Air Transportation Systems Engineering Edited by George L. Donohue, Ph.D. (Editor) George Mason University Andres G. Zellweger, Ph.D. (Editor) Embry-Riddle Aeronautical University Herman Rediess, Ph.D. (Associate Editor) Federal Aviation Administration Christian Pusch (Associate Editor) EUROCONTROL Experimental Center Volume 193 PROGRESS IN ASTRONAUTICS AND AERONAUTICS Paul Zarchan, Editor-in-Chief MIT Lincoln Laboratory Lexington, Massachusetts Published by the American Institute of Aeronautics and Astronautics, Inc. 1801 Alexander Bell Drive, Reston, Virginia 20191-4344
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Air TransportationSystems Engineering
Edited byGeorge L. Donohue, Ph.D. (Editor)George Mason University
Andres G. Zellweger, Ph.D. (Editor)Embry-Riddle Aeronautical University
Herman Rediess, Ph.D. (Associate Editor)Federal Aviation Administration
Christian Pusch (Associate Editor)EUROCONTROL Experimental Center
Volume 193PROGRESS INASTRONAUTICS AND AERONAUTICS
Paul Zarchan, Editor-in-ChiefMIT Lincoln LaboratoryLexington, Massachusetts
Published by theAmerican Institute of Aeronautics and Astronautics, Inc.1801 Alexander Bell Drive, Reston, Virginia 20191-4344
Table of Contents
Preface xxi
Chapter 1 Introduction 1
Section I: U.S. and European ATM Systems—Similaritiesand Differences
Chapter 2 Air Traffic Management Capacity-Driven OperationalConcept Through 2015 9
Aslaug Haraldsdottir, Robert W. Schwab, and Monica S. Alcabin, The BoeingCompany, Seattle, Washington
Introduction 9Preliminary Design for the NAS 9Operational Concept Development 10Functions, Agents, and Performance 11ATM System Functional Structure 12Capacity, Safety, and Separation Assurance 14Capacity-Driven Operational Concept 17National Level Flow Management 17En Route and Outer Terminal Area 19Approach/Departure Transition 20Final Approach .'V 22Surface 23Efficiency in Low Density En Route Airspace 23Conclusions ., 24References • 24
Chapter 3 Comparison of U.S. and European Airports andAirspace to Support Concept Validation 27
Diana Liang, Federal Aviation Administration, Washington, D.C.;William Marnane, EUROCONTROL, Brussels, Belgium; andSteve Bradford Federal Aviation Administration, Washington, D. C.
Introduction 27Assessment Territory 29Metrics and Measures 29Assessment and Findings 29Conclusion 46References 47
VII
viii
Chapter 4 Performance Review in Europe 49Xavier Fron, EUROCONTROL, Brussels, Belgium
Chapter 5 United States and European Airport Capacity AssessmentUsing the GMU Macroscopic Capacity Model 61
George L. Donohue and William D. Laska, George Mason University,Fairfax, Virginia
Introduction 61MCM Approach 62MCM Validation 64MCM Assessment of U.S. and European Airports 64MCM Comparisons 70Conclusions 71References 72
Section II: Economics of Congestion
Chapter 6 Forecasting and Economic Analysis for AviationSystems Engineering 77
Peter F. Kostiuk, Logistics Management Institute, McLean, Virginia;and Eric M. Gaier, Bates White and Ballentine, Washington, D.C.
Introduction : 77Evaluating National Impacts of ATM Investments 79Generating an Unconstrained Forecast 79Generating a Constrained Forecast 81Estimating and Closing the Performance Gap \ 84Estimating Airline Benefits from ATM Investments ; 87Overview of the Air Carrier Cost-Benefit Model 88Derivation of the Air Carrier Cost-Benefit Model 90LVLASO Scenario 97Conclusions 102References , 102
Chapter 7 Impact of Air Traffic Management on AirspaceUser Economic Performance 103
Joseph H. Sinnott and William K. MacReynolds, Ph.D., MITRE Corporation,McLean, Virginia
Introduction 103Airline Cost Drivers and ATM Actions 104Estimates of System-Wide Excess Cost to Airlines 106Example of the Impact of ATM Improvements on Long-Term Airline
Costs: Fleet Utilization and ATM Improvements 110
IX
The Larger Picture: The Influence of ATM on Demand-RelatedAirline Decisions I l l
Chapter 8 Effects of Schedule Disruptions on the Economicsof Airline Operations 115
Zalman A. Shavell, MITRE Corporation, McLean, Virginia
Introduction 115Scope of Disruptions 117Alternatives Available to the Airlines for
Handling Disruptions 118Cost Implications of Disruptions to the Airlines 118Snowstrom Event at Boston 119Aggregated Costs of Disruptive Events 121Conclusions . .' 125
Chapter 9 Modeling an Airline Operations Control Center 127Nicolas Pujet and Eric Feron, Massachusetts Institute of Technology,
Cambridge, Massachusetts
Introduction 127Modeling Structure and Hypotheses 128Model Identification and Calibration 132Conclusions 141References 141
Chapter 10 Pricing Policies for Air Traffic Assignment 143Karine Deschinkel, ENSAE, Toulouse, France; Jean-Loup Farges, ONERA—CERT,
