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DASC_Network_Theory.ppt 1Bruce.J.Holmes@NASA.Gov
Network Theory Implications In Air Transportation Systems
Dr. Bruce J. Holmes, NASADigital Avionics Systems Conference, Indianapolis
October 15, 2003
DASC_Network_Theory.ppt 2Bruce.J.Holmes@NASA.Gov
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”
DASC_Network_Theory.ppt 3Bruce.J.Holmes@NASA.Gov
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.
DASC_Network_Theory.ppt 4Bruce.J.Holmes@NASA.Gov
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)
DASC_Network_Theory.ppt 5Bruce.J.Holmes@NASA.Gov
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
DASC_Network_Theory.ppt 6Bruce.J.Holmes@NASA.Gov
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
NAS LayerCommunication
NavigationSurveillance
A, B, C, D, E& SUA
Airspace Services& IFR/VFR Procedures
A. Hub-and-SpokeDirected, Scheduled,
Aggregated
C. DistributedUndirected, On-Demand,
Dis-Aggregated
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(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
DASC_Network_Theory.ppt 7Bruce.J.Holmes@NASA.Gov
Scalability of 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,
Dis-Aggregated
B. Point-to-PointDirected, Scheduled,
Aggregated
Capacity Layer(Airports/Routes)
Transport Layer(Aircraft/Routings)
Operator Layer(Pilots-Crew/Missions)
Mobility Layer(Passengers/O-Ds)
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
DASC_Network_Theory.ppt 8Bruce.J.Holmes@NASA.Gov
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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Bruce.J.Holmes@NASA.Gov
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
DASC_Network_Theory.ppt 9Bruce.J.Holmes@NASA.Gov
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.
DASC_Network_Theory.ppt 10Bruce.J.Holmes@NASA.Gov
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)
DASC_Network_Theory.ppt 11Bruce.J.Holmes@NASA.Gov
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.