Purpose Technological advances and the adaption of higher levels of automation serve as a potential cause of aviation incidents and accidents. This study aims to investigate the effect of automated systems on the operator’s performance total load (work, task, information, communication and mental) in highly advanced systems. Design/methodology/approach A questionnaire was designed for aviation operators (Pilots, ATCOs) to understand the intensity to which automation has affected their working environment and personal behavior. In total, 115 responses were received from 44 countries worldwide. Approximately, 66% of respondents were pilots, 27% Air traffic controllers and 7% were both pilots and ATCOs with various experience levels. Findings Based on the results of this questionnaire, this study suggests the following: creating a total load management model to understand the best load balance an operator could perform at providing rapidly updated aviation training methods and approaches investigating the influence and consequences of adding new tools to the operator’s working station and redesigning it to achieve top operator-machine equilibrium redesigning information and alerting systems. Practical implications Intrinsic limitations include an implicit expression of bias in the way questions are phrased, ambiguity in question phrasing that leads to incorrect conclusions and challenges regarding articulating complex concepts. Originality/value In this paper, the authors aimed to assess and investigate factors leading to current and future incidents and accidents resulting from human factors, specifically caused or developed because of highly automated systems.
The complex environment of aviation created dynamic air transport systems where the quality is vulnerable and directly sensitive to the supply side due to the high strategic level of driven market environments. The significance of quality quantifications has grown rapidly. Calculating quality factors is not a simple task, due to the heterogeneous, inseparable and incomprehensible characteristics of the system. For this purpose, the analytical hierarchy process (AHP) survey was distributed among two groups of 22 experts of pilots and ATCOs and applied by creating a three-level hierarchy model of the air transport supply quality to evaluate and weigh the critical characteristics. In the hierarchical structure, 4 main criteria, 15 first-level sub-criteria, and 12 second-level sub-criteria were used for the air transport supply quality model.
Purpose The purpose of this paper is to investigate and evaluate the subjective decision-making of pilots during final approach with varying degrees of experience for landing and go-around. Design/methodology/approach In this research, the “Lorenz Attractor” was modified and used to model the subjective decision-making of pilots during the final approach. For landing and go-around situations, “hesitation frequency” and “decision-making time” were calculated for the subjective decision-making of pilots. Findings In this research, the modified Chaotic Lorenz Model was used on MATLAB with varying degrees of experience, namely, student pilots, less-skilled pilots, experienced pilots and well-experienced pilots. Based on the outcomes, the less-skilled pilot needs nearly four times more decision-making time on landing or go-around compared to the well-experienced pilot during the final approach. Practical implications Operators (pilots, air traffic controllers) need to make critical and timely decisions in a highly complex work environment, which is influenced by several external elements such as experience level and human factors. According to NASA, 80% of aviation accidents occur due to human errors specifically over the course of the aviation decision-making process in dynamic circumstances. Due to the consequences of this research the operators' training should be redesigned by assisting flight instructors on the weaknesses of pilots. Originality/value This research explores the endogenous dynamics of the pilot decision-making process by applying a novel “Chaotic Lorenz Model” on MATLAB. In addition, the operator's total decision time formula was improved by including the decision reviewing time and external factors. Moreover, subjective decision-making model created by the current authors and Wicken's information model were modified to the highly automated systems.
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