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Measuring Airline Networks Chantal Roucolle (ENAC-DEVI) Joint work - - PowerPoint PPT Presentation

Jun 16, 2023 439 likes •762 views

Measuring Airline Networks Chantal Roucolle (ENAC-DEVI) Joint work with Miguel Urdanoz (TBS) and Tatiana Seregina (ENAC-TBS) This research was possible thanks to the financial support of the Regional Council of Midi Pyrenees. Airline networks

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SLIDE 1

Measuring Airline Networks

Chantal Roucolle (ENAC-DEVI) Joint work with Miguel Urdanoz (TBS) and Tatiana Seregina (ENAC-TBS)

This research was possible thanks to the financial support of the Regional Council of Midi Pyrenees.

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SLIDE 2

Airline networks

 Airline networks are complex and dynamic

 Number of Airports served  Number of markets served  Direct or connecting flights, frequencies, schedules…

2

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SLIDE 3

US airports

3

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SLIDE 4

US domestic network in July 2005

4

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SLIDE 5

US domestic network in July 2010

5

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SLIDE 6

US domestic network in July 2013

6

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SLIDE 7

Airline networks

 Airline networks are complex and dynamic

 Number of Airports served  Number of markets served  Direct or connecting flights, frequencies, schedules…

 Airlines make different choices

7

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SLIDE 8

Frontier Airlines, July 2005

8

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SLIDE 9

Southwest Airlines, July 2005

9

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SLIDE 10

Airline networks

 Airline networks are complex and dynamic

 Number of Airports served  Number of markets served  Direct or connecting flights, frequencies, schedules…

 Airlines make different choices and their decisions evolve over

time

10

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SLIDE 11

Evolution: Delta Airlines July 2005

11

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SLIDE 12

Evolution: Delta Airlines July 2010

12

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SLIDE 13

Evolution: Delta Airlines July 2015

13

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SLIDE 14

Airline network

 Airline networks are complex and dynamic

 Number of Airports served  Number of markets served  Frequencies, schedules…

 Airlines make different choices and their decisions evolve over

time

 Two questions to address:

 Network characterization  Network evolution: what are the drivers of the choice?

 Usefulness of network analysis:

 Does the network structure affect costs, prices, profitability, delays…?

14

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SLIDE 15

Literature

 In most of the cases perfect hub-and-spoke networks (left) are

compared with fully connected networks (right),

 For instance Brueckner (2004), Alderighi et al. (2005), Barla and

Constantatos (2005), Flores Fillol (2009) or Silva et al. (2014).

 However reality is more complex

 Wojahn (2001) studies whether a mixed model can be preferred to

minimize costs

 We want to get closer to this reality

15

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SLIDE 16

Our objective

 Step 1: Network characterization  Our approach: combine Graph theory and Principal Component

Analysis (PCA)

Graph theory: set of mathematical measures and tools to study networks

Already used for airlines:

  • Wandelt and Sun (2015), Dunn and Wilkinson (2016) or Du et al. (2016) study different network

properties focusing on the country level.

  • Burghouwt and Redondi (2013) present a compilation of connectivity indicators for airports built from

graph theory.

  • Lordan et al.(2016) study resilience with a sample of airlines from Europe, North America and China.

PCA aims to explain most of the information of the dataset through a reduced number of new variables, called principal components, calculated as linear combinations of the original variables Main findings: Airline network could be characterized by three indicators: Hubness, Resilience, Size Traditional distinction between LCC and Legacies could be reconsidered

16

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SLIDE 17

Our objective

 Step 2: Network evolution: what are the drivers of the choice?  Our approach: explain the evolution of the three indicators

  • ver time

Use of macroeconomic indicators, air market characteristics, airline type as explanatory variables

Estimation of a system of equations on panel data Main findings: Network Hubness, Resilience and Size have distinct drivers Strategies in terms of network evolution depend on the type of airline

17

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SLIDE 18

Data

 Official Airline Guide, OAG

Worldwide data on frequencies, schedules and aircrafts for the last 10 years

 Monthly data for the third quarter 2005-2015  Focus on the United States Domestic Market

Advantage: most studied market with available data on fares

Disadvantage: lack of international flights

 Data cleaning:

Eliminating routes shorter than 200 miles or routes with less than 10 seats per flight

Recoding of regional/feeder airlines

 Final data set:

125358 observations

28 operating carriers ranging from 19 to 25 per year

City number from 413 to 517 and airports ranging from 435 to 537 per year.

