novel, multidisciplinary global optimization …
TRANSCRIPT
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NOVEL, MULTIDISCIPLINARY GLOBAL OPTIMIZATION UNDER UNCERTAINTY
COMPANY UNCLASSIFIED - NOT EXPORT CONTROLLED Page 1
Aditya Saraf, Steven Stroiney, Valentino Felipe Saab Sensis Corporation
NASA LEARN Project Final Briefing 01/15/2015
Bruce Sawhill, Jim Herriot, Jim Phillips NextGen AeroSciences, Inc.
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PRESENTATION OUTLINE
! Introduction: The Metroplex Problem and Its Challenges
! Overview of the Project: Our Solution for Addressing the Challenges
! Project Technical Approach
! Results to Date
! Significance of Our Innovation
! Next Steps: Plans for Phase II Research Work
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THE METROPLEX PROBLEM
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! Two or more busy airports in close proximity
! Shared entry/exit points to the terminal airspace
! Inter-dependent, crossing arrival and departure flows
! Several traffic control facilities involved
The$New$York$Metroplex$$
Ref:%Geogia%Tech,%Saab%Sensis%Corp.,%ATAC%Corp.,%Metron%Avia;on,%“Final%Briefing%for%NASA%NRA%Characteriza;on%of%and%Concepts%for%Metroplex%Opera;ons,”%at%NASA%Langley%Research%Center,%Nov.%2009%
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SIGNIFICANCE TO THE NATIONAL AIRSPACE SYSTEM
! FAA’s Future Airport Capacity Task team report(1) ! Eight metropolitan areas would require additional capacity by 2025, even after taking FAA’s
planned improvements into consideration
! RTCA NextGen Mid-Term Implementation Task Force 5(2) ! “Relieve congestion and tarmac delays at major metropolitan airports, inefficiencies at satellite
airports, and surrounding airspace”
! Key Aeronautics Challenges (National Aeronautics R&D Plan(3)) ! Increasing airport approach, surface and departure capacity, and ! Developing capability to perform four-dimensional trajectory (4DT)-based planning
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(1) FAA,$“Capacity$Needs$in$the$Na?onal$Airspace$System,$2007E2025:$An$Analysis$of$Airports$and$Metropolitan$Area$Demand$and$Opera?onal$Capacity$in$the$Future,”$FAA$Future$Airport$Capacity$Task$(FACT)$team$report,$May$2007.$
(2) RTCA,$“NextGen$MidETerm$Implementa?on$Task$Force$Report,”$September$9,$2009.$(3) Na?onal$Science$and$Technology$Council,$“Na?onal$Aeronau?cs$Research$and$Development$Plan,”$February$2010.$
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THE CHALLENGES TO AN OPTIMIZED, DE-CONFLICTED 4DT SOLUTION
! Complex interactions and network impacts ! Requires integrated planning across airport surface and terminal airspace
! Uncertain future traffic behavior ! Requires planning under the possibility of multiple different futures
! Competing and nonlinear objectives ! Requires optimization-algorithms capable of handling complex objective functions
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OUR LEARN PROJECT—OVERVIEW
Objectives:
! Develop 4DT-based traffic management tool called PROCAST by combining cutting-edge technologies from two diverse fields ! Predictive technology/Data Science: Bayesian Belief Networks (BBNs) ! Optimization technology: NGA’s Continuous Re-planning Engine (NACRE)
! Perform proof-of-concept demonstration by conducting simulation experiments using a test problem—New York metroplex traffic scheduling ! In Phase I, we focus on a single-airport, arrival-departure-surface scheduling problem ! Selected John F. Kennedy International Airport (JFK) as the focus site
! Enhance NASA simulation platform to enable terminal airspace traffic simulation and pre-pushback process modeling
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PROCAST—Probabilis;c%Robust%Op;miza;on%of%Complex%Aeronau;cs%Systems%Technology%
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WHAT IS THE INNOVATION?
