Regression analysis of top of descent location for idle-thrust descents

Stell, Laurel; Bronsvoort, Jesper y McDonald, Greg (2013). Regression analysis of top of descent location for idle-thrust descents. En: "Tenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2013)", 10/06/2013 - 13/06/2013, Chicago, IL, USA. pp..

Descripción

Título: Regression analysis of top of descent location for idle-thrust descents
Autor/es:
  • Stell, Laurel
  • Bronsvoort, Jesper
  • McDonald, Greg
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: Tenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2013)
Fechas del Evento: 10/06/2013 - 13/06/2013
Lugar del Evento: Chicago, IL, USA
Título del Libro: Tenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2013)
Fecha: 2013
Materias:
Palabras Clave Informales: Idle-thrust descents; trajectory prediction; top of de- scent; flight management system
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. In addition to recording TOD, the cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also identified for use as the independent variables in the regression analysis. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajectory parame- ters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowledge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace.

Más información

ID de Registro: 29791
Identificador DC: http://oa.upm.es/29791/
Identificador OAI: oai:oa.upm.es:29791
Depositado por: Memoria Investigacion
Depositado el: 20 Jul 2014 07:36
Ultima Modificación: 22 Abr 2016 00:10
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