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Examining Head-Down Time in Transportation: Case Study in Single-Pilot General Aviation Operations

Examining Head-Down Time in Transportation: Case Study in Single-Pilot General Aviation Operations The “head-down time” problem refers to the potential inability of an operator (e.g., pilot, car driver, or train driver) to divide his or her attention optimally between the primary visual field (e.g., out the window) and an auxiliary tool (e.g., visual display screen). This problem has become an important safety concern in road, rail, and air traffic environments. To improve transportation safety, researchers are applying technology to minimize head-down time. One of these emerging technologies is a pilot-oriented cognitive assistant called the Digital Copilot. The Digital Copilot is a working prototype that provides contextual information and aviation related reminders to the pilot in a timely manner. However, the effects of using this cognitive assistant on the pilot had not been evaluated. This research uses a cognitive task modeling software, called Cogulator, to model a pilot’s expected thoughts and actions and compare them across two different environments: one using the Digital Copilot and one using three different electronic flight bags (EFBs) without cognitive assistance. This study examines how effectively the cognitive assistant can manage mental effort, simplify tasks, and decrease head-down time during preparation for the approach phase of flight for the following five tasks: determine weather frequency, determine communication frequency, review and follow a checklist, determine if the tower is open, and determine the preferred landing runway. Results show that the Digital Copilot provides: time savings in all tasks except for determining weather frequency; savings in head-down time for all tasks; and working memory load savings, or no change, for all tasks. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Transportation Research Record SAGE

Examining Head-Down Time in Transportation: Case Study in Single-Pilot General Aviation Operations

Transportation Research Record , Volume 2672 (23): 9 – Dec 1, 2018

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References (47)

Publisher
SAGE
Copyright
© National Academy of Sciences: Transportation Research Board 2018
ISSN
0361-1981
eISSN
2169-4052
DOI
10.1177/0361198118776521
Publisher site
See Article on Publisher Site

Abstract

The “head-down time” problem refers to the potential inability of an operator (e.g., pilot, car driver, or train driver) to divide his or her attention optimally between the primary visual field (e.g., out the window) and an auxiliary tool (e.g., visual display screen). This problem has become an important safety concern in road, rail, and air traffic environments. To improve transportation safety, researchers are applying technology to minimize head-down time. One of these emerging technologies is a pilot-oriented cognitive assistant called the Digital Copilot. The Digital Copilot is a working prototype that provides contextual information and aviation related reminders to the pilot in a timely manner. However, the effects of using this cognitive assistant on the pilot had not been evaluated. This research uses a cognitive task modeling software, called Cogulator, to model a pilot’s expected thoughts and actions and compare them across two different environments: one using the Digital Copilot and one using three different electronic flight bags (EFBs) without cognitive assistance. This study examines how effectively the cognitive assistant can manage mental effort, simplify tasks, and decrease head-down time during preparation for the approach phase of flight for the following five tasks: determine weather frequency, determine communication frequency, review and follow a checklist, determine if the tower is open, and determine the preferred landing runway. Results show that the Digital Copilot provides: time savings in all tasks except for determining weather frequency; savings in head-down time for all tasks; and working memory load savings, or no change, for all tasks.

Journal

Transportation Research RecordSAGE

Published: Dec 1, 2018

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