• Abstract and Figures
• Public Full-text

OPTIMIZATION AND ANALYTICS FOR AIR TRAFFIC MANAGEMENT ADISSERTATION SUBMITTED TO THE DEPARTMENT OF MANAGEMENT SCIENCE AND ENGINEERING AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Michael Jacob Bloem May 2015 © 2015 by Michael Jacob Bloem. All Rights Reserved. Re-distributed by Stanford University under license with the author. This work is licensed under a Creative Commons Attribution- Noncommercial 3.0 United States License. http://creativecommons.org/licenses/by-nc/3.0/us/ This dissertation is online at: http://purl.stanford.edu/jh561fd9930 ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Nicholas Bambos, Primary Adviser I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Juan Alonso I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Yinyu Ye Approved for the Stanford University Committee on Graduate Studies. Patricia J. Gumport, Vice Provost for Graduate Education This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file in University Archives. iii Abstract The air traffic management system is important to the United States’economyand way of life. Furthermore, it is complex and largely controlled by human decision mak- ers. We studied and learned from these expert decision makers to facilitate the transi- tion to an increasingly autonomous air traffic management system that leverages the strengths of both computer systems and humans to provide greater value to stake- holders. In particular, we constructed decision models and corresponding solution algorithms that enable decision-support tool development. Our approach to building the decision models and algorithms leveraged expert input and feedback, operational decision data analytics, fast-time simulations, and human-in-the-loop simulations. We utilized and extended techniques from optimization, dynamic programming, and machine learning both for developing solution algorithms and for making inferences about decisions based on operational data. In this dissertation we discuss our research on three types of decisions in the air traffic management system. The first is faced by supervisors of air traffic controllers: how to configure available airspace, controllers, and other resources to ensure safe and efficient operations in a region of airspace over a period of time. We describe a prescriptive decision model and solution algorithm that returns multiple good and distinct configuration schedule advisories. The second type of decision is faced by airlines: how to assign a set of flights to a set of slots in an Airspace FlowProgram.We developed a novel heuristic that finds delay cost model noise parameters to maximize an approximation of the likelihood of operational data describing how airlines have done this historically. We thereby provide new insights into models of the cost of delay to an airline, which are fundamental to air traffic flow management research aimed at iv decision-support tool development. The third type of decision is faced by air traffic flow managers: when to implement a Ground Delay Program. We use operational data to build two types of model of the implementation of Ground DelayPrograms. The descriptive models we developed can be used to predict Ground Delay Program implementation, which may be of value in decision-support tools for stakeholders such as airlines. They also provide insights into current practice that could motivate the development of tools to support the traffic flow managers who decide when to implement Ground Delay Programs. v Acknowledgements In 1 Corinthians 4:7, the apostle Paul asks rhetorically “What do you have that you did not receive?” The answer of course is “nothing,” and so I have much and many to acknowledge. First and foremost, I am grateful to God, who through the life, death, and resurrection of Jesus Christ has graciously rescued me in the most important way and given my life meaning (Ephesians 2:8–10). My work too has profound purpose because of him (Genesis 1:26–28, 1 Corinthians 10:31, and Colossians 3:23–24), and while work can be discouraging (especially research), he has often enabled me to rejoice in the “toil” he has given me (Ecclesiastes 5:18–19). God has used many people to facilitate the work recorded here. Prof. Nick Bambos took a chance when he agreed to be my advisor. I have been an unconventional part- time PhD student and Nick had not previously studied air traffic management. In spite of the risks and challenges associated with advising me, Nick agreed to do so. I’m grateful for his sensitivity to my situation as an employee at NASA,andmore recently also as a father. The enthusiasm and curiosity he brings to our research has been invigorating. It has been a pleasure to learn from him these pastfewyears. My friend and former