Latest revision as of 21:30, 28 January 2021

Abstract

Maintaining safe separation between aircraft remains one of the key aviation challenges as the Next Generation Air Transportation System (NextGen) emerges. The goals of the NextGen are to increase capacity and reduce flight delays to meet the aviation demand growth through the 2025 time frame while maintaining safety and efficiency. The envisioned NextGen is expected to enable high air traffic density, diverse fleet operations in the airspace, and a decrease in separation distance. All of these factors contribute to the potential for Loss of Separation (LOS) between aircraft. LOS is a precursor to a potential mid-air collision (MAC). The NASA Airspace Operations and Safety Program (AOSP) is committed to developing aircraft separation assurance concepts and technologies to mitigate LOS instances, therefore, preventing MAC. This paper focuses on the analysis of causal and contributing factors of LOS accidents and incidents leading to MAC occurrences. Mid-air collisions among large commercial aircraft are rare in the past decade, therefore, the LOS instances in this study are for general aviation using visual flight rules in the years 2000-2010. The study includes the investigation of causal paths leading to LOS, and the development of the Airborne Loss of Separation Analysis Model (ALOSAM) using Bayesian Belief Networks (BBN) to capture the multi-dependent relations of causal factors. The ALOSAM is currently a qualitative model, although further development could lead to a quantitative model. ALOSAM could then be used to perform impact analysis of concepts and technologies in the AOSP portfolio on the reduction of LOS risk.

Original document

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

• http://pdfs.semanticscholar.org/eca5/0181868527fdaa09e33b060938933dc045f7.pdf

• http://arc.aiaa.org/doi/pdf/10.2514/6.2015-2894,

http://dx.doi.org/10.2514/6.2015-2894

• https://ntrs.nasa.gov/search.jsp?R=20160006019,

https://arc.aiaa.org/doi/10.2514/6.2015-2894,

https://repository.exst.jaxa.jp/dspace/handle/a-is/562408,

https://academic.microsoft.com/#/detail/2326412867