1-10 of 14 results
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Development of a Safety Performance Decision-Making Tool for Flight Training Organization
PI Marisa Aguiar
CO-I Carolina Anderson
Title 14 of the Code of Federal Regulations (CFR) Part 141 flight training organizations are actively pursuing ways to increase operational safety by introducing advanced risk assessment and decision-making techniques. The purpose of the dissertation was to create and validate a safety performance decision-making tool to transform a reactive safety model into a predictive, safety performance decision-making tool, specific to large, collegiate Title 14 CFR Part 141 flight training organizations, to increase safety and aid in operational decision-making. The validated safety decision-making tool uses what-if scenarios to assess how changes to the controllable input variables impact the overall level of operational risk within an organization’s flight department.
Utilizing SPIs determined to be most indicative of flight risk within large, collegiate flight training organizations, a predictive, safety performance decision-making tool was developed utilizing Monte Carlo simulation. In a high-risk system beset with uncertainty, applying Monte Carlo simulation addresses the need to accommodate uncontrollable inputs into the model in a manner that enables the model to produce meaningful output data. This research utilizes the validated equations drawn from the non-statistical model developed by Anderson, Aguiar, Truong, Friend, Williams, & Dickson (2020) for the mathematical inputs driving the computational nodes, including the SPIs, as the foundation to develop the safety performance decision-making tool.
The probability distributions of the uncontrollable inputs were drawn from a sample of operational data from September 2017 to September 2019 from a large, collegiate 14 CFR Part 141 flight training organization in the southeastern United States. The study conducted simulation runs based on true operational ranges to simulate the operating conditions possible within large, collegiate CFR Part 141 flight training organizations with varying levels of controllable resources including personnel (Aviation Maintenance Technicians and Instructor Pilots) and expenditures (active flight students and available aircraft).
The study compared the output from three different Verification Scenarios—each using a unique seed value to ensure a different sample of random numbers for the uncontrollable inputs. ANOVA testing indicated no significant differences appeared among the three different groups, indicating the results are statistically reliable.
Four What-if Scenarios were conducted by manipulating the controllable inputs. Mean probability was the key output and represents the forecasted level of operational risk on a standardized 0-5 risk scale for the Flight Score, Maintenance Score, Damage and Related Impact, and an Overall Risk Score. Results indicate the lowest Overall Risk Score occurred when the level of personnel was high yet expenditures were moderate.
Changes to the controllable inputs are reflected by variations to the outputs demonstrating the utility and potential for the safety performance decision-making tool. The outputs could be utilized by safety personnel and administrators to make more informed safety-related decisions without expending unnecessary resources. The model could be adapted for use in any CFR Part 141 flight training organization with data collection capabilities and an SMS by modifying the input value probability distributions to reflect the operating conditions of the selected 14 CFR Part 141 flight training organization.
Categories: Graduate
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Organizational Design of Secondary Aviation/Aerospace/Engineering Career Education Programs
PI Susan Archer
CO-I David Esser
Modern nations operate within a global economy, relying heavily on the aviation industry for efficient and effective transportation of passengers and goods. The Boeing 2018 Pilot and Technical Outlook Report indicated that over the next 20 years, the aviation industry will need almost two and a half million new aircrew and maintenance employees to meet anticipated global demand. The industry will also need engineers, aviation managers, and workers in other aviation and aerospace disciplines. Aviation and aerospace jobs require solid backgrounds in mathematics, science, and technology; the development of pre-college aviation / aerospace / engineering career education programs would presumably enhance student preparation in these areas and increase the workforce pipeline for the industry. The goal of this study was to identify and evaluate the underlying organizational factors of successful secondary aviation / aerospace / engineering career education programs, through application of measures traditionally associated with organizational theory.
Analysis of collected data involved exploratory factor analysis to identify underlying factors, confirmatory factor analysis to verify significant relationships between manifest variables and latent constructs and to ensure a good-fitting measurement model, and structural equation modeling to identify significant relationships between latent constructs and achieve the best-fitting model of these relationships for the collected data. Variables were Likert-scale responses to literature-based survey items associated with organizational vision, leadership, communication, collaboration, decision-making, flexibility, accountability, resource availability, motivation, and learning. Additionally, participants were invited to provide comments related to any of the survey items to explain or add detail to their response selection. These comments were reviewed both as they related to individual survey items and for detection of underlying themes. Participants in the study comprised stakeholders associated with career education programs in the disciplines of interest, including students, parents, alumni, school / program faculty and staff, industry members, and advisory board members.
