人工智能（AI）和可解釋人工智能（XAI）在空中交通管理中的應(yīng)用綜述：當(dāng)前趨勢和發(fā)展及未來研究軌跡

applied

sciences

/journal/applsci

Appl.Sci.2022,12,1295.

/10.3390/app12031295

Review

ASurveyonArti?cialIntelligence(AI)andeXplainableAIinAirTraf?cManagement:CurrentTrendsandDevelopmentwithFutureResearchTrajectory

AugustinDegas1,*,MirRiyanulIslam2,*,ChristopheHurter1,ShaibalBarua2,HamidurRahman2,MineshPoudel1,DanieleRuscio3,MobyenUddinAhmed2,ShahinaBegum2,MdAquifRahman2,StefanoBonelli3,GiuliaCartocci4,GianlucaDiFlumeri4,GianlucaBorghini4,FabioBabiloni4andPietroAricó4

updates

checkfor

Citation:Degas,A.;Islam,M.R.;Hurter,C.;Barua,S.;Rahman,H.;Poudel,M.;Ruscio,D.;Ahmed,M.U.;Begum,S.;Rahman,M.A.;etal.ASurveyonArti?cialIntelligence(AI)andeXplainableAIinAirTraf?c

Management:CurrentTrendsandDevelopmentwithFutureResearchTrajectory.Appl.Sci.2022,12,1295.

/10.3390/

app12031295

AcademicEditors:MaríaPazSesmeroLorente,PlamenAngelovandJoseAntonioIglesiasMartinez

Received:9December2021

Accepted:17January2022

Published:26January2022Publisher’sNote:MDPIstaysneutralwithregardtojurisdictionalclaimsinpublishedmapsandinstitutionalaf?l-iations.

Copyright:?2022bytheauthors.LicenseeMDPI,Basel,Switzerland.Thisarticleisanopenaccessarticledistributedunderthetermsand

conditionsoftheCreativeCommons

Attribution(CCBY)license

(https://

/licenses/by/

4.0/).

1

2

3

4

*

EcoleNationaledel’aviationCivile,7AvenueEdouardBelin,CS54005,CEDEX4,31055Toulouse,France;christophe.hurter@enac.fr(C.H.);minesh.poudel@enac.fr(M.P.)

Arti?cialIntelligenceandIntelligentSystemsResearchGroup,SchoolofInnovation,DesignandEngineering,M?lardalenUniversity,H?gskoleplan1,72220V?ster?s,Sweden;shaibal.barua@mdh.se(S.B.);

hamidur.rahman@mdh.se(H.R.);mobyen.ahmed@mdh.se(M.U.A.);shahina.begum@mdh.se(S.B.);

md.aquif.rahman@mdh.se(M.A.R.)

DeepBlues.r.l.,ViaManin53,00185Rome,Italy;daniele.ruscio@dblue.it(D.R.);

stefano.bonelli@dblue.it(S.B.)

DepartmentofMolecularMedicine,SapienzaUniversityofRome,PiazzaleAldoMoro5,00185Rome,Italy;giulia.cartocci@uniroma1.it(G.C.);gianluca.di?umeri@uniroma1.it(G.D.F.);

gianluca.borghini@uniroma1.it(G.B.);fabio.babiloni@uniroma1.it(F.B.);pietro.arico@uniroma1.it(P.A.)Correspondence:augustin.degas@enac.fr(A.D.);mir.riyanul.islam@mdh.se(M.R.I.)

