scholarly journals Collegiate Aviation Pilots: Analyses of Fatigue Related Decision-Making Scenarios

2019 ◽  
Author(s):  
Julius Keller ◽  
FLAVIO COIMBRA MENDONCA ◽  
Jason E. Cutter
Keyword(s):  
Decision Making ◽  
Collegiate Aviation ◽  
Aviation Pilots

scholarly journals An Analysis of Self-Reported Sleepiness and Fatigue Measures from Collegiate Aviation Pilots

2020 ◽  
Vol 38 (1) ◽  
Author(s):  
Julius Keller ◽  
Flavio Mendonca ◽  
Thomas Laub ◽  
Sarah Wolfe
Keyword(s):  
Collegiate Aviation ◽  
Aviation Pilots

Evaluating the Decision-Making Skills of General Aviation Pilots

1998 ◽  
Author(s):  
Walter E. Driskill ◽  
Johnny J. Weissmuller ◽  
John C. Quebe ◽  
Darryl K. Hand ◽  
David R. Hunter
Keyword(s):  
Decision Making ◽  
General Aviation ◽  
Aviation Pilots

Assessing General Aviation Pilots’ Interpretation of Weather Products: Traditional and New Automated Generation Products

2017 ◽  
Vol 61 (1) ◽  
pp. 94-98
Author(s):  
Jayde M. King ◽  
Yolanda Ortiz ◽  
Thomas Guinn ◽  
John Lanicci ◽  
Beth L. Blickensderfer ◽  
...  
Keyword(s):  
Decision Making ◽  
General Aviation ◽  
Aviation Accidents ◽  
Automated Generation ◽  
Human In The Loop ◽  
Product Generation ◽  
Aviation Pilots ◽  
And Training ◽  
Hazardous Weather ◽  
Aviation Weather

The General Aviation (GA) community accounts for the majority of weather related aviation accidents and incidents. Interpreting and understanding weather products is crucial to hazardous weather avoidance, and previous studies have indicated that improving usability of weather products can improve pilot decision making. The Aviation Weather Center offers two broad types of graphical weather products for assessing icing, turbulence and flight category. These are traditional human-in-the-loop products (G-AIRMETs Ice, Tango, and Sierra) and fully-automated products (CIP/FIP, GTG, and CVA). This study assessed and compared pilots’ understanding of the fully-automated products in comparison to the human-in-loop products. Participants (n=131) completed a set of weather product interpretation questions. A series of mixed ANOVAs were conducted to analyze the effects of pilot certificate and/or rating (Student, Private, Private with Instrument, Commercial with Instrument) and product generation (traditional vs. automated) on product interpretation score. Results indicated that, despite product generation, pilots performed similarly on the icing and ceiling/visibility products, but performed significantly better on the new fully automated turbulence product (GTG) than when using the traditional turbulence product (AIRMET Tango). Usability and training implications are discussed.

scholarly journals Evolution Mechanism on the Unsafe Behavioural Risks of General Aviation Pilots

2021 ◽  
Vol 32 (2) ◽  
pp. 104-117
Author(s):  
Xinyao Guo ◽  
Yue Chen ◽  
Qingmin Si ◽  
Yuansheng Wang
Keyword(s):  
Decision Making ◽  
Safety Management ◽  
Interaction Mechanism ◽  
Bottom Layer ◽  
General Aviation ◽  
Key Factors ◽  
Evolution Mechanism ◽  
Aviation Accidents ◽  
Factors Influencing ◽  
Aviation Pilots

The unsafe behaviour prevention and control of general aviation pilots has become an emphasis in the general aviation safety management with the increasing number of general aviation enterprises, lengthening of flight time and frequent occurrence of public safety events caused by general aviation accidents. How to identify the factors influencing the unsafe behaviours of general aviation pilots and clarify the inter factor evolution mechanism is hot issue in the general aviation. To accurately identify the key factors influencing the unsafe behaviours of general aviation pilots and define the interaction mechanism between factors, using the unsafe behaviours of pilots in 200 global general aviation accidents during 2015-2019 and the association rule method, the bottom-layer factors of the Human Factors Analysis and Classification System (HFACS) model were analysed. Furthermore, the influence degree, influenced degree, centrality and causality of the influencing factors in the HFACS model were calculated, and the risk transfer path at different layers was determined on the basis of the integrated decision-making trial and evaluation laboratory (DEMATEL) and fuzzy theory. Results show that the poor individual ready state is strongly associated with skill error, decision-making error and habitual violation. Moreover, 11 factors, such as poor physical environment, physical/intelligence limitation and poor technical environment, constitute the factors in the cause group for pilot unsafe behaviours. 7 factors, such as insufficient supervision, improper operation plan and failure to discover and correct problems, are the factors in the result group. Illegal behaviour, failure to discover and correct problems and decision-making error of pilots, which are of high centrality, are key factors influencing the unsafe behaviours of general aviation pilots. The conclusions obtained from this study compensate the deficiencies for the linear statistical model of risk factors and provide a novel method for regulating and controlling the unsafe behaviours of general aviation pilots.

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