Arrival air traffic separations assessment using Maximum Likelihood Estimation and Fisher Information Matrix

This article presents method for analysis of selected aspects of arrival air traffic by fitting past arrival air traffic data to one of known continuous probability distributions, with use of maximum likelihood estimation method and Fisher Information Matrix. Data modelling in the analyzed form of continuous probability distribution allows for quantitative assessment of that data. Presented work focuses on analysis of aircraft separations in the final part of descent, where all arriving aircraft should have maintained required separation values. Motivation for the research, modelling assumptions, mathematical model, and sample scenarios are presented in the article. Selected scenarios cover variety of possible arrival air traffic distributions over time, including low and high arrival air traffic intensity cases. Results present that proposed method can be used in air traffic management, especially for analysis of past arrival air traffic data, in order to build predictive models for future arrival traffic modelling.