Abstract:AirTraf?cManagement(ATM)willbemorecomplexinthecomingdecadesduetothegrowthandincreasedcomplexityofaviationandhastobeimprovedinordertomaintainaviationsafety.Itisagreedthatwithoutsigni?cantimprovementinthisdomain,thesafetyobjectivesde?nedbyinternationalorganisationscannotbeachievedandariskofmoreincidents/accidentsisenvisaged.Nowadays,computerscienceplaysamajorroleindatamanagementanddecisionsmadeinATM.Nonetheless,despitethis,Arti?cialIntelligence(AI),whichisoneofthemostresearchedtopicsincomputerscience,hasnotquitereachedendusersinATMdomain.Inthispaper,weanalysethestateoftheartwithregardstousefulnessofAIwithinaviation/ATMdomain.ItincludesresearchworkofthelastdecadeofAIinATM,theextractionofrelevanttrendsandfeatures,andtheextractionofrepresentativedimensions.WeanalysedhowthegeneralandATMeXplainableArti?cialIntelligence(XAI)works,analysingwhereandwhyXAIisneeded,howitiscurrentlyprovided,andthelimitations,thensynthesisethe?ndingsintoaconceptualframework,namedtheDPP(Descriptive,Predictive,Prescriptive)model,andprovideanexampleofitsapplicationinascenarioin2030.ItconcludesthatAIsystemswithinATMneedfurtherresearchfortheiracceptancebyend-users.ThedevelopmentofappropriateXAImethodsincludingthevalidationbyappropriateauthoritiesandend-usersarekeyissuesthatneedstobeaddressed.

Keywords:AirTraf?cManagement(ATM);Arti?cialIntelligence(AI);eXplainableArti?cialIntelli-gence(XAI);user-centricXAI(UCXAI)

1.Introduction

1.1.AirTraf?cManagement

AirTraf?cManagement(ATM)isavastandcomplexdomain[

1

]encompassingallactivitiescarriedouttoensurethesafetyand?uidityofairtraf?c.Inanutshell,ATMaimsatef?cientlymanagingandmaximisingtheuseofthedifferentresourcesavailabletoit—e.g.,theairspaceanditssubdivisionssuchasthesectors(seeFigure

1

),theairroutes(seeFigure

2

),theairport,therunways—bytheusersoftheresources—e.g.,aircrafts,airlines—,inanytime-frameoftheiruseoftheresources—i.e.,inthetaxiphaseintheairport,orany

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?ightphasesimpli?edbythetripletclimb,cruise,descent—whileensuring?ightsafety[

2

].Thistaskisachievedbymanagingresources,i.e.,AirSpaceManagement(ASM),managingtheglobaldemandbeforeandwhileaircraftsare?ying,i.e.,AirTraf?cFlowandCapacityManagement(ATFCM),andmanaging—avoidingseparationlossesbetweenaircrafts,i.e.,avoidingonetobeintheseparationzoneofanother(seeFigure

3

)—andprovidinglocalinformationtothe?yingaircraft,i.e.,AirTraf?cControl(ATC)[

3

].

Figure1.SectordivisionoftheupperairspaceofFrance[

4

].

Figure2.ExcerptofamapoftheroutenetworkinthesouthofFrance.Routesarede?nedbywaypoints—blackandgreydots.Source:EUROCONTROL.

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Nowadays,computerscienceplaysamajorroleindatamanagementanddecisionsmadeinATM,andevenifhumansremainasthemainagents,computerscienceremainsimportant,andismorelikelytohaveamorerelevantpartinthefuturewithincreasingairtraf?c—notwithstandingactualCOVIDsituation[

5

]—anditscomplexity—notablywiththeinsertionofnewaerialvehiclessuchasdrones,e-VTOLintotheairspace[

6

].

Arti?cialIntelligence(AI),beingoneofthemostresearchedtopicincomputerscience,shouldbepartofthepicture.

Figure3.Separationzoneofanaircraft.FromDegasetal.[

7

].

