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Collaborative En Route Airspace Management Considering Stochastic Demand, Capacity, and Weather Conditions

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Collaborative En Route Airspace Management Considering Stochastic Demand, Capacity, and Weather Conditions Collaborative En Route Airspace Management Considering Stochastic Demand, Capacity, and Weather Conditions by Jeffrey M. Henderson A dissertation presented to the Faculty of Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Civil Engineering Dr. Antonio Trani, Chair Dr. Hojong Baik Dr. Hesham Rakha Dr. Gerardo Flintsch Dr. C. Patrick Koelling March 26, 2008 Blacksburg, Virginia Keywords: Airspace, Thunderstorms, Equity, Air Traffic Control, Simulation Copyright 2008, Jeffrey M. Henderson Collaborative En Route Airspace Management Considering Stochastic Demand, Capacity, and Weather Conditions Jeffrey M. Henderson ABSTRACT The busiest regions of airspace in the U.S. are congested during much of the day from traffic volume, weather, and other airspace restrictions. The projected growth in demand for airspace is expected to worsen this congestion while reducing system efficiency and safety. This dissertation focuses on providing methods to analyze en route airspace congestion during severe convective weather (i.e. thunderstorms) in an effort to provide more efficient aircraft routes in terms of: en route travel time, air traffic controller workload, aircraft collision potential, and equity between airlines and other airspace users. The en route airspace is generally that airspace that aircraft use between the top of climb and top of descent. Existing en route airspace flight planning models have several important limitations. These models do not appropriately consider the uncertainty in airspace demand associated with departure time prediction and en route travel time. Also, airspace capacity is typically assumed to be a static value with no adjustments for weather or other dynamic conditions that impact the air traffic controller. To overcome these limitations a stochastic demand, stochastic capacity, and an incremental assignment method are developed. The stochastic demand model combines the flight departure uncertainty and the en route travel time un- certainty to achieve better estimates for sector demand. This model is shown to reduce the predictive error for en route sector demand by 20% at a 30 minute look-ahead time period. The stochastic capacity model analyzes airspace congestion at a more macroscopic level than available in existing models. This higher level of analysis has the potential to reduce computational time and increase the number of alternative routing schemes considered. The capacity model uses stochastic geometry techniques to develop predictions of the distribution of flight separation and conflict potential. A prediction of dynamic airspace capacity is calculated based on separation and conflict potential. The stochastic demand and capacity models are integrated into a graph theoretic frame- work to generate alternative routing schemes. Validation of the overall integrated model is performed using the fast time airspace simulator RAMS. The original flight plans, the routing obtained from an integer programming method, and the routing obtained from the incremental method developed in this dissertation are compared. Results of this validation simulation indicate that integer programming and incremental routing methods are both able to reduce the average en route travel time per flight by 6 minutes. Other benefits in- clude a reduction in the number of conflict resolutions and weather avoidance maneuvers issued by en route air traffic controllers. The simulation results do not indicate a significant difference in quality between the incremental and integer programming methods of routing flights around severe weather. iii ACKNOWLEDGEMENTS I would first and foremost like to thank Liping Fu for his ongoing encouragement to study at the doctoral level. As my Masters advisor he provided me with the positive outlook that gave me confidence in my abilities. Without him I would not have continued my studies. Antonio Trani continued this positive attitude which made my studies very enjoyable at Virginia Tech. He is a wonderful influence on all young researchers in the way he approaches his work. I will always remember the quote that he considers every day at Virginia Tech a holiday. I would also like to thank all of the members of the Air Transportation Systems Lab under the guidance of Dr. Trani. They were all instrumental in improving my knowledge of air transportation. More than that they provided friendship and support that I needed to complete my work. I must acknowledge all of the financial support that I received while studying at Virginia Tech. I thank the Virginia Tech College of Engineering, NASA, and the FAA for their generous support of research. Lastly, I would like to thank my family for all the love and emotional support that I received during my graduate studies. iv DEDICATION To Kyle. v TABLE OF CONTENTS 1 INTRODUCTION 1 1.1 Problem Statement . .4 1.2 Objective . .4 1.3 Contribution . .4 1.4 Organization of Document . .5 2 LITERATURE REVIEW 7 2.1 National Severe Weather Playbook . .7 2.2 Collaborative Decision-Making and Ground Delay Program Enhancements . 