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Identification of Causal Paths and Prediction of Runway Incursion Risk Using Bayesian Belief Networks

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Identification of Causal Paths and Prediction of Runway Incursion Risk Using Bayesian Belief Networks Dissertations and Theses 10-2013 Identification of Causal Paths and Prediction of Runway Incursion Risk using Bayesian Belief Networks Benjamin Jeffry Goodheart Embry-Riddle Aeronautical University - Worldwide Follow this and additional works at: https://commons.erau.edu/edt Part of the Aviation Safety and Security Commons Scholarly Commons Citation Goodheart, Benjamin Jeffry, "Identification of Causal Paths and Prediction of Runway Incursion Risk using Bayesian Belief Networks" (2013). Dissertations and Theses. 53. https://commons.erau.edu/edt/53 This Dissertation - Open Access is brought to you for free and open access by Scholarly Commons. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of Scholarly Commons. For more information, please contact [email protected]. IDENTIFICATION OF CAUSAL PATHS AND PREDICTION OF RUNWAY INCURSION RISK USING BAYESIAN BELIEF NETWORKS by Benjamin Jeffry Goodheart A Dissertation Submitted to the College of Aviation in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Aviation Embry-Riddle Aeronautical University Daytona Beach, Florida October, 2013 i UMI Number: 3609284 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. UMI 3609284 Published by ProQuest LLC (2014). Copyright in the Dissertation held by the Author. Microform Edition © ProQuest LLC. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code ProQuest LLC. 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, MI 48106 - 1346 Copyright 2013 by Goodheart, Benjamin J. All rights reserved ABSTRACT Researcher: Benjamin Jeffry Goodheart Title: IDENTIFICATION OF CAUSAL PATHS AND PREDICTION OF RUNWAY INCURSION RISK USING BAYESIAN BELIEF NETWORKS Institution: Embry-Riddle Aeronautical University Degree: Doctor of Philosophy in Aviation Year: 2013 In the U.S. and worldwide, runway incursions are widely acknowledged as a critical concern for aviation safety. However, despite widespread attempts to reduce the frequency of runway incursions, the rate at which these events occur in the U.S. has steadily risen over the past several years. Attempts to analyze runway incursion causation have been made, but these methods are often limited to investigations of discrete events and do not address the dynamic interactions that lead to breaches of runway safety. While the generally static nature of runway incursion research is understandable given that data are often sparsely available, the unmitigated rate at which runway incursions take place indicates a need for more comprehensive risk models that extend currently available research. This dissertation summarizes the existing literature, emphasizing the need for cross-domain methods of causation analysis applied to runway incursions in the U.S. and reviewing probabilistic methodologies for reasoning under uncertainty. A holistic modeling technique using Bayesian Belief Networks as a means of interpreting causation even in the presence of sparse data is outlined in three phases: causal factor identification, iii model development, and expert elicitation, with intended application at the systems or regulatory agency level. Further, the importance of investigating runway incursions probabilistically and incorporating information from human factors, technological, and organizational perspectives is supported. A method for structuring a Bayesian network using quantitative and qualitative event analysis in conjunction with structured expert probability estimation is outlined and results are presented for propagation of evidence through the model as well as for causal analysis. In this research, advances in the aggregation of runway incursion data are outlined, and a means of combining quantitative and qualitative information is developed. Building upon these data, a method for developing and validating a Bayesian network while maintaining operational transferability is also presented. Further, the body of knowledge is extended with respect to structured expert judgment, as operationalization is combined with elicitation of expert data to create a technique for gathering expert assessments of probability in a computationally compact manner while preserving mathematical accuracy in rank correlation and dependence structure. The model developed in this study is shown to produce accurate results within the U.S. aviation system, and to provide a dynamic, inferential platform for future evaluation of runway incursion causation. These results in part confirm what is known about runway incursion causation, but more importantly they shed more light on multifaceted causal interactions and do so in a modeling space that allows for causal inference and evaluation of changes to the system in a dynamic setting. Suggestions for future research are also discussed, most prominent of which is that this model allows for robust and flexible assessment of mitigation strategies within a holistic model of runway safety. iv ACKNOWLEDGEMENTS To James B. Crain, my grandfather, and the man to whom I owe my enduring sense of discovery, unrelenting curiosity, and more than occasional stubbornness. Thank you for always pushing me to ask why and teaching me to never give up. To my wife Cathy, and to my children Isabel and Mateo, thank you for being my source of calm in what has often been a challenging journey for all of us. Without their collective patience, love, and support, this dissertation would never have been. If I have succeeded to any degree, it is because of them. If I have failed, I owe it only to my own shortcomings. Thank you to Dr. Roger Cooke and to Daniel Ababei and Dr. Anca Hanea for so graciously answering what must have seemed like an infinite barrage of questions. To my advisor, Dr. Dothang Truong, and to my mentor, Dr. MaryJo Smith, who push me ever forward in search of answers. I am forever grateful for your humor and wisdom through this process, and I am happy to count you as dear friends. Thank you also to my entire committee for their invaluable advice, guidance, friendship, and tolerance throughout the process of creating this research. My sincere thanks are due the Transportation Research Board and the National Academies of Science. Without their support, this research would have been impossible. Finally, to Sohail Khan, whose very visceral introduction to physics showed me a world made much more interesting by science, and whose dedication to teaching young people is an inspiring example of the power of a great educator. v To those who I have not mentioned by name, please know that you have my thanks, appreciation, and gratitude for your patience with me as I navigated this research. I could not have done it without you. vi TABLE OF CONTENTS Page Committee Signature Page .................................................................................................. ii Abstract .............................................................................................................................. iii Acknowledgements ..............................................................................................................v List of Tables ..................................................................................................................... xi List of Figures ................................................................................................................... xii Chapter 1 Introduction ..................................................................................................1 Statement of the Problem ................................................................ 7 Purpose Statement ........................................................................... 9 Research Questions ......................................................................... 9 Significance of the Study ................................................................ 9 Delimitations ................................................................................. 10 Limitations and Assumptions ....................................................... 11 Definitions of Terms ..................................................................... 11 List of Acronyms .......................................................................... 15 Chapter II Review of the Relevant Literature .............................................................17 Runway Incursions........................................................................ 18 Defining Runway Incursions ............................................ 18 Runway Incursion Data .................................................... 19 Review of Runway Incursion Research and Study of Causal Factors............................................................... 22 Review of Runway Incursion Mitigation Strategies ......... 28 Probabilistic Risk Assessment ...................................................... 31 vii Predictive Safety Modeling .............................................. 32 Bayesian Reasoning ...................................................................... 34 Bayesian Belief Networks ................................................ 35 Support for Bayesian Belief Nets. ........................ 36 Bayesian Networks and Causality
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