . . . . . . . . . . . 25
7.2
Ticket Pricing Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
7.3
Cost Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
7.4
Evaluation of Operating Performance . . . . . . . . . . . . . . . . . . . . . . . . 39
7.5
Summary of the Scenario Profit-and-Loss Analysis . . . . . . . . . . . . . . . . . 41
Valuation of the Project
41
8.1
Discount Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
8.2
Results of the Simple DCF Method . . . . . . . . . . . . . . . . . . . . . . . . . . 44
8.3
Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Financing
52
9.1
History of High-Speed Rail Financing . . . . . . . . . . . . . . . . . . . . . . . . 52
9.2
Possible Funding Sources for U.S. HSR Projects . . . . . . . . . . . . . . . . . . . 56
9.3
9.2.1
Public Financing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
9.2.2
Public-Private Partnerships . . . . . . . . . . . . . . . . . . . . . . . . . . 59
9.2.3
Private Enterprise and Funding . . . . . . . . . . . . . . . . . . . . . . . . 60
Conclusion and Additional Considerations . . . . . . . . . . . . . . . . . . . . . . 61
10 Environmental Efficiency Analysis of the High Speed Rail
62
10.1 Electricity Assumption Regarding the Type of High Speed Rail . . . . . . . . . . . 63
10.2 CO2 Emission Reduction Assumptions . . . . . . . . . . . . . . . . . . . . . . . . 65
10.3 CO2 Emission Analysis Both With and Without the Chicago – St. Louis High
Speed Rail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
10.4 Other Potential Environmental Impacts for the High Speed Rail
11 Summary and Conclusions of the Cost-Benefit Analysis
iii
. . . . . . . . . . 69
70
12 Appendices
74
12.1 Appendix A: Supplemental Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
12.2 Appendix B: Supplemental Figures . . . . . . . . . . . . . . . . . . . . . . . . . . 83
12.3 Appendix C: Cost-Benefit Analysis Visuals . . . . . . . . . . . . . . . . . . . . . 86
13 References
93
iv
1
Introduction
In theory, there exist four primary methods for traveling between major metropolitan areas: by
car, by plane, by train, or by bus. In reality, however, these four options are reduced to only one or
two when one takes into account factors including, but not limited to, travel time, prices associated
with a given method, and availability. For shorter distances it may be the case that an air route
does not exist or that the ticket price and travel time do not make sense for the traveller given the
distance of the journey and the availability of alternatives. Furthermore, there may not exist a train
route between the two cities in question, thus the traveller is left to choose between driving and
taking a bus. Similarly, for longer distances, taking a train, driving a car, or riding a bus may be
out of the question given the distance between the origin and destination and the prices associated
with each option when compared to the price of a plane ticket. In short, it is ultimately up to the
traveler to choose the method that provides greatest utility and allows him or her to best execute
travel itineraries; however, oftentimes the decision is already made for the traveler purely by the
availability of means of transportation.
For many major cities separated by a distance greater than 500 miles, air travel has established
itself as the fastest way of traveling between any two of them. At the current time, the air travel industry stands unrivaled in the service it provides, namely: quick transportation over long distances.
Given the staggering difference in travel time between flying and any of the other three primary
travel methods, the question of if it is possible to alter train, car, or bus travel as to develop a viable
alternative to air travel presented itself as an interesting one to address. In choosing which other
travel method to analyze, car and bus travel were immediately ruled out as potential targets due to
speed constraints in both design and highway speed limits, leaving only rail travel to analyze.
Outside of currently established, short distance commuter rail routes in areas like the Northeastern United States, the presence of time-efficient rail lines that cover long distances is almost
completely nonexistent. This absence is further illustrated by the major player in the passenger rail
space, Amtrak, using the scenic aspect of its long distance rail lines, as opposed to the speed of the
1
trains, to attract customers [1]. With the existence of high speed rail lines in both Asia and Europe
and a rail industry that is simply unable to compete the duration of flights covering long distances,
the notion of a high speed rail line in the United States similar to those in Asia and Europe seems
like a foregone conclusion, yet such a service still does not exist.
In order to address the issue of if a high speed rail line in the United States is a reasonable
goal, we have compiled the following feasibility study to asses each aspect of such a project. This
study focuses on the economic, technical, and political feasibility of a high speed rail line in the
United States that rivals air travel in both cost and travel time. Issues such as the optimal location,
infrastructure requirements, energy requirements, financing options, and ridership projections for
a high speed rail line will all be covered in this report.
2
Route Selection & Rationale
2.1
City Identification
The first step in developing a framework for the feasibility of a high speed rail line was to iden-
tify the best route. In determining the optimal location of the rail line, the main factors that were
considered included the difference in time between a flight and a car, bus, or train ride between the
endpoints of the route, the populations of the endpoints of the route, and the number of travelers
that take a plane between the endpoints of the route. Secondary consideration was then given to
how many cities could be connected by a straight line route between the two endpoints and the
travel time between, populations of, and travelers by plane between the cities lying on this straight
line.
We identified Chicago as a highly populated, major metropolitan hub, easily linked by a straight
line to several highly populated cities, thus the search for potential routes was restricted to lines
connecting Chicago and other major cities in the United States. The search was further restricted
to cities east of Denver, Colorado as we deemed the challenges associated with establishing a line
through the Rocky Mountains to be too great.
2
We began our search for cities by looking at straight lines between Chicago and the most
populated cities in the United States, filling in other highly populated cities on the lines established
by these routes. Figure 1 shows a map of the cities identified as a result of our search, and the
accompanying table lists the name of each city along with the number it is labeled as on the map
[2].
Figure 1: A map marking all potential cities considered.
1. Austin, Texas
8. Detroit, Michigan
15. Omaha, Nebraska
2. Chicago, Illinois
9. Houston, Texas
16. Philadelphia, Pennsylvania
3. Cleveland, Ohio
10. Indianapolis, Indiana
4. Columbus, Ohio
11. Kansas City, Missouri
17. Pittsburgh, Pennsylvania
5. Dallas, Texas
12. Milwaukee, Wisconsin
18. San Antonio, Texas
6. Denver, Colorado
13. Minneapolis, Minnesota
19. St. Louis, Missouri
7. Des Moines, Iowa
14. New York, New York
20. Washington D.C.
3
2.2
Route Identification
After establishing a list of potential cities, we collected data on the population of each city,
distance of each city from Chicago, the annual number of travelers by plane between Chicago and
each city, and the time it takes to travel by train, car, and plane from Chicago to each city.1 All of
this data is displayed in Table 11 in Section 12.1.
In analyzing this data, we gave a scaled score to each city based off of its standing versus the
other cities in the metrics listed above. The equation for assigning a score is as follows.
Overall Score =
plane travelers
train time
population
+
+
+
max(population) max(plane travelers) max(train time)
drive time
nonstop time
connection time
+
+
max(drive time) max(nonstop time) max(connection time)
(1)
We chose to assign scores based off these metrics for several reasons. The scaled population value
was used as a way of ranking cities based on how many citizens would be further exposed to
Chicago, and the other cities on the route, as a result of a high speed rail line through that city. We
wished to award the largest cities the highest point values as to indicate they are the most desirable
to connect with Chicago.2 The scaled number of plane travelers was used as a mechanism for
awarding points to cities that already have a large number of people traveling from that city to
Chicago. The rationale behind this was that cities with a high number of people traveling by plane
would likely have similarly large amounts of people traveling by high speed rail in the event that
such an option was made available as a result of either the cannibalization of the population that
currently travels by train or by the adoption by new travelers who previously chose not travel by
plane due either to the price or the time associated with such a trip. Cities with smaller values for
the number of travelers by plane were given lower scores as to indicate that such cities were smaller
metropolitan areas or the citizens of such cities had less of a need to be connected with Chicago.
The scaled travel times were used as a method of indicating which cities are quick trips to and from
1
2
Data for both a nonstop flight and a flight with a connection is included.
The reason for this will be discussed late in the study.
4
Chicago, and which cities require a greater time commitment. Higher scores were assigned to cities
that take longer to travel to from Chicago as to indicate that such cities could benefit most from
the introduction of a high speed rail line connecting them to Chicago. The rationale behind this
decision was that the average traveler is not interested in taking a car or a train from a given city to
Chicago since such a trip takes upwards of twenty hours, nor is he or she particularly enthusiastic
about the required alternative: a plane ride process that takes upwards of five hours after factoring
in travel to the airport, security checkpoints, taxiing on the runway, waiting for bags, renting a car,
etc. By exposing such travelers to a new method of transportation and providing a viable alternative
to a long flying process, the potential exists to increase the number of travelers between Chicago
and the cities on a given line through adoption by new travelers who were previously deterred from
such a trek due to the length of the trip.
We summed the scaled scores for cities that fell on the same potential rail line from Chicago
and came to the following results.3
Cities on Route
Total Scaled Score
San Antonio, Austin, Dallas/Ft. Worth
St. Louis, Houston
17.954
Denver, Omaha
Des Moines
7.429
New York
Pittsburgh
6.958
Philadelphia
Pittsburgh
5.210
Washington D.C.
Pittsburgh
4.955
Milwaukee
Indianapolis
2.514
Table 1: Summed scaled scores for cities falling on the same potential rail line from Chicago.
3
Each city’s individual scaled score is presented in Table 13 in Section 12.1.
5
It is important to note that when interpreting the results in Table 1, high scores indicate a more
favorable location for a rail line from Chicago. The total scaled score for the San Antonio, Austin,
Houston, St. Louis, Dallas/Ft. Worth line is large primarily because the score for each travel time
for each city is close to 1. This reinforces the fact that a high speed rail line connecting Chicago and
all of these cities benefits the greatest number of travelers by removing the most lengthy trips by
car, bus, plane, or train with the minimal amount of development and construction. To illustrate this
point further, consider the the four routes with the highest scaled scores, reproduced in Table 2. If
the total scaled score is divided by the length of the whole route in miles as a way of further scaling
the score for each route, we see the San Antonio, Austin, Houston, St. Louis, Dallas/Ft. Worth line
remains the most highly ranked. This indicates that even with the amount of rail that needs to be
installed, the positive benefit derived from the scaled values discussed above is still greater than
the other proposed routes. Were the value of the total scaled score divided by the total length of the
route lower than one of the alternatives, we would have been forced to consider a different route
because – despite the “benefits” derived from the population, number of travelers, and high travel
times – the “costs” would have rendered the project suboptimal compared to an alternate route with
a now higher score.4
Cities on Route
Total Scaled Score
Total Scaled Score
Length of Route
San Antonio, Austin, Dallas/Ft. Worth
St. Louis, Houston
17.954
0.0112
Denver, Omaha
Des Moines
7.429
0.0074
New York
Pittsburgh
6.958
0.0084
Philadelphia
Pittsburgh
5.210
0.0068
Table 2: Summed scaled scores for cities falling on the same potential rail line from Chicago with scaled
score per mile included.
4
In this case, “benefits” are defined as the high scaled score of the route, and “costs” are defined as the amount of
rail that needs to be installed.
6
2.3
Route Selection
Figure 2: A map marking all potential cities considered with selected line highlighted.
After analyzing the data presented above, we ultimately decided upon the Midwestern Corridor
linking Chicago to Texas via St. Louis as the optimal location for introduction of high speed rail
line. Figure 2 shows the proposed route for the high speed rail line, and Table 12 provides intercity
travel data for the other cities on the route beyond Chicago. This route connects cities with an
aggregate population of over 9,100,000 people and flight traffic of over 17,200,000 round trip
travelers per year. These values are well over those of the route with the next closest total scaled
score – the Chicago, Denver, Omaha, Des Moines route – which connects cities with an aggregate
population of just over 1,200,000 people and flight traffic of roughly 3,500,000 round trip travelers
per year.
7
3
3.1
Infrastructure Required for Proposed Route
Logistical Merits of a Midwest/Texas Route
Texas and the Midwest have commonly been considered as strong candidates for high-speed
rail (HSR) systems given their accommodative topographical and logistical backdrops. First, HSR
systems must run on passages with relatively constant elevation; if the ground along a prospective
route is not flat, expensive elevated rail systems must be constructed to provide this flat surface, as
opposed to comparatively cheaper retained fill tracks. Thus, developing a high speed rail system in
topographically challenging areas is far more expensive than in flat areas with constant geography
[5]. The Texas and Midwest regions are dominated by relatively flat expanses of land, making a
largely retained fill system conceivable. Secondly, acquisition of rights-of-ways (ROWs) is often a
pressing issue with regards to HSR development. Rail planners cannot simply lay down infrastructure wherever they please, even on unutilized land; rather, they must either purchase/lease land or
develop on existing ROWs, usually existing highway or rail ROWs [6]. Developing a HSR system
on existing ROWs costs less monetary investment, but existing ROWs characterized by abrupt,
winding turns cannot be used because HSR systems require gradual, elongated turns. Looking at
Figure 3, it is clear that the Midwest and Texas regions are connected by expansive interstate and
state highways that are absent of many dramatic turns. This makes the regions accommodating for
a HSR system without the need for significant investment in purchasing/leasing new ROWs.
3.2
Infrastructural Overview & Estimated Cost
The HSR system that we are exploring is an electrified, steel-wheel-on-steel-train system with
a maximum operating speed of 220 mph. While slower HSR systems (100-130 mph) can utilize
modified existing infrastructure, a 220 mph, top-speed HSR system requires the development of
completely new infrastructure, including but not limited to new stations/terminals, communications/signaling, and obviously tracks. Similar to the HSR system proposed in the University of
Illinois’ September 2013 feasibility study, our system would be made up of two dedicated, elec8
Figure 3: A map showing interstate and state highways in the Midwest/Texas area.
trified main tracks throughout the entire system. Retained fill trackessentially embankments in the
earth that are supported by retaining walls on both sides of the trackis appropriate in flat areas.
Changing elevations and river/obstruction crossing require elevated rail structures that are much
more expensive than retained fill track [5].
We have researched and considered a “maglev” rail system that uses magnetic power to propel
the train car forwardable to reach speeds of 300+ mph – but the infrastructural investment needed
for a maglev system is enormous; the $52 billion investment that is the Tokyo-Nagoya maglev
project implies a $333 million/mile infrastructural cost, compared to the $65-100 million/mile cost
that is realistic to build a Midwestern/Texas 220 mph HSR system [7].
As mentioned earlier, the infrastructural costs of an HSR system are much higher in changing
elevations, so we studied topographical maps of the Midwest/Texas to refine the specific route that
we thought would minimize the monetary cost of infrastructure. Looking at Figure 4, we decided
that a relatively straight route through Chicago and St. Louis to Dallas would be problematic given
9
Figure 4: A map of the proposed route overlaid on a topographical map.
the Ozark Plateau in Arkansas. However, the dotted route – going south of Chicago, looping west,
and approaching Dallas from the north – is characterized by relatively changing elevations. We
believe that the solid red route – traveling west out of Chicago, crossing the Mississippi River in
southern Illinois, and taking an eastern approach into Houston – provides an extremely accommodative topography, seemingly flat through the entire trip. The area surrounding Texas’ four
potential hub cities – Dallas, Austin, Houston, and San Antonio – is very flat as well. The estimated distance of our potential route is 1,730 miles, and requires the crossing of three major rivers:
the Mississippi River, the Arkansas River, and the Red River [2]. Table 3 and Table 4 show the
distances of the individual routes and connections between the cities.
To approximate the monetary infrastructural costs associated with our HSR system given the
above route, we utilized a 2013 study by the University of Illinois that looked at the feasibility of developing a HSR system that connects Chicago, St. Louis, Champaign, and Indianapolis. The study calculated the separate per-mile costs of retained fill and elevated track infrastruc-
10
Route
Miles
CHI-STL
315
STL-HOU
735
DAL-HOU
230
DAL-AUS-SAT
260
HOU-SAT
190
Total
1730
Table 3: Distances of individual legs of route.
CHI
STL
DAL
HOU
AUS
SAT
CHI
NA
315
1280
1050
1315
1240
STL
315
NA
965
735
1000
925
DAL
1280
965
NA
230
185
260
HOU 1050
735
230
NA
265
190
AUS
1315
1000
185
265
NA
75
SAT
1240
925
260
190
75
NA
Table 4: Distances between hub cities.
ture using the Federal Railroad Administration’s Standard Cost Categories, previous HSR planning/construction projects, and publicly available manufacturers’ cost information. These per-mile
costs considered the following major inputs: track structures, stations/terminals, support facilities,
sitework and ROWs, communications and signaling, electric traction, vehicles, and a variety of
other costs. Given these inputs, the study derived an average elevated rail and retained fill cost for
their proposed route of $104.5mm per mile and $64mm per mile, respectively [5].
