European Hyperloop Network Study

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EU Hyperloop Network Study Demand Projections & Preliminary Business case

VISION

MISSION

A world where distance does not matter

Enable convenient and sustainable high-speed transportation for everyone with a global hyperloop network

PROPRIETARY & CONFIDENTIAL This document contains proprietary and sensitive information that is the property of Hardt B.V. and its subsidiaries and affiliated companies (“Hardt”). It is furnished solely to assist in the evaluation of a potential business or investment relationship with Hardt. Recipients of this document understand that all of the information contained herein is confidential, and agree that they will treat all such information in a confidential manner and will use such information only in the evaluation of the potential business or investment relationship. Recipients agree that they will not directly or indirectly disclose or distribute, or permit their agents to disclose or distribute, any such information without the prior consent of Hardt.

FORWARD LOOKING STATEMENT This document may contain forward-looking statements that involve a number of risks and uncertainties, including statements regarding the outlook of the Company’s business and results of operations. By nature, these risks and uncertainties could cause actual results to differ materially from those indicated. The Company disclaims any intention or obligation to update or revise any forward-looking statements, whether as a result of new information, future events or otherwise.


Report: EU Hyperloop Network Study Version 1.1 Date: 30-12-2019 Projectteam: Jurany Ramírez Gallego Inge Beerlage Stefan Marges Mars Geuze

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EU Hyperloop Network Study Hardt Hyperloop


EXECUTIVE SUMMARY What is hyperloop? The hyperloop is a new mode of transportation for large volumes of passengers and cargo. It is a combination of existing technologies from different industries into a mobility concept. The infrastructure consists of tubes that can be built above ground or underground. Vehicles that resemble small aircraft travel inside the tube, either separately or in short trains.

Background Hardt Hyperloop, the leading European hyperloop company, initiated the Hyperloop Implementation Program (HIP): A series of studies that explore the environmental, social, and economic impact of hyperloop transportation. The compilation of a wide business case for hyperloop Europe in 2040 is a first step for understanding the European market of transportation of passengers that includes 11 countries of west-central Europe. Since one of the main premises of Hardt Hyperloop is the alleviation of constraints of capacity and environmental inefficiencies of aviation in short-medium distance routes, our markets of reference are aviation, complemented with the international rail. Scope of the study This study is intended to support Hardt’s team by signaling the potential impact of market and concept design assumptions on a broad business case of an entire network using real data of current demand and forecast of growth from independent sources. •

• • • • •

This Study is a compilation of EU-wide concept business case based on a capacity analysis on the existing transportation market in 11 countries of west and central Europe, together with Hardt’s EU hyperloop Network. The study is intended to support Hardt’s team by signaling the potential impact of market and concept design assumptions on a broad business case of an entire network with real data of demand. In this initial stage, aviation and the potential of substitution is the main premise for the study. International rail was added given the comparability of the final service. Other modes, like car, bus, and domestic rail are by definition out of the scope, but likely to be included in a second stage depending on the findings of this study. The business case is mainly dependent on the assumption of competitive ticket prices, to determine the profitability of the network. An initial network scheme was previously identified by expert partners of Hardt, literature, and documents of public planning. It serves as a starting point for this study. To make the results comparable with public discussions, the forecast of demand for 2040 was calculated with the estimated growth of official independent European institutions. The comparison with other modes was based on expert’s technical assumptions and concept design of the project Hardt Hyperloop 1. It is out of the scope of this analysis, but a likely next step, an in-depth analysis of factors of demand for passengers, segmentation of the analysis by type of passenger, behavioral economic factors, route alignments, service design, the socio-economic impact of hyperloop, and broad ticket price systems.

The main objective of this study is:

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See Hardt Hyperloop General Concept Design, not part of this study. EU Hyperloop Network Study Hardt Hyperloop


1. 2. 3. 4. 5.

Identify the size of the market for transportation of passengers in the context a European network for 2040. Check the capacity of hyperloop to cope with the demand for transportation in a virtual hyperloop European network in 2040. Identify a first model of modal choice for hyperloop when competing with aviation and train in the proposed hyperloop routes. Estimate a likely number of passengers for hyperloop deviating from aviation and train. Calculate a profit estimation of the network based on a generalized ticket price per kilometer and passenger kilometers at the network and route level.

Figure 1. Proposed hyperloop European Network

Findings related to the motivation questions of the study What are the main markets in Europe that Hyperloop could potentially serve? • The study identified aviation and international rail like the main market to be addressed by hyperloop in the context of a European market. • Selected rail routes in the domestic markets could be added (this study only included international rail), as they serve medium distances comparable with some international routes. However, both medium and short domestic rail routes might need a different concept of service and operation than those in the medium and long distances presented in this study. What is the size of these markets, and how is currently split between the relevant current modes of transportation? • The total addressable market for hyperloop in 2017 is composed of 226 million passengers, 194 million in aviation, and 32 million in international rail.

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How would the market grow towards 2040? • With two scenarios of low and high growth for aviation and rail in 2040, it is expected that aviation will grow towards 297-357 million passengers, equivalent to a low/high growth of 5384%, while rail will see a rise in demand to 41-45 million passengers (+27-45%). What would be the potential for diversion from other modes to Hyperloop? • This study proposed a model of choice for hyperloop and the other modes (aviation, and international rail) based on the “door to door travel times.” Three scenarios of operation and services of Hyperloop, low speed at 500km/h, main speed at 500km/h, and high speed at 1000 km/h are used to obtain expected average shares of the market. On average hyperloop will get a market share of around 62%, 85%, and 95% respectively. How sensitive would be the level of diversion of passengers from other modes, the ridership forecast, and the ticket revenue in different favorable and non-favorable scenarios for hyperloop in Europe? • The percentage of market attracted by hyperloop is sensitive to operation and service variables like access, waiting, and transfer time. In general, the less time the passenger spends on the trip, the better. However, it is necessary to research segments of passengers, their preferences, and behavior. This study did not include any distinction of types of passengers and purposes of the trip. • The combination of future market growth scenarios and modal choice scenarios offered six estimations of passenger trip for hyperloop in 2040, from 207 to 334 million passenger trip. • Assuming a linear price of €0,10 per passenger-kilometer, the system will be profitable and will generate an annual profit (after cost and operational expenses) of €3-21 Billion in 2040. What would be the environmental benefit of hyperloop compared to the other modes? • A quick scan assessment is conducted of the CO2 emissions for hyperloop and aviation based, where both modes are compared on 2019 and on 2040 emissions. • Hyperloop would be able to reduce CO2 emissions between 11-25 million ton CO2-eq per annuum, a reduction of 71-79% compared to aviation.

Other findings •

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Hyperloop will face competition with the high-speed rail under 700 kilometers, substitution of aviation in distances between 500 km and 2000 km, and competition for aviation over the 2000 km. Reviewing the capacity it is unlikely the system capacity will be under pressure. Changes in service design, such as coupled vehicles, lower transit speeds or different pricing strategies, can relieve congestion in the system. Although the modal choice model assumes competitive prices, the ticket revenue was calculated with a constant price per kilometer for Hyperloop. Fix prices might neglect diminishing marginal costs of the infrastructure, making hyperloop less attractive than other modes in longer distances. In reality the system will have to guarantee that prices are indeed competitive to hold the predicted market share. This study found no financially profitable isolated links when substituting only aviation and international rail. For further assessments, wider economic benefits and or regional traffic is to be considered too.

EU Hyperloop Network Study Hardt Hyperloop


Recommendations and next steps proposal • • • • •

• • •

• •

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Modal choice sensitivity to assumptions is a good option for prefeasibility phases. When facing a real project, well design survey tools or behavioral experiments have proven to be better. Use of supernodes (regions of origin and destination) for catchment areas, economic analysis, the presence of competition in a region, stakeholders, agendas of the policy of development. Assumption of terminus by scenarios according to the context of every region. This can influence the out-of-vehicle time, which is determinant in the modal choice. Conceptualization of integration with other modes. e.g., when presence of HSR. Inclusion of demand from selected routes of domestic rail (not all of them suitable with the concept of this study). For example, domestic rail in medium distances in Spain, France, UK, and Germany. The market analysis showed that the domestic market in those countries is big. Use corridors for refining analysis. Corridors can be classified by criteria of competition with other modes, underserved routes, high demand, political interest/apathy, etc. Research on dynamic prices and economies of scale; options are non-linear prices, analysis by thresholds based on distance, etc. Include ramp-up period when needed. For that, a contextual discussion according to expectations of constraints of capacity in the future markets can give orientation. More research is needed. Concept of hyperloop for domestic rail medium distances. Different commercial plans for Hyperloop short distances and long distances (like aviation, long flights - low-cost carriers).