10 2.3 Free Flight . 14 2.4 Severe Weather . 15 2.5 Stochastic Elements of National Airspace System . 21 2.6 Conflict Detection and Resolution . 22 2.7 National Airspace System (NAS) . 26 2.8 Airspace Sector Occupancy Model (AOM) . 27 2.9 Airspace Planning and Collaborative Decision-Making Model (APCDM) . 28 2.10 Human Factors and Sector Capacity . 34 2.11 Center TRACON Automation System (CTAS) . 38 3 EXISTING APPROACH VALIDATION 44 3.1 Scenario Selection . 44 3.2 Data . 46 3.3 Trajectory Synthesis . 48 3.4 Flight Surrogate Generation . 49 3.5 Results and Discussion . 49 4 MODEL FRAMEWORK 54 5 STOCHASTIC AIRSPACE DEMAND 56 vi 5.1 Departure Uncertainty . 56 5.2 Sector Traversal Time Variation . 63 5.3 Sector Hit Rate . 67 5.4 Probabilistic Sector Demand . 67 5.5 Performance of Probabilistic Sector Demand Model . 72 6 STOCHASTIC AIRSPACE CAPACITY 75 6.1 Probabilistic Weather Forecasts . 75 6.2 Separation Distribution Prediction . 79 6.3 Expected Number of Conflict Resolution Actions . 83 6.4 Controller Workload, Dynamic Density, and Sector Capacity . 89 6.5 Second Order Congestion Effects . 91 7 ROUTING 93 7.1 Flow Constrained Area . 93 7.2 Collaborative Decision-Making . 95 7.3 Flight Grouping . 96 7.4 Graph Theoretic Network Generation . 98 7.5 Incremental Assignment . 102 7.6 k-Shortest Paths and Integer Programming (APCDM) . 102 8 SIMULATION AND VALIDATION 105 8.1 Airspace Planning in Java (AirPlanJ) . 105 8.2 Weather Capacity Restriction Scenario . 105 8.3 RAMS Simulation Tool to Compare Routing Methods . 108 8.4 Simulation Results . 109 8.5 Equity Considerations . 112 9 CONCLUSIONS 115 10 RECOMMENDATIONS 118 11 REFERENCES 120 vii APPENDIX A: ETMS DATA DESCRIPTION 130 APPENDIX B: DEMAND ANALYSIS SCRIPTS AND DATA 135 APPENDIX C: CAPACITY ANALYSIS SCRIPTS AND DATA 200 APPENDIX D: DESCRIPTION OF AirPlanJ INPUT AND OUTPUT DATA 247 APPENDIX E: AirPlanJ SOURCE CODE 256 APPENDIX F: AirPlanJ MANUAL 749 viii LIST OF TABLES 1 En Route Traffic Growth Projection (Data Source: FAA (2007)). .2 2 Sample Routing Procedure. .9 3 Impact of Thunderstorms on Aviation. 16 4 Forecast Observation Comparison Convention is RTVS. 19 5 RTVS Output Statistics. 19 6 Sources of En Route Demand Prediction Error. Influence Defined in Krozel et al. (2002). 23 7 Air Traffic Control Load Variables (Arad, 1964). 35 8 Top-Level Controller Activities (Rodgers and Drechsler, 1993). 36 9 Sector Tactical (R-side) and Planning (D-side) Controller Responsibilities. 37 10 Monitor Alert Parameter (MAP). 37 11 FAA Weather Days. 45 12 En Route Simulation Analysis Scenarios. 45 13 ETMS Messages used for Simulation Analysis. 46 14 Candidate Alternative Trajectories (Surrogates) for APCDM. 49 15 Default Parameters used for APCDM Calculations. 50 16 Study Analysis Days. 57 17 Departure Time Definitions. 57 18 Regression Coefficients for Mean and Variance of Departure Time Prediction Error at a 30 Minute Look-Ahead Time. 59 19 Bandwidth Estimation Results for Kernel Smoothing. 64 21 Comparison of Deterministic and Probabilistic Methods. 73 22 Fitted Weibull Parameters for Distributions of Bias and Centroid-to-Centroid Distance. 77 23 Performance of CCFP Forecasted Convection Tops. 78 24 Correction Factors for Washington (ZDC) Sectors Obtained by Processing Flight Radar Track Data on August 29, 2005. 84 ix 25 Correction Factors for Washington (ZDC) Sectors Obtained by Processing Flight Radar Track Data on July 27, 2005 and Compared to Correction Factors Obtained for August 29, 2005. 85 27 Average Time per Conflict derived from Simulating Flight Plans from June 11, 2004. 88 30 Properties Associated with Node Elements. 100 31 Properties Associated with Link Elements. 101 32 En Route Delay by Airline. 114 x LIST OF FIGURES 1 Commercial Aviation Demand Forecast (Data Source: FAA (2007)). Includes both domestic and international passengers. .1 2 Sample Playbook Entry . .8 3 Airspace Flow Program Iterative Process. 10 4 Thunderstorm Classifications. 15 5 Sample Collaborative Convection Forecast Log. 18 6 Sample RTVS Time Series Output for NCWF. 20 7 Conflict Resolution Maneuvers. 24 8 Trajectory Prediction for Conflict Detection. 25 9 NEXRAD Reflectivity June 11, 2006 13:00 UTC (NOAA Research, 2006). 46 10 Overview of RAMS Simulation Data Preparation and Comparison Process. 47 11 Restriction Zone Approximation of Pilot Avoidance of Severe Weather. 48 12 Alternative Trajectory (Surrogate) based on Restriction. 50 13 APCDM vs. Playbook (Historical) Delay. 51 14 APCDM vs. Playbook (Historical) Conflict Resolution Actions. 52 15 APCDM vs. Playbook (Historical) 90th Percentile Peak Flights Monitored per Sector. 52 16 APCDM vs. Playbook (Historical) 99th Percentile Peak Flights Monitored per Sector. 53 17 Proposed Capacity Restriction Response Framework. 54 18 Box-and-Whisker Diagram for Departure Time Prediction Errors at 30 Minute Look-Ahead Times by Departing Airport Type. 61 19 Tree Diagram for Clustering. 61 20 Box-and-Whisker Diagram for Departure Time Prediction Errors at 30 Minute Look-Ahead Times by Cluster. 62 xi 21 Histogram of Departure Time Prediction Errors at a 30 Minute Look-Ahead Time Frame for Large Hub Airports for 30 days in 2000-2005.
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