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Gene$c&Algorithms:%4DT%op;miza;on%over%mul;ple%flight%
domains;%flexible%objec;ve%func;ons;%incremental%“learning”%over%each%successive%planning%itera;on%for%fast%computa;on$
BBNs:&Incorporate%current%state%and%past%“learning”%(historical%dataWmining%and%SMEWdefined%causal%rela;onship%models)%for%genera;ng%realis;c%futures$
Metroplex$Traffic$
Current%State%
Select$Sta?s?callyEBest$Flight$Release$Times$To$Handle$All$
Futures$
1000s%of%Possible%Futures%
Flight%Release%Times%for%Every%Possible%Future%
Flight%Release%Times%
How&to&compute&realis$c&futures?&
Op?mize$Every$Future$Scenario$
Can&we&op$mize&over&en$re&gate>to>terminal&airspace&boundary&domain&fast?
Generate$Possible$Futures$
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PHASE I TECHNICAL APPROACH
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Metroplex$Traffic$
Current%State%
Select$Sta?s?callyEBest$Flight$Release$
To$Handle$All$Futures$
1000s%of%Possible%Futures%
Flight%Release%Times%for%Every%Possible%Future%
Flight%Release%Times%
Op?mize$Every$Future$Scenario$PROCAST&
Generate$Possible$Futures$
Bayesian(Belief(Network(
GA(Trajectory(Op8mizer(
NASA’s(
SOSS(
Airport%Surface%Traffic%Simula;on%
Pla\orm%
• Enables$planning$under$uncertainty$• Can$work$with$par?al$informa?on$• Accounts$for$interac?on$and$network$effects$
• Fast$4DT$op?miza?on$covering$surface$and$terminal$airspace$$
• Flexible$objec?ve$func?on$defini?on$
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PROCAST ELEMENTS ! Bayesian Belief Networks
! Estimating pushback readiness times and transit times on airport surface
! NACRE Genetic Algorithm for optimizing 4D trajectories
! SOSS simulation platform enhancements ! Added modeling of terminal airspace traffic ! Added pre-pushback process uncertainty models
! Simulation-based benefits assessment of PROCAST ! Modeled current-day operations at JFK as a comparison baseline ! Compared simulation performance using realistic traffic scenarios
! Concept of operations for PROCAST DST
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PROCAST ELEMENTS ! Bayesian Belief Networks
! Estimating pushback readiness times and transit times on airport surface
! NACRE Genetic Algorithm for optimizing 4D trajectories
! SOSS simulation platform enhancements ! Added modeling of terminal airspace traffic ! Added pre-pushback process uncertainty models
! Simulation-based benefits assessment of PROCAST ! Modeled current-day operations at JFK as a comparison baseline ! Compared simulation performance using realistic traffic scenarios
! Concept of operations for PROCAST DST
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WHAT ARE BAYESIAN BELIEF NETWORKS (BBNs)?
