Economical assessment of commercial high-speed transport

Abstract

The potential future market demand for a high-speed aircraft along with an estimate of the related costs and ticket prices have been assessed. To address the future demand for a high-speed aircraft, the eligible origin and destination city-pairs and the potential network for such a vehicle have first been identified. Then, the number of premium passengers flying this network, over the next 20 years, is forecasted. Based upon technical characteristics of the potential future high-speed aircraft, it is finally possible to determine the number of vehicles that would be necessary to accommodate this expected future demand.

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Source: Airbus GMF 2013

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(Source: Oxford Economics.)

Fig. 15

(Source: Airbus)

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(Source: Airbus)

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Notes

  1. 1.

    An itinerary is a flight or a sequence of connecting flights used to travel between any two cities.

  2. 2.

    By international long haul city-pairs, we mean all CPs with GC distance above 3700 km.

  3. 3.

    Airbus’ GMF traffic forecast gives the overall shape of the expected traffic evolution over the next 20 years.

  4. 4.

    Grosche [11] developped an integrated airline schedule optimization procedure to help airlines with their schedule planning. We rely on his calibration results to define the majority of the rules for itinerary building.

  5. 5.

    As robustness check, we also considered 300 km.

  6. 6.

    Intuitively, a 90% learning factor means that a doubling of the production run reduces cost by 10%.

  7. 7.

    Recall that international long-haul traffic includes all city-pairs with GC distance above 3700 km.

  8. 8.

    Section 2.4 presents the market share capture model, which substitutes the exogenous market share assumed here.

  9. 9.

    An extended range factor (ERF) is defined as the ratio between actual distance including flight path deviation and the GC distance.

  10. 10.

    This section is based on Oxford Economics’ cost and ticket price estimation results. The authors would like to thank Tom Rogers and Philip Thomas for sharing these results with us.

  11. 11.

    This is because for cluster 1, the weighted average flight time ratio over the eligible OD CPs is 0.25.

  12. 12.

    More precisely, a ticket price for the high speed aircraft of €17,000 and an average FC + B ticket price of €4300 (both as of 2012 prices) result in a fare ratio of 3.9.

  13. 13.

    An average ticket price for the high speed aircraft of €10,000, together with an average FC + B ticket price of €4300, yield a fare ratio of 2.3.

  14. 14.

    20% is the minimum development and production cost reduction required for at least one equilibrium to exist.

  15. 15.

    For comparison, the theoretical equilibrium determination for the scenario 300 seats, cluster 1 and 20% reduction in the development and production costs is depicted in Appendix A.

  16. 16.

    The theoretical equilibrium determination of the scenario 300 seats, cluster 2 and 20% reduction in the development and production costs is also depicted in the appendix.

Abbreviations

CP:

City-pair

ERF:

Extended range factor

ESA:

European space agency

FC + B:

First and business class

GC:

Great circle

GDD:

Global demand data

GMF:

Airbus global market forecast

GVA:

Gross value added

HST:

High-speed transport

OD:

Origin and destination

OE:

Oxford economics

RPK:

Revenue passenger kilometre

Sfc:

Specific fuel consumption

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Acknowledgements

The research leading to the results in this paper has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant Agreement n° 313987 HIKARI, with the coordinated support from METI, the Ministry of Economy, Trade and Industry of Japan.

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Correspondence to Paula Margaretic.

Appendices

Appendix A

See Figs. 21, 22 and 23.

Fig. 21
figure21

Eligible HST cities. The name of the city, abbreviation and distance to the coast for each city follow: AKL-Auckland (7 km), AMS-Amsterdam (21 km), BJS-Beijing (145 km), BKK-Bangkok (64 km), BNE-Brisbane (24 km), BOG-Bogota (333 km), BOM-Mumbai (18 km), BOS-Boston (6 km), BRU-Brussels (53 km), BUE-Buenos Aires (17 km), CAN-Guangzhou (42 km), CHI-Chicago (964 km), CPH-Copenhagen (4 km), DEL-Delhi (816 km), DOH-Doha (55 km), DPS-Denpasar Bali (6 km), DUS-Düsseldorf (142 km), DXB-Dubai (102 km), FRA-Frankfurt (332 km), HKG-Hong Kong (2 km), HNL-Honolulu (13 km), HOU-Houston (22 km), IST-Istanbul (22 km), JED-Jeddah (63 km), JKT-Jakarta (90 km), JNB-Johannesburg (436 km), KUL-Kuala Lumpur (47 km), LAD-Luanda (23 km), LAX-Los Angeles (18 km), LIS-Lisbon (16 km), LON-London (52 km), LOS-Lagos (15 km), MAD-Madrid (299 km), MAN-Manchester (29 km), MEL-Melbourne (24 km), MEX-Mexico City (241 km), MIA-Miami (20 km), MIL-Milan (121 km), MOW-Moscow (645 km), MUC-Munich (305 km), NYC-New York (20 km), ORL-Orlando (55 km), OSA-Osaka (12 km), PAR-Paris (153 km), PER-Perth (34 km), RIO-Rio De Janeiro (15 km), ROM-Rome (83 km), SAO-Sao Paulo (81 km), SCL-Santiago (87 km), SEA-Seattle (4 km), SEL-Seoul (25 km), SFO-San Francisco (19 km), SHA-Shanghai (41 km), SIN-Singapore (3 km), SYD-Sydney (73 km), TPE-Taipei (22 km), TYO-Tokyo (12 km), WAS-Washington (10 km), YTO-Toronto (536 km), YVR-Vancouver (11 km), ZRH-Zurich (337 km)

Fig. 22
figure22

Theoretical equilibrium determination, 300 seats, cluster 1

Fig. 23
figure23

Theoretical equilibrium determination, 300 seats, cluster 2

Appendix B

To derive the probability that individual i chooses the high-speed aircraft, we write a parametric model of passenger demand. Specifically, we assume that individual i derives utility \({U_{ij}}\) from flying high-speed or subsonic, with j indexing the two alternative choices the passenger can make. Furthermore, we assume that \({U_{ij}}\) has two components, namely, a deterministic part, which we denote as \({V_j}\), and a random component \({\varepsilon _{ij}}~\) that is unobservable to the modeller, which we assume to distribute logistically.

The utility \({U_{ij}}\) thus writes

$${U_{ij}}={V_j}+{\varepsilon _{ij}}.$$

Since \({\varepsilon _{ij}}\) distributes logistically, the probability that individual i chooses mode j then becomes

$${P_{ij}}=\frac{{{e^{{V_j}}}}}{{\mathop \sum \nolimits_{h} {e^{{V_h}~}}}},$$

where \({P_{ij}}\) denotes the probability of choosing mode j (HST say) (see Train [17] for a formal derivation).

To estimate this model, without loss of generality, we assume that the deterministic part of the utility \({V_j}\) is a linear function of measurable attributes, namely, the time ratio (TR) and the fare ratio (FR). \({V_j}\) writes

$${V_j}={a_j}+{b_j} \times TR+{c_j} \times FR,$$

with \({a_j}\), \({b_j}\), and \({c_j}\) being to be estimated parameters that capture passengers’ preferences over these attributes. Substituting the previous expression for \({V_j}\) in \({P_{ij}}\) and rearranging yields Eq. (2.4).

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Margaretic, P., Steelant, J. Economical assessment of commercial high-speed transport. CEAS Aeronaut J 9, 747–764 (2018). https://doi.org/10.1007/s13272-018-0319-y

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Keywords

  • Air passenger future demand
  • Air passenger forecast
  • High-speed aircraft
  • HST