Data Driven Aircraft Trajectory Prediction...
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DART Data Driven Aircraft Trajectory Prediction Research NEWSLETTER No. 2 -November 2017 DART AT A GLANCE Call SESAR-2-2015 Objective Data Science in ATM Duration June 2016-June 2018 Contact Project Coordinator Prof. George A. Vouros University of Piraeus Research Center, Greece [email protected] Scientific Manager Dr Georg Fucks IAIS Fraunhofer, Germany [email protected] URL: http://www.dart-research.eu SHAPING THE FUTURE: DART VISION DART explores the applicability of a collection of machine learning and agent- based models and algorithms to derive a data-driven trajectory prediction capability. Advanced visual analytics techniques are used to facilitate data exploration, quality assessment, and algorithms parameters and features selection. During the 1 st consultation meeting we had with stakeholders on September, we shaped the DART vision: To advance collaborative decision-making processes that support multi-objective optimization taking the requirements of the different actors in the ATM system into account at the planning phase (i.e. few days before operation): •Aircraft Operators (AOs): Minimizing the cost thought maximizing the adherence to the airlines preferred FPs. •Network Manager (NM): Decide which flights to modify to resolve sector imbalances and potential conflicts. •Air Navigation Service Providers (ANSPs): Minimizing the sector imbalances and potential conflicts. The overall workflow in collaborative decision making is shown in the following picture. DART DATA SOURCES (UPDATED) DART Strategic Delay Costs Surveillance Data IFS by EnAire Weather Data: NOAA forecasts, SIGMET, TAF Flight Plans DDR -2 Spanish Operationa l Data Airspace Structure DDR-2 Spanish Operational Data Reconstructed Trajectories BR&T-E Trajectory Predictor using Flight Plans Aircraft Intent Descriptions BR&T-E CHALLENGE DART aims to deliver data-driven techniques to improve the performance and accuracy of single and multiple trajectory predictions, accounting for ATM network complexity effects. RESEARCH ISSUES • What are the supporting data required for robust and reliable trajectory predictions? • What is the potential of data-driven machine learning algorithms to support high-fidelity aircraft trajectory prediction? • How the complex nature of the ATM system impacts the trajectory predictions? • How can this insight be used to optimize the ATM system CTP Collaborative Trajectory Prediction Imbalances and conflicts detection (ANSP) AOs preferred FPs Iterative Optimization process Select the flights to modify (NM) STP Single Trajectory Prediction Trajectory predictions from an individual trajectory perspective STP predict new single trajectories for these flights CTP detection of imbalances and conflicts, selection of flight to modify and proposition of new FPs Optimal trajectories AOs propose new FP for these flights
Transcript of Data Driven Aircraft Trajectory Prediction...
DARTDataDrivenAircraftTrajectoryPredictionResearchNEWSLETTERNo.2-November2017
DARTATAGLANCECall SESAR-2-2015Objective DataScienceinATM
Duration June2016-June2018
Contact
ProjectCoordinatorProf.GeorgeA.VourosUniversityofPiraeusResearchCenter,[email protected]
ScientificManagerDrGeorgFucksIAISFraunhofer,[email protected]
URL: http://www.dart-research.euSHAPINGTHEFUTURE:DARTVISIONDART explores the applicability of a collection of machinelearning and agent- basedmodels and algorithms to derive adata-driven trajectory prediction capability. Advanced visualanalytics techniques are used to facilitate data exploration,quality assessment, and algorithms parameters and featuresselection.Duringthe1stconsultationmeetingwehadwithstakeholdersonSeptember,weshapedtheDARTvision:Toadvancecollaborativedecision-making processes that support multi-objectiveoptimizationtakingtherequirementsofthedifferentactors intheATMsystemintoaccountattheplanningphase(i.e.fewdaysbeforeoperation):•Aircraft Operators (AOs): Minimizing the cost thoughtmaximizingtheadherencetotheairlinespreferredFPs.•Network Manager (NM): Decide which flights to modify toresolvesectorimbalancesandpotentialconflicts.•AirNavigationServiceProviders(ANSPs):Minimizingthesectorimbalancesandpotentialconflicts.Theoverallworkflowincollaborativedecisionmakingisshowninthefollowingpicture.