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! BBN is a directed, acyclic graph ! Nodes: Variables of interest ! Arcs: Statistical or causal
dependencies
! BBNs decompose complex joint probability distributions into smaller factors using conditional independence
! Subject matter experts design the graph structure using insights about the processes
! Machine learning is used to “learn” the parameters
! BBNs provide fast inference for ! Prediction (from causes to effects) ! Diagnosis (from effects to causes) ! Explaining away (tie-break between two or more causes)
A%Simple%BBN%Example%
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BBN FOR PREDICTING THE TIME DIMENSION
Pushback$Time$
Spot$Arrival$Time$
Departure$Runway$Queue$Length$at$Spot$Arrival$Time$
Runway(
Departure(
Time(
Departure$Gate$
Pushback$Rates$from$Adjacent$Gates$
Opposite$EDirec?on$
Traffic$$Rates$
1.$Analyze$Historical$Traffic$Data$and$Iden?fy$Key$Influencing$Factors$
2.$Use$Subject$Maeer$Exper?se$To$Model$Causal$Rela?onships$in$a$Graph$
3.$Train$the$Graph$Parameters$Using$Recorded$or$Simulated$Historical$Traffic$Data$
Condi;onal%Probability%Tables
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GENERATING REALISTIC FUTURE SCENARIOS USING BBNs
Predict(Pushback(Readiness(Times(for(All(Departure(
Flights(Currently(at(Their(Gates(
Current(State(
of(Surface(
Traffic(
Current(State(of(
PreHpushback(
Processes(
Predict(Factors(Influencing(Taxi(Time(of(Subject(Flight(
Predict(Probability(Distribu8on(For(Runway(
Departure(Time(for(the(Subject(Flight(
Sample(from(the(Probability(Distribu8on(to(Generate(
Mul8ple(Possible(Futures(
Random%Process%model%of%preWpushback%subtasks%(deWboarding,%fuelling,%boarding)%
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PROCAST ELEMENTS ! Bayesian Belief Networks
! Estimating pushback readiness times and transit times on airport surface
! NACRE Genetic Algorithm for optimizing 4D trajectories
! SOSS simulation platform enhancements ! Added modeling of terminal airspace traffic ! Added pre-pushback process uncertainty models
! Simulation-based benefits assessment of PROCAST ! Modeled current-day operations at JFK as a comparison baseline ! Compared simulation performance using realistic traffic scenarios
! Concept of operations for PROCAST DST
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THE PROBLEM ADDRESSED BY NACRE ! The most valuable resource of an airport are its
runways ! To sequence runways, consider
! Wake vortex separation (weight class of aircraft) ! Interleaving of arrivals ! Departure fix ! Frequent updates to arrival/departure information
(PROCAST)
! How NACRE works ! First optimize runway usage (arrivals and departures) ! Then organize surface traffic planning around runway usage ! Avoid using tarmac for aircraft storage
1/15/15
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SEARCH SPACE SIZE-RUNWAY SEQUENCING
5 10 15 20 25 301
106
1012
1018
1024
1030
Number of Aircraft to be sequenced
Num
ber o
f Diff
eren
t Seq
uenc
es All possible permutations
2-place permutations
1-place permutations
1/15/15
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OK!
Too Hard!
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GENETIC ALGORITHM FOR SEQUENCING
! NACRE GA encodes runway sequencing ! Position shifts encoded as elements in a genome ! Quality of solutions evaluated
! By throughput ! By sum of squares of delays (to prevent any one aircraft from getting all the pain) A
genetic algorithm (GA) encodes partial solutions, like a genome ! GAs work well when partial solutions are well – correlated to complete
solutions. In the runway sequencing problem, reorderings in different parts of the sequence contribute mostly additively to fitness.
1/15/15
COPYRIGHT 2014. NEXTGEN AEROSCIENCES, INC. ALL RIGHTS RESERVED. 17
Init. Pop.
Eval
Cull
Fitness Function
Breed End Pop.
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HOW MANY SMALL SEARCHES COVER LARGE SPACES
Original sequence
One permutation away
Net movement
One permutation away
One permutation away
1/15/15
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AN INNOVATIVE APPROACH TO OPTIMIZING SURFACE MOVEMENTS ! Surface dynamics driven ultimately by optimized runway
schedule ! Start with runway schedule, calculate taxi dynamics
backwards in time to meet schedule ! Deconflict by “rigid time translation”
! Leave gate earlier by time T sufficient for deconfliction ! Wait at runway queue for the same time T ! Runway queue acts as shock absorber ! Minimize number of aircraft in motion on airport surface
1/15/15
COPYRIGHT 2014. NEXTGEN AEROSCIENCES, INC. ALL RIGHTS RESERVED. 19
t1 t2 t3
t1-T t2-T
Initial Trajectory (with conflict)
Modified Trajectory (w/o conflict)
t3-T
Node 1 Node 2 Node 3
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NACRE SEQUENCER/OPTIMIZER SUMMARY OF FUTURE DIRECTIONS ! Optimize on additional criteria:
! Economically (based on A/C cost model, number of pax, etc.) ! Airline Network integration (priority of flight depends on context, such
as having to meet connecting flights or not) ! History of delays (spread delays around fairly by airline, aircraft, etc.