colleague Tansu Alpcan introduced me to Nick. This was not the first time that Tansu provided me with significant assistance in my academic and professional endeavors. I am grateful to other faculty members and students at Stanford for their instruc- tion, support, and encouragement. The sequence of courses on convex optimization taught by Prof. Steven Boyd were an incredible start to my training at Stanford, and I’m thankful to Eric Meuller and Stephen Schuet for helping me survive these. Prof. Ben Van Roy’s course on approximate dynamic programming, Prof. Andrew vi Ng’s course on machine learning, and Prof. Ramesh Johari’s course ongametheory were particularly formative. My collaboration with David Hattaway on our machine learning course project injected much-needed sophistication into how I handle data and write software. I doubt I would have passed the qualifying examination without the instruction and accountability provided by Jing Ma and Diana Negoescu. Cur- rent and former students in my research group, including Praveen Bammannavar, Jeff Mounzer, Lawrence Chow, Martin Valdez-Vivas, Neal Master, Kevin Schubert, and Zhengyuan Zhou, have provided helpful feedback, encouragement, and inspiration during my PhD studies. More recently, I’ve benefited from collaborations with Jon Cox and Prof. Mykel Kochenderfer in the Department of Aeronautics and Astronau- tics. Profs. Yinyu Ye and Juan Alonso have graciously provided helpful feedback on my research as part of my PhD reading committee. Many of my colleagues at NASA have been encouraging and flexible as I’ve pursued my PhD studies. The unwavering support of my supervisors over thecourseofseven years of study has made this research possible. These supervisors include Dr. Robert Windhorst, William Chan, Dr. JeffSchoeder, Tom Davis, Kathy Lee, andSandy Lozito. NASA Ames Research Center Director of Aeronautics Dr. Tom Edwards even agreed to be part of my oral defense committee, and provided fantastic feedback and guidance in that capacity. I’m especially thankful to my colleague Dr. Banavar Sridhar for initiating the process that led to my employment at NASA, as well asforyears of mentoring. Collaborators and project leaders at NASA have provided essential technical guidance. These include Dr. Heather Arneson, Haiyun Huang, Dr. Karl Bilimoria, Dr. Michael Drew, Chok Fung (Jack) Lai, Greg Wong, Dr. Pramod Gupta, Dr. Avijit Mukherjee, Dr. Kapil Sheth, Dr. Shon Grabbe, Dr. Paul Lee, Dr. Laurel Stell, and Dr. Deepak Kulkarni. I’m also grateful to Robie Remple for his meticulous review of this dissertation, which led to many corrections and improvements in the grammar and style of the document. I received valuable guidance and feedback on this research from several of the experts who make the air traffic management system work, or who did so for many years before they retired. Mark Evans and Brian Holguin answered many questions about airspace, operating position, and workstation configurations. I would also like vii to thank several individuals at Cleveland Air Route Traffic Control Center for pro- viding valuable input and feedback regarding my research. These individuals include but are not limited to Mark Madden, Brian Hanlon, Kevin Shelar, Al Mahilo, Tom Roherty, Connie Atlagovich, Bill Hikade, Steve Herbruck, Martin Mielke, Don Lam- oreaux, Rick Buentello, Dale Juhl, Todd Wargo, Mike Klupenger, Mark McCurdy, and Stephen Hughes. Raphell Taylor, Miguel Anaya, Wayne Bridges, and Bill Pre- ston, all current or former employees of Oakland Air Route Traffic Control Center, also provided useful input and feedback. David Hattaway put me in touch with Cindy Hood, Supervisory Traffic Management Coordinator at New York TRACON, who I thank for valuable insights into current Ground Delay Program decision making. Michael Brennan provided me with helpful information about airline slot utilization and related data. I would certainly not arrived at this point in my studies without the support, love, and prayers of my family and friends. My wife Sarah has been a constant reminder and demonstration of God’s permanent love for me, which thankfully does not depend on my academic accomplishments or good works in general. Although I too often take her for granted, I am grateful for her patient support through the ups and downs of graduate studies. I’m also grateful for the joyful gift of our children Andrew and Joanna (and baby #3 due in June 2015); they have provided a delightful and challenging purpose outside of research. My parents have invested tremendous toil and treasure in me and my education, and I’m confident that God has worked through their faithful prayers. Many friends and neighbors have supported and cared for me and my family these past few years.

Load more

  187

Copy Link