Data mining of air traffic control operational errors
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Data mining of air traffic control operational errors

Filetype[PDF-175.41 KB]


English
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Details:

  • Creators:
    Nazeri, Zohreh
  • Subject/TRT Terms:
  • Resource Type:
    Proceedings
  • Corporate Publisher:
    Brown University. Center for the Study of Race and Ethnicity in America
  • NTL Classification:
    NTL-AVIATION-AVIATION;NTL-AVIATION-Air Traffic Control;NTL-AVIATION-Aviation Human Factors;NTL-AVIATION-Aviation Safety/Airworthiness;NTL-SAFETY AND SECURITY-Aviation Safety/Airworthiness;NTL-SAFETY AND SECURITY-Human Factors;NTL-SAFETY AND SECURITY-SAFETY AND SECURITY;NTL-REFERENCES AND DIRECTORIES-Statistics;
  • Abstract:
    In this paper we present the results of

    applying data mining techniques to identify patterns and

    anomalies in air traffic control operational errors (OEs).

    Reducing the OE rate is of high importance and remains a

    challenge in the aviation safety community. Existing

    studies, which use traditional methods and focus on

    individual aspects of OEs, are limited to operations at a

    single facility, or events in a short period of time. A holistic

    study of historical data available on OEs has not been

    conducted. We have applied an attribute focusing

    technique to study 15 years of operational errors at all

    FAA Air Route Traffic Control Centers (ARTCCs) 1 in the

    National Airspace System (NAS) in the U.S. We have

    found ‘interesting’ patterns of common characteristics,

    anomalies, and changes in trends of operational errors.

    We interpreted the results with the help of domain experts

    and plan to do a similar analysis for OEs at other types of

    air traffic control facilities (towers and TRACONs) as well.

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