Hypothesis testing results suggested that the most important factor in predicting success for an aviation / aerospace / engineering academy or program is personal motivation related to learning. Though other underlying factors, including leadership / collaborative environment, organizational accountability, and resource availability were clearly related to perceived program success, they appeared to have indirect relationships with success. It is also important to recognize that a paired qualitative analysis of participant comments generated themes that transcended survey item topics, and the identification of these themes supported the conclusions from hypothesis testing regarding underlying factors. Personal motivation was the most commonly recurring theme in comments, supporting the hypothesis testing result indicating its predictive strength for an organization’s success.
Understanding the constructs that are most closely related to an organization’s success, as they are perceived by its stakeholders, offers current program leaders and groups interested in creating new programs evidence they can use to design the frameworks for their programs. Anticipated workforce shortages warrant study of how to increase the number of candidates not only in post-secondary academic and training programs, but to shift recruiting earlier through implementation of quality secondary-level programs that are established on a foundation of research-based strategies for success.
Categories: Graduate
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Predicting General Aviation Accidents Using Machine Learning Algorithms
PI Bradley Baugh
CO-I Bruce Conway
Aviation safety management is implemented through reactive, proactive, and predictive methodologies. Unlike reactive and proactive safety, predictive safety can predict the next accident and enable prevention before an actual occurrence. The study outlined here promotes predictive safety management through machine learning technologies using large amounts of data to facilitate predictive modeling.
The study addresses efforts to reduce General Aviation accidents, an effort that was renewed in earnest with the Federal Aviation Administration’s 1998 Safer Skies Initiative. Over the past 22 years, the General Aviation fatality rate has decreased. However, accidents still happen, and there is some evidence showing the number of accidents, representing hazard exposure, is increasing. The accident data suggest that the aviation community still has more to learn about the variables involved in an accident sequence.
The purpose of the study was to conduct an exploratory data-driven examination of General Aviation accidents in the United States from January 1, 1998, to December 31, 2018, using machine learning and data mining techniques. The goal was to determine what model best predicts fatal and severe injury aviation accidents and further, what variables were most important in the prediction model.
The study sample comprised 26,387 fixed-wing general aviation accidents accessed through the publicly accessible National Transportation Safety Board Aviation Accident Database and Synopses archive. Using a mixed-methods approach, the study employed both unstructured narrative text and structured tabular data within the predictive modeling. First, the accident narratives were culled using text mining algorithms to develop text-based quantitative variables. Next, data mining algorithms were used to develop models based on both text- and data-based variables derived from the accident reports.
Five types of machine learning models were created using SAS® Enterprise Miner™, including the Decision Tree, Gradient Boosting, Logistic Regression, Neural Network, and Random Forest. Additionally, three broad sets of variables were used in modeling, including text-only, data-only, and a combination of text and data variables. Three models, Logistic Regression (text-only variables), Random Forest (text-only variables), and Gradient Boosting (text and data variables), emerged with a similar prediction capability. The top six variables within the models were all text-based covering Medical, Slow-flight and stalls, Flight control, IMC flight, Weather factors, and Flight hours topics. The Logistic Regression (Text) model was selected as the champion model: Misclassification Rate = 0.098, ROC Index = 0.945, and Cumulative Lift = 3.46.
The results of the study provide insights to the entire General Aviation community, including government, industry, flight training, and the operational pilot. Specific recommendations include the following areas: 1) improve the quality and usefulness of accident reports for machine learning applications, 2) investigate ways to capture and publish more open-source flight data for use in safety modeling, 3) invest in additional medical education and find ways to address impairing medications and high risk medical conditions, 4) renew efforts on improving flight skills and combatting decision-based errors, 5) emphasize the importance of weather briefings, pre-flight planning, and weather-based risk management, and 6) create an aviation-specific corpus for text mining to improve text analysis and transformation.
