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My Research in the Airspace Operations Lab at NASA Ames Research Center

AUTOMATION FOR AIR TRAFFIC CONTROLLERS

Mission

Air traffic controllers' expertise lies in their ability to 

understand the performance and speed of aircraft, their current positions, their future positions, and how these will interact in the given airspace of a sector.  

 

A key component of an air traffic controller’s expertise lies in developing “situation awareness” (SA), which has been described as “the perception of elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future” (Endsley, 1988, p792).

To keep the demand on a controller’s cognitive capabilities within bounds, the number of aircraft allowed into many en route sectors is limited.

However, the Next Generation Air Transportation System (NextGen) predicts that traffic demand will increase for the next 15 years (JPDO, 2011, p2-25).

The Experiment

In July 2011, a human-in-the-loop simulation researching

flow corridors was conducted in the Airspace Operations

Laboratory (AOL) at the NASA Ames Research Center

(Homola et al., 2012). The simulation evaluated the feasibility of managing increased traffic safely by reorganizing the airspace into flow corridors and introducing automated tooling (Data Comm). 

Flow corridors are highly structured routes flown by aircraft with common avionics equipage at common speeds to increase sector capacity.

The addition of Data Comms aimed to decrease controller workload by allowing 4D trajectory amendments to be uploaded as well as transfer of communications. 

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EiC

MiC

MNC

UiC

Experimental Conditions 

Method

The study was run in the AOL at the NASA Ames Research Center using Multi Aircraft Control System (MACS) software (Prevot, 2002).

Three different mixes of equipped (Data Comm) and unequipped aircraft were tested with two corridor structures: (1) only equipped aircraft in corridors (EiC); (2) a mix of both equipped and unequipped aircraft within the corridors (MiC); (3) a 50/50 mix of equipped and unequipped traffic with no corridors (MNC); and (4) only unequipped aircraft within corridors (UiC). 

In addition to varying equipage and Data Comms, we also tested traffic density at High and Max levels: High traffic conditions included up to seven aircraft above threshold, and Max traffic conditions included up to 12 aircraft above threshold. 

Thirteen retired air traffic controllers from Oakland Center (ZOA) participated in the simulation. There were 32 one hour simulations alternated with High and Max traffic conditions, with the four equipage/corridor conditions counterbalanced throughout.

Participants were asked to manage their traffic as normal. Data were recorded for each session including throughput and losses of separation (LoS) between aircraft. Following each run, participants completed an online questionnaire that included rating the three dimensions of the Situation Awareness Rating Scale (SART, Selcon & Taylor, 1990). A final questionnaire was presented to the participants at the conclusion of the data collection which included a series of questions about the controllers' perceived SA.  

Results

During the post-study survey, participants were asked to rate the complexity of each of the conditions. Unsurprisingly, they rated the condition with the highest levels of automation (EiC) as the least complex and the condition with the lowest levels of automation (MNC) as the most complex. However, when participants were asked to rate their perceived situation awareness (SA) for each condition, the results were split - half of them felt their SA was highest with the most automation, whereas the other half felt their SA was highest with the least amount of automation. We grouped these participants into two categories - early adopters (Group 1) and laggards (Group 2) for subsequent analyses to explore further differences. 

It seems that one group (Group 2) was trying to construct and maintain their SA as they have always done, whereas another group, Group 1, may have been adapting their preexisting SA strategies to take advantage of

the automated resolutions and the task reduction that accompanied equipped aircraft. Whilst there was no statistical difference in controller performance between the two groups, there was some indication that trying to maintain a general SA (as current day controllers do) became harder with the increased traffic as both LoS events occurred in sectors where participants described their SA as better under conditions similar to the current day (Group 2) (see Homola, et al., 2012 for workload  assessments). As air traffic increases in the future, controllers will need to develop and use SA building strategies that incorporate the assistance of automation. 

Perceived Situation Awareness rankings by condition 

Survey responses

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