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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    • 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-AVIATIONNTL-AVIATION-Air Traffic ControlNTL-AVIATION-Aviation Human FactorsNTL-AVIATION-Aviation Safety/AirworthinessNTL-SAFETY AND SECURITY-Aviation Safety/AirworthinessNTL-SAFETY AND SECURITY-Human FactorsNTL-SAFETY AND SECURITY-SAFETY AND SECURITYNTL-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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