Abstract
As air traffic demand grows, robust, data-driven methods are required to ensure that aviation systems become safer and more efficient. The terminal airspace is identified as the most critical airspace for both individual flight-level and system-level safety and efficiency. As such, developing data-driven anomaly detection methods to analyze terminal airspace operations has been an active area of research. With the expansion of ADS-B technology, open-source flight tracking data has become more readily available to enable larger-scale analyses of aircraft operations. Generally, the methods developed to detect anomalies in ADS-B trajectory data detect anomalies in either the spatial or energy dimension. These methods distinctly use either spatial metrics or energy metrics, derived from ADS-B trajectory data. Motivated by the limited number of approaches that simultaneously consider both spatial and energy metrics, this paper introduces the concepts of spatial anomalies and energy anomalies and performs a novel investigation into the relationships and interdependencies, if any exist, between the two types of anomalies. To enable this analysis, spatial and energy anomalies are detected in ADS-B trajectory data (and associated derived metrics) using HDBSAN and DBSCAN clustering algorithms, respectively. Four months of ADS-B trajectory data associated with arrivals at San Francisco International Airport is extracted, cleaned and processed. The results that stem from this investigation indicate that if an aircraft is not spatially conforming to an identified set of air traffic flows representing standard spatial operations, then this aircraft is more likely to experience non-conformance to standard operations in its energy metrics. Additionally, this research reveals underlying differences between trajectories that are spatially nominal and energy-anomalous and those trajectories that are spatially anomalous and energy-anomalous. Focusing solely on energy anomalies does not provide insight into potential spatial-related decisions that may have been made to result in off-nominal energy behavior.
Highlights
In recent years, the aviation industry has seen a large increase in the volume of operations
While the spatial and energy anomaly detection methods used within the framework are clusteringbased methods, there is no restriction on the type of anomaly detection method that may be utilized for either spatial or energy anomaly detection
This paper focuses on three primary energy metrics in which to detect anomalies: specific potential energy (SPE), specific kinetic energy (SKE), and specific total energy rate (STER)
Summary
The aviation industry has seen a large increase in the volume of operations. Aviation demand is driven by economic activity, where the growing U.S and world economies provide the foundation for long term aviation growth, making efficient and safe air transportation operations more important than ever before With such a large increase in the volume and complexity of anticipated operations, maintaining or improving safety for all aviation operations is of paramount importance. To meet this objective, global efforts have been underway to modernize aviation systems to address current and future air transportation challenges. Due to the open-source accessibility of ADS-B data and the ability to derive metrics related to the position and energy management of an aircraft, many efforts have focused on exploiting ADS-B data to detect anomalies
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