Prediction and Classification of Operational Errors and Routine Operations Using Sector Characteristics Variables

Abstract

This study examined prediction and classification of operational errors (OEs) and routine operations (ROs) using sector characteristics variables. Average Control Duration, Aircraft Mix Index, Average Lateral Distance, Average Vertical Distance, Number of Handoffs, Number of Point Outs, Number of Transitioning Aircraft, and Number of Heading Changes were used as predictors in two stepwise logistic regression analyses conducted for the high-altitude and low-altitude sectors. In the high-altitude sample, variables included in the final model (Number of Heading Changes, Number of Transitioning Aircraft, and Average Control Duration) accurately classified OE and RO samples for 80% of the cases. In the low-altitude sample, variables included in the final model (Number of Point Outs, the Number of Handoffs, and the Number of Heading Changes) accurately classified OE and RO samples for 79% of the cases. Although logistic regression cannot be used to determine causation, it effectively identified variables that predicted the occurrence of OEs.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2007
Accession Number
ADA471597

Entities

People

  • Carol A. Manning
  • Elaine M. Pfleiderer

Organizations

  • Federal Aviation Administration

Tags

Communities of Interest

  • Air Platforms
  • Space

DTIC Thesaurus Topics

  • Aerospace Medicine
  • Air Traffic
  • Air Traffic Control Systems
  • Aircrafts
  • Altitude
  • Control Systems
  • Databases
  • Flight Paths
  • High Altitude
  • Information Processing
  • Information Science
  • Low Altitude
  • Personnel Management
  • Regression Analysis
  • Social Sciences
  • Statistical Analysis
  • Statistics

Readers

  • Aviation Safety and Air Traffic Management
  • Computational Modeling and Simulation