18

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SLIDE 19

19

Step 1: Network characterization

19  Graph Theory measures: a lot of correlated indicators

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SLIDE 20

Step 1: Network characterization

20

 Reduction of the information: use of PCA

Three principal components explain 94.69% of the sample variability: “Hubness”, “Resilience” and “Network Size”

Theoretical representation of network Hubness / Resilience map

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SLIDE 21

Step 1: Network characterization

21

PC2 : RESILIENCE PC1 : HUBNESS hub-and-spoke with a unique hub point-to-point path or circle hub-and-spoke with several hubs

 The distinction between LCCs and Legacies is nowadays unclear as highlighted in

Jarach et al. (2009) or Bitzan and Peoples (2016).

 No distinction in term of Hubness (PC1) between Legacies and LCCs.  Higher Resilience (PC2) values on average for LCCs

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SLIDE 22

Step 1: Network characterization

22

WN WN

Network representation Size / Hubness and Size / Resilience maps

 When the network size increases, Hubness decreases to some level between 0 and

  • 3, both for LCCs and Majors

 When the network size increases, Resilience seems to approach a level around -1,

except for LCCs (Southwest case)

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SLIDE 23

Step 2: Network evolution: what are the drivers of the choice?

23

 Simultaneous Equations Model

𝐽𝑗𝑘𝑢 = 𝛽𝑗𝑘 + 𝛾𝑗1𝑢𝑀𝐷𝐷 + 𝛾𝑗2𝑢𝑀 + 𝛾𝑗3𝑧𝑢−1

𝐻

+ 𝛾𝑗4𝑔

𝑢−1 + 𝛾𝑗5𝑒𝐸𝑀𝑂𝑋 + 𝛾𝑗6𝑒𝑉𝐵𝐷𝑃 + 𝛾𝑗7𝑒𝑋𝑂𝐺𝑀 + 𝛾𝑗7𝑒𝐵𝐵𝑉𝑇 + 𝜁𝑗𝑘𝑢

where

𝑗 ∈ {1,2,3} indexes one of three network indicators: Hubness, Resilience, Size

j indexes the airlines

t indexes the year

Explanatory variables:

 Time trends: 𝑢𝑀𝐷𝐷 and 𝑢𝑀 depending on airline type  Macroeconomic indicators: 𝑧𝐻 represents the output gap, and f the jet fuel prices,  US domestic market characteristics: dummies to control for the 4 mergers occurred

during the considered time frame

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SLIDE 24

Hubness (i=1) Resilience (i=2) Size (i=3) 𝛾𝑗1𝑢𝑀𝐷𝐷

  • 0.067

0.071 7.681 (1.015) (1.613) (10.352)***

𝛾𝑗2𝑢𝑀

0.141 0.107

  • 0.934

(2.399)** (2.895)*** (0.459)

𝛾𝑗3𝑧𝑢−1

𝐻

0.131 0.078

  • 0.364

(4.689)*** (2.342)** (0.700)

𝛾𝑗4𝑔

𝑢−1

  • 0.345
  • 0.339
  • 1.081

(2.686)*** (2.641)*** (0.521)

