The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Intelligent Modeling of Risk Factors Contributing to Runway Incursion
|
Author(s): Antonia Replogle (George Washington University, Washington, D.C., USA), John E. Bischoff (George Washington University, Washington, D.C., USA), Christopher J. Willy (George Washington University, Washington, D.C., USA)and Robert A. Roncace (George Washington University, Washington, D.C., USA)
Copyright: 2014
Volume: 1
Issue: 2
Pages: 12
Source title:
International Journal of Aviation Systems, Operations and Training (IJASOT)
Editor(s)-in-Chief: Massoud Bazargan (Embry Riddle Aeronautical University, USA)
DOI: 10.4018/IJASOT.2014070102
Purchase
|
Abstract
The purpose of this paper is to validate the use of an intelligent neural network model to identify the risk factors contributing to runway incursions. The study utilized multi-dataset fusion and a neural network model to identify risk factors. Historical runway safety data, weather data, and data on the physical characteristics of airports were obtained from multiple publicly available government websites. The results of the analysis showed that a neural network model was able to determine the factors most strongly associated with runway incursions, without the need for subjective weighting by safety experts used in most previous runway incursion studies. The Federal Aviation Administration could use a cyber-physical system, which combines human and computer processes, to analyze the runway incursion factors identified in the present study to determine which aspects of runway safety could be improved to reduce future incursions and save lives.
Related Content
Ronald Pentz, He (Herman) Tang.
© 2017.
13 pages.
|
Huseyin Onder Aldemir, Ferhan Kuyucak Sengur.
© 2017.
13 pages.
|
Karlene Petitt.
© 2017.
17 pages.
|
Jing Yu Pan, Dothang Truong.
© 2017.
14 pages.
|
Kevin M. Smith.
© 2017.
14 pages.
|
R. Devoto, M. Fantola, A. Olivo, N. Rassu.
© 2017.
26 pages.
|
Marco Michael Nitzschner, Michael Stein.
© 2017.
17 pages.
|
|
|