09 - Air Transport System Efficiency
ARTIFICIAL INTELLIGENCE AND HUMAN-MACHINE INTERACTIONS FOR STREAM-BASED AIR TRAFFIC FLOW MANAGEMENT
P. Lertworawanich¹, N. Pongsakornsathien¹, Y. Xie¹, A. Gardi¹, R. Sabatini¹; ¹RMIT University, Australia
The considerable growth in air traffic has led to airspace congestion in certain regions, with the consequent need of introducing new decision support systems and flexible schemes to optimally manage the available resources, towards maximising efficiency and safety of air operations. The objective of this research is to enhance the human-machine teaming in en-route Air Traffic Flow Management (ATFM) by implementing adaptive Human-Machine Interfaces and Interaction (HMI2).rnIn this research, a novel operational ATFM concept “stream-based management” has been implemented as a strategy to improve the efficiency of ATFM. An adaptive HMI2 is designed to improve ATCOs’ performance by introducing graphical interface adaptation and task automation features. Cognitive ergonomics devices and signal processing tools are used to collect physiological data which allow to estimate the operator’s cognitive states. These estimates are subsequently used to drive adaptation in HMI2 and task automation features to further enhancing performance of ATFM.