We believe that these per-mile costs of a Chicago – St. Louis – Indianapolis – Champaign
HSR can be used to approximate the infrastructural costs of a Midwest/Texas HSR system for two
reasons. First, all of the input costs, with the exception of ROW acquisition, are independent of
route or geography; retained fill track, stations, and trains, among the other inputs, tend to have
similar monetary cost regardless of the where the system is. Secondly, the necessary acquisition
of ROWs, which can change based on route, will be minimal along our route, just as it is in
11
the Chicago – St. Louis – Indianapolis – Champaign route. The yellow line in Figure 5 shows our
Figure 5: A map of the proposed route in relation to existing interstate and state highways.
route in relation to interstate and U.S. highways; our proposed route fits well with existing highway
rights-of-way along the entire route, with the exception of the Mississippi/Louisiana region. Thus,
just as in the feasibility study’s route, our route would not require substantial acquisition of ROWs,
allowing us to use their HSR cost per-mile metrics as a rough estimate of the total infrastructural
monetary cost of our project. The actual cost per mile of our route could actually be slightly less
due to the likelihood of having less stations per mile – especially between St. Louis and Houston –
than a Chicago – St. Louis – Indianapolis – Champaign system would have. However, as indicated
by the University of Illinois feasibility study, the monetary costs of stations is between 1-2% of the
total HSR infrastructure cost, so we ignore this in our rough estimation [5].
Given the relatively flat terrain of our route, yet the need to cross three major rivers and under-
12
standing that there may be unforeseen obstacles, we believe that our HSR system could be made
up of only 10-20% elevated rail. Given that range, and applying the cost per mile metrics outlined
in the University of Illinois feasibility study to our roughly 1,730 mile route, we estimate that the
total monetary cost of establishing the requisite infrastructure would be between $118 billion and
$125 billion.
A $118-125 billion investment to simply develop the necessary infrastructure to start running
an HSR is substantial. However, there is existing Amtrak infrastructure between Dallas, Austin,
and San Antonio – the Texas Eagle route – that could be modified into semi-high speed rail infrastructure (100-130 mph rail), which would be less costly than making entirely new 220 mph
HSR infrastructure. As an empirical example, existing Amtrak infrastructure between St. Louis
and Chicago is successfully being modified to accommodate 110 mph trains [8]. It is unlikely that
opting to modify existing infrastructure through Dallas – Austin – San Antonio would dramatically
decrease total infrastructural monetary costs, but it is an option.
4
4.1
National Political Climate
Background
One of the most significant determinants of the potential for construction of a high-speed rail
line in the United States lies in the political environment surrounding passenger rail. National
support for passenger rail projects in the US has been a long progression of peaks and valleys.
While the current national rail system is comparatively underdeveloped in relation to the national
highway and aviation infrastructure, there have been efforts throughout the past half-century to
prioritize the development of high-speed rail lines and the increase of train speed nationwide. In
order to understand the political struggles that high-speed rail faces today, one must examine the
historical context behind the issue.
Sentiments supporting high-speed rail first entered the federal rhetoric around 1960, when President Lyndon B. Johnson called on Congress to improve railroad speed as a part of his Great Society
13
infrastructure initiative [9]. Congress responded by passing the High Speed Ground Transportation Act in 1965 with overwhelming support, which established a dedicated-track service between
New York City and Washington, DC. This line was capable of speeds of up to 125 mph, faster than
even modern rail travel time between the two cities. High-speed rail continued to be studied at
the national level, with the Passenger Railroad Rebuilding Act of 1980 and the Intermodal Surface
Transportation Efficiency Act of 1991 funding studies of various high-speed rail corridors [10].
The federal trend emphasizing automobile and airplane domestic travel has had a crippling
effect on efforts to develop efficient and widespread high-speed rail projects in the US. To date,
the Acela Express route in the Northeast Corridor has been the only successful high-speed electric
route in the US, though its average speed from Washington, DC to New York is limited to 79 mph
by the fact that it shares track with conventional lines [11]. Nevertheless, with the election of
President Obama in 2008, the nation has seen a renewed vigor surrounding domestic high-speed
rail development.
4.2
Post-2008 Developments
While past national support for the development of high-speed rail could be described as lack-
luster at best, the arrival of the Obama administration in 2008 has resulted in a resurgence of support for improvement of the nation’s passenger rail system. Upon taking office, President Obama
pushed a number of legislative initiatives supporting federal funding for high-speed rail. Beginning in the fiscal year 2008, Congress passed two key pieces of legislation furthering the cause of
high-speed rail.
The first was the Passenger Rail Investment and Improvement Act (PRIIA), which established
“three new competitive grant programs for funding high-speed and intercity passenger rail capital
improvements” [12]. The capital assistance programs created the framework through which states
and other public agencies apply for federal funding assistance with appropriate intercity passenger
rail projects. It also standardized the development process by requiring State Rail Plans to be
conducted before a state could become eligible for federal grant funding. Regarding high-speed
14
rail in particular, the PRIIA authorized the appropriation of federal funding to the US Department
of Transportation to create and sustain a high-speed rail corridor development program [13].
The second piece of federal legislation that helped breathe life into the US high-speed rail
program was the American Reinvestment and Recovery Act of 2009 (ARRA). The federal economic stimulus package contained the first substantial funding for high-speed rail, appropriating
$8 billion in grant money for high-speed and intercity passenger rail [12]. In an effort to make
the most immediate impact with the funds, the ARRA waived a number of requirements for states
to demonstrate need, such as submitting State Rail Plans. Furthermore, the ARRA directed the
Secretary of Transportation to prioritize projects that support the development of high-speed rail
routes, representing a clear path towards a high-speed rail infrastructure. In short, the ARRA was
the seminal piece of legislation providing actual support to high-speed rail projects.
Following the ARRA, 2009 witnessed the establishment of the High Speed Intercity Passenger
Rail (HISPR) Program in June. This USDOT Federal Railroad Administration initiative represented a manifestation of sentiments favoring high-speed rail at the time. At its core, the HISPR
Program was structured upon three objectives [14]. First, to “build new high-speed rail corridors
that expand and fundamentally improve passenger transportation in the geographic regions they
serve.” Second, to “upgrade existing intercity passenger rail corridors to improve reliability, speed,
and frequency of existing services.” Third, to “lay the groundwork for future high-speed rail services through corridor and state planning efforts.” The strategic purpose of this program spanned
myriad issues, catalyzing economic growth by invigorating domestic manufacturing and promoting tourism, increasing passenger mobility, reducing the nation’s dependence on imported oil, and
cultivating sustainable communities. In sum, the HISPR Program seeks to solve some of the nation’s key transportation challenges through a series of strategic investments in key passenger rail
corridors, effectively and efficiently connecting communities nationwide.
Building on the newly enacted federal legislation, the fiscal year 2009 and 2010 federal budgets
included a combined $2.1 billion in appropriations dedicated for high-speed rail projects [14]. This
addition brought the total program funding to $10.1 billion. While this is undoubtedly a significant
15
step representing federal commitment to high-speed rail, only later on in this study will it be
understood, in fact, how meager this sum is relative to the true costs associated with substantial
rail infrastructure development.
The exact sentiments of the Obama administration regarding high-speed rail were articulated
in the President’s 2011 State of the Union address, in which he set the goal of giving 80 percent
of Americans access to high-speed rail within 25 years [15]. While this goal was undeniably ambitious, it is true that a real transportation revolution necessitates ambition. The large majority of
Americans live in or nearby a handful of urban centers, meaning that the population is relatively
concentrated and that routes would only need to be built or upgraded based on strategic demographical data. Nevertheless, at that point, Congress had only been able to muster $10.1 billion for
high-speed rail. This amount would need to increase several-fold for President Obama’s goal to be
realized.
In February 2011, just one month after setting such an ambitious goal and in an effort to begin
to move things along with greater haste at the federal level, the Obama administration announced
a proposal to spend $53 billion to help develop the nation’s high-speed intercity passenger rail
network [15]. This plan, which would stretch from 2011 to 2017, aims “to invest in a modern
rail system that will help connect communities, reduce congestion and create quality, skilled manufacturing jobs that cannot be outsourced” [15]. While this proposal represented a bold followup to President Obama’s 25-year goal, its timing was rather poor. With the President’s political
counterparts and many others calling for greater fiscal responsibility, the proposal embodied an
unnecessary luxury that could wait until the government had found traction once again.
In assessing the true significance of the $10.1 billion that has been spent thus far at the federal
level in the US, it is useful to consider the recent capital investments of other countries in highspeed rail. The most notable example here is China, where “the Chinese government plans to pour
over $400 billion into its program” over a five-year span from 2011 to 2016 [16]. This has resulted
in an infrastructure of over 8,300 kilometers (5,100 miles) of high-speed rail routes, making it
the world’s longest system. China is setting the standard for high-speed rail development, laying
16
down new lines at a remarkable pace. This stark contrast to the US only illuminates the feigned
dedication towards high-speed rail that has been characteristic of the US. In fact, ever since the
major federal push in 2011, the bipartisan issues that tie into high-speed rail projects have plagued
American high-speed rail stimulus.
4.3
Recent Barriers to Federal Funding for High-Speed Rail
While there was some measure of promise from 2008 to 2011 for high-speed rail in the US,
more recent efforts to gain funding have lost traction at the federal level. Despite the Obama
administration’s impassioned 2011 proposal for investing in high-speed rail, the 2011 and 2012
fiscal year congressional budgets both featured absolutely no money for high-speed rail [17]. After
strong opposition from a number of concerned members of Congress, the intended high-speed rail
appropriations ended up being cut out in the budget deal deliberations. These instances represent
a large step backwards for high-speed rail proponents, as they signify the relegated status of highspeed rail in the national political context.
Another frequent barrier to high-speed rail progress has occurred on the state level, where
some states have flat out rejected federal grant money for high-speed rail, as various public and
private interests opposed passenger rail projects in the state [18]. These occurrences are important
to note, as they mean that states essentially turned down federal stimulus money due to the status
of opposing interests. This paper will further explore this phenomenon in the following section.
5
State-Level Political Climate
The proposed route of the high-speed rail project in question crosses the territory of five states:
Illinois, Missouri, Arkansas, Louisiana, and Texas. Given the substantial historical weight of statelevel political sentiment in contributing to the success or failure of high-speed rail projects in US
states, it is therefore crucial to examine each state-level political climate regarding high-speed rail.
Such an evaluation will yield a greater understanding of the viability of a project along the route
that this paper proposes.
17
A large barrier to instituting a standard and comprehensive national rail policy is overcoming
the various attitudes of the states on the initiative, given that rail projects are executed at the state
level and must require prior state-level consent. Therefore, nationwide policy is often difficult to
implement, regardless of the level of national government support. In the past few years, a number
of state officials from around the country have opposed high-speed rail spending on the grounds
that such projects are simply too costly given the current economic climate and the fiscal situation
of most states. This is often a valid reason, since federal funding typically covers only initial
startup costs, leaving the states to find ways to fund the operation and maintenance of rail lines.
These high annual costs, coupled with the challenge of executing the actual construction, result in
governors and state officials who are often unwilling to pursue large scale high-speed rail projects.
In fact, according to Mary Ellen Curto, the executive director of the American High-Speed Rail
Alliance, the only possible way that high-speed rail will gain traction in the US is in regions and
states where the metrics can demonstrate that such a project would produce a positive business
model. According to Curto, “if these governors have data that show it’s not going to give a good
return on investment, then [by refusing federal high-speed rail funds] they’re doing what’s best for
their state” [19]. Thus, several factors at the state level may lead state politicians to conclude that
high-speed rail projects are not needed in their state.
Furthermore, for the rail line that this paper proposes to ultimately succeed, it would require an
effective multi-state agreement. Especially given the aforementioned barriers erected by several
state politicians, such a task seems to be somewhat improbable. As Todorovich, et al. assert, there
is currently “a lack of effective institutions and administrative structures for building and operating
multistate corridors” [10]. While federal legislation could be developed to allow the creation of
public benefit infrastructure corporations that partner with private entities to design, build, operate,
and maintain high-speed rail projects, no significant effort has been made in the US to enact such
a system. Nevertheless, this model has been used successfully in Europe, with Spain, France, and
the United Kingdom each featuring publically chartered infrastructure corporations [18]. While a
multi-state agreement appears to be increasingly difficult to create, it would be the keystone of the
18
route proposed in this paper.
5.1
Illinois
The political climate for high-speed rail in Illinois has been comparatively favorable. A May
2010 Illinois Senate Resolution embodied this sentiment, creating the Illinois and Midwest High
Speed Rail Commission. The Illinois Senate voted to create the commission with the “intent of
issuing a roadmap for the creation of bullet train lines in Illinois and neighboring states” [20]. Essentially, the purpose of the commission is to recommend “the best governmental structure for a
public-private partnership to design, build, operate, maintain, and finance a high-speed rail system
for Illinois and the Midwest” [20]. This commission is representative of the fervor for high-speed
rail in Illinois, which is currently undertaking a project to upgrade the Chicago-St. Louis Amtrak Lincoln Service to high-speed status. Illinois received $1.1 billion of the ARRA federal grant
money for the project and aims to replace several hundred miles of track [21]. With Illinois Governor Pat Quinn as a supporter of high-speed rail in the state, it appears likely that Illinois would
be one of the first parties to the aforementioned multi-state agreement.
5.2
Missouri
The most significant indicator of Missouri’s political climate towards high-speed rail is its exist-
ing partnership with Illinois to foster high-speed rail development, particularly along the ChicagoSt. Louis corridor. As part of the upgrade project along that route, Missouri has accepted a total
of $50.3 million in three separate federal grants, indicating its consent for high-speed rail development [22]. Therefore, while its sensible options for high-speed rail stations are largely limited
to St. Louis, the state of Missouri has shown a demonstrated commitment to high-speed rail and
would benefit from agreeing to a multi-state contract.
19
5.3
Arkansas
The political climate of Arkansas towards high-speed rail is relatively underdeveloped, but
there has been a recent push made to consider the possibilities for modernized passenger rail service along key routes. Last December, the state announced plans to spend between $1 million and
$1.25 million in federal and state funds to study the possibility of implementing train service up
to 200 mph along long-range routes [23]. The route between Little Rock, Arkansas and Memphis, Tennessee is to be examined, despite no existing funds for high-speed rail development in the
state. According to state officials, the purpose of this study is to be prepared in case federal funds
do actually become available. Therefore, it appears that the opportunity in Arkansas is somewhat
limited as it stands today. The fact that no public funding streams exist in the state for high-speed
rail could pose a problem for the possible development of the route that this paper proposes.
5.4
Louisiana
Among the five states that are included in the route proposed by this paper, Louisiana has likely
the worst political climate towards high-speed rail. This is evidenced by the fact that its governor,
Bobby Jindal, rejected $300 million in federal stimulus money in 2009 [24]. This money was
designated to go towards a proposed high-speed rail line connecting Louisiana’s economic center
and largest city of New Orleans and its capital of Baton Rouge, but Gov. Jindal worried about
the line’s future maintenance costs. It should be noted that the route would have been only eighty
miles apart, a distance that is conventionally understood to be better suited for auto and commuter
rail networks. Therefore, the climate towards high-speed rail in Louisiana appears to be less than
conducive to the multi-state agreement that would be required to construct the route that this paper
suggests, especially considering that there would likely be no stops in Louisiana.
5.5
Texas
There is certainly a large amount of potential for high-speed rail in Texas, considering that it is
home to four of the eleven most populous cities in the US in Houston, San Antonio, and Dallas-Fort
20
Worth (collectively known as the “Texas Triangle”), as well as Austin. In the nineties, there was a
private-led effort to connect the Texas Triangle by high-speed rail, but funding proved difficult and
the project was scrapped. The reason why funding the project privately proved so arduous was that
a coalition of large companies which felt that their business would be affected lobbied to create
legal barriers to prohibit development. This coalition, which was led by Southwest Airlines, also
included a number of hotel and fast food chains with several locations along traditional highway
routes.
Nevertheless, following a period of alternating progress and setbacks at the state level, the
Texas High Speed Rail and Transportation Corporation (THSRTC) formed in 2002. This grassroots
organization is working to further the cause of high-speed rail in Texas by gaining the support of
key businesses in Texas, including American Airlines and Continental Airlines. It has also done
studies of its own, including one in support of a “Texas T-Bone Corridor,” connecting Dallas-Fort
Worth to San Antonio and Houston [25].
