Predicting Pilot Misperception of Runway Excursion Risk Through Machine Learning Algorithms of Recorded Flight Data

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

Incorporating Worker Protections In Collective Bargaining Agreements As A Facilitator For Self-Reporting

The US airline industry relies on the willing participation of frontline employees to self-report safety hazards as part of an effective reporting culture. Current literature suggests fear of punitive actions as a barrier to self-reporting. Using a quantitative method, this study evaluated how employee protections from punitive actions incorporated into collective bargaining agreements (CBA) of Part 121 airline employees facilitates self-reporting. An Exploratory Factor Analysis suggests that Enhanced Reporting, Employee Protections, Roles and Responsibility, and Employee Engagement undergird self-reporting culture. All the factors had acceptable reliabilities and were significantly related to each other. Regression analysis suggested that Employee Protection was a significant predictor of Enhanced Reporting accounting for about 48% of variances. An implication for policy is to include protections in CBAs which can engender trust and facilitate enhanced self-reporting by employees. The study provides a framework for airlines and unions to improve the safety reporting culture using CBA protections

Identification and Validation of a Predicted Risk-Taking Propensity Model Among General Aviation Pilots

Risk-taking, a persistent topic of interest and concern in aviation, has been linked with unsafe behaviors and accidents. However, risk-taking propensity is a complex construct that encompasses numerous factors still being researched. Even within the limited research available about the factors affecting pilots’ risk-taking propensity, studies have yielded inconsistent results. Therefore, this quantitative study explores existing and novel factors that predict the propensity for risk-taking among general aviation (GA) pilots in the United States. This study, conducted in two stages, involved developing a prediction model using backward stepwise regression to predict pilots’ risk propensity, followed by model fit testing using additional sampling to validate the predicted model. Data was gathered using surveys from multiple local Experimental Aircraft Association (EAA) chapters in Central Florida and from Embry-Riddle Aeronautical University, Daytona Beach campus. In Stage 1, the model was constructed based on data obtained from 100 participants. Stage 2 involved validating the model using responses from another 100 participants who answered the same set of questions as in Stage 1. Model validation encompassed three methods: correlation analysis, t-test, and cross-validity coefficient. The results from these analyses demonstrated a strong fit between the regression model and the Stage 2 data, affirming the accuracy of the prediction model. The analysis identified a model comprising seven significant predictors among a set of 12, accounting for 76% of the variance, with an adjusted R2 of 75%, influencing the risk-taking propensity among GA pilots. These predictors included age, total flight hours, number of flight ratings, number of hazardous events, self-efficacy, psychological distress, and locus of control. Model prediction and cross-validation were employed to enhance the findings’ rigor and generalizability. Practical applications and suggested areas for future studies are also discussed

Evidence-Based Managerial Decision-Making With Machine Learning: The Case of Bayesian Inference in Aviation Incidents

Understanding the factors behind aviation incidents is essential, not only because of the lethality of the accidents but also the incidents’ direct and indirect economic impact. Even minor incidents trigger significant economic damage and create disruptions to aviation operations. It is crucial to investigate these incidents to understand the underlying reasons and hence, reduce the risk associated with physical and financial safety in a precarious industry like aviation. The findings may provide decision-makers with a causally accurate means of investigating the topic while untangling the difficulties concerning the statistical associations and causal effects. This research aims to identify the significant variables and their probabilistic dependencies/relationships determining the degree of aircraft damage. The value and the contribution of this study include (1) developing a fully automatic ML prediction based DSS for aircraft damage severity, (2) conducting a deep network analysis of affinity between predicting variables using probabilistic graphical modeling (PGM), and (3) implementing a user-friendly dashboard to interpret the business insight coming from the design and development of the Bayesian Belief Network (BBN). By leveraging a large, real-world dataset, the proposed methodology captures the probability-based interrelations among air terminal, flight, flight crew, and air-vehicle-related characteristics as explanatory variables, thereby revealing the underlying, complex interactions in accident severity. This research contributes significantly to the current body of knowledge by defining and proving a methodology for automatically categorizing aircraft damage severity based on flight, aircraft, and PIC (pilot in command) information. Moreover, the study combines the findings of the Bayesian Belief Networks with decades of aviation expertise of the subject matter expert, drawing and explaining the association map to find the root causes of the problems and accident relayed variables

Assessing the relationship between organizational management factors and a resilient safety culture in a collegiate aviation program with Safety Management Systems (SMS)