𝛾𝑗5𝑒𝐸𝑀𝑋𝑂

  • 1.533

0.160 148.757 (3.283)*** (0.774) (4.101)***

𝛾𝑗6𝑒𝑉𝐵𝐷𝑃

  • 1.358

0.104 259.326 (4.523)*** (0.602) (22.377)***

𝛾𝑗7𝑒𝑋𝑂𝐺𝑀

  • 0.096
  • 0.236

74.008 (0.409) (0.985) (8.299)***

𝛾𝑗8𝑒𝐵𝐵𝑉𝑇

  • 0.886
  • 0.555

88.720 (1.249) (3.828)*** (2.749)*** Constant WN

  • 0.922

2.928 409.236 Legacy AA 1.680

  • 3.665
  • 35.346

(3.101)*** (9.959)*** (75.839)*** (4.752)*** (18.206)*** (2.089)** LCC B6 3.594

  • 2.087
  • 359.558

Legacy AS

  • 0.271
  • 4.757
  • 181.121

(24.247)*** (12.668)*** (48.688)*** (0.677) (15.322)*** (7.079)*** LCC F9 5.827

  • 3.722
  • 380.930

Legacy CO 1.250

  • 3.868
  • 85.541

(44.737)*** (26.114)*** (75.420)*** (3.615)*** (22.771)*** (3.553)*** LCC FL 4.372

  • 2.436
  • 339.937

Legacy HA 3.695

  • 1.620
  • 374.144

(25.315)*** (33.692)*** (27.508)*** (12.375)*** (6.563)*** (28.786)*** LCC G4 2.290

  • 3.671
  • 322.645

Legacy DL 0.930

  • 3.830

100.584 (7.760)*** (22.403)*** (49.086)*** (2.281)** (21.224)*** (3.261)*** LCC NK 1.376 0.560

  • 395.394

Legacy NW 0.348

  • 4.060
  • 36.161

(2.052)** (1.393) (68.473)*** (1.314) (24.469)*** (1.663)* LCC SY 4.500

  • 2.965
  • 421.380

Legacy UA

  • 0.289
  • 3.718

15.540 (6.298)*** (19.657)*** (37.913)*** (1.293) (21.894)*** (1.475) LCC VX 3.725 1.335

  • 443.266

Legacy US

  • 0.998
  • 3.564

16.677 (11.992)*** (1.373) (54.925)*** (2.852)*** (19.464)*** (0.595) ρ 0.3629 Observations 211 211 211 * p<0.1; ** p<0.05; *** p<0.01

Estimation results

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SLIDE 25

Hubness (i=1) Resilience (i=2) Size (i=3) 𝛾𝑗1𝑢𝑀𝐷𝐷

  • 0.067

0.071 7.681 (1.015) (1.613) (10.352)***

𝛾𝑗2𝑢𝑀

0.141 0.107

  • 0.934

(2.399)** (2.895)*** (0.459)

𝛾𝑗3𝑧𝑢−1

𝐻

0.131 0.078

  • 0.364

(4.689)*** (2.342)** (0.700)

𝛾𝑗4𝑔

𝑢−1

  • 0.345
  • 0.339
  • 1.081

(2.686)*** (2.641)*** (0.521)

