Measuring Airline Networks - · PDF fileMeasuring Airline Networks Chantal Roucolle ... Airlines make different choices and their decisions evolve over ... (Southwest case)

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

Airline networks are complex and dynamic

Number of Airports served

Number of markets served

Direct or connecting flights, frequencies, schedules…

2

US airports

3

US domestic network in July 2005

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5


6

Airline networks





Airlines make different choices

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Frontier Airlines, July 2005

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Southwest Airlines, July 2005

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Airline networks





Airlines make different choices and their decisions evolve over

time

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Evolution: Delta Airlines July 2005

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Airline network




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

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

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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

Our objective

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

Our approach: explain the evolution of the three indicators

over 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

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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.

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Step 1: Network characterization

19

Graph Theory measures: a lot of correlated indicators


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


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


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)

Step 2: Network evolution: what are the

drivers of the choice?

23

Simultaneous Equations Model

𝐼𝑖𝑗𝑡 = 𝛼𝑖𝑗 + 𝛽𝑖1𝑡𝐿𝐶𝐶 + 𝛽𝑖2𝑡𝐿 + 𝛽𝑖3𝑦𝑡−1𝐺 + 𝛽𝑖4𝑓𝑡−1 + 𝛽𝑖5𝑑𝐷𝐿𝑁𝑊 + 𝛽𝑖6𝑑𝑈𝐴𝐶𝑂 + 𝛽𝑖7𝑑𝑊𝑁𝐹𝐿 + 𝛽𝑖7𝑑𝐴𝐴𝑈𝑆 + 𝜀𝑖𝑗𝑡

where

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

j indexes the airlines

t indexes the year

Explanatory variables:

Time trends: 𝑡𝐿𝐶𝐶 and 𝑡𝐿 depending on airline type

Macroeconomic indicators: 𝑦𝐺 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

Hubness

(i=1)

Resilience

(i=2)

Size

(i=3)

𝛽𝑖1𝑡𝐿𝐶𝐶 -0.067 0.071 7.681

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

𝛽𝑖2𝑡𝐿 0.141 0.107 -0.934

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

𝛽𝑖3𝑦𝑡−1𝐺

0.131 0.078 -0.364

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

𝛽𝑖4𝑓𝑡−1 -0.345 -0.339 -1.081

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

𝛽𝑖5𝑑𝐷𝐿𝑊𝑁 -1.533 0.160 148.757

(3.283)*** (0.774) (4.101)***

𝛽𝑖6𝑑𝑈𝐴𝐶𝑂 -1.358 0.104 259.326

(4.523)*** (0.602) (22.377)***

𝛽𝑖7𝑑𝑊𝑁𝐹𝐿 -0.096 -0.236 74.008

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

𝛽𝑖8𝑑𝐴𝐴𝑈𝑆 -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

Hubness

(i=1)

Resilience

(i=2)

Size

(i=3)

𝛽𝑖1𝑡𝐿𝐶𝐶 -0.067 0.071 7.681

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

𝛽𝑖2𝑡𝐿 0.141 0.107 -0.934

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


0.131 0.078 -0.364

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

𝛽𝑖4𝑓𝑡−1 -0.345 -0.339 -1.081

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

𝛽𝑖5𝑑𝐷𝐿𝑊𝑁 -1.533 0.160 148.757

(3.283)*** (0.774) (4.101)***

𝛽𝑖6𝑑𝑈𝐴𝐶𝑂 -1.358 0.104 259.326

(4.523)*** (0.602) (22.377)***

𝛽𝑖7𝑑𝑊𝑁𝐹𝐿 -0.096 -0.236 74.008

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

𝛽𝑖8𝑑𝐴𝐴𝑈𝑆 -0.886 -0.555 88.720

(1.249) (3.828)*** (2.749)***


(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


Estimation results

Main findings

Hubness: initial gap between LCC and Legacies is

vanishing over time

Hubness

(i=1)

Resilience

(i=2)

Size

(i=3)

𝛽𝑖1𝑡𝐿𝐶𝐶 -0.067 0.071 7.681

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

𝛽𝑖2𝑡𝐿 0.141 0.107 -0.934

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


0.131 0.078 -0.364

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

𝛽𝑖4𝑓𝑡−1 -0.345 -0.339 -1.081

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

𝛽𝑖5𝑑𝐷𝐿𝑊𝑁 -1.533 0.160 148.757

(3.283)*** (0.774) (4.101)***

𝛽𝑖6𝑑𝑈𝐴𝐶𝑂 -1.358 0.104 259.326

(4.523)*** (0.602) (22.377)***

𝛽𝑖7𝑑𝑊𝑁𝐹𝐿 -0.096 -0.236 74.008

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

𝛽𝑖8𝑑𝐴𝐴𝑈𝑆 -0.886 -0.555 88.720

(1.249) (3.828)*** (2.749)***


(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


Estimation results

Main findings

Hubness: initial gap between LCC and Legacies is vanishing

over time

Resilience: initial gap between LCC and Legacies

remains over time

Hubness

(i=1)

Resilience

(i=2)

Size

(i=3)

𝛽𝑖1𝑡𝐿𝐶𝐶 -0.067 0.071 7.681

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

𝛽𝑖2𝑡𝐿 0.141 0.107 -0.934

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


0.131 0.078 -0.364

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

𝛽𝑖4𝑓𝑡−1 -0.345 -0.339 -1.081

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

𝛽𝑖5𝑑𝐷𝐿𝑊𝑁 -1.533 0.160 148.757

(3.283)*** (0.774) (4.101)***

𝛽𝑖6𝑑𝑈𝐴𝐶𝑂 -1.358 0.104 259.326

(4.523)*** (0.602) (22.377)***

𝛽𝑖7𝑑𝑊𝑁𝐹𝐿 -0.096 -0.236 74.008

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

𝛽𝑖8𝑑𝐴𝐴𝑈𝑆 -0.886 -0.555 88.720

(1.249) (3.828)*** (2.749)***


(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


Estimation results

Main findings


over time

Resilience: initial gap between LCC and Legacies remains

over time

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

vanishing

Hubness

(i=1)

Resilience

(i=2)

Size

(i=3)

𝛽𝑖1𝑡𝐿𝐶𝐶 -0.067 0.071 7.681

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

𝛽𝑖2𝑡𝐿 0.141 0.107 -0.934

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


0.131 0.078 -0.364

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

𝛽𝑖4𝑓𝑡−1 -0.345 -0.339 -1.081

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

𝛽𝑖5𝑑𝐷𝐿𝑊𝑁 -1.533 0.160 148.757

(3.283)*** (0.774) (4.101)***

𝛽𝑖6𝑑𝑈𝐴𝐶𝑂 -1.358 0.104 259.326

(4.523)*** (0.602) (22.377)***

𝛽𝑖7𝑑𝑊𝑁𝐹𝐿 -0.096 -0.236 74.008

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

𝛽𝑖8𝑑𝐴𝐴𝑈𝑆 -0.886 -0.555 88.720

(1.249) (3.828)*** (2.749)***


(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


Estimation results

Main findings


over time

Resilience: initial gap between LCC and Legacies remains

over time

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

vanishing

Macroeconomic environment affect the network

strategical decisions but not the size

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

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))

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Measuring Airline Networks - · PDF fileMeasuring Airline Networks Chantal Roucolle ... Airlines make different choices and their decisions evolve over ... (Southwest case)