“…Even though they are based on expert assumptions and account for randomness, it is not possible to include all the variables, which affect the values of measured aviation safety data. Lališ et al (2018) make a convincing case for the use of synthetic data despite its limitations. However, the problem here may be more fundamental, since as Lališ et al (2018), p.160) points out, since "…aviation is a socio-technical system, it is unlikely that the system is deterministic.…”
Section: Current Approachesmentioning
confidence: 97%
“…The method relies upon resampling the limited number of events in an effort to balance the successfulness bias inherent in the raw data. However, even with synthetic data generation it seems clear that (Lališ et al., 2018, p. 160): …the synthetic data used to fill the gaps of existing limitations will never contain anything outside of what is inserted in the very equations behind the simulation. Even though they are based on expert assumptions and account for randomness, it is not possible to include all the variables, which affect the values of measured aviation safety data.…”
Section: Current Approachesmentioning
confidence: 99%
“…Lališ et al. (2018) make a convincing case for the use of synthetic data despite its limitations. However, the problem here may be more fundamental, since as Lališ et al.…”
While airlines generate massive amounts of operational data every year, the ability to use the collected material to improve safety has begun to plateau. With the increasing demand for air travel, the aviation industry is continually growing while simultaneously being required to ensure the level of safety within the system remains constant. The purpose of this article is to explore whether the traditional analysis methods that have historically made aviation ultra‐safe have reached their theoretical limits or merely practical ones. This analysis argues that the underlying logic governing the traditional (and current) approaches to assess safety and risk within aviation (and other safety critical systems) is abductive and therefore focused on creating explanations rather than predictions. While the current “fly‐fix‐fly” approach has, and will continue to be, instrumental in improving what (clearly) fails, alternative methods are needed to determine if a specific operation is more or less risky than others. As the system grows, so too does the number of ways it can fail, creating the possibility that more novel accidents may occur. The article concludes by proposing an alternative approach that explicitly adds temporality to the concepts of safety and risk. With this addition, a deductive analysis approach can be adopted which, while low in explanatory power, can be used to create predictions that are not bound to analyzing only outcomes that have occurred in the past but instead focuses on determining the deviation magnitude between the operation under analysis and historically commensurate operations.
“…Even though they are based on expert assumptions and account for randomness, it is not possible to include all the variables, which affect the values of measured aviation safety data. Lališ et al (2018) make a convincing case for the use of synthetic data despite its limitations. However, the problem here may be more fundamental, since as Lališ et al (2018), p.160) points out, since "…aviation is a socio-technical system, it is unlikely that the system is deterministic.…”
Section: Current Approachesmentioning
confidence: 97%
“…The method relies upon resampling the limited number of events in an effort to balance the successfulness bias inherent in the raw data. However, even with synthetic data generation it seems clear that (Lališ et al., 2018, p. 160): …the synthetic data used to fill the gaps of existing limitations will never contain anything outside of what is inserted in the very equations behind the simulation. Even though they are based on expert assumptions and account for randomness, it is not possible to include all the variables, which affect the values of measured aviation safety data.…”
Section: Current Approachesmentioning
confidence: 99%
“…Lališ et al. (2018) make a convincing case for the use of synthetic data despite its limitations. However, the problem here may be more fundamental, since as Lališ et al.…”
While airlines generate massive amounts of operational data every year, the ability to use the collected material to improve safety has begun to plateau. With the increasing demand for air travel, the aviation industry is continually growing while simultaneously being required to ensure the level of safety within the system remains constant. The purpose of this article is to explore whether the traditional analysis methods that have historically made aviation ultra‐safe have reached their theoretical limits or merely practical ones. This analysis argues that the underlying logic governing the traditional (and current) approaches to assess safety and risk within aviation (and other safety critical systems) is abductive and therefore focused on creating explanations rather than predictions. While the current “fly‐fix‐fly” approach has, and will continue to be, instrumental in improving what (clearly) fails, alternative methods are needed to determine if a specific operation is more or less risky than others. As the system grows, so too does the number of ways it can fail, creating the possibility that more novel accidents may occur. The article concludes by proposing an alternative approach that explicitly adds temporality to the concepts of safety and risk. With this addition, a deductive analysis approach can be adopted which, while low in explanatory power, can be used to create predictions that are not bound to analyzing only outcomes that have occurred in the past but instead focuses on determining the deviation magnitude between the operation under analysis and historically commensurate operations.
“…Some are closely related to aircraft technology big data and its information system, and others are related to the external environment and its response of the specific flight process. They are basic conditions for ensuring the flight safety of aircraft (Lališ et al, 2018).…”
“…Computer technology has been widely used in aviation industries. The integration of airborne electronic equipment has made aircraft manipulation more automatic and intelligent, which has greatly improved the reliability and safety of aircraft [1,2]. However, accurate manipulation by the flight crew is still critical during the flight process.…”
Human error is an important risk factor for flight safety. Although the human error assessment and reduction technique (HEART) is an available tool for human reliability derivation, it has not been applied in flight safety assessment. The traditional HEART suffers from imprecise calculation of the assessed proportion of affect (APOA) because it heavily depends on a single expert's judgment. It also fails to provide remedial measures for flight safety problems. To overcome these defects of the HEART, this study proposes an integrated human error quantification approach that uses the improved analytic hierarchy process method to determine the APOA values. Then, these values are fused to the HEART method to derive the human error probability. A certain flight task is completed to assess human reliability. The results demonstrate that the proposed method is a reasonable and feasible tool for quantifying human error probability and assessing flight safety in the aircraft manipulation process. In addition, the critical error-producing conditions influencing flight safety are identified, and improvement measures for high-error-rate operations are provided. The proposed method is useful for reducing the possibility of human error and enhancing flight safety levels in aircraft operation processes.
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