# Human Factors Analysis of the Improved FRAM Method for Take-Off Quality Lateral Shift
## 2 Traditional Systematic Analysis of Human Factors  
Most research on aircraft take-off quality has been aimed at the impact of environmental factors on aircraft take-off performance data, with relatively little consideration of human factors. In the analysis of human factors, the flight quality is analyzed through flight data mining and expert experience; an example of such a method is the analytic hierarchy process [3], but this method is too dependent on expert experience and lacks objectivity. For the analysis of safety accidents, we are familiar with FTA (fault tree analysis), ETA (event tree analysis), and FMEA (failure mode and impact analysis). These methods are based on the construction of a map and framework to form an analysis network, followed by analyzing the causes and impacts of the accident and putting forward measures and suggestions that can be applied accordingly. However, in analyses based on ETA, FTA, FMEA [4; 5; 6], and other methods, the connections between sub-modules in the system branch cannot be effectively combined, so it is easy to ignore the impact of resonance between the sub-modules. Other methods for the systematic analysis of human factors currently include the popular Human Factor Analysis and Classification System (HFACS), the Technique for Retrospective and Predictive Analysis of Cognitive Errors (TRACER), the Human Factors Investigation Tool (HFIT), the Cognitive Reliability and Error Analysis Method (CREAM), A Technique for Human Event Analysis (ATHEANA), Systems-Theoretic Process Analysis (STPA), etc. The HFACS method is logical and easy to implement. It can accurately identify important inducing patterns of aircraft collision accidents/accident symptoms and identify the influencing factors and correlations that often appear in the patterns. From the literature, ref. [7] put forward the human factors analysis framework of CAAC on the basis of HFACS. The analysis method of the TRACEr model can address the lack of thorough analysis in the cognitive field to some extent, but on the other hand, it also puts forward certain requirements for the analyst's knowledge level. The authors of [8] used the TRACEr cognitive framework as a basis to predict the probability of controller error and develop error prevention measures. HFIT can be applied to human factors analysis in many fields, but the revision of the model needs a large amount of work, which needs to be combined with practice to replace a large number of quantum elements and items. The threshold for its use is low, and its dependence on the knowledge structure of the analyst is weak. Through the screening of the expert's identification degree, more objective analysis conclusions can be formed, and more effective measures for the control of human factors can be implemented. The authors of [9] investigated human factors in the offshore oil and gas industry by using HFIT and proved their significance in accident remediation. The CREAM method's traceability analysis framework can effectively extract accident chains and assist in identifying root causes. This quantitative analysis can make predictions for various types of human error behaviors, and [PERSON] [10] used the second-generation CREAM method to quantitatively analyze the probability of accidents caused by human error. The ATHENA method needs accurate prior probability knowledge as support in the quantitative analysis part, and it is difficult to carry out a quantitative analysis in the absence of prior knowledge. The most commonly used method is to use expert knowledge to compensate for the lack of prior knowledge, but such analysis results may have some generality. After exploring the limitations of traditional human reliability analysis, [PERSON] [11] discussed the advantages and disadvantages of the ATHENA method in terms of the impact of human behavior and performance on the results.