EU Hyperloop Network Study Hardt Hyperloop


CONTENTS EXECUTIVE SUMMARY .....................................................................................................................................3 1

INTRODUCTION .......................................................................................................................................8 1.1 1.2

2

THE CURRENT TRAVEL MARKET .............................................................................................................11 2.1 2.2 2.3

3

THE STUDY AREA ....................................................................................................................................... 8 APPROACH................................................................................................................................................ 9

THE MARKET SIZE OF AVIATION IN EUROPE: QUICK OVERVIEW 2017 ................................................................. 11 THE MARKET SIZE OF RAIL IN EUROPE: QUICK OVERVIEW 2017 ........................................................................ 13 THE SIZE OF THE POTENTIAL PASSENGER MARKET FOR HYPERLOOP IN BASELINE SCENARIO 2017 ............................. 14

THE EUROPEAN CASE FOR HYPERLOOP .................................................................................................16 3.1 3.2 3.3 3.4

THE EUROPEAN HYPERLOOP NETWORK ........................................................................................................ 16 TOP TRAFFIC ROUTES IN THE NETWORK IN 2017 ............................................................................................ 17 FUTURE MARKET FOR CURRENT MODES OF TRANSPORTATION IN THE HYPERLOOP NETWORK ................................... 18 MODAL CHOICE ....................................................................................................................................... 19

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HYPERLOOP DEMAND AND RIDERSHIP FORECAST 2040 ........................................................................22

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HYPERLOOP CAPACITY...........................................................................................................................23 5.1 5.2

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QUICK SCAN BUSINESS CASE .................................................................................................................27 6.1 6.2 6.3 6.4 6.5

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MAXIMUM STANDARD CAPACITY ................................................................................................................ 23 CAPACITY VS DEMAND PER HOUR ................................................................................................................ 24

TICKET REVENUE ...................................................................................................................................... 27 COSTS .................................................................................................................................................... 28 RETURNS ................................................................................................................................................ 29 ISOLATED LINKS........................................................................................................................................ 29 QUCIK-SCAN ENVIRONMENTAL EFFECTS ..................................................................................................... 31

FINDINGS AND RECOMMENDATIONS ....................................................................................................32 7.1 7.2 7.3

FINDINGS RELATED TO THE MOTIVATION QUESTIONS OF THE STUDY .................................................................... 32 OTHER FINDINGS ...................................................................................................................................... 33 RECOMMENDATIONS AND NEXT STEPS PROPOSAL........................................................................................... 33

ANNEX 1: DISTANCES BY CURRENT GROUND INFRASTRUCTURE FOLLOWING THE HYPERLOOP NETWORK ...34 ANNEX 2: TICKET PRICES PER ORIGIN-DESTINATION PAIR IN THE NETWORK .................................................35 ANNEX 3: THEORETHICAL MODAL CHOICE MODEL ........................................................................................36

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1 INTRODUCTION The growth of the demand of aviation for passengers in Europe, around 5,4% annual average2, observed in the last 5 years is likely to overpass the limits of the adjustable capacity of the airports from 2025 onwards. In fact, in the EuroControl Regulation & Growth (most likely) scenario of 1,9% annual average growth of passengers until 2040, it is expected that at least 2% of the demand will be unaccommodated in 2025, reaching an 8% in 20403. It means that for 2040, 160 million passengers, equivalent to 1.3 million flights, would be unable to fly. In some cases, the congestion is already arriving. It is the situation of several airports in UK, Germany, Switzerland, The Netherlands, and Turkey. Rail has been unable to solve the problem4. Among other reasons, because even when using their maximum speeds, the reduction in travel time is not enough to compete with aviation in medium-long distances. Global statistics of competition between High-Speed Rail and aviation on the same routes show that HSR is a successful option for trips under 2 hours (typically 200-600 km), but rapidly decrease its market shares over longer trip times5. An example in the context of Europe is a trip by fast train between Amsterdam and Paris, which takes 3.5h for 506 km. The same trip by plane takes 1h plus the check-in/out time. Still, the aviation route was preferred by 1.5 million passengers in 2017, growing 7.4% in the last year, with a market share of 41% between the two modes. This study identifies a potential market for hyperloop in 2040, the size of the market under likely high and low scenarios of growth of demand for transportation, the share of the market for hyperloop by a theoretical modal choice model where hyperloop performs at three different operational conditions, and the calculation of the expected ticket revenue under a preliminary assumption of ticket fares. Furthermore, the study serves as a reference to improve and adjust the concept of hyperloop, point out possible segments of interest, and future methodological challenges. Hyperloop is economically and financially viable for a European network, and a serious option for competition with high-speed rail in distances under 500 kilometers, total substitution of aviation in distances between 750 km and 2000 km, and strong competition for aviation over the 2000 kilometers.

1.1 THE STUDY AREA The study area for market analysis and network identification is presented in Figure 2. For this analysis, the European hyperloop network was focused on 11 countries of west and central Europe, that were previously identified by Hard like relevant markets. From West to East, the countries included in the analysis are Portugal, Spain, France, Italy, Switzerland, Belgium, UK, The Netherlands, Germany, Austria, and Poland.

Report Mott Macdonald – EC: Annual Analyses of the EU Air Transport Market 2016: Link Report EUROCONTROL: European Aviation in 2040: Challenges of Growth: Annex 1: Flight Forecast 2040: Link 4 Report European Court of Auditors: Report No 12 of 2018. A European high-speed rail network: not a reality but an ineffective patchwork: Link 5 See paper by Zhang, Graham, and Chun 2018. Transportation Research Part A: Quantifying the substitutability and complementarity between high-speed rail and air transport: Link 2 3

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Figure 2. Study area of the hyperloop Market Analysis in Europe

The solid lines indicate the most popular ground base infrastructure that currently connects those countries, while the dash lines indicate, alternative routes that were not evaluated in this study, but that would be alternative or complementary cases for the network in the future due to political interest, government plans, or regional agendas of development. In any case, hyperloop would be likely to be integrated into other modes of transportation and infrastructure.

1.2 APPROACH This study adopts a holistic approach of analysis that includes recent techniques of forecasting under uncertainty in combination with more traditional approaches. The analysis use scenario planning, theoretical simulations, adapted classic models of demand, and expert verifications to estimate the ridership forecast of hyperloop and the ticket revenue. Figure 3 explains the workflow of the analysis. The demand forecasting and ticket revenue analysis addresses the following questions: • What are the main markets in Europe that hyperloop could potentially serve? • What is the size of these markets, and how is currently split between the relevant current modes of transportation? • How would the market grow in the future? • What would be the potential for diversion from other modes to hyperloop • How sensitive would be the level of diversion of passengers from other modes, the ridership forecast, and the ticket revenue in different favorable and non-favorable scenarios for hyperloop in Europe.

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Figure 3. Methodological approach for estimation of demand and price ticket revenue

To avoid biases for overestimation, a cautious approach was adopted on areas of high uncertainty. It is the case of future demand of international trips made by car and buses, where the quality of the data does not allow to estimate a market for this mode; therefore, no assumptions were made. Similarly, this analysis did not estimate any induction of the demand foster by hyperloop, although it is likely that hyperloop will attract new users, at this point we assume zero induction of demand.

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2 THE CURRENT TRAVEL MARKET 2.1 THE MARKET SIZE OF AVIATION IN EUROPE: QUICK OVERVIEW 2017 To understand better the context of our network, this study collected the data of all the traffic of commercial passengers between pairs of airports in 2017 for EU28. Then, the analysis was narrowed down to the preselected 11 countries. Summary: Looking at the traffic between countries inside Europe, in 2017: • 425 airports across EU28 transported 678 million passengers over three thousand routes. • When narrowing down the analysis to the 11 countries for the proposed hyperloop network: 232 airports transported 393 million passengers over 1.6 thousand routes

EU28 EU11

Airports

Routes

Links

Passengers

425 232

3.053 1.637

6.106 3.274

678.333.561 393.297.975

Domestic

International

32% 33%

68% 67%

216.317.198 128.864.201

462.016.363 264.433.774

Table 1. Air market for EU28 and the 11-countries of the proposed hyperloop network in 2017

Rank Country Traffic % Rank Route Traffic 1 TR-TR 52.629.366 7,8 1 Istanbul Izmir 3.350.888 2 ES-UK 31,843,299 4,7 2 Izmir Istanbul (Sb) 2.619.727 3 IT-IT 29,625,383 4,4 3 Ankara Istanbul (Sb) 2.461.229 4 ES-ES 24,180,472 3,6 4 Barcelona (EP) Madrid 2.342.016 5 DE-DE 23,567,051 3,5 5 Toulouse Paris 2.326.481 6 FR-FR 23,434,613 3,5 6 Antalya Istanbul 2.298.676 7 UK-UK 20,363,608 3,0 7 Antalya Istanbul 2.270.013 8 DE-ES 19,882,469 2,9 8 Nice Paris 2.171.416 9 NO-NO 14,831,576 2,2 9 Oslo Trondheim 2.088.130 10 DE-UK 13,292,347 2,0 10 Ankara Istanbul 2.062.705 Rest 424,671,714 62,6 Total: 678,321,898 100 Table 2. Top 10 air traffic between countries EU28 (left) and the Top 10 air traffic passengers between cities EU28 (right)

