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Demand Management at Congested Airports: How Far Are We from Utopia?

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Demand Management at Congested Airports: How Far Are We from Utopia? DEMAND MANAGEMENT AT CONGESTED AIRPORTS: HOW FAR ARE WE FROM UTOPIA? by Loan Thanh Le A Dissertation Submitted to the Graduate Faculty of George Mason University in Partial Fulfillment of the the Requirements for the Degree of Doctor of Philosophy Systems Engineering and Operations Research Committee: George L. Donohue, Dissertation Director Chun-Hung Chen, Dissertation Co-Director Karla Hoffman, Committee Chair Jana Kosecka Daniel Menasc´e,Associate Dean for Research and Graduate Studies Lloyd J. Griffiths, Dean, The Volgenau School of Information Technology and Engineering Date: Summer Semester 2006 George Mason University Fairfax, VA DEMAND MANAGEMENT AT CONGESTED AIRPORTS: HOW FAR ARE WE FROM UTOPIA? A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at George Mason University By Loan Thanh Le Bachelor of Science University of Natural Sciences, Ho Chi Minh City, Vietnam, 1998 Master of Science University of Paris I-Pantheon-Sorbonne, Paris, France, 1999 Director: George L. Donohue, Professor Co-Director: Chun-Hung Chen, Associate Professor Department of Systems Engineering and Operations Research Summer Semester 2006 George Mason University Fairfax, VA ii Copyright c 2006 by Loan Thanh Le All Rights Reserved iii Acknowledgments Early 2002, professor George L. Donohue gave me this invaluable opportunity of pur- suing a Ph.D. degree in Air Transportation, and I began my quest in the Department of Systems Engineering and Operations Research at George Mason University. With- out his trust in my capability, none of this would have happened. Over the years, I have learned so many things, accomplished a few things, and met people who have been genuine professors, colleagues and friends. I would like to thank all of them who made this experience possible and so enjoyable. I have had the privilege of working with Professor George Donohue, my research advisor, mentor, and role model, to whom I owe deep gratitude for many things. Dr. Donohue introduced me to the wonderful world of air transportation. His broad knowledge and outstanding vision in the aviation system guided me throughout the journey. Dr. Donohue has high expectations of his students, and I thank him for challenging me to carry through with the research. Beyond his academic virtues, I am also grateful for many discussions with him that teach me the values of integrity and tolerance. I look forward to working with Dr. Donohue in the future. In the same manner, Dr. Chun-Hung Chen, my research advisor, exerted a strong influence on me in daily research process. Not only did Dr. Chen convey to me invaluable knowledge in discrete event simulation, he also made sure that my research was on the right track. Dr. Chen demonstrated how to be a good researcher and a good mentor by his academic rigorousness, diligence, and understanding towards his students. My sincere gratitude goes to Dr. Karla Hoffman, my committee chair, who taught me invaluable knowledge in optimization theory, and difficult but fascinating prob- lems of the airline industry. Dr. Hoffman’s work ethics and professional qualities have always been a great source of inspiration for me, and will stay as such in my future endeavors. She also kindly helped revise this dissertation with great care and attention. I am deeply grateful for her time and efforts. Without her help, this dissertation could not have been written as it is. It is a pleasure for me to have Dr. Jana Kosecka in my committee. I would like to express my thanks for her suggestions and warm encouragements throughout the completion of this dissertation. I am also very grateful to Dr. John Shortle, Dr. Lance Sherry, Dr. Donald Gross, and Dr. Alexander Klein for their thoughtful comments and advice about my research. Their insights were always very helpful. I also would like to thank my colleagues at Center for Air Transportation System Research, Arash Yousefi, Richard Xie, Danyi Wang, Bengi Menzhep, Babak Ghalebsaz, Ning Xu, and Jianfeng Wang, for enriching discussions regarding my research, and their warm iv friendship. Many thanks to Angel Manzo and Alerie Karen who were exceptionally helpful in taking care of all my paperwork throughput the program. Last but not least, I deeply appreciate the distant support of my parents. Their self-giving love and constant encouragement stand by me in my pursuit of the doctor- ate. I also would like to thank my relatives in Virginia for sharing with me so many relaxing and comforting moments. Finally, I thank Michael C. Ahlers for all of his computer technical help, for the extra RAM he gave me to help boost my laptop’s speed, and for always being there for me. I can not express enough my thanks to all the people who have helped make this experience possible and memorable! v Table of Contents Page Abstract . xiii 1 Introduction and Problem Statement . 