# Human Factors Analysis of Air Traffic Safety Based on HFACS-BN Model
## 4 Results
### Model Validation  
A BN model without verification is incomplete. This study adopts the following two methods:  
* Content validity: In order to check the validity of Content, it is necessary to ensure that the BN network only considers the relevant human factor variables and their relations related to ATC. In this regard, all human factors are derived from the literature review and the viewpoint of front-line ATCOs, who also provide great help for the subsequent input of conditional probability between nodes.
* Predictive validity: The most direct and effective verification method is to compare the diagnosis results of the BN model with the database used, which include countermeasure preferences and sensitivity verification.  
Firstly, as mentioned above in Section 3.2.2, there are questions designed for the preference of countermeasures. According to their prior knowledge, the respondents were required to select the six most influential factors from lists that would reduce human errors and improve ATC performance, then compare them with the posterior key human factors calculated by the Bayesian network. Figure 10 shows the countermeasure preferences of 26 frontline ATCOs and relevant experts. Interestingly, \"Increase the salary of ATCOs\" (chosen by 68.20% of the respondents) is the most popular response.  
Figure 8: Marginal probability distribution by different levels.  
Figure 9: Posterior probabilities from Bayesian network (BN) inferences.  
Higher salaries may have a positive effect on ATC mental state and physical fatigue. Followed by \"Reasonable collocation of team members\" (53.13%) and \"Improve the selection criteria of ATCOs\" (41.35%). Understandably, air traffic controllers selected with higher standards tend to have better professional qualities, which can make up for the possible consequences of insufficient training. At the same time, reasonable collocation of team members can also reduce ATC work burden and pressure. In contrast, \"Regular safety awareness training\" and \"Strengthen organizational management\" are the least popular measures chosen by 13.32% and 15.53%, respectively. The possible explanation is that respondents are satisfied with the current situation of safety awareness training and organizational management. The above results are roughly consistent with the observations in Section 4.1. However, respondents' preference for countermeasures only indicates their observation and understanding of ATC performance in this region, and cannot be extended to other regions of the world.  
Secondly, the sensitivity analysis of the target node \"ATC Performance\" was conducted to determine how much uncertainty can be reduced by each human factor. The mathematical function and analysis were shown in Section 3.3. Figure 11 shows the human factors with relatively high sensitivity.  
Figure 11: Sensitivity function for different human factors.  
Figure 10: Countermeasure preferences by ATC professionals.  
The study found that the target variable was relatively sensitive to training, physical fatigue, mental state, ineffective monitoring, and ATC software/hardware. This finding is also consistent with previous observations.