The above reveals the following: • 68% of the aviation passengers in EU28 were moving in international routes, e.g., MadridBerlin, while a significant 32% were flying in domestic routes, e.g., Madrid-Barcelona. • When consolidating the traffic at the country level, most of the top connections are domestic. Turkey is an example of a high level of domestic air traffic with 52 million passengers per year. • Between countries, a first overview highlights the relevance of aviation traffic between Spain (ES) -United Kingdom (UK) with 4.7% of the total traffic, Germany (DE)-Spain (ES) with 2.9%, and Germany (DE)-United Kingdom (UK) with 2%. • The top routes by pairs of airports are domestic. Airports in Turkey, France, and Spain are dominant in the list. Narrowing down the analysis to the 11 countries for the proposed hyperloop network, results in a network of 232 airports transporting 393 million passengers over 1.6 thousand routes (see Table 1). This proposed network contains the following characteristics: • 67% of the aviation passenger in the 11-countries of the hyperloop network was moving in international routes; still, a significant 33% were flying in national routes.

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Consolidating the traffic of passengers at the level of countries (See Matrix 1), the highest traffic occurs between Spain (ES) and the United Kingdom (UK) with 31.8M passengers in total. Domestic traffic remains strong. Germany, Spain, France, Italy, and the UK sum up together 93% of the total domestic traffic, equivalent to 30% of the traffic for the 11-countries of the hyperloop network.

AT BE CH DE ES FR IT NL PL PT UK Total

AT 0.5 0.2 0.7 3.3 0.5 0.5 0.5 0.5 0.1 0.1 0.8 7.8

BE 0.2 0.0 0.5 1.0 2.2 1.1 1.6 0.1 0.4 0.7 0.7 8.4

CH DE ES FR IT NL 0.7 3.5 0.6 0.5 0.5 0.5 0.4 1.0 2.2 1.0 1.4 0.1 0.7 3.3 2.6 1.7 1.0 1.0 3.3 23.5 10.0 3.4 5.8 2.1 2.1 9.9 24.0 5.5 5.4 3.5 1.9 3.5 4.9 23.3 4.5 1.9 1.1 5.8 5.3 4.7 29.6 2.4 1.0 2.1 3.5 1.8 2.3 0.0 0.2 1.8 0.4 0.5 0.6 0.6 1.0 1.9 1.8 2.9 0.9 1.1 2.7 6.6 15.7 5.3 6.4 5.3 15.2 63.0 71.1 50.6 58.4 18.5

PL 0.1 0.4 0.2 1.7 0.4 0.5 0.5 0.5 2.2 0.1 2.9 9.4

PT UK Total 0.1 0.9 8.2 0.7 0.7 8.1 1.2 3.2 16.1 1.9 6.6 62.7 1.9 16.1 71.3 2.9 5.1 50.1 0.9 6.5 58.9 1.1 5.2 18.1 0.1 3.3 10.1 4.7 3.7 18.9 3.8 20.3 70.7 19.2 71.7 393.3

Matrix 1. Air passengers between and within countries in the 11-countries of the proposed hyperloop network in 2017, values in millions.

A view of the most popular routes by pair of airports confirms the relevance of domestic national traffic. As an example, Madrid-Barcelona is the most popular route by pairs of airports.

Top 15 Airport pairs by 2017 traffic Millions 0

0.5

1

1.5

2

2.5

3

Madrid-Barajas -- Barcalona-El Prat Paris Orly -- Toulouse Paris Orly -- Nice Catania -- Rome-Fiumicino Munich -- Berlin

Berlin -- Frankfurt Madrid-Barajas -- Palma Barcalona-El Prat -- Palma Munich -- Hamburg London Heathrow -- Amsterdam Palermo -- Rome-Fiumicino Dusseldorf -- Munich Frankfurt -- London Heathrow London Gatwick -- Barcalona-El Prat Madrid-Barajas -- Lisbon

Table 3. Top 15 traffic by pair of airports in 2017 (Blue is domestic, Orange is international)

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Looking only at the international routes, the most popular are London-Amsterdam with 1.6 million passengers, Frankfurt-London with 1.4 million, Barcelona-London with 1.4 million, and Madrid-Lisbon 1.4 million.

EU Hyperloop Network Study Hardt Hyperloop


A quick view of the entire database indicates that some cities are served by more than one airport, which means that virtually the same routes can be served by a different combination of airports. For example, a passenger going from a small city close to Amsterdam in The Netherlands to London may choose to go from Amsterdam-Schiphol to London Heathrow, or from Rotterdam Airport (close also to Amsterdam) to London-Gatwick.

Because of the many combinations of airports, or other modes + airports, that a passenger may choose to make the same trip, an initial way to see the flow of passengers in the hyperloop network is identifying areas that contain airports and cities and understanding the flow of passengers between those areas.

After exploring the traffic between pairs of airports and build the traffic at national levels, it was identified that a lot of the flow of passengers might occur between a cluster of cities and airports located in geographical proximity with other clusters. E.g., all the airports of London serving London as a region and connecting to all the airports in the South of Spain.

2.2 THE MARKET SIZE OF RAIL IN EUROPE: QUICK OVERVIEW 2017 •

For the 11-countries of the hyperloop network, the traffic of passengers between and within the countries reached 4 Billion per year in 2017, this includes urban trains, intercity, and international trains. In the 11-countries of the hyperloop network, international passengers of rail were calculated to be 53 million in 2017. Those are passengers that buy tickets to cross at least one national border. This number can be underestimating the real flow of passengers crossing borders due to the ticketing process per trajectory or because of transfers in the borders.

Statistics of international rail at the level of stations or cities are difficult to consolidate in a homogenous database, among other reasons, because rail has not standard operations and reporting systems as aviation does. This section focuses only on the market of the 11-countries of the proposed hyperloop network, where the following has been identified: • The characteristic of the service highly varies within and between countries. Some routes are patchworks of different kind of rail gauge, different operators, and speeds ranging from 70km/h to 350km/h6. • Urban trains and short intercity trains are out of the scope of this study. The nature of urban transportation up to 50 km is not comparable with the concept of hyperloop in this study – a replacement of short-medium distance flights. But, could be part of a different approach of the hyperloop technology. • The United Kingdom-France have the highest traffic of passenger by train with 35.6 million in total, followed by France-Belgium with 8.1 million, Belgium-United Kingdom with 2.4 million, and the Netherlands-France with 1.1 million. • Sharing borders, France, Belgium, United Kingdom, and the Netherlands together concentrate 89% of the international traffic in the 11-countries of the hyperloop network. • Some national routes by train are still in competition with aviation and may be interesting for this case, e.g., Madrid-Barcelona, Marseille-Paris.

European Court of Auditors: Report No 12 of 2018. A European high-speed rail network: not a reality but an ineffective patchwork, Link 6

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As in the case of aviation, looking at the traffic between areas can give a better picture of the flow of passengers before looking at the pairs of stations.

AT AT 142.7 BE 0.0 CH 0.1 DE 0.6 ES 0.0 FR 0.0 IT 0.3 NL 0.0 PL 0.0 PT 0.0 UK 0.0 Total 143.7

BE 0.0 56.0 0.0 0.0 0.0 4.1 0.0 0.6 0.0 0.0 1.3 62.1

CH 0.1 0.0 68.2 0.0 0.0 0.5 0.4 0.0 0.0 0.0 0.0 69.2

DE 0.2 0.0 0.0 449.2 0.0 0.0 0.0 0.1 0.0 0.0 0.0 449.5

ES 0.0 0.0 0.0 0.0 537.8 0.7 0.0 0.0 0.0 0.0 0.0 538.5

FR 0.0 4.0 0.5 0.0 0.4 971.7 0.5 1.1 0.0 0.0 17.3 995.5

IT 0.2 0.0 0.4 0.0 0.0 0.5 468.3 0.0 0.0 0.0 0.0 469.4

NL 0.0 0.4 0.0 0.1 0.0 1.1 0.0 124.7 0.0 0.0 0.0 126.4

PL 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 118.2 0.0 0.0 118.3

PT 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 124.1 0.0 124.1

UK Total 0.0 143 1.1 62 0.0 69 0.0 450 0.0 538 17.8 997 0.0 470 0.0 127 0.0 118 0.0 124 907.5 926 926.4 4,023

Matrix 2. Rail passengers in the 11-countries of the hyperloop network in 2017, values in millions.

