A human-relatable Course of Action Planner for Air Traffic coordinators

Traffic Managers and Supervisors at operational Air Traffic Control (ATC) facilities, and Air Traffic Control System Command Center (ATCSCC) Specialists, have a critical and significant responsibility in the Air Traffic Management (ATM) system to deal with widely varying contingencies and issues. In this regard, the role of these Traffic Management and Supervisory Specialists is to allow the Air Traffic Controller to remain focused on keeping aircraft separated from each other and operating in the most safe and efficient manner possible. While much attention has been focused on decision support tools and information systems for the controllers, only minimal attention has been given to decision support tools for the Supervisors and Traffic Managers other than those for specific traffic management initiatives. In this paper, we address a conceptual design of a decision support tool (DST) to support the 'big picture' of ATM for the Traffic Manager, Supervisor or other 'Coordinator' as we will refer to them in this paper. Most of the DSTs that address the needs of Traffic Management provide powerful capabilities for the Traffic Management Coordinator (TMC) to receive and analyze information. However, with the dramatic increase in the number of DSTs and other information tools that are used in the ATM operation, the TMC suffers from significant and problematic information overload. This information overload can be easily observed in any Air Route Traffic Control Center (ARTCC) Traffic Management Unit (TMU), as well as at the ATCSCC and in ATC Towers and Terminal Radar Approach Control (TRACON) facilities. In this concept for an Air Traffic Course of Action Planner (ATCOAP), concepts from the field of Artificial Intelligence are applied to support planning, coordination and execution of traffic management tasks.