NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
RNAV STAR Procedural AdherenceFlight crews and air traffic controllers have reported many safety concerns regarding area navigation standard terminal arrival routes (RNAV STARs). However, our information sources to quantify these issues are limited to subjective reporting and time consuming case-by-case investigations. This work is a preliminary study into the objective performance of instrument procedures and provides a framework to track procedural concepts and assess design functionality. We created a tool and analysis methods for gauging aircraft adherence as it relates to RNAV STARs. This information is vital for comprehensive understanding of how our air traffic behaves. In this exploratory archival study, we mined the performance of 24 major US airports over the preceding three years. Overlaying radar track data on top of RNAV STAR routes provided a comparison between aircraft flight paths and the waypoint positions and altitude restrictions. NASA Ames Supercomputing resources were utilized to perform the data mining and processing. We assessed STARs by lateral transition path (full-lateral), vertical restrictions (full-lateralfull-vertical), and skipped waypoints (skips). In addition, we graphed aircraft altitudes relative to the altitude restrictions and their occurrence rates. Full-lateral adherence was generally greater than Full-lateralfull-vertical, but the difference between the rates was not always consistent. Full-lateralfull-vertical adherence medians of the 2016 procedures ranged from 0 in KDEN (Denver) to 21 in KMEM (Memphis). Waypoint skips ranged from 0 to nearly 100 for specific waypoints. Altitudes restrictions were sometimes missed by systematic amounts in 1000 ft. increments from the restriction, creating multi-modal distributions. Other times, altitude misses looked to be more normally distributed around the restriction. This tool may aid in providing acceptability metrics as well as risk assessment information.
Document ID
20170012335
Acquisition Source
Ames Research Center
Document Type
Presentation
Authors
Matthews, Bryan L.
(SGT, Inc. Moffett Field, CA, United States)
Stewart, Michael J.
(San Jose State Univ. Moffett Field, CA, United States)
Date Acquired
December 19, 2017
Publication Date
August 29, 2017
Subject Category
Astronautics (General)
Report/Patent Number
ARC-E-DAA-TN46326
Meeting Information
Meeting: Machine Learning Workshop 2017
Location: Moffett Field, CA
Country: United States
Start Date: August 29, 2017
End Date: August 31, 2017
Sponsors: NASA Ames Research Center
Funding Number(s)
CONTRACT_GRANT: NNX17AE07A
CONTRACT_GRANT: NNA08CG83C
CONTRACT_GRANT: NNA14AA60C
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
rnav
data mining
adherence
star
No Preview Available