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MIT ICAT
MIT International Center for Air Transportation
Philippe A. [email protected]
&
Prof. R. John [email protected]
Massachusetts Institute of Technology
Global Airline Industry Program – Industry Advisory Board Meeting
October 25th 2007
Scalability of Air Transportation Networks through Scalability of Air Transportation Networks through the Development of Multithe Development of Multi--Airport Systems: Airport Systems:
A Worldwide PerspectiveA Worldwide Perspective
MIT ICAT
Introduction
Air Transportation System Demand/Capacity Inadequacy Problem
0
0.5
1
1.5
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3.5
1990 1995 2000 2005
Del
ays
(in m
in.)
Mill
ions
0
200
400
600
800
1000
1200
1400
1970 1980 1990 2000 2010
Reve
nue
Pass
enge
r Kilo
met
ers
(bill
ion)
North America
Europe
Asia and Pacific
Latin America & CaribbeanMiddle East
2
Growth of demand for air transportation • FAA forecast growth rate (2005-2017
forecast):(enplanements air carrier: +3.1% per year, regional carriers: +4.3%, general aviation turbojet operations: +6.0%)
• Factors adding pressure to the system:Entry of Very Light Jets and Unmanned Aerial Vehicles
Key infrastructure constraints in the air transportation system
Demand/capacity inadequacy problem:• leading to the generation and propagation of
delays throughout the systemRecord of 22.1 million minutes of flight delays in 2006
Implications:• Degradation of the quality of air service • Economic impacts
Need: • Understand how the air transportation
system evolved to meet demand in the past and how it will to so in the future
* Data source: ICAO traffic data & FAA OPSNET data
U.S. Flight Delays from 1990 to 2007
MIT ICAT
Analysis of the U.S. Air Transportation Network
Analysis of the U.S. Air Transportation Network
Air transportation system is fundamentally a network system
Described and represented using network abstractions
Use of tools from network theory• Recent theories of scale free and scalable networks
U.S. Air Transportation Network in 2005
3*
MIT ICAT
0.0
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0.6
0.8
1.0
0 200 400 600 800 1000
Degree (k)
Freq
uenc
y p(
k)Nu
mbe
r of a
irpo
rtsN
umbe
r of n
odes
1.E-06
1.E-05
1.E-04
1.E-03
1.E-02
1.E-01
1.E+00
0 200 400 600 800 1000
Degree (k)
Cum
ulat
ive
Freq
uenc
y p(
K>k
)Nu
mbe
r of a
irpo
rts w
ith
degr
ee g
reat
er t
han
Num
ber o
f nod
es w
ith
degr
ee g
reat
er th
an
4
Networks that exhibit power law degree distribution are also called “scale free” networks
Scale Free & Scalable Networks: Definitions & Properties
Degree distribution
Scale free networks are represented by an affine function on a log–log scale
log-log scale
Scaling “up”time
Large Scale Networke.g. www in 1999
Degree Distribution
Networks that can grow without constraints are scalable (i.e. scale “up”)
e.g. kin = 2kout = 2k = 4
Notations and basic network characterization concepts: Un-weighted networks:
Degree (k) = number of connections in and out of a node
Weighted networks:- Flight Weighted degree (weighted by the frequency of flights on each arc)
- Passenger weighted degree (weighted by the number of passenger traveling through each arc)
fi
fj
fj
fj
pi
pj
pj
pj
γα −kkp )(
Background on Network Theory
linear-linear scale
MIT ICAT
Analysis of the U.S. Air Transportation Network
Analysis of the U.S. Air Transportation Network:(Airport Level)
U.S. Air Transportation Network (airport level)
1
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1000
10000
1000 10000 100000 1000000
Num
ber o
f airp
orts
with
flig
ht w
eigh
ted
degr
ee g
reat
er t
han
Flight weighted degree (i.e.number of flights)
100000 1000000
Distribution with correction applied*
Power Law DistributionNon Power Law
Distribution
+Airport size
The air transportation network exhibits a partial power law distribution
• Power law distribution for small and medium size nodes
• Non power law distribution (between 250,000 and 970,000 flights)
Limits to scale in this network and capacitated nodes (capacity constrained airports) are present in the non-power law part of the distribution
5*
MIT ICAT
Analysis of the U.S. Air Transportation Network
Analysis of the U.S. Air Transportation Network:(Airport Level)
