Abstract
In order to address the continuing growth of demands on airspace capacity, various navigation methods have been developed such as Area Navigation (RNAV), which allows pilots and air traffic controllers to have a higher degree of freedom in the airspace, but at the same time, the airspace becomes more complex than ever, and maintaining the safety and efficiency becomes challenging. To develop assistant tools for such situation, this paper proposes (i) a trajectory pattern identification framework that can identify complex and diverse trajectory patterns in the RNAV terminal airspace, and (ii) a recurrent neural network-based real-time trajectory pattern classification framework that is necessary for real-time air traffic applications. The proposed frameworks are tested with the real air traffic data recorded at Incheon International Airport, South Korea, in 2019, and evaluated by predicting estimated time of arrival in a real-time manner.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Transportation Research Part C: Emerging Technologies
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
