Airport capacity constraints & air travellers airport choice behaviour from global constraints to local effects

Transcription

1 Cologne Business School Airport capacity constraints & air travellers airport choice behaviour from global constraints to local effects Dr. Marc C. Gelhausen

2 Cumulative distribution of global ATMs on airports in Top 1,000 Airports (41%) handle about 52 m ATMs (95%) 80 Share of ATMs Worldwide (n = 55,043,014) Top 100 Airports (4.1%) handle nearly 28 m ATMs (51%) Gini Coefficient = % of the global air traffic is handled by less than 5% of 2,500 airports ,04 2,5 4,96 7,42 9,89 12,3 14,8 17,3 19,7 22,2 24,7 27,1 29, , ,4 41,9 44,3 46,8 49,3 51,7 54,2 56,6 59,1 61, ,5 68,9 71,4 73,9 76,3 78,8 81,3 83,7 86,2 88,6 91,1 93, ,5 Share of Airports Worldwide in % (n = 2,438) Reichmuth/Berster/Gelhausen (2011), CEAS Aeronautical Journal 2 (1-4), pp. 21 Slide 1

3 Why consider capacity constraints in airport choice? Limited airport infrastructure: Runways Terminals Night curfews Noise/emissions/political restrictions Affects available airport capacity to handle air passenger demand Slide 2

4 Hourly variation of flight movements at Frankfurt Airport Frankfurt Intl Airport: Peak Week 2008: September 2008 (9,459 ATMs) ATMs per Hour Monday Tuesday Wednesday Thursday Friday Saturday Sunday Total Arrival Departure Source: OAG, DLR Slide 3

5 Development of air traffic volume at LHR & FRA Europe 116 Year 2000 = Frankfurt London Heathrow Europe Frankfurt London Heathrow Reichmuth/Berster/Gelhausen (2011), CEAS Aeronautical Journal 2 (1-4), pp. 21 Slide 4

6 Traffic ranking by hours of operation of the year 2008 at London Heathrow Airport; CUI = 0.85 Flight Movements at London Heathrow in th Hour or 5% Peak Hour: th or Average Hour: 73 (77% of Hours with ATMs/Hour > 5) ATMs per Hour Capacity Utilization Index: Introduced by DLR in the global analysis as the ratio of average day time demand to 5% peak hour demand, as a proxy of capacity in the absence of comparable capacities of airports world wide, for airports with high traffic volumes ,121 1,401 1,681 ATMs in 2008: 479,294 1,961 2,241 2,521 2,801 3,081 3,361 3,641 3,921 4,201 4,481 4,761 5,041 Hours of the Year 5,321 5,601 5,881 6,161 6,441 6,721 7,001 7,281 7,561 7,841 8,121 8,401 8,681 Source: OAG, DLR Slide 6 Slide 5

7 CUI analysis of airports worldwide Reichmuth/Berster/Gelhausen (2011), CEAS Aeronautical Journal 2 (1-4), pp. 21 Slide 7 Slide 6

8 General model structure of the capacity constraints forecast and hypothesis about interdependencies Attitude of Population towards Air Transport Government and social Values Welfare Level Age Structure Tourism... Location and Size of an Airport Noise Number of Flights Airport Category... Democracy Ministry of Environment... Intermodal Substitution Railway Km Country Size... Acceptance & speed to improve airport capacities +: Positive wrt Improvement -: Negative wrt Improvement Slide 7

9 Main questions in modelling capacity constraints How is the individual air passenger affected: Given his chosen destination... Does he change his departure airport ( he is crowded out)? or Does he pay a higher price at his favourite airport ( other passengers are crowded out)? or Does he cancel his air trip altogether ( he is crowded out)? Slide 8

10 Possible consequences of capacity constraints at airports Low Redistribution of demand among neighbouring airports Mixed strategy Travel disutility Restricted growth of local demand Airport capacity expansion High / No Airport capacity expandable? Yes Gelhausen (2009), Journal of Airport Management 3(4), pp. 366 Slide 9

11 Forecasting philosophy of a nested logit-model Traveller: Which alternative is the best for me? Evaluation of alternatives by means of utility Lack of observability, measurement errors, Forecaster: Which alternative is most likely the best for him? Choice probabilities Summing up over homogenous populations Market segment specific market shares of all alternatives Gelhausen (2008), Journal of Airport Management 2(4), pp. 355 Slide 10

12 Modelling capacity constraints in airport choice - conceptual Idea: The higher the loss in personal welfare (utility) from alternative to alternative, the higher the efforts to get a slot for the best alternative, e.g. by early booking or paying higher prices. Realisation: Increase so-called synthetic price to reduce airport attractiveness and thus redistribute excess demand until capacity constraints are met. Slide 11

13 Modelling capacity constraints in airport choice (I) 1 P i e e j V i V j P i σ A <σ B σ A σ B 0 0 V i max(v j ), i j Slide 12

14 Modelling capacity constraints in airport choice (II) 1 P i MS1 Reduction of P i depends on level of P i and MSi MS1 MS2 MS1 > MS2 MS2 0 0 P i e e j V sp i V sp j V b x x sp sp i k k,i i k V i max(v j ), i j Decrease of V i to meet capacity constraints Slide 13

15 Example: Airport choice in the Cologne region Gelhausen (2008), Journal of Airport Management 2(4), pp. 355 Slide 14

16 Willingness-to-pay by market segment Market segment 1 Euro equals... DOM Leisure DOM Business 2.74 EUR Short stay EUR Holiday EUR Business 1.00 INT Leisure 5.39 INT Business 4.45 Slide 15

17 Total market share of DUS wrt capacity constraints Market share per airport FRA DUS CGN DTM HHN NRN Share of unsatisfied demand potential at DUS due to limited capacity Gelhausen (2009), Journal of Airport Management 3(4), pp. 366 Slide 16

18 Market shares by segment at DUS wrt capacity constraints Market share of DUS per market segment DOM L - Berlin DOM B - Berlin EUR S - Barcelona EUR H - Barcelona EUR B - Barcelona INT L - Dallas INT B - Dallas Share of unsatisfied demand potential at DUS due to limited capacity Gelhausen (2009), Journal of Airport Management 3(4), pp. 366 Slide 17

19 Market shares by segment at STR wrt capacity constraints Gelhausen (2011), Journal of Air Transport Management 17, pp. 116 Slide 18

20 Effects of capacity deficit at DUS on market segments at CGN Market share of CGN per market segment DOM L - Berlin DOM B - Berlin EUR S - Barcelona EUR H - Barcelona EUR B - Barcelona INT L - Dallas INT B - Dallas Share of unsatisfied demand at DUS due to limited capacity Gelhausen (2009), Journal of Airport Management 3(4), pp. 366 Slide 19

21 Conclusions Capacity constraints at one airport affect the whole airport system Demand is distributed among more airports, benefiting remote airports However, spill-over effects may lead to further capacity-constrained airports Welfare of air travellers is reduced due to higher prices and crowding out effects Slide 20

22 Thank you for your attention Contact: Dr. Marc C. Gelhausen DLR - German Aerospace Center Institute of Air Transport and Airport Research Linder Höhe Cologne/Germany Marc.Gelhausen@dlr.de Tel: