Is it possible to optimize AIR TRAFFIC GLOBALLY?

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Brett McLaughlin
7 years ago
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1 Is it possible to optimize AIR TRAFFIC GLOBALLY? Elaborated by: Dr Jan Malawko MSc Miron Kursa for

2 Agenda: Inspiration for the study Solution approach Functional components Scope of used models Calculation methodology Scope of optimizer applications Example results 2

3 Inspiration for the study Many air travel connections are performed in a very inefficient manner with high values of Detour Factor. Is this reflected in tickets fares? What about extra CO2 emissions and hub performance quality (MCT, luggage handling complains)? 3

4 Inspiration for the study Is it possible to reduce aviation environmental impact specially over very dense populated areas and reduce congestion? Source: NATS 4

5 Inspiration for the study Many modern expensive commercial planes are protected and stored on huge boneyards with the hope that they will fly again. Are aircraft manufactures producing too many planes? Is the production profile satisfying real demand? 5

6 Inspiration for the study There are many airport locations considered or advertised as optimal. Where is really the optimum? What global network concept is the optimal one? Academic institutions with its wide range of projects are mainly focused on deep knowledge exploration with the outcome of neutral results some times showing unexpected magnitudes. This project is globally optimisation oriented and it could be potentially position in the area of interest for wide spectrum of entities: Central and Regional Governmental Institutions, Airlines, Airports, ANSPs, Environmental Agencies, Aircraft Manufactures Other aviation services and equipment providers 6

7 Solution approach Simulate commercial aviation air transport at the most accessible level with lowest possible energy consumption and minimal environmental footprint. Global air traffic model should satisfy at the most possible extend, the demand for aviation mobility as vital human necessity and expression of freedom. As real life has shown, mobility can't be limited by any telecom or another technology achievement. They have been rather even important stimulating factors for its development. 7

8 Solution approach UTILITY FUNCTION: MINIMIZE AIR TRANSPORT NETWORK ENERGY CONSUMPTION SET OF BOUNDARY CONDITIONS: SATISFY AIR TRANSPORT MOBILITY DEMAND TAKING INTO ACCOUNT: A/D & ATC CAPABILITIES, A/C PERFORMANCE, ENVIRONMENTAL RESTRICTIONS. 8

9 Global Air Transport Optimizer - GATO basic functional components: World Airport Data Base GLOBAL STATISTICS & FORECASTING /weather, traffic, fleet/ Air Transport Intelligence /trends & industry, economy, resources, social phenomena dynamics/ CALCULATION MODELS System functionality is based on publicly available, unrestricted DATA SOURCES. 9

10 Scope of Global Air Transport Optimizer - GATO models: models Real Air Transport demand Optimized route network structure Tailored fleet assignment TARGET RESULT: MINIMUM ENERGY, OPTIMIZED GLOBAL AIR TRANSPORT SCHEDULE CONCEPT 10

11 World Airport Data Base (7769 commercially suitable airports/landing strips with ICAO code assigned) Based on: AIP s, EUROCONTROL database, airport s websites 11

12 EARTH IS NOT FLAT More accurate method, Meeus' algorithm, which also accounts for Earth being an ellipsoid is being applied WGS84 Earth model was used for that correction. 12

13 e or M an th 25 Hypothetical Centre of the World Air Travel M & O s D ed st te All 398 potential hub airport /RWY 3500m/, with a minimal total detour factor for routes connecting all remaining airports as a feeders. 13

14 Hypothetical Centre of the World Air Travel All 398 potential hub airport /RWY 3500m/, with a minimal total detour factor for routes connecting all remaining airports as a feeders, detour factors are smaller than

15 GLOBAL WIND 20 years of data on 2.5deg global grid, collected from the NCEP/NCAR Reanalysis. Treated as a full time series to avoid problems with averaging. 15

16 Restricted areas Overall solution: generate a shortest possible path for each day over 20 years of meteo data and use it as a population for statistical reasoning; for instance about 16 competitiveness of hub airport location.

