air transportation system architecture analysis · 2020. 12. 30. · © 2005 philippe a. bonnefoy,...

© 2005 Philippe A. Bonnefoy, Roland E. Weibel, Engineering Systems Division, Massachusetts Institute of Technology 1

Air Transportation System Air Transportation System Architecture AnalysisArchitecture Analysis

Project Phase IIProject Phase II

Advanced System ArchitectureAdvanced System Architecture

Spring 2006Spring 2006

March 23rd, 2006

Presentation by: Philippe Bonnefoy

Roland Weibel

Instructors: Chris Magee, Joel Moses and Daniel Whitney


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


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


Overview of the System

Infrastructure layer

Operator layer

Transport layer

Mobility layer

System layer

Ground

Airspace

Demand layer

National Airspace System(airports layout and airspace structure)

Crews & Pilots

Aircraft routes

Movements of People and goods

Layer attributes

Population, income,location of businesses

SUPP

LY

Scheduled

On-Demand

DEM

AND

Data sources

FAA Form 5010 airportdatabase, airway

ETMS, OAG

DB1B database

ArcGIS, Census


Infrastructure Layer Analysis


Navigation Infrastructure Analysis

• 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

Nodes & Link Highlighted

Image removed for copyright reasons.Chart of jet routes.


Degree Sequence

0

500

1000

1500

2000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Degree

Freq

uenc

y

Other PointsVOR/VORTAC

020406080

100120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Degree

Freq

uenc

y

Victor Airways-All Points (left)-VOR/VORTAC (below)

0100200300400500600

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Degree

Freq

uenc

y

Other PointsVOR/VORTAC

05

101520253035

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Degree

Freq

uenc

y

Jet Routes-All Points (left), VOR/VORTAC (right)

NavAid Network n m C (Watts) r0.1928 -0.0166

-0.07280.2761Jet Routes 1787 4444Victor Airways 2669 7635


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


Navigation Distance Results

6.0%

6.5%

7.0%

7.5%

8.0%

8.5%

9.0%

0 200 400 600 800 1000 1200

Number of Airport Pairs

% D

ista

nce

Red

uctio

n

∑ ∑>

=airports airportsn

i

n

ij,jijdd

)

dd̂1%reduction −=


Transport Layer Analysis


0.001

0.01

0.1

1

1 10 100 1000 10000

Degree

Cum

ulat

ive

Prob

abili

ty p

(>k)

Wide/Narrow Body &Regional Jets

WB/NB/RJ + with primary &secondary airportsaggregated

Preliminary Analysis of the Wide-Body/Narrow Body &Regional Jet Flight Network

Degree Distribution

Regional JetsRegional Jets

Wide Body JetsWide Body Jets Narrow Body JetsNarrow Body Jets

(Images removed forcopyright reasons.)


Analysis of the Wide-Body/Narrow Body & Regional Jet Route Network

y = 1.85x-0.49

0.01

0.1

1

10

1 10 100 1000 10000

Degree

Cum

ulat

ive

Prob

abili

ty p

(>k)

WB/NB/RJ + w ith primary &secondary airports aggregatedBeyond Pow er Law

Pow er (WB/NB/RJ + w ith primary &secondary airports aggregated)

Coefficient of the degree distribution power law function: γ = 1.49

Hypotheses for the exponential cut-off:- Nodal capacity constraints- Connectivity limitations between core and secondary airports- Spatial constraints

Degree Distribution Analysis

Network Characteristics

Network n m Density Clustering coeff. rCentrality vs.connectivity

-0.3913/20

most central also part of the top 20 most

connected

0.64Scheduled

transportation network

249 3389 0.052


Preliminary Analysis of the Light Jet Route Network

Light JetsLight Jets

0.0001

0.001

0.01

0.1

1

1 10 100 1000 10000

Degree

Cum

ulat

ive

Freq

uenc

y (n

(>k)

)

Degree Distribution

Image removed for copyright reasons.


Analysis of the Light Jet Route Network

Degree distribution identified as resulting from sub-linear preferential attachment.

Degree Distribution Analysis

Network Characteristics

Network n m Density Clustering coefficient r

0.12 0.0045Light Jet Network(Unscheduled)

900 5384 0.005

0.0001

0.001

0.01

0.1

1

1 10 100 1000 10000

Degree

Cum

ulat

ive

Freq

uenc

y (n

(>k)

)

Power law network degree signature

Light Jet Network ⎥⎦

⎤⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛−−

−=−−

−

γµ

γγγ

12exp.

11kkank

with: γ = 0.57µ = 0.16a = 0.13


0

10

20

30

40

50

60

70

80

90

0 200 400 600 800 1000 1200 1400

Scheduled Traffic WB/NB/RJ(weighted degree)

Uns

cehd

uled

Tra

ffic

LJ(w

eigh

ted

degr

ee)

Interactions between Transport Layers

TEB

ATL

SEA

ORD

CMH

CLT

DFW

Scheduled

On-Demand

Transport layer


Demand Layer Analysis


Analysis of the Demand Layer

0.00001

0.0001

0.001

0.01

0.1

10.0 0.1 1.0 10.0

Size of population basin (b) [in millions]

Cum

ulat

ive

Den

sity

Fun

ctio

n p(

>b)

• Single Layer Analysis

Population/Airport Gravity Model

based on 66,000 Census Track data

• Non scale free nature of distribution of population around airports

⎭⎬⎫

⎩⎨⎧ === ∑

∈jctjictiCct

cti ddctCtspbi

,, min..

Notations:

Pct: population of census track ct

bi: size of population basin around airport i

ct: census track

di,j: Euclidean distance


Questions & Comments

Thank you

Air Transportation System Architecture AnalysisProject Phase IIAdvanced System ArchitectureSpring 2006MotivationObjective of the projectOverview of the SystemInfrastructure Layer AnalysisNavigation Infrastructure AnalysisDegree SequenceNavigation Architecture AnalysisNavigation Distance ResultsTransport Layer AnalysisPreliminary Analysis of the Wide-Body/Narrow Body &Regional Jet Flight NetworkAnalysis of the Wide-Body/Narrow Body & Regional Jet Route NetworkPreliminary Analysis of the Light Jet Route NetworkAnalysis of the Light Jet Route NetworkInteractions between Transport LayersDemand Layer AnalysisAnalysis of the Demand LayerQuestions & Comments

air transportation system architecture analysis · 2020. 12. 30. · © 2005 philippe a. bonnefoy,...

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