Extant research advocates for assessing and continuously improving resilient safety culture in high-reliability organizations (HROs) such as aviation that has a fully functional Safety Management Systems (SMS). Perceptions on the relationship between four (4) organizational management factors (Principles, Policy, Procedures, Practices) and resilient safety culture in a collegiate aviation program was assessed using an online survey instrument drafted using Reason (2011) concept on safety resilience. Sample was drawn from aviation students, flight instructors, faculty and administrators. Structural Equation Model (SEM) and Causal Path Analysis (CPA) techniques were used to assess conceptual models. Results suggest good reliability and construct validity for survey instrument. All the measurement models had acceptable fit based on various goodness-of-fit indices. The results suggest all four management factors had significant predictive relationship with resilient safety culture. Practices had the weakest predictive relationship and Policy had the highest. Procedures strongly mediated path between Policies and Practices and there was no significant causal relationship between Principles and Practices. Results suggest that more focus should be placed on resilient safety practices in the collegiate aviation program. Significant benefit of this study is the validation of an instrument that explores the relationship between resilient safety culture and organizational management factors and adds to literature on resilient safety culture in collegiate aviation programs. Future studies using this survey instrument and models in other collegiate aviation programs, airlines and airports are highly recommended

Determining Contributing Factors for Passenger Airline Pilot Perceived Fatigue

Fatigue is a recurring concern for pilots and continues to be a common contributing cause of aircraft accidents. The purpose of the dissertation was to determine factors that influence fatigue in commercial airline pilots. The ability to accurately associate fatigue in pilots before a flight begins could have a profound and meaningful impact on aviation safety. Seven factors were identified in the literature review as having possible predictive capabilities of perceived fatigue in pilots working for passenger carriers, including time awake, perceived stress, sleep quality, hours of sleep, age, typically scheduled start time, and hours on duty. An electronic survey instrument was used to gather quantitative data from U.S. passenger-carrying airline pilots. Data collected from 271 responses were randomly assigned to two separate groups. First, a regression equation was created utilizing half of the data collected from a survey instrument. The regression identified that age, hours on duty, and sleep quality (JSS) were significant independent variables (IVs) contributing to fatigue. Next, the regression equation was used to create predicted values of perceived fatigue. Then the second half of the dataset was used to validate if the equation could be utilized to identify contributing factors for passenger airline pilots\u27 perceived fatigue. Data were created with the regression equation and compared to perceived fatigue. The model was a moderate fit for the second data set. The analysis identified age as a negative predictor, indicating that fatigue (FSS) decreases as age increases. Age also had the smallest effect size of the significant IVs. These two items, while counterintuitive, are possibly explained by variances in schedules between pilot seniority. Sleep Quality (JSS) had the most significant effect on fatigue, while hours on duty had a larger effect than age but a smaller effect than sleep quality. Four variables studied were not significant predictors of fatigue and were not used in model creation: time awake, perceived stress, hours of sleep, and typically scheduled start time. Safely operating a flight involves weighing the implications of fatigue and other possible hazards resulting in many possible predictive factors. Heinrich’s domino theory was used to derive the fatigue factors in this dissertation. The significant predictor variables, age, hours on duty, and sleep quality form a potential “domino” for a fatigue- related accident. These fatigue factors may not cause an accident but could be a “domino” in a series of factors. While some fatigue factors have been studied, the factors studied in this dissertation have not previously been studied in the same way by creating a model with this population. Additionally, previous fatigue studies have not typically researched U.S.- based passenger-carrying pilots. Analyzing risks associated with fatigue in passenger- carrying pilots at commercial airlines is particularly complex because many factors can influence fatigue, including scheduling software, union contracts, and norms and practices. Airlines and regulators could use the prediction equation to potentially reduce fatigue-related risks. The equation created can predict fatigue in advance of scheduled flights and serve as a starting point for future fatigue researchers

Public Opinions of Unmanned Aerial Technologies in 2014 to 2019: A Technical and Descriptive Report

The primary purpose of this report is to provide a descriptive and technical summary of the results from similar surveys administered in fall 2014 (n = 576), 2015 (n = 301), 2016 (ns = 1946 and 2089), and 2018 (n = 1050) and summer 2019 (n = 1300). In order to explore a variety of factors that may impact public perceptions of unmanned aerial technologies (UATs), we conducted survey experiments over time. These experiments randomly varied the terminology (drone, aerial robot, unmanned aerial vehicle (UAV), unmanned aerial system (UAS)) used to describe the technology, the purposes of the technology (for economic, environmental, or security goals), the actors (public or private) using the technology, the technology’s autonomy (fully autonomous, partially autonomous, no autonomy), and the framing (promotion or prevention) used to describe the technology’s purpose. Initially, samples were recruited through Amazon’s Mechanical Turk, required to be Americans, and paid a small amount for participation. In 2016 we also examined a nationally representative samples recruited from Qualtrics panels. After 2016 we only used nationally representative samples from Qualtrics. Major findings are reported along with details regarding the research methods and analyses

