# An Unsupervised Learning Approach for Analyzing Unsafe Pilot Operations Based on Flight Data
## 4 Experiment
### Further Analysis on Impacting Factors  
In general, the overall proportions of these four types are shown in Figure 6. It can be seen that among all the types, the third type of adverse event has the largest proportion, and the lowest proportion is the first type. In addition, we also investigate how the different types are correlated with different airlines, airports, and pilots.  
_Airline impact_: Figure 7 shows the distribution of the adverse events with respect to different airlines, from which we see that airline A accounts for about 60% of the overall occurrences. Given that airline A has a total number of 23,445 flights, while airline B has 63,059 flights in total, it is interesting to observe that the occurrence probability of airline A (6807/23,445 \(\approx\) 29%) is about four times that of airline B (4578/63,059 \(\approx\) 7.3%). This result is mainly due to the different pilot training programs of the two airlines. From the results, it can be seen that airline B has more rigorous restrictions on the pilots' forward stick operations during the flare. We also analyzed the distribution of the four types of adverse events in each of the two airlines. As we can see, airline A has a higher proportion of the third type of events, while the proportion of the first type is relatively  
Figure 5: The K-means clustering results represented by lines with different colors.  
Figure 6: The overall proportions of the four types of adverse events from K-means clustering.  
lower. According to these results, airlines can train pilots to avoid these adverse events in a more targeted manner.  
_Airport impact:_ We investigate the adverse event distribution with respect to different airports, and the results indicate a significant difference between the coastal and inland airports, as shown in Figure 8a,b. The left one is an airport from Guangzhou (CAN), a coastal city of China, while the right one is an airport from Changchun (CGQ), a northeastern inland city of China (the difference between coastal and inland airports is widely observed; we only show two representative airports due to the space limit). It can be clearly seen that the inland airport has a significantly higher proportion of the first type of adverse events (caused by the wind influence) than the coastal airport. We think the result is mainly due to the different wind conditions of these two airports. To validate our assumption, we further investigated the monthly wind statistics data of the two airports, which are available from [[https://www.windfinder.com/windstatistics/](https://www.windfinder.com/windstatistics/)]([https://www.windfinder.com/windstatistics/](https://www.windfinder.com/windstatistics/)), accessed on 22 November 2022. From the statistics, we found that these two airports exhibit significantly different characteristics. Specifically, for the Guangzhou airport, the proportion of east winds is significantly higher than the west winds, while for the Changchun airport, the southwest winds dominate the wind directions. Moreover, the average wind speed of the Changchun airport is stronger than the Guangzhou airport.  
_Pilot impact:_ Finally, we analyze the results from different pilots. Because there are hundreds of pilots in our dataset and it is impractical to show all of them, we only chose two representatives, as shown in Figure 9a,b. The most significant difference between the two pilots is observed on the third and fourth adverse event types. The percentage of the fourth type of adverse event for pilot 1 is as high as 51.2%, while that value for pilot 2 is only 5%. The results indicate that for pilot 1, many of the adverse events were due to personal factors, e.g., operational habits. Based on the results, the airlines can train their pilots in a more personalized way.  
Figure 8: Distributions of four types of adverse events in coastal and inland airports.  
Figure 7: The distribution of four types of adverse events with respect to different airlines.