1.2.AIandXAIforATM

‘Arti?cialIntelligence’termwas?rstusedin1956forthe?rst“DartmouthSummerResearchProjectonArti?cialIntelligence”,andisgenerallyreferstoanymachinethatexhibitstraitsassociatedwithahumanmind,suchaslearningandproblem-solving.Sincethen,thedisciplinehasknownseveral‘summer’withimportantinterest,and‘winter’,disinterestfromthe?eld,associatedwithscepticism[

8

].Inparticular,AIingeneralhasexperiencedanewbloomduringthe2010s,boostedbytheincreasingaccesstomassivevolumesofdata,andthediscoveryoftheveryhighef?ciencyofcomputergraphicscardprocessorstoacceleratethecalculationoflearningalgorithms[

9

].Thisbloomhasbeenmaterialisedbysomesigni?cantpublicsuccessesandhasboostedfunding,suchasWatson—IBM’sIA—winningthetelevisiongameshowJeopardyagainsttwoofitschampions[

10

],GoogleXbeingabletohaveanAIrecognisecatsonvideos[

11

],orlaterinthedecade,AlphaGo—anditssuccessorAlphaGoZero—beatingoneoftheworldplayersofGo[

12

].EXplainableArti?cialIntelligence(XAI),methodsandtechniquesenablinghumanstounderstand(i)theAIalgorithm(i.e.,globalexplanationorinterpretability),or(ii)itssolutions(i.e.,localexplanationorjusti?cation),beingstronglylinkedtothesystemsitexplains,followedthesametendency,andisactuallyinitsthirdgeneration—accordingtoMulleretal.[

13

].Arti?cialIntelligenceinAirTraf?cManagementroughlyfollowedthesametendencies,withsomedelay.Asthefollowingshows,AIinATMroughlyevolvedfromAIsystemsusedtooptimisethetraf?c,toAIsystemstopredictvariousobjects—likepredicting4Dtrajectories—duringthelastdecade.

DespitehistoricalresearchworkinAIforATM,aresearcherfacingaproblemintheATMdomain—toourknowledge—will?ndnogeneralguidepresentinghowtoresolvethisproblem(orsimilarones),northelimitationofcurrentwork,whichisdetrimentaltothedomainanditsevolution.Somereviewexistinthedomain,buttheyarespecialisedintoacategoryofAIalgorithms—e.g.,meta-heuristics[

14

,

15

],multi-agentsystems[

16

]—,focusedonotheraspects,e.g.,communications[

17

],orareoutdated[

18

],andfocusmoreonthetechniquesthantheintegrationfortheendusers.

Unfortunately,despiteseveralresearchworkalreadycarriedinAIforATMdomain,ithasnotbeen‘fullyoperational’norhasitbroughtanybene?tstoendusers.SlowprogresswithintheuseofAIintheATMdomainisexplainablebythefactthattheATMdomainisacriticaldomainwithlifeatstake,andthatsafetyisthetopmostpriority.Historically,safetyhasbeenachievedinATMwithhuman-in-the-loop—inparticularbutnotrestrictedto,AirTraf?cController(ATCO)—,andwillmostlikely,ascontendbytheauthors,evolvebydesigningtightlyhuman-centeredsystems,requiringthosesystemstobeunderstandablebytheend-user,andtoadapttoitscharacteristics—mental

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andphysical—andtoitspsychologicalstate.Forexample,iftheoperator’sworkloadisexceedstheircognitivecapacity,orifsomekindofincapacitationisoccurring,theircognitivestatecouldbeautomaticallydetectedbythesystem,andusedbythisassessmenttoexecuteactionsautonomouslyalonganescalatingscaleofautomation(i.e.,adaptiveautomation)[

19

,

20

].Inotherdomainssuchashealthcareandcriminaljustice,amongothers,theincreasinginterestinAItosupporthigh-consequencehumandecisionshasspurredthe?eldofXAIandUser-CentriceXplainableArti?cialIntelligence(UCXAI)[

21

]—UserCenteredDesign(UCD)referstothemethodsemployedwhendesigningsystemsforend-userstovalidatenovelalgorithms/workingmethods/interactiontechniques[

22

–

25

].ThisprimordialaspectisyettobefullyassessedinATM,buttheinterestisgrowing[

26

,

27

].

Basedonthetwopreviousobservations,thegoalofthisarticleistodepictthetrendsofAIandXAI,andsetthetrajectorythattheseworksmusttakeinordertoreachend-users.Ourmainresearchquestionsstemfromthetwopreviousobservations:

?RQ1:WhatarethecurrenttrendsofAIandXAIinATMtasks?