The main high-speed rail project in Texas is currently a line being privately developed from
Houston to Dallas-Fort Worth [26]. This project is majority funded by a Japanese railway company,
and will seek no public money. Regardless, the project has public support and is a promising
sign for the ability to gain a multi-state agreement in a situation where a rail line crosses into
Texas. Furthermore, Texas has lots to be gained in term of economic stimulus from such a project,
meaning that it would likely be receptive to building the line that this paper proposes.
6
6.1
Energy Requirements and Considerations
Current Energy Considerations
To date, most high-speed rail lines around the world have featured diesel locomotives, with
only the newer and faster lines using electric bullet trains. There are several advantages to diesel
engines, including their efficiency per passenger/mile compared to automobiles and their long life
span [27]. These engines rely on the diesel combustion cycle, or direct-injection compression-
21
ignition. Their average thermal efficiency, which is defined as the ability to convert fuel into work,
or the amount of energy output over the amount of energy input, can be 40% or greater, according to
a study conducted by Argonne National Laboratory [27]. Furthermore, diesel locomotives typically
have long and industrious lifespans, traveling up to one million miles and lasting over forty years
before needing replacement [27]. These reasons make it easy to realize why Amtrak has used
primarily diesel locomotives for the majority of its fleet.
6.2
Proposed Utilization of High-Efficiency High-Speed Electric Trains
While diesel locomotives may be reliable and durable, they simply are not capable of the speed
necessary to make possible a project like the one that this paper proposes. In order for a highspeed train to be a viable form of transportation alongside air travel between Chicago and Texas,
it must be able to travel at speeds of roughly 220 mph while maximizing energy efficiency. In
short, only an electric-multiple-unit (EMU) train is capable of fulfilling these needs. Electrically
powered locomotives have a number of advantages over diesel engines, mainly that the efficiency
per passenger/mile is much higher for an EMU. EMUs also tend to be less complex technically
than diesel locomotives, therefore making them both easier and cheaper to maintain.
Given that the EMU train technology is the only appropriate categorization for the route proposed by this paper, the specific train model must be decided upon. For this route, it would be
advisable to utilize the Bombardier ZEFIRO380. This choice is relatively straightforward, as the
ZEFIRO380 is currently the only EMU in the world that is capable of sustaining speeds of 380
kph, or roughly 220 mph [28]. Furthermore, the ZEFIRO380 features unique aerodynamics and
Bombardier’s energy-saving ECO4 technologies, giving it the lowest energy consumption per seat
in very high-speed (VHS) rail travel [29]. These best-in-class ratings make the ZEFIRO380 an
appropriate choice for the proposed route.
The ZEFIRO380 comes in two lengths, an 8-car version and a 16-car version, seating 495
and 1,013 respectively [30]. The trains are built with three types of cars: motorized end cars;
intermediate trailer cars both with and without pantograph (the device transmitting power to the
22
train); and intermediate motorized cars [29]. Each train base unit has its own complete system
for propulsion, 400V AC auxiliary supply and 110V battery supply. The high voltage supply is
connected between the train base units; one pantograph at a time feeds all the main transformers
in the ZEFIRO380 EMU [29].
6.3
Energy Considerations of Proposed High-Speed Rail System
The proposed EMU train would run on an electric traction engine, drawing on a system of
overhead catenary cables running parallel to the tracks that conduct the electricity along the route
at 25 kV 50 Hz AC. If the route that this paper proposes follows a similar structure as the proposed
EMU line in California, then the electricity for the train will be obtained from dedicated power
substations running along the overhead lines and situated roughly every thirty miles [31]. These
power stations would draw electricity from the regional grids along the route.
With electricity requirements of 10 MW for the 8-car trainset and 20 MW for the 16-car trainset, the high-speed rail in question will certainly place a large strain on the power grids with which
it interacts [30]. This is where the train’s efficient technologies are key, lest it overwhelm the grids.
For comparison, the planned 800-mile California rail system is estimated to use 3 billion kWh/year
once operational, or a little over 1% of the state’s current total electricity consumption [31]. To
find an estimate for the system proposed in this paper, the California usage measurement can be
extrapolated as needed. Given an estimated route of roughly 1,730 miles in total, or about double
the California system, meaning a yearly energy demand of nearly 6 billion kWh/year. To put this
number into perspective, it is the equivalent to the yearly energy consumption of about 1,000,000
average households [31].
It should be noted that while the EMU trains do not run on diesel fuel, they are not always the
cleanest rail option. This is due to the fact that electric trains draw their power from the grid, much
of which is fueled by non-renewable sources [32]. Thus, when considering the true environmental
impact of high-speed rail, the original energy source must be investigated. While the California
high-speed project has committed to use only electricity generated from renewable sources, this
23
could prove very costly, given the premium that most consumers still pay for electricity produced
by renewables [33]. One possible way to get around this would be to develop an alternative energy
plan that would allow integration of solar or wind infrastructure alongside the train corridor, but
this would only add to the project’s initial costs [33].
6.4
Opportunities for Energy Efficiency: Regenerative Braking Technology
In discussing the technology behind the EMU high-speed rail systems, it is key to examine one
aspect of the train that allows it to better utilize its energy – regenerative braking. While locomotive
traction horsepower and downhill grades generate the mechanical energy a vehicle requires in order
to move, much of that energy is dispersed while braking [27]. The ZENFIRO380 is equipped with
regenerative braking technology and is therefore able to harness a braking train’s kinetic energy
and pump the electrical current back into the catenary line. This excess energy is then used as
mechanical energy by the following EMU train on the tracks [34]. Nevertheless, regenerative
brakes do become ineffective below a certain speed, which is the reason why electric trains also
require the use of mechanical brakes when fully stopping. Also, the capacity of the regenerative
braking mechanism depends on the grade of the route, the average speed, and frequency of stops
[27]. For a full diagram of the mechanisms involved in the regenerative braking process, refer to
Figure 22 and Figure 23 in Section 12.2. In sum, regenerative braking is a rather energy efficient
technology that allows modern EMU trains to cut their actual electricity consumption.
7
Operational Performance
In the following, this feasibility report will explore the operational performance and opera-
tional profitability of the proposed high-speed rail system between the Chicago metropolitan area,
St. Louis, and the Texas hubs Dallas/Fort Worth, San Antonio/Austin, and Houston. In order to
evaluate the operational performance of the system, we developed estimates of the expected ridership of the system, a ticket pricing scheme, and an operational cost structure to project operating
profit (defined here as revenue from ticket sales minus operating expenses) for the years 2020 to
24
2040. Based on these projections, we evaluated the operational profitability of the system under
different scenarios, which then allowed us to assess different financing possibilities and structures
as operational profitability is an essential prerequisite in order to attract private investor participation in the financing of the project. Lastly, we attempted to project the total present value of the
project through the simple discounted cash flow method under two different discount rates, which
we will discuss below.
Our projection period assumes completion and full operation of the system by 2020. Planning
and construction of similar electric, steel-wheel-on-steel-rail high-speed rail systems has taken
anywhere between 2 years (Paris – Lille – Channel Tunnel, France), 3 years (Beijing–Shanghai,
China), 5 years (Barcelona – Madrid, Spain), 5.5 years (Tokyo – Shin-Osaka, Japan), and 7 years
(Cologne – Frankfurt, Germany)5 . Hence, given the size and novelty of our project in the U.S., we
tentatively assume a construction period of 5-6 years for the purposes of our model, which may be a
fairly optimistic estimate given the scope, novelty, and complexity of the project. Nevertheless, our
assumption of the length of the construction and planning period has no bearing on our assessment
of the operational profitability and total project value as longer or shorter project durations would
merely shift the overall projection period. Lastly, the projection period in our model also assumes
the calculation of a terminal value after 2040 to assess the remaining value of the project beyond
the projection period. For the calculation of the terminal value in our model, we use the perpetuity
growth method, which we will elaborate on later.
The following three subsections will discuss the development of ridership estimates, the creation of a ticket pricing scheme, and the evaluation of an operational cost structure.
7.1
Estimation of the Expected Ridership
To estimate the expected ridership of our proposed HSR system, we begin by developing esti-
mates of total travel demand between the different cities. For simplicity, we define total travel as
airline and car travel between the cities. We obtained data on 2012 airline travel between the dif5
Duration of projects based on years between begin of construction and full operation of the HSR system.
25
ferent cities from the Airtime Blog, whose data is based on data from the Bureau of Transportation
Statistics [35].
Route
Airline Travelers
CHI-STL
1,600,000
CHI-DAL
1,900,000
CHI-HOU
1,700,000
CHI-AUS
589,000
CHI-SAT
418,000
STL-DAL
1,300,000
STL-HOU
469,000
STL-AUS
699
STL-SAT
45,000
DAL-HOU
3,000,000
DAL-AUS
2,100,000
DAL-SAT
2,100,000
HOU-AUS
995,000
HOU-SAT
991,000
AUS-SAT
5,000
Total
17,212,699
Table 5: Airline Travelers between each hub city on the route.
As there exists no appropriate and available data on car trips between the different cities, we
had to find a way to extrapolate the number of car travelers between the different cities from the
data on airline travelers, for which we used the travel data and statistics from the 2012 Domestic
Travel Market Report developed by the U.S. Travel Association [2]. According to the report,
total domestic travel in 2012 consisted of 2,030.3m total domestic person-trips6 , which was split
between 459.0m business trips and 1,571.3m leisure trips7 . Hence, business travel accounted for
22.6% of overall travel and leisure travel accounted for 77.4% of total travel. Furthermore, the
6
Defined as a one-person-trip of 50 miles or more, one way, away from home or including one or more nights away
from home.
7
U.S. Travel Association’s Travel Forecast Model, BLS, Department of Labor; OTTI, BEA, Department of Commerce
26
report also states that 48% of business trips are car trips while 79% of all leisure trips are car trips,
with the rest being airline trips [36]. For a visualization of these distributions, please see Figure 6.
From these splits, we can then extrapolate the distribution of overall travel between airline and car
Figure 6
travel and then calculate the number of 2012 car travelers between the cities based on the number
of 2012 airline travellers. Hence, airline travel accounts for:
0.226 × (1 − 0.48) + 0.774 × (1 − 0.79) = 0.226 × 0.52 + 0.774 × 0.21
(2)
= 0.28
(3)
= 28% of overall travel
(4)
Knowing that airline travel accounts for 28% of overall travel and that car travel makes up the
other 72%, we extrapolated the following estimation of car travelers in 2012 between the different
cities.
27
Route
Airline Travelers
Car Travelers
CHI-STL
1,600,000
4,113,061
CHI-DAL
1,900,000
4,884,261
CHI-HOU
1,700,000
4,370,128
CHI-AUS
589,000
1,514,121
CHI-SAT
418,000
1,074,537
STL-DAL
1,300,000
3,341,862
STL-HOU
469,000
1,205,641
STL-AUS
699
1,797
STL-SAT
45,000
115,680
DAL-HOU
3,000,000
7,711,990
DAL-AUS
2,100,000
5,398,393
DAL-SAT
2,100,000
5,398,393
HOU-AUS
995,000
2,557,810
HOU-SAT
991,000
2,547,527
AUS-SAT
5,000
12,853
Total
17,212,699
44,248,056
28.006% of travel
71.994% of travel
Table 6: Airline Travelers and projected number of car travelers between each hub city on the route.
Now, in order to estimate the ridership of our high-speed rail system, we assume that an existing high-speed rail line would capture a certain percentage of the overall travel market between the
different cities. We further suppose that the demand for alternative modes of transportation such
as electric, steel-wheel-on-steel-rail high-speed rail depends strongly on the price of fossil-fueled
modes of transportation. In order to proceed, we first need to determine how many people would
use an existing high-speed rail line at the current cost of driving of $0.20 per mile. The cost of
driving will serve as our proxy for the cost of fossil fuel transportation. For the past 3 years, the
cost of driving as estimated by the AAA was between $0.17 per mile and $0.20 per mile for a
medium sedan8 , which serves as the basis for our baseline driving cost assumption of $0.20 per
8
AAA (2010). Your Driving Costs, 2010 Edition; AAA (2011). Your Driving Costs, 2011 Edition; AAA (2012).
Your Driving Costs, 2012 Edition.
28
mile. As we have 2012 (FY2013) ridership data on the Amtrak connection between Chicago and
St. Louis and since we have the total number of car and airline travelers between these two cities,
we can extrapolate a base case HSR diversion rate based on this line. Currently, about 655,000
people annually travel on the Amtrak line between Chicago and St. Louis9 while 1.6m people
fly and approximately 4.1m people take the car between these two cities. Hence, approximately
655,000
(655,000+4,100,000+1,600,000)
= 10% of travelers currently use an existing “high-speed” rail line be-
tween Chicago and St. Louis at the current cost of driving. Therefore, we assume that an existing
high-speed rail system would capture 10% of all travelers between the different cities at the current
cost of driving of $0.20 per mile as our base case. As the outputs of our model (i.e. operating profits
and total project value) are highly sensitive to the baseline market capture assumption, we consider
two additional upside and downside scenarios and sensitize the outputs of our model with respect
to the base case assumption by evaluating the outputs for a baseline market capture assumption
between 5% and 65%.
Besides the baseline case of a 10% diversion from both the airline and car travel market, we also
consider an upside and downside case for the diversion of travelers from the airline and car travel
markets. It is important to consider that even though we have the same baseline case for the two
markets, we will obtain different upside and downside market capture percentage due to different
demand elasticities in the airline and car travel market. To determine our upside and downside
scenarios, we assume, in accordance with a 2013 HSR feasibility study conducted by the University
of Illinois for the Illinois Department of Transportation [37], that the demand for alternative modes
of transportation depends strongly on the price of fossil-fueled modes of transportation. Hence,
we develop our ridership estimates for three different scenarios by varying the cost of driving, i.e.
we develop estimates for an assumed cost of $0.15 per mile, of $0.20 per mile, and of $0.30 per
mile. With the downside case being $0.15 per mile and the upside case being $0.30 per mile (the
terms “downside” and “upside” are defined with respect to the ridership estimates for our model),
we analyze driving costs within 75 percent to 150 percent of the current level (see above). Similar
9
Amtrak Press Release (14 Oct. 2013): http://www.amtrak.com/ccurl/730/658/FY13-Record-Ridership-ATK-13122.pdf
29
to the University of Illinois study, we also assume that “any significant change in driving cost is
likely to be accompanied by increases in the costs of other modes that rely on fossil fuel” [37]
and accordingly assume that the cost of flying also decreases by 25% or increases by 50% for our
scenarios. However, this 25% cost decrease for the downside case as well as the 50% cost increase
for the upside case have different effects on the demand in the car and air travel market as the
markets display different elasticities of demand.
The medium-term elasticity of vehicle miles traveled (VMT) with respect to gasoline prices
(here VMT serves as our proxy variable for the demand for car travel while the gasoline price
serves as the proxy for driving costs) is estimated to fall anywhere in the range of -0.15 to -0.25.
This range is based on a brief survey of the current state of research in Gillingham (2013), who
himself estimates the elasticity of VMT to gasoline prices at -0.22; on Austin (2008), who finds
elasticities from -0.10 to -0.16 in the short run and -0.26 to -0.31 in the long run; on Graham
and Glaister (2002), who estimate the short-term elasticity at -0.15 and the long-term elasticity at
-0.30; as well as on Schimek (1998) who finds a range from -0.19 to -0.32 for the VMT elasticity
with respect to gasoline prices. In accordance with these studies, we use -0.20 as the medium-term
demand elasticity of car travel with respect to driving costs [38] [39] [40] [41].
The air travel demand elasticities for different markets were estimated by InterVistas Consulting and the IATA in 2007. For domestic U.S. long-haul flights, their study finds an air travel
demand elasticity with respect to air fares of -0.80, assuming a “combination of the route own-price
elasticity with cross-price elasticities when all national routes have prices which vary identically”
[42]. Hence, we assume an air travel demand elasticity of -0.80 when evaluating the effect of the
cost increase and decrease on the demand for U.S. air travel.
Assuming demand elasticities of -0.20 for car travel demand and of -0.80 for air travel demand,
we see that a 25% cost (price) decrease would lead to a 5% demand increase in car travel and to
a 20% demand increase in air travel. Similarly, a 50% cost (price) increase would lead to a 10%
demand decrease in car travel and to a 40% demand decrease in air travel. We assume that these
travelers are diverted to the HSR market. Hence, we have an upside case of an additional 40% and
30
10% of market capture in the air travel and car travel markets, respectively. Correspondingly, we
have a downside case of an additional -20% and -5% of market capture in the air travel and car
travel markets, respectively. Please refer to Table 7 for a quick overview of the different scenarios.
