Arriving air traffic separations generalized model identification

Abstract

Quick development of computer techniques and increasing computational power allow for building high-fidelity models of various complex objects and processes using historical data. One of the processes of this kind is an air traffic, and there is a growing need for traffic mathematical models as air traffic is increasing and becoming more complex to manage. This study concerned the modelling of a part of the arrival process. The first part of the research was air separation modelling by using continuous probability distributions. Fisher Information Matrix was used for the best fit selection. The second part of the research consisted of applying regression models that best match the parameters of representative distributions. Over a dozen airports were analyzed in the study and that allowed to build a generalized model for aircraft air separation in function of traffic intensity. Results showed that building a generalized model which comprises traffic from various airports is possible. Moreover, aircraft air separation can be expressed by easy to use mathematical functions. Models of this kind can be used for various applications, e.g.: air separation management between aircraft, airports arrival capacity management, and higher-level air traffic simulation or optimization tasks.