?RQ2:WhatarethelimitationthatarisefromtheuseofAIandXAIinATMtasks?

?RQ3:HowcouldthegeneralXAI?eldbene?tAIandXAIinATM?

?RQ4:WhatlimitationmayarisefromtheuseofgeneralXAIinATM?

?RQ5:WhatshouldthetrajectoryofAIandXAIbeforthisdomain?

Toanswerthesequestions,thisarticleisdividedintotwoparts:(i)The?rstpartofthisarticleisdedicatedtothereviewofresearchworkofthelastdecadeofAIin

ATM,presentingthemethodologyemployed(Section

2

),theextractionofrelevanttrendsandfeatures,theclusteringoftheseworkintorepresentativegroups(Section

3

),andtheextractionofrepresentativedimensions,allowingustocreateadesignspacerepresentingthoseworks,usedthentoanalysethepublications(Section

4

);(ii)thesecondpartofthisarticleisbasedonthedimensionsextractedinthe?rstpart,toanalysegeneralandATMXAIwork,analysingwhereandwhyXAIisneeded,howitiscurrentlyprovided,andthelimitations(Sections

5

and

6

),thensynthesisethe?ndingsintoaconceptualframework(Section

7

),thatisthenappliedtodifferentscenarios(Section

8

).Finally,weconcludethedifferent?ndingsofthisarticle(Section

9

).

2.PaperSelection

Thissectionprovidesdetailsfortheprocedureinvolvedintheselection,inclusion,andexclusionofresearcharticles.ThereviewwasconductedusingthePreferredReportingItemsforSystematicReviewsandMeta-Analysis(PRISMA)guidelines[

28

].Overall,thereviewwasperformedusingdifferentwell-rankedconferencesandjournals,judgerepre-sentativeofthedomain,namely,TransportationResearchPartC:EmergingTechnologies(TR_C)andIEEETransactionsonIntelligentTransportationSystems(IEEETrans.onITS)(toptwojournalonTransportationaccordingtoGoogleScholarmetrics[

29

]),JournalofAirTransportManagement(JATM)(the?rstATMjournal[

29

]),andInternationalConferenceonResearchinAirTransportation(ICRAT)andAirTraf?cManagementResearchandDevelopmentSeminar(ATMseminar)(thetwoATMconferencessupportedbyEURO-CONTROLandtheFederalAviationAdministration).Figure

4

describesthecompletepipelineofthepaperselection.

2.1.Identi?cation

ThefocusofthisresearchisonEnglishpublishedarticlesfrom2010untiltheendofDecember2021.Theyear2010hasbeendeterminedasastartingpointofthesearchingprocesssoastobeabletodeterminetrendsandevolutionofthevast?eldfromthebeginningofthenewbloomofAIinterest—seeSection

1.2

—,andbesuretofullycapturetheessenceoftheresearchspace.Apartfromthis,theinclusionandexclusioncriteriaofthisreviewareshowninTable

1

.

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Figure4.PRISMA?owofthereviewmadeonInternationalConferenceonResearchinAirTrans-portation(ICRAT),AirTraf?cManagementResearchandDevelopmentSeminar(ATMseminar),TransportationResearchPartC:EmergingTechnologies(TR_C),JournalofAirTransportManagement(JATM),andIEEETransactionsonIntelligentTransportationSystems(IEEETrans.onITS).

Tobe?rstlyselected(Identi?cation),articlesofthedifferentjournalsandconferencesrequired:(i)TobeintheATMdomain,acharacteristicthatiseitherobtainedbythedatabaseused—i.e.,ATMspeci?cconferencesorjournals,orusingkeywordsto?lter,indetail,“airtraf?c”O(jiān)R“airplane”O(jiān)R“aircraft”,namedinthefollowing“ATM_Filter”;and(ii)toworkwiththeAIalgorithm,characteristicthatwas?lteredusinggeneralregularexpression“Predict*”O(jiān)R“Estimat*”O(jiān)R“Optimi*”O(jiān)R“Cluster*”O(jiān)R“Analy*”O(jiān)R“Visu*”O(jiān)R“Learn*”O(jiān)R“Explain*”O(jiān)R“Model*”O(jiān)R“Plan*”O(jiān)R“Con?ict”O(jiān)R“Classif*”.Thenumberofarticlesidenti?edpersourcearerepresentedinFigure

