1998 - Draper Laboratory

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(2) Allocating arrival slots at congested airports is performedon a dynamic basis, according to predictedairport capacity over the next few hours. Wheneverarrival capacity at one or more airports is predicted tobe exceeded by demand, available slots for arrivals atthese airports are allocated among the airlines on anFSFS basis to ensure fairness. For example, supposethat 15-minute slot-allocation intervals are in use andthat airline XYZ originally had six arrivals scheduled toarrive at A between 10:00 and 10:14. On a particularday, when capacity is low, XYZ might then receive fourslots on a FSFS basis, with the other two slots movedto the 10:15 to 10:29 interval. Note that, while thenumber of slots that XYZ will receive in the interval isspecified, the identity of XYZ’s flights that will occupythese slots is not. This is a fundamental aspect of thispartially decentralized concept and applies irrespectiveof whether the intervals into which slots are allocatedare 1, 10, or 15 minutes long.(3) Each airline (or, more generally, each aircraft operator)is now free to use its slots in each interval in the way itdeems best. Thus, each airline may schedule any oneof its flights into any one of its arrival slots. Eachairline must also keep the ATFM operator informed asto which flight has been assigned to each slot, and mostimportant, as to what slots, if any, will be left unuseddue to flight cancellations. Any slots left unused by aparticular airline will be awarded by the ATFMoperator to other airlines on an FSFS or other agreedonbasis.(4) The ATFM operator then estimates a Controlled Timeof Arrival (CTA) for each flight, (taking into accounteach airline’s preferred sequencing of its own flights)and sends to each airline the list of that airline’s CTAs.The point of “arrival” is not necessarily the runway; infact, in the presence of congestion, this point willusually be the boundary between en route airspace andthe transitional airspace into the terminal area of eachcongested airport.(5) Little or no use is made of departure slot assignments(known currently as Expected Departure ClearanceTimes (EDCT)). Thus, each aircraft operator isresponsible for determining the take-off time that ismost appropriate for meeting the CTA of each one of itsflights. This means that the aircraft operators will alsodecide how to best allocate a predicted delay betweendelay taken on the ground and delay taken whileairborne. In other words, the airlines will determinethe size of their own MARS.(6) Free Flight (user-preferred routing) is permitted inlarge portions of en route airspace and is used totravel from the airport of origin to the “arrival point” atwhich the CTA has been specified.(7) Air traffic management in the transitional area to theairport of arrival, in the airport's terminal area, and onthe airport’s surface is supported by advanced decisionsupportand automation aids such as CTAs and SurfaceManagement Automation (SMA). (An SMA-likeconcept is described in Ref. [7].)(8) The ATFM operator checks for compliance on the partof aircraft operators with slot allocations and withCTAs. The ATFM operator also continuously monitorstraffic operations to ensure safety.Numerous additional details can be specified with regard to thepartially decentralized concept described by (1) through (8), butare superfluous for the purposes of this paper. It should also benoted that many other plausible variations on the above themesmerit investigation. The important point, however, is that aconsensus is beginning to take shape on the part of the FAA andthe airlines about the desirability of moving toward a system thatcomplies with the general framework outlined here.Evaluation MetricsA set of key metrics related to congestion, delay costs, schedulereliability, and the use of aircraft and other resources has beenidentified for the evaluation of alternative ATFM systems. Eachof these metrics quantifies an aspect of performance that is ofparticular interest to one or more of the three principalstakeholders identified in the previous sections. Finegranularity metrics are listed in Table 2; aggregate metrics can bederived from those.AscentThe <strong>Draper</strong> ATFM Simulation Testbed<strong>Draper</strong> and MIT have been working together to investigate airtraffic flow management concepts since 1989. Since 1991, aspart of that collaboration, an ATFM simulation testbed, ATFMSystem Concept Evaluator for New Technologies (ASCENT), hasbeen designed and implemented to evaluate the system-wideimpact of new procedures, technologies, and improvedinfrastructure under existing or anticipated future approaches toATFM. The current version of ASCENT contains:(1) Models for a national network of capacitated 3 andnon-capacitated 4 airports.(2) Algorithms for planning ground holds and forallocating mandated delay between the ground and theair.Evolutionary Concepts for Decentralized Air Traffic Flow Management9
Table 2. ATFM performance evaluation metrics.ATFM ASCENTSimulation TestbedFigure 3. Representative ASCENT windows.(3) Algorithms for tactical planning of arrivals at airports.(4) A system-level simulation of a day’s activities in theNational Airspace System (NAS).(5) Database and analysis capabilities.Supporting utility programs include:(6) Models to simulate the evolution of airport weatherand capacity.(7) A tool for generating realistic demand schedules atairports.Figure 3 illustrates a subset of the many output window formatsavailable from ASCENT’s graphical user interface.ASCENT has been designed so that it can be used by a singleanalyst, requiring a minimum of overhead activity associatedwith defining and setting up scenarios and performing analyses.It is capable of evaluating candidate ATFM approaches across aspectrum of scenario variations. Flight schedules (demand) andairport capacities (supply) have been determined to be the mostsignificant defining factors for any given scenario. Tools havebeen created to allow user interaction in the creation of each ofthese scenario components.Through the demand-generation tool Pseudo-OAG Generator(POAGG) (Ref. [8]), the user can easily generate OAG-likehypothetical flight schedules for a network of airports. POAGGuses a combination of heuristics and mathematical programmingto create reasonably realistic flight schedules that satisfyuser-specified parameters. These input parameters include foreach airport:(1) The number and hourly distribution of arrivals.(2) The percentage of flights that connect to each of theother airports in the network.(3) Directional travel times between airport pairs.(4) The presence, if any, of shuttle flights and theircharacteristics.(5) The presence, if any, of airline flight “banks” and theircharacteristics.Once defined, a parameter set can be saved and edited to createnew scenarios.Airport capacities can be defined explicitly by the user throughan interactive graphical input format, and can be required tochange dynamically over time. Alternatively, the capacity can begenerated automatically by a Markov model of weather andcorrelated capacity at individual airports (Ref. [9]). Thetransition probabilities for the Markov model are estimated usingNational Oceanic and Atmospheric Agency (NOAA) data. Alsoin the testbed environment is a saw-tooth wave weather model(Ref. [10]) that has more realistic spatial and temporalcorrelation characteristics. The airport arrival and departure...................................................................................3At a capacitated airport, capacity can be less than demand. ATFM planning deals with all flights into and out ofcapacitated airports.4At non-capacitated airports, demand is always less than capacity.Evolutionary Concepts for Decentralized Air Traffic Flow Management10
