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Aeronautical Decision-Making
Published in Monica Martinussen, David R. Hunter, Aviation Psychology and Human Factors, 2017
Monica Martinussen, David R. Hunter
Risk assessment and management is one component of the broader process of pilot decision-making. As noted earlier, poor pilot decision-making has been implicated as a leading factor in fatal general aviation accidents (Jensen and Benel 1977), and poor risk assessment can contribute significantly to poor decision-making. To address the question of risk perception among pilots, O'Hare (1990) developed an Aeronautical Risk Judgment Questionnaire to assess pilots’ perceptions of the risks and hazards of general aviation. Hazard awareness was assessed by (a) having pilots estimate the percentage of accidents attributable to six broad categories, (b) ranking the phases of flight by hazard level, and (c) ranking detailed causes of fatal accidents (e.g., spatial disorientation, misuse of flaps). O'Hare found that pilots substantially underestimated the risk of general aviation flying relative to other activities, and similarly underestimated their likelihood of being in an accident. On the basis of these results, he concluded that, “… an unrealistic assessment of the risks involved may be a factor in leading pilots to ‘press on’ into deteriorating weather” (O'Hare 1990, p. 599).
Expertise Differences in Attentional Strategies Related to Pilot Decision Making
Published in Don Harris, Wen-Chin Li, Decision Making in Aviation, 2017
Angela T. Schriver, Daniel G. Morrow, Christopher D. Wickens, Donald A. Talleur
Safe and efficient flight depends on the ability of pilots to make appropriate and timely decisions during flight. Errors related to pilot judgment and decision making have been found to be a contributing factor in more than a third of accidents in commercial (Shappell et al., 2007) and general aviation (O’Hare, 2003) operations. Although the quality of pilot decision making depends on many factors, a critical one is flying experience, which may improve pilots’ ability to understand and respond to problems (O’Hare, 2003).
Using neural networks to predict high-risk flight environments from accident and incident data
Published in International Journal of Occupational Safety and Ergonomics, 2022
NNs have been applied to a diverse range of classification problems such as the stock market [20] and cancer survival [21]. In the transportation field, they have been successfully applied to specific complex prediction problems. A decision-making tool for aircraft safety inspectors [22] evaluated the ability of a hybrid two-stage NN to analyse the relationships between aircraft operation and maintenance data, and service difficulty reporting (SDR) profiles. When compared with actual SDR numbers, 13 out of 19 NN models developed had R2 values above 0.80. Classifications of marine accidents based on the river stage, traffic level, utilizations, location, weather and time were performed with 80% accuracy [23]. Predictions of the landing speed of McDonnell-Douglas MD80 aircraft in no/low-gust and high-gust conditions based on airport topology, the environment and flight and aircraft parameters were 95% correct [24]. Predictions of pilot decision-making in the resolution of a disruptive passenger incident [16] were 100% correct. Liu et al. [25] developed a NN to predict fatal accidents in GA operations from an analysis of flight environment-type factors and achieved a classification accuracy of over 78%. Harris and Li [26] developed a NN model based on the theoretical model of error causation underpinning the human factors analysis and classification system (HFACS) [27,28]. They found that 74% of unsafe acts (errors) implicated in 523 military aviation accidents could be correctly predicted from their preconditions.
Predicting and mitigating failures on the flight deck: an aircraft engine bird strike scenario
Published in Ergonomics, 2022
Victoria Banks, Craig K. Allison, Katie Parnell, Katherine Plant, Neville A. Stanton
The remedial measures generated from this analysis are under review by an aerospace engineering manufacturer who is looking to utilise the outputs to inform a future interface which convey engine health to the airline pilot. This interface aims to improve pilot decision making, minimise flight disruptions and improve flight safety. The outputs of this analysis provide valid and usable findings that identify how current processes can be enhanced through future technologies. A comparison between ‘work-as-done’, as presented by the HTA, and ‘work-as-envisaged’ should be the focus of the next stage of research. Otherwise, there is a risk that the expected benefits of the new technology are unlikely to be realised in reality (Damodaran 1996).
Generating Design Requirements for Flight Deck Applications: Applying the Perceptual Cycle Model to Engine Failures on Take-off
Published in International Journal of Human–Computer Interaction, 2021
Katie J. Parnell, Rachael A. Wynne, Thomas G. C. Griffin, Katherine L. Plant, Neville A. Stanton
Following the SWARM interview, participants were informed on the development of the engine condition monitoring tool. The researchers explained the concept of the tool which could aid pilot decision making during the bird-strike scenarios. The system was purposely described so as not to fully divulge its intended full capability. Instead, the participants were asked what information (if any) they would want the assistant system to give them and how they may want this information to be presented to them. They were encouraged to think open-mindedly, without being limited to current availability of information. This was key to determining what information users may require of a new system.