Abstract:

Good situation awareness (SA) is highly important for air traffic controllers (ATCos) to maintain the safe, orderly and expeditious flow of air traffic. If ATCos’ situation awareness can be accurately monitored and alerted before it has been lost, it will be highly possible to improve ATCos’ performance and prevent accidents. At present, many ways exist to evaluate ATCos’ situation awareness, but they are basically diagnostic and retrospective methods, early warning and forecasting cannot be achieved. ATCos’ situation awareness could be affected by many factors, but it can be demonstrated by some performance and physiological indicators. In order to improve the prediction accuracy of SA, multi-sensor data which are relevant to ATCos’ situation awareness will be collected by radar control simulation experiment, psychological measurement, and expert ratings. The ultimate aim of the research is to establish SA computational models by two different approaches, which address demonstration indicators and contributing variables of the above multi-sensor data respectively. An evaluation of the two prediction Models will also be made. The research results are expected to provide supports for monitoring ATCos’ situation awareness.

Key words: situation awareness, multi-sensor data, real-time human factor engineering, air traffic controllers, human factors