Toulouse, France; and Daniel Delahaye LOG, Toulouse, France
Introduction 143Model Formulation 144Identification Problem 147Optimization Problem 148Principle of Resolution 148Numerical Experiments 150Conclusion and Future Work 156References 157
Section III: Collaborative Decision Making
Chapter 11 Improved Information Sharing: A Step Toward theRealization of Collaborative Decision Making 161
Peter Martin, EUROCONTROL Experimental Centre, Bretigny-sur-Orge,France; Alison Hudgell, U.K. Defence Evaluation Research Agency, Great Malvern,Worcestershire, United Kingdom; Nicolas Bouge and Sophie Vial, Aerospatiale,Les Mureaux, France
Introduction 161Collaborative Decision Making 162Project Overview 162
Chapter 13 Data Flow Analysis and Optimization Potentialfrom Gate to Gate 191
Matthias Poppe, DFS Deutsche Flugsicherung GmbH, Langen, Germany;and Georg Bolz, Lufthansa AG, Frankfurt/Main, Germany
Introduction 191Definitions 192ATM Process Model 192ATM Process Model Simulations 195Identification of Potentials 198Conclusions 201References 203
Chapter 14 Effect of Shared Information on Pilot/Controllerand Controller/Controller Interactions 205
R. John Hansman and Hayley J. Davison, Massachusetts Institute of Technology,Cambridge, Massachusetts
Introduction 205Why Humans Are Necessary in ATM 206ATM Interaction Architecture 208Interaction Assumptions 209Shared Information in Controller/Pilot Interactions 209Shared Information in Pilot/Airline Interactions 214Shared Information in Intrafacility Controller/Controller Interactions 215Shared Information in Cross-Facility Controller/Controller
Interactions 217Shared Information in Airline/ATM Interactions 221Flight Information Object 222
XI
Conclusions 223References 223
Chapter 15 Modeling Distributed Human Decision Making inTraffic Flow Management Operations 227
Keith C. Campbell, Wayne W. Cooper Jr., Daniel P. Greenbaum, andLeonard A. Wojcik, MITRE Corporation, McLean, Virginia
Introduction 227TFM Operations and Implications for Modeling 228Baseline Schedule Disruption Scenarios Modeled by IMPACT 229Airline and FAA Agents in IMPACT 231Basic Analysis of Airline and FAA Decision Making
with IMPACT 231Other Analyses with IMPACT 235Conclusions 236References 237
Chapter 16 Assessing the Benefits of Collaborative DecisionMaking in Air Traffic Management 239
Michael O. Ball, University of Maryland, College Park, Maryland; Robert L. Hoffman,Metron Scientific Consulting, Inc., Reston, Virginia; Dave Knorr and James Wetherly,Federal Aviation Administration, Washington, D.C.; and Mike Wambsganss, MetronScientific Consulting, Inc., Reston, Virginia
Introduction 239Improvements in the Quality of Information and Information
Distribution 240System and User Impact 245Collaborative Routing ". .-.- 249Conclusions 250References 250
Section IV: Airport Operations and Constraints
Chapter 17 Fast-Time Study of Airline-Influenced ArrivalSequencing and Scheduling 253
Gregory C. Carr and Heinz Erzberger, NASA Ames Research Center, Moffett Field,California; and Frank Neuman, Raytheon STX Corporation, MoffettField, California
Chapter 18 Capacity-Related Benefits of Proposed Communication,Navigation, Surveillance, and Air Traffic Management Technologies 269
Tara J. Weidner, Seagull Technology, Inc., Los Gatos, California
Introduction . 269Assumed Technology Scenarios 269Capacity-Related Benefits Defined 271Analysis Methodology Overview 272Model Assumptions and Results 278Conclusions 279References 286
Chapter 19 Collaborative Optimization of Arrival and Departure TrafficFlow Management Strategies at Airports 289
Eugene P. Gilbo, John A. Volpe National Transportation Systems Center,Cambridge, Massachusetts; and Kenneth W. Howard, Arcon Corporation,Waltham, Massachusetts
Chapter 20 Analysis, Modeling, and Control of Ground Operations atHub Airports 305
Kari Andersson and Francis Carr, Massachusetts Institute of Technology, Cambridge,Massachusetts; William D. Hall, Charles Stark Draper Laboratory, Cambridge,Massachusetts; and Nicolas Pujet and Eric Feron,-Massachusetts Institute of
. Technology, Cambridge, Massachusetts
Introduction 305Available Data 307Models 5 314Applications 331Conclusions 339References 340
Chapter 21 Conceptual Design of a Departure Planner Decision Aid 343Ioannis Anagnostakis, Husni R. Idris, John-Paul Clarke, Eric Feron, R. John Hansman,
Amedeo R. Odoni, and William D. Hall, Massachusetts Institute of Technology,Cambridge, Massachusetts
Introduction 343Departure Process—Results from Field Observations 344Overview of the Proposed Departure Planner Architecture and