! Use “hot start” capability of GA for bigger/harder problems ! For sufficiently rapid replanning cycles, much of old solution is still
valid ! GAs strong point is incorporation of “partial solutions”
! Scale the GA to bigger/harder problems ! Preserve speed so as to keep using BBN capability in real-time ! Parallelizable on inexpensive hardware (GPU card, for instance) ! Metroplex ground/TRACON problem
1/15/15
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PROCAST ELEMENTS ! Bayesian Belief Networks
! Estimating pushback readiness times and transit times on airport surface
! NACRE Genetic Algorithm for optimizing 4D trajectories
! SOSS simulation platform enhancements ! Added modeling of terminal airspace traffic ! Added pre-pushback process uncertainty models
! Simulation-based benefits assessment of PROCAST ! Modeled current-day operations at JFK as a comparison baseline ! Compared simulation performance using realistic traffic scenarios
! Concept of operations for PROCAST DST
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PROCAST ELEMENTS ! Bayesian Belief Networks
! Estimating pushback readiness times and transit times on airport surface
! NACRE Genetic Algorithm for optimizing 4D trajectories
! SOSS simulation platform enhancements ! Added modeling of terminal airspace traffic ! Added pre-pushback process uncertainty models
! Simulation-based benefits assessment of PROCAST ! Modeled current-day operations at JFK as a comparison baseline ! Compared simulation performance using realistic traffic scenarios
! Concept of operations for PROCAST DST
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SIMULATION-BASED BENEFITS ASSESSMENT SIMULATION TRAFFIC SCENARIO
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Departures(
31L(
Arrivals(
31R(
Arrivals(
31L(
! Selected one of the most commonly used runway configurations for simulation
! Derived realistic traffic scenarios from recorded surface surveillance (ASDE-X) data and airline schedules (OAG)
! Selected three 2-hour busy-traffic time-periods from 2013 for simulation ! Scenario #1: November 24, 2013; 7 to 9 PM
Local time; 82 departures, 63 arrivals
! Simulation Parameters ! Planning Horizon: 45 minutes ! Planning Frequency: Once every 5 minutes
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SIMULATION-BASED BENEFITS ASSESSMENT
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! Compared simulated JFK surface and terminal operations as controlled by PROCAST against simulated current-day baseline operations
$$ Baseline(Opera8ons( PROCAST(Opera8ons(
Scheduling$method$for$Departures&
• Simple,$determinis?c$departuresEonly$planning$similar$to$currentEday$Departure$Management$Tools$
• Uses$nominal$pushback$readiness$?me$es?mates$
• Combined$arrivalEdeparture$planning$$• Assumes$periodic$updates$of$preEpushback$process$state$
• BBNs$generate$mul?ple$futures$$• Including$es?mates$of$pushback$readiness$
?mes$and$?mes$of$arrival$at$key$nodes$in$the$surfaceEterminal$network$
• GA$op?mizes$arrival$and$departure$opera?ons$over$each$future$
• Sta?s?cal$assessment$selects$best$flight$release$?mes$
Scheduling$method$for$Arrivals&
Determinis?c$arrivalsEonly$planning$based$on$currentEday$Traffic$Management$Advisor$(TMA)$scheduling$algorithms$
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TWO OTHER VARIANTS OF PROCAST
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$$ BBNsHonly(Opera8ons( NACREHonly(Opera8ons(
Scheduling$method$for$Departures&
• Assumes$periodic$updates$of$preEpushback$process$state$
• BBNs$generate$mul?ple$futures$
• DeparturesEonly$planning$similar$to$baseline$for$each$future$scenario$
