# Characterization and Prediction of Air Transport Delays in China
## 6 Discussion and Conclusions  
In this contribution we presented the results of a set of statistical and data mining analyses aimed at characterizing the appearance and evolution of delays in the Chinese air transport network. These analyses leveraged on a data set comprising more than 11 million flights, which allowed describing how the behavior of the system has evolved during two consecutive years, and giving a first estimation of the underlying causes. The evolution of delays through time suggests that these have not diminished, in spite of efforts for improving the coordination between airports, and between civil and military air space users. As discussed in Section 4, summer peaks for years 2016 and 2018 are not different in a statistically significant way. On a positive note, the situation has not worsened in spite of a significant increase in traffic--see Figure 3.  
Moving to delay causes, the results of Figure 6 indicate that a significant fraction of the delays appearing in the system can be predicted, provided some variables (like weather conditions and traffic levels) are known, or at least can be estimated in advance. From an operational point of view, this conclusion has major consequences. First of all, it supports the idea that real-time prediction models can be developed and deployed, ingesting weather forecasts and scheduled traffic patterns and yielding predictions of the delay levels. These could be used to improve the allocation of resources, or even warn passengers of forthcoming major disruptions in their trips. In addition, these results points to a relevant conceptual issue: if the appearance of an abnormal delay can be predicted, it also means that selective resources can be put in place for its mitigation. In contrast, only broad-spectrum mitigation strategies can be implemented if delays were completely random, as are for instance those due to random equipment failures, with an important reduction of their cost effectiveness. Predictability thus here implies actionability. Regarding the factors associated with the appearance of delays, the most important ones are the presence of thunderstorms and the season of the year (the temperature being a proxy of the latter). The relevance of thunderstorms is self-evident, as aircraft have to reroute around them, and could even make an airport temporarily suspend its operations. This is in line with what is reported in the literature for other airports, see for instance [38; 39; 40]. These adverse weather phenomena are nevertheless not enough to explain all delays, and, as shown in Figure 5, extreme delays can also appear when no thunderstorm is recorded. The solution to this puzzle resides in the second factor, i.e., the season of the year. China customarily suffers from extreme weather events during summer, including Super Typhoons, partly because of the presence of the East Asian Summer Monsoon [41]. With the monsoon, masses of warm and moist air arrive over China, which also result in an increase in the observed temperature and rain--note the low \(p\)-values for these two variables in Table 3. While typhoons may be far away from a given airport, they are still capable of strongly affecting its operation, both by being in the path of flights arriving or departing from it, or through the generation of reactionary delays. In synthesis, results indicate that most extreme delays in China can be explained either by extreme weather events near an airport, or by disrupting events en-route. As shown in the insets of Figure 6, the second most important element to achieve a good delay prediction is the traffic volume, even though it has a minor effect in the case of some airports (e.g., ZGGG and ZUUU). This seems to partly support the hypothesis of the importance of the limited availability of airspace resources, as suggested by previous analyses [1]--even though the opposite has also been defended [20]. These insights can potentially be used to improve the system at two levels.