2.3 THE SIZE OF THE POTENTIAL PASSENGER MARKET FOR HYPERLOOP IN BASELINE SCENARIO 2017 Summary: Considering hyperloop like a potential substitute of aviation and international train, with data of 2017, the number of potential passengers along the hyperloop network in the 11-countries of the sample would be 226 million, equivalent to 51% of all the passengers moving in the group of eleven countries. Definition 1: Potential passengers for the hyperloop network are passengers moving in the same routes proposed in the network using current, comparable modes of transportation, like aviation and international train. Aviation Rail (Int.) Total EU11 393.297.975 54.874.327 448.172.302 In HL Network 194.295.794 32.049.599 226.345.393 Not in HL Network 199.002.181 22.824.728 221.826.909 Table 4. Potential Market for Hyperloop in the 11-countries of the Hyperloop network in 2017

% 100 51 49

These numbers are initial estimations of the market. Some segments of passengers in cars and coaches (e.g., Flixbus) in international trips, and some national medium distance trips are likely to increase the size of the potential market, although those are out of the scope of this stage. In the 11-countries of the hyperloop network, 221 million potential passengers were traveling in routes different to the hyperloop network, e.g., between Paris (within the network) and Bordeaux (not in the network).

Recommendation: Based on the previous evidence of a greater market in domestic distances, this study recommends identifying the characteristics of the portion of the market for passengers by cars and domestic rail that would be comparable with the passengers of short-flights or international trains and include them to the addressable market.

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3 THE EUROPEAN CASE FOR HYPERLOOP The 226 million passengers traveling by the current, comparable modes of transportation (air, and international rail) in the same routes of the network are the potential market for hyperloop in the base scenario of 2017. This section presents the distributions of those passengers across the origindestination areas of the hyperloop network. Definition 2: Supernodes, city nodes, or just nodes are for now, virtual areas of origin and destination. It could contain several stations, being a network inside like in rail, or at least one station like an airport. At this stage, there is no physical assessment of the stations of Hyperloop, but it should be a next step in a deeper feasibility study.

3.1 THE EUROPEAN HYPERLOOP NETWORK Summary: The proposed European hyperloop network follows the area of influence of 46 airports and 15 regional train stations connected to the area of 37 cities in 11 countries (Figure 4). The number of passengers traveling by air or international rail between any of the 46 airports or the 15 train stations was aggregated in city nodes. Following the route of the hyperloop network across the eleven countries of interest for the study, we localized the areas of origin and destination for aviation and rail. In those areas, passengers choose between different airports or rail service to move to other areas. For example, passengers in the cities of Marseille and Nice located in the South of France in proximity of 199 km may choose between their city airports when going to London or opt for the high-speed service going through Lyon and Paris.

Figure 4. Hyperloop European Network

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The location of the city node does not assume a specific number of stations, but the presence of demand for a similar service as hyperloop. A city node could be a network of other small cities nodes, airports nodes, or train-station-kind of nodes all together when existing infrastructure that allows connectivity, or an isolated node of a city or just an airport when connectivity is low. The distribution of airports and passengers of rail in every area was done by following a heuristic criterion: presence of a main airport7, and presence of a main city or capital city in the path of the infrastructure.

3.2 TOP TRAFFIC ROUTES IN THE NETWORK IN 2017 From the total market of 226 million passengers, the biggest routes in terms of passengers are LondonParis with 17 million, followed by Paris-Brussels with 6,4 million, London-Edinburgh/Glasgow with 5.9 million, and Amsterdam/Rotterdam-London with 4.7 million. Table 5 presents the top 10 routes in the addressable market in 2017, the flow of passengers if the network operated with the current market numbers, and the approximated distance of ground infrastructure. Detailed OD matrixes of passengers and ground distances applicable to the proposed hyperloop network are presented in the annex. #

Route

Traffic with network (million) 151,9

Distance (km)

London

Isolated Traffic (million) 17,1

1

Paris

2 3

Paris

Brussels

6,5

147,4

330

Amsterdam/Rotterdam

Brussels

6,5

87,0

260

4

London

Edinburgh/Glasgow

6,0

52,9

665

5

Amsterdam/Rotterdam

London

4,7

230,9

650

6

Paris

Montpellier/Toulouse

4,2

195,3

970

7

Paris

Amsterdam

3,5

234,4

590

8

London

Barcelona

3,1

341,1

1.815

9

Paris

Nice

3,1

152,3

985

10

London

Madrid

3,0

382,3

2.435

500

Table 5. Top traffic routes (aviation and rail) in the hyperloop market 2017

Distances between the nodes were collected for air and ground-based modes of transportation (see annex). The minimum distance between any two nodes is the link Eindhoven-Brussels with 125 km of ground base route, while the maximum distance is in the link between Warsaw-Lisbon with 4,415 km, both located at extreme points of the network. Interestingly, the cities in the top routes: Paris, London, Amsterdam/Rotterdam, and Brussels are not only highly generator of passengers but also geographically neighbors, and the political center of the European Union. These cities are located at less than 500 km distance from each other. All these routes offer frequent connections by air, high-speed rail and road. Once the network is operating, the traffic flow in these top routes will increase at least by 800% due to the transit of passengers from other regions coming through the same infrastructure.

According to Eurostat (2019), main airports are those reporting more than 150 000 passenger movements per year: Link 7

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3.3 FUTURE MARKET FOR CURRENT MODES OF TRANSPORTATION IN THE HYPERLOOP NETWORK Consistent with the scope of the study, and the analysis of the market presented previously, this section introduces the estimation of the future growth of the market for the comparable modes of transportation defined: Aviation, and International rail. Summary: Under two scenarios of growth in 2040, the potential market size for the proposed hyperloop network will reach between 338-403 million passengers. For 2040, Eurocontrol8 estimated several scenarios of growth for aviation and rail. The study uses the so-called low growth scenario, in which aviation is expected to grow by 53% and a likely scenario (from now the high scenario) in which aviation grows by 84%. The input for rail the demand and ridership forecast are the WLO-scenarios9 for the projected growth on the rail network. These forecasts are extrapolated to the EU. The growth forecasts indicate a substantial demand growth for rail transportation between 2018-2040, of +42% in the high and +27% in the low scenario. Figure 12 describes the calculation of the future market for Hyperloop. Eurocontrol is an intergovernmental organization and an independent source of forecasts for transportation in Europe; its estimations are based in a standard model that includes data from real operations, macroeconomic variables, prices, and plans and changes on operation and services of the main stakeholders of the business. The study uses this external source to ensure the reliability of the estimations and the comparison with public sources.

Figure 5. Future Relevant Market in 2040

Recommendation: The study recommends a follow up on the domestic rail and personal car driving behavior to define its compatibility with the hyperloop concept or the conditions in which a portion of those markets are likely to be attracted by hyperloop. 8 9

Report EUROCONTROL: European Aviation in 2040: Challenges of Growth: Annex 1: Flight Forecast 2040: Link Report CPB/Pbl: Nederland in 2030 en 2050: Twee referentiescenario’s: Link

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3.4 MODAL CHOICE Summary: Based on a modal choice model of the “door to door travel time” for aviation, international high-speed rail, and hyperloop, where hyperloop offers similar time services as rail; hyperloop will have an average market share of 62%, 85%, and 95% in each of the three scenarios of operational speed defined in the section of capacity. Passengers in the relevant market in 2040 will have at least three alternatives of transportation between the cities of the network; aviation, rail, and hyperloop. In a theoretical model, assuming competitive prices in the three modes, and keeping relative consistency of individual behavior, demographic factors, and economic factors, passengers will base their decision on the comparison between the “door to door travel time” offered by every mode. Figure below sketches the main variables in a classic “door to door” model. The model is set to represent the theoretical assumption that individuals prefer the mode that provides the smaller door to door travel time (see details in the annex). The in-vehicle time is defined as the time that a passenger spends inside of the vehicle during the trip, it is a function of the distance and the operational speed between the stations (airports) of origin and destination. Access and egress time refer to the part of the trip between the stations (airports) and the initial/final destination, and transfer time refers to the average time for transfers during the trip.

Figure 6. Modal Choice Model

The model of modal choice was tested in the three scenarios of the operational speed of hyperloop compare with the average parameters of aviation and international high-speed rail. The following table outlines the parameter assumption for every mode.