4

,anddetailsaboutthekeyword?lterscatchesarerepresentedinTable

2

.Thekeywordsusedwere?rstre?nedinapreliminarystudyperformedtoplanasystematicreview,usingdifferentkeyword-extractiontechniques,ATMdomaininsightsfrominterviewsperformed,andgeneralauthordomainknowledge.Inanutshell,thesekeywordsrepresentmaintasksforwhichAIcanbeemployedinanATM,alongwithsomespeci?cAIkeywords,suchasthetheoryorframeworkemployed—e.g.,neuralnetwork,geneticalgorithm.

Table1.Exclusionandinclusioncriteria.

Criteria

Principles

Inclusion

?Paperspublishedfrom2010toendof2021.

?Fulltext.

?Peer-reviewedstudies.

?PaperintheATMdomain.

?PaperintheAIdomain.

?Paperanswersthede?nedresearchquestions.

Exclusion

?PapersnotintheEnglishlanguage.

?Reviewpapers.

?Paperslessthan4pages.

?Notpeerreviewedstudies.

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Table2.Paperselectionresultattheidenti?cationphase.

Keyword

ICRAT

ATMSeminar

TR_C

JATM

IEEETrans.onITS

Total

“Predict*”

39

23

15

18

5

100

“Estimat*”

17

12

6

20

2

57

“Optim*”

26

27

24

26

5

108

“Cluster*”

5

3

0

7

0

15

“Analy*”

33

38

12

171

1

255

“Visual*”

2

2

1

1

0

6

“Learn*”

13

11

10

14

4

52

“Explain*”

0

0

0

3

0

3

“Model*“

38

43

30

134

4

249

“Plan*”

9

9

12

37

0

67

“Con?ict”

17

13

9

2

5

46

“Classif*”

1

1

0

8

0

10

Total(withoutduplication)

160

141

88

371

18

778

2.2.Screening

Thereviewprocessusedconferencesandjournaldatabases,duplicatescameonlyfromexternaldatabasesfrompreviouswork,andforwardbackwardreferencesearching,hencethelownumberofexclusionforduplication(29excluded).

Selectedpaperswerethenmanuallyscreenedforrelevance,basedona?rstsuper?-cialreadingusinganempiricalkeywordlist—containingmostlyprimarykeywordsandmethodsemployed—resultingintheexclusionof383papers.

2.3.Eligibility

Theremaining366articleswereselectedforafull-textreviewandcontentanalysis.Forinclusioninthe?nallist,articlesmustberelatedwithATM—excludingexamplearticleswheredronesareusedforsurveillance—,andAIorXAI.Theseinclusioncriteriaresultedin257relevantarticles.Inregardtoexclusioncriteria,thearticlesaboutpassengerexperienceandsecuritywerenotincludedinthisreview,asthiswasnotthecorefocusofthisstudy.Furthermore,onlythearticleswritteninEnglishwereconsideredinthisreview.

2.4.Inclusion

Inclusionratewashighlydifferentdependingonthesourcesofthepapers.Ononeside,paperscomingfromATMdomainconferences—ICRATandATMseminar—onlyneededtobeconsistentwiththeAI?eld,hencethelowexclusionrateattheidenti?cationphase—285outof383+343=726,roughly39percent,seeFigure

4

.

Ontheotherside,paperscomingfrommoregeneraljournals—TR_CandIEEETrans.onITS—neededtobeconsistentwithboththeAIandATMdomain,hencethehighexclusionrateattheidenti?cationphase—(n=7186),around98.5percent.Inbetween,paperscomingfromtheJournalofAirTransportManagementtargetingATMandotherAeronauticalissues,theexclusionratewasmedium-high—(n=870),70percent.