Market Capture by HSR
% of air travel
% of car travel
Upside Scenario
50%
20%
Baseline Scenario
10%
10%
Downside Scenario
-10%
5%
Table 7
The downside diversion rate of -10% for the air travel market means that in the case of a 25%
decrease of the costs of air travel, the number of air travelers would actually increase and air travel
would divert travelers from other modes of transportation such as high-speed rail. Based on these
three different scenarios, we can estimate the hypothetical 2012 ridership of our high-speed rail
system based on the 2012 air travel data and the extrapolated 2012 car travel numbers. We use the
term ‘hypothetical’ since there existed no complete high-speed rail system between these cities in
the year 2012 as we envision it. Please refer to Table 8 for an overview of the hypothetical 2012
ridership estimates under the three different scenarios for each of the routes.
We believe these numbers to be reasonable, but fairly conservative, estimates of the hypothetical 2012 ridership of the HSR system between Chicago, St. Louis and Texas. In particular, the
downside ridership scenario is fairly pessimistic as our model assumes that the 10% increase in air
travel under the 25% decrease of air travel costs comes entirely from a diversion of high-speed rail
travelers. A similar feasibility study conducted by the University of Illinois for the Illinois Department of Transportation in 2013 estimated the current ridership of an HSR system between O’Hare,
Chicago, St. Louis, and Indianapolis at an annual ridership of 14.6m for the driving cost scenarios
of $0.30 per mile [37]. Hence, it makes sense for our upside estimates to be slightly higher than the
one developed in the University of Illinois study as we are studying the connection of the Chicago
metropolitan area not only with St. Louis and/or Indianapolis, but with a much larger number of
inhabitants in the three Texas hubs Dallas/Fort Worth, Houston, and San Antonio/Austin.
31
Route
Upside Scenario
Baseline Scenario
Downside Scenario
CHI-STL
45,653
571,306
1,622,612
CHI-DAL
54,213
678,426
1,926,852
CHI-HOU
48,506
607,013
1,724,026
CHI-AUS
16,806
210,312
597,324
CHI-SAT
11,927
149,254
423,907
STL-DAL
37,093
464,186
1,318,372
STL-HOU
13,382
167,464
475,628
STL-AUS
20
250
709
STL-SAT
1,284
16,068
45,636
DAL-HOU
85,600
1,071,199
3,042,398
DAL-AUS
59,920
749,839
2,129,679
DAL-SAT
59,920
749,839
2,129,679
HOU-AUS
28,391
355,281
1,009,062
HOU-SAT
28,276
353,853
1,005,005
AUS-SAT
143
1,785
5,071
Total
491,133
6,146,075
17,455,961
Table 8: Hypothetical 2012 HSR System Ridership.
From these hypothetical 2012 ridership estimates, we can extrapolate the annual ridership estimates for the projection period (2020 to 2040) by assuming an annual 3% growth rate of overall
travel demand and correspondingly of the HSR ridership. This estimated 3% annual growth rate is
based on the 1995 – 2001 transportation sector compound annual growth rate (CAGR) of 4.21%10 ,
the 1995 – 2001 rail transportation CAGR of 3.52%11 , and the 2000 – 2013 Amtrak ridership
CAGR of 3.23% . Please refer to Table 9 for an overview of the estimated ridership on the different routes for 2012, 2020, 2030, and 2040 under the baseline scenario defined above, assuming a
3% annual ridership growth rate.
In the model, we project the ridership for every single year between 2012 and 2040 on every
10
Calculation based on data from U.S. Department of Commerce, Bureau of Economic Analysis, Industry Economic
Accounts
11
Calculation based on data from U.S. Department of Commerce, Bureau of Economic Analysis, Industry Economic
Accounts.
32
route between the different cities.
Route
2012 (Hypothetical)
2020E
2030E
2040E
CHI-STL
571,306
723,714
972,610
1,307,107
CHI-DAL
678,426
859,410
1,154,975
1,552,190
CHI-HOU
607,013
768,946
1,033,399
1,388,801
CHI-AUS
210,312
266,417
358,042
481,179
CHI-SAT
149,254
189,070
254,094
341,482
STL-DAL
464,186
588,017
790,246
1,062,025
STL-HOU
167,464
212,139
285,096
383,146
STL-AUS
250
316
425
571
STL-SAT
16,068
20,354
27,355
36,762
DAL-HOU
1,071,199
1,356,963
1,823,645
2,450,826
DAL-AUS
749,839
949,874
1,276,551
1,715,578
DAL-SAT
749,839
949,874
1,276,551
1,715,578
HOU-AUS
355,281
450,059
604,842
812,857
HOU-SAT
353,853
448,250
602,411
809,589
AUS-SAT
1,785
2,262
3,039
4,085
Total
6,146,075
7,783,403
10,460,243
10,460,243
Table 9: Projected HSR System Ridership: Baseline scenario of 10% diversion to HSR
7.2
Ticket Pricing Scheme
Having projected the ridership of the high-speed rail system for our projection period, we then
developed a ticket-pricing scheme in order to determine the ticket prices for each route and to
project the revenues from ticket sales of the high-speed rail system. In order to develop a ticketpricing scheme, we first calculated the weighted average cost of travel between each of the two
different cities based on the costs of airline and car travel between each of the cities. The cost
of airline travel was defined as the cost of the cheapest, round-trip economy airfare between the
two different cities based on Google Flights search results. The cost of car travel was defined as
the number of miles between the cities times the baseline costs of owning and operating a car of
33
$0.20 per mile as given by the AAA (please see above for a discussion of driving costs). We then
calculated the weighted average of these two costs based on the distribution of total travel between
car travel (72%) and airline travel (28%) defined in the previous section. We then priced the train
tickets for each of the routes by applying a multiple between 0.50 and 1.25 to the weighted average
cost of travel for each route, with our base case being a multiple of 1.0. For example, a multiple of
0.75 would mean that a HSR ticket would cost 75% of the weighted average cost of travel between
the two cities. In our base case, we price tickets exactly at the weighted average cost of air and
car travel between two respective cities. As the outputs of our model, i.e. operating profits and
total project value, are highly sensitive to the ticket pricing multiple, we sensitize our outputs with
regards to the ticket pricing multiple. Please refer to Figure 7 for an overview of different HSR
ticket pricing schemes and a comparison of the HSR ticket price with the cost of airline and car
travel.
Figure 7
A green field indicates that the HSR ticket price is cheaper than the corresponding airfare
34
or cost of car travel; similarly, a red field indicates that the HSR ticket price is more expensive
than the corresponding airfare or cost of car travel. We vary the pricing multiples in 0.125 steps
between 0.50 and 1.25. We notice that for pricing multiples between 0.75 and 1.00, the HSR ticket
price tends to be more expensive than the airfare but cheaper than the cost of car travel for the long
distance routes between Chicago and Texas. Similarly, the HSR ticket is cheaper than the airfare for
the inner-Texas connections, but more expensive than the cost of car travel on these comparatively
short routes. Over the range of all ticket pricing multiples, we notice that the HSR ticket prices
are most competitive compared with the airfares and costs of car travel for the routes between
St. Louis and all other destinations (Chicago and the Texas hubs). For a ticket pricing multiple
between 0.50 and 0.75 the HSR ticket prices become cheaper and more competitive than both other
modes of transportation on more and more routes; eventually, at a ticket pricing multiple of 0.50,
the HSR is cheaper than both other modes of transportation on every route besides the car route
from San Antonio to Austin. For a pricing multiple between 1.0 and 1.25, the HSR ticket price
becomes more expensive and less competitive on an increasing number of routes. For example, at a
pricing multiple of 1.125, the HSR ticket is only cheaper than car travel for the ultra-long distances
Chicago-Houston and Chicago-San Antonio, while still being more competitive than the airfares
for inner-Texas travel and for the Chicago-St. Louis route. At a multiple of 1.25, the HSR ticket is
only cheaper than airfares on the Chicago-St. Louis route and on the inner-Texas connections.
We can draw the following conclusions from these observations. First, high-speed rail seems
to be most competitive compared with airline and car travel for rail track distances between 250
and 1100 miles, such as the connections between St. Louis and all other destinations. For rail track
distances above 1100 miles such as the Chicago-Texas connections, airfares tend to be cheaper
even when we price tickets below the weighted average cost of travel. For distances shorter than
250 miles such as the inner-Texas connections, car travel tends to be cheaper. However, the slightly
more expensive ticket prices if compared with long-distance airfares or short-distance car travel can
be justified by the increased comfort, service, and time savings of high-speed rail. For example,
high-speed rail does not require lengthy drives to an airport and the search for airport parking as
35
well as complicated, stressful, and time-consuming check-in and security processes. Furthermore,
high-speed rail cars offer a lot more seat comfort than economy class airplane seats and high-speed
rail operators usually do not charge for or limit luggage. Lastly, if compared with driving, highspeed rail also increases seating comfort and removes the negative effects on the driver such as
stress and tiredness. The use of high-speed rail reduces congestion and the probability of accident
as opposed to car travel, making high-speed rail system a considerably safer mode of transportation
than highway car travel.
Lastly, for the purposes of our model, we assume that ticket prices grow at 2% annually during
our projection period, representing an adjustment for annual inflation. Hence, we have the following ticket prices for each of the routes for 2013 (hypothetical), 2020, 2030, and 2040 under our
base case ticket pricing multiple of 1.0x:
Route
2012 (Hypothetical)
2020E
2030E
2040E
CHI-STL
$139.86
$163.87
$199.76
$243.50
CHI-DAL
$330.43
$387.15
$471.94
$575.29
CHI-HOU
$378.54
$443.52
$540.65
$659.05
CHI-AUS
$400.07
$468.75
$571.40
$696.54
CHI-SAT
$425.10
$498.07
$607.14
$740.10
STL-DAL
$255.06
$298.84
$364.28
$444.06
STL-HOU
$314.48
$368.46
$449.15
$547.51
STL-AUS
$330.28
$386.97
$471.72
$575.02
STL-SAT
$347.77
$407.47
$496.70
$605.47
DAL-HOU
$106.86
$125.20
$152.62
$186.04
DAL-AUS
$95.01
$111.32
$135.69
$165.41
DAL-SAT
$131.18
$153.70
$187.35
$228.38
HOU-AUS
$98.30
$115.18
$140.40
$171.15
HOU-SAT
$106.44
$124.71
$152.03
$185.32
AUS-SAT
$106.68
$124.99
$152.36
$185.73
Table 10: Projected HSR Ticket Prices: Baseline Multiple of 1.0x
36
7.3
Cost Structure
Having developed ridership estimates and a ticket pricing scheme for each of the routes, we
calculated revenues from ticket sales for each of the routes by multiplying the number of riders
times the price per ticket on each route and determined the overall annual revenue from operations
of our high-speed rail system. In order to project the operating profit of the system, we then
determined an operating cost structure. The costs of maintaining and operating a high-speed rail
system can be divided into two categories, i.e. into fixed operating costs independent of the number
of riders and variable operating costs, which depend on the number of riders. The total annual
operating expenses are defined as the sum of the fixed operating costs and the variable operating
costs.
Fixed operating costs include the annual expenses for infrastructure maintenance and operation, which include the costs of labor, energy, and materials expanded on the maintenance and
operations of the track system, stations, energy supplying and signaling systems, as well as on
traffic management and safety systems. A 2008 European study estimates the cost of infrastructure maintenance at $140,000 per mile per year for a two-track rail system [43], while another
British feasibility study put forward the considerably higher estimate of $493,000 per mile per
year [44]. In his cost-benefit analysis of a proposed Texas high-speed rail line, Harvard economist
Edward Glaeser chooses an estimate of $200,000 per mile per year [44]. In accordance with these
estimates, we chose an infrastructure maintenance expense of $150,000 per mile per year as our
baseline scenario, bringing the initial annual track maintenance expense to $266.25m for our 1730mile-long track. Similar to all other cost and revenue drivers, we also assume a 2% annual increase
in the fixed infrastructure maintenance expenses, holding the total number of track miles constant
at 1730 miles over our entire projection period between 2020 and 2040. We also considered scenarios with a infrastructure maintenance expense of $100,000 and $200,000 per mile per year, but
found the impact of these variations on the operational profitability and on the total project value
to be minimal as the total infrastructure maintenance expenses only account for 3% to 9% of total
expenses, depending on the choice of scenario.
37
We will define variable operating costs to depend on the number of riders or, more specifically,
on the number of passenger miles travelled, which are defined as the number of riders times the
number of miles traveled. We calculate the annual passenger miles of our high-speed rail system by
multiplying the number of riders on each route times the length of the route for each route between
the different cities. Basing our variable cost assumptions on the total annual number of passenger
miles simplifies our model as we do not have to make assumptions about operating frequencies
and schedules, about the number of train sets, cars, and seats, or about seat load factors. On the
down side, this approach neglects the acquisition costs of rolling stock as well as the implicit
costs of operating with less-than-optimal seat load factors, i.e. the costs of operating with empty
seats. For the purposes of our high-speed rail system model, variable operating costs include
the costs of operations of the actual high-speed rail transportation services, i.e. the cost of train
operations, the maintenance costs of rolling stock and other equipment, the energy costs, as well as
selling, general, and administrative expenses. The variable costs of electric, steel-wheel-on-steelrail high-speed rail service operations vary widely depending on country and train operator. For
example, a 1997 study estimates the operating costs of HSR services at $0.105 per passenger mile
[44]. Another more recent European study estimates the variable costs of operating an electric
HSR system at $0.50 per passenger mile [43], while Amtrak’s operating expenses come in at
approximately $0.45 per passenger mile. For the purposes of our model, and in accordance with
the cost-benefit analysis performed by Glaeser [44], we averaged between the low estimate of
$0.10 and the high estimate of $0.50 to assess our variable costs of the HSR operations at $0.30
per passenger mile. As with all other cost and revenue drivers, we assume an annual increase of
2% for the variable costs of operating the HSR system. As the outputs of our model are highly
sensitive to this cost assumption, we also consider two additional scenarios with assumed costs
of $0.15 and $0.45 per passenger mile and sensitized the total project value with respect to the
variable operating cost assumption.
38
7.4
Evaluation of Operating Performance
Having calculated the annual revenues and operating expenses of our proposed high-speed rail
system, we can now evaluate its operational performance by calculating the system’s operating
income, which we define as revenue from ticket sales minus total operating expenses. In this
section, we will present profit-and-loss calculations for the years 2020, 2025, 2030, 2035, and
2040 for 15 different scenarios to give the reader an impression of the range of profitability (or
lack thereof) of our proposed high-speed rail system between Chicago, St. Louis, and the Texas
hubs. It is worth noting that we will be fairly conservative and pessimistic on the cost side by
never assuming the ‘low’ scenario for variable operating costs throughout our entire analysis. We
will consider the following 15 scenarios (displayed on the next page) to evaluate the operating
profitability of the Chicago-St. Louis-Texas high-speed rail system. See Section 12.1 for the
Profit-Loss Analysis for each ridership scenario.
39
Scenario
I
Ridership1
Low
Ticket
Prices2
Low
Variable
Operating
Costs3
Baseline
II
Low
Low
High
III
Low
Baseline
Baseline
IV
Low
High
Baseline
V
Low
High
High
VI
Baseline
Low
Baseline
VII
Baseline
Baseline
High
VIII
Baseline
Baseline
Baseline
IX
Baseline
High
Baseline
X
Baseline
High
High
XI
High
Low
Baseline
XII
High
Baseline
High
XIII
High
Baseline
Baseline
XIV
High
High
Baseline
XV
High
High
High
Description / Comment
• Low utilization of the system due to low demand for
domestic U.S. HSR services.
• Low demand causes low prices; costs at baseline
level.
• Low utilization of the system due to low demand for
domestic U.S. HSR services.
• Low demand causes low prices – no power to
increase prices without destroying demand; costs
are high due to uncontrollable, external factors (e.g.
electricity costs). Extremely pessimistic scenario.
• Baseline revenue and cost scenario with low
demand for HSR.
• Possibly low demand for HSR due to high prices.
• High prices due to market power or due to low, but
inelastic demand; costs are at baseline level.
• Possibly low demand for HSR due to high prices.
• High prices due to high operating costs.
• Average demand for domestic U.S. HSR system.