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Scenarios 1

Modes Hyperloop Low speed

Speed 500 km/h

Access time 24 min

Waiting time 20 min

Egress time 24 min

2

Hyperloop Baseline

700 km/h

24 min

20 min

24 min

3

Hyperloop High speed

1000 km/h

24 min

20 min

24 min

Base

Air

800 km/h

45 min

120 min

45 min

Base

International HSR

250 km/h

24 min

20 min

24 min

+

Table 6. Modal choice assumptions

This theoretical model of the utility of the “door to door” travel time was used to estimate the probability to choose hyperloop when competing against aviation and high-speed rail in Europe. The model used the sample of the 406 routes in Europe, from the combination of 46 airports, 11 regional rail zones, in 37 cities of West and central Europe. The average modal choice will range between 62% and 95%, with an average in the main scenario of 85%; dispersion is high in all the scenarios. Figure 7 describes the non-linear relation between the ground distance and the probability to choose hyperloop. Hyperloop will strongly compete with HSR and short-haul flights under 600 kilometers distance, it will substitute aviation in distances between 500 km and 2000 km, and will face competition with aviation over 2000 km. In distances below the 600-700 km, Hyperloop will have at least 58% of the market share. In this scenario, the out-of-vehicle travel time reduces the relative differences between the three modes, which explains why the shorter the distance, the lower the share for hyperloop independently of the speed (in-vehicle time). Improvements in the profile of speed in this segment will not significantly increase the market share; better out-of-vehicle time would be more relevant. Distances between 700-2000km are likely to be dominated by hyperloop, in particular in the scenarios of speeds of 700km/h (main) and 1000km/h (high speed), the main advantage here is for one side the combination of competitive in-vehicle times and superior services-related to aviation, and in the other side competitive service with superior in-vehicle time in relation to HSR. Beyond the 2000 km distance the profiles of speed become a very important factor since the out-ofvehicle time will be offset by the advantages in speed. HSR is not a competitive mode due to the longer in-vehicle times. If hyperloop reaches 700km/h it will keep the dominium of the market with at least 60%, still with some variation explained by the relative advantage of a smaller out-of-vehicle time which decreases rapidly as the distance increase. In this same range of distance, if hyperloop reaches the 1000 km/h it would take at least 95% of the market. This is totally explained by the advantage in in-vehicle times. The low-speed scenario of 500 km/h will be less favorable for hyperloop in this segment, due to disadvantages in relation with aviation in the in-vehicle time.

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Figure 7. Relation distances and probability to choose hyperloop over HSR or aviation

Although we acknowledge that behavioral and socioeconomic factors are relevant variables of the modal choice, the degree of uncertainty at the moment of this study does not allow to find dominant assumptions other than the classical rational consistency of the passengers when comparing the three modes. Recommendation: This study recommends that in the future, the modal choice analysis should include socio-economic behavior of the passengers. As uncertainty will decrease with time, a good design survey instrument or behavioral economics experiments will determine the degree of influence of these variables in the modal choice. Scenario planning with behavioral experts is also a new option implemented recently for official government institutions when designing the long-term outlooks10.

Paper by Glenn Lyons and Cody Davidson, Guidance for transport planning and policymaking in the face of an uncertain future: Link 10

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4 HYPERLOOP DEMAND AND RIDERSHIP FORECAST 2040 Summary: Hyperloop will generate between 207 and 334 million passenger trips annually in 2040. Run up periods after 2040 should be analyzed carefully in future studies. The combination of the three profiles of operation and the two scenarios of future demand ended in six scenarios for hyperloop in 2040, see table below. In the baseline scenario – with high demand growth and hyperloop operating at 700km/h – the system will generate at least 311 million passenger trips from current aviation and international rail. If the market grows at a low expected rate, hyperloop will have between 207-280 million passenger trips. If the market grows at a high11 rate, hyperloop will have between 247 and 334 million passenger trips. A detailed overview of the passengers per O-D pair can be reviewed in the annex. Future Market Growth (Million passenger trips year) Low High Low-Speed Scenario

207

247

Baseline Scenario

261

311

High-Speed Scenario

280

334

Table 7. Demand & ridership forecast for Hyperloop in 2040

Ramp up period note The literature advice to account for a period before the expected total market share is achieved. For example, if the expected total market share for hyperloop is 85%, that share will not be achieved in the first year, because social and individual behavior, instead it will take “X” years. Historical experiences of HSR service in Europe have shown mix evidence, with rump up periods between 2 years for TGV in France and more than five years for Thalys -Paris, Brussels, Amsterdam, Cologne-. Popular routes with high demand are likely to have shorter ramp-up periods12. This study did not include a ramp up period in the main analysis and advice a route specific approach. However, a maximum ramp up period for this case is not expected to be longer than 5 years for the next reasons: first, the current routes selected for the EU case are among the most popular in Europe with growing demand. Second, by 2040 it’s expected that many airports will face physical capacity constraints translated in unaccommodated demand. Third, the local and international agendas of social and economic development are advocating for a quick transition to environmentally friendly modes of transportation.

Likely rate of EuroControl, Report EUROCONTROL: European Aviation in 2040: Challenges of Growth: Annex 1: Flight Forecast 2040: Link 12 Nash, HSR Overseas Experience Report, High Speed Rail Study Phase 1, 201: Link 11

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5 HYPERLOOP CAPACITY 5.1 MAXIMUM STANDARD CAPACITY The study estimated three scenarios of maximum standard capacity for a one-tube hyperloop between every link of the network. Keeping everything else constant, the scenarios are based in the operational speed, a low-speed scenario of 500km/h, main speed scenario of 700 km/h, and high-speed scenario of 1000km/h. Summary: based on operating speed assumptions, hyperloop would be able to move between 81157 million passengers per year, equivalent to 14-27 thousand passengers per hour in every link of the network. Capacity can be increased by changes in operational and design concept parameters; for example, coupling vehicles13 can double the standard capacity. The maximum standard capacity of a one-tube hyperloop system to move passengers between any origin point A to a destination point B in a given period is determined by the maximum number of vehicles operating, and the number of passengers per vehicle. The vehicle capacity is preceded by the “minimum safe time headway” needed between the vehicles in operation, while the passenger capacity depends additionally on the number of seats per vehicle, adjusted to the period of operation. Figure below shows a basic input-output scheme of passenger capacity.

Table 8. Maximum passenger capacity between two origin/destination points.

*The size of the vehicles and the number of seats could keep some correlation. The capacity of the Hyperloop was tested in three theoretical scenarios, low-speed scenario of 500km/h, baseline scenario of 700 km/h, and high-speed scenario of 1000km/h. The rest of the inputs remains constant in the three scenarios, and the value of the parameters was based on expertise criteria and concept designs14 (see table below).

13 14

See Hardt Hyperloop General Concept Design. See Hardt’s Hyperloop General Concept Design for more details.

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Keeping everything else constant, hyperloop would be able to move between 81 million passengers per year in the high-speed scenario, and 157 million passengers per year in low-speed scenario. With 16 hours of operation per day, hyperloop would move between 14 and 27 thousand passengers per hour. Operational factors like speed, number of seats, emergency braking rate, number of tubes, among others, can be changed and then produce many other scenarios of capacity. Parameters of Capacity Number of seats Number of operating hours Operating speed Annual Passenger Capacity Hourly Passenger Capacity

Low-Speed Scenario

Baseline Scenario

High-Speed Scenario

60 seats per vehicle

60 seats per vehicle

60 seats per vehicle

16 hours per day

16 hours per day

16 hours per day

500 km/h

700 km/h

1000 km/h

157.680.000

116.800.000

81.760.000

27.000

20.000

14.000

Table 9. Scenarios of capacity for Hyperloop

5.2 CAPACITY VS DEMAND PER HOUR This section analyzes the use of the maximum standard capacity of the hyperloop system in the six scenarios of demand in peak hours (100% increase over the average demand). Summary: A one-tube hyperloop system operating in any of the six scenarios have enough capacity to move the total number of passengers in normal operational hours. In peak hours, four links in the center of the network would require a virtual coupling of vehicles. Traffic in current modes is not uniformly distributed along the operating hours of the day. Accounting for peak hours of demand, and in the extreme case where the peak hour occurs at the same time in the entire network, only four links would require adjustment to the standard operating parameters to cope with such increase in traffic. An example of a quick-fix for these links would be doubling the capacity by the coupling of extra-vehicles15.

Additional vehicles of 60 passengers can be coupling creating 120 passenger two-wagon vehicles. See Hardt Hyperloop General Concept Design. 15

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The boxplot shows the summary of the distribution of use of capacity in peak hours, emphasizing the greater and average values. Only in the case of high demand and speeds of 1000km/h and 700km/h, four links would fall beyond the standard maximum capacity (Paris-Lille, Lille-London). Overall, In the six cases, 75% of the links (upper and lower border of the boxes) would use less than 85% of the standard maximum capacity, and more than 15%.

Figure 8. Scenarios of use of capacity in peak hours in 2040

Capacity will not be an issue in the network. A potential pressure in a particular link can be solved by conceptualizing efficient services, coupling of vehicles, adjustments in speed, etc. The next map offers a visualization of the traffic in the network in the scenario with more use of capacity – 1000km/h in high growth future. In red those links with traffic over the standard maximum capacity.

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Figure 9. Map with use of Maximum capacity in peak hours in 2040 (Scenario high growth and speed 1000km/h).