Pastthisphase,exclusionsweremostlyinthescreeningandintheeligibilityphases,duetothefactthatAItechniqueswerenotused:Inthescreeningphase—n=383,roughly51.1percent—clearlyde?nedinthetitle/abstracttheuseoftechniquesnotfromtheAI?eld;andintheeligibilityphase—n=109,around29.8percentdidnotmeetthecriteria.Asaresult,theoverallexclusionrateisabitlowcomparedtosomesystematicreview,butstillhighingeneral—around97.2percent.

3.PaperClustering

Thissectionprovidesdetailsaboutdataextractionfromthepublications,thedifferentstatisticsontheextracteddata,andtheclusteringofthepublicationsintorepresentativegroups.

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3.1.DataExtraction

Theaimofthissectionistocreateaninformationextractionformtoderiveoutthe

accuratedatafromtheselectedarticles.Inthisstep,therelevantdatawerederivedfromselectedarticlesthroughtheuseofspreadsheetsandreferencemanagementsoftware.

Thefollowingprimaryfeatureswereextractedinthissystematicreview:Author(s),Publication,Year,TitleoftheStudy,SourceType,TheoryorFramework,Objective,and

Factors.ThedescriptionoftheseitemsispresentedinTable

3

.

Table3.Extractedfeaturesfromprimarypapers.

Data

Description

Author(s)

Nameoftheauthor(s).

PublicationYear

Theyearofpublishingthepaper.

TitleoftheStudy

Thetitleofeachpaperthatisvisibleinthesearchingstep.

SourceType

Journal,bookchapter,andconferenceproceeding.

AITheoryorFramework

TheAItheoryorframeworkthatthestudyhadadopted,e.g.,NeuralNetwork(NN).

XAITheoryorFramework

TheXAItheoryorframeworkthatthestudyhadadopted,e.g.,LIME.

Objective

Themainobjectiveofpapers.

Factors

Theexaminedfactorsofthestudies,detailedinTable

4

.

Theextractedfactorswerethenre?nedinapreliminarystudyperformedtoplanthesystematicreview—andofcourseadaptedifneededintherestofthestudy—,usingdifferentkeywordextractiontechniques,ATMdomaininsightsfrominterviewsperformed,andgeneralauthordomainknowledge.There?nedextractedfactorsarepresentedinTable

4

.

3.2.FirstDataClusteringonAdditionalExtractedData

Theclusteringofthepaperwasperformedintwosteps.Thefirststepwasperformedinthepreliminarystudy,andresultedintheadditionalextractedfeaturepresentedinTable

4

.

Indetail,whileextractingthedifferentfeaturesofTable

3

,itseemedpromising,at?rst,tocategorisethepublicationsbythespeci?cpartoftheATMworldeverypublicationwasbene?tingto.However,althoughinteresting,thefeaturewasnotselectiveenoughtoassessanytrendsorcategoriesinthedifferentworks.Nonetheless,itseemedthatthemainObjectivefromTable

3

,wasapromisingfeaturetofullycategorisetheDesignSpaceofAIinATM.Thisclusteringwasperformedbyre?ningtheObjectivefeaturebyextractingthesubject,thetime-frame,andanycomplementaryinformationpiecesaboutthesubjectfromthefeature.ForexamplethefollowingObjective“Predictthefuturelocationofageneralaviationaircraft”from[

30

],canbecutinto“Predict”“thefuture”“l(fā)ocationofageneralaircraft”.Afterthisquitesimplephase,thedifferentextracteddata—i.e.,subject,time-frame,complementaryinformationpieces—haveanalysedandclusteredintomoregeneralcategories.Inanutshell,thecategoriesareregroupedinto“Object”,subdividedinto“Complement”,and,ifany,subdividedinto“Sub-Complement”.AllObjectcategoriesexceptTime-Framearenamedinthefollowing“MaterialObject”andrepresentthegeneralsubjectoftheobjective,namely,“Aircraft”,“Traf?c”,“Airport/ControlledTraf?cRegion(CTR)”,“Airspace”,“ATCO”,and“Pilot”(thedistributionofthepublicationsinfunctionoftheobjectfeatureisrepresentedinFigure

5

).Thesecategorieshavebeencreatedas:(i)Theyrepresentedadistinctpartforpractitioners;and(ii)thepublicationsacrossthosecategoriesappearedtohaveapartialdistinctionofObjectiveandemployedalgorithmsandmethodologies.

