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Multivariate analysis of operational...

Ceric, Damir.

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Multivariate analysis of operational errors in air traffic control.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Multivariate analysis of operational errors in air traffic control./
作者:	Ceric, Damir.
面頁冊數:	38 p.
附註:	Adviser: Kevin Jordan.
Contained By:	Masters Abstracts International46-02.
標題:	Engineering, Aerospace. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1448878
ISBN:	9780549277095

Multivariate analysis of operational errors in air traffic control.
Ceric, Damir.

Multivariate analysis of operational errors in air traffic control. - 38 p.

Adviser: Kevin Jordan.

Thesis (M.A.)--San Jose State University, 2007.

Improving safety by reducing the frequency of operational errors in an air traffic control environment has long been the goal of the FAA. Complexity embedded in air traffic control operations, configuration of airspace, and human information processing capacity limitations allow for multiple factors to influence incidents, creating several categories of operational error. This study examines several predictors of operational errors, and is tailored towards recognizing error patterns. A single operational error may have multiple categories. Identifying unique contributions of any of them is essential, because it leads to a better human error prediction model. Results in this study revealed the influence of a number of contextual factors in prediction of error type frequency. Furthermore, the severity conformance metric (an aircraft proximity index) predicted the frequency of error categories to some extent. The number of aircraft in a sector did not contribute significantly towards the prediction of error categories.

ISBN: 9780549277095Subjects--Topical Terms:

1018395
Engineering, Aerospace.

Multivariate analysis of operational errors in air traffic control.
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502 $a Thesis (M.A.)--San Jose State University, 2007.
520 $a Improving safety by reducing the frequency of operational errors in an air traffic control environment has long been the goal of the FAA. Complexity embedded in air traffic control operations, configuration of airspace, and human information processing capacity limitations allow for multiple factors to influence incidents, creating several categories of operational error. This study examines several predictors of operational errors, and is tailored towards recognizing error patterns. A single operational error may have multiple categories. Identifying unique contributions of any of them is essential, because it leads to a better human error prediction model. Results in this study revealed the influence of a number of contextual factors in prediction of error type frequency. Furthermore, the severity conformance metric (an aircraft proximity index) predicted the frequency of error categories to some extent. The number of aircraft in a sector did not contribute significantly towards the prediction of error categories.
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650 4 $a Psychology, Experimental. $3 517106
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