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Evaluating Network Analysis and Agent Based Modeling for Investigating the Stability of Commercial Air Carrier Schedules

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Evaluating Network Analysis and Agent Based Modeling for Investigating the Stability of Commercial Air Carrier Schedules Old Dominion University ODU Digital Commons Engineering Management & Systems Engineering Management & Systems Engineering Theses & Dissertations Engineering Spring 2012 Evaluating Network Analysis and Agent Based Modeling for Investigating the Stability of Commercial Air Carrier Schedules Sheila Ruth Conway Old Dominion University Follow this and additional works at: https://digitalcommons.odu.edu/emse_etds Part of the Aerospace Engineering Commons, Systems Engineering Commons, and the Transportation Commons Recommended Citation Conway, Sheila R.. "Evaluating Network Analysis and Agent Based Modeling for Investigating the Stability of Commercial Air Carrier Schedules" (2012). Doctor of Philosophy (PhD), Dissertation, Engineering Management & Systems Engineering, Old Dominion University, DOI: 10.25777/fnr1-7j24 https://digitalcommons.odu.edu/emse_etds/58 This Dissertation is brought to you for free and open access by the Engineering Management & Systems Engineering at ODU Digital Commons. It has been accepted for inclusion in Engineering Management & Systems Engineering Theses & Dissertations by an authorized administrator of ODU Digital Commons. For more information, please contact [email protected]. EVALUATING NETWORK ANALYSIS AND AGENT BASED MODELING FOR INVESTIGATING THE STABILITY OF COMMERCIAL AIR CARRIER SCHEDULES Sheila Ruth Conway B.S. May 1989, Massachusetts Institute of Technology M.S. May 1989, Massachusetts Institute of Technology A Dissertation Submitted to the Faculty of Old Dominion University in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY ENGINEERING MANAGEMENT OLD DOMINION UNIVERSITY May 2012 Approved by: Dr. Charles Keat :or Dr. Resit Unal, Member Dr. Ghaith Rabadi, Member Dr. J, MemberA ABSTRACT EVALUATING NETWORK ANALYSIS AND AGENT BASED MODELING FOR INVESTIGATING THE STABILITY OF COMMERCIAL AIR CARRIER SCHEDULES Sheila R. Conway Old Dominion University, 2012 Director: Dr. Charles Keating For a number of years, the United States Federal Government has been formulating the Next Generation Air Transportation System plans for National Airspace System improvement. These improvements attempt to address air transportation holistically, but often address individual improvements in one arena such as ground or in-flight equipment. In fact, air transportation system designers have had only limited success using traditional Operations Research and parametric modeling approaches in their analyses of innovative operations. They need a systemic methodology for modeling of safety-critical infrastructure that is comprehensive, objective, and sufficiendy concrete, yet simple enough to be deployed with reasonable investment. The methodology must also be amenable to quantitative analysis so issues of system safety and stability can be rigorously addressed. The literature suggests that both agent-based models and network analysis techniques may be useful for complex system development and analysis. The purpose of this research is to evaluate these two techniques as applied to analysis of commercial air carrier schedule (route) stability in daily operations, an important component of air transportation. Airline­ like routing strategies are used to educe essential elements of applying the method. Two main models are developed, one investigating the network properties of the route structure, the other an Agent-based approach. The two methods are used to predict system properties at a macro-level. These findings are compared to observed route network performance measured by adherence to a schedule to provide validation of the results. Those interested in complex system modeling are provided some indication as to when either or both of the techniques would be applicable. For aviation policy makers, the results point to a toolset capable of providing insight into the system behavior during the formative phases of development and transformation with relatively low investment. Both Agent- Based Modeling and Network Analysis were found to be useful in this context, particularly when applied with an eye towards the system context, and concentrated effort on capturing the salient features of the system of interest. iv ACKNOWLEDGEMENTS As are many engineering endeavors, the creation of this dissertation began as a solution to a problem; one for which I am most grateful. While working at NASA, I had the pleasure to work with Dr. Bruce Holmes. Unlike so many in our field of Air Transportation System research, Bruce had the ability to take a broad, system-wide view. His interest was in true system modernization, or in his terms, "transformation", as he saw the