Literature DB >> 19899356

False alerts in air traffic control conflict alerting system: is there a "cry wolf" effect?

Christopher D Wickens1, Stephen Rice, David Keller, Shaun Hutchins, Jamie Hughes, Krisstal Clayton.   

Abstract

OBJECTIVE: The aim is to establish the extent to which the high false-alarm rate of air traffic control midair conflict alerts is responsible for a "cry wolf' effect-where true alerts are not responded to and all alerts are delayed in their response.
BACKGROUND: Some aircraft collisions have been partly attributed to the cry wolf effect, and in other domains (health care and systems monitoring), there is a causal connection between false-alarm rate and cry wolf behavior. We hypothesized that a corresponding relationship exists in air traffic control (ATC).
METHOD: Aircraft track and alert system behavior data surrounding 495 conflict alerts were analyzed to identify true and false alerts, trajectory type, and controller behavior. Forty-five percent of the alerts were false, ranging from 0.28 to 0.58.
RESULTS: Although centers with more false alerts contributed to more nonresponses, there was no evidence that these were nonresponses to true alerts or that response times were delayed in those centers. Instead, controllers showed desirable anticipatory behavior by issuing trajectory changes prior to the alert. Those trajectory pairs whose conflicts were more difficult to visualize induced more reliance on, and less compliance with, the alerting system.
CONCLUSION: The high false-alarm rate does not appear to induce cry wolf behavior in the context of en route ATC conflict alerts. APPLICATION: There is no need to substantially modify conflict alert algorithms, but the conflict alert system may be modified to address difficult-to-visualize conflicts.

Mesh:

Year:  2009        PMID: 19899356     DOI: 10.1177/0018720809344720

Source DB:  PubMed          Journal:  Hum Factors        ISSN: 0018-7208            Impact factor:   2.888


  7 in total

1.  Understanding reliance on automation: effects of error type, error distribution, age and experience.

Authors:  Julian Sanchez; Wendy A Rogers; Arthur D Fisk; Ericka Rovira
Journal:  Theor Issues Ergon Sci       Date:  2014-03

2.  Effect of Remote Cardiac Monitoring System Design on Response Time to Critical Arrhythmias.

Authors:  Noa Segall; Jeffrey A Joines; Ron'Nisha D Baldwin; Diane Bresch; Lauren G Coggins; Suzanne Janzen; Jill R Engel; Melanie C Wright
Journal:  Simul Healthc       Date:  2022-04-01       Impact factor: 1.929

3.  Advice Taking from Humans and Machines: An fMRI and Effective Connectivity Study.

Authors:  Kimberly Goodyear; Raja Parasuraman; Sergey Chernyak; Poornima Madhavan; Gopikrishna Deshpande; Frank Krueger
Journal:  Front Hum Neurosci       Date:  2016-11-04       Impact factor: 3.169

4.  Supporting dynamic change detection: using the right tool for the task.

Authors:  Benoît R Vallières; Helen M Hodgetts; François Vachon; Sébastien Tremblay
Journal:  Cogn Res Princ Implic       Date:  2016-12-19

5.  Alerts for community pharmacist-provided medication therapy management: recommendations from a heuristic evaluation.

Authors:  Margie E Snyder; Heather Jaynes; Stephanie A Gernant; Julie DiIulio; Laura G Militello; William R Doucette; Omolola A Adeoye; Alissa L Russ
Journal:  BMC Med Inform Decis Mak       Date:  2019-07-16       Impact factor: 2.796

Review 6.  AI and Ethics When Human Beings Collaborate With AI Agents.

Authors:  José J Cañas
Journal:  Front Psychol       Date:  2022-03-04

7.  Human Performance Analysis of Processes for Retrieving Beidou Satellite Navigation System During Breakdown.

Authors:  Mo Wu; Liang Zhang; Wen-Chin Li; Lingyun Wan; Ning Lu; Jingyu Zhang
Journal:  Front Psychol       Date:  2020-02-21
  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.