• Conservative scenario: low pricing despite baseline
cost level.
• More pessimistic version of scenario VII on the cost
side. Adjustment of pricing to baseline level due to
high variable operating costs.
• Overall baseline scenario.
• Average demand for domestic U.S. HSR system.
• High prices due to market power or due to inelastic
demand; costs at baseline level.
• Average demand for domestic U.S. HSR system.
• High prices due to high operating costs.
• High demand for domestic U.S. HSR system.
• Conservative scenario: low pricing despite baseline
cost level.
• More pessimistic version of scenario XI on the cost
side, more optimistic version of scenario VII on the
demand side.
• Pricing adjusted to baseline level due to high
variable operating costs coupled with high demand.
• Baseline revenue and cost scenario with high
demand for HSR.
• High demand for domestic U.S. HSR system.
• High pricing possible due to high demand for HSR.
• High demand for domestic U.S. HSR system.
• High pricing necessary and possible due to high
operating costs and high demand for HSR.
1
Based on percentage of air and car travel market diverted to HSR. Low = 5% of air travel, 20% of car travel; Baseline = 25% of
air travel, 25% of car travel; High = 65% of air travel, 35% of car travel. See section on ridership estimation for details.
2
Based on ticket pricing multiple. Low = 0.875x; Baseline = 1.0x; High = 1.125x.
3
Based on variable operating cost assumption. Low = $0.15 per passenger mile; Baseline = $0.30 per passenger mile; High =
$0.45 per passenger mile.
7.5
Summary of the Scenario Profit-and-Loss Analysis
From these profit-and-loss analyses for our 15 different scenarios, we can draw the following
conclusions. For detailed tables displaying the profit-and-loss calculations under the 15 different
scenarios, please refer to the profit-and-loss analysis tables in the appendix Section 12.1. First,
we notice that the project only becomes operationally profitable in scenarios VII, IX, XIII, XIV.
Hence, it is impossible to achieve operational profitability in the downside ridership case as scenarios I through V all produce operating deficits during the projection period. Furthermore, it is
also impossible for the project to become operationally profitable if the HSR system is faced with
variable operating costs higher than the base case scenario of $0.30 per passenger mile since scenarios II, V, VII, X, XII, and XV are operationally unprofitable. Lastly, a ticket pricing multiple
below 1.0x also leads to operational unprofitability as scenarios I, II, VI, XI, too, produce operating deficits during the projection period. Even though we chose fairly conservative and pessimistic
scenarios in our analysis by never assuming a low cost environment for the operation of the HSR
system, it is worth noting that only 4 out of 15 investigated scenarios become operationally profitable during the projection period. These 4 operationally profitable scenarios all assume a base
case operating cost structure of $0.30 per passenger mile and assume either a high (1.125x) or base
case (1.0x) ticket pricing multiple. Hence, it is likely that the project may not become operationally
profitable under a variety of scenarios – or at least not within our projection period – which means
that the project’s continued operation would require public operating subsidies, which need to be
taken into account in the social cost-benefit-analysis and in the decision about the project.
8
Valuation of the Project
Having projected the operational profitability of the proposed Chicago-St. Louis-Texas high-
speed rail system and evaluated the operational performance under different scenarios, we will now
assess the present value of the project’s future operating surplus through the simple discounted cash
flow method. It is essential to note that this does not represent a net present value calculation of the
41
project, i.e. we will not subtract the initial capital investment from the present value of the future
operating profits; we will merely calculate the present value of the future operating income of the
project. Calculating the present value of the operating income from the project allows us to assess
how much of the capital construction cost could be covered by selling claims to future operating
income (equity investments) or by borrowing against future operating income (debt investments).
A positive present value of the project’s operation, i.e. operational profitability, is a necessary
requirement to be able to attract private financial participation in the development of this HSR line.
We performed a discounted cash flow analysis for all 15 different operating scenarios and we
performed our analysis by assuming two different discount rates. First, we assumed a discount
rate of 7%, the opportunity return of private sector investments as defined in class as well as the
discount rate used for the California HSR project [45]; second, we assumed a discount rate of 11%,
the rate used in the University of Illinois / Illinois Department of Transportation study [37]. In our
analysis, we discounted the operating income for every year in the projection period to the present
(i.e. 2013) by applying our respective discount rate. We then calculated the sum of these present
values to obtain the present value of all operating income in the projection period from 2020 to
2040. In our analysis, we did not project operating income beyond 2040 but since we assume a
perpetual operation of the project, we calculate the so-called terminal value of the project after
2040 to assess the remaining value of the project operation beyond 2040. In order to calculate
the terminal value in our model, we use the perpetuity growth method, which values the operating
income beyond the projection period as a growing perpetuity based on the operating income in
year one after the period. We chose a baseline perpetuity growth rate of 5.0% (also referred to
as “terminal value growth rate” in our model), which corresponds to
1
2
of the project’s operating
income CAGR between 2030 and 2040 under the overall base case scenario (scenario VIII). After
calculating the terminal value in the year 2040, we then discount the terminal value to the present
(i.e. 2013) by applying our discount rates. By adding the terminal value to the present value of all
operating income in the projection period, we obtain the present value of the operating income from
the entire project. As mentioned at several instances above, we also sensitized the present value of
42
the project operation in our base case scenario with respect to the discount rate, the terminal value
growth rate, the market capture percentages, the assumed ridership growth, the operating costs per
passenger mile, and the ticket pricing multiple. Such a sensitivity analysis allows us to assess the
full effects of our assumptions on the model outputs and assists in the determination of the most
realistic range for the present value of the project.
In the following, we will briefly discuss the choice of our discount rates before elaborating
on the results of the discounted cash flow (DCF) analysis under the two different discount rate
scenarios and the 15 different operating scenarios. Lastly, we will present the sensitivity analyses
of the project value and develop a final assessment of the approximate range of the present value
of the project, before presenting a brief discussion of financing possibilities and structures for this
project.
8.1
Discount Rates
As discussed above, we conducted our project valuation with two different discount rates. First,
we assumed a discount rate of 7%, which corresponds to the private sector opportunity return rate
and to the discount rate used for the California HSR project [45]. The private sector opportunity
return of 7% is calculated as the weighted average of the before-tax return on investments (ROI) in
ordinary private businesses (return of 9%; approx.
2
3
of all investments) and the before-tax return
on investments (ROI) in housing (return of 3%; approx.
1
3
of all investments). This 7% discount
rate is further consistent with the U.S. Department of Transportation guidance for TIGER II grants
and OMB Circular A-4 and A-94. Even if the project may be entirely publicly funded by federal
and state governments, we use the private sector opportunity return of 7% as the discount rate as
opposed to the riskless rate since the source of the project funding as well as the (riskless) interest
paid do not affect the fact that real resources are used for the project, which could be put to other
productive uses in the private sector that would earn an approximate 7% return. Furthermore, 7%
represents the academic estimate of an average rate of return over an infinite number of investments
in the economy and also corresponds to the historical academic estimate of the so-called equity risk
43
premium, which is another measure of the opportunity rate of return of private sector investments.
Moreover, we use the private sector opportunity return rate without assessing a risk discount or
premium as the discount rate of 7% is measuring the opportunity returns foregone in an alternative
use of the resources, which is not affected by the degree of riskiness of the investment that is being
undertaken. Hence, we decided, in accordance with Harberger and Sjaastad, to use the estimated
opportunity return on private sector investment of 7% as the appropriate estimate of our discount
rate for benefits that will occur within 25 years, i.e. within our projection period. The present
value of the project calculated with this interest rate will be the opportunity value that society has
to forego in order to built this HSR system between Chicago, St. Louis, and Texas. However, this
does not indicate how a private investor, who is considering funding the project, will evaluate the
project, which leads us the introduction of an additional interest rate.
Secondly, we also conducted our DCF analysis with an assumed discount rate of 11%, which
correspond to the rate used in the University of Illinois study [37]. This discount rate of 11% is
used to reflect the fact that if this infrastructure project had to compete for funding in the private
sector, a risk premium over the private sector opportunity return would be required to compensate
for the uncertainty about the generation of future operating income. Hence, we also perform our
analysis with a discount rate that is higher than the private sector opportunity rate of return of 7%
to assess how much of the capital construction costs could be financed by tapping private sector
equity and debt resources. The present value of the project operation calculated with this interest
rate of 11% will be the value of the project to a potential debt or equity investor in the private
funding market.
8.2
Results of the Simple DCF Method
Figure 8 summarizes the results of our DCF analyses under the 15 different scenarios and under
the assumption of the two discount rates of 7% and 11%. Columns 4 and 6 indicate how much of
the project’s capital construction cost could be financed by the present value of the project’s future
operating profitability assuming the two different discount rates.
44
Figure 8
We notice that, in accordance with Section 7.5, only scenarios VIII, IX, XIII, and XIV are
operationally profitable and therefore produce a positive operating income present value. For a
detailed discussion of the drivers behind the profitability of these scenarios, please refer to Section
7.5 above. Our overall base case, i.e. scenario VIII, which assumes a 10% diversion rate from
the air and car travel market, ticket pricing at 1.0x times the weighted average travel costs, and a
variable operating cost of $0.30 per passenger mile, produces a positive present value of the future
operating profits under both discount rate assumptions of 7% and 11%. Assuming the private sector
opportunity return rate of 7% as the discount rate for the project, the project has a total present
45
value of $3.3bn, which means that 2.7% to 2.8% of the initial capital construction costs could be
covered by the projects operating surpluses, with the exact percentage depending on the percentage
of elevated tracks as part of the full track mileage. Under the 11% discount rate, which is supposed
to reflect the attitude of a potential investor in the private, debt or equity funding markets, the
project has a total present value of $646m, which could help to finance up to 0.5% of the overall
construction costs of $118bn to $125bn. However, even under the most optimistic scenario, i.e.
scenario XIV, which assumes high demand for HSR, a high pricing multiple of 1.125x, and an
average cost structure, our HSR project is facing a minimum funding gap of approximately 65%
under the 7% discount rate, i.e. gap of c. $77bn - $81bn, or a funding gap of approximately 91%
under the 11% discount rate, i.e. a gap of c. $107bn - $114bn. In other words, there is no scenario
under which the overall project would ever become net present value-positive when taking into
account the initial capital construction costs, regardless of which discount rate we assume, which
is not an unfamiliar occurrence for national infrastructure projects of this scope, which tend to
have extraordinarily high construction costs. Hence, we can conclude that a private investor is very
unlikely to undertake the whole project independently and that the project most certainly requires
funding by the federal and by the respective state governments should it ever become a reality.
Nevertheless, there are operating scenarios in which the present value of the operating surplus is
positive and could be used to induce the participation of private investors in the project by selling
ownership claims to future operating income in exchange for an upfront equity contribution or by
issuing debt backed by the future stream of operating surpluses. Even if small compared with the
initial capital requirements, it is encouraging to see that the project’s present value is positive in
our base case scenario, which we deem to be a fairly realistic, but appropriately conservative and
not overly optimistic base case. In the following, we will explore the public, private, and mixed
financing possibilities and structures for the proposed HSR project in Section 9 after we briefly
discuss the outcomes of our sensitivity analysis of the project’s present value.
46
8.3
Sensitivity Analysis
Having evaluated the present value of the project’s entire operation under our 15 different
operating scenarios and by assuming two different discount rates, we will now perform several
sensitivity analyses on our base case scenario, i.e. scenario VIII, in order to assess the full effects
of our assumptions on the model outputs and to determine the most realistic range for the present
value of the project. Scenario VIII is characterized by a HSR market capture percentage of 10%
of the total air and car travel markets, a pricing scheme that prices the HSR tickets at the weighted
average cost of travel (pricing multiple of 1.0x), and by moderate variable operating costs of $0.30
per passenger mile. We will perform 3 sensitivity analyses on the present value of the project’s
operating income in the operating scenario. First, we will sensitize the project value with respect
to the terminal value growth rate and with respect to the discount rate to evaluate the impact of
our DCF model assumptions on the outputs of the model. Second, we will sensitize the project
value with respect to our ridership assumptions, i.e. we will sensitize it with respect to the market
capture assumption and with respect to the assumed ridership growth rate. Third, we will sensitize
the project value with respect to the main profit and loss drivers, i.e. we will evaluate the sensitivity
of the project value with respect to the variable operating costs per passenger mile and with respect
to the ticket pricing multiple. The second and third sensitivity analysis will differ for the two
different discount rate assumptions of 7% and 11%; the first analyses will be equivalent as we are
sensitizing the model output with respect to the discount rate.
Please refer to Figure 9 and Figure 10 for a visualization of the impact of our DCF assumptions,
i.e. of the terminal value growth rate and of the discount rate, on the present value of the project’s
operations.
We see that, depending on the discount rate and on the terminal value growth rate, the present
value of the project’s overall operating profits varies anywhere between approximately $300m and
$6.1bn. Our base cases, which assume a terminal value growth rate of 5% (corresponding to the
operating profit CAGR between 2030 and 2040 in the base scenario) in combination with our two
discount rates of 7% and 11%, are bolded in the table and we observe the same present values as
47
Figure 9
Figure 10: Variation of the Project Value: Discount Rate / Perpetuity Growth Rate
presented in the DCF output table for scenario VIII. Generally, we can observe that the lower the
discount rate, the higher the project value; similarly, the higher the terminal value growth rate, the
higher the project value. We further note that we do not obtain a value for a discount rate 7% and
a terminal value growth rate 7% as these parameters lead to a division by zero in the calculation of
48
the terminal value through the perpetuity growth method.
From the point of view of society, the values in the column corresponding to a discount rate
of 7% represent the opportunity value of the project that society has to forego in order to build
the HSR system since 7% is the private sector opportunity return. From the point of view of an
investor in the private funding markets, the column corresponding to a discount rate of 11% (or
above) seems to be most relevant as the values in this column correspond to the expected value
of the project to an investor in the private funding markets. Hence, we see that, in our overall
base case scenario VII, which we deem to be an appropriately conservative and realistic operating
scenario, the value of the project to a private investor varies between $500m and $830m (11%
discount rate). By further restricting the choice of the terminal value growth to values between 3%
and 5% (we believe that any operating profit growth rate above 5% is difficult to maintain in the
long run and that any growth rate below 3% represents a mismanagement of the system), we can
further limit the range of the present value of the project to between $550m and $650m.
In the following, we will now evaluate the sensitivity of the project’s present value with respect
to the ridership assumptions, i.e. we will sensitize the present value of the operating profits with
respect to the market capture assumption and with respect to the assumed ridership growth rate. We
will perform this sensitivity analysis for both discount rates 7% and 11%. Please refer to Figure 11
and Figure 12 for visualizations of the sensitivity analyses.
Figure 11: Discount rate of 7%
49
Figure 12: Discount rate of 11%
With a discount rate of 7%, the total present value of the project falls anywhere in between
($3.47bn) and $96.33bn, depending on the market diversion assumption and the ridership growth
rate assumption. With a discount rate of 11%, the present value of the project’s operation falls
anywhere between ($1.06bn) and $22.00bn. Intuitively, the higher the ridership growth rate or the
higher the travel market diversion percentage, the higher the value of the project in both discount
rate scenarios since both the ridership growth rate and the market diversion percentage are important drivers of the projects operating revenue. Regardless of the choice of discount rate, we can
observe that it is difficult for the project to be profitable and to have a positive present value if
the HSR system is unable to capture more than 5% of the air and car travel market. According to
the two tables above, the present value is only barely positive for low ridership growth rates when
the HSR system diverts 15% of both air and car travel markets. Hence, we can conclude that it is
essential for the success of the project that the HSR system diverts significantly more than 5% of
all air as well as car travelers between the different cities; more realistically, the project will only
be successful if the HSR system captures at least 15% of both travel markets in the long run.
Lastly, we will sensitize the project value with respect to the main revenue and cost drivers, or
– in other words – with respect to the most important profit or loss drivers, i.e. we will evaluate the
sensitivity of the project’s present value with respect to the variable operating costs per passenger
mile and with respect to the ticket pricing multiple. Please refer Figure 13 and Figure 14 for
50
visualizations of the sensitivity analyses under the two different discount rate scenarios of 7% and
11%.
