Mining airport surveillance for operational insights

The technology and procedural changes planned for operational improvements in air transportation, such as those planned for the Next-Generation Air Transport System (NextGen), must be justified in terms of both safety and efficiency. Performance metrics that give insight into the frequency, severity, and causality of the problems being addressed are needed to provide quantitative justification of the improvements under consideration. Estimation of benefits in a mixed-equipage environment with incremental adoption of operational improvements requires performance metrics that can be applied to individual flights. This need is particularly acute in the airport environment, where performance depends on the close coordination of many aircraft and multiple operational needs. Broad measures such as average taxi-in or taxi-out times do not give sufficient insight into causality to predict the efficiency benefits of specific changes. Safely benefits are particularly difficult to predict, as incidents showing obvious hazards are relatively rare; precursors to safely lapses should be more common but are difficult to define and detect. These needs can be addressed by a providing fine-grain analysis of both the specific characteristics and the context of each movement on the airport surface. A process that supports quantitative investigation of causal factors underlying both safety and efficiency shortfalls has been developed and used to improve the prediction of operational improvement benefits. The process has been applied to tasks performed for the FAA´s Air Traffic Organization (ATO) and other customers. The process provides both detailed metrics of aircraft movement in the full airport terminal area environment and detection and characterization of unusual events and patterns in those movements. Such events may be instances of or precursors to safety incidents, and may also reveal instances of degraded efficiency in airport operations. Results from initial applications of- the process to operational airport surveillance data are presented. These examples include the analysis of aircraft surface trajectories to find anomalies in surface movement, specifically sudden stops on taxiways and runways, irregular turns at taxiway intersections, and unusual taxi routes indicating confusion or inefficiency. The use of contextual information such as proximate traffic and visibility conditions to infer causality is also described.