Safety is a top priority for civil aviation. Data mining in digital Flight Data Recorder (FDR) or Quick Access Recorder (QAR) data, commonly referred to as black box data on aircraft, has gained interest for proactive safety management. New anomaly detection methods, primarily clustering methods, have been developed to monitor pilot operations and detect any risks from such flight data. However, all existing anomaly detection methods are offline learning — the models are trained once using historical data and used for all future predictions. In practice, new flight data are accumulated continuously and analyzed every month at airlines. Clustering such dynamically growing data is challenging for an offline method because it is memory and time intensive to re-train the model every time new data come in. If the model is not re-trained, false alarms or missed detections may increase since the model cannot reflect changes in data patterns. To address this problem, we propose a novel incremental anomaly detection method based on Gaussian Mixture Model (GMM) to identify common patterns and detect outliers in flight operations from digital flight data. It is a probabilistic clustering model of flight operations that can incrementally update its clusters based on new data rather than to re-cluster all data from scratch. It trains an initial GMM model based on historical offline data. Then, it continuously adapts to new incoming data points via an expectation–maximization (EM) algorithm. To track changes in flight operation patterns, only model parameters need to be saved, not the raw flight data. The proposed method was tested on three sets of simulation data and two sets of real-world flight data. Compared with the traditional offline GMM method, the proposed method can generate similar clustering results with significantly reduced processing time (57 %–99 % time reduction in testing sets) and memory usage (91 %–95 % memory usage reduction in testing sets). Preliminary results indicate that the incremental learning scheme is effective in dealing with dynamically growing data in flight data analytics.

Flight schedules are highly sensitive to delays and witness these events on a very frequent basis. In an interconnected and interdependent air transportation system, these delays can magnify and cascade as the flight itineraries progress, causing reactionary delays. The airlines, passengers and airports bear the negative economic implications of such phenomenon. The current research draws motivation from this behavior and develops a multi-agent based method to predict the reactionary delays of flights, given the magnitude of primary delay that the flights witness at the beginning of the itinerary. Every flight is modeled as an agent which functions in a dynamic airport environment, receives information about other agents and updates its own arrival and departure schedule. To evaluate the performance of the method, this paper carries out a case study on the flights in Southeast Asia, which covers eleven countries. The model is tested on a six-month ADS-B dataset that is collected for the calendar year 2016. Through the reactionary delay values predicted by the multi-agent based method, the flights are first classified as delayed or un-delayed in terms of departure. The classification results show an average accuracy of 80.7%, with a delay classification threshold of 15 minutes. Further, a delay multiplier index is evaluated, which is a ratio of the total delays (primary+reactionary delays) and the primary delays for each aircraft. The majority of delay multiplier values range between 1-1.5, which signifies that for except a few outliers, the primary delays do not significantly cascade into reactionary delays for the flights in Southeast Asia. The outliers represent scenarios where primary delays magnify and propagate as reactionary delays over subsequent flight legs. Therefore, the proposed method can assist in better flight scheduling by identifying itineraries which experience higher reactionary delays.