# Human Factors Analysis of Air Traffic Safety Based on HFACS-BN Model
## 5 Conclusions  
As reviewed in Section 1, traditional accident analysis or risk assessment methods are difficult to quantify rare accidents related to aviation safety. HFACS-BN model provides an additional method for identifying the major human factors that may lead to aviation accidents. Through the analysis of 142 aviation accidents/incidents related to ATC human factors worldwide from 1980 to 2019, the ATC performance model identified 25 major human factors (Figure 1). In addition, the subjective data of the human factors perceived by ATCOs are further collected to supplement the objective data of aviation accident report. CPTs are elicited by the noisy MAX model, and two inference methods of probability prediction and probability diagnosis are used to analyze the causal relationship and posterior probability among variables in the BN model.  
Concerning the four levels of the HFACS framework, the influence of human factors on various levels is quite different. Unsafe acts (79.4%) and unsafe supervision (56.9%) contribute the most to ATC performance, while preconditions for safe acts (40.3%) has the lowest Influence. Overall, the top five most influential factors for ATC Performance are training, physical fatigue, mental state, ineffective monitoring, and ATC software/hardware; specific values are described in Section 4. The above results further imply that the formulation of aviation safety policy should pay more attention to the supervision level and individual factors. Furthermore, due to the complexity between human factors and accident risk, the coupling relationship and chain reaction between human factors should be fully utilized in the safety-related capacity building for ATCO; for example, reasonable allocation of team members may increase work efficiency and reduce the physical strain.  
The contribution of this study is mainly reflected in the following aspects. Firstly, the HFACS framework and BN model are used to provide a systematic and operable method for aviation safety research. Secondly, it makes full use of the ATCO's professional knowledge to overcome the issues of database availability, as mentioned above in Section 3.2.2, to calculate the order of key factors, and to derive effective countermeasures against human errors. Thirdly, the modeling method and data analysis strategy of this study can also be further applied to security research in non-aviation fields. Examples include areas involving human-factor-related accident risk mechanisms such as public transport or mining systems.  
This study is, however, not free of limitations, including:  
* Almost all accidents have multiple contributory factors and that pilot error is often the probable cause.
* Air traffic control systems vary in size and structure around the world so that where one investigative agency may find ATC, or an associated controller or controllers, responsible for a particular accident, another one may not.
* These data are limited to summarized accident reports based on queried keywords and not based on independent evaluations of all accidents, probable causes, or contributing factors.
* Expert opinions were gathered from one ATC system (i.e., China) and perceptions may not reflect a comprehensive global view.
* The use of expert evaluation and the noisy MAX algorithm to evaluate the prior probability of the root node in the BN model will inevitably involve cognitive bias.  
Finally, the current BN model in this study may change with the passage of time and the cognition of various human factors by the frontline ATC. More research should be accumulated in the future to monitor the changes in human factors and causal relationships related to aviation safety, reducing the contribution of human errors to the risk of aviation accidents.