Page 2 and 3: Letter from thePresident and CEO,Vi
Page 4 and 5: Information TechnologyMilton AdamsE
Page 6 and 7: BiographyMilton Adams has been at D
Page 9 and 10: Figure 1 represents a functional de
Page 11 and 12: Programs. In effect, these controll
Page 13: Although the terminal area traffic
Page 17 and 18: In the experiments, a nominal capac
Page 19 and 20: [3] Wambsganss, Michael C. “Colla
Page 21 and 22: Guidance, Navigation, and Control A
Page 23 and 24: A Control Lyapunov FunctionApproach
Page 25 and 26: x( 0) ∈ X and w(t) ∈Wfor all t
Page 27 and 28: (b) Select a quadratic RCLF V i (x)
Page 29 and 30: at each grid point. In the case w 1
Page 31 and 32: References[1] Ball, J.A. and A.J. v
Page 35 and 36: Relative and Differential GPSData T
Page 37 and 38: The first term on the right in the
Page 39 and 40: H R# δρ R,GPS -H A# δρ A,GPSThi
Page 41 and 42: selection; and (3) shown that the a
Page 45 and 46: Segmentation of MR ImagesUsing Curv
Page 47 and 48: (3)where ν now represents a contin
Page 49 and 50: Experimental ResultsThe results of
Page 51 and 52: Table 1. A summary of segmentation
Page 53 and 54: Guidance, Navigation,and ControlJim
Page 55 and 56: BiographyGeorge SchmidtGeorge Schmi
Page 57 and 58: clock and ephemeris errors, as well
Page 59 and 60: maintained in a rigid structure, wh
Page 61 and 62: Table 5. “Typical” absolute GPS
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tightly-coupled system, however, ca
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Concluding RemarksRecent progress i
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As real-time systems evolve into th
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Advanced Fault-TolerantComputing fo
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The Viking and Voyager were both in
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Containment Regions (FCRs). There a
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well as reversing the whole process
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As real-time systems evolve into th
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Automated Station-Keepingfor Satell
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Figure 2. Minimum elevation angles
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anomaly M and/or the ascending node
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However, since optimization and rec
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is maintained in the Northern Hemis
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autonomy. It must have the ability
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[31] Neelon, Joseph G., Jr., Paul J
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Draper’s primary goal is to Drape
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)Rotordynamic Modelingof an Activel
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Eq. (9) becomes:λ[ R ] { Φ } = [
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chosen to be 24, for a total of 48
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InertialInstruments/MechanicalDesig
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BiographyJeffrey Borenstein is curr
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process step. Process information i
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Figure 4. Control chart for boron d
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References[1] Barbour, N., J. Conne
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Draper Laboratory continues to engi
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Validating the Validating Tool:Defi
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calculates miscellaneous terms, suc
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Table 1. Suggested specification sh
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User Accuracy as aFunction of Simul
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20-min averaging, this clock lockin
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Table 2. Sample high-level summary
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AcknowledgmentR.L. Greenspan, J.A.
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Systems IntegrationRich MartoranaPe
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BiographyAnthony Kourepenis is an A
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control is employed to maintain the
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Table 1. Summary of automotive yaw
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Resolution (60 Hz) deg/h10000000100
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References[1] Greiff, P., B. Boxenh
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Guidance, Navigation, and Control A
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An Integrated Safety AnalysisMethod
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Infrastructure ModelsSystemRequirem
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Figures 6 and 7 illustrate the bloc
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Notice that each flight track descr
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Table 7. Safety statistics at 1700-
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Guidance, Navigation, and Control A
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An Optimal Guidance Law forPlanetar
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Note that the states in the three d
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Crossrange (Kft)10090807060504030Cl
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The 1997 Charles StarkDraper PrizeT
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The 1997 Charles StarkDraper Prize1
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“Draper encourages its personnel
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Gimballed Vibrating GyroscopeHaving
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Optical Source Isolator withPolariz
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Hunting Suppressor forPolyphase Ele
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Sensor Having an Off-Frequency Driv
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proof mass from transients and enha
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1997 Published PapersThe following
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monitoring of space structures and
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measured by kinematic degrees of fr
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i.e., what percent of the earth’s
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McConley, M. W.; Dahleh, M. A.; Fer
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unaffordable, or even misguided. Bu
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The Draper DistinguishedPerformance
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Educational Activitiesat Draper Lab
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1998 - Draper Laboratory

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