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

Introduction 49Background 49European Challenge . 50Other Limitations on Growth 57Conclusions 58

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

Airline Operational Aspects 162Airport Operational Aspects -. . 165Information Gaps 166Issues Outstanding 173Conclusions 175References 175

Chapter 12 Air Traffic Control/Air Carrier CollaborativeArrival Planning 177

Cheryl Quinn, NASA Ames Research Center, Moffett Field, California; andRichard E. Zelenka, Logicon/Sterling Federal Systems, Herndon, Virginia

Introduction 177Current Research: ATC/Air Carrier Information Exchange 180Future Research 185Human Factors Issues Associated with ATC-Airline

Collaborative Tools 187Conclusions 187References 188

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

Introduction 253Priority-Scheduling 254Scope 255Fast-Time Simulation 255Order Deviation 261Simulation Inputs/Outputs 262Results and Discussion 263Conclusions 266References 267

xii

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

Introduction 290Mathematical Formulation 292Numerical Examples 295Conclusions 302References 303

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

Operational Context 347Conclusions 364References 365

XIII

Chapter 22 Modeling Air Traffic Management Automation MeteringConformance Benefits 367

Tara J. Weidner, Seagull Technology, Inc., Los Gatos, California; and Steve Green,NASA Ames Research Center, Moffett Field, California

Introduction 367ATM Interruptions Model 368Illustrative Application 375Conclusions 381References 382

Section V: Airspace Operations and Constraints

Chapter 23 Effect of Direct Routing on Air TrafficControl Capacity 385

S. A. N. Magill, Defence Evaluation and Research Agency, Malvern,Worcestershire, United Kingdom

Introduction 385Workload and Capacity 386Simulation 387Results 389Discussion 394Concluding Remarks 395References : 396

Chapter 24 Performance Measures for Future Architecture 397Steve Bradford, Dave Knorr, and Diana Liang, Federal Aviation Administration,

Washington, D.C.

Introduction 397Architecture 398Metrics 398Architecture and Performance : . . . . 399Analysis 401Conclusions 407

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

XIV

Chapter 26 Operational Assessment of Free Flight Phase 1Air Traffic Management Capabilities 421

Dave Knorr, Federal Aviation Administration, Washington, D.C; Joseph Postand Jeff Biros, CNA Corporation, Alexandria, Virginia; and Michelle Blucher,MITRE Corporation, McLean, Virginia

Introduction 421System Description 422Collaborative Approach 424Metrics Definitions 425Measurement Process 429Preliminary pFAST Results 430Conclusions 434References 435

Chapter 27 CENA-PHARE Experiment: Requirements forEvaluation of Novel Concepts in Air Traffic Control 437

Didier Pavet, CENA, Athis-Mons, France

Introduction 437Evaluation Methodology 439Lessons Learned 441Discussion: Requirements for Future Developments of Novel Concepts 445Conclusions 446References 447

Chapter 28 Restriction Relaxation Experiments Enabled byUser Request Evaluation Tool 449

Michael J. Burski, Federal Aviation Administration, Washington, DC;and Joseph Celio, MITRE Corporation, McLean, Virginia

Introduction 449URET Utilization 450URET Benefits , • . • . 451Conclusions : . , . . . . 460References 460

Section VI: Safety and Free Flight

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

Introduction 585Methods , 586Results 589Lessons Learned . 593Concluding Remarks 596References 597

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

Introduction 599Defining Controller Workload, Taskload, Sector Complexity,

and Performance 600Purpose of Study 603Method 603Results 606Conclusions 609References 613

Chapter 37 Controller Roles—Time to Change 615Robert Graham, Alan Marsden, Isabelle Pichancourt, and Franck Dowling,

EUROCONTROL Experimental Centre, Bretigny-sur-Orge, France

Introduction 615Controller Tools and Transition Trials—C3T 616C3T Study Concepts 617Model-Based Study—Hypothesis 618Model Based Study Scenarios 618RAMS Model 620Model-Based Study Preliminary Results : 620Real-Time Simulation Hypotheses 622Real-tTiime Simulation—Preliminary Results 623Conclusions 625References 626

Chapter 38 Trajectory Orientation: Technology-Enabled ConceptRequiring Shift in Controller Roles and Responsibilities 627

Kenneth J. Leiden, Micro Analysis and Design, Boulder, Colorado;and Steven M. Green, NASA Ames Research Center, Moffett Field, California

Introduction 627Background '..... 628Trajectory Orientation Concept 629Research Approach 632Results and Discussion 633Conclusions 644References 644

Section VIII: Emerging Issues in Aircraft Self-Separation

Chapter 39 Cooperative Optimal Airborne Separation Assurance in FreeFlight Airspace 649

Colin Goodchild, Miguel A. Vilaplana, and Stefano Elefante, Universityof Glasgow, Glasgow, United Kingdom

Introduction 649Operational Methodology 651

XVIII

Planning Algorithm 652Computed Example 655Conclusions 660References 662

Chapter 40 Operational Efficiency of Maneuver Coordination Rulesfor Airborne Separation Assurance System 665

R. Schild and J. K. Kuchar, Massachusetts Institute of Technology, Cambridge,Massachusetts

Introduction 665Evaluation of Rule Systems 666Rule Design 667Rule Evaluation Criteria 668Rule Evaluation 670Human Factors and Rules 674Conclusions 675References 676

Chapter 41 Probabilistic Approaches TowardConflict Prediction 677

G. J. Bakker, H. J. Kremer, and H. A. P. Blom, National Aerospace LaboratoryNLR, Amsterdam, The Netherlands

Introduction 677Conflict Prediction Approaches 678Collision Risk Modeling 680Comparison of Approaches 681Discussion of Results 689Conclusions >. 692References 693

Chapter 42 Safe Flight 21:1999 Operational Evaluation of AutomaticDependent Surveillance Broadcast Applications . . . . . . 695

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

Index 717