Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 23, Issue 8, Pages 11729-11738Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2021.3106774
Keywords
Aircraft; Airports; Atmospheric modeling; Reliability; Data models; Trajectory; Machine learning; ADS-B; flight identification; TICC; clustering; machine learning
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Funding
- Purdue Polytechnic Institute
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The paper discusses the importance of aircraft operational statistics, especially for nontowered general aviation airports. Research shows that utilizing ADS-B data and machine learning technology can improve access to aircraft operational information, aiding in enhancing noise and emissions models to address public concerns.
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.
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