Categories: Graduate
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Examining Unstable Approach Predictors Using Flight Data Monitoring Information
PI David Carroll
CO-I David Esser
The approach and landing phase of flight is statistically the most dangerous part of flying. While it only accounts for 4% of flight time, it represents 49% of commercial jet mishaps. One key to mitigating the risks involved in this flight segment is the stabilized approach. A stabilized approach requires meeting rigorous standards for many flight parameters as the aircraft nears landing. Exceeding any of these parameters results in an unstable approach (UA). The energy management (EM) accomplished by the flight crew, represented by the EM variables in the study, influences the execution of a stabilized approach.
While EM is a critical element of executing a stabilized approach, there appears to be a lack of studies that identify specific EM variables that contribute to UA probability. Additionally, several possible moderating variables (MVs) may affect the probability of a UA. Fortunately, modern jet transport aircraft have Flight Data Monitoring (FDM) systems that capture a wealth of information that enable the analysis of these EM variables. This study used FDM data to answer the questions about what influence a set of EM variables has on the probability of a UA event. The analysis also determined what impact a set of possible MVs, not directly related to EM, has on these EM variables influence.
The analysis used logistic regression (LR) to investigate FDM information. The LR provided estimations of odds ratios for each of the variables and the interaction factors for the MVs. These statistics defined a model to evaluate the influences of the EM and MVs, providing answers to the research questions posed. The results determined the model was a good fit to the data but had poor discrimination. The model supported three of the original seven EM hypotheses and none of the 28 MV hypotheses.
The study identified three specific EM variables that significantly influenced the probability of a UA event. Of the MVs, only one significant influence was revealed but was opposite that hypothesized. Identifying the EM variables, and examining their impacts, shows their importance in preventing UAs. Further, the results help prevent future UAs by informing the design of training programs. Additionally, the current effort fills gaps in the current body of knowledge, as there appears to be a lack of studies in the areas investigated.
A gap in the body of knowledge filled by investigating an area of limited research and the results provide practical application in the analysis of EM-related events. Aviation safety practitioners now have additional information to identify trend issues that may lead to the increased probability of a UA event. Finally, this study was one of very few granted access to actual operational FDM information by an air carrier. The data were crucial in evaluating the proposed model against real-world flight operations, comparing theory to reality. Without access to such closely held information, the research for this dissertation would not have been possible.
Categories: Graduate
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Cost Optimization Modeling for Airport Capacity Expansion Problems in Metropolitan Areas
PI Woo Jin Choi
CO-I Dothang Truong
The purpose of this research was to develop a cost optimization model to identify an optimal solution to expand airport capacity in metropolitan areas in consideration of demand uncertainties. The study first analyzed four airport capacity expansion cases from different regions of the world to identify possible solutions to expand airport capacity and key cost functions which are highly related to airport capacity problems. Using mixedinteger nonlinear programming (MINLP), a deterministic optimization model was developed with the inclusion of six cost functions: capital cost, operation cost, delay cost, noise cost, operation readiness, and airport transfer (ORAT) cost, and passenger access cost. These six cost functions can be used to consider a possible trade-off between airport capacity and congestion and address multiple stakeholders’ cost concerns.
This deterministic model was validated using an example case of the Sydney metropolitan area in Australia, which presented an optimal solution of a dual airport system along with scalable outcomes for a 50-year timeline. The study also tested alternative input values to the discount rate, operation cost, and passenger access costs to review the reliability of the deterministic model. Six additional experimental models were tested, and all models successfully yielded optimal solutions. The moderating effects of financial discount rate, airport operation cost, and passenger access costs on the optimal solution were quantitatively the same in presence of a deterministic demand profile.
This deterministic model was then transformed into a stochastic optimization model to address concerns with the uncertainty of future traffic demand, which was further reviewed with three what-if demand scenarios of the Sydney Model: random and positive growth of traffic demand, normal distribution of traffic demand changes based on the historical traffic record of the Sydney region, and reflection of the current COVID- 19 pandemic situation. This study used a Monte Carlo simulation to address the uncertainty of future traffic demand as an uncontrollable input. The Sydney Model and three What-if Models successfully presented objective model outcomes and identified the optimal solutions to expand airport capacity while minimizing overall costs. The results of this work indicated that the moderating effect of traffic uncertainties can make a difference with an optimal solution. Therefore, airport decision-makers and airport planners should carefully consider the uncertainty factors that would influence the airport capacity expansion solution.