Figure 13: Discount rate of 7%
Figure 14: Discount rate of 11%
Regardless of the choice of discount rate, we can observe that in the base case cost scenario of
$0.30 per passenger mile or at costs per passenger mile above this level, tickets have to be priced
exactly at the weighted average cost of travel or above (pricing multiple of greater than or equal to
1.0x) in order for the project to be operationally profitable. Hence, we can conclude that the HSR
system will not be able to compete with conventional air and car travel by offering lower prices
than the two other modes of transportation, which means that the HSR service has to compete by
offering higher comfort (as opposed to airline travel) as well as time savings (as opposed to car
travel) in order to attract and retain demand from the air and car travel markets. At a pricing mul51
tiple of 1.0x (and above, for the most part), HSR tickets will be competitive compared to airfares
on low to medium distances such as the inner-Texas routes or the St. Louis connections. However,
the ticket price will most likely not be cheaper than the cost of car travel on these distances. Similarly, HSR ticket prices will be competitive with regards to the cost of car travel on long distances
such as the Chicago-Texas connections, but HSR ticket prices will most likely not be cheaper than
airfares for these distances if the system is operationally profitable. Price competition with airlines
(as well as with car travel) only becomes possible when the costs per passenger mile drop below
$0.30, at which point the pricing multiple could be set to a level where HSR travel becomes be
cheaper than both airline and car travel on certain routes. As mentioned previously during the
discussion of ticket pricing, the HSR system seems to have the biggest competitive advantage on
medium distances between 250 and 1100 miles, such as the routes connecting St. Louis to all the
other cities.
9
Financing
After evaluating the operational performance of the HSR system under different scenarios and
calculating the present value of the operating surpluses of the project under these same scenarios,
we will now turn to a discussion of the availability of funding and financing for a high-speed rail
infrastructure project of this scope. We will proceed by first providing a brief historical overview
of the financing of high-speed rail construction and operation projects in different countries before
turning to a discussion of possible funding sources for this project. Lastly, we will provide a brief
conclusion of the section and explore additional possible considerations.
9.1
History of High-Speed Rail Financing
Reviewing the history of high-speed rail development, construction, and operation projects, we
can identify three key financing trends in the development of high-speed rail systems.
The first phase encompasses early Japanese and French projects such as the Tokaido Shinkansen
line and the Paris-Lyon TGV line, which were funded and (initially) operated entirely by public
52
entities and agencies [37]. The Tokaido Shinkansen line was constructed between 1959 and 1964,
with the first train running in the year 1964, while the TGV Sud-Est line opened in 1981 after a
2-year construction period. Both projects became extraordinarily successful and quickly achieved
financial profitability, as both lines were able to recover their full capital construction costs within
the first decade of operations. The extraordinary operational profitability of these early projects
seems surprising if compared with the projections of our model, which predicts an inability of the
HSR system to recapture its construction costs in the near future; however, we need to take into account several key factors and differences when comparing the projects. Both the Japanese and the
French project were developed in regions with a much higher population density than the American Midwest, providing for the opportunity to introduce more frequent and heavily used stops
along the line and thus increasing ridership of the systems. For example, the Tokaido Shinkansen
line had a ridership of 22m passengers annually in 1967, which is a figure that our HSR could
only ever achieve within the projection under the most optimistic diversion and ridership scenario.
Furthermore, the Tokaido Shinkansen line was also constructed in a time when domestic and international commercial air travel only started developing and was far its current scope; thus the
line provided a viable substitute to air travel from the beginning on, which further explains its exceptional ridership and operational profitability. Our system on the other hand is competing with
already existing, well-established, quick and cheap air carriers in the domestic U.S. travel market,
introducing a completely novel and therefore unfamiliar (and perhaps untrusted) way of traveling
into an established travel market. Lastly, different consumer attitudes towards car and airline travel
certainly also need to be taken into account in a comparison of these two early projects and our
proposed U.S. HSR system.
The second phase of high-speed rail construction and operation projects was sparked by EU
directive 91/440 which required EU member states to liberalize rail services and to open the domestic rail markets to induce increased private sector participation in high-speed rail services [37].
The increased participation of private sector actors was aimed at increasing competition in the operation of HSR systems and to increase, broadly construed, ‘consumer surplus’ in high-speed rail
53
markets. Owners of high-speed rail infrastructure were forced to make rail systems accessible for
all kinds of operators, public as well as private, which had not been the case previously as most
European lines were operated and managed by a single state-owned entity. This directive generally
led to a separation of the infrastructure owners from the HSR system operators, which could now
compete to operate trains on different routes. A similar development also took place in Japan when
the government privatized its high-speed rail system operations after the Japan National Railways
became financially insolvent in 1987. The Japanese high-speed rail operations were restructured
into private companies such as the Central Japan Railway Company (JR Central), which now operates the Tokaido Shinkansen line, while the government and retained ownership of existing infrastructure assets and continued the development of new projects. This separation of infrastructure
owners from high-speed rail operators in Europe and Japan has produced both public and private,
operationally profitable, and financially successful rail operating companies [37].
This approach to financially and organizationally separate the construction and ownership of
high-speed rail infrastructure from the HSR operation seems to be a realistic approach for our
project as well – given operationally profitability of the system. For example, the proposed U.S.
high-speed rail line between Chicago, St. Louis, and the Texas cities could be constructed and
therefore owned by the federal and state governments through a public agency belonging to the
Department of Transportation, while it would be operated by one or more private (and possibly
competing) rail operators. The private operator(s) would pay the public owner an annual franchise
fee while competing for customers and travel market share in order to achieve operational profitability and financial success, which could be a successful public-private partnership, given that
the ridership and cost structure allow for operational profitability of the system.
The third phase of HSR developments consists of projects in which private companies have
not only participated in the operation of the HSR system, but have actively participated in the
construction and development of the high-speed rail infrastructure through so-called public-private
partnerships [37]. Public-private partnership have been structured in several ways, including, but
not limited to, the provision of private equity and debt sources in the (partial) financing of capital
54
construction costs. Other aspects of public-private partnerships involve EU, federal, or state credit
and loan guarantees in privately financed construction projects to secure or ‘insure’ the private
stakeholders against operational unprofitability.
Overall, only two HSR projects have been developed that were entirely privately financed.
These two projects include the Taiwanese HSR Corporation and the Channel Tunnel project [37].
The Taiwanese corporation planned to finance the construction and operation of a HSR line through
the projected operating surplus, similar to the way we modeled and valued the operating performance of our HSR system. However, it quickly became clear that the project required public credit
support to finance construction and initial operation as initial ridership turned out below expectations. The Channel Tunnel project on the other hand has encountered even more severe problems
than the Taiwanese corporation. During its construction, the private developers, i.e. the Eurotunnel group, had to seek additional public government and state funding on several occasions. Upon
completion and initial operation of the tunnel’s rail line, it became clear that the private HSR system operator would require operating subsidies or would need to be restructured to stay financially
solvent. The public equity contributed to the Channel Tunnel project has declined drastically and
value and no dividends have been distributed to shareholders [37].
From this brief historical overview of HSR project financing, we can draw the following general
conclusions:
• Traditionally, the infrastructure construction and development costs of large-scale highspeed rail projects have often been almost entirely publicly financed. In many countries
and cases, state-owned, public entities retain ownership of the high-speed rail infrastructure
assets.
• In addition, since the 1990s, the actual operation of high-speed rail systems has seen the
emergence of financially successful public and private operators.
• However, in more recent cases, private actors have not only been involved in the operation
of high-speed rail systems, but have also contributed directly in the financing of the capital
55
construction costs of the project. In some projects, equity or debt funding from private
sources has covered significant portions of the construction and development costs. The
involvement of private funding has been particularly successful in public-private partnerships
that involve the combination of private funding with government grants as well as with public
credit and loan backing.
• Projects that have initially relied entirely on private financing have not been able to be sustainable and had to be restructured to include public funding participation.
9.2
Possible Funding Sources for U.S. HSR Projects
Having explored the historical developments in the funding and financing of high-speed rail
projects, we will now turn to a discussion of possible funding sources for the proposed ChicagoSt. Louis-Texas HSR rail line. Generally, we distinguish between public, public-private (mixed),
and private financing possibilities and structures. Furthermore, we can subdivide public financing
sources into federal, state, and municipal funding sources, which provides an efficient outline for
the following discussion.
9.2.1
Public Financing
Public financing sources include government grants and budget allocations, municipal bond
issues backed by project operating surplus, private activity bonds, as well as loans and loan guarantees under the Transportation Infrastructure Finance and Innovation Act (TIFIA).
First of all, there is a strong argument to be made for the social utility and necessity of government grants and budget allocations to (high-speed rail) infrastructure construction and development in the U.S. at this point in time. Ever since the construction and completion of the U.S.
interstate highway system over 50 years ago, which at the time constituted one of the largest and
most ambitious public infrastructure projects ever, U.S. investment in infrastructure (and in surface
transportation in particular) as a percentage of GDP has lagged the investment of other developed
countries [12]. In order to compensate for this history of underinvestment in infrastructure and
56
in surface transportation, McKinsey estimates that the U.S. would have to increase infrastructure
spending by 1% of GDP in the coming years, i.e. by c. $150bn to $180bn annually, in order to
erase the competitive disadvantage compared to other developed countries by 2030 [12]. Similarly
to McKinsey, we argue that throughout the history of any developed nation, large-scale, forwardlooking infrastructure projects have often paved the road to the next period of economic growth
and that a strategic, visionary investment such as the Chicago-St. Louis-Texas HSR line could
lay the foundation for the region’s future competitiveness and economic growth. We also believe
that the current point in time provides an extraordinarily strong window of opportunity for such
an investment given the extremely low borrowing rates and the severe underemployment and underperformance of the U.S. construction sector ever since the burst of the mortgage bubble, which
could potentially be remedied by increasing the public investment in infrastructure and surface
transportation such as HSR systems [46].
Hence, our Chicago-St. Louis-Texas HSR system could apply for budget allocation grants,
loans, credit lines, or credit guarantees from the federal government. While the Passenger Rail
Investment and Improvement Act of 2008 was passed before the financial crisis in 2008, the U.S.
High-Speed Intercity Passenger Rail funding program first became available through the American
Recovery and Reinvestment Act in 2009, which as aimed at serving as a counter-cyclical spending
program in response to the ensuing financial and economic crisis. 40 states as well as Amtrak
applied for over $75bn of project funding under the U.S. High-Speed Intercity Passenger Rail
program; however, only a total of $10.1 billion had been allocated to passenger HSR projects
across the U.S., which has mostly been spent by now [37]. Hence, in order for this ChicagoSt. Louis-Texas line to become a reality with the help of federal grants and infrastructure budget
allocations, U.S. Congress would have to reauthorize spending on passenger HSR projects and
allocate additional funds, which is largely uncertain given the current political climate.
In addition to government grants and budget allocations, loans and credit/loan guarantees under the Transportation Infrastructure Finance and Innovation Act (TIFIA) provide another possible
source of federal funding for our project. The TIFIA program offers assistance to transportation
57
infrastructure financing by providing secured loans with repayment terms of up to 35-years with
the possibility to begin repayment up to 5 years after project completion; by offering loan guarantees that back the project’s third-party loans by the full credit of the U.S. government; and by
offering so-called stand-by credit that provides optional operating assistance for the initial 10-years
of project operation after completion. Under the new federal surface transportation reauthorization, MAP-21, signed into law by Obama in 2012, the Transportation Infrastructure Finance and
Innovation Act (TIFIA) program saw a large increase in funding availability. For example, MAP21 increased the amount of loans and credit assistance under the TIFIA program to $17.5bn for
FY2013 and FY2014, after only providing $1.2bn under the program in FY2012 [37].
In conclusion, given the uncertainty surrounding the availability of federal grants and funding under U.S. High-Speed Intercity Passenger Rail program and given the uncertainty regarding
Congress’ willingness to reauthorize funding of these projects, federal financial assistance would
most likely take the form of loans as well as loan and credit guarantees under the TIFIA program,
which recently saw a large increase in funding specifically aimed at surface transportation projects
such as rail and highway systems.
In light of this outlook for direct grants from the federal government, state and municipal
funding and grants may provide additional, but politically and financially equally uncertain and
unreliable, sources of financial support in the construction and development of the proposed high
speed rail system as our track would pass through 5 different states (Illinois, Missouri, Arkansas,
Louisiana, and Texas). For example, states could access their current surface transportation funding programs and tap resources such as the gasoline tax, vehicle registration and license fees, or
general sales tax, income tax, and gaming tax reserves [37]. In addition, states could consider the
introduction of (airport and road) congestion pricing and taxes alongside the development of the
HSR system. The revenue of such a congestion pricing system would not only help in the financing of the project but would provide an added incentive to use the HSR system, thereby increasing
future ridership to operationally profitable levels.
The states could further consider new bond programs through their existing bonding and fi-
58
nancing authorities. Many states have the power to issue tax-exempt Private Activity Bonds for
infrastructure projects that are undertaken in cooperation with private actors [37], which could assist in incentivizing the participation of private funding sources in the Chicago-St. Louis-Texas
HSR project.
9.2.2
Public-Private Partnerships
Many forms of public-private partnerships have been mentioned and discussed above, which
we will not take up again in this section. Example of previously discussed public-private partnerships include the partial funding of the project construction through private debt and equity
contributions backed by the future stream of operating surplus as well as public-private cooperation under the TIFIA program or through Private Activity Bonds.
Nevertheless, considering the limited availability of direct grants and funding on the federal
and state level and given the historical success of public-private partnerships in HSR projects,
public-private partnerships generally deserve serious evaluation and consideration in the construction and development of these projects. Therefore, we will briefly elaborate on an additional form
of public-private partnerships, which includes the structured financing of infrastructure projects
through private sponsors and specialized infrastructure banks such as the European Investment
Bank [47]. The European Commission and the European Investment Bank have recently developed the Europe 2020 Project Bond Initiative, which provides structured, public-private funding
to infrastructure developments. The project has been developed in order to take advantage of the
demand for long-term debt securities with ratings of A- or higher and to profit from institutional
investors that are seeking to increase their exposure to the infrastructure sector, for which there are
currently limited opportunities. Under the Europe 2020 Project Bond Initiative, the project’s debt
issuance is divided into tranches of senior debt (rated A- or higher) and subordinate debt (rated
BBB). The senior tranche can then be sold as a project bond to institutional investors in the capital
markets. The subordinate tranche on the other hand is financed and backed fully by the European
Investment Bank [13]. Such an initiative, led by a public entity such as an infrastructure bank
59
and aimed at increasing the participation of private institutional investors in public infrastructure
projects, may well be a future possibility for infrastructure financing in the U.S. and provides an
additional example of possible public-private partnerships worth considering.
9.2.3
Private Enterprise and Funding
There is not much more to be said at this point about the possibility and availability of private
enterprise and funding in the construction and development of our proposed high-speed rail system
as the subject has been discussed at multiple occasions throughout this paper. As the overall project
is net present value-negative when accounting for the initial capital construction costs (please see
above for details; in particular, refer to Section 8.2), it is highly unlikely, or in other words virtually
impossible, that a rational, profit-maximizing, and self-interested independent private actor alone
would undertake the construction and development of the proposed HSR system. Even under the
most generous and optimistic operating scenario, which assumes a diversion rate of 55% from
other modes of transportation, an annual ridership growth rate of 6%, a baseline cost structure of
$0.30 per passenger mile, and a 7% discount rate, the maximum present value of future operating
income is $96.3bn (Figure 11) while the project is facing an estimated upfront capital construction
cost of $118bn to $125bn, leaving a minimum funding gap of $22bn to $29bn for the project.
Considering that the largest private bond issue ever had a volume of $49bn (Verizon, September
2013) with the second-largest ever coming in at a volume of $17bn (Apple, April 2013) and taking
into account that both issues were conducted by large, established, and profitable corporations, it is
unlikely to assume the possibility of a private debt financing of the funding gap of $22bn to $29bn
by a currently non-existent private HSR operating company for our proposed system. Hence, given
these factors and the extreme optimism of the operating scenario leading to this minimum funding
gap, we discard a discussion of a private bond financing through securities such as fixed rate,
floating rate, zero-coupon, convertible or asset-backed bonds in this feasibility report as such an
inquiry lies beyond the scope and limitations of this study.
