Detecting Anomalies by Fusing Voice and Operations Data

Our innovation will detect, in near real-time, NAS operational anomalies by uniquely combing with analytical methods our existing Microsoft Azure based TFMData flight information warehouse, live Air Traffic Control (ATC)-Pilot voice communication records, and IBM Watson capabilities such as natural language processing. Implementation of our proposed capability will fill one of the gaps for monitoring and predictive safety tools in the terminal area. In the enroute domain, predictive metrics such as the Monitor Alert Parameter (MAP) and "going red" forecasts help traffic flow managers balance traffic and workloads, thereby increasing safety. However, this relies on the assumption that ATC-pilot communication is of superior quality, unambiguous, and strictly procedural. Also, pilots reacting to controller resolutions by changing the trajectory of the aircraft (either using lateral or vertical maneuvers) may react late, react wrongly, or not react at all. We aim to find these anomalies by correlating actual flight trajectory data and ATC voice communication data. While these anomalies could be precursors to unsafe events, we view them as indicators of inefficiencies in flight operations. Identifying these inefficiencies through innovative data mining methods can uncover unique and recurring problems that otherwise go undetected. Our concept will also provide better insight into the frequency and content of controller instructions and interventions.