# Characterization and Prediction of Air Transport Delays in China
Within the former one, most works have focused on Europe (see, for instance, [6; 7; 8]) and US [9; 10; 11; 12; 13], mainly due to the larger data availability. On the other hand, the propagation of delays [14] is usually represented through networks of airports (see [15] for a review), in which links describe the circumstances/likelihood of propagation between pairs of airports [16; 17; 18; 19]. Note that the picture is further made more complex by the presence of multiple definitions for delays, e.g., departure delay, en-route delay or arrival delays; additionally, delays can be estimated for aircraft or individual passengers [17]. Most studies, including the present one, focus on landing delays for flights, calculated as the difference between the actual and scheduled arrival time of a flight, as these are the most relevant from the passenger's perspective. When comparing the causes for air transportation delays throughout the world, China stands out as a special case, as here delays are mainly caused by a limited aerospace for civil aviation (as opposed to, for instance, airport capacity) [20]. While a few research works focusing on the study of delays can be found, e.g., [1; 21; 22; 23], a framework for describing the evolution and causes of delays is hitherto missing in the literature, possibly due to a lack of public operational data sets. In addition, detailed aggregated information about the cause of delays, and in some cases about individual flights, are easily obtainable in Europe and US--respectively through the Eurocontrol's Network Operations Portal and the US Bureau of Transportation Statistics' RITA. Yet, this does not hold in China, for which only annual statistics are published by the Civil Aviation Administration of China (CAAC). The objective of our research is to bridge this gap, and present a comprehensive study of the evolution of air transport delays in China between 1 May 2016 to 31 October 2018. Based on the operational data, we are interested in identifying the major factors driving delays in the Chinese domestic air transportation system. Our analysis is organized around two main topics. We firstly describe the temporal evolution of delay statistical metrics, in order to understand if and how much the system predictability has increased in the last year. Secondly, we further assess the presence of relationships between weather conditions and delays, by means of several statistical and data mining tests, to understand whether the former ones have a significant impact on the dynamics of the system. Through our data-driven experiments, we find that a considerable fraction of the delays can be predicted rather well, provided some input variables, such as weather conditions and traffic levels. Notably, the largest factors appearing with the occurrence of delays are the presence of thunderstorms and season of the year. This study contributes towards understanding the generation of delays and prediction of delays in air transportation systems and, eventually, should lead to novel strategies for improving passengers' experience. The remaining part of this study is structured as follows. Section 2 summarizes the state-of-the-art delay analysis in air transportation networks. Section 3 describes the flight data and methods used in our study for delay characterization and prediction. Section 4 presents statistical analysis on the flight data used in this study, with a focus on temporal evolution and identification of seasonality. Section 5 identifies the relevance of weather phenomena for the occurrence and predictability of delays in the Chinese air transportation system. Section 6 concludes our study and presents some directions for future work.