Application of Machine Learning to the Analysis and Assessment of Airport Operations

Abstract

Tremendous progress has been made over the last two decades toward modernizing the National Airspace System by way of technological advancements and the introduction of procedures and policies that have maintained the safety of the United States airspace. However, as with any other system, there is a need to continuously address evolving challenges pertaining to the sustainment and resiliency of the National Airspace System. One of these challenges involves efficiently analyzing and assessing daily airport operations for the identification of trends and patterns to better inform decision making so as to improve the efficiency and safety of airport operations. This research effort provides a repeatable methodology that leverages supervised and unsupervised machine learning techniques to categorize airports as a means to facilitate the analysis of their operations. In particular, it provides a means for stakeholders to assess the impacts and effectiveness of traffic management decisions and procedures on daily airport operations.