This research demonstrated the effectiveness of combining MINLP and the Monte Carlo simulation to support a long-term strategic decision for airport capacity problems in metropolitan areas at the early stages of the planning process while addressing future traffic demand uncertainty. Other uncertainty factors, such as political events, new technologies, alternative modes of transport, financial crisis, technological innovation, and demographic changes might also be treated as uncontrollable variables to augment this optimization model.
Categories: Graduate
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Student Engagement in Aviation MOOCs: Identifying Subgroups and Their Differences
PI Jennifer Edwards
CO-I Mark Friend
The purpose of this study was to expand the current understanding of learner engagement in aviation-related Massive Open Online Courses (MOOCs) through cluster analysis.
MOOCs, regarded for their low- or no-cost educational content, often attract thousands of students who are free to engage with the provided content to the extent of their choosing. As online training for pilots, flight attendants, mechanics, and small unmanned aerial system operators continues to expand, understanding how learners engage in optional aviation-focused, online course material may help inform course design and instruction in the aviation industry. In this study, Moore’s theory of transactional distance, which posits psychological or communicative distance can impede learning and success, was used as a descriptive framework for analysis. Archived learning analytics datasets from two 2018 iterations of the same small unmanned aerial systems MOOC were cluster-analyzed (N = 1,032 and N = 4,037). The enrolled students included individuals worldwide; some were affiliated with the host institution, but most were not. The data sets were cluster analyzed separately to categorize participants into common subpopulations based on discussion post pages viewed and posts written, video pages viewed, and quiz grades. Subgroup differences were examined in days of activity and record of completion. Pre- and postcourse survey data provided additional variables for analysis of subgroup differences in demographics (age, geographic location, education level, employment in the aviation industry) and learning goals. Analysis of engagement variables revealed three significantly different subgroups for each MOOC. Engagement patterns were similar between MOOCs for the most and least engaged groups, but differences were noted in the middle groups; MOOC 1’s middle group had a broader interest in optional content (both in discussions and videos); whereas MOOC 2’s middle group had a narrower interest in optional discussions. Mandatory items (Mandatory Discussion or Quizzes) were the best predictors in classifying subgroups for both MOOCs. Significant associations were found between subgroups and education levels, days of activity, and total quiz scores. This study addressed two known problems: a lack of information on student engagement in aviation-related MOOCs, and more broadly, a growing imperative to examine learners who utilize MOOCs but do not complete them. This study served as an important first step for course developers and instructors who aim to meet the diverse needs of the aviation-education community.Categories: Graduate
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An Exploratory Study of General Aviation Visual to Instrument Meteorological Condition Contextual Factors
PI James Hartman
CO-I Mark Friend
The purpose of this dissertation was to bridge the existing literature gap of outdated contextual factor (CF) research through examination and determination of current General Aviation (GA) Title 14 Code of Federal Regulations (CFR) Part 91 visual flight rules (VFR)-into-instrument meteorological condition (IMC) contextual factors. Contextual factors are a multifaceted arrangement of pertinent events or occurrences contributing to pilot accidents in weather-related decision-making errors.
A total of 46 contextual factors were identified and examined from the reviewed research literature. The study examined and determined the presence of the 46 contextual factors, frequencies, and manifestations in the GA VFR-into-IMC Aviation Accident Reports (AARs) archived in the National Transportation Safety Board (NTSB) online safety database. Significant relationships were identified among the contextual factors and pilot age, flight experience, weather, flight conditions, time of day, and certification level using point biserial and phi correlations. Contextual factor significant effects on the crash distance from departure and crash distance from the planned destination were revealed using multiple regression. A qualitative methodology was used on secondary data. Three subject matter experts (SMEs) for the main study analyzed a sample of 85 accidents for the presence of the 46 contextual factors. Raters then reported the presence of the contextual factors and provided opinions on how the contextual factors were manifested. Qualitative analysis revealed the presence of 37 out of 46 contextual factors. Highest frequency factors included number of passengers on board (CF29), accident time of day (CF1), crash distance from the planned destination (CF15), not filing of a flight plan (CF21), and underestimating risk (CF43). Raters described numerous manifestations of the contextual factors including 62% of the accident flights had passengers on board the aircraft (CF29). Quantitative analysis discovered several significantly weak to moderate relationships among pilot age, flight experience, weather, flight conditions, time of day, certification level, and the contextual factors. Several contextual factors had significant effects on the crash distance from departure and crash distance from the planned destination. Findings indicated the contextual factors were extensive in GA accidents. Additional research should focus on all flight domains, including further study of GA Part 91 VFR-into-IMC accidents. It is recommended the GA Part 91 pilot community be trained on the contextual factors assessed.