Chapter 25 Analytical Identification of Airport and AirspaceCapacity Constraints 409
William R. Voss, Federal Aviation Administration, Washington, D.C; and JonathanHoffman, MITRE Corporation, McLean, Virginia
Introduction 409Background 410How to Find Airspace Problems 410Definition of an Airspace Problem 412Data Sources 414Results 414Conclusions 419References 419
Dave Knorr, Federal Aviation Administration, Washington, D.C; Joseph Postand Jeff Biros, CNA Corporation, Alexandria, Virginia; and Michelle Blucher,MITRE Corporation, McLean, Virginia
Chapter 29 Accident Risk Assessment for AdvancedAir Traffic Management 463
H. A. P. Blom, G. J. Bakker, P. J. G. Blanker, J. Daams, M. H. C. Everdij,and M. B. Klompstra, National Aerospace Laboratory NLR,Amsterdam, The Netherlands
Introduction 463Accident Risk Assessment Methodology 467Mathematical Framework . 471RNP1 in Conventional and Airborne Separation Assurance
Scenario Examples 474
XV
Concluding Remarks 476References 477
Chapter 30 Human Cognition Modelling in Air TrafficManagement Safety Assessment 481
Henk A. P. Blom, Jasper Daams, and Herman B. Nijhuis, NationalAerospace Laboratory NLR, Amsterdam, The Netherlands
Introduction 481Human Modeling Approaches 483Modeling for En-Route ATC 488Reduction of the ATCo Model 497Example Application 503Concluding Remarks 507References 509
Chapter 31 Probabilistic Wake Vortex Induced AccidentRisk Assessment 513
J. Kos, H. A. P. Blom, L. J. P. Speijker, M. B. Klompstra, and G. J. Bakker,National Aerospace Laboratory NLR, Amsterdam, The Netherlands
Introduction 513Risk Assessment Methodology 514Wake Vortex Risk Assessment 516Single Runway Approach 519Concluding Remarks 524Appendix: Stochastic Wake Vortex Model 525References 530
Chapter 32 Free Flight in a Crowded Airspace? 533J. M. Hoekstra, R. C. J. Ruigrok, and R. N. H. W. van Gent, National Aerospace
Laboratory NLR, Amsterdam, The Netherlands
Introduction 533Free Flight \ 533Air Traffic Growth 534NLR Free Flight Study 534Distrust in Distributed System . . 537(Un)Predictability of a Distributed System 538Complex Geometry Examples 539Robustness and Redundancy of a Distributed System 542Effective Conflict Rate for Air and Ground . 543Conclusions 543References 544
Chapter 33 Managing Criticality of Airborne Separation AssuranceSystems Applications 547
Andrew D. Zeitlin, MITRE Corporation, McLean, Virginia;and Beatrice Bonnemaison, CENA, CS-SI, Toulouse, France
Introduction 547Operational Safety Assessment of ASAS 548
XVI
Operational Environment of ASAS Applications 551Operational Hazards and Mitigating Factors Associated with ASAS 552Operational Hazard Identification 553Allocation of Safety Objectives and Requirements for ASAS
Applications 556ASAS Simulations and Trials 559Conclusions and Future Work 560References 560
Chapter 34 Analysis of Aircraft Separation Minima Usinga Surveillance State Vector Approach 563
Tom G. Reynolds and R. John Hansman, Massachusetts Institute of Technology,Cambridge, Massachusetts
Introduction 563Model of a Separation Assurance Budget 564Need for Surveillance of Intent 566State Vector Modeling Approach 567Intent States I(t) 569State Uncertainty 571Relationships Between State Uncertainty and the Current
Separation Minima 574Conformance Monitoring 577Conclusions 581References 581
Section VII: Cognitive Workload Analysis and the ChangingRole of the Air Traffic Controller
Chapter 35 Passive Final Approach Spacing Tool Human FactorsOperational Assessment 585
Katharine K. Lee, NASA Ames Research Center, Moffett Field, California; andBeverly D. Sanford, Cadence Design Systems, Inc., San Jose, California
Chapter 36 Evaluating Taskload Measures Derived fromRoutinely Recorded Air Traffic Control Data 599
Carol A. Manning, Federal Aviation Administration Civil Aeromedical Institute,Oklahoma City, Oklahoma; Scott H. Mills, SBC Technology Resources, Inc. Austin,Texas; Cynthia M. Fox and Elaine Pfleiderer, Federal Aviation Administration
XVII
Civil Aeromedical Institute, Oklahoma City, Oklahoma; and Henry Mogilka,Federal Aviation Administration Training Academy, Oklahoma City, Oklahoma
James J. Cieplak, Edward Hahn, and Baltazar O. Olmos, MITRE Corporation,McLean, Virginia
Introduction 695Operational Evaluation 1999 696Method of Test 699Results . 703Conclusions 711Selected Bibliography 712
Chapter 43 Conclusions and Observations 713Introduction 713U.S. Air Traffic Management System 713European Air Traffic Management System 714
XIX
Public-Private Nature of Air Transportation 714Safety Is Much Discussed But Little Analyzed 715Air Traffic Controller—Pilot Cognitive Workload Substitution Function . . 716Final Comments 716