60
9.3
Conclusion and Additional Considerations
In conclusion, the overview of the historical financing structures of HSR projects, provided a
guideline for our discussion of the availability of financing for our proposed high-speed rail system. We discussed the possibility of public, public-private, and entirely private financing structures
and conclude that both an entirely public and an entirely private financing of the proposed HSR
is highly unlikely given the current federal and state funding and political outlook as well as the
projected operational performance of our system. Nevertheless, our proposed high-speed rail system between Chicago, St. Louis, and Texas is operationally profitable in scenarios that involve a
diversion rate of at least 10% from other modes of transportation and a variable cost structure of
no more than $0.30 per passenger mile. In these scenarios, which we judge to be fairly realistic but
appropriately conservative in terms of ridership estimates and cost structure, anywhere between
2.5% to 35% (for a 7% discount rate) or between 0.5% to 9.0% (for a 11% discount) of the total
capital construction costs of $118bn to $125bn could be supported by the future operating surplus
from the HSR system. These factors, along with the historic success of public-private partnerships
in the development of HSR systems, merit a serious evaluation and consideration of public-private
partnerships for our project. Besides the partial funding of the project’s construction cost through
private debt and equity contributions backed by the future stream of operating surplus, we explored
additional public-private partnerships such as public-private cooperation under the TIFIA program,
Private Activity Bonds, or the Europe 2020 Project Bond Initiative.
Lastly, we briefly want to mention additional financing considerations that often appear in
the literature about infrastructure financing and that may merit further consideration beyond this
feasibility study. First, another opportunity to support the funding of the project could be afforded
by a process known as value capture. Value capture involves project funding through increased
state and local taxes along the infrastructure corridor due to increased property development around
the infrastructure asset. However, as the majority of our track runs through sparsely populated
areas in the American Midwest, we judge the impact of property value capture on the proposed
project to be minimal. Second, a process known as capital recycling, i.e. the development and
61
following divestiture of infrastructure asset by a public entity with the goal to realize a positive
return on investment, has been proposed. Again, based on the enormous development costs and
the projections of our operating scenarios, we judge this consideration to be of limited effectiveness
for our project.
Encountering similar financing challenges, the California HSR project decided on a staged and
incremental development, construction, and operating approach, which has the effect of significantly decreasing the initial and overall capital construction costs [3]. Such a staged and incremental approach to construction and operation is a matter worth of further exploration, but unfortunately lies beyond the scope and limitations of this initial study. It would be worth modeling and
projecting the construction in different stages, with separate financing and operating start dates for
every stage and with the operating surpluses from the initial stages supporting the construction of
the later stages. Such incremental approaches represent an important area of further research in the
field of U.S. high-speed rail development.
10
Environmental Efficiency Analysis of the High Speed Rail
Some of the major reasons why the high-speed rail has received significant attention compared
to other modes of transportation are the perceived environmental benefits that the rail offers. The
chief talking points include the potential for the high-speed rail to be a “greener” alternative to
airplanes, automobiles and buses. In addition, the high-speed rail already has existing precedents
that have been operating at capacity and across several continents. Other alternative fuels are
not yet economically feasible, such as the biofuels that power aircraft which cost exponentially
more than the current cost of jet-fuel or batteries that power vehicles between cities which are
not sold to the public yet [53]. Hence, the high-speed rail offers a realistic possibility for a perceived environmentally-friendly method of transportation. However, it is important to note that
this environmentally-friendly method of transportation is entirely perceived; thus, this portion of
the paper attempts to analyze the potential environmental impact of the high speed rail in the Midwest relative to other methods of transportations and includes other environmental deliberations to
62
consider.
Prior to addressing the analysis, it is imperative to mention the uncertainties of the three most
important assumptions for the environmental impact analysis: the choice of the type of the high
speed rail, diversion from other modes of transportation to the high-speed rail, and passenger
ridership. I will address all of these in the upcoming sections of the paper; nonetheless, due to the
large degree of uncertainty with respect to these three key assumptions, it is difficult to deduce a
single conclusion regarding the environmental impact from this analysis, as the analysis is highly
dependent upon these forward-looking assumptions.
10.1
Electricity Assumption Regarding the Type of High Speed Rail
The Center for Neighborhood Technology (CNT) and the Center for Clean Air Policy (CCAP)
have composed a study regarding high-speed rail emissions, and in order to understand the energy
efficiency of the high-speed rail, it is important to note the three broad categories that the highspeed rail falls into: the diesel-powered, electric powered, and magnetic levitation (MagLev) rails.
The diesel-powered high-speed rails are essentially passenger-carrying locomotives, and while the
fossil fuel emissions rooting from the burning of diesel are not appealing from an environmental
standpoint, the durability of these trains and low annual maintenance capital expenditures make
this the most realistic train for the CCAP and CNT studies [50]. However, for the purposes of this
paper and analysis regarding a high-speed rail in the Midwest, we will assume the more energyefficient electric high speed rail. The Figure 15 details the pounds of CO2 per passenger mile for
each type of train given its precedent.
The CCAP study employs the Danish IC3 as the diesel-powered high-speed rail precedent
for the reasons stated above; however, as mentioned in the policy and political climate portion
of this paper, we will assume the high-speed rail powered is powered by a source of electricity.
The MagLev train is also powered by electricity, but since it is the fastest train at 300 miles per
hour, it is also clear from the chart above that it is the least energy-efficient with respect to CO2
emissions per passenger mile. Hence, for this paper, we will use the Japanese Shinkansen, French
63
Figure 15
TGV and the German ICE as our precedents, with each having 0.22, 0.15 and 0.11 pounds of CO2
per passenger mile respectively, which is lower than the Danish IC3’s 0.26 pounds of CO2 per
passenger mile.12 These are not perfect precedents for our study, as both Japan and France have
lighter train weight requirements than the US, and as result, the analysis may present an overly
optimistic view of emissions savings [50]. This holds true as lighter trains abroad will have less
CO2 per passenger mile, which will therefore inflate the overall perceived potential environmental
benefit in the US.
Accordingly, our first key assumption to the rest of the paper is that the high-speed rail in the
Midwest will be powered by electricity. Furthermore, the French TGV 0.15 CO2 emissions per
passenger mile will be used for the rest of the analysis in the paper, as the French TGV is median
pounds of CO2 per passenger mile among the available precedents of Japanese Shinkansen, French
TGV and the German ICE.
12
CO2 per passenger mile - all CO2 emission per passenger mile data mentioned in the Center for Clean Air Policy
study assumes 70 percent occupancy.
64
10.2
CO2 Emission Reduction Assumptions
CO2 emissions reductions from the high-speed rail are entirely dependent upon the diversion
of passengers from other methods of transportation, such as automobile, plane, train and bus. Fortunately, the Center for Clean Air Policy study on the high-speed rail includes analysis regarding
the emissions reductions from the high-speed rail based upon the passengers diverted from other
methods of transportation. See Figure 16 for this data [50].
Figure 16: CO2 Emissions by Corridor Detail (Excludes California)
When looking at the Midwest bar from the chart above, it is clear that the majority of the
emissions reduced are from passengers diverted from airplane and automobile travel, while both
bus and train emissions reduction seem to be minimal. This is due to the fact that bus and train
travel in the Midwest has only a small portion of ridership as well as relatively similar emissions
per passenger mile to the high-speed rail. The following table further illuminates why airline
and automobile travel are the important figures to consider when analyzing emissions reduction
65
analysis [50].
Figure 17: Summary CO2 Emissions Factors by Mode
The high speed rail that this CCAP and CNT table provide is the diesel-powered Danish IC-3;
however, as previously mentioned, the high speed rail for this study employs the electric highspeed rail, specifically the French TGV with a 0.15 pounds of CO2 emissions per passenger mile.
Given the low ridership share in the Midwest and that the difference in the CO2 emissions between
the conventional rail, bus and electric high speed rail are between 0.06 pounds of CO2 emissions
per passenger mile, the emissions reductions from passengers diverted from bus and train travel to
the high speed rail are negligible. Hence, the emissions reductions and energy efficiency analysis
of this section only considers passengers diverted from airplane and automobile travel.
10.3
CO2 Emission Analysis Both With and Without the Chicago – St. Louis High Speed
Rail
This portion of the paper attempts to analyze the “with and without principle” leg of the environmental benefit-cost analysis. In the evaluation of the operating and performance value of the
project portion of the paper, the economic conclusion of the high-speed rail mentioned details how
the rail would be operationally feasible; yet when the initial fixed costs of development are considered, the high-speed rail project becomes highly improbable. That said, the analysis in this section
66
assumes that the high-speed rail is already built so that emissions reduction rates can be forecasted.
Given that this paper has already discussed the distance estimates and the passenger ridership
estimates in the route methodology and operational performance sections of the paper respectively,
we can now analyze CO2 emission estimates for each trip. The table in Section 12.2 employs the
following equation to calculate the CO2 emission estimates: (Miles between each high speed rail
stop) × (2012 historical total passengers for each stop × % estimated diversions to the HSR)
× (CO2 emissions per passenger mile by each mode of transportation [car,automobile,HSR])13 =
Total estimated emissions for each stop by each mode of transportation
This model has the same downside, base-case and upside scenario toggle that consists of the
demand elasticities of airline and automobile travel with respect to fossil fuel estimates included
in the estimation of expected ridership section; however, in this section, the model also includes
downside, base-case and upside scenarios for the percentage by which airplane and automobile
passengers are diverted to the high speed rail. The upside case of 50% airline diversion and 20%
automobile diversion numbers are derived by the CCAP study, which we believe to be overly
optimistic and possibly unrealistic [50] On the other hand, Randal O’Toole’s “High Speed Rail
is Not interstate 2.0” estimates a more negative outlook for the high-speed rail, criticizing the
CCAP study for inherent bias in the study. Using Toole’s outlook, we assumed a downside case of
approximately 2% for airline diversion and 5% for automobile diversion to the HSR, and for our
base-case, we chose a conservative 10% for both airline and automobile diversion. Given these
base-case assumptions of 10% diversion, we have modeled the following to create a base-case
scenario both with and without the HSR:
The “without the high speed rail” carbon emissions scenario assumes that the passengers who
would divert to the high speed rail instead use their original mode of transportation of either automobile or airplane. The net carbon saved is the difference between the two scenarios, leading
to a significant amount of 73% carbon emissions saved. However, given that this is only the base
13
CO2 emissions per passenger mile by each mode of transportation - the automobile and airplane figures are from
the “Summary Emissions Factors by Mode” table and the high speed rail figure is from the French TGV precedent of
0.15 pounds of CO2 emissions per passenger mile.
67
Figure 18
case, it is important to also analyze the sensitivity of the net carbon emissions saved as well as the
percentage carbon saved with respect to our airline and automobile diversion rate assumptions.
Figure 19
Figure 20
The first sensitivity table displays that in terms of percentages, the high-speed rail shows stability to save between 71% to 75% of carbon emissions regardless of the upside or downside scenario,
which is a clear benefit for the high speed rail. However, the second sensitivity table shows significant variability in the pounds of CO2 emissions saved depending upon the diversion rates for
each alternative mode of transportation. Thus, the conclusion from our model is that the highspeed rail provides a potential 70% more energy-efficient mode of transportation; however, the
68
amount of CO2 emissions saved is highly variable depending upon the diversion rates selected for
the automobile and airplane methods of transportation.
The next natural step for this “with or without” leg of the benefit-cost analysis is to determine
a dollar amount for the pounds of CO2 emissions saved with respect to the car and airplane diversion rates. However, calculating this dollar amount given the current research available would
not prove much, if anything, given that the range of values per ton of CO2 emitted is so wide.
The Environmental Protection Agency, Council on Economic Advisors, and the Department of
Transportation currently estimate the cost of CO2 to be between $5.50 and $72 per ton of CO2 ,
while some private consulting firms estimate that figure to be as high as $893 per ton of CO2 [51].
However, if the European Union Carbon Emissions Trading Scheme (EU ETS) $12.12 dollar price
per ton of CO2 14 is applied to our base case scenario, the high speed rail would save $151,372,391
per year. If we assume that these annual environmental benefits of $151,372,391 per year continue throughout the entire operation of the project, we can value the overall future environmental
benefits of the project to society as a constant perpetuity. Furthermore, we can then compare the
present value of all future environmental benefits to the present value of the constructions. Hence,
using a discount rate between 7% and 11%, the present value of all future environmental benefits
ranges from $1.4 billion to $2.1 billion, which represents only 1% to 2% of the present value of
the overall construction costs of $118 billion to $125 billion. While this dollar amount for the CO2
emissions saved seems informative for policy decisions, it is important to recognize that the dollar
amount yielded would depend entirely on the unit cost of CO2 emitted, which could either fall into
the benefit or cost side of the benefit-cost analysis.
10.4
Other Potential Environmental Impacts for the High Speed Rail
A potentially large environmental cost that has yet to be considered is the high initial developmental fixed cost. The largest assumption made in the CO2 emission analysis above is that the
high-speed rail has already been developed, and as a result, the environmental cost in the analy14
$12.12 dollar price per ton of CO2 – as of 2012 price
69
sis above is potentially overly optimistic. Moreover, little is known regarding the environmental
cost of developing a high-speed rail in the Midwest. However, if studies from California serve
as an example, then the developmental environmental costs are potentially wide-ranging as well
depending upon high-speed rail ridership. For example, at 75% ridership, the prospective California high-speed rail’s energy return on investment is recouped in eight years; however, if at 25%
occupancy, the rail will never be able to recoup its initial energy fixed cost [49]. In addition to the
infrastructure environmental cost, other greenhouse gases not mentioned in the CCAP report such
as SO2 cause significant respiratory and cardiovascular problems as well as add acidification to
the environment. Hence, the initial fixed cost of development on the environment remains outside
of the scope of this paper since little data is available for the hypothetical Chicago to St. Louis
high-speed rail line.
11
Summary and Conclusions of the Cost-Benefit Analysis
In the following, we will briefly explain to what extent we considered all five legs of a cost-
benefit analysis in our model and paper; i.e. we will elaborate on the with-and-without principle
and the present-value principle in our analysis, we will explain whose costs and whose benefits we
are accounting for, in how far we attempted to quantify the seemingly unquantifiable, and in how
far we allowed for uncertainty in our analysis. Graphics visualizing the next few paragraphs are
included in Section 12.3.
First, we conducted the analysis of the HSR system’s projected ridership and environmental
benefits under the with-and-without-principle. We assumed that with a HSR system currently in
place, a certain amount of travelers between each of the cities would choose to utilize this mode of
transportation. As described above, we extrapolated this base case diversion rate from the Chicago
– St. Louis route and from there developed our hypothetical and projected ridership estimates.
Then, without the HSR system, we assumed that those who would normally divert with the HSR
system in place would instead use their original mode of transportation of either automobile or
air travel. Needless to say, with the HSR system, we assumed that those travelers who wished to
70
choose HSR would divert from other modes of transportation to HSR. Hence, we calculated the
net amount of carbon saved and the percentage of carbon saved as the difference between the two
‘with’ and ‘without’ scenarios. Hence, assuming a dollar cost of $12.12 per ton of CO2 , the HSR
would save approximately $151,372,391 per year for every year of its operation.
Second, the present-value-principle was used all throughout our analysis in order to be able
to compare costs and benefits occurring at different points in time. The following paragraph will
briefly describe the application of the present-value-principle in our model and analysis; for a detailed discussion of the choice of discount rates please refer to the earlier section on discount rates.
In our project valuation, we calculated the present value of future operating surplus by discounting
all future streams of operating income to the present. We then compared this present value of all
future operating surplus to the present value of all construction costs in order to evaluate what
percentage of the construction costs could be supported by the system’s operational profitability.
We arrived at the present value of construction costs by using the present estimate of construction
costs and by assuming construction costs to be a true ‘upfront’ cost, i.e. by assuming that all construction costs would have to be paid before construction begin. In the valuation of environmental
benefits, we valued the environmental benefits based on 2012 travel data, 2012 hypothetical ridership estimates, and 2012 CO2 dollar cost estimates. We then assumed a perpetual operation of the
HSR system with annual environmental benefits of (at least) $151m, which allowed us to value the
present value of all future environmental benefits as a constant perpetuity.
Based on the present-value-principle, we were able to draw the following two conclusions.
Based on the calculation of the present value of future operating surpluses, we saw that the overall
project would be net present value negative under all operating scenarios when accounting for
upfront construction costs and we concluded that, even under the most generous and optimistic
operating scenario, the project still has a minimum funding gap of $22bn to $29bn. Based on the
calculation of the present value of all future environmental benefits, we saw that the dollar value
of the environmental benefits only accounts for 1.0% - 2.0% of the total construction costs.
Third, we considered whose benefits and whose costs we are accounting for in our analysis.