𝛾𝑗5𝑒𝐸𝑀𝑋𝑂

  • 1.533

0.160 148.757 (3.283)*** (0.774) (4.101)***

𝛾𝑗6𝑒𝑉𝐵𝐷𝑃

  • 1.358

0.104 259.326 (4.523)*** (0.602) (22.377)***

𝛾𝑗7𝑒𝑋𝑂𝐺𝑀

  • 0.096
  • 0.236

74.008 (0.409) (0.985) (8.299)***

𝛾𝑗8𝑒𝐵𝐵𝑉𝑇

  • 0.886
  • 0.555

88.720 (1.249) (3.828)*** (2.749)*** Constant WN

  • 0.922

2.928 409.236 Legacy AA 1.680

  • 3.665
  • 35.346

(3.101)*** (9.959)*** (75.839)*** (4.752)*** (18.206)*** (2.089)** LCC B6 3.594

  • 2.087
  • 359.558

Legacy AS

  • 0.271
  • 4.757
  • 181.121

(24.247)*** (12.668)*** (48.688)*** (0.677) (15.322)*** (7.079)*** LCC F9 5.827

  • 3.722
  • 380.930

Legacy CO 1.250

  • 3.868
  • 85.541

(44.737)*** (26.114)*** (75.420)*** (3.615)*** (22.771)*** (3.553)*** LCC FL 4.372

  • 2.436
  • 339.937

Legacy HA 3.695

  • 1.620
  • 374.144

(25.315)*** (33.692)*** (27.508)*** (12.375)*** (6.563)*** (28.786)*** LCC G4 2.290

  • 3.671
  • 322.645

Legacy DL 0.930

  • 3.830

100.584 (7.760)*** (22.403)*** (49.086)*** (2.281)** (21.224)*** (3.261)*** LCC NK 1.376 0.560

  • 395.394

Legacy NW 0.348

  • 4.060
  • 36.161

(2.052)** (1.393) (68.473)*** (1.314) (24.469)*** (1.663)* LCC SY 4.500

  • 2.965
  • 421.380

Legacy UA

  • 0.289
  • 3.718

15.540 (6.298)*** (19.657)*** (37.913)*** (1.293) (21.894)*** (1.475) LCC VX 3.725 1.335

  • 443.266

Legacy US

  • 0.998
  • 3.564

16.677 (11.992)*** (1.373) (54.925)*** (2.852)*** (19.464)*** (0.595) ρ 0.3629 Observations 211 211 211 * p<0.1; ** p<0.05; *** p<0.01

Estimation results

Main findings

Hubness: initial gap between LCC and Legacies is vanishing over time

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SLIDE 26

Hubness (i=1) Resilience (i=2) Size (i=3) 𝛾𝑗1𝑢𝑀𝐷𝐷

  • 0.067

0.071 7.681 (1.015) (1.613) (10.352)***

𝛾𝑗2𝑢𝑀

0.141 0.107

  • 0.934

(2.399)** (2.895)*** (0.459)

𝛾𝑗3𝑧𝑢−1

𝐻

0.131 0.078

  • 0.364

(4.689)*** (2.342)** (0.700)

𝛾𝑗4𝑔

𝑢−1

  • 0.345
  • 0.339
  • 1.081

(2.686)*** (2.641)*** (0.521)

𝛾𝑗5𝑒𝐸𝑀𝑋𝑂

  • 1.533

0.160 148.757 (3.283)*** (0.774) (4.101)***

𝛾𝑗6𝑒𝑉𝐵𝐷𝑃

  • 1.358

0.104 259.326 (4.523)*** (0.602) (22.377)***

𝛾𝑗7𝑒𝑋𝑂𝐺𝑀

  • 0.096
  • 0.236

74.008 (0.409) (0.985) (8.299)***

𝛾𝑗8𝑒𝐵𝐵𝑉𝑇

  • 0.886
  • 0.555

88.720 (1.249) (3.828)*** (2.749)*** Constant WN

  • 0.922

2.928 409.236 Legacy AA 1.680

  • 3.665
  • 35.346

(3.101)*** (9.959)*** (75.839)*** (4.752)*** (18.206)*** (2.089)** LCC B6 3.594

  • 2.087
  • 359.558

Legacy AS

  • 0.271
  • 4.757
  • 181.121

(24.247)*** (12.668)*** (48.688)*** (0.677) (15.322)*** (7.079)*** LCC F9 5.827

  • 3.722
  • 380.930

Legacy CO 1.250

  • 3.868
  • 85.541

(44.737)*** (26.114)*** (75.420)*** (3.615)*** (22.771)*** (3.553)*** LCC FL 4.372

  • 2.436
  • 339.937

Legacy HA 3.695

  • 1.620
  • 374.144

(25.315)*** (33.692)*** (27.508)*** (12.375)*** (6.563)*** (28.786)*** LCC G4 2.290

  • 3.671
  • 322.645

Legacy DL 0.930

  • 3.830

100.584 (7.760)*** (22.403)*** (49.086)*** (2.281)** (21.224)*** (3.261)*** LCC NK 1.376 0.560

  • 395.394

Legacy NW 0.348

  • 4.060
  • 36.161

(2.052)** (1.393) (68.473)*** (1.314) (24.469)*** (1.663)* LCC SY 4.500

  • 2.965
  • 421.380

Legacy UA

  • 0.289
  • 3.718

15.540 (6.298)*** (19.657)*** (37.913)*** (1.293) (21.894)*** (1.475) LCC VX 3.725 1.335

  • 443.266

Legacy US

  • 0.998
  • 3.564

16.677 (11.992)*** (1.373) (54.925)*** (2.852)*** (19.464)*** (0.595) ρ 0.3629 Observations 211 211 211 * p<0.1; ** p<0.05; *** p<0.01

