Automatic Planning of Ground Traffic
12 pages
English

Automatic Planning of Ground Traffic

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12 pages
English
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Automatic Planning of Ground Traffic Charles Lesire ONERA, Toulouse, France Ground traffic optimization is a major issue of air traffic management: optimal ground circulation could decrease flight delays and consequently decrease costs and increase pas- senger wellness. This paper proposes a planning algorithm for ground traffic based on contract reservation. It deals with time and speed uncertainty to ensure the feasability of the planned trajectories while avoiding conflicts between aircrafts. The algorithm efficiency is improved using pre-computed heuristics. The different algorithm versions are evaluated through simulations of a large european airport, namely the Frankfurt airport. I. Introduction One of the major issues of Air Traffic Management concerns the optimization of airport traffic. Indeed, the air traffic growth is having a hard impact on airport congestion. Flight delays are obviously impacted leading to an economic interest on ground traffic optimization methods. This optimization may also take into account ecologic issues such as noise and pollution reduction. Ground traffic optimization can hardly be performed by human controllers: managing several aircrafts moving on the airport during rush hours on quite complex taxiway networks may be difficult. It is especially the case when hard weather conditions occur (e.g. fog). A lot of researches have tried to help ground controllers either by defining new visualization displays (DST,1 AMAN,2 DMAN, etc.) or by improving traffic predictivity by sharing flight data between airports and controllers (CDM3).

  • green aircrafts

  • algorithm

  • end while

  • ground traffic

  • frankfurt airport

  • current point

  • aircraft

  • start point


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Nombre de lectures 7
Langue English

Exrait

AutomaticPlanningofGroundTrafficCharlesLesireONERA,Toulouse,FranceGroundtrafficoptimizationisamajorissueofairtrafficmanagement:optimalgroundcirculationcoulddecreaseflightdelaysandconsequentlydecreasecostsandincreasepas-sengerwellness.Thispaperproposesaplanningalgorithmforgroundtrafficbasedoncontractreservation.Itdealswithtimeandspeeduncertaintytoensurethefeasabilityoftheplannedtrajectorieswhileavoidingconflictsbetweenaircrafts.Thealgorithmefficiencyisimprovedusingpre-computedheuristics.Thedifferentalgorithmversionsareevaluatedthroughsimulationsofalargeeuropeanairport,namelytheFrankfurtairport.I.IntroductionOneofthemajorissuesofAirTrafficManagementconcernstheoptimizationofairporttraffic.Indeed,theairtrafficgrowthishavingahardimpactonairportcongestion.Flightdelaysareobviouslyimpactedleadingtoaneconomicinterestongroundtrafficoptimizationmethods.Thisoptimizationmayalsotakeintoaccountecologicissuessuchasnoiseandpollutionreduction.Groundtrafficoptimizationcanhardlybeperformedbyhumancontrollers:managingseveralaircraftsmovingontheairportduringrushhoursonquitecomplextaxiwaynetworksmaybedifficult.Itisespeciallythecasewhenhardweatherconditionsoccur(e.g.fog).Alotofresearcheshavetriedtohelpgroundcontrollerseitherbydefiningnewvisualizationdisplays(DST,1AMAN,2DMAN,etc.)orbyimprovingtrafficpredictivitybysharingflightdatabetweenairportsandcontrollers(CDM3).ThedevelopmentoftheASMGCaaimsatcombiningallthesemethodstoimprovegroundtrafficefficiency.4FollowingtheconceptofASMGC,someworkshavedeveloppedmethodsforgroundtrafficoptimization.Gotteland5usesgeneticalgorithmtogenerateaircrafttrajectories.Thealgorithmisusedforreal-timecontrolofaircrafts–mainlyforsimulationissues–anddoesnotprovideacompletetrajectoryoftheaircraft(usefullforpilotorcontrollerunderstandingofaircraftbehaviors).Deauetal.6workonrunwaysequencingtohelpfluidifyinggroundtrafficbutdoesnotconsidertherelationbetweenrunwaysandcompletetaxiwaytrajectories.ConstraintpropagationandrelaxationmethodsareusedbyvanLeeuwenetal.7butnosignificantresultsaregivenonuseabilityofsuchalgorithms.Theworkpresentedinthispaperproposesanalgorithmforgroundtrafficplanningbasedonaclassicalpath-findingalgorithm,namelyA,improvedtodealwithtimeconstraints,andtimeandspeeduncertainty.InsectionII,groundtrafficanduncertaintymodelsarepresented.ThenthealgorithmisintroducedandevaluatedinsectionIII.Someimprovementsarethenmadetomakethealgorithmmoreefficient,usingamoreaccurateheuristic(sectionIV.A),andapruningalgorithmtoreducethestate-space(sectionIV.B).II.GroundTracModellingTheairportstructureisrepresentedbyagraphG=(N,E),wherethesetofnodesNrepresentstheairportwaypoints(taxiwayintersections,gates,runwayaccesses,etc.)andthesetofedgesErepresentstheaAdvancedSurfaceMovementGuidanceandControlSystem21fo1AmericanInstituteofAeronauticsandAstronautics
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