1 1.1 Airport congestion and congestion management measures . 2 1.1.1 Runway and airport expansion . 3 1.1.2 Improvement of technology . 5 1.1.3 Demand management . 6 1.2 Congestion management by demand management in the US . 7 1.3 Motivation . 12 1.4 Statement of the problem . 15 1.5 Contributions of this dissertation . 17 1.5.1 Primary hypothesis . 17 1.5.2 Research scope . 18 1.5.3 Contributions . 19 1.6 The potential readers . 21 1.7 Dissertation outline . 21 2 Literature Review of Prior Research . 23 2.1 Congestion Management by Demand Management Measures . 23 2.1.1 Administrative options . 24 2.1.2 Market-based options . 27 2.1.3 Hybrid options . 37 2.1.4 Summary . 37 2.2 Route development, flight scheduling and fleet assignment models . 40 2.3 Delay and cancellation estimation models . 43 2.3.1 Analytical models . 43 2.3.2 Simulation models . 47 3 The current slot allocation rules aggravate the congestion problem . 51 4 Scheduling Models . 54 vi 4.1 General approach . 54 4.2 Profit-maximizing airline scheduling sub-models . 56 4.2.1 The timeline network . 57 4.2.2 Interaction of demand and supply through price . 59 4.2.3 Piecewise approximation of non-linear revenue functions . 60 4.2.4 Nesting revenue functions . 62 4.2.5 Assumptions . 64 4.2.6 Formulation . 65 4.3 Airport’s allocation problem . 67 4.4 Solution method . 70 4.5 Implementation details . 72 5 Parameter estimation for scheduling models . 74 5.1 Timeline networks . 74 5.1.1 Arcs and arc lengths . 75 5.1.2 Arc costs . 77 5.2 Nonlinear revenue functions and piecewise linear approximation . 80 5.2.1 Assumptions . 80 5.2.2 Processing segment fares . 82 5.2.3 Extrapolating the 10% ticket sample . 83 5.2.4 Breaking down data from by-quarter-of-the-year to daily and by-time-of-day . 85 5.3 Model validation: Unconstrained profit maximizing schedules . 87 5.3.1 Flight schedules by time of day . 88 5.3.2 Supply and price . 89 5.3.3 Flight frequencies and fleet mix . 89 6 A Stochastic Queuing Network Simulation Model for Evaluating Schedule Delays and Cancellations . 96 6.1 Stochastic queuing network simulation model . 97 6.1.1 Modeling objectives . 97 6.1.2 Queuing network model . 97 6.1.3 Runway capacity submodel . 100 6.1.4 Delay propagation submodel . 102 6.1.5 Cancellation and cancellation propagation submodel . 103 vii 6.2 Parameter estimation . 105 6.2.1 Gate-out delay distributions . 106 6.2.2 Taxi time distributions . 106 6.2.3 En route time distributions . 106 6.2.4 Cancellation and cancellation propagation . 107 6.3 Model calibration and application . 110 6.3.1 Estimating delays and cancellations of alternative schedules . 110 6.3.2 Assessing impacts of changes in separation standards on airport capacity and delay . 113 6.3.3 Assessing impacts of changes in fleet mix on delay estimates . 115 7 Demand Management at LaGuardia Airport: How Fare Are We From Utopia?117 7.1 Assumptions and parameters . 117 7.2 Baseline statistics . 119 7.3 Investigated scenarios . 121 7.4 Profit maximizing . 123 7.5 Seat throughput maximizing . 127 7.6 Compromise scenarios . 130 7.6.1 Seat-maximizing within 90% profit optimal . 132 7.6.2 Seat-maximizing within 80% profit optimal . 139 7.7 Discussion . 144 7.7.1 Research questions and answers . 145 8 Conclusion and Future Work . 148 8.1 Contributions . 150 8.2 Recommendations for future work . 152 Bibliography . 154 A Appendix A: Airport Codes, Locations and Names . 161 B Appendix B: Problem formulations for ORD-LGA market in MPL . 164 C Appendix C: Implementation of solution algorithm (column generation) in C/Cplex Concert Technology API . 172 D Appendix D: Price elasticities estimates for several key markets . 218 viii List of Tables Table Page 1.1 New runways, runway extensions, and reconfigurations included in the OEP [1] . 4 1.2 Runways, Runway Extensions, Reconfigurations or New Airports with Environmental Impact Statements (EISs) or Planning Studies Under- way[1]................................... 5 2.1 Review of demand management measures . 38 5.1 Aircraft types and seating capacities categorized to fleets . 76 5.2 Hourly costs for each fleet of 25-seat increment . 79 5.3 Example of demand extrapolation . 83 6.1 Wake Vortex Separation Standards (nmiles/seconds) [2] . 101 6.2 Example of delay propagation (unit: minute) . 103 7.1 Daily average statistics of 67 markets in study, and overall statistics (Source: ASPM Q2, 2005) . 119 7.2 Scenarios investigated . 123 7.3 Daily statistics of profit-maximizing scenarios (* queuing delay esti- mates do not include international, non-daily and non-schedule opera- tions) . 125 7.4 Daily average statistics of fall-off markets in profit-maximizing scenario at different runway capacity levels, Source: ASPM Q2, 2005.
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