Evaluating Importance Ratings as an Alternative to Mental Models in Predicting Driving Crashes and Moving Violations

The present study investigated the extent to which importance ratings (i.e., a measure of perceived importance for driving-related concepts) are a viable alternative to traditional mental model assessment methods in predicting driving performance. Although mental models may predict driving–related outcomes—crash involvement and moving violations—common mental model assessment techniques are associated with administrative limitations and challenges, which can affect how valid mental models are as assessments of knowledge structure. Importance ratings, as a measure of driving-related knowledge that may be associated with fewer administrative limitations, were hypothesized to provide equal predictive validity for driving–related performance outcomes in a sample of undergraduate students. To investigate the extent to which the measurement of mental models and importance ratings contribute to the prediction of driving crashes and moving violations, students completed Pathfinder, a common computer-based mental model assessment method, and paper-and-pencil importance ratings. In addition, students completed a test of driving knowledge and reported driving behaviors and outcomes including at-fault crashes and moving violations that occurred over the past five years (i.e., from 2005 to 2009). A group of expert drivers completed mental model and importance ratings assessments as well. Data across expert raters were combined and analyzed for appropriateness to serve as referent scores for each assessment. Students' mental model accuracy as well as importance rating accuracy was based on the extent to which student mental models and ratings agreed with those provided by the group of expert drivers. The results suggest that importance rating and mental model accuracy predicted crash involvement and moving violations. Whereas mental model accuracy was a stronger predictor of the number of moving violations, importance rating accuracy predicted the number of at-fault crashes slightly better than mental models. Although inconclusive, these results suggest that importance ratings may be a viable alternative to traditional mental model assessment in predicting some driving outcomes. Future research is warranted on importance ratings and other alternative mental model assessments

An Evaluation of the Relationships between Collegiate Aviation Safety Management System Initiative, Self-Efficacy, Transformational Safety Leadership and Safety Behavior mediated by Safety Motivation

The study conceptualized Safety Management System (SMS) initiative, self-efficacy, and transformational safety leadership as constructs that relates to safety behavior (measured by safety compliance and safety participation) when mediated by safety motivation using a quantitative approach. Structural equation modeling techniques was used to derive a final measurement model that fit the empirical data and was used to test the study hypotheses. Utilizing a sample of 282 collegiate flight students and instructors from a large public university in the US, a 46-item survey was used to measure respondent’s perceptions on the study variables. The results indicate that perceptions of SMS policy implementation have direct, positive significant effect on safety compliance and SMS process engagement has direct, positive significant effect on safety participation. Self-efficacy had direct, positive significant effect on both safety compliance and safety participation. Safety motivation fully mediated the effect of transformational safety leadership on safety participation. There were indications that respondents were not familiar with the Emergency Response Plan of the collegiate aviation program\u27s SMS. The theoretical and policy implications of this study to improve proactive safety in collegiate aviation are discussed

A Study Of Factors Contributing To Self-reported Anomalies In Civil Aviation

A study investigating what factors are present leading to pilots submitting voluntary anomaly reports regarding their flight performance was conducted. The study employed statistical methods, text mining, clustering, and dimensional reduction techniques in an effort to determine relationships between factors and anomalies. A review of the literature was conducted to determine what factors are contributing to these anomalous incidents, as well as what research exists on human error, its causes, and its management. Data from the NASA Aviation Safety Reporting System (ASRS) was analyzed using traditional statistical methods such as frequencies and multinomial logistic regression. Recently formalized approaches in text mining such as Knowledge Based Discovery (KBD) and Literature Based Discovery (LBD) were employed to create associations between factors and anomalies. These methods were also used to generate predictive models. Finally, advances in dimensional reduction techniques identified concepts or keywords within records, thus creating a framework for an unsupervised document classification system. Findings from this study reinforced established views on contributing factors to civil aviation anomalies. New associations between previously unrelated factors and conditions were also found. Dimensionality reduction also demonstrated the possibility of identifying salient factors from unstructured text records, and was able to classify these records using these identified features