Noncompliance and Decision-Making of Airline Pilots: An Analysis of Narratives From the Aviation Safety Reporting System

The purpose of this study was to identify self-reported flight deck noncompliance in aviation safety reports and explore the relationship between adaptive expertise, deliberate vs. non-deliberate actions (errors), and intentional vs. unintentional noncompliance. The heuristics for assessing adaptive thinking and behavior were based on subscales of the Adaptive Expertise Survey (AES; Fisher & Peterson, 2001). We analyzed a random sample of 200 ASRS reports from 2019 and coded them with respect to (a) whether they described intentional or unintentional noncompliance by one or more flightcrew members, (b) whether the decision making was deliberate, and (c) whether the decision-making process involved correlates of adaptive or routine (non-adaptive) expertise. We found that unintentional noncompliance was associated most frequently with non-deliberate actions and non-adaptive behaviors. Adaptive behaviors were strongly associated with deliberate actions and intentional noncompliance. Our on-going research to investigate adaptive expertise and its relationship with predictors of noncompliance is discussed.

AIRLINE PILOT SITUATION AWARENESS: PRESENTING A CONCEPTUAL MODEL FOR META-COGNITION, REFLECTION AND EDUCATION

The dissertation research summarized here, utilized the Grounded Theory Method to develop a conceptual model of pilot situation awareness from 223 Aviation Safety Reporting System (ASRS) narratives. The application of Latent Semantic Analysis aided the theoretical sampling of ASRS reports. A multistage model was developed involving attention, perception, interpretation, decision making, and action in support of goal-driven behavior. Narrative report coding identified several categories of situation awareness elements that pilots direct their attention to in building and maintaining situation awareness. Internal to the aircraft, flight crews directed their attention to the aircraft’s flight state and automation state. They also directed their attention to the condition of the aircraft, the functioning of the crew, and the status of the cabin. External to the aircraft, flight crews directed their attention to airport conditions, air traffic control, terrain, traffic, and weather. Pilots were also aware of the passage of time. Twelve characteristics of situation awareness were identified from narrative report coding which were subsequently compared with existing theoretical perspectives of situation awareness.

Ensembled Machine Learning Model for Aviation Incident Risk Prediction

With the fabulous development of air traffic request expected throughout the following two decades, the security of the air transportation framework is of expanding concern. In this paper, we encourage the "proactive security" worldview to expand framework wellbeing with an emphasis on anticipating the seriousness of strange flight occasions as far as their hazard levels. To achieve this objective, a prescient model should be created to look at a wide assortment of potential cases and measure the hazard related with the conceivable result. By using the episode reports accessible in the Aviation Safety Reporting System (ASRS), we construct a half breed model comprising of help vector machine and K-closest neighbor calculation to evaluate the hazard related with the result of each perilous reason. The proposed system is created in four stages. Initially, we classify all the occasions, in view of the degree of hazard related with the occasion result, into five gatherings: high hazard, decently high hazard, medium hazard, respectably medium hazard, and okay. Furthermore, a help vector machine model is utilized to find the connections between the occasion outline in text configuration and occasion result. In this application K-closest neighbors (KNN) and bolster vector machines (SVM) are applied to group the everyday nearby climate types In equal, knn calculation is utilized to highlights and occasion results subsequently improving the forecast. At long last, the forecast on hazard level order is stretched out to occasion level results through a probabilistic choice tree

Natural Language Processing Based Method for Clustering and Analysis of Aviation Safety Narratives

The complexity of commercial aviation operations has grown substantially in recent years, together with a diversification of techniques for collecting and analyzing flight data. As a result, data-driven frameworks for enhancing flight safety have grown in popularity. Data-driven techniques offer efficient and repeatable exploration of patterns and anomalies in large datasets. Text-based flight safety data presents a unique challenge in its subjectivity, and relies on natural language processing tools to extract underlying trends from narratives. In this paper, a methodology is presented for the analysis of aviation safety narratives based on text-based accounts of in-flight events and categorical metadata parameters which accompany them. An extensive pre-processing routine is presented, including a comparison between numeric models of textual representation for the purposes of document classification. A framework for categorizing and visualizing narratives is presented through a combination of k-means clustering and 2-D mapping with t-Distributed Stochastic Neighbor Embedding (t-SNE). A cluster post-processing routine is developed for identifying driving factors in each cluster and building a hierarchical structure of cluster and sub-cluster labels. The Aviation Safety Reporting System (ASRS), which includes over a million de-identified voluntarily submitted reports describing aviation safety incidents for commercial flights, is analyzed as a case study for the methodology. The method results in the identification of 10 major clusters and a total of 31 sub-clusters. The identified groupings are post-processed through metadata-based statistical analysis of the learned clusters. The developed method shows promise in uncovering trends from clusters that are not evident in existing anomaly labels in the data and offers a new tool for obtaining insights from text-based safety data that complement existing approaches.

Construction and application research of knowledge graph in aviation risk field

Since the causes of aviation accidents and risks are complicated, concealed, unpredictable and difficult to be investigated, in order to achieve the efficient organization and knowledge sharing of the historical cases of aviation risk events, this paper put forward the method of constructing vertical knowledge graph for aviation risk field. Firstly, the data-driven incremental construction technology is used to build aviation risk event ontology model. Secondly, the pattern-based knowledge mapping mechanism, which transform structured data into RDF (Resource Description Framework) data for storage, is proposed. And then the application, update and maintenance of the knowledge graph are described. Finally, knowledge graph construction system in aviation risk field is developed; and the data from American Aviation Safety Reporting System (ASRS) is used as an example to verify the rationality and validity of the knowledge graph construction method. Practice has proved that the construction of knowledge graph has a guiding significance for the case information organization and sharing on the field of aviation risk.