Airport code
Flight weighted degree (i.e. annual number of
operations)
Airport code
Flight weighted degree (i.e. annual number of
operations)ORD 964360 LGA 386589ATL 949708 BOS 381064DFW 795974 MIA 366561LAX 629735 IAD 361754DEN 556178 SEA 354658CVG 512830 MEM 343970MSP 498523 SLC 339080DTW 498053 SFO 331498IAH 494410 PIT 324287LAS 490290 JFK 311827PHX 484252 MCO 292520PHL 430218 MDW 280940EWR 420197 STL 274770CLT 417485 BWI 266166
DCA 264784
1
10
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1000
10000
1000 10000 100000 1000000
Num
ber o
f airp
orts
with
flig
ht w
eigh
ted
degr
ee g
reat
er t
han
Flight weighted degree (i.e.number of flights)
100000 1000000
Distribution with correction applied*
Power Law DistributionNon Power Law
Distribution
6
Flight weighted degree distributionAirports in the non power law part of the distribution: 29 airports
Some of these nodes are clearly constrained by capacity:
• Slot restricted airports (i.e. ORD, LGA, DCA)
• Airports that exhibit high level of delays that are indicative of congestion and capacity constraints (i.e. J.F. Kennedy JFK, Newark EWR, Philadelphia PHL, Boston Logan BOS and San Francisco SFO).
29 airports
29 airports in the non power law part of the distribution
*
MIT ICAT
Analysis of the U.S. Air Transportation Network
Regional Airport System Analysis
Hypothesis: Emergence of secondary airports in the vicinity of primary airports, leading to the development multi-airport systems, allows the network to scale
Need to shift the focus from an airport level perspective to a regional airport system perspective
29 airports in the non-power law part of the distribution: basis for a case study analysis of regional airport systems.
7*
Regional airport systems in the U.S.
BOS
LGA/ JFK/ EWR
DCA/ IAD/ BWIPHL
CLT
MIA
MCOTPA
IAH
DFW
DENSLC
PHX
LASSFO
LAX
SAN
SEAMSP
ORD
STL
MEM CVG
DTWPIT
ATL
Definitions: • Regional airport system: all airports
within 50 miles of one of the identified airports.
• Primary airport: airport serving between 20% and 100% of the traffic in the region
• Secondary airport: airport were defined as serving between 1% and 20% of traffic
• Other airport: serving less than 1%
MIT ICAT
Analysis of the U.S. Air Transportation Network
Primary & Secondary Airports in the United States
Total of 16 primary and 16 secondary airports were found in the 11 multi-airport systems in the United States,14 remaining regional airports systems were single airport systems.
SFO
LAX
MSP
DAL
HOU
DTWORD
STL
CVG
ATL
DCA
PHL
LGA / JFK / EWR
BOS
MIA
PHX
BUR
OAK
ONT
SNA
FLL
BWI ISP
MHT
MDWPVD
LGBDFW
IAH
IAD
LegendPrimary airport
Secondary airport
SLC
DEN
SEA
MEM CLT
PIT
LAS
MCOTPA
SJC
SFB
MLBPIESRQ
SAN
8*
MIT ICAT
Analysis of the U.S. Air Transportation Network
Analysis of the U.S. Air Transportation Network:(Regional Level)
Primary and secondary airports in each of the regional airport system serve the demand for air transportation within the region
Airports part of multi-airport systems are aggregated into single nodes
New network composed of 11 multi-airport nodes and 2159 single airport nodes
Power law flight weighted degree distribution across the entire range of flight weighted degree.
Mechanisms by which airports emerged in a region are key to the ability of the system to scale and to meet demand. 1
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1000 10000 100000 1000000 10000000
Num
ber
of n
odes
(si
ngle
air
port
&
aggr
egat
ed m
ulti-
airp
ort
syst
ems)
with
fli
ghts
wei
ghte
d de
gree
gre
ater
tha
n
Flight weighted degree (i.e. number of flights)
Power Law Distribution(across entire range of flight weighted degree)
9
U.S. Air Transportation Network(Airport level)
Legend: Flight frequency (flights per month)over 1250901 to 1250601 to 900301 to 600151 to 300
31 to 1505 to 302 to 40 to 1
Multi-airport system node
MIT ICAT
Worldwide Case Study Analysis of Multi-Airport Systems
Extension of the Case Studies to other Regions of the World
Asia/Pacific
Legend
Europe
North America
Latin America &Caribbean
Africa
Middle East
Overall analysis of multi-airport systems covers:• 63 case studies of multi-airport systems,• accounting for 175 airports,• in 31 different countries,• across all regions of the world.
