Esa Rantanen, Associate Professor, RIT

 

Esa Rantanen, Associate Professor, RIT

Sage 4101

April 17, 2013 12:00 PM - 1:30 PM

Abstract: World air traffic is projected to grow at an increasing rate through 2025. In the U.S., the aviation industry is forecasted to grow from 731 million passengers in 2011 to 1.2 billion in 2032. The air traffic control (ATC) infrastructure that underlies safe air transportation is undergoing massive modernization to accommodate the predicted growth in air traffic. These efforts, known collectively as NextGen, represent an unprecedented influx of new technologies and procedures to an already immensely complex system. NextGen will fundamentally change the task environments, working methods, strategies, workload, and performance of the human operators within the system. To properly design and deploy new technologies, we need a deeper understanding of fundamental cognitive constructs, such as situation awareness, that underlie air traffic controller performance. This talk will describe an approach to the problem of accurately representing controller situation awareness through the theories developed to account for human performance in multiple object tracking (MOT) and multiple identity tracking (MIT) tasks. However, despite the apparent similarity between ATC and MIT tasks, the current theories of MOT/MIT performance do not account for several specific characteristics of the former. I will present relevant theories, examine their applicability to ATC tasks in the light of recent empirical findings, and discuss “black-box theories” (as situation awareness arguably is) and structural models of cognition. I will also discuss the use of models to properly divide responsibility and authority between controllers and pilots, to guide the use of automation to augment human limitations, and most importantly, help predict human performance and set quantifiable thresholds for the amount of traffic that controllers can safely supervise. 

Do Velocity Vectors Support Multiple Object Tracking?

Multiple Identity Tracking and Entropy in an ATC-like Task

 

 

 

 

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