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6 QUICK SCAN BUSINESS CASE The quick scan business case provides an initial assessment of the economic potential of the proposed European hyperloop network. The network has been analyzed based on several assumptions that are appropriate for this early stage of development. Insights are used to analyze under what circumstances a positive business case for an initial social cost-benefit analysis can be generated.

6.1 TICKET REVENUE Summary: The ticket revenue is estimated between €21-€41Billion. This section assumes a constant ticket price of €0,10 per kilometer16. This assumption only applies to this section. The previous modal choice section assumed a competitive price in every link for all the modes and did not identify a particular parameter for the prices. The final numbers of passenger trips were distributed between the ground distances to get the number of passenger kilometer (PaxKm). The proposed European hyperloop network will generate between 210-416 billion passenger kilometers.

PaxKm Billion 416 370 311

210

349

251

500 km/hr

700 km/hr Low

1000 km/hr

High

Figure 10. Final demand for Hyperloop 2040 (passenger kilometer per year)

The ticket price per every origin-destination pair was calculated with a first assumption of ticket fares at €0.10 per kilometer. Table 10 presents a representative list of ticket prices in the network (see the complete matrix in the annexes). Because of the assumption of a fixed price per kilometer, the longest distances do not get benefits from economies of scale and diminishing marginal costs. For example, a trip between Lisbon and Amsterdam will be charged at 315 Euro, closer to a business class air ticket, while Paris-Dusseldorf will be charged at 60 Euro, very competitive with average fares of air and rail in the same route.

16

Fare reference from the Dutch rail market in 2017

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Route Amsterdam-Eindhoven Dusseldorf/Cologne-Marseille Lyon- Marseille Frankfurt-Munich Stuttgart -Milan Paris-Dusseldorf Eindhoven- Birmingham Lyon-London Brussels-Warsaw Edinburgh-Berlin Lisbon-Amsterdam Lisbon-Warsaw

Distance 125 205 305 400 500 605 700 990 1.525 2.005 3.150 4.415

Ticket-Price 12,5 20,5 30,5 40,0 50,0 60,5 70,0 99,0 152,5 200,5 315,0 441,5

Table 10. Ticket prices when assuming 0,10 Euro per kilometer

This first assumption on price evidence reveals the need for research about the system of prices in the market. However, prices in the air market are very heterogeneous; low-cost carriers and fix infrastructure working at minimum return presents a challenge for the assumption of competitive fairs. Finally, the ticket revenues are calculated between €21 billion and €41 billion. Table 11 summarizes the ticket revenue in every scenario. Future Market Growth (bEUR / year) Low

High

Low Speed Scenario

21

25

Baseline Scenario

31

37

High-Speed Scenario

35

41

Table 11. Estimated ticket price revenues for EU hyperloop network

Recommendation: The study recommends using some non-lineal-threshold prices for different segments of the route based on categories of prices observed in the market in future stages.

6.2 COSTS Summary: Assuming a construction cost of €30 million per kilometer, the total network of 9.588 km is estimated to cost €288 Billion. For further research, the cost estimates have to be detailed on route level. This purpose of this cost-benefit is to provide guidance on the profitability of the proposed hyperloop network for AAS. Cost estimates at this stage are subject to uncertainty. Therefore, a generalized capital cost is considered, because: - The costs are determined based on a high-level route alignment scenario; - Designs are conceptual and at a limited level of development; - No alignments studies have been performed on the proposed network; - No thorough ground condition investigations have been conducted; - Preliminary infrastructure performance information is available; Table 12 describes the parameters used for the business case.

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Cost and Financial Parameters Network Kilometers

9.588

Construction costs / km

€ 30 M

Total construction

€ 288B

Debt share

80%

Payback time

30 years

Interest rate

5%

OPEX infra per km

€ 100.000

OPEX passengers per km

€ 0,01

Table 12. Costs and financial assumptions

6.3 RETURNS Summary: All scenarios generate profits in 2040 with financial assumptions for costs and repayments if the whole network is operational. The annual profit for 2040 was calculated with cost and financial parameters taken from previous studies at Hardt. The four scenarios are profitable after cost and operational expenses. The annual profits are estimated between €3 Billion and €21.5 Billion after discounting repayment and interest rates of building costs, and annual operational costs. Repayment + interest rate

Annual OPEX

Annual expenses

Scenario 1: 500Km/h-Low

15B

€ 3,1 B

€ 18,0 B

€ 3,0 B

Scenario 2: 700Km/h-Low

15B

€ 4,1 B

€ 19,0 B

€ 12,1 B

Scenario 3: 1000Km/h-Low

15B

€ 4,4 B

€ 19,4 B

€ 15,5 B

Scenario 4: 500Km/h-High

15B

€ 3,5 B

€ 18,4 B

€ 6,7 B

Scenario 5: 700Km/h-high

15B

€ 4,7 B

€ 19,6 B

€ 17,4 B

Scenario 6: 1000Km/h-high Table 13. Annual returns 2040

15B

€ 5,1 B

€ 20,1 B

€ 21,5 B

2040

Annual return

6.4 ISOLATED LINKS Although the network makes a profitable case, isolated links with only aviation passengers and international rail will not be able to cover the repayment of the debt and interest rate of the construction costs. The exception is the link between Paris and London, currently served by aviation and high-speed rail. Table 14 illustrates the sequential links of the network, the infrastructure distance, the expected passengers in 2040 in the baseline scenario (high growth and speed of 700km/h) without network effects, the annual repayment plus interest rate of the constructions costs, the annual ticket revenue and the difference between the ticket revenue and the expected annual payments.

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Route Lisbon Madrid Barcelona Paris Paris Paris Brussels Amsterdam + Rotterdam Rome Bologna Milan Stuttgart + Munich Berlin Vienna London

Distance (km)

Passengers (mil.) 2040

Madrid Barcelona Montpellier + Toulouse Lyon London Brussels Eindhoven

625 621

2,2 3,6

Annual Debt Payments (€ million) 643,8 639,6

0,7

355,4

24,5

-331

1,0 24,2 7,5 0,2

505,7 516,0 339,9 141,1

46,8 1.214,3 249,0 2,4

-459 698 -91 -139

Eindhoven Bologna Milan Zurich

123 374 222 281

-

126,7

-

-127

0,3 1,0

385,2 228,7 289,4

11,0 27,5

-374 -229 -262

Zurich Warsaw Munich Outside London

543 574 454 952

0,2

559,3

9,1

-550

0,2 0,7 10,0

591,2 467,6 980,6

14,0 33,8 955,6

-577 -434 -25

345 491 501 330 137

Table 14. Financial viability of isolated links 2040 (Scenario High growth at 700km/h)

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Annual Ticket Revenue (€ million) 136,7 222,4

Ticket RevenueDebt Payments (€ million) -507 -417


6.5 QUCIK-SCAN ENVIRONMENTAL EFFECTS As the new mode of transportation for high-speed and medium- to long-distance travel, hyperloop competes with rail and aviation in terms of speed and sustainability. To estimate the sustainability of hyperloop, a model for the CO2 emissions during construction and operation has been developed. This model is used to compare the sustainability of hyperloop as an alternative to rail and aviation on three cases of increasing order of magnitude. Reduction of greenhouse gas emissions Hardt commissioned CE Delft for the execution of the “Prospective LCA” to ensure an independent assessment. Hardt provided the inputs, based on a product under design. CE Delft modeled these to determine the environmental impact. Following from the LCA results it is observed that hyperloop produces less greenhouse gas emissions over its lifetime compared to aviation. As a result, the modal shift of passengers from aviation to hyperloop leads to reduction of GHG. In order to determine the reduction of greenhouse gas emissions, the following are used per mode per year. The emissions are depicted in CO2 equivalents per passenger-kilometer. For the future projections towards 2040, a linear reduction of CO2 emissions is considered. The reduction targets for 2050 are set in the Dutch Climate Act and Paris Agreements: 49% less CO2 emissions by 2030, and 95% less CO2 emissions by 2050, compared to 1990. ICAO and IATA have set goals for 2050 to reduce emission of aviation worldwide by 50% in 2050 as compared to 2005. Given the aviation market doubling towards 2050 compared to 2005, the emissions per aviation pkm are to be reduced by 75%. Year 2019

2040

Mode Environmental Impact Aviation17 107 gr CO2-eq/pkm Hyperloop 30 gr CO2-eq/pkm High-Speed Rail 45 gr CO2-eq/pkm Aviation (49% of 2019 emissions) 53 gr CO2-eq/pkm Hyperloop (36% of 2019 emissions) 11 gr CO2-eq/pkm High-Speed Rail (36% of 2019 emissions) 16 gr CO2-eq/pkm Table 15. Environmental impact per mode for 2019 and 2040.