10* World regions defined based on the IATA statistics regions
MIT ICAT
Worldwide Case Study Analysis of Multi-Airport Systems
Fundamental Regional Level Scaling Mechanisms
Multi-airport systems evolve according to two fundamental mechanisms;
• Construction of new high capacity airports• Emergence of secondary airports
Emergence of a secondary
airport Re-emergence of a primaryairport
Construction of new airport and transfer of traffic
Strengthening of secondary airport role
Failure of a Secondaryairport to emerge
Failure to transfer traffic to an external primary airport
Closure /failure of primary to re-emerge
Failure to transfer traffic to an external primary airport
Emergence of Secondary Airport
Construction of New High Capacity Airport
Legend
Primary airport
Secondary airport
Primary airport (Emerged)
Closed primary airport --
11
MIT ICAT
Worldwide Case Study Analysis of Multi-Airport Systems
Mechanisms by which Airports Emerge in Multi-Airport Systems
Mechanisms by which primary & secondary airports emerge by world regions:
Legend
Construction of new airportEmergence of existing airport
North AmericaEurope
Latin America & Caribbean
Africa
Middle East
Asia / Pacific
12
Emergence Existing Airport
Emergence Existing Airport
Construction New Airport
Emergence Existing Airport
Construction New Airport
Emergence Existing Airport
Construction New Airport
Emergence Existing Airport
Construction New Airport
Emergence Existing Airport
MIT ICAT
System Dynamics Model
Model of Scaling Dynamics in the Air Transportation System
Regional LatentDemand
A
13
Airlines
Regional LatentDemand
Airport Utilization Ratio
Airport Infrastructure Provider
Delays
Airport System
Secondary/Other Airport
Substitute/No Travel
Externalities:Cost of
operations
Pressure to reduce
congestion
InfrastructureEnhancement
Process
Airport Attractiveness
to Airlines
Airport Attractiveness to Passengers
Need for Airport
Enhancement
Regional EconomicImpacts(GRP)
Environmental constraintsLand availability
Need to manage demand
Passenger Mode/Airport
Choice
Airline Network
Planning /Scheduling
Process
RegulatoryProcess
Access to Capital
Forecast Demand
Airport regulatory constraints
Implement Demand Mgt
Regulator
Passengers
Fare Freq,
DemandPopulation
Socio Econ Factors
DemandGeneration
Regional Latent Demand
Airport Enplanements
Flights (acft. mvts.)
Airport Capacity
Airport Attractiveness
Alternative Choices
Regulatory framework
Secondary/Other Airport
Need for Airport
Construction
A. Revenue
A. Cost A. Profit
Airport attractiveness
Forecast demand
Airport set / Alternatives
MIT ICAT
Key Factors Influencing the Dynamics of Multi-Airport Systems
Availability of Existing Airport Infrastructure
Analysis of regional airport system capacity coverage charts: (active civil and jointly operated airports with at least one runway longer than 5000ft)
United States: Extensive set of existing under-utilized airport infrastructureEurope:
• Limited existing under-utilized civil airport resources (presence of military airfields)• Explaining the conversion of existing military airfields into secondary airports
Asia: Very limited existing under-utilized civil airport resources
14Data source: DAFIF (Category A & B airports) excluding existing primary & secondary airports
MIT ICAT
Key Factors Influencing the Dynamics of Multi-Airport Systems
Entry of Low Cost Carriers at Secondary Airports
Low Cost Carriers (LCCs) generally concentrate at secondary airports
Entry of low-cost carriers changes the dynamics at the airport and regional level:
Entry of one LCC, followed by of entries of both LCCs and other airlinesIncreased competition at the airport and regional levelYield decrease followed by stimulation of demand and traffic shifts
10%
36%
Presence of Low Cost Carriers vs. Other Airlines at Primary & Secondary Airports
15* Data source: Airport websites
MIT ICAT
Key Factors Influencing the Dynamics of Multi-Airport Systems
Construction of Airports:Drivers & Constraints
Historically, airports in Europe and the United States were built prior to or during World War II in Europe & North America.
