Latest revision as of 15:50, 1 February 2021

Abstract

NASA is collaborating with the FAA and aviation industry to develop and demonstrate new capabilities that integrate arrival, departure, and surface air-traffic operations. The concept relies on trajectory-based departure scheduling and collaborative decision making to reduce delays and uncertainties in taxi and climb operations. The paper describes the concept and benefit mechanisms aimed at improving flight efficiency and predictability while maintaining or improving operational throughput. The potential impact of the technology is studied and discussed through a quantitative analysis of relevant shortfalls at the site identified for initial deployment and demonstration in 2017: Charlotte-Douglas International Airport. Results from trajectory analysis indicate substantial opportunity to reduce taxi delays for both departures and arrivals by metering departures at the gate in a manner that maximizes throughput while adhering to takeoff restrictions due mostly to airspace constraints. Substantial taxi-out delay reduction is shown for flights subject to departure restrictions stemming from traffic-flow management initiatives. Opportunities to improve the predictability of taxi, takeoff, and climb operations are examined and their potential impact on airline scheduling decisions and air-traffic forecasting is discussed. In addition, the potential to improve throughput with departure scheduling that maximizes use of available runway and airspace capacity is analyzed.

Original document

The different versions of the original document can be found in:

• https://zenodo.org/record/1267425 under the license https://creativecommons.org/share-your-work/public-domain/cc0/

• https://zenodo.org/record/1267425/files/article.pdf

• http://hdl.handle.net/2060/20170000653,

https://repository.exst.jaxa.jp/dspace/handle/a-is/578152,

https://ntrs.nasa.gov/search.jsp?R=20170000653,

https://academic.microsoft.com/#/detail/2565538975 under the license cc0

• http://xplorestaging.ieee.org/ielx7/7756173/7777937/07778084.pdf?arnumber=7778084,

http://dx.doi.org/10.1109/dasc.2016.7778084