71
Benefits related to the time and money savings and related to increased comfort of introducing
an alternative mode of transportation primarily fall on residents and business in the regions and
cities containing stations. We note that the proposed route is inherently flawed, as there are few
incentives for states without stations (Arkansas, Louisiana) to support HSR construction. The
economic benefits and the profits of an operationally profitable HSR system also fall mainly on
the operating company (or public operating agency) and on the residents and businesses of states,
regions, and cities connected to the system through stations. When considering the environmental
benefits, our analysis becomes broader as the we can assert that every ton of CO2 saved anywhere
benefits not only the residents and environment of the respective region or country, but benefits the
world population globally due to the nature of our atmosphere. Analyzing the costs and financing
possibilities of the project, we see that the initial construction costs will most likely have to be
undertaken by a consortium of federal and state governments as well as private investors; however,
it is difficult to further specify the precise allocation of construction costs at the current point. Other
forms of costs are the unrealized revenue for competing forms of transportation and for businesses
along traditional routes, which fall mainly on the residents and business of the states, regions, and
cities along the route that are traditionally connected by other modes of transportation.
Fourth, we quantified the seemingly unquantifiable in our analysis during the selection of our
proposed route between Chicago – St. Louis – Texas Triangle. Without the introduction of a
quantitative framework for the selection of our route, the choice of a route would have become
entirely arbitrary and would have only been qualitatively justifiable. We selected the route and the
cities along the route by establishing framework for assigning scaled scores to each city. We then
picked the route with the highest aggregate scaled score, which allowed us to quantitatively justify
the choice of the optimal U.S. HSR route.
Fifth and lastly, we allowed for uncertainty in our cost-benefit analysis by introducing upside,
base case, and downside scenarios for the performance of our HSR system and by conducting sensitivity analyses on the most important assumptions and inputs. For instance, we developed upside,
base case, and downside scenarios for our ridership estimates, for our ticket pricing structure, and
72
for our cost structure. We also sensitized the outputs of our economic model and analysis with
respect to our DCF assumptions (discount rate and terminal value growth rate), with respect to our
ridership assumptions (diversion rate and ridership growth rate), and with respect to our profit and
loss drivers (ticket pricing multiple and variable costs per passenger mile). Additionally, we conducted our simple DCF analysis by assuming two different discount rates of 7% and 11%. In our
environmental cost-benefit-analysis, we also allowed for uncertainty by conducting a sensitivity
analysis of the total amount of CO2 saved (in lbs.) and of the percentage of CO2 saved with respect
to diversion rates from the two other, fossil-fueled modes of transportation.
In conclusion, we question the economic, technological, political, and environmental viability
of the proposed high-speed rail system and doubt its feasibility at the current point in time. In
summary, our assessment is based on the extraordinarily high initial construction costs of $118bn
to $125bn, on the questionable operational profitability under a variety of scenarios, on the comparatively small present value of environmental benefits compared to construction costs, on the
high electricity demands of the system, as well as on the doubtful political support and current
lack of available public funding on federal and state levels. As mentioned in the conclusion of
section 9, it would be worth modeling the construction, operation, and financing of such a project
in different, incremental stages, which represents an important area of further research in the field
of U.S. high-speed rail development.
73
12
12.1
Appendices
Appendix A: Supplemental Data
74
75
Distance
(miles) [2]
790
1084
925
759
1199
New York
Houston
Dallas/Ft. Worth
Philadelphia
San Antonio
820,445
183
1120
283
1004
92.1
471
345
409
297
461
332
356
525
Austin
Detroit
Denver
Milwaukee
Omaha
Cleveland
Minneapolis
St. Louis
Pittsburgh
Des Moines
Columbus
Kansas City
1,300,000
1,000,000
429,000
930,000
1,600,000
2,400,000
1,200,000
827,000
341,000
2,300,000
1,500,000
589,000
797,000
2,400,000
418,000
1,800,000
1,900,000
1,700,000
7.2
-
-
9.5
5.5
9.2
6.0
9.0
1.5
19.0
5.8
29.0
5.0
17.5
32.0
19.3
22.0
-
7.5
5.3
5.0
6.8
4.5
6.3
5.0
7.0
1.5
14.3
4.0
16.8
3.0
10.5
18.0
11.3
14.0
16.3
1.3
1.0
1.0
1.3
1.0
1.3
1.0
1.3
-
2.5
1.0
2.5
1.0
1.5
2.5
2.0
2.2
2.3
3.5
2.5
3.0
2.5
2.5
2.5
2.3
3.3
-
3.5
2.2
4.0
2.8
2.6
4.2
2.8
3.5
3.5
Annual Travelers Train Time Drive Time Nonstop Flight Connecting Flight
by Plane [3]
(hours) [2] (hours) [2]
(hours) [2]
(hours) [2]
5,200,000
21.2
12.0
2.0
3.5
Table 11: Traveler data and travel time data for routes between Chicago and various cities.
145,786
189,885
203,433
305,704
319,294
382,578
396,815
408,958
594,833
600,158
713,777
790,390
1,222,684
701
1,327,407
1,526,006
1,939,022
2,100,263
8,175,133
Population [4]
Washington D.C.
/Baltimore
Indianapolis
End City
76
Dallas/Ft. Worth
Dallas/Ft. Worth
Dallas/Ft. Worth
St. Louis
Austin
San Antonio
St. Louis
St. Louis
Austin
St. Louis
Houston
San Antonio
Austin
Dallas/Ft. Worth
Houston
Houston
Houston
San Antonio
San Antonio
Austin
630
825
196
905
304
81
779
239
162
699
5,000
45,000
469,000
991,000
995,000
1,300,000
2,100,000
2,100,000
22
2.3
24
-
-
-
15.5
5.75
8.3
13
1.3
13.5
12.5
2.83
2.5
9.5
3
4
-
-
2
2
1
0.75
1.5
1
1
3
3
2.75
3
2.5
3
3
2.6
2.5
Distance Annual Travelers Train Time Drive Time Nonstop Flight Connecting Flight
(miles) [2]
by Plane [3]
(hours) [2] (hours) [2]
(hours) [2]
(hours) [2]
197
3,000,000
3.5
1
2.5
Table 12: Traveler data and travel time data for routes between other cities on proposed route beyond Chicago.
End City
Start City
City
Score
New York
4.969
San Antonio
4.243
Houston
4.157
Austin
4.008
Dallas/Ft. Worth
3.773
Denver
3.742
Philadelphia
3.220
Washington D.C./Baltimore
2.966
Kansas City
2.270
Minneapolis
2.246
Omaha
2.180
Pittsburgh
1.989
St. Louis
1.770
Cleveland
1.697
Detroit
1.697
Indianapolis
1.638
Columbus
1.511
Des Moines
1.506
Milwaukee
0.269
Table 13: Each city’s individual scaled score.
77
Scenario I: Profit-and-Loss Analysis
Ridership
Passenger
Miles
Revenue
Operating
Expenses
Operating
Profit
2020E
2025E
2030E
2035E
2040E
621,972
721,036
835,878
969,012
1,123,350
351,757,732
407,783,619
472,732,977
548,027,084
635,313,591
$126,073,229
$161,365,191
$206,536,512
$264,352,743
$338,353,602
$383,142,074
$444,762,446
$518,882,782
$608,507,886
$717,432,251
($257,068,845)
($283,397,255)
($312,346,270)
($344,155,142)
($379,078,649)
Scenario II: Profit-and-Loss Analysis
Ridership
Passenger
Miles
Revenue
Operating
Expenses
Operating
Profit
2020E
2025E
2030E
2035E
2040E
621,972
721,036
835,878
969,012
1,123,350
351,757,732
407,783,619
472,732,977
548,027,084
635,313,591
$126,073,229
$161,365,191
$206,536,512
$264,352,743
$338,353,602
$444,963,111
$523,889,185
$620,159,647
$738,135,411
$883,346,702
($318,889,882)
($362,523,994)
($413,623,135)
($473,782,668)
($544,993,100)
Scenario III: Profit-and-Loss Analysis
Ridership
Passenger
Miles
Revenue
Operating
Expenses
Operating
Profit
2020E
2025E
2030E
2035E
2040E
621,972
721,036
835,878
969,012
1,123,350
351,757,732
407,783,619
472,732,977
548,027,084
635,313,591
$144,083,690
$184,417,361
$236,041,729
$302,117,421
$386,689,831
$383,142,074
$444,762,446
$518,882,782
$608,507,886
$717,432,251
($239,058,384)
($260,345,085)
($282,841,054)
($306,390,465)
($330,742,421)
Scenario IV: Profit-and-Loss Analysis
Ridership
Passenger
Miles
Revenue
Operating
Expenses
Operating
Profit
2020E
2025E
2030E
2035E
2040E
621,972
721,036
835,878
969,012
1,123,350
351,757,732
407,783,619
472,732,977
548,027,084
635,313,591
$162,094,151
$207,469,532
$265,546,945
$339,882,099
$435,026,059
$383,142,074
$444,762,446
$518,882,782
$608,507,886
$717,432,251
($221,047,923)
($237,292,915)
($253,335,838)
($268,625,787)
($282,406,192)
Scenario V: Profit-and-Loss Analysis
Ridership
Passenger
Miles
Revenue
Operating
Expenses
Operating
Profit
2020E
2025E
2030E
2035E
2040E
621,972
721,036
835,878
969,012
1,123,350
351,757,732
407,783,619
472,732,977
548,027,084
635,313,591
$162,094,151
$207,469,532
$265,546,945
$339,882,099
$435,026,059
$444,963,111
$523,889,185
$620,159,647
$738,135,411
$883,346,702
($282,868,960)
($316,419,654)
($354,612,703)
($398,253,313)
($448,320,643)
Scenario VI: Profit-and-Loss Analysis
Ridership
Passenger
Miles
Revenue
Operating
Expenses
Operating
Profit
2020E
2025E
2030E
2035E
2040E
7,783,403
9,023,097
10,460,243
12,126,288
14,057,691
4,401,923,808
5,103,036,148
5,915,817,507
6,858,053,864
7,950,364,044
$1,577,690,258
$2,019,336,644
$2,584,614,096
$3,308,130,939
$4,234,183,479
$1,806,766,597
$2,266,905,387
$2,851,102,299
$3,593,590,861
$4,538,136,224
($229,076,339)
($247,568,743)
($266,488,202)
($285,459,923)
($303,952,745)
Scenario VII: Profit-and-Loss Analysis
Ridership
Passenger
Miles
Revenue
Operating
Expenses
Operating
Profit
2020E
2025E
2030E
2035E
2040E
7,783,403
9,023,097
10,460,243
12,126,288
14,057,691
4,401,923,808
5,103,036,148
5,915,817,507
6,858,053,864
7,950,364,044
$1,803,074,581
$2,307,813,307
$2,953,844,682
$3,780,721,073
$4,839,066,833
$2,580,399,896
$3,257,103,597
$4,118,488,922
$5,215,759,875
$6,614,402,661
($777,325,315)
($949,290,290)
($1,164,644,240)
($1,435,038,802)
($1,775,335,828)
Scenario VIII: Profit-and-Loss Analysis
Ridership
Passenger
Miles
Revenue
Operating
Expenses
Operating
Profit
2020E
2025E
2030E
2035E
2040E
7,783,403
9,023,097
10,460,243
12,126,288
14,057,691
4,401,923,808
5,103,036,148
5,915,817,507
6,858,053,864
7,950,364,044
$1,803,074,581
$2,307,813,307
$2,953,844,682
$3,780,721,073
$4,839,066,833
$1,806,766,597
$2,266,905,387
$2,851,102,299
$3,593,590,861
$4,538,136,224
($3,692,016)
$40,907,920
$102,742,383
$187,130,211
$300,930,609
Scenario IX: Profit-and-Loss Analysis
Ridership
Passenger
Miles
Revenue
Operating
Expenses
Operating
Profit
2020E
2025E
2030E
2035E
2040E
7,783,403
9,023,097
10,460,243
12,126,288
14,057,691
4,401,923,808
5,103,036,148
5,915,817,507
6,858,053,864
7,950,364,044
$2,028,458,904
$2,596,289,971
$3,323,075,267
$4,253,311,207
$5,443,950,187
$1,806,766,597
$2,266,905,387
$2,851,102,299
$3,593,590,861
$4,538,136,224
$221,692,306
$329,384,583
$471,972,968
$659,720,345
$905,813,963
Scenario X: Profit-and-Loss Analysis
Ridership
Passenger
Miles
Revenue
Operating
Expenses
Operating
Profit
2020E
2025E
2030E
2035E
2040E
7,783,403
9,023,097
10,460,243
12,126,288
14,057,691
4,401,923,808
5,103,036,148
5,915,817,507
6,858,053,864
7,950,364,044
$2,028,458,904
$2,596,289,971
$3,323,075,267
$4,253,311,207
$5,443,950,187
$2,580,399,896
$3,257,103,597
$4,118,488,922
$5,215,759,875
$6,614,402,661
($551,940,992)
($660,813,626)
($795,413,655)
($962,448,668)
($1,170,452,474)
Scenario XI: Profit-and-Loss Analysis
Ridership
Passenger
Miles
Revenue
Operating
Expenses
Operating
Profit
2020E
2025E
2030E
2035E
2040E
22,106,265
25,627,220
29,708,972
34,440,841
39,926,374
12,502,255,962
14,493,541,207
16,801,986,566
19,478,107,423
22,580,464,951
$4,480,924,318
$5,735,279,549
$7,340,769,264
$9,395,687,329
$12,025,843,233
$4,654,015,644
$5,911,191,270
$7,515,541,331
$9,563,756,813
$12,179,544,169
($173,091,326)
($175,911,720)
($174,772,067)
($168,069,484)
($153,700,936)
Scenario XII: Profit-and-Loss Analysis
Ridership
Passenger
Miles
Revenue
Operating
Expenses
Operating
Profit
2020E
2025E
2030E
2035E
2040E
22,106,265
25,627,220
29,708,972
34,440,841
39,926,374
12,502,255,962
14,493,541,207
16,801,986,566
19,478,107,423
22,580,464,951
$5,121,056,364
$6,554,605,199
$8,389,450,588
$10,737,928,376
$13,743,820,838
$6,851,273,467
$8,723,532,420
$11,115,147,471
$14,171,008,802
$18,076,514,579
($1,730,217,103)
($2,168,927,221)
($2,725,696,883)
($3,433,080,426)
($4,332,693,741)
Scenario XIII: Profit-and-Loss Analysis
Ridership
Passenger
Miles
Revenue
Operating
Expenses
Operating
Profit
2020E
2025E
2030E
2035E
2040E
22,106,265
25,627,220
29,708,972
34,440,841
39,926,374
12,502,255,962
14,493,541,207
16,801,986,566
19,478,107,423
22,580,464,951
$5,121,056,364
$6,554,605,199
$8,389,450,588
$10,737,928,376
$13,743,820,838
$4,654,015,644
$5,911,191,270
$7,515,541,331
$9,563,756,813
$12,179,544,169
$467,040,719
$643,413,930
$873,909,256
$1,174,171,563
$1,564,276,669
Scenario XIV: Profit-and-Loss Analysis
Ridership
Passenger
Miles
Revenue
Operating
Expenses
Operating
Profit
2020E
2025E
2030E
2035E
2040E
22,106,265
25,627,220
29,708,972
34,440,841
39,926,374
12,502,255,962
14,493,541,207
16,801,986,566
19,478,107,423
22,580,464,951
$5,761,188,409
$7,373,930,849
$9,438,131,911
$12,080,169,423
$15,461,798,443
$4,654,015,644
$5,911,191,270
$7,515,541,331
$9,563,756,813
$12,179,544,169
$1,107,172,765
$1,462,739,580
$1,922,590,580
$2,516,412,610
$3,282,254,274
Scenario XV: Profit-and-Loss Analysis
Ridership
Passenger
Miles
Revenue
Operating
Expenses
Operating
Profit
2020E
2025E
2030E
2035E
2040E
22,106,265
25,627,220
29,708,972
34,440,841
39,926,374
12,502,255,962
14,493,541,207
16,801,986,566
19,478,107,423
22,580,464,951
$5,761,188,409
$7,373,930,849
$9,438,131,911
$12,080,169,423
$15,461,798,443
$6,851,273,467
$8,723,532,420
$11,115,147,471
$14,171,008,802
$18,076,514,579
($1,090,085,058)
($1,349,601,571)
($1,677,015,560)
($2,090,839,379)
($2,614,716,136)
12.2
Appendix B: Supplemental Figures
Figure 21: Selected rail route.
83
Figure 22
Figure 23
84
Figure 24
85
12.3
Appendix C: Cost-Benefit Analysis Visuals
Figure 25
86
Figure 26
87
Figure 27
88
Figure 28
89
Figure 29
90
Figure 30
91
Figure 31
92
13
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