17 Air Route Structure ANSPs & Air Operators tend to have optimal/shortest route corresponding to GC According to IATA Air Distances Manual majority of Air Routes are GC based. Difference between GC and RL is minor 17

18 18

19 Methodology validation l Difference between GC and time from those OCRred tables. Latitudally orientented routes can take additional adventage 19

20 Discontinued long-range routes Today, the majority of the longest routes are around hours flight time (distance kkm) Airline Flight number Distance [nm] Newark to Singapore Singapore Airlines Route Los Angeles to Singapore Singapore Airlines New York to Bangkok km (mi) Scheduled duration Aircraft Type 15,345 (9,535) [8,285] 18 hr 50 min Airbus A SQ 37 14,114 (8,770) [7,621] 18 hr 05 min Airbus A Thai Airways International TG ,963 (8,676) [7,539] 17 hr 00 min Airbus A Mumbai to Atlanta Delta Air Lines DL ,695 (8,510) [7,395] 17 hr 00 min Boeing LR Los Angeles to Bangkok Thai Airways International TG ,309 (8,270) [7,186] 17 hr 20 min Airbus A Manila to Toronto Philippine Airlines PR ,230 (8,221) [7,143] 16 hr 05 min Boeing ER New York to Hong Kong United Airlines UA ,990 (8,072) [7,014] 15 hr 40 min Boeing Washington, D.C. to Johannesburg South African Airways SA ,813 (7,973) [6,929] 17 hr 00 min Airbus A Detroit to Hong Kong Delta Air Lines DL ,645 (7,857) [6,828] 15 hr 45 min Boeing LR Taipei to Newark EVA Air BR 32 12,552 (7,677) [6,777] 14 hr 55 min Boeing ER Mumbai to New York Delta Air Lines DL 17 12,540 (7,790) [6,771] 16 hr 00 min Boeing LR New York to Mumbai Air India AI ,540 (7,790) [6,771] 16 hr 00 min Boeing LR Minneapolis to Hong Kong Northwest Airlines NW 97 12,073 (7,501) [6,420] 15 hr 10 min Boeing Delhi to Chicago American Airlines AA ,044 (7,484) [6,503] 15 hr 55 min Boeing ER 20

21 TOP MARKETS Setting up an efficient hub is a long term process with gradual consequent increase of destinations and frequencies

22 Possible scope of GATO applications: determination of an airport optimal network value (including wind variation sensitivity as a significant factor for reliable connections). identification of airport dominant routes flight planning validation setting up optimal air transport network structure defining directional hub traffic distribution optimization of traffic distribution among airline alliance group members route and alternative hub location analysis in case of airport or area restriction situations (weather avoidance, local conflicts, volcanic eruptions, technical problems, emergency/contingency planning etc.) fleet assignment optimization evaluation of environmental impact of specific route structure. 22

23 Example Results 23

24 AFRICA SOUTH-NYC /DIRECTIONAL HUB TRAFFIC DISTRIBUTION CONCEPT/ 24

25 AFRICA SOUTH-NYC /DIRECTIONAL HUB TRAFFIC DISTRIBUTION CONCEPT/ 25

26 ASIASOUTH AMERICA/CARRIBEAN 26

27 ASIASOUTH AMERICA/CARRIBEAN no wind correction applied Singapore Panama City, direct distance 18799km. via Istanbul +738km (Df=1.039) (best) via Dubai Al Maktoum +909km (Df=1.048) via Auckland +1607km (Df=1.085) 27

28 ASIASOUTH AMERICA/CARRIBEAN wind correction applied Singapore Panama City, direct distance 18799km. via Istanbul +721km (+518,+916)km. via Dubai Al Maktoum +951km (+754,+1124)km. via Auckland +1739km (+1527,+1955)km. 28

31 North/South AMERICA EAST Airline Alliances Hub's typical dominating directions 31

32 North/South AMERICA EAST, Airline Alliances Hub's typical dominating directions 32

33 MANY THANKS FOR YOUR ATTENTION COMMENTS / QUESTIONS PLEASE NOW? or LATER: J.MALAWKO@ICM.EDU.PL M.KURSA@ICM.EDU.PL 33

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