need for a fundamental change in air traffic management and air transportation as a whole. Not a small feat for a massive, safety-critical system. However, it seemed that the complex system network theory Bruce thought would help describe and control the future system may not suffice. With this inspiration, I was determined to help derive useful tools for such transformative work. Bruce, you are no doubt my muse for this study, and for this and all your thoughts and wisdom you have shared on system transformation, complexity and other topics over the years, I thank you. This pursuit would not have begun without my supervisor at NASA, Dr. Kelli Wilshire, who provided encouragement and also endorsed financial support and education leave. There were many others at NASA who helped formulate my thinking in complex systems and modeling. Many of us self-organized a Langley Networkers Working Group to explore such notions, which largely prepared me to tackle such a messy problem. Additionally, Dr. Jeremiah Creedon, then the Langley Center Director, challenged me to create a "little gold making machine" for ATM, demonstrating a small but scalable transformed system and its value. That assignment helped me understand the worth of an appropriate exploratory platform, and the fact that one didn't really yet exist. Thanks all. V I am grateful to The Boeing Company for their generous financial sponsorship. I'd also like to thank my Manager John Olsen who has gently, but consistently, asked for progress reports, and didn't let me lose sight of the value of finishing. In fact, over the years, many people have encouraged me to complete the project and help move me off "top-dead-center" when I seemed to be stalled: The first real push was from the other graduate students and faculty at the Santa Fe Institute Computational Social Science Modeling and Complexity Workshop. I learned so much there; particularly to focus on capturing the essence of the system in the modeling, and that an old programmer can learn new tricks. More recendy, an instrumental shove came from Chuck Adler and other colleagues at Boeing who wouldn't let me give up when time was short, and much support and assistance from Maria Consiglio. I can't sufficiendy express my gratitude towards my committee. Fitting with the overall graduate experience in the EMSE Department at Old Dominion, they have been nothing but supportive, not to mention extremely patient. I have learned a lot from this undertaking, and value this wonderful opportunity to return to school with a real purpose. I would particularly like to thank Dr. Charles Keating. Chuck not only co-chaired this dissertation, but also made remote completion of this program possible. Finally, throughout my education I've been fortunate to have had incredible backing: my parents have always been so encouraging, and for that I will always be indebted. And thankfully my husband Charlie and daughter Anna have been sympathetic and willing to put up with many long nights, not to mention having been a most vocal cheering squad. So nice to have such a wonderful support network right in your own home. With much love. NOMENCLATURE Alpha, a Network Tolerance Parameter: Capacity/Demand ABM Agent Based Modeling ACES Airspace Concept Evaluation System ADS-B Automatic Dependent Surveillance - Broadcast AGNA Applied Graph & Network Analysis (software) ANTs Active Nonlinear Tests of Complex Simulation Models ATC Air Traffic Control ATCSCC Air Traffic Control System Command Center ATM Air Traffic Management ATS Air Transportation System ATSP Air Traffic Service Provider BA Barabasi-Albert small world network C,C(p) Clustering Coefficient, and average for the network CAASD Center for Advanced Aviation System Development (@ MITRE) CDM Collaborative Decision Making, an FAA Program CRAF Civil Reserve Air Fleet DFW Dallas-Ft Worth Airport DOD Department of Defense DOT Department of Transportation EGlob, ELoc Network Efficiency- Global (path length), Local (cluster) EAS Essential Air Service EU European Union ER Erdos-Renyi random network FAA Federal Aviation Administration (USA) FAR Federal Aviation Regulation GIS geographic information system GUI Graphic User's Interface HaS Hub and Spoke HWP America West Airline vii ICAO International Civil Aviation Organization IMPACT Intelligent agent-based Model for Policy Assessment of Collaborative Traffic flow management IT Information Technology k Number of links at each node I, L(p) Path Length Between Nodes, average or characteristic length N Number of Nodes in Network NA Network Analysis NAS National Airspace System NASA National Aeronautics and Space Administration NextGen Next Generation Air Transportation System NGATS Next Generation Air Transportation System NRC National Research Council OEP Operational Evaluation Partnership (w/FAA, top 35 airports by commercial flights) ORD Chicago O'Hare Airport FAR Part 121 Operating
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