Air Transportation System
Architecture Analysis
Project Phase II
Advanced System Architecture
Spring 2006
March 23rd, 2006
Presentation by:
Philippe Bonnefoy
Roland Weibel
Instructors: Chris Magee, Joel Moses and Daniel Whitney
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
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Motivation
• The air transportation system is facing and will continue
to face significant challenges in terms of meeting
demand for mobility
• Current multi-agency effort to establish a roadmap for
the “Next Generation of Air Transportation System”
• Navigation in current system under most conditions
requires use of fixed-location of current infrastructure to
facilitate mobility
• Future (evolved) architecture of the system require
understanding of the structure of the current system
• Lack of integrated quantitative analysis of structure of
the current system
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
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Objective of the project
• Better understand the architecture of the current system
through network analyzes
• Understand
– the network characteristics of individual system layers
– Influence of constraints, desired properties (i.e. safety, capacity,
etc.) in explanation of network characteristics
– comparison of network characteristics across different layers,
through coupling of infrastructure or comparison of different
network characteristics across layers
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
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DEMAND
Overview of the System
System layer
Layer attributes
Demand layer
Population, income,
location of businesses
ArcGIS,
Census
Mobility layer
Movements of
People and goods
DB1B
database
Transport layer
Aircraft routes
ETMS, OAG
Operator layer
Crews & Pilots
Infrastructure
layer
National Airspace
System
(airports layout and
airspace structure)
Data sources
On-Demand
SUPPLY
Scheduled
Airspace
Ground
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
FAA Form
5010 airport
database,
airway
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Infrastructure Layer Analysis
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
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Navigation Infrastructure Analysis
Nodes & Link Highlighted
Image removed for copyright reasons.
Chart of jet routes.
• Nodes: FAA-Defined
Navigational Aids of Different
Types
– VORs, Reporting Points, etc
• Links: Air Routes Between
Nodes
– Victor (low alt) & Jet Routes (high alt)
• Network Metrics
– Clustering Coefficient (Watts method) – Proxy for robustness of
network
– Correlation Coefficient
• Architecture Analyses
– Shortest-Path Navigational vs. Direct Distance between
Airports
– Nodal Betweenness/Centrality
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
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Degree Sequence
Frequency
2000
Other Points
Victor Airways
-All Points (left)
-VOR/VORTAC (below)
VOR/VORTAC
1500
1000
500
120
100
1
2
3
4
5
Frequency
0
6
80
7 8 9 10 11 12 13 14 15
60
Degree
40
20
0
1
600
500
3
4
5
6
7 8 9 10 11 12 13 14 15
Degree
VOR/VORTAC
400
300
200
35
100
30
25
Frequency
Frequency
2
Other Points
0
1
2
3
4
5
6
7
8
20
9 10 11 12 13 14 1515
Degree
Jet Routes
-All Points (left), VOR/VORTAC (right)
10
5
0
1
2
3
4
5
6
7 8 9
Degree
10 11 12 13 14 15
NavAid Network
n
m
C (Watts)
r
Jet Routes
1787
4444
0.1928
-0.0166
Victor Airways
2669
7635
0.2761
-0.0728
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
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Navigation Architecture Analysis
• End Nodes: Navaids corresponding to published
airports
• Geodesic (shortest path by navigational distance)
computed between top 1,000 airport pairs
– Airports ranked based on 2004 FAA traffic data
– A-Star search algorithm implemented to find shortest distance along
network
• Results – Dynamics Along Network
– Navigational Distance Compared to Shortest Path Distance by
Airport Ranking – Maximum “direct-to” efficiency
– Betweenness centrality to be calculated for navigation nodes as
measure of their utilization
• Number of shortest-paths through nodes as a proportion to total
shortest paths
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
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Navigation Distance Results
) n airports n airports
d = ∑ ∑ d ij
% Distance Reduction
i
% reduction = 1 −
j, j>i
d̂
d
9.0%
8.5%
8.0%
7.5%
7.0%
6.5%
6.0%
0
200
400
600
800
1000
1200
Number of Airport Pairs
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
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Transport Layer Analysis
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
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Preliminary Analysis of the Wide-Body/Narrow
Body &Regional Jet Flight Network
Narrow Body Jets
Wide Body Jets
(Images removed for
copyright reasons.)
Regional Jets
Degree Distribution
Cumulative Probability p(>k)
1
0.1
Wide/Narrow Body &
Regional Jets
0.01
WB/NB/RJ + with primary &
secondary airports
aggregated
0.001
1
10
100
1000
10000
Degree
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
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Analysis of the Wide-Body/Narrow Body &
Regional Jet Route Network
Degree Distribution Analysis
10
Cumulative Probability p(>k)
WB/NB/RJ + w ith primary &
secondary airports aggregated
Beyond Pow er Law
Pow er (WB/NB/RJ + w ith primary &
secondary airports aggregated)
1
Coefficient of the degree distribution
power law function: γ = 1.49
Hypotheses for the exponential cut-off:
- Nodal capacity constraints
0.1
y = 1.85x
- Connectivity limitations between core
and secondary airports
- Spatial constraints
-0.49
0.01
1
10
100
1000
10000
Degree
Network Characteristics
Network
Scheduled
transportation
network
n
m
Density
Clustering
coeff.
r
Centrality vs.
connectivity
13/20
249
3389
0.052
0.64
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
-0.39
most central also part
of the top 20 most
connected
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Preliminary Analysis of the Light Jet Route
Network
Image removed for copyright reasons.
Light Jets
Degree Distribution
Cumulative Frequency (n(>k))
1
0.1
0.01
0.001
0.0001
1
10
100
1000
10000
Degree
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
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Analysis of the Light Jet Route Network
Degree Distribution Analysis
1
Cumulative Frequency (n(>k))
Power law network
degree signature
0.1
0.01
Degree distribution identified as
resulting from sub-linear preferential
attachment.
nk = a.k
Light Jet
Network
−γ
0.001
with:
0.0001
1
10
100
1000
10000
Degree
⎡ ⎛ k 1−γ − 21−γ
exp ⎢− µ ⎜⎜
⎣ ⎝ 1− γ
⎞⎤
⎟⎟⎥
⎠⎦
γ = 0.57
µ = 0.16
a = 0.13
Network Characteristics
Network
Light Jet Network
(Unscheduled)
n
m
Density
Clustering
coefficient
r
900
5384
0.005
0.12
0.0045
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
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Interactions between Transport Layers
On-Demand
Scheduled
Transport layer
90
80
TEB
Unscehduled Traffic LJ
(weighted degree)
70
CMH
60
50
40
CLT
ATL
30
20
ORD
10
0
0
200
400
SEA
DFW
600
800
1000
1200
1400
Scheduled Traffic WB/NB/RJ
(weighted degree)
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
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Demand Layer Analysis
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
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Analysis of the Demand Layer
•
Single Layer Analysis
Notations:
Pct: population of census track ct
Population/Airport Gravity Model
bi =
∑ pct
ct ∈C i
bi: size of population basin around
airport i
⎫
⎧
s.t. Ci = ⎨ct d ct ,i = min d ct , j ⎬
j
⎭
⎩
ct: census track
di,j: Euclidean distance
1
0.0
•
Non scale free nature of
distribution of
population around
airports
Cumulative Density Function p(>b)
based on 66,000 Census Track data
0.1
1.0
10.0
0.1
0.01
0.001
0.0001
0.00001
Sizeofof
population
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute
Technology
basin (b) [in millions]
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Questions & Comments
Thank you
© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology
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