Estimation results

Main findings

Hubness: initial gap between LCC and Legacies is vanishing

  • ver time

Resilience: initial gap between LCC and Legacies remains over time

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SLIDE 27

Hubness (i=1) Resilience (i=2) Size (i=3) 𝛾𝑗1𝑢𝑀𝐷𝐷

  • 0.067

0.071 7.681 (1.015) (1.613) (10.352)***

𝛾𝑗2𝑢𝑀

0.141 0.107

  • 0.934

(2.399)** (2.895)*** (0.459)

𝛾𝑗3𝑧𝑢−1

𝐻

0.131 0.078

  • 0.364

(4.689)*** (2.342)** (0.700)

𝛾𝑗4𝑔

𝑢−1

  • 0.345
  • 0.339
  • 1.081

(2.686)*** (2.641)*** (0.521)

𝛾𝑗5𝑒𝐸𝑀𝑋𝑂

  • 1.533

0.160 148.757 (3.283)*** (0.774) (4.101)***

𝛾𝑗6𝑒𝑉𝐵𝐷𝑃

  • 1.358

0.104 259.326 (4.523)*** (0.602) (22.377)***

𝛾𝑗7𝑒𝑋𝑂𝐺𝑀

  • 0.096
  • 0.236

74.008 (0.409) (0.985) (8.299)***

𝛾𝑗8𝑒𝐵𝐵𝑉𝑇

  • 0.886
  • 0.555

88.720 (1.249) (3.828)*** (2.749)*** Constant WN

  • 0.922

2.928 409.236 Legacy AA 1.680

  • 3.665
  • 35.346

(3.101)*** (9.959)*** (75.839)*** (4.752)*** (18.206)*** (2.089)** LCC B6 3.594

  • 2.087
  • 359.558

Legacy AS

  • 0.271
  • 4.757
  • 181.121

(24.247)*** (12.668)*** (48.688)*** (0.677) (15.322)*** (7.079)*** LCC F9 5.827

  • 3.722
  • 380.930

Legacy CO 1.250

  • 3.868
  • 85.541

(44.737)*** (26.114)*** (75.420)*** (3.615)*** (22.771)*** (3.553)*** LCC FL 4.372

  • 2.436
  • 339.937

Legacy HA 3.695

  • 1.620
  • 374.144

(25.315)*** (33.692)*** (27.508)*** (12.375)*** (6.563)*** (28.786)*** LCC G4 2.290

  • 3.671
  • 322.645

Legacy DL 0.930

  • 3.830

100.584 (7.760)*** (22.403)*** (49.086)*** (2.281)** (21.224)*** (3.261)*** LCC NK 1.376 0.560

  • 395.394

Legacy NW 0.348

  • 4.060
  • 36.161

(2.052)** (1.393) (68.473)*** (1.314) (24.469)*** (1.663)* LCC SY 4.500

  • 2.965
  • 421.380

Legacy UA

  • 0.289
  • 3.718

15.540 (6.298)*** (19.657)*** (37.913)*** (1.293) (21.894)*** (1.475) LCC VX 3.725 1.335

  • 443.266

Legacy US

  • 0.998
  • 3.564

16.677 (11.992)*** (1.373) (54.925)*** (2.852)*** (19.464)*** (0.595) ρ 0.3629 Observations 211 211 211 * p<0.1; ** p<0.05; *** p<0.01

Estimation results

Main findings

Hubness: initial gap between LCC and Legacies is vanishing

  • ver time

Resilience: initial gap between LCC and Legacies remains

  • ver time

Size: LCC increase their size over time; initial gap is vanishing

slide-28
SLIDE 28

Hubness (i=1) Resilience (i=2) Size (i=3)

𝛾𝑗1𝑢𝑀𝐷𝐷

  • 0.067

0.071 7.681

(1.015) (1.613) (10.352)***

𝛾𝑗2𝑢𝑀

0.141 0.107

  • 0.934

(2.399)** (2.895)*** (0.459)

