Delays analysis in air transportation with ADS-B large dataset

Civil aviation is an important part of global transportation system. Numerous advantages of civil aviation stimulate a continuous growth in the amount of services provided by air transport except Covid-19 global lockdown. Civil aviation uses a wide network of flight routes to provide main transportation services. Nominal transportation network operation grounds on wellplanned air traffic. However multiple factors may affect the system, which significantly degrade network performance and could reduce the safety of air transportation. Low precision of air traffic or action of rare factors results in delays of provided services. In this paper, we provide a statistical analysis of air traffic delays based on probabilities. We use a kernel probability density function to fit punctuality data and estimate risks to be delayed at a particular time. As input data for delay analysis, we use a database of historical flight trajectories archived by Automatic Dependent Surveillance-Broadcast (ADS-B) technology. Air traffic punctuality depends on many factors, however, in trajectory data, we have only the result of their action with no data about reasons. We propose a specially developed software for automatic delay analysis based on ADSB large datasets. Considered examples of delay analysis for particular flights.