Air transport delays are a major source of direct and opportunity costs in modern societies, being this problem is especially important in the case of China. In spite of this, our knowledge on delay generation is mostly based on intuition, and the scientific community has hitherto devoted little attention to this topic. We here present the first data-driven systemic study of air transport delays in China, of their evolution and causes, based on 11 million flights between 2016 and 2018. A significant fraction of the delays can be explained by a few variables, e.g., weather conditions and traffic levels, the most important factors being the presence of thunderstorms and the season of the year. Remaining delays can often be explained by en-route weather phenomena or by reactionary delays. This study contributes towards a better understanding of delays and their prediction through a data-driven methodology, leveraging on statistics and data mining concepts.
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