Categories: Graduate
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An Investigation of Factors that Influence Passengers’ Intentions to Use Biometric Technologies at Airports
PI Kabir Kasim
CO-I Scott Winter
This research investigated the factors that influence passengers’ intentions to choose the use of biometrics over other methods of identification. The current study utilized a quantitative research method via an online survey of 689 persons from Amazon ® Mechanical Turk ® (MTurk) and employed structural equation modeling (SEM) techniques for data analysis. The study utilized the theory of planned behavior (TPB) as the grounded theory, while perceived usefulness and perceived ease of use were included as additional factors that could influence individuals’ intentions to use new technology.
The study further assessed the impact of passengers’ privacy concerns on the intentions to use biometrics and investigated how the privacy concerns moderate the influencing factors of passengers’ behavioral intentions. Because of the coronavirus (COVID-19) pandemic that became prevalent at the time of the study, a COVID-19 variable was introduced as a control variable to examine if there were any effects of COVID-19 on passengers' behavioral intentions while controlling for the other variables.
Results showed that for the TPB factors, attitudes and subjective norms significantly influenced passengers’ behavioral intentions to use biometrics, while the effect of perceived behavioral control (PBC) on passengers’ intentions was not significant. The additional factors of perceived usefulness and perceived ease of use did not significantly influence passengers’ intentions. In addition, the hypothesized relationships between privacy concerns and four factors, behavioral intentions, attitudes, PBC, and perceived ease of use were supported, while the relationships between privacy concerns and perceived usefulness and between privacy concerns and subjective norms were not supported.
The examination of the moderating effects found that privacy concerns moderated the relationships between passengers’ intentions and three factors: attitudes, subjective norms, and perceived usefulness. However, because the interaction plots showed that the moderating effects were weak, the effects were not considered to be of much value and were therefore not added to the final model. Results also showed that the control variable (COVID-19) did not significantly influence passengers’ behavioral intentions and passengers’ privacy concerns while controlling for the other variables.
Practically, the study contributed a research model and specified factors that were postulated to influence passengers’ behavioral intentions to use biometrics at airports. Further research would be required to determine additional factors that influence behavioral intentions. Finally, although the moderating effects were not used in the final model, the findings suggest that stakeholders can customize biometric systems and solutions appropriately to cater to passengers’ concerns.
Categories: Graduate
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Predicting Pilot Misperception of Runway Excursion Risk Through Machine Learning Algorithms of Recorded Flight Data
PI Edwin Odisho
CO-I Dothang Truong
The research used predictive models to determine pilot misperception of runway excursion risk associated with unstable approaches. The Federal Aviation Administration defined runway excursion as a veer-off or overrun of the runway surface. The Federal Aviation Administration also defined a stable approach as an aircraft meeting the following criteria: (a) on target approach airspeed, (b) correct attitude, (c) landing configuration, (d) nominal descent angle/rate, and (e) on a straight flight path to the runway touchdown zone. Continuing an unstable approach to landing was defined as Unstable Approach Risk Misperception in this research. A review of the literature revealed that an unstable approach followed by the failure to execute a rejected landing was a common contributing factor in runway excursions.
Flight Data Recorder data were archived and made available by the National Aeronautics and Space Administration for public use. These data were collected over a four-year period from the flight data recorders of a fleet of 35 regional jets operating in the National Airspace System. The archived data were processed and explored for evidence of unstable approaches and to determine whether or not a rejected landing was executed. Once identified, those data revealing evidence of unstable approaches were processed for the purposes of building predictive models.
SAS™ Enterprise MinerR was used to explore the data, as well as to build and assess predictive models. The advanced machine learning algorithms utilized included: (a) support vector machine, (b) random forest, (c) gradient boosting, (d) decision tree, (e) logistic regression, and (f) neural network. The models were evaluated and compared to determine the best prediction model. Based on the model comparison, the decision tree model was determined to have the highest predictive value.
