The Woodruff School

SUMMARY

The projected growth in air transportation demand over the next twenty years is likely to exceed the capacity of the unaided air traffic controller. Consequently, air navigation service providers have made several efforts to improve capacity and throughput with airspace redesign, trajectory based operations, and incorporation of new traffic flow management tools. Additionally, there has been significant investment in the study and development of automated aircraft conflict-resolution algorithms. Ideally, the algorithms are designed to enable the required levels of service and safety for the predicted increase in air traffic demand. Despite numerous advances, there are still concerns about the safety and realizability of automated tactical conflict-resolution algorithms in governing traffic. Given the safety concerns of automated systems and the slow uptake of supporting technologies, it is likely that humans will continue to play a critical role in tactical air traffic control for the coming decades. With this in mind, the development of conflict-resolution algorithms will need to be designed to support, not replace, human controllers. Furthermore, if a human-in-the-loop control architecture is to remain with the support of conflict-resolution decision-support tools, then there is an imperative to understand how the design and implementation of conflict-resolution algorithms has implications into controller taskload. It is expected that the exploration of the relationship between controller taskload and conflict-resolution algorithms will lead to an understanding of the requirements needed to effectively reduce air traffic controller workload. More specifically, studying the relationship will reveal information can be used to aid air traffic controllers in conflict-resolution decision making. Ultimately, the proposed research aims to explore and quantify how engineering methods such as optimization not only influence human work practice, but how human-factors issues can be addressed in conflict-resolution decision-support tools.

SUBJECT:	Ph.D. Proposal Presentation

BY:	Adan Vela

TIME:	Wednesday, December 8, 2010, 3:00 p.m.

PLACE:	MARC Building, 401

TITLE:	Bounds on Controller Taskload: Implementing Conflict Resolution Algorithms in an Airspace

COMMITTEE:	Dr. William Singhose, Chair (ME) Dr. John-Paul Clarke (AE) Dr. Eric Feron (AE) Dr. Karen Feigh (AE) Dr. Wayne Book (ME) Dr. Kok-Meng Lee (ME)

SUMMARY The projected growth in air transportation demand over the next twenty years is likely to exceed the capacity of the unaided air traffic controller. Consequently, air navigation service providers have made several efforts to improve capacity and throughput with airspace redesign, trajectory based operations, and incorporation of new traffic flow management tools. Additionally, there has been significant investment in the study and development of automated aircraft conflict-resolution algorithms. Ideally, the algorithms are designed to enable the required levels of service and safety for the predicted increase in air traffic demand. Despite numerous advances, there are still concerns about the safety and realizability of automated tactical conflict-resolution algorithms in governing traffic. Given the safety concerns of automated systems and the slow uptake of supporting technologies, it is likely that humans will continue to play a critical role in tactical air traffic control for the coming decades. With this in mind, the development of conflict-resolution algorithms will need to be designed to support, not replace, human controllers. Furthermore, if a human-in-the-loop control architecture is to remain with the support of conflict-resolution decision-support tools, then there is an imperative to understand how the design and implementation of conflict-resolution algorithms has implications into controller taskload. It is expected that the exploration of the relationship between controller taskload and conflict-resolution algorithms will lead to an understanding of the requirements needed to effectively reduce air traffic controller workload. More specifically, studying the relationship will reveal information can be used to aid air traffic controllers in conflict-resolution decision making. Ultimately, the proposed research aims to explore and quantify how engineering methods such as optimization not only influence human work practice, but how human-factors issues can be addressed in conflict-resolution decision-support tools.