Development of a Reliable Method for General Aviation Flight Phase Identification

Research Areas: Artificial Intelligence and Machine Learning

Principal Investigator: John Mott

Aircraft operations statistics have typically received significant attention from U.S. airport owners and operators and state, local, and federal agencies. Accurate operational data is beneficial in assessing airports' performance efficiency and impact on the environment, but operational statistics at nontowered general aviation airports are, for the most part, limited or not available. However, the increasing availability and economy of capturing and processing Automatic Dependent Surveillance-Broadcast (ADS-B) data shows promise for improving accessibility to a wide variety of information about the aircraft operating in the vicinity of these airports. Using machine learning technology, specific operational details can be decoded from ADS-B data. This paper aims to develop a reliable and economical method for general aviation aircraft flight phase identification, thereby leading to improved noise and emissions models, which are foundational to addressing many public concerns related to airports.

Representative Publications

Q. Zhang, J. H. Mott, M. E. Johnson and J. A. Springer, "Development of a Reliable Method for General Aviation Flight Phase Identification," in IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2021.3106774.
Zhang, Q., & Mott, J. H. (2022). An improved framework for classification of flight phases of general aviation aircraft. Transportation Research Record: Journal of the Transportation Research Board.