DARTDATASOURCES(UPDATED)
DART
StrategicDelayCosts
SurveillanceDataIFSbyEnAire
WeatherData:NOAAforecasts,SIGMET,TAF
FlightPlansDDR-2Spanish
Operational Data
AirspaceStructure
DDR-2SpanishOperational
Data
ReconstructedTrajectoriesBR&T-E
TrajectoryPredictorusingFlightPlans
Aircraft IntentDescriptionsBR&T-E
CHALLENGEDARTaimstodeliverdata-driventechniquestoimprovetheperformanceandaccuracyofsingleandmultipletrajectorypredictions,accountingforATMnetworkcomplexityeffects.
RESEARCHISSUES•Whatarethesupportingdatarequiredforrobustandreliabletrajectorypredictions?•Whatisthepotentialofdata-drivenmachinelearningalgorithmstosupporthigh-fidelityaircrafttrajectoryprediction?•HowthecomplexnatureoftheATMsystemimpactsthetrajectorypredictions?•HowcanthisinsightbeusedtooptimizetheATMsystem(e.g.adherencetopreferredflightplans,betterutilizationof
CTPCollaborativeTrajectoryPrediction
Imbalancesandconflicts
detection(ANSP)
AOsp
referr
edFP
s
IterativeOptimizationprocess
Selecttheflightstomodify(NM)
STPSingleTrajectory
Prediction
Trajectorypredictionsfromanindividual
trajectoryperspective
STPpredictnewsingletrajectoriesfortheseflights
CTPdetectionofimbalancesandconflicts,selectionof
flighttomodifyandpropositionofnewFPs
Optim
altra
jector
ies
AOsproposenewFPfor
theseflights
DARTALGORITHMSHIDDENMARKOVMODELS(HMM):trajectorypredictionusingdiscretespatio-temporalstate-transitionmodels.SIMILARITY-BASEDRETRIEVAL(CLUSTERING):trajectorypredictionbymeansofanN-dimensionalclusteringtaskofdataseries.
REGRESSIONMODELS:trajectorypredictionusinganN-dimensionalregressiontaskw.r.t.dataseries.REINFORCEMENTLEARNING:MarkovDecisionProcess(MDP/POMDP):trajectorypredictionusingastate-action-rewardmodelinordertoobtainanoptimalpolicyonanyinstantoftheflight.JOINT(COLLABORATIVE)TRAJECTORYPREDICTIONS:Formulatetheproblemasamulti-agentMarkov-Decision-Processandsolveitviacollaborative,decentralizedreinforcementlearningmethods.
COMMUNICATION&DISSEMINATIONACTIVITIES
• DARTPresentedinSID2017,Belgrade,Servia,thearticle
DART:AMachine-LearningApproachtoTrajectoryPredictionandDemand-CapacityBalancing
• AlongsidetheInternationalConferenceforResearchinAir
Transportation(ICRAT)2018,DART(incollaborationwithdatAcronproject)willbeorganizingaworkshoponData-EnhancedTrajectoryBasedOperations.Pleasevisitthefollowingwebpageforadditionaldetails:http://icrat.org/icrat/upcoming-conference/data-tbo-workshop/
VISUALIZATIONSANDVISUALANALYTICSVisualization, interaction techniques and interfacessupport the exploration and evaluation of results oftrajectory prediction algorithms, particularly,comparisonofpredicted trajectories to realonesandcomparison of predictions obtained with differentalgorithmsordifferentparametersettings.Visuallysupporteddetectionofclustersoftrajectoriesandflightplans:
Historical data provide recurrent patterns oftrajectories (enriched with contextual information –e.g.weather)thatdata-drivenmethodslearn.Predictionofasingletrajectory involveschoosingthepattern thatfitsbetteraflightplan w.r.t.contextualdata. Visual explorationof results is beneficial for analystsandstakeholders(e.g.AircraftOperators),tofine-tuneprediction algorithms and to understand better thereasonsfordeviations.
Linkingpointstocomparetrajectories
Theabovehistogramshowsthestatisticaldistributionoftheproportionsofthematchedpointsbetween
trajectories(e.g.proportionsarehigherforthebluetrajectorythanfortheredone).
RESULTS&BENEFITS•Data-driventrajectorypredictioncapabilities;
•Agent-basedmultipletrajectorypredictionabilities;
•Interactivevisualinterfacesforsupportinginteractiveexplorationofmodellingresultsinspaceandtime,supportingdecisionmaking.
This project has received funding from the SESAR Joint Undertaking under grant agreement No 699299 under European Union’s Horizon 2020 research and innovation programme
The opinions expressed herein reflect the author’s view only. Under no circumstances shall the SESAR Joint Undertaking be responsible for any use that may be made of the information contained herein.