The emission reductions are determined based on the pkm per year, predominantly for hyperloop compared to aviation. As illustrated in the table, the GHG-reduction of hyperloop is projected between 72-80%. In absolute terms, a substantial difference can be observed between the 2019 and 2040 emissions, where the absolute reduction in 2019 terms is +-2x larger than the absolute reduction in 2040 emissions. Scenario

Aviation Emissions (ton CO2 per annum) 30.021.119

Hyperloop Emissions (ton CO2 per annum) 8.417.136

Reduction

%

21.603.983

71,96%

2019 emissions - High demand

35.716.444

10.013.956

25.702.488

71,96%

2040 emissions - Low demand

14.768.454

3.000.302

11.768.152

79,68%

2040 emissions - High demand

17.570.186

3.569.491

14.000.695

79,68%

2019 emissions - Low demand

Table 16. CO2-emission reduction scenarios

To put this in perspective: the total EU emissions in 2017 accounted to 3.5 billion ton CO218. Hyperloop would reduce the total CO2 emissions of the EU with 0,33 - 0,72%.

17 18

Based on the A320 NEO, the current airplane with lowest CO2 emissions per pkm. “Fossil CO2 emissions of all world countries, 2018 report, EU”: Link

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7 FINDINGS AND RECOMMENDATIONS 7.1 FINDINGS RELATED TO THE MOTIVATION QUESTIONS OF THE STUDY What are the main markets in Europe that Hyperloop could potentially serve? • The study identified aviation and international rail like the main market to be addressed by hyperloop in the context of a European market. • Selected rail routes in the domestic markets could be added (this study only included international rail), as they serve medium distances comparable with some international routes. However, both medium and short domestic rail routes might need a different concept of service and operation than those in the medium and long distances presented in this study. What is the size of these markets, and how is currently split between the relevant current modes of transportation? • The total addressable market for hyperloop in 2017 is composed of 226 million passengers, 194 million in aviation, and 32 million in international rail. How would the market grow towards 2040? • With two scenarios of low and high growth for aviation and rail in 2040, it is expected that aviation will grow towards 297-357 million passengers, equivalent to a low/high growth of 5384%, while rail will see a rise in demand to 41-45 million passengers (+27-45%). What would be the potential for diversion from other modes to Hyperloop? • This study proposed a model of choice for hyperloop and the other modes (aviation, and international rail) based on the “door to door travel times.” Three scenarios of operation and services of Hyperloop, low speed at 500km/h, main speed at 500km/h, and high speed at 1000 km/h are used to obtain expected average shares of the market. On average hyperloop will get a market share of around 62%, 85%, and 95% respectively. How sensitive would be the level of diversion of passengers from other modes, the ridership forecast, and the ticket revenue in different favorable and non-favorable scenarios for hyperloop in Europe? • The percentage of market attracted by hyperloop is sensitive to operation and service variables like access, waiting, and transfer time. In general, the less time the passenger spends on the trip, the better. However, it is necessary to research segments of passengers, their preferences, and behavior. This study did not include any distinction of types of passengers and purposes of the trip. • The combination of future market growth scenarios and modal choice scenarios offered six estimations of passenger trip for hyperloop in 2040, from 207 to 334 million passenger trip. • Assuming a linear price of €0,10 per passenger-kilometer, the system will be profitable and will generate an annual profit (after cost and operational expenses) of €3-21 Billion in 2040. What would be the environmental benefit of hyperloop compared to the other modes? • A quick scan assessment is conducted of the CO2 emissions for hyperloop and aviation based, where both modes are compared on 2019 and on 2040 emissions. • Hyperloop would be able to reduce CO2 emissions between 11-25 million ton CO2-eq per annuum, a reduction of 71-79% compared to aviation.

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7.2 OTHER FINDINGS •

Hyperloop will face competition with the high-speed rail under 700 kilometers, substitution of aviation in distances between 500 km and 2000 km, and competition for aviation over the 2000 km. Reviewing the capacity it is unlikely the system capacity will be under pressure. Changes in service design, such as coupled vehicles, lower transit speeds or different pricing strategies, can relieve congestion in the system. Although the modal choice model assumes competitive prices, the ticket revenue was calculated with a constant price per kilometer for Hyperloop. Fix prices might neglect diminishing marginal costs of the infrastructure, making hyperloop less attractive than other modes in longer distances. In reality the system will have to guarantee that prices are indeed competitive to hold the predicted market share. This study found no financially profitable isolated links when substituting only aviation and international rail. For further assessments, wider economic benefits and or regional traffic is to be considered too.

7.3 RECOMMENDATIONS AND NEXT STEPS PROPOSAL • • • • •

• • •

• •

33

Modal choice sensitivity to assumptions is a good option for prefeasibility phases. When facing a real project, well design survey tools or behavioral experiments have proven to be better. Use of supernodes (regions of origin and destination) for catchment areas, economic analysis, the presence of competition in a region, stakeholders, agendas of the policy of development. Assumption of terminus by scenarios according to the context of every region. This can influence the out-of-vehicle time, which is determinant in the modal choice. Conceptualization of integration with other modes. e.g., when presence of HSR. Inclusion of demand from selected routes of domestic rail (not all of them suitable with the concept of this study). For example, domestic rail in medium distances in Spain, France, UK, and Germany. The market analysis showed that the domestic market in those countries is big. Use corridors for refining analysis. Corridors can be classified by criteria of competition with other modes, underserved routes, high demand, political interest/apathy, etc. Research on dynamic prices and economies of scale; options are non-linear prices, analysis by thresholds based on distance, etc. Include ramp-up period when needed. For that, a contextual discussion according to expectations of constraints of capacity in the future markets can give orientation. More research is needed. Concept of hyperloop for domestic rail medium distances. Different commercial plans for Hyperloop short distances and long distances (like aviation, long flights - low-cost carriers).

EU Hyperloop Network Study Hardt Hyperloop


Marseille

Montpellier+Toulouse

Barcelona

Madrid

Sevilla+Malaga

Valencia+Alicante

Brussels

Eindhoven

London

Birmingham+Bristol

Manchester

Edinburgh+Glasgow

Dusseldorf+Cologne

Hannover+Hamburg

Berlin

Warsaw

Frankfurt

Stuttgart

Zurich

Milan

Nice

Bologna

Rome

Munich

Vienna

X

Lyon

Lisbon Amsterdam+Rotterdam Paris Lyon Marseille Montpellier+Toulouse Barcelona Madrid Sevilla+Malaga Valencia+Alicante Brussels Eindhoven London Birmingham+Bristol Manchester Edinburgh+Glasgow Dusseldorf+Cologne Hannover+Hamburg Berlin Warsaw Frankfurt Stuttgart Zurich Milan Nice Bologna Rome Munich Vienna

Paris

Lisbon

Distances ground infrastructure (km)