The Flight Data Recorder data were then analyzed to determine predictive accuracy of the target variable and to determine important predictors of the target variable, Unstable Approach Risk Misperception. Results of the study indicated that the predictive accuracy of the best performing model, decision tree, was 99%. Findings indicated that six variables stood out in the prediction of Unstable Approach Risk Misperception: (1) glideslope deviation, (2) selected approach speed deviation (3) localizer deviation, (4) flaps not extended, (5) drift angle, and (6) approach speed deviation. These variables were listed in order of importance based on results of the decision tree predictive model analysis.
The results of the study are of interest to aviation researchers as well as airline pilot training managers. It is suggested that the ability to predict the probability of pilot misperception of runway excursion risk could influence the development of new pilot simulator training scenarios and strategies. The research aids avionics providers in the development of predictive runway excursion alerting display technologies.
Categories: Graduate
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Pilot Acceptance of Personal, Wearable Fatigue Monitoring Technology: An Application of the Extended Technology Acceptance Model
PI Rachelle Strong
CO-I Dahai Liu
The research problem of pilot fatigue has been referenced as a causal factor for aircraft accidents in many United States National Transportation and Safety Board (NTSB) accident reports; however, the United States Code of Federal Regulations 14 CFR Part 117, Flight and Duty Limitations and Rest Requirements for Flight Crew Members, does not provide a tangible means of measuring fatigue for aircraft crew members. This problem is relevant to the airline industry and the travelling public because pilot fatigue is preventable as a causal factor in aviation accidents, and pilots need an accurate way to measure it. Adoption of a technology-based solution has been recommended by the NTSB.
The purpose of this study was to determine the factors that affect United States certified airline transport pilots’ behavioral intention to use personal, wearable fatigue monitoring technology (FMT), such as a Fitbit or Apple Watch, to assess their personal fatigue levels. FMT could potentially be used to help meet pilots’ legal requirement to be aware of their personal fatigue levels, per 14 CFR Part 117. The theoretical framework for this study is the Extended Technology Acceptance Model, and the research question is: What factors affect pilots’ behavioral intention to use personal, wearable fatigue monitoring technology, and to what degree? There were ten hypotheses tested that corresponded to different relationships in the model.
The data for this study was collected using an online survey distributed to certified airline transport pilots in the United States, in which the survey questions corresponded to observed variables pertaining to each of the eight factor constructs in the model. The data was analyzed using confirmatory factor analysis (CFA) and structural equation modeling (SEM) techniques to test the hypotheses. The results of the study contributed to the theoretical body of knowledge by demonstrating that a modified version of the Extended Technology Acceptance Model was applicable to U.S. airline transport pilot behavioral intention to use FMT. Six of the ten original hypotheses were supported, and four were not supported.
It was determined that the primary factors that positively affect a pilot’s behavioral intention to use FMT are perceived usefulness and perceived ease of use. Perceived usefulness is positively affected by the external factors of job relevance, results demonstrability, and perceived image or social status, which act as secondary factors positively influencing behavioral intention to use FMT. A tertiary factor influencing behavioral intention to use FMT is subjective norms, which positively influence perceived image, thus positively affecting perceived usefulness and intention to use FMT. Output quality, subjective norms, and perceived ease of use were determined to not have a statistically significant effect on pilots’ perceived usefulness of FMT, and subjective norms were determined not to have a statistically significant effect on pilots’ behavioral intention to use FMT.
The practical significance of this study is that pilots find FMT devices most useful when it is applicable to their jobs, provides tangible results, and increases their social status perception. It is beneficial if others around them think they should use FMT, and that if they use FMT, their social status perception increases. Practical solutions to increase the likelihood of pilot FMT device usage should include wearable device applications that provide features that directly apply to the pilot profession, report data in ways that make sense to pilots, and also make the pilot look and feel stylish. Nearly 87 percent of pilots already wear a watch while flying, and over 40 percent of pilots already wear some form of FMT for personal use, so the challenge going forward is to make the right improvements to the devices to increase usage. Such improvements may include new aviation-themed applications that appeal to pilots and provide results that can help them make more informed decisions, while simultaneously improving the aesthetic to drive an increase in social pressures to wear the FMT devices regularly.
Categories: Graduate
1-10 of 14 results