A clustering-based quantitative analysis of the interdependent relationship between spatial and energy anomalies in ADS-B trajectory data

Abstract

As air traffic demand grows, robust, data-driven anomaly detection 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 is paramount. 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. This paper makes a distinction between spatial metrics in ADS-B trajectory data and 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. In particular, it proposes a novel, unified framework for detection of spatial and energy anomalies in ADS-B trajectory data (and associated derived metrics). The framework consists of three main parts -a data processing procedure, a spatial anomaly detection method, and an energy anomaly detection method. The framework is demonstrated utilizing four months of ADS-B trajectory data associated with arrivals at San Francisco International Airport, and the relationship between the spatial and energy anomalies in this terminal airspace is explored. The results that stem from the implementation of this framework indicate that if an aircraft is spatially not 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. Aviation operators, such as air traffic controllers, may benefit from this observation, as it may factor into decision-making in instances where there is the potential to instruct an aircraft to spatially deviate from standard operations. Additionally, this research revealed underlying differences between trajectories that are spatially nominal yet energy-anomalous and those trajectories that are spatially anomalous and energy-anomalous. Focusing solely on energy anomaly detection does not provide insight into potential spatial-related decisions that may have been made to result in off-nominal energy behavior.

Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model

“…Samantha et al [24] applied the HDBSCAN algorithm on the basis of a weighted Euclidean distance function to improve the identification of terminal airspace air traffic flows. To improve the accuracy of the anomaly detection models from surveillance data, Deshmukh et al embedded a data preprocessing step that involves clustering of the source dataset using DBSCAN [25] and HDBSAN [26] algorithm. Olive et al [27] proposed an algorithm that computes a clustering on subsets of significant points of trajectories while keeping a dependency tree of their temporal chaining and then associates trajectories to root-to-leaf paths in the dependency tree based on the clusters they cross.…”

Unstable Approach Detection and Analysis Based on Energy Management and a Deep Neural Network

“…The kinetic energy of the aircraft is determined by the aircraft's speed, and the potential energy is decided by the aircraft's altitude or the aircraft's gliding angle. The energy used in energy management is based on energy height, which is also termed specific energy in the study [10] and defined as real energy divided by gravity and has the same unit as height [16]. The specific total energy is defined as follows:…”

“…Along with improvements in data collection and the industry moving to carry out a more proactive approach to improve flight safety, machine learning algorithms, or deep learning, have come into sight in data analysis and provided more prospective information such as the forecast of flight trajectory or the prediction of flight parameters. Many works have analyzed the safety of the approach and landing phases with unsupervised learning methods such as clustering frameworks, density-based spatial clustering of applications with noise (DBSCAN), and SVM [7][8][9][10][11]. Corrado [10] analyzed anomalies through spatial and energy aspects by applying HDBSCAN and DBSCAN and discussed the relationship between spatial anomalies and energy anomalies.…”

“…Many works have analyzed the safety of the approach and landing phases with unsupervised learning methods such as clustering frameworks, density-based spatial clustering of applications with noise (DBSCAN), and SVM [7][8][9][10][11]. Corrado [10] analyzed anomalies through spatial and energy aspects by applying HDBSCAN and DBSCAN and discussed the relationship between spatial anomalies and energy anomalies. They had good results in detecting anomalous flights and abnormal flight parameters.…”

Aircraft Trajectory Prediction Based on Bayesian Optimised Temporal Convolutional Network–Bidirectional Gated Recurrent Unit Hybrid Neural Network

“…As a new technology promoted by the Civil Aviation Administration of China, the ADS-B technology gener-ates massive amounts of data that can hide important aircraft flight information [29]. The dynamic data (generated in real time) implies the future movement trend of the aircraft.…”