Amsterdam+Rotterdam

ANNEX 1: DISTANCES BY CURRENT GROUND INFRASTRUCTURE FOLLOWING THE HYPERLOOP NETWORK

3.150

2.560

2.070

1.765

1.590

1.245

625

1.150

1.600

2.890

3.025

3.060

3.235

3.375

3.725

3.165

3.465

3.840

4.415

2.930

2.755

2.535

2.255

1.955

2.475

2.850

2.980

3.435

590

1.080

1.385

1.560

1.905

2.525

3.050

2.260

260

125

650

825

965

1.315

265

565

940

1.515

470

645

865

1.145

1.445

1.365

1.740

870

1.325

490

795

970

1.315

1.935

2.460

1.670

330

465

500

675

815

1.165

605

905

1.280

1.855

810

985

1.205

1.285

985

1.505

1.880

1.210

1.665

X

305

480

825

1.445

1.970

1.180

820

955

990

1.165

1.305

1.655

1.095

1.395

1.770

2.345

1.300

1.295

1.075

795

495

1.015

1.390

1.520

1.975

X

175

520

1.140

1.665

875

1.125

1.260

1.295

1.470

1.610

1.960

1.400

1.700

2.075

2.650

1.165

990

770

490

190

710

1.085

1.215

1.670

X

345

965

1.490

700

1.300

1.435

1.470

1.645

1.785

2.135

1.575

1.875

2.250

2.825

1.340

1.165

945

665

365

885

1.260

1.390

1.845

X

620

1.145

355

1.645

1.780

1.815

1.990

2.130

2.480

1.920

2.220

2.595

3.170

1.685

1.510

1.290

1.010

710

1.230

1.605

1.735

2.190

525

975

2.265

2.400

2.435

2.610

2.750

3.100

2.540

2.840

3.215

3.790

2.305

2.130

1.910

1.630

1.330

1.850

2.225

2.355

2.810

1.500

2.790

2.925

2.960

3.135

3.275

3.625

3.065

3.365

3.740

4.315

2.830

2.655

2.435

2.155

1.855

2.375

2.750

2.880

3.335

X

2.000

2.135

2.170

2.345

2.485

2.835

2.275

2.575

2.950

3.525

2.040

1.865

1.645

1.365

1.065

1.585

1.960

2.090

2.545

135

390

565

705

1.055

275

575

950

1.525

480

655

875

1.155

1.315

1.375

1.750

880

1.335

X

525

700

840

1.190

140

440

815

1.390

345

520

740

1.020

1.320

1.240

1.615

745

1.200

X

175

315

665

665

965

1.340

1.915

870

1.045

1.265

1.545

1.485

1.765

2.140

1.270

1.725

X

140

490

840

1.140

1.515

2.090

1.045

1.220

1.440

1.720

1.660

1.940

2.315

1.445

1.900

X

350

980

1.280

1.655

2.230

1.185

1.360

1.580

1.860

1.800

2.080

2.455

1.585

2.040

1.330

1.630

2.005

2.580

1.535

1.710

1.930

2.210

2.150

2.430

2.805

1.935

2.390

300

675

1.250

205

380

600

880

1.180

1.100

1.475

605

1.060

X

375

950

505

680

900

1.180

1.480

1.400

1.775

905

1.360

X

575

880

1.055

1.275

1.555

1.855

1.775

2.150

1.280

1.735

1.455

1.630

1.850

2.130

2.430

2.350

2.725

1.855

2.310

175

395

675

975

895

1.270

400

855

X

220

500

800

720

1.095

225

680

X

280

580

500

875

445

900

X

300

220

595

725

1.180

X

520

895

1.025

1.480

X

375

945

1.400

1.320

1.775

X

X

X

X

X

X

X

X

X

X

X

X

Ticket price when assuming € 0.10 per kilometer.

34

455

EU Hyperloop Network Study Hardt Hyperloop


ANNEX 2: TICKET PRICES PER ORIGIN-DESTINATION PAIR IN THE NETWORK

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EU Hyperloop Network Study Hardt Hyperloop


ANNEX 3: THEORETHICAL MODAL CHOICE MODEL Hardt developed a theoretical modal choice model based on the utility of the travel time, under perfect market competition for aviation, high-speed rail, and hyperloop. The model is based on a set of factors that determine the probability of a passenger to choose one mode over the other. A theoretical model of the utility of the “door to door” travel time was used to estimate the probability to choose hyperloop when competing against aviation and high-speed rail in Europe. Dataset The model uses a sample of 406 routes in Europe, from the combination of 46 airports, 11 regional rail zones, in 37 cities of West and central Europe. Real data of infrastructure distances and average operative parameters were used for the comparison with aviation and high-speed rail, while conceptual design parameters were assumed for Hyperloop. Model The modal choice model is assuming that the only factor of choice is door-to-door travel time, where all types of travel time (i.e. travel time, process time, egress time, access time) are equally weighted. Although we acknowledge that behavioral and socioeconomic factors are relevant variables of the modal choice, the degree of uncertainty at the moment of this study does not allow to find dominant assumptions other than the classical rational consistency of the passengers when comparing the three modes. Door-to-door travel time is defined as the sum of two components: 1.

2.

In-vehicle time: The time the passenger travels within the transportation mode. This is dependent on the speed of the transportation mode and the distance to overcome. For HSR the length of the track from one station to the other is used to estimate the length with an average speed of 250 km/h. Since aviation does not follow a particular track, trip times found online are used as in-vehicle time. Out-of-vehicle time: This is the sum of Access time, the time needed to get to the station, Process time, the time needed to move within the station to the vehicle and Egress time, the time needed to get to the final destination.

Figure 11. Modal Choice Model

The functional shape is a standard multinomial logit(MNL) modeling approach, which gives the choice probabilities of each alternative as a function of the systematic portion of the utility of all the

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EU Hyperloop Network Study Hardt Hyperloop


alternatives. This modeling approach assumes that the general expression for the probability of choosing a mode alternative n (n = {1,...,M}) from a set of M alternatives is: 𝑖𝑗

𝑖𝑗

𝑃𝑛 =

exp (−𝜇 . 𝑉𝑛 ) ∑ exp (−𝜇 . 𝑉𝑛𝑖𝑗 )

where 𝜇 is a scale parameter, and 𝑉𝑛 is the disutility of traveling from i to j with mode n, calculated as follows: 𝑖𝑗

𝑖𝑗

𝑖𝑗

𝑖𝑗

𝑖𝑗

𝑉𝑛 = 𝑉𝑂𝑇 · ( 𝐼𝑉𝑇𝑛 + 𝜔 1 · 2 · 𝐴𝐴𝑇𝑛 + 𝜔2 · (𝐴𝑊𝑇𝑛 + 𝑁𝑇 𝑛 · 𝐴𝑇𝑇𝑛 ) + 𝐹𝑛 + 2 · 𝐴𝐴𝐹 𝑛 Where 𝐼𝑉𝑇𝑛 is the in-vehicle time for every pair of cities i and j, 𝑁𝑇 𝑛 is the number of transfers needed 𝑖𝑗 between a pair of cities i and j, and the 𝐹𝑛 is the fare ticket between the pair cities i and j ( constant for the three modes). The parameters are defined in the next table: 𝑖𝑗

Parameter (units) 𝜇 VOT 𝑢𝑛 𝜔1 𝜔2 𝐴𝐴𝑇𝑛 𝐴𝑊𝑇𝑛 𝐴𝑇𝑇𝑛 𝐴𝐴𝐹 𝑛

𝑖𝑗

Definition

Air (n = 1)

Rail (n = 2)

HL (n = 3)

Scaled parameter -1.8 Value of time 12 12 Operating speed Km/h 800 250 Weight for AAT 1.5 1.5 Weight for AWT NT and ATT 1.5 1.5 Average access time 0.75 0.25 Average waiting time 1.5 0.25 Average transfer time 0.4 0.3 Average ticket fare Table 17. Generalized parameters for modal share model.

12 700 1.5 1.5 0.25 0.25 0.3 -

Scaled parameter As the scaled parameter 𝜇 determines the sensitivity of the function to the relative variability between the modes, its calibration is key for the analysis. The function was tested with real data of aviation and high-speed rail from experiences in Europe and Japan19. The results show that the model is able to predict the market share of the control group using either distance-based measures or in-vehicle time measures as the main variables.

Figure 12. Test Calibration: Modal choice model for HSR and Aviation (without Hyperloop)

Probability to choose a mode per O-D pair distance based on total travel time

A similar exercise can be found in the following study High Speed Rail Study Phase 2 Report Appendix 1A: Previous HSR demand studies in Australia and overseas: Link 19

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EU Hyperloop Network Study Hardt Hyperloop


After calibration, the theoretical mode of hyperloop is added to the modal mix on the dataset with all O-D pairs of the European Case. As a result, passengers in the relevant market in 2040-2050 will have at least three alternatives of transportation between the cities of the network; aviation, rail, and hyperloop. The figure below illustrates the probability to choose any of the three modes per O-D pair distance, based on the parameters of table 17.

Probability to choose any mode P.HL

P.Air

P.HSR

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 0

500

1000

1500

2000

2500

3000

3500

4000

Distance between O-D pair Figure 13. Probability to choose a mode per O-D pair distance

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EU Hyperloop Network Study Hardt Hyperloop

4500

5000


Other Factors Beyond the straightforward impact of the comparison of the door to door travel time on the probability to choose a mode of transportation over another, other factors might play a substantial role. These factors are subject to a more political discussion, so we present here an analytical view of what could happen to the probability too choose hyperloop if these factors move in two extreme directions. •

Ticket prices: If ticket prices for hyperloop are significantly higher than the other competitive modes of transportation, or the other modes of transportation manage to lower their ticket prices -for example as an strategy to cover their fix costs- less users would be expected to choose hyperloop over the other modes. The difference in price although should be higher enough to offset the advantage of the door to door travel time offered by hyperloop, which we consider unlikely based in the conceptual design of hyperloop. Conversely, if hyperloop offers a relative lower price than the other modes, the probability to be chosen would go up from the current estimation of 72% of market share.

Environmental pressures: Concerns related to the CO2 emitted in the industry of transportation are increasing. Some of the trends are related to the possibility to tax CO2 emissions in the aviation industry within the European Union, while a second possibility is to leave the decision to the consumers who will reward or punish the modes of transportation according to their preferences for a more or less sustainable environment.

Taxes to CO2 emissions have been discussed in the EU as a policy to cope with the high emissions in the sector. Such measure would be likely to increase the ticket prices of aviation and would give an advantage to the other competitive modes in the market. Preferences for friendly environmental modes of transportation can benefit the conceptual design of hyperloop as well as some forms of high-speed rail; that would depend on the capacity of the users of distinguishing the differences between the two modes. If hyperloop meets the design assumptions, it would offer a significant reduction of CO2 emissions comparing to the two modes which will increase the probability for it of taking a higher market share.

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EU Hyperloop Network Study Hardt Hyperloop