network theory implications in air transportation systems

DASC_Network_Theory.ppt [email protected]

Network Theory Implications In Air Transportation Systems

Dr. Bruce J. Holmes, NASADigital Avionics Systems Conference, Indianapolis

October 15, 2003


Outline

• Air Transportation Transformation Concept Space

• A Proposed Air Transportation Network Topology

• Implications of Scale Free Power Law Behavior in Air Transportation Networks

• Innovation Diffusion and Organizational Network Dynamics

• Network Robustness and Resilience

• Technologies and Scalability of Air Transportation Systems

““A problem well posed is half solved”A problem well posed is half solved”““A problem well posed is half solved”A problem well posed is half solved”


Transformation Concept Space(Notional)

Joint Planning OfficeFor the Transformation

Of The Air Transportation System

Centralized Distributed

Aggregated

Dis-Aggregated

Hierarchical

Scalable

On-Demand

Scheduled

Current Current StateState

Future Future StateState

The vision is to expand the concept space along all dimensions.


Proposed Topology for Air Transportation Networks

Q: What network characteristics, topologies, and technology strategieswould lead to scalable air transportation system behavior?

NAS LayerCommunication

NavigationSurveillance

A, B, C, D, E,SUA & TFR

Architecture

Airspace Services& IFR/VFR Procedures

A. Hub-and-SpokeDirected, Scheduled,

Aggregated

C. DistributedUndirected, On-Demand,

Disaggregated

B. Point-to-PointDirected, Scheduled

Aggregated

Capacity Layer(Airports/Routes)

Transport Layer(Aircraft/Routings)

Operator Layer(Pilots-Crew/Missions)

Mobility Layer(Passengers/O-Ds)


Power Law Distribution in Air Transportation(Physical & Transport Layers)

0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000

Links to Destinations

Nodes: Trip Originations

Hub-and-Spoke

On-Demand, Fractionals, SATS, SSO/L

UAVs PAVs RIAsLSAs

Known/Predicted

DivertedInduced

Examples of Scalable Behaviors in Air Transportation Topology

• Physical layer (airports-infrastructure) supports growing access to more runways in more weather

• Transport layer (new aircraft) supports growing access to more markets/communities

• NAS layer (airspace architecture & procedures) supports ubiquitous airspace access and services

Emergent Industry


Primal Questions

1. What are the comparative mobility metrics (e.g., door-to-door speeds) for networks A, B, and C?

2. What are the optimal sizes, costs, performance of aircraft for these networks?

3. What are the comparative energy consumptions for optimized operations of these networks?

4. What are the comparative noise constraint optimization issues for these networks?

5. What are the comparative infrastructure costs at each layer of these networks?

6. What are the comparative degrees of resistance to disruptions of these networks?

7. What are the comparative degrees of vulnerabilities of these networks?

8. What are the percolation behaviors for “events” in these networks?

9. What changes occur within the network when one of the layers is fundamentally altered?

10.What topology of topologies (system of systems) expands the transformation concept space?

Air Transportation TopologyAs framework for primal questions



A, B, C, D, E& SUA



Aggregated


Dis-Aggregated

B. Point-to-PointDirected, Scheduled,

Aggregated





Power Law Distribution in Air Transportation(Mobility & Capacity Layers)

0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000Links to Destinations

No

des

: O

rig

inat

ion

s

Scheduled AirlinesAggregated Transport

On-Demand, Dis -AggregatedFractionals , SSOL, SATS

UAVs, PAVs, RIAs, HLAs

Known/PredictedDiverted

Induced


Scalability of Networks

Q: What network characteristics, topologies, and technology strategieswould lead to scalable air transportation system behavior?



A, B, C, D, E,SUA & TFR

Architecture



Aggregated


Dis-Aggregated

B. Point-to-PointDirected, Scheduled,

Aggregated





Scale-Free: On-Demand

Scale-Free: Single-pilot

Scale-Free: Lower $/mph

Scale-Free: All Runway Ends

Scale Free:

ADS-B

Airborne Internet

Collaborative Sequencing

DAG-TM

Dynamic Sectors

Fanning

Intersecting Runways

Paired Approaches

Parallel Tracks

RNP

Self-Separatio

n

Virtual P

rocedures

WakeVAS


Network Diffusion/PercolationRole in Innovation Life Cycles

• Innovation life cycles are shaped by network behaviors

• Rates of diffusion are functions of: Scale free nature of the network (growth by preferential attachment) Thresholds of vulnerability (existence of need) Existence of a well-connected percolating cluster (incubator for innovation) Distribution of early adopters (potential for growth of links) The size of the clusters of early adopters (existence of highly linked groups) Links between early adopters and innovators (ability to legitimize the innovation)

• These conditions enable global cascades to occur. Global cascades exhibit self-perpetuating growth, ultimately altering the state of the entire system.