• Sta?s?cal$assessment$selects$best$flight$release$?mes$
• Combined$arrivalEdeparture$planning$$• Only$one$future$scenario$is$generated$using$nominal$es?mates$of$pushback$readiness$?mes,$as$in$baseline$opera?ons$
• GA$op?mizes$arrival$and$departure$opera?ons$over$only$one$future$scenario$
$Scheduling$method$for$Arrivals&
ArrivalsEonly$planning$similar$to$baseline$for$each$future$scenario$
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DELAY DISTRIBUTION FOR DEPARTURES
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November(24,(2013,(7H9(PM(Local(Time(Traffic(Scenario((82(Departures)(
Total(Delays(in(M
inutes
8.4% 6%
16%
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DEPARTURE BENEFITS OVER MULTIPLE TRAFFIC SCENARIOS
Traffic(Scenario(BBNsHonly(
Savings(
NACREHonly(
Savings(
PROCAST(
Savings(
November$24,$7E9$PM$(82%departures,%63%arrivals)% +(8%( H(6%( +16%(
November$27,$8E10$PM$(72%departures,%60%arrivals)% +(12%( +(0.2%( +24%(
October$27,$11$AME1$PM$(62%departures,%90%arrivals)% +(8%( +(5%( +17%(
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PROCAST ESTIMATED ANNUAL SAVINGS
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! Assuming similar conditions prevail for 100 days per year: Quantity Savings Gate Delay 2,400 hours Total Delay in Metroplex 3,000 hours Fuel 155,000 gallons Fuel Cost $ 322,000 Operating Costs $ 5 million CO2 Emissions 9.8 metric tons Passenger Time 14,000 person-days Passenger Time @ $30/hr $ 10 million Passenger Time NAS-wide $ 15 million
Fuel burn rate = 8 kg / min taxiing, 40 kg / min airborne, cost = $ 993.60 / metric ton Assumptions: Operating costs = $ 27 / min at gate, $ 41 / min taxiing, $ 78 / min airborne
1 minute savings in NYC = 1.5 minute savings NAS-wide*
*Stroiney$S.,$Levy$B.,$Khadikar$H.,$Balakrishnan$H.,$$“Assessing$the$Impacts$of$JFK$Ground$Management$Program,”$DASC,$Syracuse,$NY,$2013.$
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PROCAST ELEMENTS ! Bayesian Belief Networks
! Estimating pushback readiness times and transit times on airport surface
! NACRE Genetic Algorithm for optimizing 4D trajectories
! SOSS simulation platform enhancements ! Added modeling of terminal airspace traffic ! Added pre-pushback process uncertainty models
! Simulation-based benefits assessment of PROCAST ! Modeled current-day operations at JFK as a comparison baseline ! Compared simulation performance using realistic traffic scenarios
! Concept of operations for PROCAST DST
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En(Route(Delays(
Ramp(Airport(C(Ramp(Airport(C(
Ramp(Airport(B(Ramp(Airport(B(
Ramp(Airport(A(Ramp(Airport(A(
PROCAST CONCEPT OF OPERATION
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Metroplex(Coordina8on(
Center(
(
Ramp(Airport(A(
TRACON(
Ramp(Airport(B( Ramp(Airport(C(
Tower(Airport(A( Tower(Airport(C(Tower(Airport(B(
PROCAST(
Gate(Release(Times(
Runway(Departure(Times,(Taxi(Merge(Sequences(
ATCSCC(
TMI(Mods(
Center(
Departure(Reroutes(
TRACON(Delays(or(Path(Stretches(
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SUMMARY AND KEY LESSONS LEARNED
! PROCAST showed significant benefits in proof-of-concept simulation experiments ! 3000 hours of delays saved, $322K annual savings in fuel cost, $ 5 million
savings in operating cost, $ 15 million in passenger time savings
! Predictive component by itself (BBNs-only) showed benefit ! Speed of computation limited our ability to assess scheduling over a large
number of possible futures
! Optimization-only component (NACRE-only) did not show benefit ! Apparently sensitive to uncertainty in gate pushback readiness times