• Meet the needs of military activity and growing demand for commercial traffic• Environmental constraints limiting the construction of new airports in the three decades
In Asia, phase of construction of primary airports in Asia is more recent (1970s and 1990s/2000s)
• Demand forecasts (double digit growth rates)• Weaker environmental constraints (than in Europe and United States)
16* Data source: Airport websites and other sources
World War II
MIT ICAT
Key Factors Influencing the Dynamics of Multi-Airport Systems
Projected Rate of Growth of Demand for Air Transportation
GDP driving air transportation activity (and conversely)Projected rate of growth of RPKs related to type and capacity of airport infrastructure developed
• Secondary airports (small incremental capacity strategy) in Europe and United States (small to medium average annual rate of growth)
• New high capacity airports in anticipation of medium to high average annual rate of growth
China
Europe
US
Japan
Brazil
Australia
Germany
Ireland
PortugalAustria
Poland
Hungary
Finland
Cyprus
Singapore
South Africa
Malaysia
Mexico
Saudi Arabia
Indonesia
CanadaFrance
Belgium
U.K.
Spain
Italy
Netherlands
Czech Rep
Malta
Greece
Cyprus
India
Russian Fed.
Malaysia
Thailand
New Zealand
Switzerland
Philippines
Vietnam
Turkey
Chile
Columbia
Egypt
Mauritius
Bangladesh
Kenya Morocco
Ethiopia
UzbekistanAlgeria
Trinidad
Tunisia
Lebanon
YemenCuba
Syrian A.R.Venezuela
Suriname
Costa Rica
KazakstanBolivia
Turkmenistan
Azerbaijan
NamibiaGabon
Romania
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met
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apita
GDP per capita
Legend
Population
RPK per capita vs. GDP per capita
17* Data source: ICAO traffic data & CIA World Fact book data
MIT ICAT
Key Factors Influencing the Dynamics of Multi-Airport Systems
Future Needs for Airport Infrastructure:Projected Demand/Airport Capacity Adequacy
Indi
aC
hina
Japa
nIta
lyN
ethe
rland
sBr
azil
Spai
nH
unga
rySo
uth
Afric
aIre
land
Ger
man
yPo
rtuga
lBe
lgiu
mSi
ngap
ore
Pola
ndLu
xem
bour
gFr
ance
Euro
peC
zech
Rep
Mal
taU
.K.
Gre
ece
Can
ada
Uni
ted
Stat
esFi
nlan
dAu
stra
liaC
ypru
sAu
stria
0
1
2
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latio
n / #
Airp
orts
(with
runw
ays
long
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an 5
000f
t)M
illio
ns
Analysis of future needs for airport infrastructure:• Population / Number of airports used as a proxy for latent demand vs. existing
infrastructure capabilities
China and India: • high population/airport infrastructure ratios • will require significant future development of airport infrastructure
United States and Europe:• Significant number of existing airports that can accommodate future growth• Future emergence of secondary airports
18* Data source: CIA World Fact book
MIT ICAT Conclusions
Air transportation network• is not entirely scalable at the airport level due to airport capacity constraints,• scales to meet demand through regional level mechanisms.
Fundamental regional level scaling dynamics;• construction of new high capacity airports,• emergence of secondary airports.
Differences & similarities across the world• United States & Europe
Concentration of traffic at primary airportsConstruction of new large airports: pre/during WWIISignificant limitations to the development of new large airports; environmental barriers & constraintsEmergence of secondary airports (Southwest effect) 1970s to 2000sMore recent trend in Europe, especially with the growth of LCCs after deregulation in the early 1990s
• Asia Pacific Construction of multi-primary airport systems due to lack of existing infrastructure and projected demand
Future Evolution of the System and Needs• Regional level scaling mechanisms will be key to meeting future demand• Need to protect exiting airport infrastructure (both civil and military airports) in the United States
and in Europe• Need to develop a phased development approach for future primary airports in Asia
taking advantage of the lessons learned in the United States and in Europe 19