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Table4.Additionalextractedfeatures.

Object

Complement

Sub-Complement

Description

TimeFrame

Pre-Flying

Flying

Post-Analysis

Nottime-framed

ApublicationfocusedonbeforetheAircraftsare

?ying/moving;containstheATMtime-framecalledStategic,Pre-Tactical,andpartofTacticalphase.

ApublicationfocusedonwhileimplicatedAircraftsare?ying.ApublicationfocusedonafterimplicatedAircrafts

havelanded.

Thepublicationisnottimeframed

“MaterialObject”

Aircraft

Traf?c

Airport/CTRTraf?c

Airspace

ATCO

Pilot

State

Thepublicationdealswithanydescriptoroftheactualphysicalstateoftheaircraft,suchasmassorTrueAirSpeed(TAS).

Trajectory

Indicators

Route/FlightPlan4DTrajectory

Thepublicationdealswithanydescriptorofthetrajectory,notrelatedtothedirectphysicalstateoftheAircraft,suchasPhaseofFlight(PoF),orDescentLength.

Thepublicationfocusesonthedescriptionofthe

intended?ight.

Thepublicationfocusesonthedescriptionoftheactual?ight.

Indicators

Thepublicationdealswithanydescriptorofthetraf?c,suchastimebufferseparation,ordelay.

5DTraf?c

Con?ictavoidance

Optimisation

Prediction

Simulation

Analysis

ThepublicationdealswiththeavoidanceofseparationlossesbetweenAircrafts.

Thepublicationdealswiththeoptimisationof

Aircrafttrajectories.

ThepublicationdealswiththepredictionofAircrafttrajectories,andtheirpotentialinteractions.ThepublicationdealswiththesimulationofAircrafttrajectories,andtheirpotentialinteractions.

ThepublicationdealswiththeanalysisofAircrafttrajectories,andtheirpotentialinteractions.

State

Thepublicationdealswithanydescriptorofthestateoftheairport,e.g.,therunwaycon?guration.

GroundTraf?c

Indicators

“5D”Traf?c

Thepublicationdealswithanydescriptorofthegroundtraf?c,e.g.,Taxi-Speed,orEstimatedTake-OffTime(ESOT),ArrivalRunwayOccupancy.

Thepublicationfocusesonthetrajectoriesofthe

taxiingaircrafts

CTRTraf?c

Thepublicationdealswiththearrivalofaircrafts(e.g.,thesequencingofarrivingaircraft),thedeparture,orboth(e.g.,optimisationofdepartureandarrival).

State

StaticStructuralState

StateofEnvironment

Thepublicationdealswithanydescriptorofthestateofallorapartoftheairspace,e.g.,thecapacityofasector,withoutmodifyingit.

Thepublicationfocusesontheweather,thewindoranyotherenvironmentaldescriptor.

Structure

Sector

Route

Thepublicationdealswiththestructureofthesector(s),e.g.,thecon?gurationofthesectors,ortheirgeometricalstructure.Thepublicationdealswiththeroutenetworkstructure.

Demand/CapacityBalancing

Thepublicationfocusesonthebalancingofthedemandandcapacity.

ThepublicationsfocusesontheAirTraf?cCOntroller(ATCO).

ThepublicationfocusesonthePilot.

MostpublicationstargetingtheATCOorthePilotfocusedonpredictingoranalysingtheirbehaviourandtheirdecisions—thecommandforATCOandthe?ightdecisionsforthePilot—,oranalysingtheiraudiotransmissions.Publicationstargetingthosetwocategoriesarelessrepresentedthantheothers(seeFigu