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SIGNIFICANCE OF PROCAST ! Helps NASA address key aeronautics technical challenges
! Provides optimization tools and predictive capabilities that can be utilized in multiple existing NASA programs ! Predictive and optimization support for IADS traffic scheduling algorithms ! Coordinating surface planning with gaps in overhead en-route traffic streams ! Predicting Traffic Management Initiatives (TMIs) ! Evaluating candidate TMIs for Traffic Flow Management decision support
! Provides a platform for enhancing and validating NASA’s airport surface simulation tool SOSS
! Applicable to any problem with three features: (i) Complex interactions/ network effects, (ii) Uncertainty, and (iii) Competing objectives ! ATM safety assessment ! Passenger-focused air traffic management ! Non-ATM areas such as road transportation
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NEXT STEPS Phase(I(Findings( Next(Steps((Phase(II)(
Single$airport$showed$benefits;$coordina?on$across$metroplex$airports$may$be$even$more$beneficial$
Extend$algorithms$to$a$New$York$metroplexEwide$scope$including$JFK,$EWR,$LGA,$TEB$
Current$op?miza?on$capability$does$not$fully$address$delay$equity$among$airlines$and$airline$economic$objec?ve$op?miza?on$
Incorporate$equity$considera?ons$and$airline$economic$considera?ons$(e.g.,$based$on$aircral$cost$model,$AOC$data,$number$of$pax,$etc.)$
Current$op?miza?on$does$not$fully$integrate$runway$sequence$planning$with$ramp/taxiway$CD&R$
Enhance$op?miza?on$algorithms;$explore$exis?ng$NASA$algorithms$
Computa?on$?me$limited$ability$to$assess$op?miza?on$over$large$number$of$possible$futures$
Explore$the$itera?on$space;$assess$computa?on$accelera?on,$e.g.,$leverage$paralleliza?on$
Current$modeling$in$SOSS$limited$to$a$single$airport$ Modeling$traffic$on$mul?ple$metroplex$airport$surfaces$and$in$terminal$airspace$
Discussions(with(NASA(IADS,(ATDH2,(and(New(York(
TBO(research(ac8vity(planners(and(researchers(
• NASA(AFH(branch(seminar(
• Mul8ple(mee8ngs(throughout(the(year((
• Definite(interest(in(New(York(metroplex(
traffic(management(DSTs—analyze(test(cases(
• Enhanced(SOSS(will(benefit(IADS(research(• Poten8al(to(benefit(NASA(Traffic(Flow(
Management/Machine(Learning(research(
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PUBLICATIONS
! Papers ! Digital Avionics Systems Conference 2014: “Robust, Integrated Arrival-
Departure-Surface Scheduling Based On Bayesian Networks” ! AIAA Aviation Technology Integration and Operations Conference 2015
(submitted): “A Robust And Practical Decision Support Tool For Integrated Arrival Departure Surface Traffic Management”
! Presentations ! NASA AFH Branch Seminar—1/6/2015 ! Presentations to NASA SOSS simulation group—multiple ! NASA Open House Poster presentation—October 2014 ! Presentation to FAA/JPDO representative, Sherry Boerner—July 2014 ! Presentation to NASA SARDA research group—June 2014
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ACKNOWLEDGEMENTS
Thanks to… ! NARI—for the support of this project and for fostering collaboration with NASA
and LEARN researchers
! Robert Windhorst, Yoon Jung—for letting us use SOSS
! Zhifan Zhu and Sergei Gridnev—for SOSS software support
! NASA SARDA, ATD-2, IADS, and New York TBO researchers—for positive feedback throughout the project
! Kristin Rozier, Johann Schumann—for pointers on Bayesian Belief Network software
! Kris Ramamoorthy and Katy Griffin (ex-Saab employees)—for your technical contributions
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QUESTIONS?((SIMULATION&PLAYBACK&VIDEO)&
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