𝛾𝑗3𝑧𝑢−1

𝐻

0.131 0.078

  • 0.364

(4.689)*** (2.342)** (0.700)

𝛾𝑗4𝑔

𝑢−1

  • 0.345
  • 0.339
  • 1.081

(2.686)*** (2.641)*** (0.521)

𝛾𝑗5𝑒𝐸𝑀𝑋𝑂

  • 1.533

0.160 148.757 (3.283)*** (0.774) (4.101)***

𝛾𝑗6𝑒𝑉𝐵𝐷𝑃

  • 1.358

0.104 259.326 (4.523)*** (0.602) (22.377)***

𝛾𝑗7𝑒𝑋𝑂𝐺𝑀

  • 0.096
  • 0.236

74.008 (0.409) (0.985) (8.299)***

𝛾𝑗8𝑒𝐵𝐵𝑉𝑇

  • 0.886
  • 0.555

88.720 (1.249) (3.828)*** (2.749)*** Constant WN

  • 0.922

2.928 409.236 Legacy AA 1.680

  • 3.665
  • 35.346

(3.101)*** (9.959)*** (75.839)*** (4.752)*** (18.206)*** (2.089)** LCC B6 3.594

  • 2.087
  • 359.558

Legacy AS

  • 0.271
  • 4.757
  • 181.121

(24.247)*** (12.668)*** (48.688)*** (0.677) (15.322)*** (7.079)*** LCC F9 5.827

  • 3.722
  • 380.930

Legacy CO 1.250

  • 3.868
  • 85.541

(44.737)*** (26.114)*** (75.420)*** (3.615)*** (22.771)*** (3.553)*** LCC FL 4.372

  • 2.436
  • 339.937

Legacy HA 3.695

  • 1.620
  • 374.144

(25.315)*** (33.692)*** (27.508)*** (12.375)*** (6.563)*** (28.786)*** LCC G4 2.290

  • 3.671
  • 322.645

Legacy DL 0.930

  • 3.830

100.584 (7.760)*** (22.403)*** (49.086)*** (2.281)** (21.224)*** (3.261)*** LCC NK 1.376 0.560

  • 395.394

Legacy NW 0.348

  • 4.060
  • 36.161

(2.052)** (1.393) (68.473)*** (1.314) (24.469)*** (1.663)* LCC SY 4.500

  • 2.965
  • 421.380

Legacy UA

  • 0.289
  • 3.718

15.540 (6.298)*** (19.657)*** (37.913)*** (1.293) (21.894)*** (1.475) LCC VX 3.725 1.335

  • 443.266

Legacy US

  • 0.998
  • 3.564

16.677 (11.992)*** (1.373) (54.925)*** (2.852)*** (19.464)*** (0.595) ρ 0.3629 Observations 211 211 211 * p<0.1; ** p<0.05; *** p<0.01

Estimation results

Main findings

Hubness: initial gap between LCC and Legacies is vanishing

  • ver time

Resilience: initial gap between LCC and Legacies remains

  • ver time

Size: LCC increase their size over time; initial gap is vanishing

Macroeconomic environment affect the network strategical decisions but not the size

slide-29
SLIDE 29

Step 2: Network evolution: what are the drivers of the choice?

29

 Mergers mapping

 Following the merger:

Increase in Size

Decrease in Hubness the year of the merger

Hubness recovers its initial level the years after

slide-30
SLIDE 30

Conclusions and further research

30  We propose a methodology to determine the drivers of network evolution  Step 1: building three network indicators: Hubness , Resilience and Size  Step 2: analysis of these indicators over time given macroeconomic and market

conditions

 We apply the methodology to the US domestic networks  Level and evolution of the networks depend on the type of airline  LCCs and Legacies differ in terms of Resilience while there seems to be a

convergence in terms of Hubness and Size.

 Next steps: study the impact of these indicators over the airline’s cost structure

Pels et al. (2000), the level of competition (Hendricks, Piccione, and Tan 1997), the prices (Tan and Samuel 2016), the level of congestion and delays (Mayer and Sinai 2003; Brueckner 2002; Fageda and Flores-Fillol (2016))