DASC_Network_Theory.ppt 4

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[email protected]

Power Law Distribution in Air Transportation(Physical & Transport Layers)

0 2000 4000 6000 8000100001200014000160001800020000

Links to Destinations

Nodes: Trip Originations

Hub-and-Spoke

On-Demand, Fractionals,SATS, SSO/L

UAVsPAVsRIAsLSAs

Known/Predicted

DivertedInduced

Examples of Scalable Behaviors in Air Transportation Topologies

• Physical layer (Airports-infrastructure) supports growing access to more runways in more weather

• Transport layer (New aircraft) supports growing access to more markets/communities

Emergent Industry

Figure D.- The Substitution of Cars for Horses (N. Nakicenovic, 1986)

50%

45%

40%

35%

30%

25%

20%

15%

10%

5%

0%

Horses Cars

1900 1905 1910 1915 1920 1925 1930

As a percentageof all “vehicles”

Over a period of about 16 years,cars displaced horses for transport.

Cars Displace Horses


Organizational Architectures

Network-basedValue Web

Hierarchy-basedValue Web

Independ. Prog. Assess.

Benik

Aerospace Systems Concepts & AnalysisVacant

Business Mgmt Offices

Program Offices

R&T Competencies Systems EngineeringJurczyk

Airborne SystemsArbuckle

AtmosphericSciences

McMaster

Aerodynamics,Aerothermodynamics,

and AcousticsKumar

Structures andMaterialsShuart

Space Access & Exploration

Saunders

AerospaceVeh. Sys. Tech.

Tenney

Airspace SystemNewsom

AviationSafety Finelli

Earth & SpaceScience

Sandford

TechnologyCommercialization

MgmtSupp. Off.Buonfigli

Agency Functions

Wind TunnelFac. Group

Gloss

Office of DirectorD. C. Freeman, Acting Director

Vacant, Deputy DirectorR. M. Martin, Assoc. Dir. for Program IntegrationD. L. Dwoyer, Assoc. Dir. for R&T Competencies

L. M. Couch, Assoc. Dir. for Business ManagementC. M. Darden, Asst. Dir. for Planning

Revised 5/03

Systems Mgt. OfficeM. Gilbert

Human ResourcesRay, Acting

ProcurementStone

Chief CounselKurke

Chief Financial OffWinter

EducationMassenberg

Equal Opport.Merritt

External AffairsFinneran

LMS SupportSuddreth

Logistics Mgt.Puckett

Chief Info. OfficerMangum

Safety &Mission Assur.

Phillips

Security & Environ. Mgmt.

LeeProject

Implementation Vacant

Mgt. Info. Sys.Vacant

Hdq. Function

NIA Mgt. Off.Harris

CALIPSOVacant

Research &Facilities Mgt. Off.

Lundy

For InfluenceIn System Advancements

For Process ControlIn Component Advancements

AIAA_Awards052203.ppt 13

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Value Web for Air Transportation Innovation Consumer Value Criteria for Disruptive InnovationsConsumer Value Criteria for Disruptive Innovations

Alternative Business ModelsAlternative Business Models((e.g.,e.g., On-Demand, Pt-to-Pt, UAVs ...) On-Demand, Pt-to-Pt, UAVs ...)

AirframeAirframeOEMsOEMs

ManufacturersManufacturersProvidersProviders

ServiceServiceProvidersProviders

Web Value Criterion:Mobility (Time)

Web Value Criterion:Web Value Criterion:Mobility (Time)Mobility (Time)

AffordableAffordableSpeedSpeed

AffordableAffordableReliabilityReliability

AffordableAffordableMaintainabilityMaintainability

Travel and CargoTransportation

Service Providers

Aircraft ManufacturersInternet Agents

Dispatch,Catering, Fuel, etc.

Partnerships& Alliances

Airports, DOTAir Traffic Services

FAA

RegulatorsCertifiersInsurers

Expected rewards from new consumers of disruptive innovationsExpected rewards from new consumers of disruptive innovationsdrive new value network toward new value criterion.drive new value network toward new value criterion.

Etc…

Engines, Avionics,Interiors, etc.

NASAUniversities

R&D Organizations

Flight TrainingEngineering,

Design, Testing

Materials Vendors& Sub-sub Component

Suppliers, etc.


Topological Robustness

NetworkRobustness(Tolerance to attackor to adoption ofnew ideas)

NetworkVulnerability(Exposure to attackor to new ideas)

High

HighLow

Low

DistributedUndirectedNetworks

(Highly vulnerable andhighly robust)

CentralizedDirectedNetworks

(Low vulnerability andlow robustness)


Summary

• Air Transportation Network TopologyProvides Mental Model for System of Systems

• Power Law Distribution of Nodes and LinksSheds Light on Scalability Issues for Aircraft, Airport, and Airspace

• The JPO Air Transportation System Transformation Visionis to Expand the Concept Space In All Dimensions.

• Network Theory Provides an Approachto Air Transportation System Robustness and Resilience Analysis.

Modern developments in network theory from complexity scienceModern developments in network theory from complexity scienceoffers a new way to think about air transportation systems and offers a new way to think about air transportation systems and

new tools for analyzing the dynamics of complex transportation topologies.new tools for analyzing the dynamics of complex transportation topologies.

Modern developments in network theory from complexity scienceModern developments in network theory from complexity scienceoffers a new way to think about air transportation systems and offers a new way to think about air transportation systems and

new tools for analyzing the dynamics of complex transportation topologies.new tools for analyzing the dynamics of complex transportation topologies.

network theory implications in air transportation systems

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