TFM Tools: Guidance for Use, Integration, and Training
| Full Text: |
 Pdf
(0.89 MB) |
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Document Number: |
DOT/FAA/TC-17/37 |
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Publication Date: |
09-2017 |
| Authors: |
Anthony J. Masalonis, Ph.D., Spectrum Software Technology, Inc. Brion Woroch, Ph.D., Engility Corporation, Inc. Carolina Zingale, PhD. |
Woroch, B., Zingale C.M., & Masalonis, A. J. (2017). Traffic Flow Management Tools: Guidance for Use, Integration, and Training: Part Task Experiment 1 (DOT/FAA/TC-17/37). Atlantic City International Airport, NJ: Federal Aviation Administration William J. Hughes Technical Center.
Abstract
Objective
The purpose of this study is to develop a better understanding of human behavior when using the types of decision support tools (DSTs) planned for the Traffic Flow Management (TFM) domain and other applicable Air Traffic Control domains. Background: DSTs are typically not 100% accurate or reliable because they base decisions on probabilistic information, such as weather predictions. DSTs may provide one or more recommendations. User trust in automation and user workload can influence the extent to which users implement the suggested recommendations and how well the task is performed.
Method
Sixteen volunteers from the FAA William J. Hughes Technical Center with no experience with TFM tools and procedures served as participants. We designed a task that could be quickly learned by novices and focused on several key aspects of the types of tasks performed by TFM personnel. We focused on four factors that might impact DST use; situation-specific training, DST reliability, the number of recommendations made by the DST, and overall task workload.
Results
Some of the factors had direct impact on both objective measures of task performance and subjective measures. Several of the factors interacted in meaningful ways that illustrate the complex nature of DST use and provide insights and recommendations for DST development and deployment.
Conclusion
We found that DST reliability and task workload played important roles in task performance. The interaction of the factors and training highlights a need to consider these multiple factors when developing and deploying DSTs in the operational environment.
Applications
An R-Side CP that presents only red alerts will likely enhance controller performance, and controllers will likely accept it. Yellow alerts may represent information overload, but controllers indicate a need for some information about near-losses of separation. Future research may need to consider the potential benefits of using a buffer zone to present a limited number of near-conflicts.
