Multi-Scale Data Mining for Air Transportation System Diagnostics

With the rapid growth of air traffic, it often happens that aircraft deviate from the original flight plan during actual flight. This paper proposes an anomaly detection method for aircraft trajectory deviation to realize single-point and successive multipoint anomaly detection from a data-driven perspective. Given the one-to-many relationship between reporting points of planned and real trajectories, a matching algorithm is used to match these points. Four trajectory deviation features (which are the position deviation, distance deviation, altitude deviation, and flight stage) are defined. On this basis, a one-class support vector machine is trained to detect single-point anomalies using the deviation features as input. Furthermore, successive multipoint anomaly detection of the aircraft is realized by considering the deviation of successive segments of the trajectory. Taking the flights taking off and landing at four Chinese hub airports as examples, the proposed method obtained an F score, which is a balance of the precision and recall, over 0.92, indicating it achieves high accuracy for anomaly detection.

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Multi-Scale Data Mining for Air Transportation System Diagnostics

Cited by 3 publications

References 10 publications

Airline network and competition characterization using big data approaches

Airline network and competition characterization using big data approaches

ADS-BI: Compressed Indexing of ADS-B Data

Data-Driven Method for Detecting Flight Trajectory Deviation Anomaly

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